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withHuman is the approval layer for AI agents. It lets teams put their most consequential agent actions behind human approvals, so their agents can do real work while someone accountable stays in the loop. Rather than blocking agents entirely or letting them run unchecked, withHuman intercepts the tool calls an agent wants to make and holds the risky ones until a person has looked at them. It is framework-agnostic, open source, and built on open standards, and it is aimed at teams running agents in production — support, payments, SRE, finance, sales and marketing — where an action such as a refund, a payment, a merge or a data deletion cannot simply be taken back. Agents are increasingly capable of taking real action: calling Stripe, merging pull requests, rolling back deployments, updating CRM records or paying invoices. The problem is that a tool call is a real-world event. Once a refund is issued, a payment goes out or a table is dropped, there is no undo. Teams therefore face an uncomfortable choice: keep agents on a short leash and lose the value of autonomous work, or let them run and accept that some actions will be wrong. withHuman exists for the actions you can't take back. Its stated approach is to let agents draft everything and ship nothing until someone accountable has looked at it, while still allowing the routine majority of calls to pass without a human ever being bothered. Everything starts with interception. Each tool call an agent makes is intercepted by AAP, the Agent Approval Protocol, and sent to withHuman for approval. If the agent supports AAP, withHuman just works. Coverage spans coding agents through hooks — Claude Code and Codex among them — and agent frameworks through SDKs for LangGraph, CrewAI, Mastra and others. There is also an MCP gateway, and for teams building their own agents the open Agent Approval Protocol is available directly. In the product's own illustration, a support agent's stripe.refund call is held by withHuman before it reaches Stripe, with the arguments captured as a structured request. Approval pipelines are how withHuman avoids approval fatigue. Instead of sending every single call to a person, teams write policy rules that approve or deny routine requests automatically, and only the tough stuff goes to humans. Rules can be conditioned on the agent, the tool, the arguments and the agent context. A simple example from the site: refunds under $20 approve automatically, while a $75 refund that exceeds the $20 threshold skips the automatic rule and is escalated. For calls that rules can't describe — such as judging whether a support reply is routine — there is an AI judge. Teams can also use webhooks to their own policy service, so existing policy logic can participate in the pipeline. In the demo, three of four calls are decided without a person touching them. Escalation paths make sure the request reaches the right human rather than a generic queue. Teams write rules that decide which requests go to which team or person, routing by agent, tool, arguments or team. Teams then set their own escalation order and on-call flows, and requests escalate when nobody answers in time. The example on the site shows a $2,400 refund held because it is over $1,000: it goes to Sam Alvarez, the finance lead, while under-$1,000 refunds go to the support team and out-of-hours requests go to the on-call person. Reviewers are met where they already work — Slack and Discord, iPhone and Android apps with push, plus email, text message and WhatsApp — and each reviewer's own notification rules pick the channels. Every decision is kept on the record. withHuman maintains an append-only audit log of run history for production, capturing each request, the decision and any notes, with reviewer identity attached to every decision and the full request retained alongside it. Entries such as db.delete_rows approved by d.chen, stripe.refund approved by s.alvarez with a note that it was verified with finance, or k8s.apply denied by m.okafor are all recorded. The point is straightforward: when someone later asks who approved this, the answer is available, with the identity of the person who said yes and the exact request they saw. withHuman's distinctive approach is that it is a protocol, not a walled garden. Your agents speak to withHuman via AAP, the Agent Approval Protocol, an open spec started by withHuman to let agents wait on human decisions. That means integrations are made with AAP, not with withHuman specifically, so one integration works with any provider. The spec is built in the open, with issues, proposals and changes happening in public, and the aim is stated plainly: to be the best provider, never the only one. The spec, SDKs, adapters and conformance tests are open source, so teams can swap providers without rewriting a single agent. The withHuman core — the queue, the audit trail and the web UI — is also open source and self-hostable, so it can run on your own infrastructure or be run for you. Following a single consequential action end to end shows how the pieces fit together. A customer asks for a refund; the support agent finds a duplicate charge and decides to refund it; the agent's tool call is intercepted and held by withHuman before it reaches Stripe. The rules check it: the approval pipeline approves or denies the call automatically, or asks a human to take a look. In the example, a $75.00 refund exceeds the automatic $20 threshold, so the request goes to the Payments team, where Chris is on call. Chris is notified on his preferred platform, sees the tool call and the request context — amount, reason, customer — and approves or denies. The approved refund then reaches Stripe and the agent carries on, with the resulting stripe.refund reference recorded. Getting started is done through a CLI that connects every supported agent on a machine. A single curl-and-sh install command onboards coding agents such as Claude Code, Codex, OpenClaw, Pi, Hermes and DeepSeek, places the hooks in position, and finishes in the browser where the request appears for approval or denial. Supported integrations listed on the site include coding agents, an MCP gateway, LangGraph, Deep Agents, Pydantic AI, OpenAI Agents, Claude Agent SDK, Google ADK, CrewAI, AI SDK, Mastra and an HTTP API. Setup examples use pip installs for the Python frameworks, npm packages for the TypeScript ones, and a simple POST to /api/aap/v1/requests for anything else, with a timeout field so a call does not wait forever. Use cases are framed around the actions you can't take back. An AI SRE can request approval for kubernetes.rollout_undo before rolling payments-api back after a 5xx rate spike. Coding agents surface Bash commands. Customer support routes stripe.refund. Finance and accounting routes bill.pay. Sales routes docusign.send. Marketing routes campaign.send. Personal assistants can route calendar.create_event. In every case the agent can draft the work — the refund, the rollback, the payment, the contract send — and nothing ships until someone accountable has approved it. withHuman is aimed at companies running AI agents, not hobby projects — the kind that want one set of rules for every agent the company runs. The reviewer experience is built for team members who are on call for a particular domain, whether that is the Payments team, a finance lead or an SRE. On availability, the project is open source with a hosted version; teams can self-host the core or let withHuman run it for them. The Product Hunt launch notes an offer of three months of Scale free using the code PRODUCTHUNT. In short, withHuman gives agents the freedom to do real work while keeping humans accountable for the decisions that matter. By intercepting tool calls, deciding routine requests automatically, routing risky ones to the right person, and recording every decision, it turns human approval from a blocker into a workable layer that sits underneath every agent a company runs.

Navige is a cost-control layer for AI agents that sits between your agents and everything they pay for. On one side are the agents themselves and the tools and frameworks they run on; on the other side are the models and metered services those agents call. Navige plugs into both and routes every call through one governed layer so that, in the company's words, every run gets cheaper and faster, with the same result. The product is built for teams that run AI agents in production and want to manage programmatic AI spend — the money spent on models, agent traffic and metered AI tools — rather than only reviewing it after the fact. Most cost tooling for AI agents works backwards. As Navige puts it, everyone else shows you what the run cost after it has run; Navige changes the step before it runs. The company frames the underlying problem as a set of familiar failure patterns: runs that never finish, where agents keep running without producing an output; big model, small task, where a step is answered by a larger model than its outcome required; chasing, not remembering, where the same work is redone instead of reused; one budget, many agents, where spend is not governed centrally; and misread, then paid for, where an error is only discovered after the call has been billed. The stated consequence is that the bill is decided before the dashboard sees it — by the time cost reporting catches up, the money has already been spent. Navige lightens every run in four stated ways. The first is cache and reuse, described as never pay twice: when a verified answer is still valid, Navige reuses it instead of paying for another model or tool call. The second is context trimming and tool payload capping, described as only pay for what's used: oversized tool results are limited and accumulated history is trimmed before the next request is sent to the model. Together these two controls target both halves of token spend — the context a model is asked to read and the output it is asked to produce — by removing repeated work and excess context rather than by degrading the answer. The third control is a loop and failure guard, described as stopping runaway jobs: repeated failures are blocked before another call adds to the bill, which addresses the 'runs that never finish' pattern directly. The fourth is smart routing, described as the right model, once it's earned: Navige chooses the model based on the task complexity. Routing is deliberately conservative — if Navige is unsure, it keeps your chosen model — so the substitution only happens where the system is confident the outcome will hold. Governance is built into the same layer. Users can set budgets, catch runaway loops and approve sensitive actions. Tool calls are checked against your rules before they run, not flagged afterwards, which means the guardrail acts at the moment of decision rather than in a post-hoc report. Costs and savings are visible by job, with estimates clearly labelled so that projected figures are never confused with measured ones. Changes can be previewed before the controls are switched on, so teams can see what a policy would do before it takes effect. The Watchtower tier is described as free forever. Architecturally, Navige is a middle layer rather than a replacement. Your agents, frameworks, keys and providers stay exactly as they are; you change one line of config and both ends are plugged in once. On the tools and frameworks side this covers LangChain, LangGraph, CrewAI, OpenAI Agents, MCP and Agentforce. On the models side it covers Anthropic, OpenAI, Google, AWS Bedrock, Mistral and Meta Llama, with the site summarising it as 'Claude, GPT, Gemini or Bedrock. One line of config, any framework.' Because the layer sits in the path of every call, the bill is influenced as work runs rather than reconstructed afterwards. Navige calls this approach Sufficiency Intelligence: 'Sufficiency isn't about using less AI. It's about using exactly enough.' That framing matters because the product is explicitly not a discount: it does not propose a cheaper answer to the same question, it proposes the same answer reached at the level it needed, and where a step needs a frontier model, sufficiency keeps it there. The promised outcome is straightforward: the same result with smaller bills, plus faster runs. Because the four controls act before a call is made, savings arrive during the run instead of appearing as a retrospective insight. Because routing defers to your chosen model when it is unsure, and because measurement checks results as well as cost, the spend-versus-quality trade-off is handled explicitly: a step that got cheaper and started being wrong is reported as a failure, never as an opportunity taken. Users also gain visibility — cost and savings broken out by job, with estimates labelled — and control, through budgets, approvals and rules that are enforced before execution. The most direct use case is repetitive agent work: the same conclusion reached the same way many times, where a verified answer can be reused rather than recomputed. Navige's own calculator treats repetitive work as carrying the most opportunity and complex work the least. A second scenario is the multi-agent environment, where several agents touch the same work and share one budget; a governed layer makes that budget observable and enforceable. A third is agents that use premium models by default or send large prompts and context, where smart routing and context trimming apply. A fourth is agents that make paid tool, search or data API calls, where a still-valid held answer can replace another paid call. Before committing, teams can run the sufficiency calculator, which asks for monthly programmatic AI spend and the kind of work involved and returns an estimated opportunity range broken into overspecified intelligence, repeated or unnecessary execution, excess context and output, and tools and APIs. Once connected, Navige leaves a slice of your jobs running untouched and compares the two so the difference is observed rather than assumed. The documented workflow is 'Plug in your agents. Switch on when you trust.' Navige's stated audience is teams running AI agents at scale: the site reports 51+ agents connected and $9K+ in AI spend managed, priced to the millionth of a dollar and updated live, with tool calls governed against rules before they run. The supported integration surface spans agent frameworks and orchestration tools — LangChain, LangGraph, CrewAI, OpenAI Agents, MCP and Agentforce — and model providers — Anthropic, OpenAI, Google, AWS Bedrock, Mistral and Meta Llama. Teams that do not see their platform are invited to say so. On pricing, the Watchtower tier is described as free forever, with dedicated pricing and enterprise pages for the other options; the site also notes that the spend figure entered into the calculator covers models, agent traffic and metered AI tools while excluding employee seat licences. The takeaway is that Navige treats AI agent cost as something to be governed during execution rather than audited afterwards. By sitting between agents and the models and tools they pay for, and by applying caching, context trimming, loop guards and smart routing under user-defined budgets and approvals, it aims to deliver the same output at a lower bill — using exactly enough intelligence for each step.

Chatway is a live chat and AI customer support platform with an all-in-one inbox, automation, and MCP. It is built for customer support and sales teams that want to handle every customer conversation from one place while adding AI where it helps most. Chatway lets businesses bring their own AI agent — connecting ChatGPT, Claude, Gemini, Cursor, or any MCP-compatible assistant — to manage customer conversations, reply to messages, search contacts, and access a knowledge base. Alongside that, Chatway ships with a built-in AI Support Agent, a shared inbox, a help center, automation, and iOS and Android mobile apps. Over 50,000 businesses use Chatway to build better customer connections. Customer support today is scattered. Conversations arrive through a website chat widget, WhatsApp, Facebook Messenger, Instagram, and email, and agents are expected to answer quickly without losing context or repeating themselves. Support teams also face rising volumes and the expectation of 24/7 availability, which is difficult to staff around the clock. Chatway addresses this by centralising every customer conversation in one unified inbox and by offering automation and AI so that routine questions can be handled instantly. The optional MCP connection means teams that already rely on an AI assistant can put that assistant to work on support, rather than starting over with a new tool. Chatway's built-in AI Support Agent answers questions and supports customers 24/7 across a website, WhatsApp, Instagram, Messenger, and Email. It is trained on your website and knowledge base, so it delivers instant, accurate answers in multiple languages and seamlessly hands conversations over to your team when human support is needed. For teams that already use an AI agent, Chatway supports Bring Your Own AI Agent with MCP and Webhooks. You connect your own agent — including Claude Code, OpenAI Codex, GitHub Copilot, Gemini, Devin, Cursor and others — through MCP and choose what it can access, so it can tag, hand off and help your team. It works alongside Chatway's built-in AI Support Agent rather than replacing it. Every conversation can live in a single all-in-one inbox. Chatway integrates with WhatsApp, Facebook Messenger, Instagram, X, Email, and Phone so customers can reach out through the channel they prefer while your team works from one place. Team collaboration tools let agents share notes, assign tasks, and work on conversations together, which improves efficiency and effectiveness. Live Visitors Insights shows who is browsing your website in real time so you can instantly start a conversation at the right moment. Multilingual Support translates incoming messages into agents' preferred languages in real time, supporting more than 20 languages, and the chat widget can be customised for diverse audiences. Automations and Smart Workflows let you create custom rules that respond to chats, form submissions, page visits, and more — engaging visitors, qualifying leads, and simplifying repetitive tasks. Deep eCommerce integration connects Chatway with platforms such as Shopify and WooCommerce so your team and AI agents get real-time order and cart insights, access to store data, and the ability to answer questions with relevant, up-to-date product information. A self-service Help Center lets you publish FAQs, guides, and documentation, while FAQ and Canned Responses give agents a library of common answers they can send in seconds. Analytics reports on your support activity so you can better understand customer interactions, and Segmentation categorises and labels conversations using specific criteria for more personalised support. Notifications keep you updated on all incoming messages with email and push alerts, and Full Customisation lets you personalise the chat widget to match your business's style and branding. Chatway's unique approach is that it does not force a choice between a human team and an AI agent. Human agents can reply to customers from the Chatway web or mobile app, work alone or with a team, and bring in an AI agent whenever they want. Conversations can be handed between AI and humans without losing context. Two paths are available for developers: connecting an existing AI assistant through MCP and webhooks, or building your own agent against the REST API. With the API route, you receive a signed webhook event the moment a visitor writes, and you reply through the REST API — and the API is included on every plan. The outcomes are practical: faster first responses, round-the-clock coverage, and less repetition for agents. Because the AI agent is trained on your own website and knowledge base, answers stay accurate and relevant. Because conversations are centralised in one inbox, agents do not have to switch between tools. And because handoffs between AI and humans preserve context, customers do not have to start over. Chatway reports over 1,000 five-star reviews across the iOS and Android app stores, and over 50,000 businesses already use it to build better customer connections. Chatway is positioned for a wide range of industries. eCommerce teams use it to recover carts, answer questions, and boost sales. SaaS teams use it to convert trial users into customers with support. Customer support teams use it to resolve issues quickly and improve satisfaction. Finance teams use it for secure client questions, education teams for student support, travel businesses for guiding travellers and answering booking questions, and real estate teams for responding to inquiries and closing deals. Professional services use it to connect with clients quickly and offer personalised service, HR and recruitment teams use it to engage candidates and streamline hiring, and nonprofits use it to engage supporters and build relationships. Chatway is aimed at customer support and sales teams of all sizes, including developers who want to build their own agents. It integrates with WhatsApp, Facebook Messenger, Instagram, X, Email, and Phone, with eCommerce platforms like Shopify and WooCommerce, and with site platforms such as Shopify, Wix, and Duda. Supported AI agents include Claude Code, Claude Desktop, ChatGPT, Codex, Cursor, Muse, Gemini, Grok, VS Code, Cline, Devin, OpenCode, Goose, Manus, and any MCP client. Pricing is free to start with no credit card required, and the API is included on every plan. In short, Chatway is a live chat and AI customer support platform that unifies every conversation, automates the routine, and lets you bring whichever AI agent you already trust. It lets support and sales teams deliver faster, more consistent answers across web, mobile, and social channels.

Daddy Issues is a companion app for dads that delivers a weekly family briefing, helping fathers stay one step ahead before anyone has to remind them. It is built for dads, by dads, and its central promise is straightforward: think ahead, fight less. The app uses AI to connect your kids, your calendar, your responsibilities and your tasks, then surfaces what is coming up, what you might be missing, and what is worth handling now. Rather than waiting for a question you did not anticipate — the familiar "Did you think about this?" — you start each week with a clear picture of what actually matters and what you can act on early. The problem Daddy Issues addresses is not a lack of effort or intention. As the site puts it, you are involved, you care, you do a lot — and still you hear "Did you think about this?" You find out things too late. You react instead of anticipate. That is where it breaks: not effort, not intention, but timing. The app frames the deeper issue as ownership versus helping, stating plainly that helping is not the same as owning, and that if someone has to remind you, you are not ahead — you are catching up. The product exists so dads can stop being the one who gets reminded, the one who reacts late, or the one who "helps" but does not lead, and instead become the one who already handled it. At the core of the app is the weekly briefing — a summary of your family life that is explicitly described as not just a summary, because the AI connects the dots you do not see. The briefing is organized around four questions: what's coming up, what actually matters, what AI spots early, and what to handle now. A sample week shown on the site illustrates the format: a kindergarten trip on Thursday with a snack needed, a doctor follow-up that is overdue, and a birthday on Sunday where the gift is missing — closed out with a simple count of three items needing attention that week. The value is that the briefing is built around your actual week rather than a generic checklist, so the things that would otherwise surface too late appear while there is still time to act. Once the briefing has surfaced something, the next step is to turn it into actions and add it to your calendar. The app does not stop at awareness; it moves from what the AI noticed to what you are going to do about it, with "Done" as the stated end state of that loop. This matters because the gap between knowing something is coming and actually handling it is where family logistics usually fall apart — the kindergarten snack, the overdue follow-up, the missing gift. By converting the briefing into concrete actions and calendar entries, Daddy Issues closes that gap in the same place the problem was identified. The app also learns how your family works. The mechanism described is deliberate rather than automatic: you create tasks, and the AI understands patterns from them. Over time it starts anticipating what matters — earlier. As a result, things stop slipping, including the ones you did not track before. That last point is significant: the system is designed to catch items that never made it onto a list in the first place, not merely to remind you of things you already wrote down. The more you use it, the more the briefings reflect the real rhythm of your household rather than an idealized version of it. The overall methodology is described in three steps. First, connect your basics — calendar, kids, routines. Second, get your weekly briefing, built by AI around your actual week. Third, act early, before things pile up. Onboarding is presented as low-friction, taking about one minute with no setup stress, alongside an invitation to "show me my first week." Rather than asking dads to adopt a heavyweight planning system, Daddy Issues positions itself as a lightweight layer on top of the basics they already have, producing one briefing a week and letting the actions flow from there. The stated benefits are deliberately modest and human: less stress, fewer reminders, more trust. Daddy Issues does not promise perfection — the origin story notes that the founder built something that actually helps you stay ahead, "not perfectly, but consistently." Consistency is the point. Fewer reminders matters because it means the mental load is not being pushed back onto someone else; more trust matters because the dynamic shifts from catching up to leading. The outcome described is that you do not just help more — you take ownership. For dads who feel they are contributing but still behind, that reframing is the real benefit. Concrete use cases appear throughout the material. A typical week might include a kindergarten trip on Thursday that needs a snack packed, an overdue doctor follow-up that should be handled, and a child's birthday on Sunday where a gift is still missing. The question at the top of the site — "backup clothes for daycare?" — captures the kind of everyday, easily forgotten item the app is designed to catch. Beyond individual items, the weekly briefing supports a recurring ritual: reviewing what is coming up for the family, turning the important items into tasks or calendar entries, and acting before the week fills up and things pile up. Daddy Issues is explicitly built for dads — the site and its tagline describe it as a companion app for dads, created by a dad who was tired of being behind. The origin story emphasizes that it did not start as a product but as a personal fix: trying to keep track of everything, still missing things, still being reminded, and then building something that genuinely helps you stay ahead. The basics you connect are your calendar, your kids and your routines, and the app produces a briefing you can turn into actions and add to your calendar. Product Hunt lists the product under the Calendar, Virtual Assistants and Family topics. In short, Daddy Issues is a weekly family briefing that uses AI to help dads think ahead rather than react. It connects kids, calendar, responsibilities and tasks, surfaces what is coming up and what is worth handling now, learns from the tasks you create so it can anticipate earlier, and turns its findings into actions. The value proposition is stated plainly: think ahead, fight less — less stress, fewer reminders, more trust, and the shift from helping to owning.

Tiden is an intent verification platform for coding agents. It verifies that your agents built what you actually asked for, and it keeps your specifications versioned beside the code. Tiden is built for engineering teams that ship agent-written code every day, and it works beside Claude Code, Cursor, Codex and Copilot. The product reads your code and writes down each spec, keeps your intent as requirements that branch and merge like code, and checks every change against that record rather than against the session that produced it. A verdict comes from tests run in your own environment, and no model grades the diff. You decide what merges. Coding speed went up. Delivery failed to keep pace. Agents write features in minutes, and then someone, usually you, spends the day checking them. The problem is not that the pipeline stops running; it is that the pipeline cannot tell you whether the agent built the right thing. Most checks run inside the session that wrote the code, so the tests agree with it by construction. Put another way, green tests only prove the agent agreed with itself. Tiden exists to close that gap, giving teams a way to show that agent-written code actually does what was asked before it reaches production, and to keep that proof attached to the code as it evolves. Tiden maps what your product does. It reads your code and writes down each spec, then marks the ones that no test proves. The website shows a product snapshot in which Tiden summarises components and specs, for example components such as web (TypeScript, 1,204 files), api (Go, 2,318 files) and billing (Go, 396 files), alongside connected sources such as Jira with 312 issues and error tracking across 3 environments. Each spec is labelled either as proven by a test or as having no test yet; the example given is TIDEN-377, "Billing tier caps volume", marked with no test. The snapshot reports how many specs sit under each area, including Web App, Public API, Billing, Reporters, Search and Auth, so a team can see at a glance where proven coverage exists and where it does not. Mapping the codebase this way turns implicit knowledge about product behaviour into an explicit, inspectable record. Each promise is proven by a test. When an agent starts a session, it begins from a working contract, and the session cannot close while a promise is open. The website illustrates this with an intent called rate-limit-public-api, whose task is to add rate limiting to the public API, worked on by Claude Code. The session moves through four states, Started, Coding, Settle and Close, and carries a list of promises. In the example, "Free returns 429 above 60 requests a minute" is running with 2 tests passed, "Every 429 carries Retry-After in seconds" is running with 1 test passed, and "Pro returns 429 above 600" is still open, so the interface reports that it cannot close yet because 1 promise is open. Attaching promises to a session this way means an agent cannot quietly declare work finished while something remains unproven. The spec changes with the code. Because green tests only prove the agent agreed with itself, Tiden drafts the update on every merge, even ones that skip Tiden. The example shows a Git change to api/public/v1/limiter.go, where a TODO comment about rate limits is replaced by an Allow function that consults the tier limits for a workspace key. Beside that diff, Tiden shows what the code was supposed to do: the spec TIDEN-377, "Billing tier caps volume", moves from the previous line "Unlimited for all tiers" to a new set of statements, that free returns 429 above 60 requests per minute, that Pro returns 429 above 600, and that every 429 carries Retry-After in seconds. In this way the record of intent stays current even when a change arrives outside the normal flow, so the documentation of what the product promises does not drift away from what the code does. You review what was promised and proven. Each branch shows what changed and what proves it, while code review itself stays with your reviewer. On a branch such as intent/rate-limit-public-api heading into main, Tiden reports a pass with 3 of 3 promises delivered and 0 open, listing the spec (TIDEN-377, 3 lines) and the tests that were added: free_tier_returns_429_above_60, retry_after_header_in_seconds and pro_tier_returns_429_above_600, all running and passed. When merging into main, the choices presented are to merge, to ask for a test, or to accept the risk. Release level reporting gives one verdict per release, part by part, with coverage and component figures, for example release 2.6.0 in production reporting 205 of 205 coverage and 12 of 12 components, plus per component status such as Web App pass, Public API pass and Billing pass with risk indicators. Errors in production point back to the release, and a warning such as a feature flag warning on Search, first seen in 2.5.0 with no test covering it, can be fixed with the agent, which adds the missing test. Tiden's approach differs from checks that happen inside the generating session. Because most checks run inside the session that wrote the code, those tests agree with the code by construction. Tiden instead keeps your intent as requirements that branch and merge like code, effectively versioning specifications alongside the software, and checks every change against that record rather than against the session that produced it. The verdict comes from tests run in your environment, and no model grades the diff. The system is built around an explicit, versioned record of what the product is supposed to do, proven by tests, updated when code changes, and traceable back to releases in production. The stated outcome is straightforward: ship agent code you can prove is right. Teams get an account of what was promised and what proved it, so review focuses on what changed and what backs it rather than on re-deriving whether the agent built the right thing. Because a session cannot close while a promise is open, work is not quietly finished without tests. Because a verdict is issued per release, part by part, and production errors point back to the release, failures can be connected to a specific change instead of being hunted down manually. And because the record lives outside the session, merges that skip Tiden still cause an update to the spec to be drafted, keeping intent and reality aligned. Concrete scenarios appear throughout the site. A team adding rate limiting to a public API can watch an agent work through that intent, see which promises are proven by tests and which remain open, and decide whether the branch is ready. A reviewer can look at a branch and see its promises, the tests added and the spec lines that changed before choosing to merge, ask for a test, or accept the risk. A release owner can read one verdict per release with coverage and component counts, and trace a production error back to the release that introduced it. When a coverage gap appears, such as a feature flag warning first seen in an earlier release with no test covering it, the gap can be routed to the agent, which fixes it and adds the missing test. Onboarding onto an existing codebase follows the same path: connect GitHub, and Tiden maps the rest. Tiden is aimed at engineering teams that ship agent-written code every day. It works beside Claude Code, Cursor, Codex and Copilot, and connects to the tools teams already use: GitHub, Jira, Linear, Notion and Azure DevOps. The recommended starting point is to connect GitHub, after which Tiden maps the rest of the product. Installation is available through a shell script served from tiden.ai, and the product offers a free trial alongside a separate pricing page. Tests that produce a verdict run in your environment, which is where the evidence is generated. In short, Tiden treats intent as something to be written down, versioned and proven, not something to be re-checked by hand after every agent session. It maps what your product does, marks what is unproven, holds a session contract open until each promise is proven by a test, drafts spec updates on every merge, reports one verdict per release part by part, and traces production errors back to a release. For teams whose coding agents now write the code, it is a way to verify that the code does what was actually asked for.

AirJelly is an always-on desktop AI agent that presents itself as your proactive, self-organizing second brain. Rather than waiting for a prompt, it observes authorized on-screen activity to understand who you are and what you are working on, then proactively captures your intent, organizes your tasks and gets things done. The website describes it as the world's first context-aware proactive agent, built for anyone whose work moves faster than their notes. That includes product, operations and engineering teams, founders and executives, researchers and PhD students, and anyone juggling more context than one brain can hold. AirJelly brings scattered context together so that tools like Codex and Claude Code can act with the full picture, and it also tracks your personal interactions so you never miss a follow-up or forget a promise. The problem AirJelly addresses is the gap between the fast-moving reality of modern work and the static tools people use to track it. Most AI assistants wait for you to ask, and they only understand what you paste into them. Meanwhile, the most important context of a working day — a chat message confirming a review, a note saved weeks ago, a task agreed inside a meeting — stays scattered across tabs, documents, calls and inboxes. Notes apps and to-do lists require you to stop and write things down, which means the fastest-moving details are usually the ones that get lost. AirJelly's premise is that this scattered context is exactly where work slips through the cracks, so the product sets out to observe, remember and act continuously instead of reactively, aiming to free your time for the things AI cannot do — or simply for you to enjoy. The first of AirJelly's three core steps is watching. According to the website, AirJelly captures authorized screen context across your work apps. It reads across every app you use, including Slack, Zoom, Google Docs, Calendar and email, and the FAQ summarises this simply: if it is on your screen, AirJelly can see it. This ongoing observation is what allows the agent to know who you are and what you are working on at any given moment, without you having to explain it. Because the watching happens continuously in the background rather than only when you open an app, the agent builds up a picture of your work as it actually happens. The second step is remembering. AirJelly builds a living timeline of your work in which every task, meeting and message is searchable, forever. In the feature list this appears as the Timeline — a full log of everything AirJelly observed, across every app — and as Memory, which lets you ask about anything from your past work, anytime. The product also describes an activity log and the idea of gathering context for the agent, so that everything it has seen can be brought back when it is relevant. This matters because recall is usually the hardest part of knowledge work: the note you saved, the decision that was made, or the person who promised something are all retrievable rather than lost in a scrollback you will never revisit. The third step is acting. AirJelly surfaces what matters, creates tasks, prepares briefs and sends reminders — before you ask. Tasks are auto-captured from conversations, meetings and messages, complete with due dates, so you do not have to write anything down yourself. Proactive alerts deliver follow-ups, reminders and briefs at the right moment, tying the promise you made in a chat to the moment it needs to be honoured. A Daily Report provides an end-of-day summary of what happened, what is pending and what is next, giving a structured close to the day. Taken together, these features turn observation into output: not just a record of activity, but a stream of concrete items that need attention. AirJelly's overall approach can be described as moving from context to memory, and then from memory to action. The website frames this with three pillars — context for the agent, an activity log, and deadline alerts — and with a three-step loop of watches, remembers, acts. Scattered context is pulled together from the apps you already use, stored as a searchable timeline, and then handed to an agent that can reason over the full picture instead of a partial prompt. AirJelly also explicitly positions itself as a companion for other AI agents, bringing scattered context together so that Codex and Claude Code can act with the full picture. In contrast to regular AI assistants that wait to be asked, AirJelly watches your workflow continuously and acts before you ask, which the site presents as its central differentiator. The benefits AirJelly claims follow directly from that design. You no longer need to take notes to keep track of your own work, because tasks are extracted automatically from conversations, meetings and messages with their context and due dates attached. You are less likely to drop a follow-up or forget a promise, because personal interactions are tracked and reminders arrive at the right moment. You spend less time re-reading documents or hunting through open tabs, because a one-page summary and a searchable memory can surface what you need — including notes you had forgotten you saved. And when an important moment is coming up, such as an interview, a brief is already prepared. The stated outcome is that everything in your life is managed by AirJelly, freeing your time for what AI cannot do, or simply for you to enjoy. The website illustrates this with concrete scenarios. In a product and operations setting, a team confirmed a review in a chat message; AirJelly caught it, created the task and attached the conversation, with the due time set and the context attached. In research and academia, a user had fourteen tabs open across papers, docs and notes; AirJelly read through all of it and produced a single research summary, including a note the user had forgotten they had saved, with key points extracted. For founders and executives, an interview thirty minutes away was spotted on the calendar and AirJelly prepared a brief covering background, recent work and talking points. These examples all follow the same pattern: context is observed, kept, and then converted into something useful before the user asks for it. AirJelly is available as a free download for Apple Silicon Macs and Windows x64 PCs, with Linux support stated as coming soon, and the site invites users to download and start in minutes, as well as to join its Discord community. It works with the apps already on your screen — Slack, Zoom, Google Docs, Calendar, email and more — rather than requiring a new place to work. On privacy, the site states that screenshots are encrypted and stored on your device, that capture can be paused at any time, and that specific apps can be excluded, with further privacy controls documented on a dedicated page. In summary, AirJelly's primary value proposition is proactivity: an always-on desktop agent that watches your workflow, remembers everything in a searchable living timeline, and acts before you ask by creating tasks, preparing briefs and sending reminders. For teams, founders, researchers and anyone carrying more context than one brain can hold, it aims to close the gap between the work that is happening on screen and the notes that never quite get written.

Kai is an AI executive assistant built by Morgen, the team behind the calendar app that 248,000 people already use. It is designed for solopreneurs, freelancers, startup founders, executives, teams, AI power users and people with ADHD who want an assistant handling the everyday chores of email, meetings, tasks and calendar management. Kai works across the accounts you already use — Google and Microsoft email and calendars — and captures Zoom, Google Meet and Microsoft Teams calls, so there is nothing to import and no new workflow to learn. It is available on web, Mac, iPhone and Android. Its stated purpose is to proactively manage your calendar, email and tasks so you can focus on what actually matters. The contrast Kai draws is cost and coverage. Its landing page states that an assistant costs $6,000 a month, positioning Kai as an alternative that you can start free with no credit card required. The problem it addresses is the fragmentation of a knowledge worker's day: email arrives, meetings happen, commitments are made out loud, and the follow-through lands on one person. Kai's own framing is "Imagine your day with an assistant handling all the chores" — it tracks your email, meetings and DMs, and chases what you promised. Rather than a tool you must open and prompt, Kai is described as an embedded assistant that works in the background, surfacing what needs attention without waiting for you to ask. Meetings are handled end to end. Before a call, the brief is already written, so you walk in knowing what you last agreed with that person. During the meeting, Kai provides a live transcript with no bot joining the call, because notes are taken from your own device. Afterward, the summary and tasks land. Kai's page notes that recording, summaries and share links are table stakes among notetakers; its stated difference is what happens after the call. Action items become real tasks on the same list your email feeds, each linked to its meeting, and a follow-up email can be drafted from how you actually write to that person. Email triage is the second core area. Kai reads your inbox in the background and sorts threads by what each one wants from you, using categories such as Needs Reply, FYI, Waiting Reply and Done. Replies are drafted in your voice, but the send stays yours — nothing is sent unless you say so. The reason this matters is timing: it works whether the email lands during your working day or at 1am, so by the time you look, the message is already sorted, the task is filed and a reply is drafted. You review everything in a triage workspace where you can accept, edit or reject each AI suggestion. Tasks and calendar complete the loop. Kai keeps your tasks on track in one ranked list, captured from what was actually agreed, so nothing slips; accepted items become tasks linked to their source, so you act with the context in front of you. That list is where meetings and email converge. Kai does not sync with external task tools today, so it works best as the home for the commitments Kai catches, next to whatever you already run. On the calendar side, Kai puts every account in one grid, spots a clash and moves it, and can block focus time on your calendar. It also gives you a booking page that reads free/busy from every calendar you have marked as busy in Kai, across your Google and Outlook accounts, so you can share one link. Setting Kai up is designed to require no imports and no new workflow to learn. You connect your email and calendar accounts — Google, Microsoft, one account or several — and Kai works across all of them at once. Kai then analyses your inbox and starts building your memory, reading what is already there and starting to remember the people, the projects and the preferences. From then on it handles your email and listens in your meetings, so the inbox arrives sorted, replies are drafted in your voice and notes are taken from your device. You approve the drafts, the follow-ups and the tasks; accepted items become tasks linked to their source. Kai also offers workflows that run on their own, and it provides MCP for the AI tools you already use, so Claude or ChatGPT gets the context of the meeting or email a task came from. You can also chat with Kai about any of it. The outcome Kai promises is time back for the work only you can do: Kai clears the inbox, and the calendar stays honest. For founders and executives, it means entering a call knowing what you last agreed and leaving it with what was agreed turned into tasks. For freelancers and solopreneurs, mail that needs you is sorted with the reason attached, a reply is drafted in your voice for you to press Send, and what you promised yourself sits in the list. Teams get meetings recorded on their own laptop with no bot, mail sorted by what needs a reply, and each person starting with a brief on their day. For users with ADHD, everything owed today sits in one ranked list, and Kai flags a task before it slips. Day to day, Kai fits into several explicitly described scenarios. Before a call you receive the brief; after it, action items become tasks and a follow-up email is drafted. If you need to schedule a meeting with someone outside your company, you share a booking page that reads free/busy across your Google and Outlook accounts, or you ask Kai to draft a reply suggesting times in your voice. For AI power users, you can ask ChatGPT what is due and it reads Kai, or tell Claude to move the meeting and approve the change, without pasting your day into the prompt. You can also ask Kai to hold your building time or let it block focus time on your calendar. Kai is explicitly positioned for five groups: founders and executives, freelancers and solopreneurs, teams, AI power users, and people with ADHD. It connects with Gmail, Google Calendar, Microsoft Outlook, Zoom, Google Meet and Microsoft Teams. Kai is available on web, Mac, iPhone and Android, including a mobile app on iPhone and Android. Pricing starts from $20 per user, per month, and Kai is described as free until it has earned it, with a free start and no credit card required; Product Hunt sign-ups get 2x trial credits. Every action runs through an approval step first: Kai does not create a task or block calendar time without your approval, and it learns from your corrections over time. If it miscategorises an email or suggests the wrong action item, you edit or reject it. Kai's value proposition is a proactive, embedded assistant rather than another chatbot you have to prompt. By connecting directly to your email, calendar and meeting tools, it sorts, drafts, records and captures in the background while keeping you the decision-maker on anything that sends or syncs. Start free and see it on your own week.

PostNitro for Agents is a social media API for AI agents that designs the post and publishes it, not just writes the caption. It gives an agent a design engine: on-brand carousels, single-image posts and animated video built from over 100 templates and the user's brand kit, then scheduled and published natively to LinkedIn, Instagram, TikTok, Threads, Facebook and Pinterest. The product is aimed at creators, marketing teams and developers who want their AI agent to carry a social post from a raw idea to a finished, on-brand publication in one chain, with no human in the loop unless they want one. Most social APIs an agent can reach will publish text and pre-made media — they assume the design already exists and that a person made it. That leaves the hardest and most time-consuming part of social publishing untouched: producing the artwork, keeping it on brand, sizing it for each network and turning a long article or thread into a readable visual format. PostNitro addresses that gap by putting the design engine itself behind the API, so an agent can convert a topic, article or X thread into a carousel, image post or video and then schedule it. The company frames this as giving an agent a design engine rather than just a publishing API. The creation tools cover carousels, single-image posts and short video, generated from a topic, raw text, an article URL or an X post. Agents can also add AI-generated images and bake them straight into slides, so the artwork appears inside the post rather than alongside it. Alongside generation, there is an import path: a user can bring their own slide content and let PostNitro design it, using the same templates and the same brand kit with no AI writing step involved. That distinction matters for teams that already have approved copy and only need a consistent visual treatment applied to it. Brand is handled through brand kits that agents can list, read, create and update. A brand kit holds fonts, colours, logos and headshots, which means the output stays on-brand without anyone having to describe the brand in a prompt. Because the agent works from the kit rather than from a text description, results are consistent across posts and across sessions. A browsing set of tools rounds this out: agents can list templates, AI presets and audio tracks, then pick deliberately instead of accepting a default. That gives an agent or its operator a way to steer the look and sound of the output before anything is generated. Scheduling and publishing tools let an agent create, read, update and delete scheduled posts, connect and inspect social accounts, and publish natively to six networks: LinkedIn, Instagram, TikTok, Threads, Facebook and Pinterest. Output can take the form of PNG, PDF, MP4 or an editable design, depending on what the caller asks for. X (Twitter) is supported as a content source and as an export format, but not as a publishing destination. Recently PostNitro added direct publishing to Facebook Pages and Pinterest boards, extending where an agent can deliver without a manual upload step. Operating the product follows a simple three-step flow. First, connect the agent: add PostNitro to Claude or ChatGPT in one click with no API key, or point any MCP client, the CLI or the REST API at it. Second, ask for a post — for example, turn an article into a six-slide LinkedIn carousel on the brand kit and schedule it for Thursday at 9am. Third, approve it or let it run: the post is designed on the brand kit and then scheduled, or it is saved as a draft on the calendar so a person can review it and hit schedule. That choice between full automation and human review is available on every job. There are six documented ways into the same engine, all sharing one account, one brand kit and one template library. Claude connects in one click via Claude's Connector Directory using OAuth, so there is no API key to fetch. ChatGPT installs in one click from its plugin directory with the same OAuth sign-in. The MCP server exposes 35 tools at mcp.postnitro.ai for any MCP client. The CLI, installed with npm i -g @postnitro/cli, takes JSON in and returns a designed post out, and is built to be script-safe for CI and automations. The REST API, the layer the others are built on, designs and publishes in one chain. A Skill for OpenClaw and Claude Code lets an agent drive PostNitro end to end. Long-running work is handled explicitly: every generator has a fire-and-forget form and an and-wait form, plus status and output polling, so an agent can choose between streaming and blocking depending on its own architecture. Authentication is unified across the MCP server, CLI and REST API, which all use the same PostNitro API key, so moving between interfaces needs no new setup. Claude and ChatGPT are the exception and the easier case — they connect through their respective directories with OAuth, so there is no key to fetch at all. The MCP server implements OAuth 2.1 with dynamic client registration, which is what removes the key step. A distinctive part of the product is the handoff between agent and human. If the caller asks for the DESIGN output, PostNitro returns an editable design instead of a rendered file. An agent can produce the work at 3am, and a person can then open it in the editor, change the headline and publish. PostNitro contrasts this with publish-only APIs, which return something that cannot be edited, describing its own approach as the one place where the human and the agent share a workspace rather than a handover. For teams that want oversight without slowing the agent down, this makes review a matter of light editing rather than rebuilding. PostNitro is explicit about one boundary: video output is not AI-generated. The video that an agent produces is the carousel slides rendered as an MP4, with animated slide text and audio chosen from the available tracks. It is a design feature rather than a generative video model, and the company notes that this is how carousels reach TikTok, which static-export tools cannot do. Being clear about that distinction helps set expectations for anyone comparing the product with generative video tools, and it keeps the output tied to the brand kit that the slides were built on. Agent access is priced separately from the broader product. It is included in Creator+ at $30 per month with 200 credits per month, and in Team+ at $60 per month with 500 credits per month. On the Starter plan, agent access is a $10 per month add-on with 250 credits. Credit volume can be increased, and the cost per output is stated plainly: an imported slide costs 1 credit and an AI-generated slide costs 2. There is no free tier for the API, so teams evaluating agent-driven publishing should plan for a paid plan or the Starter add-on from the start. Typical uses are described through the prompts the product suggests pasting into an agent. A user might ask it to turn a latest blog post into a LinkedIn carousel, which returns a carousel PDF. Another might announce a launch as an Instagram post on the brand kit, producing an image post. A third might request a 20-second TikTok video from five tips, producing an MP4. A planner-style prompt can ask for next week's schedule — three carousels, Monday to Wednesday at 9am — saved as drafts for review. Each of these maps directly onto the create, schedule and publish tools the agent can call. PostNitro says the product is used by more than 85,000 creators, with its editor embedded in over 200 platforms and a 4.9-star rating on Product Hunt. On the technical side, supported clients include anything that speaks MCP: Claude (Desktop, Code and the web app), Cursor, ChatGPT, VS Code and agent frameworks that support MCP servers. Anything that can run a command works with the CLI, including OpenClaw through the Skill, n8n, Make and CI pipelines, while anything that can make an HTTP request works with the REST API. That breadth means the same engine can be driven from a chat window, a script or a scheduled job. The core value proposition is a design engine and a publishing pipeline in one chain. Rather than handing an agent a caption it can already write and a media file someone else had to produce, PostNitro lets the agent build the carousel, image post or video on the brand kit and then schedule it natively to six networks — six ways in, 35 MCP tools, editable design output when a human wants to finish the job, and paid agent access that starts with Creator+, Team+ or a Starter add-on.

MausBot is an open source Grok Bot alternative that gives you your own team of AI bots inside a familiar chat app, running on your own machine. Instead of a single assistant, MausBot lets you give each bot a job, a brain and a computer: bots run in parallel on your computer and come back when something needs you. The app is free and open source under Apache 2.0, available for Mac, Windows and Linux, and it works with your own provider account or API key, so provider fees and limits apply. Its stated purpose is putting your business on autopilot across areas such as business, sales, support, hiring, invoices and SEO. MausBot is designed for people who want agentic AI teammates doing real work in a chat interface they already understand, while keeping their own machine, logins and subscriptions at the centre. The problem MausBot addresses is the sense that AI agent software usually arrives as another hosted subscription. In the words on the site, MausBot is the open source Grok Bot alternative, and the builders describe making an open source version of the Grok Bot complete with all the features. The stated claim is that it does not need any subscriptions at all and instead uses the existing subscriptions you already have. Users quoted on the page frame the same point, arguing that nobody wants another monthly seat and that intelligence on your machine should come with your machine, not be billed like headcount. MausBot's answer is local-first by default: transcripts, keys and events live in ~/.openmausbot on your own disk, and nothing leaves unless you send it. The core capability is giving each bot a job, a brain and a computer. In the free app, every bot gets a computer in the form of a cloud desktop from a provider you connect, spun up on its own with a live screen preview, or you can point the bot at your Mac. Each bot also has its own brain: Claude and Codex are shown side by side with defaults marked, and you can switch a bot's model mid-conversation. Supported models listed on the site include Claude from Anthropic, Codex from OpenAI, Grok from xAI, Cursor, Kimi from Moonshot AI, Droid from Factory, Antigravity from Google, OpenCode, Qwen from Alibaba, Hermes from Nous Research and Pi. Because the brain is chosen per bot, different bots in the same team can think with different models while living inside one chat app, which lets you match a model to the nature of the work rather than accepting one engine for everything. Connected apps give bots hands. MausBot offers Gmail, Slack, GitHub, Notion, Linear and hundreds more over Composio: OAuth once and every bot can use them. Credentials are handled through a keys screen where you paste credentials once and the provider fleet hot-reloads, while secrets are write-only so the UI never sees them. Routines sit on a calendar, with every bot's routines placed on one week so you can see what runs when, plus a next indicator for the upcoming run. Bots can also talk back: hit call and it becomes a conversation, where the bot hears you, narrates its work, and asks for approvals out loud, with Approve and Not now controls on screen. MausBot manages agents the way a messaging app manages contacts. You can pin, mark unread, duplicate, hide and delete bots, and the site describes the product plainly: it is a messaging app, and your agents behave like contacts. Bot rows show their latest activity, such as reminders sent, a plan ready, a launch draft delivered or pull requests reviewed. On top of that sits a catalogue of jobs worth giving a bot: Sales Follow-up, Chief of Staff, Recruiter, Bookkeeper, Bug Fixer, Support Triage, Competitor Watch and SEO Growth. Each job lists when it runs and which tools it uses, and each is designed so the bot reports back and waits for your approval before anything goes out. The approach is deliberately local. Bots run on the claude, codex and grok CLIs already installed on your computer, using your logins and your subscriptions, with no proxy in the middle. A single local harness, one server on 127.0.0.1, owns every agent process and normalizes each provider's native protocol into one event stream, which is what allows different engines to appear uniformly as bots in the app. Nothing leaves unless you send it: transcripts, keys and events live in ~/.openmausbot on your own disk, and the site states that local-first is the default, not a setting. Getting started follows a short path: download the desktop app, connect one provider through the onboarding engine check, create your first bot with a name, engine and model, then send one small useful task. The outcomes described on the site follow from that design. Because bots run in parallel on your own machine and come back when something needs you, work proceeds without you driving each step, while approvals keep you in control of what actually gets sent, merged or prepared. Running on existing subscriptions removes a barrier for adopters, and testimonials highlight that the bots, the machine and the keys are yours. Others point to configuration flexibility, cross-agent capability, and the option to run on your own infrastructure, for example on an Azure VM for an always-on agent, or with only your own local models and your own agent computer. A contributor notes the merged MCP server for external agent orchestration, which lets one agent communicate with the rest, and another notes that dynamic personality makes the agent experience feel more alive. Concrete use cases come straight from the job catalogue. Sales Follow-up finds the deals that went quiet, works out who owes the next step and drafts each follow-up, running weekdays at 9:00 AM with Gmail and HubSpot, and nothing sends until you approve. Chief of Staff reads your inbox, calendar and team channels before your day starts and sends one brief covering what changed, what is waiting on you and what it can clear, weekdays at 7:30 AM with Gmail, Google Calendar and Slack. Recruiter finds people who fit an open role, checks each one against your must-haves and keeps a shortlist with the reason they made it, using its own browser and Google Sheets. Bookkeeper pulls overdue invoices, drafts a polite reminder for each one and keeps a running sheet of who owes what, with Stripe, Gmail and Google Sheets, running Mondays at 9:00 AM. Bug Fixer reproduces the bug, writes a test that fails for the same reason, fixes it and prepares a pull request with the evidence. Support Triage answers the questions you have answered a hundred times and turns technical ones into tickets, while Competitor Watch checks pricing pages, changelogs and docs and writes a short brief, and SEO Growth turns search data into a short list of fixes and content briefs. MausBot is for individuals and teams who already pay for AI subscriptions and want agentic teammates running on their own hardware, from solopreneurs running daily operations with an autonomous AI team on a laptop, to developers working with coding agents, to teams wanting shared always-on automation. Integrations named on the site include Gmail, Slack, GitHub, Notion, Linear and hundreds more through Composio, with computers and apps from Cua, ASCII Box and Daytona, and models from Anthropic, OpenAI, xAI, Cursor, Moonshot AI, Factory, Google, OpenCode, Alibaba, Nous Research and Pi. Pricing is straightforward: the desktop app is free and open source under Apache 2.0 for Mac, Windows and Linux, phone apps are companions that start from your computer, and Cloud plans start with a 3-day free trial that you can cancel any time. Bring your own provider account or API key, and provider fees and limits apply. MausBot's primary value proposition is simple: a whole team of AI bots, each with a job, a brain and a computer, living in a chat app on your own machine. It is free, open source under Apache 2.0, built on the CLIs and subscriptions you already have, and local-first in the way it stores transcripts, keys and events. For anyone who wants real work done by agents, from sales follow-ups and daily briefs to invoices, bug fixes, competitor monitoring and SEO, without adding another hosted subscription, MausBot is the open source Grok Bot alternative that keeps your bots, your machine and your keys in your hands.

OpenCode is an open source AI coding agent that helps you write code in your terminal, IDE, or desktop. It is offered as a terminal-based interface, a desktop app, a web app, and an IDE extension, so the agent can follow developers into the environment they already work in rather than forcing them into a single tool. Free models are included, and users can also connect any model from any provider, including Claude, GPT, Gemini and more, which means one agent can be pointed at many different models over time. For developers, the appeal of an agent that writes code is obvious, but the practical questions are usually about lock-in, cost, and privacy. OpenCode answers those questions by being open source, by including free models while still allowing any model from any provider, and by stating that it does not store any of your code or context data so that it can operate in privacy sensitive environments. The FAQ on the site reflects those same concerns, with questions about how to use OpenCode, whether extra AI subscriptions are required, whether existing subscriptions can be used, whether it can only be used in the terminal, how much it costs, and what happens with data and privacy. Together, these choices are meant to let teams adopt an AI coding agent without committing to one vendor's model catalog or handing over code and context they would rather keep local. Model choice sits at the center of OpenCode. The agent connects to 75+ LLM providers through Models.dev, including local models, so a developer can select a hosted model from a major provider or run a model locally on their own hardware. Beyond the providers it supports directly, OpenCode also lets you log in with GitHub to use your Copilot account, and log in with OpenAI to use your ChatGPT Plus or Pro account. That means existing AI subscriptions can be brought into the agent instead of requiring a separate purchase, and teams can standardize on OpenCode as the interface while individual developers use whichever underlying model and account they already have. OpenCode is deliberately editor- and platform-agnostic. It is available as a terminal interface, a desktop app, a web app, and an IDE extension, which means the same agent experience can be used from a command line, from a dedicated desktop application, or from inside an editor. Because the agent is available across those surfaces, a developer can move between them as a task changes, and the choice of editor is not a constraint on whether OpenCode can be used on a given project. The website lists several installation routes, including curl, npm, bun, brew, paru, and yay, with a single install command shown as curl -fsSL https://opencode.ai/v2/install | bash. Documentation is linked directly from the site, so setup and usage details are a click away rather than buried in the product. Within a session, several features are designed to make the agent more useful across a real project. OpenCode is LSP enabled, meaning it automatically loads the right LSPs for the LLM, so the model is given language-aware context instead of working blind against a codebase. Multi-session support lets you start multiple agents in parallel on the same project, so separate tasks can progress at the same time rather than queuing behind one another. Share links let you share a link to any session for reference or to debug, turning an agent session into something a teammate can inspect and discuss. Privacy is treated as a first-class concern. OpenCode states that it does not store any of your code or context data, so that it can operate in privacy sensitive environments, and it links to a dedicated enterprise page for more detail on that approach. Local models are also part of the picture, since the 75+ providers OpenCode connects to through Models.dev include local models, which matters for developers who want the model itself to run on their own machines. For model reliability, the project also offers Zen, which gives access to a handpicked set of AI models that OpenCode has tested and benchmarked specifically for coding agents. The stated goal is to remove the worry about inconsistent performance and quality across providers by letting developers use validated models that work. The project's scale is part of what it offers users. The website reports over 208,000 GitHub stars, 950 contributors, and over 13,000 commits, and states that OpenCode is used and trusted by over 16 million developers every month. For anyone evaluating an agent, those numbers suggest a large, active open source project behind the tool. Because OpenCode is open source, developers can inspect how it works, and the site's FAQ answers the questions people typically ask before adopting it: what OpenCode is, how to use it, whether extra AI subscriptions are needed, whether existing subscriptions can be reused, whether the terminal is the only option, what it costs, how data and privacy are handled, and whether it is open source. The practical use cases follow directly from those features. A developer can run OpenCode from the terminal for everyday coding tasks, or from a desktop app or IDE extension when a graphical workflow is preferable. Multi-session support suits projects where several tasks can be investigated in parallel, such as fixing one area of a codebase while another agent works elsewhere. Share links are useful when a session needs to be handed to a colleague for reference or debugging. Privacy-first operation is relevant for teams working in privacy sensitive environments, and Zen's validated models help when consistent behavior matters more than experimenting with every provider. OpenCode is aimed at developers and teams who want an AI coding agent that they can inspect, extend, and run in the environments they already use. Its integrations are broad: 75+ LLM providers through Models.dev, including local models; GitHub login for Copilot accounts; OpenAI login for ChatGPT Plus or Pro accounts; and a terminal interface, desktop app, web app, and IDE extension for access. Free models are included, and the Product Hunt listing lists the product under the Open Source, Developer Tools, GitHub, and Development topics. The website also invites visitors to join a waitlist to be the first to know when new products are released. Overall, OpenCode's value proposition is straightforward: an open source AI coding agent that works where you work, connects to the models you already have access to — including free models, 75+ providers through Models.dev, and local models — and is designed so that it does not store your code or context data. With LSP awareness, parallel multi-session agents, shareable sessions, and a model lineup validated through Zen, it gives developers a flexible, privacy-conscious way to bring agentic coding into their daily workflow without being tied to a single provider.

Toby is an AI mock interviewer designed for anyone preparing for a real job interview. You paste a job description, and Toby builds the questions a real panel would ask, from the warm-up to the curveball, then interviews you on a Google Meet, Teams or Zoom-style call. Afterwards you receive a scored debrief that breaks down every answer: what worked, what to fix, and a stronger version of each answer built only from what you said. Toby Higgins, the AI interviewer, asks the questions, listens, and tells you exactly what to fix, positioning the whole product as "the interview before the interview." The core promise is that this is not a generic list of questions. Toby is built for the interview you have actually got. Preparing for an interview usually means reading a wall of sample questions that have nothing to do with the company, the role or the round you are facing, and then hoping the real conversation goes well. Toby instead researches the company, maps every round and plays the interviewer, then shows you exactly what to fix. Practice happens in a Meet, Teams or Zoom-style call "so the real thing feels familiar," and the closing message on the site sums up the outcome: walk in having done it already, having practised the hard questions before they count. The first part of the product is preparation. Tell Toby the role and it builds your brief: what the company does, how it makes money, what has happened lately and how the team works. The brief covers the company's stage, size, HQ and business model at a glance, recent news and culture signals, dated, and talking points worth raising in the room. In the worked example shown on the site, a candidate preparing for a Senior Product Manager, Payments role at Fernleaf is given a summary of the company as spend management for UK and EU small businesses (company cards, bill pay and expenses in one account), plus its Series B stage, £45m in funding, roughly 200 staff and a London HQ. Recent news items are dated, such as hiring a new CFO from a listed payments company in August 2026 and launching bill pay in Ireland and the Netherlands in June 2026. Suggested talking points include asking how SEPA failure rates compare with Faster Payments, noting that interchange is most of the company's revenue so authorisation rate is a revenue metric, and offering a short written plan for your first 90 days in light of the company's memo culture. The second part maps the interview itself. Toby maps the interview process and what each round is assessing, then lists the questions most likely to come up in each one. Every round is shown with how long it runs and what it tests; in the example these are a 30-minute recruiter screen, a 45-minute hiring manager round, a 60-minute product case and a 45-minute values interview. Questions are tagged by competency and difficulty, and marked when real candidates have reported them. The site lists sample questions such as "What made you apply to Fernleaf, and why this payments role in particular?" tagged customer focus, motivation and easy, and a question about leading a squad of six engineers without line management tagged cross-functional leadership, leadership and medium. Users can also browse separate company interview guides. Practice is shaped around the round you are facing and the time you have. You pick the round (recruiter screen, hiring manager, product case or values) and the length, choosing 10, 20 or 30 minutes, whatever you've got. Real mode runs the interview straight through, like the day itself, with the full debrief at the end. Coach mode gives you a note or two after each answer and a chance to try again, and coach notes only appear once you have finished talking. The call is taken in a Google Meet, Teams or Zoom-style environment so the real thing feels familiar, with your camera on or off, and Toby will wait while you think. If you leave the call, he waits: "he's very patient," and you can rejoin. The debrief is where the feedback lands. You get a score against the role's rubric, then every answer is broken down: what worked, what to fix, and a stronger version built only from what you said. Each competency is scored 1–5, with the quote behind it, and the debrief also reports pace, talk time, filler words and answer length, alongside the full transcript, question by question. In the example on the site, a recruiter screen debrief scores 60 out of 100 with the headline "Solid examples, light on measurable impact," customer focus scored 3/5, talk time 35%, pace 136 wpm against a 130–160 ideal, and one filler word ("actually" ×1). The stronger version is built only from what the user said and adds bracketed prompts — such as adding the result as a number, from what, to what, over what time — that the user fills in with their real details. Overall, the method is simple and explicitly described. You paste a job description; Toby researches the company, maps every round and plays the interviewer; you take the call; and you get a scored debrief and a stronger version of each answer. Everything it tells you is anchored to your own words rather than a generic script, which is what makes the feedback specific to your role, your company and your rounds rather than to interviews in general. The stated benefits follow directly from that loop. Because you have already sat the interview once, in the same style of call, familiarity replaces novelty on the day. Because each competency is scored with the quote behind it, you know exactly what to fix rather than vaguely feeling underprepared. Because delivery metrics such as talk time, pace and filler words are surfaced, you can adjust how you speak as well as what you say. And because the suggested rewrites are drawn only from your own answers, the improved version still sounds like you, with placeholders prompting you to add the numbers and details you left out. Typical use cases are the rounds the product supports. You might rehearse a recruiter screen before an introductory call, work through hiring manager questions that probe your background and domain experience, run a longer product case round, or prepare for a values interview. Between rounds, Coach mode lets you pause after each answer, take a note such as "Good, specific example. Now size it: from what, to what, over what time?" and try again. Separately, the prep brief lets you research a company before the interview, gathering its business model, latest news and talking points worth raising in the room. Pricing is pay by the minute rather than a subscription. Your first 5 minutes on a call are free — enough to warm up and see how Toby's feedback works — and prep briefs, likely questions for every round, Real or Coach mode and a debrief on what you answered are included; you save a card to see your debrief, and it is not charged. After that, minutes cost £0.50 each, bought in packs from £5, are only used on a call, and last 6 months. There is no subscription, you can stop whenever you like, and every round and both modes are available. The pricing panel lets you choose a mock interview of 10, 20, 30 or 60 minutes, with £10.00 shown in the example. Toby runs on the web, with sign-up through app.usetoby.ai. In short, Toby's value proposition is that you can practise the hard questions before they count. It turns a job description into a researched prep brief, a mapped interview process and a realistic mock interview call, then converts that call into scored, quotable feedback and stronger answers built only from what you said — so that on the day, the conversation is something you have already had.

MentionBird is an AI visibility platform that tracks what AI assistants say when buyers ask them what to buy. It monitors the prompts that matter to a business across ChatGPT, Perplexity, Gemini, and Claude, as well as Google AI Overviews, Grok, Microsoft Copilot, and DeepSeek, and shows which sources each model cites when it answers. From there, MentionBird produces a per-prompt plan to get a brand into those sources. The product is aimed at marketing and SEO teams, founders, e-commerce stores, SaaS companies, and agencies that want their brand to be named in AI answers rather than only ranked in Google. The problem MentionBird addresses is that the shortlist for a purchase now forms inside the AI answer. Buyers no longer start with ten blue links; they ask AI, get two or three brand names, and shop from that list. If AI does not say your name, you were never in the running, and you would never know it. MentionBird's own launch story makes the point bluntly: the team took the top three Product Hunt launches and asked ChatGPT, Gemini and Claude the exact questions their buyers ask, such as 'Best browser for Claude Code?' and 'How do I track agent run costs?'. The result was nine answers and zero mentions, which means AI sent those buyers elsewhere. AI search pulls from pages it trusts, including listicles, Reddit threads, comparisons and YouTube videos, and if a brand is not there, it is not in the answer. Tracking the right prompts is the starting point. Users add the questions buyers actually ask, such as 'Best running shoes for daily use', 'Best marathon shoes for men', 'Best running shoes for flat feet' and 'Most comfortable shoes for long runs', and MentionBird keeps them in one workspace as prompts ready to track. The product guidance is to start with the prompts customers might use when searching for products or services like yours, which for most brands means tracking 20 to 50 real questions. A focused set of pre-purchase questions is described as more valuable than hundreds of broad prompts, and users can add, label or regroup prompts at any time as they learn what matters. Once prompts are tracked, MentionBird shows exactly which sources AI cites for each one. For a tracked prompt, the interface lists the sources AI checks and marks each as cited or not cited, covering domains such as runnersworld.com, runrepeat.com, YouTube shoe reviews and zappos.com listings, or verywellfit.com, rei.com expert advice, YouTube channels and fleetfeet.com. This matters because every prompt has its own set of sources: AI answers are built from a small set of pages, reviews and videos that the model trusts for that specific question. Sources decide the shortlist, so getting into them means getting recommended, while staying out means your competitor takes the customer. Seeing per-prompt citation status turns an opaque AI answer into a concrete list of places where a brand must earn a mention. AI Ranking Advice converts that list into work. Choose any tracked prompt, or customer question, and MentionBird reviews the answers, the named brands and the cited sources, then recommends practical steps while showing the effort required for each one. Advice is rated by impact and effort, for example 'Get featured in trusted running shoe roundups' marked medium effort and high impact because it targets the most-cited sources, 'Publish a guide to daily running shoes' marked medium effort to answer the questions buyers ask before choosing, and 'Add fit and durability details to product pages' marked low effort to give AI clearer product facts to quote. Recommendations may include better product pages, clearer product details, comparison guides, videos, or mentions from trusted websites. MentionBird also proves whether the plan is working. Every plan a team executes shows up in the numbers through daily checks across the supported AI platforms, with AI visibility data checked and updated once every 24 hours. Tracking is prompt by prompt: cited, not cited, or no brand named yet, and that gap is yours to take. It reports share of AI recommendations against each competitor week over week, and every score change is traced back to the exact prompt, model or citation that caused it. Example reporting shows brands such as Nike at 31 percent visibility share and up four points, Adidas at 27 percent and unchanged, Puma at 19 percent and down three points, and New Balance at 12 percent and up one point. The methodology is deliberately action-oriented. Other tools chart AI mentions and tell you when you dropped, leaving the 'now what?' to you; MentionBird is built around the question of what to do to get mentioned for a specific prompt. Its three steps are: pick the prompts that bring you customers, track exactly which sources AI cites for each one, and get ranking advice to win those sources. Setup requires no code, plugins or tracking tags: users add their brand, prompts and competitors, and MentionBird then checks AI answers without making any changes to the website, with setup taking only a few minutes. Unlike SEO rank trackers that report a webpage's position in Google search results for keywords, MentionBird tracks brand mentions across AI answers and the sources behind them. The payoff is visibility that turns into consideration. Because AI answers influence sales by shaping which brands buyers consider, and a buyer may visit only the brands included in the answer, appearing in those answers is directly relevant to revenue. If a brand is missing, a competitor may reach that buyer first. MentionBird's customers report discovering that they win category questions but lose comparison questions, that they were invisible on the exact questions engineering buyers ask about automating environment provisioning, and that AI was recommending an ATS to every buyer who asked while never naming theirs. One customer reports being in 83 percent of 'alternatives to the category leader' answers after MentionBird found the gap and told them exactly what to fix first. Another notes that having 3,300+ reviews was not enough while AI still failed to recommend them. Concrete use cases include e-commerce and retail brands competing on product-category prompts, such as running shoes or apparel, where being cited in trusted roundups, review sites and YouTube reviews determines whether AI names the brand. Software companies use it to find the questions where they are invisible among engineering or business buyers and then publish content that answers them. Agencies manage several clients in one workspace, while marketing and SEO teams track their brands and products and compare their visibility with competitors. Mentions of AI ranking advice also point to comparison guides and clearer product pages as fixes. Beyond those, MentionBird states it supports e-commerce stores, software companies, healthcare providers, law firms, financial services, real estate, travel and education, as well as home services, automotive companies and local businesses, meaning any business whose customers research online. MentionBird is designed for teams whose buyers ask AI what to buy, including marketing and SEO teams, founders, and agencies. Competitors can be added so a brand's AI mentions can be compared with theirs, and businesses managing several brands or product lines can keep them in one workspace, using tags to organize prompts by brand, product or project. Subscription plans can be changed at any time, with upgrades applying immediately and downgrades beginning with the next billing period, while saved prompts, labels and past data remain available. During the Product Hunt launch, code PH19 gives Starter at $19/mo for three months, and a free AI visibility report is available to start. MentionBird's core proposition is simple: AI search is the new shortlist. MentionBird shows which sources AI trusts for the prompts that drive sales, why competitors get recommended, and what to fix to win the mention, then proves the plan worked with daily tracking. For brands that suspect AI is sending their buyers elsewhere, it replaces guesswork with a prompt-by-prompt picture of the gap and a ranked set of actions to close it.

nestd.ai is an AI tool that turns property photos into structured property features and then into professional listing descriptions. You can paste an Airbnb or Booking.com listing URL, or upload your own photos, and the platform analyzes each room to understand what makes every space unique — the light, the style, the feel, and the neighborhood character — before writing copy that is ready to publish. Instead of starting from a blank form or a blank page, users get a room-by-room breakdown of materials, finishes, appliances, and style, followed by descriptions, highlights, and insights. The product is built for landlords, Airbnb and Booking.com hosts, real estate agents, and agencies or catalog operators who need listing copy that matches what is actually in the photos. The product addresses a familiar set of frustrations. As the site puts it: 'I spend 45 minutes staring at a blank page for every listing,' 'My descriptions all sound the same. I'm running out of ways to say spacious,' 'I've tried ChatGPT but it writes generic fluff that doesn't match my photos,' and 'Guests ask for a quiet place with a real kitchen and my catalog filters can't find it.' Those quotes capture two related problems. On the writing side, hosts, landlords, and agents lose time staring at blank pages and reusing interchangeable language that does not reflect the actual unit. On the discovery side, catalog search built on checkbox filters cannot answer natural-language requests from guests and buyers. nestd.ai addresses both by first turning photos into a structured, reviewable feature graph, and then writing copy — and powering search — from that same analysis. Getting started requires no manual feature forms. You can drop interior, exterior, and amenity shots directly into the app, or import a listing by pasting an Airbnb or Booking.com URL, which pulls the title, room details, location when available, and photo links. Smartphone photos work perfectly, and the AI is described as trained on real-world listing photos, not just professional shots. Once the images are in, nestd.ai groups them into rooms. The import flow opens the app with your URL ready and requires the user to confirm ownership before analysis begins; users must own or manage the listing and are responsible for complying with the platform's terms. From those photos, nestd.ai builds a feature analysis. It identifies each space — kitchen, bedrooms, bathrooms, living room — and extracts materials, finishes, appliances, furniture, and condition signals. Every property gets deep analysis: room-by-room identification, feature extraction of materials, appliances and finishes, style classification such as modern, traditional or luxury, and quality assessment covering condition and renovation level. The analysis is surfaced as a list of structured features such as open plan, vaulted ceiling, ceiling beam, kitchen island, breakfast bar, pendant lighting, recessed lighting, fireplace, built-in cabinets, refrigerator, range, oven, range hood, dishwasher and kitchen sink, alongside descriptors such as white, light wood, black, gold, beige and gray. Users review the analysis before generating any copy, so they can see exactly what the system detected in each room. Location intelligence is built in. Add the address and nestd.ai researches nearby amenities, transit and accessibility signals, schools, neighborhood vibe, and local characteristics. That context is then woven into the descriptions for guests or buyers who care about location. The sample outputs show how this appears in practice: the Brookhaven villa description references Wertheim Wildlife Refuge, Row Boat Beach, and Painters' Restaurant, while the Warsaw apartment description mentions cafes, parks, the Vistula waterfront, and public transport. The platform also suggests a listing name, property type, and room counts from the photos, plus a location when EXIF GPS data is available, and lets users review and edit anything before continuing. Descriptions are generated from the analysis rather than from a blank prompt. When generating, users choose a style — marketing or social — along with a length, a language, and a target audience such as families, couples, digital nomads, professionals, or a custom audience. The AI adapts the wording so it speaks to the right guests or buyers. Descriptions can be regenerated as many times as needed, and style, tone, or length can be adjusted until they are right; an additional prompt can be added if desired. The dashboard shows the complete property breakdown — rooms identified, features extracted, style classification, and location insights — so users can see the analysis behind the copy. Published examples include a social-style description for a luxury villa with vaulted ceilings, a fireplace and a barn door into a spa bathroom, a marketing-style description for a one-bedroom coastal apartment in Cabarete with turquoise accents and a private balcony, and a marketing-style description for a small one-bedroom Warsaw apartment with a galley kitchen and stone-veneer feature wall. The differentiating idea is that photos replace manual feature forms. Generic one-shot prompts often miss room-level detail; nestd.ai analyzes each room in the photos separately and highlights what makes that specific property unique. The result is copy that mentions the marble countertops in the kitchen and the barn doors in the bedroom because the system looked at each room, not just the first photo. No prompt writing is required: upload your photos and location, and the platform automatically analyzes rooms, features, style, and neighborhood context. Structured features then power both the descriptions and, for catalogs and platforms, natural-language search — so guests and buyers can find properties by how they actually think, rather than by checkbox filters. The stated benefits are speed and specificity. Landlords can list rental properties faster, generating descriptions for multiple units in minutes instead of hours. Airbnb and Booking.com hosts can import a listing in one click and produce compelling descriptions intended to get more bookings without hiring a copywriter. Real estate agents can speed up their listing process with consistent, high-quality descriptions for every property they represent. Because the copy is generated from a structured analysis of each room, descriptions are accurate and specific rather than generic AI filler, and they can be tailored to the audience reading them. As the site notes, every description written by hand is an hour that could be spent on showings, client calls, or closing deals. Concrete scenarios include a landlord publishing multiple rental units who wants descriptions for each unit without starting from a blank page every time, and a short-term rental host who pastes an Airbnb or Booking.com URL and confirms ownership to begin analysis. A real estate agent photographing a listing with a smartphone can upload interior, exterior, and amenity shots to receive a room-by-room feature breakdown and marketing copy. Agencies and catalog operators running larger inventories can use the same photo analysis as the basis for natural-language search across their catalog, and hosts can use natural-language search in-app. Users who do not want to upload photos can enable demo mode in the app to search example properties using natural language. nestd.ai is aimed at landlords, Airbnb and Booking.com hosts, real estate agents, and agencies and catalog operators, with an Enterprise path for organizations that want natural-language search across a catalog. The product runs on the web at nestd.ai, with the application at app.nestd.ai; no mobile or desktop app is mentioned. Pricing is free to start: users get free credits with no credit card required and no expiration, and the homepage advertises instant demo access. Importing a listing requires that you own or manage it. For catalog and platform search, the site points interested organizations to an Enterprise page and offers a call-booking link. In short, nestd.ai turns property photos into property intelligence: structured, reviewable features first, then professional listing descriptions written from that analysis, with optional location research and audience targeting. For hosts, landlords, and agents the value is faster, more specific listing copy; for agencies and portals it is a structured feature graph that also powers natural-language property search. Upload photos or paste a listing URL and get structured features and professional copy in minutes.

EmbeddingGemma 2 is an open, lightweight multimodal embedding model from Google DeepMind that natively maps combinations of text, images, audio, and video into a unified embedding space. Described as the most capable model for on-device multimodal embeddings, it lets developers organize, search, and connect information directly on consumer hardware. Rather than sending data to the cloud, it is designed to run locally, so a single query can search across multiple media types at once. It is aimed at developers who want to power smarter on-device search tools and privacy-first retrieval augmented generation (RAG) pipelines, and it expands on the original EmbeddingGemma by moving beyond text alone. The original EmbeddingGemma was introduced to provide a lightweight option for high-quality text embeddings, helping apps organize, search, and connect information directly on consumer hardware. The developer community's response blew past expectations: EmbeddingGemma has surpassed more than 20 million downloads, and builders have used it to power smarter on-device search tools and privacy-first retrieval augmented generation (RAG) pipelines. However, information on modern devices is rarely purely text. EmbeddingGemma 2 responds by expanding beyond text to unify code, images, video, and audio in a shared embedding space. It can help find a specific video clip from a voice memo, or search through hours of audio recordings based on a text query, all processed by a single, natively multimodal model. EmbeddingGemma 2 is best-in-class for its size. It achieves leading scores among sub-1B multimodal embedders across benchmarks such as MTEB (Massive Text Embedding Benchmark) Code and MAEB (Massive Audio Embedding Benchmark), while matching or outperforming many larger models across text, vision, and audio tasks. The model is also modular by design: it requires as little as 270M parameters for text-only workloads, with optional vision (170M) and audio (300M) encoders available for full multimodal support. This modularity means developers can adopt only the capabilities they need rather than paying the cost of the full multimodal model, which is especially valuable when deployment targets have limited resources. Storage efficiency is another core feature. Using Matryoshka Representation Learning (MRL), developers can dynamically truncate output vectors from 768 dimensions down to 512, 256, or 128 dimensions. This provides up to 6x storage reduction for local vector databases and memory usage, which matters greatly on devices where space and memory are constrained. Combined with optimizations for on-device performance, EmbeddingGemma 2 runs efficiently within tight resource constraints. With quantization, on a Google Pixel 11 Pro, it requires as little as ~191MB active RAM for text-only weights and ~567MB for the full multimodal model. EmbeddingGemma 2 is also extended context ready. It features an 8K token context window, which is 4x larger than EmbeddingGemma 1, allowing it to process up to 5.5 minutes of audio, 29 images, 58 video frames, or interleaved combinations thereof directly on local hardware. On quality, EmbeddingGemma 2 matches the strong multilingual text performance of EmbeddingGemma while delivering a significant 9.92-point improvement on code performance in MTEB Code, rising from 68.76 to 78.68, making it well-suited for local codebase indexing, semantic code search, and coding agent retrieval. Across image, video, documents, and audio, it sets a new standard in quality-per-parameter for sub-1B models and even outperforms some specialist models more than twice its size. The distinctive approach behind EmbeddingGemma 2 is that it is built from the same technology as Gemini Embedding models and runs on the Gemma 4 architecture. Released under a commercially permissive Apache 2.0 license, it has 740 million parameters, making it optimal for on-device inference. Because EmbeddingGemma 2 is built on Gemma 4 and shares its text tokenizer and audio encoder, developers can run both models together in a unified pipeline with a lower combined total memory footprint. This design enables on-device RAG pipelines that understand complex multimodal data, letting an embedding model handle retrieval while a generative model handles reasoning within the same resource budget. The benefits for users flow directly from running embeddings locally. Generating embeddings on-device helps ensure data privacy, reduces pipeline latency, and empowers developers to build cross-modal search and retrieval that works entirely offline. Because nothing needs to be sent to a remote service, sensitive data stays on the device, and pipelines avoid network round trips that would otherwise add delay. Developers also gain the ability to pair EmbeddingGemma 2 with generative models such as Gemma 4 to build on-device RAG pipelines that understand complex multimodal data, all within the resource limits of consumer hardware. Concrete use cases are demonstrated in the Google AI Edge Gallery. Instant Media Search lets you use text or an image to find the top matches in your media library based on semantic similarity. Video Moments Finder lets you locate specific moments in video using text or audio queries. In the Google AI Edge Foresight app, EmbeddingGemma 2 handles local file retrieval while Gemma 4 provides contextual reasoning. And the MediaPipe Decision Task API allows developers to create real-time decision engines that leverage multimodal context for classification, routing, and predictive capabilities. EmbeddingGemma 2 is built for developers and builders working on on-device applications. Model weights are available on Hugging Face and Kaggle, with Gemini Enterprise Agent Platform Model Garden availability coming soon, and the LiteRT Community on Hugging Face offers models optimized for on-device use. For deployment, Google AI Edge MediaPipe provides turnkey embedding, retrieval, and decision tasks, while LiteRT supports custom model integration. Developers can build for the browser with transformers.js or WebGPU. The model works with familiar tools including transformers, sentence-transformers, MLX, vLLM, llama.cpp, SGLang, Ollama, and LMStudio, and embedding vectors can be stored with Qdrant. Fine-tuning guidance is available from Unsloth. It is released under the Apache 2.0 license, and resources include a developer guide, documentation, and guides for inference and fine-tuning. In summary, EmbeddingGemma 2 brings best-in-class, natively multimodal embeddings to on-device hardware under an open, commercially permissive license. By unifying text, code, images, video, and audio in a shared space, scaling from as little as 270M parameters up to a 740M-parameter multimodal model, and supporting flexible vector truncation, it enables private, low-latency, offline search and retrieval augmented generation pipelines that developers can run directly on consumer devices.

Manifold MCP is a hosted marketing MCP server that gives AI agents live marketing data across SEO, AI answers, social, ads and lead research, inside the AI app the user already works in. According to the website, Manifold "gives your agent the tools to grow on every channel" and covers SEO, AEO, social, ads and lead research in Claude, ChatGPT, Cursor and other MCP apps. Connect it once in a minute and the agent can look things up on every channel whenever it needs to. Every client uses a single URL, https://mcp.manifoldmcp.com/mcp, and a single credit balance, and the product states there are no API keys to set up. New workspaces get 250 free credits, so marketers, growth teams and founders can start asking questions in plain words immediately. The problem Manifold is built around is channel fragmentation. Marketing research normally means jumping between an SEO tool for rankings, a separate dashboard for AI visibility, another tool for TikTok and YouTube creators, ad libraries for competitor creative, Reddit for community threads, and a data provider for contact emails. The site describes the alternative as "no pile of tabs": instead of opening each tool itself, the user asks their agent a question and the agent picks the right tools and brings back the answer. Manifold is explicitly positioned as the data your agent works from rather than a scheduler or an autoposter, which matters because it lets teams keep using the AI clients they already pay for and trust while gaining live marketing data they would otherwise have to collect by hand. For SEO, Manifold lets an agent see every keyword a site ranks for, who outranks it and why, and what to fix on the site. The workflow example on the site asks the agent which keywords rank between positions 5 and 15 that could reach page one, with volumes such as "crm for startups" at 72,900 and "simple crm" at 96,600. The underlying tool is exposed as seo_get_ranked_keywords. For AI answers, the aeo_run_ai_answers tool lets an agent ask ChatGPT, Claude, Gemini, Perplexity and Google's AI Overviews any prompt on demand and report which brands each answer names, in what position, and which pages it cites. This is billed per prompt and engine rather than as a monthly fee for tracked prompts, and a free check shows whether a site lets AI crawlers read it, so teams can see whether AI assistants recommend them for the questions their buyers actually ask. Manifold's social coverage finds the TikTok, YouTube and Instagram creators a brand's buyers watch, their audience, and their top videos with transcripts, so the agent can answer questions like which sales creators on TikTok and YouTube get the most views and what their top videos are. The site shows YouTube Shorts and TikToks from named creators as examples of what the data surfaces, along with view and like counts. Alongside social, the ad libraries capability returns competitors' live ads on Meta and Google, including what each ad says and how long it has run, for example three live Attio ads on Meta, an image ad live 47 days and two creator videos live 76 and 42 days. Being able to see creative that is already running, and for how long, gives teams a factual basis for writing their own ads. The People capability spots posts on LinkedIn and X about the problem a product solves and then returns each author's verified work email, so an agent can answer questions like who posted about leaving their CRM this week and hand back a contactable list. Reddit coverage finds every thread that asks for what the product makes, reads the replies, and helps draft an answer, with example queries such as finding threads this month asking for a CRM and example communities such as r/CRM and r/CRMSoftware. The site lists tools including reddit_search_posts, linkedin_search_posts and aeo_run_ai_answers to illustrate how the agent calls these capabilities on demand. Manifold works in three steps. First, the user connects their agent by adding Manifold to the AI app they already use; the site says it takes a minute with no API keys to set up. Second, the user asks for what they need in plain words and the agent picks the tools, with Manifold reading live data from Google, AI answers, Reddit, social, ads and leads. Third, the agent returns answers you can act on, such as what to fix on a page, who to reach out to, or which threads to join, and can take the next steps itself. Because Manifold is hosted, the agent signs in once and uses Manifold's own provider accounts; there is no need for a Reddit API app, a LinkedIn login or a DataForSEO deposit, and clients without OAuth use a Manifold API key instead. Data is fetched live from specialist providers when the agent asks: search, keyword, backlink and AI answer data come from DataForSEO, while Reddit, TikTok, Instagram, YouTube, Facebook, LinkedIn, X and the ad libraries come from the platforms' public data. Results are cached briefly, most for a day to a week, so a repeat question comes back fast and costs less. The stated outcome is answers built on real data rather than guesses. Your agent writes from Manifold's data, drafting blog posts, Reddit replies, ad copy and outreach emails in the chat with real numbers behind them, and the user checks the drafts and uses them where they want. Cost control is part of the design: every call is paid from one credit balance, each tool states its price before it runs, and the agent can cap what a call spends. A three-step example on the site shows an agent answering what to fix on a /pricing page, namely to put "crm for startups" in the title because the site ranks 7th for it, to answer "is there a free plan?" on the page because ChatGPT gets it wrong, and to link to it from the four best-ranking posts, which illustrates how concrete the output is. Concrete workflows follow the same pattern of asking in plain words and letting the agent choose the tools. A SEO lead might ask which keywords the site ranks 5 to 15 for that could reach page one, then ask what to fix on a specific page, and finally have the agent draft the changes or an outreach email about them. A growth marketer might check whether ChatGPT and Gemini recommend the brand for "best CRM for startups", then look at what competitors are running on Meta this week, then ask which TikTok and YouTube creators in the niche get the most views. A founder doing outbound might ask who posted on LinkedIn about leaving their CRM this week, request verified work emails for those authors, and then ask the agent to draft the outreach. In each case the agent returns real numbers and drafts the follow-up content in the chat. Manifold is aimed at marketers, growth teams and founders who already work inside AI clients and want marketing data there rather than in another dashboard. It works with Claude, ChatGPT, Cursor, Claude Code, Codex, Gemini CLI, Grok, OpenCode and any other client that supports remote MCP servers, all through one URL and one credit balance; in Claude you add the URL as a custom connector and sign in, and in Claude Code you run claude mcp add --transport http manifold followed by the URL, with per-client steps in the docs. Pricing is free to start with 250 free credits per new workspace, then a Pro plan from $20 a month for 2,500 credits or a Max plan at a lower rate per credit, with top-ups from $10 between renewals. Manifold is a marketing data MCP server founded in London in September 2026 by Jesse Sibley, and the site notes it is unrelated to Manifold Markets, the prediction market. Manifold MCP's value proposition is simple: connect your agent once and it can pull live SEO, AI answer, social, ad and lead data on demand, then turn that data into drafts and decisions in the same chat. It replaces a pile of tabs with plain-language questions, keeps costs visible through a single credit balance with per-tool pricing, and gives marketers real numbers to act on rather than estimates. Free credits make it easy to test what the agent can answer before committing to a paid plan.

Kudos is a free financial companion that streamlines your purchases and returns the value back to you. It is built around a simple promise: set up once and earn forever. The product pairs the Kudos app with a free browser extension so that, wherever you shop, Kudos automatically maximizes your card offers and benefits, lowers your bills, and keeps finding new ways to save. According to the website, Kudos works at more than 2 million stores and supports more than 3,000 credit cards. It is used by 500,000+ members who want to take control of their finances today, and it holds a 4.8 star rating across 10,000+ reviews. Most money apps show you charts. Kudos does the work instead. The problem the product targets is that value embedded in your wallet routinely goes unclaimed: most people don't know their cards come with personalized offers they have to turn on by hand, benefits expire before they are used, bills quietly creep upward, and forgotten subscriptions keep charging month after month. Kudos states that its AI finds money you're missing and gets it back — activating card offers that are worth roughly $800 a year in early testing, calling providers to lower your bills at about $216 a year per negotiation, and cancelling subscriptions you forgot at around $247 a year each. You keep 100% of the savings. One testimonial describes the AI calling a cellphone company within 12 hours of subscribing and reducing a bill from $420 to $315 a month with no plan changes. The first feature group centers on card-linked offers and earning at checkout. Visit your issuer portal — Amex, Chase, BofA, Wells Fargo, Citi — with Kudos installed, and it automatically activates hundreds of offers across all your cards with no manual clicking required. Kudos auto-activates every offer worth $750+ a year and reminds you when to use them. The free browser extension then stacks rewards, benefits, and offers automatically: it picks the best card, autofills card details, applies offers, and stacks cash back at checkout. Because the offers are activated in the background and surfaced when you shop, members report that they actually use them instead of forgetting, earning cash back on purchases they were already making. The second group is benefits tracking and card guidance. Kudos tracks every benefit across all your cards and makes sure you use them before they expire, which the site frames as taking back +$3,000 in missed benefits every year. For in-store shopping, the Cheatsheet shows the best card to use based on your location, so you never miss a reward. Personalized card discovery tools include Dream Wallet, Card Explore, Test Drive, and Approval Odds, and a Test Drive feature helps members select the right card for their setup. Kudos also analyzes your transaction history and proactively finds personalized ways to earn more rewards and save money, so you understand your spending and get more from every purchase. The third group is the AI concierge and conversational layer. Kudos AI connects the dots across your cards, bills, and spending to answer questions, uncover hidden opportunities, and take action to save you money — you can ask your wallet anything. On the bills side, Kudos AI cancels unused subscriptions, negotiates your bills, and handles the calls to save you money in minutes. For recurring subscriptions, Kudos automatically detects them across all your connected cards and helps you decide what is worth keeping; Premium members can tap "Cancel for Me" and Kudos handles the entire cancellation process — phone calls, live chat, emails — while you get real-time status updates until it is done. By comparison, the site notes that Rocket Money takes up to 10 days and up to 50% of savings, while Kudos takes no cut. Kudos follows a four-step flow the website lays out as Activate, Connect, Automate, and Earn & Save. First you download Kudos and securely connect your cards. Next, Kudos learns how you spend in order to find ways for you to earn and save. Then it automates the work — activating offers, negotiating bills, and using the best card. Finally, you watch your savings pile up and keep more of what you earn. The learning step is what makes the automation personal: because Kudos understands your card mix and your transaction history, the recommendations it surfaces at checkout and the actions it takes on your behalf are tailored to your spending rather than generic advice. The outcomes the site reports are concrete. The average member earns over $3,000 per year with Kudos Premium, and on average Premium members earn an additional $754/year and save $324/year. A member dashboard shows one number for everything Kudos has earned or saved you, along with your wins and your next best money move. Members describe the product as surfacing useful actions instead of just charts, saving them time and helping them earn and save without constant checking — a personal finance experience without the busywork. Concrete workflows include online checkout, where the extension picks the best card, autofills details, applies activated offers, and stacks cash back; in-store shopping, where the Cheatsheet tells you which card to use based on your location; a benefits review, where Kudos tracks credits across your cards and reminds you before they expire; subscription cleanup, where Kudos detects recurring charges and cancels the ones you don't want; bill negotiation, where the AI calls providers such as a cellphone company on your behalf; and card selection, where Test Drive and Approval Odds help you decide whether a particular card fits your setup. Kudos is aimed at people who have multiple credit cards, or who aren't sure which card fits their setup, and at anyone who wants to stop leaving money on the table without doing manual work. The product integrates with Plaid for secure account connections and supports offers from Amex, Chase, BofA, Wells Fargo, and Citi, plus Boost Cash Back at 15,000 partner stores. Security is presented prominently: Plaid connection with user control over what is shared, Zero-Knowledge Architecture so sensitive data is encrypted on your device and invisible to anyone, SOC 2 (Type 1) compliance from independent audits, and 256-bit bank-level encryption. Pricing includes a free tier plus Premium options — a VIP Lifetime access plan at $190 total ($240 total list, with a $50 limited-time promotion) and a yearly subscription at $141.99 yearly. Premium includes an Automated Benefits Tracker, AI negotiations to lower bills, auto-activate card-linked offers, 2 years of transaction history and dashboards, a 500 Boost welcome bonus (lifetime only), earning 1.25x more Boost at 15,000 partner stores, welcome bonus tracking with smart reminders, and early access to new autopilot features. Lifetime access covers one account for the lifetime of the product. The takeaway is simple: Kudos turns a passive wallet into an active one. Instead of asking you to study charts, it activates your card offers automatically, keeps your benefits from expiring, negotiates your bills, cancels what you no longer want, and tells you the best card to use — set up once, put your wallet to work forever, and keep 100% of what it saves you.

MePersona is an AI unified inbox built for content creators, designed to help them answer fan comments and direct messages without living inside a dozen apps. It drafts replies in the creator's own voice, gathers fan DMs from twelve platforms into a single place, and waits for approval before anything is sent. Its stated purpose is simple: stop drowning in DMs and reclaim personal time while still giving every fan a real answer. MePersona works in English and Spanish, so bilingual creators no longer have to choose which half of their audience receives a real response. Creators build audiences on multiple platforms, and each platform brings its own stream of comments and DMs. The result is fragmented attention: messages spread across X (Twitter), Instagram, TikTok, YouTube, Telegram, Discord, LinkedIn, Bluesky, Facebook, Mastodon, WordPress and Pinterest, with Twitch, WhatsApp and Reddit listed as coming soon. Answering every fan personally costs hours, while replying only to some leaves other fans feeling ignored. MePersona's positioning is that replying to every fan yourself costs hours of personal time, and its pricing page notes that Premium costs less than a dollar a day. Rather than replacing the creator with generic bot replies, MePersona is presented as a way to keep a real voice present at scale, with the creator retaining final control over what is sent. Everything in MePersona runs off a single AI persona. Training it is quick: the creator reads a short sample aloud and MePersona maps their real voice from that sample, then learns to talk, write and post like them. The Product Hunt description adds that the persona improves with every reply the creator approves and that it scores how much each draft sounds like the creator. Because every other part of the product runs off this one brain, the inbox, the vault, the marketing agent and any connected tools all draw on one consistent voice rather than separate systems. The unified inbox brings fan DMs from the supported platforms into one place. For each message, MePersona prepares a drafted reply, and the Product Hunt description notes that it remembers what each fan said last time, so context carries across conversations. Nothing posts until the creator hits Send. Every AI-drafted reply, post and campaign waits in the Review Queue until it is approved, so the creator can check each draft rather than publishing automatically. Once the persona has earned trust, auto-reply can be switched on for the routine messages the persona is sure about, while anything less certain still goes through review. This staged approach is what the FAQ contrasts with ordinary DM automation. Beyond the inbox, MePersona includes an auto-tagged content vault that stores material, alongside a Marketing Agent that plans campaigns in the creator's voice. Paid plans include one free Marketing Agent plan per month. Vault storage scales by plan, from 10 GB on Free through 100 GB on Premium, 500 GB on Pro and 1 TB on Pro Elite. Premium and above also add post scheduling across all platforms, a unified inbox and an analytics dashboard. Scheduling means the creator can plan posts from the same place they plan marketing instead of moving between tools, and the analytics dashboard gives visibility into how that activity performs. The persona is not locked inside MePersona. A Persona API, included with the Pro Elite plan at 1,500 AI calls a month, lets creators connect it to tools they already use. The documented scenarios include pointing an autonomous agent or runtime at the persona so it acts in the creator's voice, building n8n and Zapier workflows that pull new fan messages and draft replies that sound like the creator, powering a chat on the creator's own website so it talks like a person rather than a bot, and letting agencies and builders add voice-matched drafting inside their own tools and dashboards. Discord bots are also named as a possible connection point. A Creator Studio is described as on its way and marked coming soon. It is framed as create it, post it, answer it, all in your voice. The Create step generates images and videos, or remixes content already stored in the Vault. The Post step adds captions, music and templates, then schedules the result from the same place marketing is planned. The Reply step covers what happens when fans respond: the persona drafts replies in the creator's voice and the creator approves them. Studio is stated to work with a MePersona account. Because it is labeled coming soon, these capabilities are announced rather than currently available. MePersona's approach is a chain of small confirmations. First the creator trains a persona by reading a short sample aloud. The persona then produces drafts for comments, DMs, posts and campaigns, and each draft is scored for how much it sounds like the creator. Those drafts wait in the Review Queue, which makes approval the default state and publishing an explicit action. Approvals feed back into the persona, which the Product Hunt description says improves with every reply the creator approves. Only once a persona has demonstrated reliability does the creator enable auto-reply for routine messages. The FAQ positions this against plain DM automation and also addresses whether fans will know it is AI, whether the account could be banned, and whether the product can create content as well as reply to it. The stated outcome is time back. The pricing page argues that Premium costs less than a dollar a day, while replying to every fan yourself costs hours. Creators who adopt it can answer every fan rather than a subset, which as the Product Hunt description puts it stops bilingual creators from choosing which half of their audience gets a real answer. Being present across platforms at once means fans on Instagram, TikTok, YouTube or Discord are not deprioritized because the creator only had time for one app. The Review Queue protects the creator's output, since no unvetted message leaves the account, and auto-reply absorbs the routine traffic once trust is established. Concrete workflows appear throughout the site. A creator opens the unified inbox, sees a fan message with a draft already prepared, checks that it sounds right, and sends it. A bilingual creator runs the same flow in English and Spanish without splitting the audience. A creator with a large following switches on auto-reply for routine questions while keeping other messages in review. A Marketing Agent plans a campaign in the creator's voice, and the resulting drafts wait in the Review Queue. A developer connects the Persona API to n8n or Zapier so new fan messages are pulled and drafted automatically, or embeds a chat on their own website that speaks in the creator's voice. Once Creator Studio ships, the same pipeline extends to images, videos and captioned reels. MePersona is built for creators across niches, with Beauty, Fitness, Gaming, Music, Cooking, Lifestyle, Education and Comedy named on the site, plus a short five-question quiz that asks what kind of creator you are. Pricing starts with a Free plan at $0 that includes an authentic persona, 300K tokens per month (roughly 300 replies), two platforms, 10 GB of vault storage and post scheduling, with no card required. Premium is $29 per month or $299 per year with 2.5M tokens (roughly 2,500 replies), unlimited platforms and 100 GB of storage. Pro is $99 per month or $999 per year with 10M tokens (roughly 10,000 replies) and 500 GB. Pro Elite is $149 per month or $1,599 per year with 25M tokens (roughly 25,000 replies), 1 TB of storage and an API key. Paid plans begin with a 7-day free trial and offer a full refund within the first 30 days. Enterprise pricing is custom. Payments run through Stripe with Apple Pay supported, and the project has a Discord community. MePersona combines a unified inbox, a trainable voice persona and a review-first approval flow so creators can respond to every fan on every platform without losing their voice or their time. One persona powers replies, content, campaigns and connected tools, and the creator always hits Send.

Ghost Core is a personal AI computer from Ghost, described on its website as the personal AI computer. It runs AI models entirely on device, connects to your apps, files, and devices, builds context on your life over time, and works continuously to help you, not just when you ask. The site frames the product as Intelligence you own, and states that everything you do with Core is run locally using models on-device, so that no data ever leaves your home and your AI is owned entirely by you. It is aimed at people who want a private, always-available assistant that lives in their home rather than somewhere else. The problem Core addresses is presented on the site in two parts. The first is reactiveness: the site states plainly that Core doesn't wait to be asked, that it understands what is happening in your life and thinks proactively, and that it understands what to take action on without waiting for a prompt. Much of the value of an assistant disappears if the user has to think of the right question first. The second is ownership and privacy: the site's repeated claim is that intelligence stays yours on Core, that everything is run locally using models on-device, and that no data ever leaves your home. Together these two ideas — proactive help and local, owned intelligence — define what Ghost says makes Core different from assistance that lives elsewhere. Why it matters to Ghost's audience is stated in the copy itself: your AI is owned entirely by you, and the longer you use Core, the better it gets because context accumulates rather than resets. Local intelligence is the first of the three pillars the site highlights, alongside continuous context and a proactive agent. According to Ghost, Core runs its models entirely on device, and everything you do with Core is processed locally. That design is offered as a guarantee rather than a setting: the site says no data ever leaves your home, and that your AI is owned entirely by you. Practically, this is the difference between a device that sends what it learns about you somewhere else and a machine that keeps it in the room. Ghost also lists no subscription among the headline product facts and offers 30 day returns, so the ownership pitch extends to the commercial model as well as the technical one. Continuous context is the second pillar. Core runs 24/7, continuously taking in the context of your life, and it can connect to anything, from your apps, files, and screen history to wearables and smart-home devices. The site's stated consequence is simple: the longer you use Core, the better it gets. Rather than starting each interaction from zero, Core accumulates an ongoing picture of routines, activity, and surroundings. The examples shown on the page illustrate the texture of that context — a higher recovery score than usual, the observation that you work out more on early mornings, a package arriving today, groceries running low, rain tomorrow that may change the usual commute, four days of tacos in a row, stress peaking on Sunday nights, a refill pickup that could fit between gas and work, and a weekend trip that may need an earlier departure. The proactive agent is the third pillar, and it is the most visible part of the product. The site's framing is that Core doesn't wait to be asked: it understands what is happening in your life, thinks proactively, and understands what to take action on without waiting for a prompt. The kitchen scenario on the homepage shows how this plays out in a concrete setting. Core combines what it has learned about you with what it can sense nearby and produces short, timely nudges — you said you wanted to eat more fruit, take an apple with you; you have already had two coffees today, maybe water first; and don't forget your keys. These are not requests the user typed; they are observations and suggestions generated from accumulated context and the moment at hand. How Core works overall is a combination of on-device models, continuous data capture, and connections to the surrounding environment. The site states that Core is compatible with your devices and connects over Wi-Fi and Bluetooth. Its context sources are grouped into three areas: Digital, which captures context from the apps you use every day, including email, calendar, finances, files, recordings, and browser; Home, which connects to the devices in the world around you, including light, fridge, speaker, and cameras; and Health, which learns from your routine, recovery, and activity through sources such as health records, Whoop, Oura, and Eight Sleep. All of that feeds a set of models running locally on the machine, which then acts as the proactive agent described above. The hardware is specified in detail on the site. Core ships with an NVIDIA RTX PRO 4000 Blackwell SFF Edition GPU with 24 GB of GDDR7 ECC memory and 432 GB/s of memory bandwidth, paired with an AMD Ryzen 5 7600 CPU with 6 cores and 12 threads, 64 GB of DDR5 memory, and a 1 TB NVMe SSD. The models listed as running on Core are Qwen 3.8-Next, Qwen 3.8-27B, Gemma 4-31B, and Muse-Glimmer-30B. In other words, the components that would normally sit in a data centre are packaged as a single object for the home — a brushed steel tower shown on the site from the front, back, and underside, with vents, a power input, and a light edge that glows. The benefits Ghost claims follow directly from those design choices. Because everything runs locally, the user gets assistance without personal data leaving the home, and the AI is owned entirely by the user. Because there is no subscription, there is no recurring fee attached to continued use. Because context is continuous rather than session-based, the product is described as getting better the longer it is used. And because Core operates proactively rather than only on request, it can surface things the user had not thought to ask about — health and recovery observations, incoming packages, weather effects on a commute, or a reminder to pick up groceries. Several concrete scenarios appear across the site. In the kitchen, Core notices what is present and what you have said about your intentions, and prompts you to take an apple, drink water instead of a third coffee, or remember your keys. In a daily-planning context, it surfaces that a package is arriving, that groceries are running low, or that a refill pickup could fit between gas and work. Around health and routine, it points out a recovery score higher than usual, that you tend to work out on early mornings, or that you are most stressed on Sunday nights. For travel and weather, it flags that rain tomorrow may change the usual commute or that a weekend trip may need an earlier departure. Core is presented as a personal device for the home, priced at $3,499 with no subscription and a 30 day return window. Batch 1 is listed as sold out, and the site invites visitors to reserve for Batch 2 or to order now. Its context integrations span everyday app sources such as email, calendar, finances, files, recordings, and browser; smart-home hardware including light, fridge, speaker, and cameras; and health and sleep trackers such as Whoop, Oura, and Eight Sleep, plus health records. Connectivity is over Wi-Fi and Bluetooth, and the machine's stated purpose is to keep intelligence and control in the hands of the people who use them. Taken together, Ghost Core is a personal AI computer whose value proposition is ownership: on-device models, no subscription, continuous context that improves with use, and a proactive agent that acts without waiting for a prompt. The site's own summary line is the clearest statement of intent — Ghost is building Core, the personal AI computer designed to keep intelligence and control in the hands of the people who use them.

HireKey is a recruiter-informed AI job search copilot built for tech job seekers who want to apply strategically and interview with confidence. The platform brings an entire job search into one workspace, guiding candidates through a simple sequence: find your fit, apply, practice, and keep moving. It helps you assess how well a role matches your documented experience, tailor your resume to a specific opportunity, prepare your answers out loud with an interactive AI voice coach, and track every application through to the final decision. Instead of scattering notes, resumes, and reminders across different tools, HireKey keeps the whole search together in one place, informed by the perspective of the people on the other side of the hiring process. Many job seekers describe the same frustrating pattern: sending applications and hearing nothing. It is difficult to tell whether the problem is the role, the resume, the way experience is explained, or simply the competition. Typical ATS resume checkers reduce a candidate to a single score, and a high score does not reliably lead to interviews because an algorithm cannot predict an employer's decision. HireKey was created to answer the questions that actually stall a search: Is this role worth pursuing? Does my resume show what I can do? Can I explain my experience clearly? And what is next for each application? The goal is to help people decide where to focus their effort with a clearer reason, rather than investing more time in applications that were never going to move forward. The first pillar is role-fit analysis, which answers the question "Is this role worth pursuing?" HireKey compares your resume with the job description and shows the experience you can support, the parts that need a clearer explanation, and the areas where there may be a genuine gap. Crucially, it separates two things that are usually blended together: your actual fit for the role and the way your resume is worded. That distinction lets you understand your strengths and gaps, and decide whether to apply, improve your materials, or move on with a clearer reason. A free resume checker is also available without an account; it reviews a resume on its own for positioning, evidence, scan visibility, and seniority signals, processing the file on your device. Role-fit analysis in the full app goes further by comparing your documented experience with a specific job description. Resume tailoring is the second pillar, aimed at candidates who ask whether their resume really shows what they can do. HireKey turns vague bullets into a clearer account of what you actually did, and brings the experience that matters for the specific role to the front. You review the original and tailored wording side by side, see what changed and why each rewrite is clearer, and keep the final say over every change. The tailoring is grounded in your experience and is meant to make real experience clearer and more relevant. It is not intended to invent qualifications, results, or responsibilities, or to turn a contributing role into ownership you did not have. Cover letter drafting works the same way: it produces a draft grounded in your background and the job so you can connect your experience to the opportunity and give it your own voice before sending. Interview preparation is the third pillar, and it is where HireKey moves beyond the resume. You work through your answers in a live audio conversation with an AI voice coach, on your own schedule. You can choose questions for a recruiter phone screen or a hiring-manager conversation, or add your own. Because the coach responds to your answers with follow-up questions, practice resembles a real conversation rather than a static list of prompts. You receive feedback during practice and a debrief on what to improve afterwards. For candidates who prefer to prepare on paper, role-specific questions and answer guidance can be saved as a PDF and reviewed offline. This out-loud practice targets the question many job seekers find hardest to answer: can I explain my experience clearly when it counts? The fourth pillar is the job application tracker, which keeps every opportunity in one place from the first application to the final interview. Applications are organized by stage in either a table or a Kanban view, with columns for Not Applied, Applied, Phone Screen, Interview, Offer, Rejected, and Withdrawn. Stages, deadlines, notes, and follow-ups stay together, and each opportunity remains connected to the preparation you have done for it, so your next step does not live in another spreadsheet. Two further workspace tools round out the search. Company research briefs build context before an interview and help you turn what you learn into more specific questions and a clearer reason for your interest, while cover letter drafts help you make your case for a particular role. What makes HireKey different is that it is recruiter-informed. The product combines AI with ten years of tech recruiting experience at Microsoft, Amazon, and Stripe, so the guidance reflects how hiring is actually evaluated rather than only how a resume parses. It considers the experience you have documented, the role's requirements, seniority, scope, and evidence of impact, and then explains which gaps need clearer communication and which ones require actual experience. It deliberately avoids presenting a single score as an answer, because a score or recommendation cannot predict an employer's decision. The workflow reinforces this: find your fit, apply, practice, and keep moving, with each step feeding the next so preparation stays connected to the opportunities you are actually pursuing. The benefits are practical. Candidates can see where they stand before investing hours in an application, present their experience more clearly and with less risk of understating their contribution, and arrive at interviews having already said their answers out loud. Getting real-time feedback and a post-practice debrief means improvement happens before the interview rather than after a rejection. Keeping applications, notes, and deadlines in a single tracker removes the anxiety of wondering what comes next and which follow-up is overdue. Because everything is connected, the research you do, the resume you tailor, and the practice you complete all support the same opportunity, and every change to your resume remains under your control. Concrete scenarios show how this plays out. A candidate who finds an appealing job posting runs role-fit analysis first to decide whether the role is worth pursuing, then uses resume tailoring to bring the relevant experience forward before applying. Someone preparing for a recruiter screen selects recruiter-screen questions and rehearses out loud with the AI voice coach, using follow-up questions to expose weak spots, then saves the question set and guidance as a PDF. A candidate juggling many active applications moves each one through the Kanban board from Applied to Phone Screen and Interview, keeping notes, deadlines, and follow-ups with the opportunity. Before a hiring-manager conversation, they generate a company research brief and convert the findings into specific questions. Those who are not hearing back start with the free resume checker to test whether their resume clearly supports the roles they are targeting. HireKey is built for professionals applying to competitive roles, including engineers, product managers, designers, data professionals, marketers, sales professionals, and operators. A free plan provides five resume tailoring sessions, five interview practice sessions, two live voice coaching sessions, five cover letters, and five company research briefs per month, plus unlimited job application tracking in the dashboard. Premium is $39 per month and raises those allowances to fifty resume tailoring sessions, fifty interview practice sessions, and ten live voice coaching sessions, with unlimited cover letters, unlimited company research briefs, unlimited tracking, and access to Pipeline Intelligence and Application Insights. Additional credits are available for Premium members, including a five-session voice pack and a fifteen-session resume tailoring pack, each priced at $9.99. Sign-up happens in the HireKey web app after onboarding. The takeaway is simple: your job search does not have to be a series of blind applications. HireKey gives tech candidates a single, recruiter-informed workspace to understand their fit, tell their experience more clearly, practice the conversation out loud, and keep every opportunity moving. You have already done the work; HireKey helps make that experience count.

Foley is a voice-first to-do list that turns spoken thoughts into tasks with the details already in place. You say what needs doing — “I called the dentist,” “Change Sam's pull request review to 4 PM,” “Add bread to the shopping list” — and Foley works out the task, the project, the date and the reminder before anything lands. The product is for people who want to get a thought out of their head quickly, without doing the filing admin that normally comes with it. Foley runs on Mac, Windows, the web, iPhone and Android, with a reduced capture experience on Apple Watch (watchOS 10 or later) and Wear OS watches (Wear OS 3 or later). Most task apps assume you will stop what you are doing, open an app, create a task, choose a project, set a date and pick a reminder. Foley starts from the opposite premise: the thought arrives in a handful of words, and the system should do the work of turning it into something actionable. The site frames this as “Out of your head. Into your day.” Rather than forcing you to translate a half-formed thought into a structured record on the spot, Foley files the task with a project and a time, keeps the original words as part of the record, and only asks a question when it genuinely cannot tell what you meant. When plans change, you say so and it moves the task rather than leaving you to unpick and rebuild the list by hand. Capture is the centre of the product. A shortcut brings Foley to you wherever you are working, and you can say a sentence or several things at once — for example, “Add two tasks: buy milk, and call the electrician.” Foley works out the tasks, the dates and the project from what you said, and you review what it understood before the tasks land in your list. On the phone the capture sheet listens while you talk, then shows what it saved with the project and the time already set, and says so out loud in one sentence, by the same rules on every device. The words stay on the screen whether or not the voice plays, and when voice replies are off the app says why. The watch strips capture back further: tap the mark, say what needs doing, and it goes to the same list as everywhere else. While the microphone is open the wordmark pulses and the clock counts up, so waiting never looks like broken; you stop when you are done and nothing asks you to confirm. When Foley is not certain, it asks rather than guesses. If two tasks could fit what you said, it asks which one you mean, so you stay in charge of the change. On the watch, a question like “Which area, and how many people?” arrives with the likely answers as chips you can tap or say; because a short answer is where recognition is least reliable, what you said is read back before it goes anywhere, and you can send it or say it again. Capture history keeps the original words beside what Foley changed, so you can review the result and undo a capture when you need to. Alongside projects, Foley keeps simple lists for shopping, packing and little things to remember — add another item, tick things off, or say “Add bread to the shopping list.” Day to day, tasks from across your projects come together at your Daily desk, where you can open one, change a detail or check it off. The Timeline groups what is overdue, what is due today and what the week holds across every project, so the day is one glance, and a calendar view shows the shape of the day so that when plans change you can move a task or change its time while keeping the rest of the day in view. Projects give each part of life a home: folders keep related projects together, and projects hold the work and its context. Task context connects related work, keeps notes and attachments with the task, and shows the dependencies that explain what comes next — “The invoice to Acme is blocked by the board deck.” Planning turns the list into a plan: ask Foley to plan your day around the tasks already there, follow the plan back to the work, and adjust it as you go. For shared work, you can share a project through your Foley account so tasks and comments stay with the work, and in Your setup you connect your tools, choose your capture shortcut, and decide which projects AI may use as context, picking either Foley-managed AI or bringing your own key. The approach running through all of this is that Foley asks instead of guessing and shows its work. What it saved appears on the capture sheet with the project and time already set, it speaks a one-sentence result by the same rules on every device, and the original words sit beside the change in history. Signed in, the phone, the desk and the browser share one list, and the watch shows the same day in the order the service sends it. AI sits behind capture and planning rather than in front of it: you can use hosted, Foley-managed AI, or bring a personal key from a provider such as OpenRouter and pay that provider for usage. You also control which projects the AI may use as context. The outcome Foley is built for is a lighter head and a clearer day. Instead of holding a thought until you can write it down properly, you say it and move on, with the project, date and reminder already worked out. Instead of an undifferentiated list, you get a Daily desk and a Timeline that show what is late, what is due today and what the week holds. Instead of being told what to do by a system that guessed wrong, you get a question and you give an answer. And instead of a plan you have to maintain by hand, you can ask for a plan and change it in a sentence when the day shifts. Concrete uses the product describes include emptying your head in one pass — “Add two tasks: buy milk, and call the electrician” — and reviewing what Foley understood before the tasks land. It covers errands and chores with times: “Water the plants on Thursday evening” is filed as a task on Thursday at 18:00 in the Home project, and “Post the parcel” sits in Errands at 12:30. It handles changed plans with “Change Sam's pull request review to 4 PM,” disambiguates with “Complete the report expenses task,” and answers “What should I work on first?” from the list — for example naming an overdue parking permit renewal before a parcel due at 12:30. It keeps small checklists, links blocked work such as an invoice held up by a board deck, and captures from the wrist on an Apple Watch or Wear OS watch, including follow-up questions such as which area and how many people. Foley is for anyone who wants fast capture across the devices they already use. Desktop task management is free: a home for your tasks on your computer, local desktop task management, no sign-in needed to get started, and the option to bring a personal AI key for AI features, with your AI provider charging for personal-key usage. Foley Pro is Foley-managed AI with less to set up: hosted AI without a personal key, one account across devices, and monthly or annual billing, at $8 per month after an introductory $4 for the first month applied at checkout. Signing in is what enables hosted AI, sync and sharing, and the site notes that website pricing may differ from store prices. The desktop app runs on Mac and Windows, the mobile app on iPhone and Android, and the watch apps on watchOS 10 or later and Wear OS 3 or later. Taken together, Foley is a voice-first to-do list that carries a thought the whole way from the moment it is spoken to the moment it is planned and done: tasks filed with project, date and reminder; questions when the intent is unclear; history to undo a wrong call; the same list on Mac, Windows, the web, iPhone, Android and your wrist; and a free desktop tier or Foley Pro for managed AI and sync.

OpenScreen is a free, open-source screen recorder and video editor for Windows, macOS and Linux. A raw capture goes in and a finished demo comes out: you record your screen, and the app turns the take into something presentable on the first attempt. It is built for anyone who needs to show software, a website or a workflow on screen — developers, makers, product teams and people recording demos — and who want the result to look deliberate without commissioning an editor or re-shooting. It is MIT licensed, carries no watermark, requires no account, and performs recording, transcription and rendering on your own machine. Screen recordings usually look worse than the thing being recorded. A cursor crawls across the frame at the wrong size, the desktop wallpaper competes with the interface, dead air and filler words fill the audio track, and something private can flash by in a password field. Fixing that normally means a second pass in a video editor, or a re-shoot. OpenScreen addresses the category Screen Studio defined: a tool that records the screen and then frames, zooms, captions and cuts the result so it reads as intentional rather than improvised. It continues the original OpenScreen project, which its creator archived after v1.5.0, and moves the whole pipeline — capture, transcription, styling and render — onto the local machine. Recording uses the capture path each operating system itself provides, not a workaround layered on top. macOS goes through ScreenCaptureKit, Windows through Windows Graphics Capture, and Linux through PipeWire and the ScreenCast portal; on Windows, system audio works without an extra driver. You pick a window or a display before you start. Crucially, the pointer is recorded as data rather than burned into the pixels. That separation is the reason the cursor can be restyled, resized and smoothed after the take, and the reason zooms can be planned from where you actually clicked rather than guessed at from the image. Once a take exists, the editor styles it. You can put an image, a color or a gradient behind the recording, so a narrow window or an odd desktop no longer forces a re-shoot. Framing controls include padding, motion blur, shadow and roundness, and every effect composites live while you adjust it. The cursor gets its own controls: size, smoothing, motion blur and click bounce, so every move reads on screen. Five cursor packs ship with the app — Studio Ink, Prism Glow, Pop Coral, Pixel Candy and Star Sprout — and each can be drawn as a 3D object with a shadow. Zooms are where the tool is most distinctive. Each zoom can stay flat, turn the screen left or right, or orbit it while following the zoom's focus, with depth of field softening the far side — a 3D camera move rather than a crop. Around the screen you can place device frames: a window, a laptop, a phone or a plain screen, in a light or a dark treatment, over a background animated with Drift, Aurora or Waves. Webcam layouts cover picture in picture, dual frame, vertical stack, or no webcam at all, and a Full Camera stretch can give part of the timeline to the camera as it grows to full screen and then eases back. A segmentation model separates you from the room on your own computer, with nothing uploaded, and the same area supports blur, cut out or replace. The same take can be framed for 16:9, 9:16, 1:1, 4:3, 4:5, 16:10 or 10:16. Blur regions — smooth or mosaic, as a rectangle, an oval or a freehand shape — cover an email, a key or a password before the video is shared. The timeline is where edits land. Zooms, speed ramps, trims and comments each appear as a pill on the timeline. Zooms are placed automatically: each take is zoomed where you clicked, by a fixed rule, with no AI and no network call, and this can be turned off in Record mode. Transcript editing takes a different approach — the video is edited like text. Delete a word or a silence in the transcript and the cut lands on the timeline, non-destructively. That is a faster way to strip filler and pauses than scrubbing an audio waveform, and it keeps every edit reversible. Transcription runs on your machine. whisper.cpp ships with the app, the model downloads once on first use at 264 MB, and after that it works with the network off; the audio never leaves the laptop. It covers 100 languages. What comes back is editable text: you set the typeface, the size, the color and the position, then burn it into the render. Export writes the file locally — MP4 from 720p up to source resolution, at 24, 30 or 60 fps, encoded in H.264, or a GIF. The encode runs on your machine and counts frames while it does. There is no queue, no account and no watermark, and the file is on disk when the bar fills. An AI agent goes further than the click-based zoom rule. Given a provider key that you supply — off by default, and nothing runs until you connect one — the agent reads the actual transcript and the actual timeline, so it answers with timecodes you can go and check: which spans it will cut, and how much that saves. Every edit it makes is an ordinary undoable one. The chat panel and caption translation are the places text can leave the machine, and each needs a key you provide. Taken together, the approach is: capture natively, treat the pointer as data, describe the look before you edit, and keep everything local. The finished-frame defaults — wallpaper, padding, shadow, rounded corners and a little motion blur — are on before you touch anything, so the first export already looks deliberate. Edits are expressed as objects on a timeline rather than baked into pixels, which is why a cursor can be restyled after a take and why deleting a transcript word becomes a normal cut. The AI layer, where it is used, reads that structure instead of acting as a black box. The outcome is a demo that looks finished on the first take. Because the pointer is separate data, a recording made with an awkward cursor can be restyled afterward. Because transcription is offline, sensitive audio stays on the laptop. Because export is local, there is no queue to wait in and no upload to trust. Because the license is MIT, there are no paywalls, no premium tier and no usage caps — every feature ships free for personal and commercial use. OpenScreen is one source tree with native capture on each platform. Distribution includes a Microsoft Store listing (winget install --source msstore OpenScreen) and an .exe installer for Windows; a signed and notarized .dmg for macOS, where cursor shape and clicks activate once Accessibility is granted; and .deb, .rpm, .pacman, an AppImage and a Nix flake for Linux, which needs PipeWire and xdg-desktop-portal. The .exe installer is not code-signed, so SmartScreen warns before it runs. The interface ships in 15 languages: English, Arabic, Czech, German, Spanish, French, Italian, Japanese, Korean, Russian, Turkish, Vietnamese, Brazilian Portuguese, Simplified Chinese and Traditional Chinese. In short, OpenScreen is a free and open-source answer to the screen-demo problem: record natively, frame the result automatically, restyle the cursor, cut by editing the transcript, transcribe offline and export locally — with an optional AI agent that proposes edits you can verify, and no account, watermark or upload anywhere in the process.

History Monk is a browser extension that replaces your browser's built-in history page with a fully searchable one. Instead of scrolling through days of visits, you type a few letters and the page you want shows up as you type — even with a typo. It works in Chrome, Edge and Opera, where it takes over the built-in history page so the History menu and its shortcut open History Monk, and in Firefox 140 and later, where you open your browsing history from the toolbar button or with Alt+Shift+H. It is made for anyone who spends the day in a browser and needs to find, reopen, export or clear out pages they have visited, and the whole thing runs on your own computer. Browser history pages were designed to record visits, not to help you find them again. They show a long chronological list with a single search box that only matches exact strings, so a half-remembered title or a misspelled word returns nothing useful. At the same time, the history fills up with search result pages, sign-in screens, duplicate URLs and links you clicked once and never needed again. History Monk addresses both sides of that problem: fast, forgiving search across everything you have visited, and a set of cleanup tools that sort the junk into groups you can review and remove. Because everything happens locally, tidying up your history does not mean handing your browsing data to a third party. Search is the centre of the product. History Monk is ready to search 80 milliseconds after it opens, and results update with every letter you press — no Enter key, no waiting for a page to load. It is built to stay fast on large archives, with the page citing 100,000 pages searched with no lag. Typo correction works against the words in your own browsing history, so typing “plannig” still finds the planning doc. When a word is not enough, 11+ power filters let you narrow things down: require two words in any order (“tailwind config”), match an exact phrase in quotes (“release notes”), restrict to one site with site:github.com, exclude one with -site:youtube.com, match the page title with title:invoice or the address with url:/pull/, and combine several of them in a single query. Date and time filters such as after:yesterday, a specific date like 2026-08-01 and before:17:30 limit results to a window, visits:>10 finds pages you opened more than ten times, a regular expression such as /issues\/\d+/ can be mixed with anything above, and sort:new orders results by best match, newest or most visited. A selected: filter can show only the rows you have ticked. Recently closed keeps every tab and window you close, newest first, so the last tab you closed sits on top and can be reopened with one click. The same list is searchable: type a word and only matching closed tabs stay. In Chrome, the Devices view lists the tabs open on your other signed-in computers and phones, grouped by device with the time each tab was last used, and you can search across all of them and open a page on the machine you are using. It uses the Chrome sync account you already have, with no extra account, and the product notes that Firefox does not share other devices' tabs with extensions. A third view, By group, keeps a search together with the pages you opened from it, one card per search — Google, Bing, DuckDuckGo, YouTube and dozens more are recognised with no setup. From a card you can reopen every page in a new tab group or delete the search and its pages in two clicks, while pages you also visited at other times stay. Sites turns cleanup into a bulk operation. You tick the sites you want gone — shift-click to select a range, Ctrl+A to tick every site — and delete every page from them, including older visits that are not visible on screen. You can also right-click any page or link and choose “Delete all history of this site”. Deleting a site always asks first, and sites on your protected list can never be removed. Export takes what you filtered and writes it out: search or filter first, then export just those results or a whole Cleanup group as CSV for Excel and Google Sheets, JSON ready for your own scripts, or an HTML page you can search and sort offline. Every file uses the same five columns — title, URL, site, last visited and visit count — and is created in your browser and saved to Downloads, with nothing uploaded. Cleanup itself sorts your history into 24 groups of likely junk, covering search result pages, duplicate pages that differ only by tracking parameters or a fragment, sign-in and OAuth callback pages, shortened links, links with tracking parameters, pages visited once and never again, untitled or failed pages, social feeds and video, AI chat conversations, meeting and call links, preview and staging sites, cart and checkout pages and more. You see every page in a group before anything is deleted, every delete waits for a second click to confirm, protected sites are never touched, and there are one-click deletes for the last hour, day or week. Timeline lays your browsing history out one day at a time, newest first, with a new session starting after 30 minutes of quiet and the gap showing how long you were away. Analytics shows how you really browse: a weekday-by-hour heatmap of your history, a focus score, streaks, rising and fading sites, and the terms you search for most — and clicking any cell opens those pages. The weekly digest condenses the last seven days into one card with pages, top sites, your busiest hour and late-night share, which you can copy as an image or text. Keyboard users can open History Monk from any tab with Alt+Shift+H, jump to search with /, move with the arrow keys, open with Enter and open in the background with Ctrl+Enter. The interface can be made your own with 28 themes taken from official palettes — Nord, Dracula, Catppuccin, Solarized, Rosé Pine and 23 more — plus 8 languages (English, Español, Français, Deutsch, Português, 日本語, 简体中文 and हिन्दी), a 12- or 24-hour clock, date order, row density and text size. Under the hood, History Monk reads your browsing history through the browser's history API and searches it inside the extension page, so search, Analytics and Cleanup all run locally. There is no account to sign up for and no third-party trackers or analytics. In Chrome and Edge, site icons come from the browser's icon cache, so showing results makes no web requests, and the only network requests the extension makes are to verify a license key and to look up the current price. It has no content scripts, so it never runs inside the pages you visit and only does work while its history page is open. Two settings-like features give you control over what appears: hidden sites stay out of search, Sites, Analytics and Cleanup without the underlying history being touched, while protected sites stay in your browsing history and cannot be deleted from any view. The practical payoff is time. A page you half-remember turns into a few keystrokes instead of a scroll through days of visits, and a typo no longer costs you a second attempt. Closing a tab by mistake becomes one click rather than a hunt. Research that started on a phone can be picked up on a laptop through the Devices view. Instead of manually picking through thousands of entries, you review a cleanup group, confirm, and remove hundreds of junk pages at once — and you can still export exactly the pages you filtered before you clear anything. Because everything stays on your machine, tidying up does not trade privacy for convenience. Typical workflows come straight from the product's own examples. You remember the Lisbon flight you looked at yesterday, type a few letters and it appears. You searched for cheap flights to Lisbon and opened three results; the By group card lets you open those pages again in a new tab group or delete the search and its pages together. You are shopping for a surprise and want a single site's history gone, so you tick it in Sites or right-click and choose to delete all history of that site. You have been researching Tailwind grid gap and want to see where the time went, so you open Timeline or the Analytics heatmap and click a cell to reopen those pages. You want a record of the GitHub pull requests you read this week, so you filter with site:github.com and export CSV, JSON or HTML. Or you simply want the junk gone: you open Cleanup, look through the search result, duplicate and sign-in groups, and clear them out. History Monk is aimed at people who treat the browser as their workspace: developers and designers moving between documentation, repositories and design files, researchers running many searches in a sitting, and anyone who wants a private, searchable record of where they have been. It is available for Chrome, Edge and Opera — where it replaces the built-in history page — and for Firefox 140 and later, and in Chrome it can also show tabs from other devices signed in to the same sync account. It is free for 7 days with no account; when the trial ends it keeps working for 3 opens a day, and a license removes that limit and enables deleting from Cleanup. The extension's only outbound requests are to verify a license key and to look up the current price. History Monk takes the browser's weakest tool — the history page — and rebuilds it around search you can trust, filters that narrow fast, one-click recovery of closed tabs, bulk deletion by site, exports in three formats, and a cleanup system that never deletes without showing you what is going. All of it runs on your computer, which is the point: the wise search once, and History Monk remembers the rest.

Microsoft-Decision-1 is a new decision-scoring model from Microsoft, available in Microsoft Foundry and through OpenRouter. It is purpose-built to deliver structured outputs that software can immediately act on, and it is designed for routing, classification, prioritization, verification, and workflow control. The model is intended to make it easier to incorporate decision intelligence into existing applications, agents, and workflows in a secure, trusted environment. Give Microsoft-Decision-1 a set of fixed options and it returns a calibrated probability for each option in a single pass, as structured output that software can act on directly. It is positioned as a model for fast decision-making, delivering top performance in latency and quality on structured decision tasks to outperform both LLMs and other decision models. Decision models are quickly emerging as an important new category in AI. Unlike LLMs, which are designed to generate text or reason through complex problems, decision models are purpose-built to deliver structured outputs that software can immediately act on. Microsoft frames this as a shift: once you understand the capability of making decisions and classifying things at very low cost with high performance, all kinds of useful tasks get unlocked. Traditional large language models are oriented toward generation and reasoning rather than acting as reliable decision engines, and Microsoft-Decision-1 is designed specifically for that gap. By focusing on structured decision tasks rather than open-ended text generation, the model aims to outperform both LLMs and other decision models on latency and quality, so teams do not have to choose between the flexibility of a general model and the reliability of a purpose-built decision component. Accuracy is a central claim for Microsoft-Decision-1. Microsoft reports that the model achieved the highest accuracy in its 36-benchmark comparison, spanning nearly 150,000 questions across benchmarks kept blind from training. The accuracy figure is reported as a mean score across 36 benchmarks, covering 147,137 questions. This breadth — dozens of separate benchmarks and well over a hundred thousand questions — is intended to demonstrate that the model's strength is not limited to a single task type but holds across a wide range of structured decision problems. For teams evaluating a model for routing or classification, that breadth matters because it suggests the model can be trusted across varied inputs rather than only on a narrow distribution of examples. Microsoft also notes that Strands-Decider 2B was scored as the average of the 23 of 36 benchmarks it could answer, illustrating how comparisons were handled for models that could not complete the full benchmark set. Latency is the second headline capability. Microsoft states that in its benchmarking, Microsoft-Decision-1 was the fastest measured, running 2.5 times quicker than H2O-Lightning-4B v1.1, the runner-up, and 35 times quicker than GPT-6 Sol. The latency figures are described as JevBench v1.6.1 adjusted median (p50), checked on October 7, 2026, with Microsoft-Decision-1 measured through Foundry in the same region. The Product Hunt listing echoes this with the claim that the model is about 35 times faster than GPT-6 Sol at median latency. Speed matters for decision tasks because routing, classification, verification, and workflow control often sit directly in the critical path of an application or agent loop. When a decision must be made on every request, a model that returns answers in a fraction of the time can change what is practical to build — for example, making it feasible to score many inputs cheaply and quickly instead of batching decisions or skipping them altogether. Calibration is the third dimension Microsoft highlights. In the benchmark comparison, Microsoft-Decision-1 placed third on calibration, 1.5 behind Jev. Calibration is defined in the source material as a measure where 100 means a model's confidence exactly matches how often it is right — in other words, a well-calibrated model's stated probability reflects its true likelihood of being correct. Because Microsoft-Decision-1 returns a calibrated probability for each fixed option in a single pass, downstream software can act on those probabilities directly, for example by applying thresholds, comparing options, or routing based on confidence. The model produces structured output that software can act on directly, rather than free-form text that must be parsed or interpreted. That combination — calibrated probabilities delivered as structured output in a single pass — is what makes the model suitable for use as a decision component inside larger systems. How Microsoft-Decision-1 works overall can be summarized from the source material: it is a decision-scoring model that is post-trained from Qwen3.5-9B. Rather than generating prose, it is given a fixed set of options and returns a calibrated probability for each option in a single pass, as structured output. This single-pass, fixed-option approach is the model's distinctive methodology. The model is available in Microsoft Foundry and through OpenRouter, so teams can access it through those platforms rather than deploying a bespoke system. Microsoft describes it as delivering top performance in latency and quality on structured decision tasks, and the benchmarking data presented alongside the launch compares it against both LLMs and dedicated decision models on accuracy, median latency, and calibration. For users, the stated benefits center on speed, cost, and reliability of decisions. Microsoft says the model makes it easier to incorporate decision intelligence into existing applications, agents, and workflows in a secure, trusted environment. Because it returns calibrated probabilities that software can act on directly, it reduces the need to translate model output into actionable signals. The combination of high accuracy across a broad benchmark set, the fastest measured median latency, and calibrated confidence is intended to give teams confidence when embedding the model in automated decision paths. Microsoft also emphasizes low cost: making decisions and classifying things at very low cost with high performance is framed as the capability that unlocks useful tasks. On Product Hunt, the model is listed at $0.042 per million input tokens, with free output. The source material names specific use cases for Microsoft-Decision-1: routing, classification, prioritization, verification, and workflow control. It is also described as a model for agents and workflows, and more broadly as a decision model for agent control. These use cases share a common shape — they are structured decision tasks where a system needs to choose among a fixed set of options and act on the result. Routing describes directing a request, item, or task to the right destination or handler. Classification describes assigning items to categories. Prioritization describes ranking or ordering work. Verification describes checking whether something meets a condition. Workflow control describes deciding how a process should proceed. In each case, the model returns calibrated probabilities for the available options in a single pass, giving the surrounding application or agent a structured signal it can act on. Microsoft-Decision-1 is aimed at teams that want to incorporate decision intelligence into existing applications, agents, and workflows. Its intended environments are those where a secure, trusted model of this kind is valuable. It is available in Microsoft Foundry and through OpenRouter, giving teams two routes to access it. The model is post-trained from Qwen3.5-9B, which is the base model identified in the source material. Pricing is described on Product Hunt as $0.042 per million input tokens, with free output, and the model is presented as making decisions and classifying at very low cost. The benchmarking in the launch material reports accuracy as a mean score across 36 benchmarks (147,137 questions), latency as JevBench v1.6.1 adjusted median (p50) checked October 7, 2026, and calibration on a scale where 100 means confidence exactly matches how often the model is right. Taken together, Microsoft-Decision-1 represents Microsoft's entry into the emerging category of decision models — models purpose-built to deliver structured outputs that software can immediately act on. Its value proposition rests on three measured dimensions: the highest accuracy in Microsoft's 36-benchmark comparison spanning nearly 150,000 questions, the fastest measured latency (2.5 times quicker than the runner-up H2O-Lightning-4B v1.1 and 35 times quicker than GPT-6 Sol), and competitive calibration (third, 1.5 behind Jev). By returning calibrated probabilities for fixed options in a single pass as structured output, and by being available in Microsoft Foundry and through OpenRouter, it gives teams a practical way to add fast, reliable decision-making to their applications, agents, and workflows.

Cue is a free macOS app that turns your headphones into a focus switch. Put your AirPods in and your Mac turns on your Focus mode, blocks distracting sites and starts a timer; take them out and it is break time. It is made for people who work on a Mac and want a focus session to begin without opening another app, and its interface lives in the notch next to the camera. Cue is free, private and built for macOS 14 or later on Apple Silicon. Most focus tools wait for you to open them, choose a session and press start, which means the moment you actually need help is exactly the moment you have to remember to ask for it. Cue takes a different route: it starts from something you already do many times a day, putting your headphones in and taking them out. macOS also has no public way to set a Focus mode, so Cue sets up its own shortcuts once and then switches Focus for you with no more prompts. The result is a focus session that begins and ends with your ears instead of with an app window, and nothing to remember. Your ears start it, not an app. Cue starts when your headphones go in and ends when they come out. Any Bluetooth headphones work, and Cue tells them apart by itself; AirPods Pro, Max, 3 and 4 are the most precise because they report when they are actually in your ears. AirPods start the session when they are in your ears, while other headphones start it when they connect. While you focus, Cue blocks sites: built-in social, video and news lists, plus your own sites, which you paste as a comma-separated list and Cue splits for you. Apps you list are asked to quit. Both the sites and the apps come back when you take a break, so the blocking is scoped to the session rather than to a permanent setting. The product lives in the notch. A small dot and a timer sit next to the camera; point at the notch to open it and tap to pin it. On a MacBook Air M1, iMac, Mac mini or Mac Studio there is no camera cutout, so Cue draws a floating 190 × 32 pill instead, and if you plug into an external display the same pill floats at the top of the screen. Inside the notch you get now playing, with artwork and play, previous and next controls for whatever is playing, from Spotify to a YouTube tab, read on your Mac only. Notifications from any app land in the notch instead of the corner and tuck away by themselves. Volume and brightness show in the notch rather than as a big box in the middle of the screen. You can also give the notch your own colors: pick a preset such as Peach, Lilac, Mint, Sunset or Aurora, or hit Mix and Cue blends three to five colors into three options so you keep the one you like, and the buddies wear the new accent. Two tiny buddies hang under the notch and react to everything you do. They run while you work, nap on your break, dance when music plays and blush when you point at them. New in 2.6 they leave the notch: outside a focus session they roam your whole screen, walking on windows, climbing their sides, hanging from the menu bar and jumping between them, and you can pick one up and throw it or click it to cheer. When focus starts they jump back onto the notch. The loop button sprays 20 to 30 buddies out of the notch and the power button adds ten more on each tap, up to 60; loop again and they all go home. Skins are free, with all 17 outfits available from day one, and the skin you pick is worn on the screen too, with eleven dance moves that change every phrase and stay smooth on the beat. A music panel shows what is playing on the notch with artwork and controls, and the buddies dance to the beat if you let Cue hear it. Sound stays in reach with per-app volume, an output switch and mic mute on ⌃⌥M, plus an orange or green dot that shows when the mic or camera is in use. Open your AirPods case and a card shows the battery of each bud with one-tap connect. Small islands cover keep awake, Caps Lock and a nudge to unplug at full charge, and a single switch hides the notch from recordings. Face Unlock is an opt-in extra for Macs without Touch ID, offered for convenience rather than security, and the maker states plainly that a photo of you could open it. The mechanics are simple. Step one: put them in and focus starts, with Focus mode on, sites blocked and the timer running. Step two: take them out and break starts, after a 10 second grace period in case you were only adjusting a bud, Focus turns off and your sites come back. The break ends after 15 minutes by default, and you can set it anywhere from 5 to 60 minutes in Settings. Step three: see your day, with today against your goal, your streak, your longest session, your sessions today and the last seven days in one place. Cue also greets you by name and reminds you to drink water and rest. The benefits are mostly about removing decisions. There is no Dock icon and no window in the way, so Cue never asks for screen space; a dot and a timer are all you see until you point at them. Because the trigger is your headphones, starting a session costs zero clicks, and the site and app blocking is scoped to the session so nothing is permanently locked. Cue states that it is private and free: no accounts, no analytics, and your data stays on your Mac. It is made with Swift only, and the maker notes it is light on the machine, with two roaming buddies costing no more than the notch alone. In practice, Cue fits a few clear situations. If you put your AirPods in to start work, the session begins the moment your buds go in and the social, video and news lists close themselves; when you pull them out to grab a coffee, the sites come back and a break timer runs until the break ends. If you are writing, coding or studying with music on, the notch shows the track and its controls without a window to switch to. If you are on a Mac without a camera cutout, the same experience follows you as a floating pill at the top of the screen, so an iMac or Mac mini user is not left out, and if you are on an external display the pill floats there too. Cue is for Mac users who already wear headphones while they work and want focus to follow their ears: writers, developers, designers, students and anyone who loses time to social media, video or news sites. It requires macOS 14 (Sonoma) or later and the download is built for Apple Silicon. Cue is free, with no ads and no accounts. The app is not notarized by Apple, so the first launch needs a one-time confirmation in System Settings, Privacy & Security, and setup asks for Accessibility and Motion permission, with optional Full Disk Access if you want to pick your own Focus modes. Optional update checks are off by default, and the only data Cue sends is feedback you write yourself plus two anonymous one-time setup signals. Cue is a small, free Mac app with one big idea: your headphones are the switch. Put them in and the Mac focuses, take them out and it relaxes, with the timer, the blocking and the buddies all living in the notch. It is private, light and built for macOS 14 or later on Apple Silicon.

Baby Desk is a macOS app that turns your Mac into a full-screen playground for babies and toddlers while everything underneath stays untouched. With a single Accessibility permission it intercepts input at the system level — before macOS or any other app can see it — so every keypress, click and scroll is swallowed by the play screen instead of reaching your documents, windows or applications. It is built for parents and caregivers who need to hand a Mac to a small child without risking open projects, unsaved files or a carefully arranged desktop, and the website states the intent plainly: the point is not the fun, it is the wall. The problem Baby Desk solves is the ordinary reality that small children love keyboards, and keyboards are packed with destructive shortcuts. One accidental mash of ⌘Q quits an application, ⌘W closes a window, ⌘Tab switches away from what you were doing, ⌘Space opens Spotlight, ⇧⌘3 takes a screenshot, and ⌥⌘⎋ can force quit a program. Media and volume keys, mouse clicks and scrolling are just as easy for a toddler to trigger. Adults routinely lose work, close documents or have their desktop reorganised simply because a child was curious for a few seconds. Baby Desk's answer is not to teach the child restraint, but to build a wall: input is intercepted before the rest of the system sees it, so nothing dangerous can happen in the first place. The core capability is system-level input interception. With one Accessibility permission, Baby Desk absorbs keystrokes and pointer events before macOS acts on them, which is what allows it to swallow shortcuts that no ordinary window could block. The site notes that multi-display setups are fully covered, so external monitors become part of the playground too. The dangerous inputs it swallows whole are listed explicitly: ⌘Q (quit app), ⌘W (close window), ⌘Tab (switch apps), ⌘Space (Spotlight), ⇧⌘3 (screenshot), ⌥⌘⎋ (force quit), media and volume keys, and every click and scroll. Because these events are intercepted rather than merely covered by a window, the protection does not depend on which app happens to be frontmost — the input never reaches other applications at all. On screen, Baby Desk offers three ways for a child to be completely mesmerized, and each one turns input into something harmless. The Starlight Keyboard pops a giant letter or emoji for every key pressed, and each one plays its own xylophone note tuned to a pentatonic scale, so even chaotic mashing sounds lovely rather than discordant; fast mashing earns fireworks. Sparkle Drawing lets the mouse paint a glowing rainbow trail that lingers on screen — real drawings that stay long enough to admire before they gently fade away. Balloon Pop floats balloons up the screen, and any key press or click pops them in a burst of confetti, with an endless supply of balloons and endless giggles. Together, the three activities mean that whatever a small child does with their hands produces letters, colors or confetti instead of closing a document. Being able to end playtime is treated as a parent-only action. The secret exit is ⌘⌥⇧L, or a parent can hold any screen corner for three seconds. An optional second factor of Touch ID or a PIN means a lucky mash can never unlock the Mac on its own. A play timer can be set to 1, 3 or 5 minutes, after which a calm 'that's all for today' screen appears, so a session ends on its own rather than depending on the child stopping. Volume cap and dim mode keep the stimulation gentle, and the system volume is never touched, so a baby can enjoy the fireworks, rainbow trails and confetti without the session becoming overwhelming. Every display is covered, so external monitors become part of the playground too. Baby Desk's approach comes with a deliberate trade-off that the site explains openly. The Mac App Store requires sandboxing, and a sandboxed app cannot intercept system-level input — which is the entire point of the product — so Baby Desk ships directly, signed with a verified Apple Developer ID and notarized by Apple, the same way professional tools such as keyboard remappers are distributed. The Accessibility permission is what lets Baby Desk swallow keystrokes before macOS acts on them; it is used only while the play screen is on, and a friendly onboarding walks you through granting it. If you would rather not grant it, the app still runs in a clearly labelled partial mode, and either way it shows you the exact protection level you have rather than pretending. The app is also explicit about its limits. No software can intercept the hardware power button, a forced shutdown, or some system trackpad gestures, and instead of pretending otherwise Baby Desk labels exactly what it can and cannot do — Full protection with Accessibility granted, or a clearly labelled partial mode without it, with no surprises. The payoff for users is that your work survives tiny hands. Input never reaches other apps, and when you exit, your desktop comes back exactly as you left it: same windows, same focus, same volume. There is no need to save everything, close documents or move to another room just to keep a child occupied for a few minutes, and there is no cleanup afterwards. In practice, Baby Desk fits the moments when a parent needs a few minutes of calm. A caregiver working from home can open the play screen and let a toddler mash the keyboard while open documents, windows and unsaved work stay exactly as they were. A parent can start a one, three or five minute session, step away, and return to the calm end-of-session screen that closes playtime automatically. Multi-display desks work too, since every display becomes part of the playground, so a child can interact with whichever monitor is in front of them without leaving part of the input unprotected. Households that would rather not grant the Accessibility permission can run the partial mode and still see exactly which protection level is active, while volume cap and dim mode keep sessions gentle for younger children. The requirements are straightforward: macOS 13 Ventura or later, on either Apple Silicon or Intel. The interface is available in English, 한국어 and 日本語, and it follows your system language. Baby Desk is a one-time purchase that is yours forever, priced at a $1.99 launch offer (regularly $4.99), and a demo is available on the website so you can try the keyboard-mashing experience before buying. There is no subscription attached to the product. Baby Desk does not try to entertain a child at the expense of the parent's computer. It builds a wall around the input, hides three simple and delightful activities on the other side of it, and gives the parent the only key that opens the gate. For anyone who has watched a toddler reach for a keyboard, that is a trade of five minutes of chaos for five minutes of wonder — without a single lost document.

TapNoise is a Mac app that gives your keyboard the sound of a dream keyboard. It ships with 20 keyboard sounds, from a deep thock to tiny paws, and it adds Tappy, a little keycap character that lives on your desktop and reacts to everything you do. TapNoise plays its sounds under every key and in every app on your Mac, and it also tells you when your AI coding agent is done or needs you. It is built for Mac users who want typing to sound and feel better, and for developers who look away from the terminal while an agent works. You can try everything free for three days with no card, and then pay once for one Mac or two. Most keyboards on modern MacBooks are quiet, flat and identical, which is why so much of the appeal of a custom mechanical board comes down to how it sounds and feels under your fingers. TapNoise exists to bring that tactile pleasure back to the Mac you already own, without buying new hardware. It also addresses a very specific modern annoyance: while an AI coding agent runs, you either stare at the terminal waiting for it or you look away and lose time checking back. Rather than being only a novelty sound toy, TapNoise uses the same sensors that make the sounds to do practical work, so the app is fun first and surprisingly handy second. The heart of the app is its library of 20 keyboard sounds. Ten are grouped as Keyboards: Thock, Creamy, Clicky, Silky, Hushed, Bumpy, Pillowy, Clunky, Chunky and Retro. Ten more are grouped as Fun: Typewriter, 8-bit, Bubble, Piano, Rain, Paws, Kalimba, Woody, Marbles and Laser. Thock is described as deep and heavy, like a custom board, while Paws is a tiny-paw sound and Rain brings a soft patter. These are the app's own sounds and they play under every key, in every app. From the menu bar you can turn TapNoise on or off, set the volume and switch sounds with one click, and Tappy, Clean Mode and Guard Mode live there too, so you never have to dig through a settings window just to change what you hear. TapNoise takes keyboard realism further than playing one sample for every press. Most keyboard sound apps play every key the same; TapNoise feels how hard you press through the MacBook's motion sensor, so every key plays as loud as you hit it. Type softly and it whispers, hit hard and it clacks. Every key also gets a take of its own, so fast typing never sounds like a machine gun, and Space, Return and Delete sound like the bigger keys they are. The stereo image follows the key from left to right, which means the sound tracks your hands across the board. On top of raw typing, the app adds sound for the rest of the MacBook: slap it and it says ouch, the lid creaks like an old door and creaks faster the faster you move it, and the charger and USB plugs each get their own little sounds. Tappy is the companion that turns typing into something you can watch. Tappy sits in a corner of your screen, can be dragged anywhere, presses along with every key, and shows your words per minute underneath. Tappy has moods: Happy while typing along with you, Sweating when you are typing fast, On fire when you are really fast, Dizzy when someone slaps the Mac, Snacking when the charger goes in, Ears covered when Caps Lock is on, Sleepy when it is late, Asleep when you have been away, Party when you level up, and Bye when the lid is closing. Every tap is XP, so you type, level up, unlock hats for Tappy, keep a daily streak going, watch your typing speed, and get a card of your week to share. Hats unlock at levels: the party hat at level 2, sunglasses at level 4, headphones at level 6, a beanie at level 8, a chef's hat at level 10 and a crown at level 15. The tools section is where the app earns its keep beyond sound. Knock commands let you knock twice on your MacBook to pause the music, mute your mic in a call, or answer a waiting AI agent, while three knocks lock the screen. Guard Mode is designed for leaving your MacBook at a café table: if someone lifts it or pulls the charger, it sounds a loud alarm, and turning it off takes your Touch ID or password. Clean Mode holds the keyboard and trackpad still so you can wipe them without typing nonsense or clicking anything, shows a countdown so you know when it is done, and lets you hold Esc to stop early. A shortcut cheat sheet appears when you hold Command for a second, showing every shortcut in the app you are using, and disappears when you let go. Break buddy reminds you to stretch after a long stretch of work and nudges you when you start hammering the keys. Song Mode lets Piano, Kalimba or 8-bit play the next note of a song on every key, so you can type an email and hear Für Elise. Typing speed appears live under Tappy with your best today and your best ever, and Quiet in calls makes TapNoise go silent while any app is using your microphone, so nobody in the meeting hears your thock. For developers, TapNoise acts as an audio notification layer for AI coding agents. If you are coding with Claude Code, Codex or Cursor, you can look away while the agent works and TapNoise will chime when your agent is done or needs you, so you stop checking the terminal. A rising little chime plays and Tappy throws a party when the agent is done; two quick taps play and Tappy sweats until you answer when it needs you; a falling note plays and Tappy covers its ears on an error. When an agent is waiting, you can knock twice on your MacBook and its window jumps to the front, or you can simply click Tappy. Connection is done in one click from Settings, Agents, Connect: TapNoise backs up your settings first, adds one small hook, and Remove takes it out again. It works with Claude Code for done, needs you and errors, Cursor for done and errors, and the Codex CLI for done. The cues are made from the keyboard sound you picked, so they sound like part of your Mac rather than another alert, and they stay quiet in calls and follow your volume. TapNoise is private by design, and the app states this plainly: it hears keys, never words. Instead of reading characters, TapNoise learns which key position went down, like the third key on the second row, so it can play the right sound. It never sees what you type, keeps only counts such as taps today, and has no analytics. There is no account to sign up for and no tracking in the app, and your stats, levels and hats stay on your Mac. Network calls are kept to a minimum: TapNoise goes online to activate and check your license key with Dodo Payments, and once a day to look for an update. Nothing about your typing is sent. From AI agents it hears only done, needs you or error, never your code or prompts, and the hook it adds to each agent's settings file is backed up first so it can be removed cleanly. The app is also built to be light: on a MacBook Air, typing at full speed uses about 4% of one CPU core and around 30 MB of memory, and the motion sensor slows down when you are not typing and turns off with the features that use it. The outcomes TapNoise aims for are straightforward. Typing becomes more satisfying because every keypress has weight, texture and direction instead of a single flat click. Fast typing stays clean and natural rather than turning into a machine-gun rattle. Your MacBook itself becomes expressive, reacting to slaps, lid movement and plugs. Tappy turns ordinary typing into light progression with levels, hats, streaks and a weekly card. Developers regain attention: instead of watching a terminal, they get a chime and a knock-to-answer shortcut that brings the agent's window back to the front. And the practical tools quietly cover small daily problems such as wiping a keyboard, protecting a laptop at a café table, muting your keyboard during calls, and remembering shortcuts or taking a stretch break. Concrete scenarios show how this plays out. A developer runs Claude Code or Cursor in the background, looks away to read documentation, hears the chime when the agent finishes, and knocks twice on the MacBook to jump back to the terminal. A writer at a café enables Guard Mode so that anyone lifting the laptop or unplugging the charger triggers a loud alarm, and later uses Clean Mode to wipe the keys without triggering a flood of accidental typing. Someone on a Zoom or FaceTime call keeps the satisfying thock on their desk while TapNoise automatically goes quiet because the microphone is in use. A typist picks Song Mode with Piano, Kalimba or 8-bit and types an email note by note, then checks the live words-per-minute figure under Tappy and keeps a daily streak going. Someone learning an unfamiliar app holds Command for a second to see every shortcut, and Break buddy nudges them to stretch after a long session. TapNoise is aimed at Mac users generally, and most specifically at MacBook owners who care about how their keyboard feels and sounds, plus developers using AI coding agents. It requires macOS 14 or later. Any Mac with macOS 14 or later plays the key, click, charger and USB sounds, with the built-in keyboard or any keyboard you plug in, and Guard Mode sounds its charger alarm. The motion-based features, which include harder press meaning louder sound, slaps, knocks and Guard Mode's lift alarm, need a MacBook with a motion sensor: MacBook Pro with M1 Pro or M1 Max and later, and MacBook Air with M2 and later. Lid sounds need a lid angle sensor, which covers the same models plus the 2019 16-inch MacBook Pro. Sounds start within a few milliseconds of the key; with AirPods or other Bluetooth headphones, Bluetooth itself adds delay, so TapNoise can play its sounds through the MacBook speakers instead. Pricing is a one-time purchase: a three-day free trial with no key and no card, then Solo at US$9.99 for one Mac or Duo at US$14.99 for two Macs, plus tax, with launch prices of US$4.99 and US$7.99 while launch licenses last. Checkout is by Dodo Payments, every update is included, there is no subscription, and there is a 14-day money-back guarantee. TapNoise is made independently by Rudra Satani, who also makes NotchMind. In short, TapNoise gives your keys a voice: 20 keyboard sounds, a pressure-sensitive feel, a small desktop companion called Tappy, and a set of genuinely useful tools built on the same sensors. It turns an ordinary Mac keyboard into something satisfying to type on, and it keeps developers in the loop with their AI agents without staring at a terminal.

Skymir is a native Google Drive sync client built specifically for Linux. It mirrors your Google Drive folders to local disk, keeps them in perfect two-way sync, lets you open Google Docs, Sheets, and Slides with a double-click, and shows sync status in a native GTK system tray. It is designed for Linux users who need reliable access to their Google Drive files without relying on Google's own desktop client, which is available only for Windows and macOS. The product's main purpose is to provide a safe, fast, and fully integrated Google Drive experience on Debian-based Linux distributions. Google has never released a Google Drive desktop application for Linux. Google Drive for desktop runs on Windows and macOS only. This leaves Linux users without an official, native way to sync their Drive files to local disk. Many have turned to tools like rclone, which is excellent for bulk transfers but not optimized for keeping a working folder in sync with Drive's native documents. The creators of Skymir experienced this firsthand: a sync run using rclone's bisync deleted 1,437 of their Google Docs because the native Docs, Sheets, and Slides were treated as missing files. That event led to building Skymir from scratch with a safety-first approach, ensuring that Google Docs become shortcuts rather than "missing files," and that every deletion goes to a trash you can restore from on both sides. True mirror mode is the core of Skymir. It mirrors your entire Google Drive — or just the folders you choose — in perfect sync with local disk. You can edit on either side, and changes flow both ways. Full offline access is available, so you can work on your files even without an internet connection. Deletions propagate safely to the trash on both sides, with a 30-day recovery period and never permanent. A sync that would delete more than half your files is refused, preventing catastrophic data loss. Because Skymir is built from scratch on the Google Drive API, your files are real files on disk, not a virtual filesystem. Skymir generates .gdoc, .gsheet, and .gslides shortcut files for every native Google Docs, Sheets, and Slides file in your Drive. These shortcuts let you open the live document directly from your file manager. Double-click a .gdoc file in Nemo, Nautilus, or Dolphin and the document opens in your browser. This bridges the gap between local file management and Google's cloud-native documents without requiring you to manually navigate the web interface. It also means your Google Docs are never treated as "missing files" during sync, which was the root cause of the data loss that inspired Skymir. Quick, incremental sync keeps your files up to date without unnecessary work. When you save a file locally, it is on Drive within seconds. Changes made in Drive — whether on the web, your phone, or another computer — come down within about a minute. Skymir asks Drive only for what changed since the last check using the changes.list API endpoint, so it never rescans your whole Drive. This makes syncing fast and efficient, even for large Drives. The background daemon runs continuously, keeping everything in sync while the tray icon shows current status. Skymir also includes a Recent Activity window that lists downloads, uploads, deletions, and shortcut creations with timestamps, giving you a clear audit trail of sync actions. The native GTK tray is a real AppIndicator3 icon that matches your Mint, GNOME, Cinnamon, KDE, or XFCE theme. It shows sync status at a glance, so you can see whether everything is up to date. Quick actions are available directly from the tray menu: sync now, pause, open folder, and open settings. This provides a native Linux experience, unlike tools that rely on command-line interfaces or non-native toolkits. The tray also displays trial status and last sync time, keeping you informed without needing to open the main application window. Skymir offers two sync modes to fit different needs. Full Drive Sync mirrors every top-level folder in your My Drive and automatically picks up new folders you create later — the same model as Google Drive Desktop on Windows and Mac. Selective mode lets you choose exactly which folders to mirror, so you can leave large or noisy folders behind. You pick at first-run setup, and you can switch between the two any time in Settings → Folders. Either way, only your My Drive is mirrored; shared drives are not supported yet. Installation is straightforward: add the skymir.app apt repository or download the .deb directly. The app works on Ubuntu, Linux Mint, Debian, Pop!_OS, and other Debian-based distributions. Skymir's approach is built on safety, transparency, and independence. It is an independent third-party application built on top of Google's public Drive API, not affiliated with or endorsed by Google. Your Google Drive data moves only between your computer and Google — Skymir never sees or stores it. The app sends only a small amount of data to its own server: email address and OS version at setup, license key and a random machine identifier at activation, and a weekly "still alive" ping. No usage telemetry, crash reports, or file information is collected. There is no vendor lock-in: if you uninstall Skymir, your local files stay on your disk and your Drive content stays in Google Drive. Pricing is simple and transparent. Skymir costs $19 as a one-time purchase with lifetime updates and no subscription ever. A 14-day free trial offers full functionality with no credit card required. After the trial, you need to purchase a license to continue using the graphical app and background sync. Each license activates one machine at a time, and you can move between computers anytime via deactivation. A 30-day no-questions-asked refund policy is available. Email support is provided at support@skymir.app. Use cases for Skymir are varied. You can mirror your entire Google Drive for full offline access on a Linux laptop. Selective sync lets you keep only the folders you need, leaving large or noisy folders in the cloud. Writers, developers, and researchers can open Google Docs from their file manager with a double-click. Teams and individuals working across multiple devices can rely on quick incremental sync to keep files current. Anyone who has experienced data loss with other sync tools will appreciate Skymir's safety features, including trash recovery and the refusal to delete more than half your files. The app is tested on Linux Mint 22.3, Ubuntu 24.04.4 LTS, Ubuntu 22.04.5 LTS, and Debian 13 "Trixie" across Cinnamon, GNOME, and KDE Plasma. In summary, Skymir is the native Google Drive client that Google never built for Linux. It combines true mirror mode, double-click Google Docs shortcuts, quick incremental sync, and a native GTK tray into a single application. Built safety-first from the ground up on the Google Drive API, it protects your files with trash recovery and deletion safeguards. With a one-time price of $19, a 14-day free trial, and no subscription, Skymir offers a compelling, reliable solution for Linux users who need Google Drive sync that just works.

GitGlow is a local-first visual Git workbench for independent developers who want to understand exactly what will ship. It combines precise staging, clear commit history, conflict resolution, a checkout-aware terminal, coding-agent review, and release preparation into a single desktop application for macOS, Windows, and Linux. Instead of juggling a command line, a hosted Git client, and separate tools for watching AI coding agents, developers can open a clone and work through one calm workspace where their own changes and their agents' work sit side by side. The product's stated purpose is simple: review the diff, stage precisely, and ship with confidence. Its tagline, "Review your code and your agents' changes before you ship," captures that intent. Modern development workflows have two sources of change that are hard to reconcile: your own edits and the edits produced by coding agents. Agents can touch many files quickly, which makes it difficult to know what was actually changed, which files were reviewed, and whether earlier verification still applies after a later edit. At the same time, Git tooling often splits the work across several applications — one for staging and history, another for resolving conflicts, another terminal, and yet another for observing agent runs. GitGlow addresses that fragmentation by tying every piece of work back to the checkout it belongs to. Review marks, check results, and agent activity are anchored to real Git state, so a developer can tell the difference between what was inspected, what was tested, and what is still current. Staging is the heart of GitGlow's daily loop. The product lets you review files, hunks, or selected lines before composing a commit, so you can stage only what belongs in that change. This is described as a free capability for a local repository, meaning the precise staging workflow is available without a GitGlow account in a private local clone. From there, GitGlow shows how the work connects: you can explore real repository topology, compare revisions, and trace the history of a file or a line. Core history is free and history editing is a Pro capability. The same workspace covers blame and reflog, and it keeps branches and refs legible by supporting branches, remotes, stashes, tags, and trusted worktrees. Daily branch actions are free, while managed worktrees are part of Pro. GitGlow also keeps your shell beside the diff. A checkout-aware terminal lets you run Git, tests, and installed agent CLIs in terminal tabs that understand which checkout is active, with terminal tabs in the active checkout included on the Free plan. Conflict handling is framed around resolving the real operation: GitGlow explains the roles involved in merge, rebase, cherry-pick, and revert conflicts, and continuation and recovery are free. Moving between repositories is designed to be safe — GitGlow keeps registrations, context, and workspaces without flattening local data. One active repository or checkout is free, while concurrency across checkouts and workspaces is a Pro feature. Pro also adds multi-repository fetch, which fetches selected repositories together and reports clear per-repository results. Pro adds a layer that answers a question Git alone does not: did I actually review and verify this? Review confidence lets you mark files reviewed and run an explicit check with a timeout and cancellation. Results stay tied to the checkout that was tested, and later edits bring files back to your attention and make earlier results stale — so a passing check never silently applies to code that has since changed. The GitHub connection, also Pro, lets you review authorized GitHub repositories, pull requests, issues, workflows, and checks. Release Workspace brings checks, merged work, notes, and tags into one guarded sequence. Release publication is available only through Release Workspace, after a fresh guarded preview and explicit final confirmation. Agent work becomes reviewable in Agent Observatory, which follows authentic provider and Git signals across live, observed, and managed runs. Basic observation is free; managed controls are Pro. Observed and managed runs can carry repository, checkout, event, and attributed-file context into the same Changes workflow, so you can open the exact files an agent changed. The Activity Tape lets you search or replay sanitized operational detail in context, with the latest Replay free and advanced retention on Pro. GitGlow never exposes or stores hidden reasoning; an encrypted local flight recorder can retain sanitized provider-visible plans and commentary, commands, bounded output and tests, approvals, collaboration, relative paths, statistics, and safe text diff hunks, while excluding raw prompts, full transcripts, final replies, credentials, absolute paths, binary or oversized bodies, and contents or hunks from secret-prone files. MCP support gives agents bounded Git context through a packaged MCP sidecar: reads are free, and Pro can preview an allow-listed Git or GitHub action, but every MCP-originated write waits for native approval in Agent Observatory while the desktop app is open. GitGlow excludes arbitrary shell or Git commands, destructive cleanup, history editing, force pushes, and PR merges. Insights round out the picture with activity, commits, graphs, trends, complexity, work types, rhythm, sessions, and exports, and Indie Log turns evidence into a work log — written manually, or as a managed analysis-only report pack built from selected sources. The unifying idea behind GitGlow is that agent context is connected to the checkout. Rather than treating agent activity as a separate stream of logs, GitGlow attributes runs to the repository, checkout, events, and files they touched, then surfaces them inside the same review workflow used for human changes. Everything is local-first: local status, diffs, history, repository paths, previews, and audit records stay on your device. Optional GitHub, agent-provider, billing, and consented analytics connections have separate, explicit data boundaries — if you connect GitHub, GitGlow sends only the GitHub requests needed for installed repositories through the GitGlow API, and tokens never enter the desktop webview or MCP responses. Observed sessions started elsewhere remain owned by their originating desktop or CLI client; GitGlow monitors supported hook events and independent Git changes but hides message, approval, and interrupt controls unless the provider exposes a supported channel. Runs launched by GitGlow are Managed and can expose those controls. For users, the outcome is a workflow where confidence is earned rather than assumed. Precise staging produces cleaner commits; checkout-aware checks mean verification results have a clear scope; review freshness prevents shipping on the strength of a stale test run; and agent observability means AI-generated changes arrive as reviewable diffs with attributed files rather than opaque output. The Activity Tape and Replay provide a way to return to the moment that matters when something needs to be explained or investigated. Because everyday local work — staging, commits, branches, conflicts, history, blame, and reflog — is free and requires no account, developers can adopt GitGlow without a subscription and add Pro capabilities only when they need concurrent workspaces, managed agents, GitHub integration, or release tooling. Concrete scenarios include preparing a first reviewed commit after installing GitGlow and opening a clone; running the daily Git loop of reviewing changes, working with refs, resolving conflicts, and inspecting history; connecting supported agent clients, reviewing events, and opening the exact changed files an agent touched; and catching up across a workspace by fetching several selected repositories together and reading per-repository results. Developers working with coding agents can use Review confidence to mark files reviewed, run tests in a checkout-aware terminal, and let later edits pull those files back into view. When a release is due, Release Workspace gathers checks, merged work, notes, and tags into one guarded sequence that must be previewed and confirmed. GitGlow's own documentation suggests a four-step path: start with one repository, run the daily Git loop, understand agent work, and find every control through searchable reference documentation. GitGlow is aimed at independent developers who work locally and want a calmer, more accountable Git workflow, including those who use coding agents such as Codex or Claude and need to review what those agents changed. Pricing is simple: the Free tier works with a private local clone with no account and includes one active repository or checkout, precise staging, commits, branches, sync, merge, conflict resolution, stashes, tags, topology history, compare, blame, reflog, basic observation, the latest Replay, manual Indie Log, analytics, export, read-only MCP, and terminal tabs in the active checkout. Pro is €2.99/month or €29/year and adds concurrent terminal sessions, review freshness, verification tied to checkout state, multi-repository fetch, guarded history editing, concurrent workspaces, managed agents, advanced Observatory review and Replays, GitHub integration, Release Workspace, managed Indie Log, and MCP writes. AI provider subscriptions and API usage are separate, and existing subscriptions retain their current terms. Desktop builds are published for macOS, Windows x64, and Linux x86_64/aarch64; version 2.0.5 has been manually checked on macOS while the Windows and Linux packages are build-validated for architecture, version, payloads, sidecars, and checksums without runtime claims. Agent tooling is exposed through a packaged MCP sidecar, and the product's own comparison notes that GitKraken covers a broader provider ecosystem and shared cloud workspaces while both products include an embedded terminal. GitGlow's value proposition is straightforward: it turns Git and coding-agent activity into one reviewable, checkout-anchored workspace. By combining precise staging, legible history, conflict resolution, a checkout-aware terminal, review marks with freshness, GitHub checks, release preparation, and bounded agent observability in a local-first desktop app, it lets developers answer the question that matters before shipping — what exactly am I about to release, and have I actually verified it?

Fluence is an Android app that turns the PDFs, ebooks, reports and saved articles you already have into something you can listen to, follow along with, and ask questions about. It is built for people who want their reading to move with them, and for people who care about where their documents go. Everything Fluence does happens on the phone in your hand: reading the file, making the speech, and answering your questions. Whether you are working through a long report, listening to a novel, or catching up on articles you saved weeks ago, Fluence reads the document aloud while you follow the text, and it can answer questions using the document's own passages. Most people already have a reading pile they never get through: PDFs, ebooks, reports and saved web articles that sit unread because there is simply no time to sit down with them. Listening is one way to get through that pile, turning otherwise idle moments into reading time. The usual options for listening to text, however, involve sending your documents to a server, creating an account, and paying a recurring subscription. Fluence takes a different approach by keeping the entire job on the device: the reading, the speech, and the question answering all run on the phone, and there is nothing to upload before the app can start working. The reading experience is designed around following along. The sentence being spoken lights up as you listen, so your eye can track the text while your ears hear it, which helps you keep your place and stay oriented inside a long document. Beyond simply moving through the text, Fluence lets you bookmark a line or write a note beside it, so the passages and thoughts that matter to you stay attached to the document itself rather than living in a separate place. Because your library and your notes stay on your phone, the record of what you read and what you thought about it stays with you. The voice that reads to you arrives with the app. There is no voice pack to pick and nothing to download first, and the built-in voice reads 31 languages. That means you can start listening immediately after installing the app, without waiting for a download or choosing between different packs. If the built-in voice is not what you want, Fluence also lets you use your own voice, made from a short recording you speak yourself. The voices live on your phone along with everything else, which is consistent with the app's on-device approach to reading and speech. Fluence also lets you ask about what you read. You can put a question to the document and get an answer drawn from the document itself, with the app showing you the passages the answer came from. Because the answer is grounded in the source text and the source passages are shown, you can check the answer against the document rather than taking it on trust. This turns a long PDF or ebook into something you can interrogate, not just listen to. One of Fluence's more distinctive capabilities is turning any book or document into a single audio file. Fluence prepares the audio for the whole document ahead of time while you do something else, and tells you when it is ready. What you get is one file to keep, to play in the car, or to send to another app. Preparing the audio is free; saving it out is part of the full version of the app. This makes it possible to build your own audiobooks from material that was never available as an audiobook in the first place. Privacy is central to how Fluence works. The app does all the work on the device in your hand: reading the file, making the speech, answering your questions. There is no account, no server and no subscription, and the app works offline. Your library, your notes and your recorded voice stay with you. For anyone who has hesitated to send contracts, reports, medical documents or personal reading to a cloud service simply to hear them read aloud, this on-device design removes that step entirely. Fluence opens what you already read. It supports PDF, EPUB, Word, OpenDocument, Rich Text, Slides, Text, Markdown and web articles, and it presents books and documents side by side in one library. That format coverage means you do not have to convert files before using them, and you can keep the ebooks you bought alongside the reports you were sent without splitting them across different apps. Fluence is sold as a one-time purchase rather than a subscription. It costs €14.99, and buying once means it stays yours, on every phone you sign into with the same Google account. Ten documents are free, so you can see how it feels before paying. The app is available on Google Play for Android, and because nothing leaves your phone, it keeps working offline for good. The benefits follow directly from how Fluence is built. You get a reading app that works wherever you are, including without a connection, because the speech is generated on the device. You get through more of the material you have already collected by listening to it rather than only reading it. You keep control of sensitive documents, since they are never uploaded. You avoid a recurring bill, since the app is a single payment. And you end up with audio you can keep, because any document can be prepared as a single audio file you can save, play in the car, or send to another app. In practice, Fluence fits a range of everyday situations. A commuter can load a PDF report or an EPUB novel and listen with the text lighting up sentence by sentence, keeping their place even if they look away. A driver can prepare a document's audio ahead of time and play the resulting single file in the car. A student or researcher can bookmark lines, write notes beside them, and then ask the document questions, checking the answers against the passages the app shows. Someone who reads in more than one language can listen in any of the 31 languages the built-in voice supports, or record a short sample to hear the text in their own voice. And anyone handling confidential material can keep the whole workflow on the phone, with the library, notes and recorded voice never leaving the device. Fluence's core promise is simple and specific: your books and documents, read aloud by a voice you choose, with everything happening on your phone. It combines reading-along highlighting, bookmarks and notes, an on-device voice covering 31 languages, the option of your own recorded voice, document-grounded question answering, and single-file audiobook export, then wraps all of it in a one-time purchase with no account, no server and no subscription. Ten documents are free to try, and for anyone who wants to listen to their own documents without handing them over, that combination is the point.

Skreno is an AI-powered screen recorder that runs entirely in your browser, built for capturing screens, screenshots, and meetings without installing a desktop application. It combines recording, screenshots, annotation, editing, and sharing in one place, so a capture arrives not as a raw file but as a shareable link carrying a transcript, an AI summary, and comments on the exact second. The product is described as a video studio in your browser: record, edit and share in one place, with a free full editor, no watermark on any plan, and no account needed to try. It is aimed at individuals and teams who need to explain something visually and quickly. The website frames a familiar set of friction points that Skreno is designed to remove. Recording often means installing an app just to hit record, and screenshots have to be cropped and labeled by hand. Drawing a simple arrow forces you into a separate editor, while sharing usually means uploading, waiting, and then pasting a link. Explaining a problem in writing can take three paragraphs where a quick video reply would do, and feedback ends up chased across five different threads. Engineering hand-offs stall on refrains such as "it works on my machine," "I can't reproduce it without the error," and "no stack trace, no idea where to look." In support, customers describe issues in words that don't match the screen, so each ticket takes three replies before anyone can reproduce it. Skreno records right in the browser instead of requiring an installed application. Screen recording is the core capability, and the site also covers screenshots, where Skreno grabs and labels each step rather than making you crop and label every shot by hand. Annotations let you mark things up right where you captured them, and pinpoint annotation lets you draw and mark up while you record, so you never have to open a separate editor just to draw an arrow. Instant sharing means the link is live the second you stop recording, eliminating the upload-then-paste step. A video reply lets you respond with a quick video instead of typing three paragraphs, and feedback can be collected right on the video instead of across multiple threads. Studio is a recorder and editor that runs in your browser, included with every plan, and it can download up to 4K on your own machine. Within the editing studio you can trim, caption, blur, and polish, with exports up to 4K and no download required to edit. Trimming happens right in the browser instead of downloading the raw file just to cut ten seconds. Captions, blur, and backgrounds are gathered in one editor rather than split between one app for captions and another for backgrounds. Every edit updates the same link, so you don't re-upload the whole thing after each tiny change. When recording, you can capture your screen and camera as separate tracks, then pick a layout, cut silences, and add animated captions and titles. Network and console logs are captured behind whatever broke, directly addressing debugging refrains like "it works on my machine" and "no stack trace." Bug reports come with the console log attached, and a network panel highlights failed requests with the error response body available. A Web SDK installs with a single script tag on your own site, letting you add Skreno to the products you build. You can also connect your AI assistant so it searches your recordings over MCP and acts on them. The Meeting Notetaker records the call and writes the notes, so meetings arrive with a transcript and an AI summary. Transcripts and AI generate a transcript and recap automatically, described as AI transcripts in seconds. To gather social proof, you can collect customer stories by sending a link so the customer records themselves and you receive the testimonial. A request link lets anyone record for you with no account needed. In support, you can send a Request link so customers record the bug themselves, build a library for your ten most repeated tickets, and keep recordings attached to the ticket rather than in a separate folder. Skreno's approach is to keep the whole workflow inside the browser. Recording, editing, and sharing happen in one place, and one link is used to share. That link carries a transcript, an AI summary, and comments on the second, plus the console and network log behind whatever broke. Because every edit updates the same link, what you shared stays current without re-uploading. The Studio recorder and editor runs in the browser, is included with every plan, and downloads up to 4K on your own machine, while AI produces transcripts and recaps automatically and an AI assistant can search recordings over MCP. The stated benefits follow directly from removing each friction point: no app to install, no manual labeling of screenshots, no separate editor for a single arrow, and no upload-and-wait before sharing a link. Feedback arrives on the video instead of across threads, and edits stay linked so recipients always see the current version. Support teams can get bugs reproduced with fewer back-and-forth replies, and engineering hand-offs include the console and network logs rather than a vague description. Meetings produce notes automatically, and testimonials can be collected without scheduling a call. The site lists concrete use cases: Support, where request links let customers record bugs themselves and recordings stay attached to tickets; Product & Design; Engineering, where dev logs and the Web SDK support debugging and hand-offs; Guides; Meetings & calls through the Meeting Notetaker; Testimonials, where a link lets customers record their own story; and a video editing studio for trimming, captioning, blurring, and polishing clips. Skreno targets individuals and teams who need to record, edit, and share video quickly, spanning support, product and design, engineering, and people producing guides, meetings, testimonials, or edited video. It runs in the browser and is also distributed as a Chrome Extension. Pricing is framed around free being a real plan rather than a trial: there is no credit card required, no countdown, and no watermark on any plan, with the full editor included. A separate pricing page lists additional plans. Skreno's core promise is to record once and explain nothing: capture your screen, screenshots, or meeting in the browser, then share a single link with a transcript, AI summary, second-accurate comments, and the developer logs behind any bug. With a free full editor, up to 4K exports, and no watermark on any plan, it replaces a patchwork of recording tools, editors, and feedback threads with one web-based video studio.

ReSO AI is a generative engine optimization platform that helps brands get discovered and recommended when customers ask AI. It is built for founders, CMOs, and growth teams who want to turn buyer intent expressed in ChatGPT, Perplexity, and Google AIO into visibility, citations, and traffic. Rather than simply tracking where a brand appears, ReSO analyzes intent, competitors, content gaps, and technical issues, then turns those findings into priority actions that can improve how AI platforms mention, cite, and recommend the brand. Onboarding the ReSO agentic employee is presented as the starting point: a buyer searching on ChatGPT should receive an answer that recommends your brand. Buyer behavior is shifting toward AI-led discovery, and the product states that in 2026 AI will dictate discovery. Buyers increasingly research on ChatGPT rather than on traditional search result pages, which means a brand that is absent from AI answers can lose influence before a prospect ever reaches its website. Traditional SEO tools were built to track keyword rankings, not to decode the intent behind the questions people ask large language models. ReSO positions itself against that gap, offering a way to understand how LLMs interpret intent, what they cite in a category, and which technical signals limit a site's ability to be read and trusted by AI systems. The discovery side of the platform is built on a proprietary intent model that analyzes 30+ intent-driven search variables to decode what customers are looking for. It maps those variables to real buyer queries on AI and organizes them by stage, including Discovery, Feature Comparison, and Persona, where Discovery covers the exploratory phase in which people look for the best options, trends, and possibilities before narrowing down what fits their needs. Users can curate a list of 75 high search intent-based prompts, track brand share of voice across the entire awareness-to-conversion funnel, and map their visibility against competitors so they can see where they are mentioned and where rivals are recommended instead. Content creation is handled through citation intelligence. Each piece of content is built around signals that influence AI visibility: prompt trends, citation behavior, and content gaps across the category. The platform supports a consistent weekly publishing cadence, a 100% customized content strategy intended to make the brand a category leader on LLMs, and content created in formats that improve mention potential. This matters because getting cited by AI search engines requires clear, authoritative answers, strong entity signals, useful source material, and structured content that AI systems can interpret easily, so the content work is aimed at the sources and authority signals that AI platforms actually draw on. A third pillar is the technical engine, which audits the technical issues that affect how AI systems and search engines access, interpret, and trust a website. It delivers technical edits intended to increase LLM visibility, entity mapping and code-level changes to make the website AI-readable, and improvements to discoverability across search platforms. Because AI systems must be able to reach, parse, and trust a site before they can cite it, these fixes complement the content and prompt work rather than standing alone. Overall, ReSO replaces the work of an SEO agency with an agentic employee that handles the process end to end, and it is designed to replace agencies, consultants, and fragmented internal workflows with one system. Its methodology is to measure first, through an AI visibility check, then act on the gaps it finds in brand presence, content, technical setup, and competitive positioning, and then keep publishing and optimizing on an ongoing cadence. The platform states that it works alongside traditional SEO while extending visibility into AI search, combining content, technical optimization, authority, and AI visibility tracking so that both traditional search and AI platforms are addressed inside one search visibility strategy instead of treating AI search as a separate channel. Reported outcomes from the company's own case studies include a 3X traffic increase from LLM-driven search, a +38% month-on-month citation growth, a +25% signup conversion directly from ChatGPT referrals, and a +30% quarter-over-quarter visibility score growth. A publicly listed fintech in India, SMC Global, saw a 132% increase in clicks from ChatGPT, a 162% increase in website clicks up from 37,200, and a 516% increase in impressions up from 923,000. An HRTech EoR platform achieved a 12X increase in citations from LLMs in three months, a 364% increase in blog traffic from 824 to 3,825 monthly clicks, a 5x increase in AI mentions from 10 to 50, and a 41% increase in AI visibility up from 8%. A US-based LegalTech company recorded a 92% increase in brand mentions up from 288, a 60% increase in total traffic up from 23,400, and a 55% increase in impressions up from 2,080,000. Concrete workflows described in the content include improving a brand's visibility in ChatGPT by identifying the questions buyers ask LLMs and where the brand currently appears in those answers, then prioritizing the pages, topics, and site improvements most likely to increase mentions and recommendations. Another is getting cited by AI search engines by analyzing which sources AI platforms cite in a category, finding gaps in content and authority signals, and optimizing the website accordingly. A third is competitive analysis: measuring share of voice across intent-driven prompts, seeing where competitors are mentioned or recommended, and locating the buyer-intent stages with the largest visibility gaps. The Discovery stage content shows the exploratory questions buyers ask, such as the best budgeting apps for managing personal finances, the best fitness trackers for everyday use, or the best tools for automating B2B lead qualification, which illustrates the kind of prompt-level work the platform supports. ReSO is aimed at founders, CMOs, and growth teams across high-growth startups and enterprise, and it is described as trusted by founders and CMOs. Its content highlights B2B brands in particular, noting that B2B buyers increasingly use ChatGPT, Perplexity, or Google AIO to research solutions, compare vendors, and shortlist providers, and that the platform also serves verticals such as fintech, HRTech, and LegalTech. Access begins with an AI visibility check from the onboarding flow, and the brand offers a detailed AI visibility analysis across brand, content, technical setup, and competitive gaps, stated as worth $500 at no cost for the time being, with no credit card and no commitment required to get started. The platform notes that AISO, GEO, AEO, LLMO and similar terms overlap, with AISO focused broadly on AI search, GEO on generative engines, AEO on direct-answer visibility, and LLMO on large language models, while the underlying goal is the same: making a brand easier for AI platforms to understand, mention, cite, and recommend. In short, ReSO AI's core value proposition is turning AI answer engines into a growth channel: it shows brands how LLMs see them, why competitors are recommended instead, and what to change in content and on-site technical signals so that the brand becomes the one AI recommends to buyers.

API Claws is Buda's developer-facing Agent API. With a few calls to Buda's REST API at /api/v1, third-party developers can give their products a cloud-hosted AI Agent with conversation capability and a private knowledge base, waking on demand and consuming zero resources at rest. In practice, you are not integrating a raw model API; you are integrating a managed Agent capability layer that already includes model access, runtime, knowledge base, session management, and operational scaling. It is built for smart hardware makers building watches, earbuds, and IoT devices that want a built-in AI assistant; software developers building mini-programs, apps, and browser extensions that want to embed AI chat; and SaaS providers who want to offer customers an "AI-powered private space." Smartwatches, IoT devices, and chat plugins all want built-in AI Agents. But running a large model on-device isn't viable: there is not enough compute, too much power draw, and poor results. Calling an LLM API directly also falls short, because a raw model API only gives you model output. You are left to manage sessions yourself, build retrieval, build orchestration, design multi-tenant support, and run operations. API Claws closes that gap. It gives you an operational AI Agent instead of a text or structured completion, with a built-in session model, Drive-based knowledge included, a managed Agent runtime, Space-based isolation built in, and operations managed by Buda. At the heart of API Claws are Spaces and Agents. A Space is the tenant boundary, customer boundary, or device fleet boundary, controlled by the Space API at /api/v1/spaces. You use a Space whenever you need isolation between customers, teams, devices, or paid accounts. Inside a Space lives the managed Claw Agent, controlled by the Agent API at /api/v1/spaces/{spaceId}/agents. Each tenant or product line can have its own assistant role and instructions, so different users can have different Agent configurations; to do that, you create separate Spaces and Agents per user as needed. Buda also provides a concise developer-facing hosted Agent collection through the API Agents API at /api/v1/api-agents, which lets you list or provision API Claws with a plain REST resource name. The Drive API at /api/v1/api-agents/{agentId}/drive/files holds durable knowledge, manuals, policies, state, and long-lived memory. This matters because the useful pattern is not "send everything in every prompt." Instead, your product treats Drive as the Agent's durable brain: you write product manuals, policies, release notes, and user-specific state into Drive, and the Agent keeps improving from files rather than receiving the same giant prompt every time. Files persist across sessions, and updating a file updates what the Agent can use on the next run. Buda can create, overwrite, browse, rename, and delete Drive files, list files and folders, and issue presigned upload and download URLs, so a device or third-party app gets an Agent that improves over time. The Chat Session API at /api/v1/api-agents/{agentId}/sessions covers user conversations, async Agent runs, status, and messages. When you send a message, Buda returns a session.id, starts the Agent run asynchronously, and you poll the session until the status becomes completed, waiting_for_input, failed, or cancelled. Each end user gets an independent Chat Session with isolated context. The Embed API adds a hosted iframe surface and a frontend-safe path: your backend mints a short-lived embed URL or embed session token, and a browser, mobile app, mini-program, or extension can talk to Buda without seeing your main API Key. Agents also support scheduled tasks, which can be listed, created, enabled, disabled, and run immediately with run history. The way API Claws works is deliberately infrastructure-first. A request authenticates with your API Key, resolves to a Space (tenant boundary), reaches the Agent and its Drive (knowledge base), and runs inside a Chat Session, gated by a credit check against the Space's usage balance rather than a per-request rate limit. The Agent sleeps when idle and wakes automatically when a message arrives, so it consumes zero resources when idle and no infrastructure sits behind every device. The recommended lifecycle is provision, seed, run, read, learn, and update: the API call wakes the Agent, the session captures the immediate conversation, and Drive carries forward the durable knowledge. API Claws sits on top of OpenClaw; OpenClaw is the underlying Agent runtime and capability model, while API Claws is the developer-facing integration layer built on top of it. The key value is that your team can focus on the product experience while Buda provides the Agent infrastructure layer behind it. With API Claws, the developer does not need to separately build or operate model configuration and provider switching, inference machines or GPU infrastructure, the Agent runtime and tool sandboxing, file ingestion and knowledge base processing, session and context management, tenant isolation for different users or customers, or wake/sleep orchestration for idle agents. For hardware companies, this changes the cost structure: there is no need to ship a device powerful enough to run a full AI stack locally, no need to keep a user's laptop or phone acting as the primary runtime, and no need to maintain a separate AI backend team just to support one device line. Your hardware can stay lightweight while the AI capability keeps improving in the cloud. API Claws is used across several concrete scenarios. A smart hardware fleet can map one Space per customer, fleet, or premium device group, one Agent per device model or assistant role, with manuals, firmware notes, troubleshooting guides, and device state snapshots in Drive; the device backend starts sessions when the user speaks or a device event occurs. A WeChat mini program can use one Space per merchant, course, customer, or end-user account, with an Agent per business function such as support, tutor, or shopping assistant, and its backend mints an embed session for the user's openid. A SaaS copilot can use one Space per customer tenant and one Agent per workflow such as onboarding, reporting, or support. A customer support widget can run one support Agent per brand or queue and create one embed URL per visitor. An internal enterprise Agent can use one Space per department or project with multiple Agents for ops, sales, research, or engineering. API Claws is for two common buyer types: chat apps, SaaS tools, and third-party software that already have users, conversations, forms, or workflows; and hardware manufacturers building smartwatches, earbuds, voice terminals, or other connected devices. Integration is straightforward: register a developer account and get an API Key, create a Space, create an Agent inside it, add durable knowledge to Drive, start a conversation, and poll the session result. Buda exposes the endpoints in an OpenAPI JSON at /api/v1/openapi.json and an interactive Swagger UI at /api/v1/doc, and an official Rust CLI, buda-cli, wraps every /api/v1 route. Buda runs a self-built Kubernetes cluster (Claw Computer) with elastic scaling. On billing, Buda charges per Space, not per end user: you buy Spaces from Buda with volume pricing available, charge your end users a subscription or activation fee, and keep the margin. In summary, API Claws turns a product's AI ambitions into a few REST calls. It gives hardware and software teams a managed, multi-tenant Agent capability layer, with model access, runtime, Drive knowledge base, sessions, embeddings, and scaling included, so they can ship a cloud AI brain behind watches, apps, and SaaS tools without operating inference infrastructure, and with an Agent that keeps improving over time.

put·here is a local-first spatial workbench for macOS, described by its maker as a place to put things down and make sense of them later. It is aimed at anyone whose thinking does not arrive pre-sorted: instead of demanding a title, a folder, or a tag the moment something is saved, it offers a canvas where text, links, pictures, and files land wherever you drop them. Capture happens by pasting, dragging, or pressing ⌃⌥P from any app, and stepping back lets the whole canvas read at a glance. The product frames its purpose in three moves — collect, connect, and try — gathering links, notes, images, and files, connecting your thinking, and optionally letting AI research the next question with sources. Most note apps want an answer the moment you save something: a title, a folder, a tag. The maker's own story describes the alternative that most people actually live with — thoughts going wherever was closest, such as a chat file-transfer tool, the desktop, or a temporary note, until weeks later everything still existed only in theory. The deeper problem put·here targets is that a question can be scattered across twelve tabs, a few notes, and a screenshot, and that ideas show up before they have a shape. Rather than forcing structure prematurely, put·here keeps scattered material in one space with positions, groups, and drawn connections, so the thread between things is preserved until it is needed. It also matters that this happens locally: the app is local first, needs no account, and does not require you to know where anything belongs. Capture in put·here is deliberately undemanding. Text, links, pictures, and files can be pasted, dragged in, or sent from any app with the ⌃⌥P keyboard shortcut. Cards sit on an open canvas rather than in a list, and every canvas remembers its position and zoom, so a workspace looks the same when you return to it. Because items stay where they were dropped, spatial arrangement itself becomes a memory aid — the maker notes that letting positions leave a clue is part of the point. Working cards have tabs, and the writing page uses block editing, so a card can hold more developed material once a thought starts taking shape. Tidying exists too, but it is always your choice: you decide when to tidy the canvas, and the change is undoable. Tidying is labelled Beta and described as more accurate in the current release. The second half of the premise is connection. put·here keeps not just what you found but the thread between things: positions, groups, and the connections you draw yourself. Connections are drawn by you, and relationship labels are optional, which keeps the graph honest to what you actually intended rather than what an algorithm guessed. AI can help with grouping, but only under your control: you start AI grouping, you can undo it, and the groups you made yourself are kept. This matters because the value of a research pile rarely comes from any single item — it comes from the relationship between a question, a PDF chapter, a reading link, and a half-formed idea. Making those relationships explicit, and visible on a canvas, is what lets a scattered question be picked up again later. AI in put·here is optional and framed as research rather than autocomplete. You right-click a question card and choose "Hand it to AI". The AI then reads your material, researches the web, and brings findings back with their sources, in a workspace of its own — the copy is explicit that AI works in its space while you stay in charge of yours. The flow is shown as three steps: question, research, evidence. An example workspace asks "Can I see an eigenvector?", tells the AI to use saved linear algebra notes to find a visual explanation while keeping the sources, and lists the starting material as a chapter from a linear algebra PDF. Crucially, AI does not change your main canvas on its own; it creates source and finding cards in a separate workspace, leaving your own arrangement intact. Built-in put·here AI includes a monthly free allowance shown in Settings, and you can alternatively bring your own API key or run a local model. Storage is where put·here diverges most from typical note apps. Every canvas maps to a real Finder folder. Notes are plain Markdown, and images and files keep their original formats, so nothing is locked in a proprietary database. Even if put·here is removed, what was saved stays yours — a claim the product states directly. Housekeeping also respects the filesystem: deleted items go to Recently Deleted and then to Finder Trash. Because canvases are folders, the same files can be viewed through Finder as an ordinary directory (an example shows still-thinking.md, linear-algebra.pdf, and reading-trail.webloc) or through the canvas as a spatial arrangement. It is described as the same files, a different way to see them. Each canvas additionally remembers its own position and zoom, so layout is part of the workspace rather than something rebuilt each time. The overall approach is spatial and local-first rather than hierarchical. Instead of a filing tree that must be decided upfront, put·here treats placement as a lightweight form of meaning: drop something where it feels right, and let the canvas carry the context until the shape of the idea becomes clear. Capture is frictionless and multi-source; organization is deferred; connections are user-drawn; research is handed off, on request, to an AI that returns sourced findings in a separate area. Everything rests on ordinary files on the Mac. The privacy model follows from that: AI sends only the text its feature needs, built-in AI relays it through the company's server, your own key connects directly to your provider, and a local model processes everything on your Mac. Built-in AI uses an anonymous device ID to count the free allowance, and the app includes no behavioral analytics. Publishing, phone capture, previews, and update checks have their own separate data flows described in the privacy policy. Practically, the benefit is that you can save something without first deciding what it is. Thoughts arrive before they have a shape, and put·here accommodates that instead of fighting it. Keeping a question in one place — with its tabs, notes, and screenshots together — means it can be resumed rather than reconstructed. Connections you draw yourself preserve the reasoning behind a pile of material. AI research that returns sources lets findings be checked rather than taken on trust. And because everything is stored as real files in real folders, your notes stay yours: notes as plain Markdown, images and files in their original formats, and no account required to get started. Updates download in the background and install when you quit, a behaviour introduced from version 0.12.0 and switchable in Settings. put·here names three broad situations on its own site: exploring a product direction, researching an open question, and learning a difficult concept. A product direction is a typical example — ideas, competitor links, and reference screenshots can be spread across a canvas while the shape of the thing is still unclear. Researching an open question suits the AI flow, where a question card is handed to AI together with your saved material, and findings come back as sourced cards in a separate workspace; the site's worked example uses linear algebra notes to answer "Can I see an eigenvector?" Learning a difficult concept is supported by collecting a PDF chapter, a weekend reading link, and a note of the question itself — the sample canvas includes a note titled "Why don't eigenvectors turn?", an idea described as still taking shape, a PDF of Linear Algebra Chapter 2, and a reading item about learning, memory, and drawing things out. Sharing is possible through an export share bundle; hosted publishing to a link requires an invitation code and is not part of the standard download experience. put·here is a Mac app — version 0.13.2, requiring macOS 14 or later, running on both Intel and Apple silicon, a 19 MB download notarized by Apple. That makes it suited to Mac users who keep research material, design references, reading notes, or unresolved questions, and who prefer local storage over a hosted account. There is no account and no subscription required for the current release: 0.13.2 is free to download with no account, and AI is optional. Built-in put·here AI carries a free monthly allowance shown in Settings; alternatively you can supply your own API key, where your provider may charge for usage, or use a local model. The site links to a manual covering the AI, disk storage, and privacy, plus a full privacy policy. There is no stated third-party integration list beyond that, and phone capture is mentioned only as having its own data flow. put·here's value proposition is captured in its own line: put it here, make sense of it later. It gives Mac users a spatial, local-first place to collect notes, links, images, and files without deciding where they belong, to connect ideas manually, and to run sourced AI research when a question needs answering. Because every canvas is a real folder of plain files on your Mac, the workspace stays yours — free to start, no account, and no need to know where it belongs.

Pawse is a free, open-source macOS menu bar app that reminds you to drink water, keep your habits and take breaks from the screen. Instead of sending a notification, Pawse sends a cute 3D pet that walks onto your screen and asks you a simple question, such as "Did you drink water?". You answer yes or not yet. The app is built for Mac users who spend long stretches of the day in front of their screens and want gentle, hard-to-ignore nudges around hydration, movement, posture, eye rest and screen-time breaks, all collected in one small app that lives quietly in the menu bar until it is time to care. Most reminder apps rely on notifications, and notifications are easy to dismiss. As Pawse's own page puts it, notifications get swiped away, banners get muted and reminders get ignored. The problem is not that people do not know they should drink water or stand up; it is that an impersonal banner is trivial to postpone. Pawse changes the delivery mechanism rather than the message. A small character walks across the screen, looks hopeful, and asks a direct question. It is much harder to say no to someone small and hopeful who walked all the way across your screen just to ask if you are okay. That single design decision is what the whole product is built around. Water tracking is the first of the three main areas. You set a daily goal and a glass size, and your pet visits on your schedule, only during your active hours, and stops once you hit the goal. A one-click log lets you record a drink the moment you are asked, and the app keeps track of streaks and a month calendar so you can see continuity over time. The website shows an example of six of eight glasses logged today alongside an eleven-day streak. Because the pet only appears during active hours and stops after the goal is reached, the reminders stay relevant to your actual day rather than firing on a fixed timer regardless of whether you are at your desk. Habits are the second area, and Pawse deliberately keeps them open-ended: any habit, any interval. You create a habit with an emoji and a goal, and the pet asks about it. The examples on the site include stretching every 30 minutes, walking every 45 minutes and resting your eyes every 20 minutes, but the habit itself can be anything. Each habit has its own interval, so a 30-minute stretch routine and a 45-minute walking reminder can run side by side without interfering with each other. The habit screens show progress such as three of four stretches done, along with streaks and a monthly calendar. The FAQ explains the setup directly: open Pawse, go to Habits, add a habit, turn on the pet reminder and set the interval you want. Screen time is the third area, and it is where Pawse measures rather than assumes. The app tracks active screen use from keyboard and mouse activity and pauses the count when you step away; stepping away for three minutes counts as a break automatically. After 30 minutes of continuous screen time, adjustable to anything you like, Pawse shows a calm full-screen break with tips such as the 20-20-20 eye rule. The screen time view includes an hourly chart, auto-pause, a daily limit and 20-20-20 tips, and the site shows an example day with four hours and 28 minutes of screen time and four breaks taken. For habits you are serious about, there is an optional hard block. The pet covers the whole screen and it only unlocks when you confirm you did it. Hard block can be set per habit, it applies to water, any habit or breaks, locked breaks cannot be snoozed or ended early, and there is always an emergency exit: holding Esc for five seconds unlocks the screen. The overall approach is to merge three kinds of self-care tracking — hydration, habit intervals and screen-time breaks — into a single app and deliver all of them through the same character. Rather than three separate utilities each with its own notification style, Pawse has one pet that handles every reminder type. The pet is not a static icon: it walks onto the screen, has moods, reacts to your answers and leaves. Saying yes makes it bounce with joy; saying not yet makes it droop and float back later. The dashboard itself is organised into Today, Habits and Screen time views, so the same app that interrupts you also shows you the record of what you have actually done. The outcome Pawse aims at is breaks you will actually take and habits you will actually keep. Because reminders arrive as a character rather than a banner, they are harder to dismiss, and because the pet only appears during active hours and stops when a goal is reached, the interruptions stay tied to real activity. Recording a drink or a stretch takes one click, so logging stays low friction. Streaks and monthly calendars turn scattered good intentions into visible continuity. And because the app makes no network requests and has no analytics or accounts, your history stays on your Mac, which makes it reasonable to track personal health habits inside it. The website walks through a concrete day. At 9:00, Momo walks in with a bottle before your first meeting and asks about water. At 10:30, Mochi arrives for the 30-minute stretch habit and waits for an answer. At 12:00, after 30 minutes of screen time, a calm countdown appears and you take your eyes off the screen. At 15:00, if you answer "not yet", Boo droops, sniffles and floats back a little later rather than disappearing. At 18:00, the goal is reached: eight of eight glasses and four stretches, and the streak grows to twelve days. Other described scenarios include setting a stretch reminder every 30 minutes on a Mac, using hard block so a break cannot be snoozed, and creating your own habits around anything you care about. Pawse runs on macOS 13 Ventura, macOS 14 Sonoma, macOS 15 Sequoia and newer, on both Apple silicon (M1, M2, M3, M4) and Intel Macs, delivered as a universal download of about 5MB. It is a menu bar app, so it lives in the menu bar rather than the Dock. It is completely free and open source under the MIT licence, with no ads, subscriptions, in-app purchases or accounts, and the site describes it as $0 forever. Five hand-built 3D pets ship with it — Momo the Chibi, Mochi the Cat, Pip the Penguin, Yuzu the Capybara and Boo the Ghost Bunny — each with its own walk, voice and personality, and you can drop in any .usdz 3D model to use your own pet instead. Pawse is a small, free Mac app with a simple thesis: the reminder is not the hard part, the delivery is. By turning water, habits and screen-time breaks into a question asked by a tiny animated pet with a voice, moods and an optional hard block, it makes the small daily decisions that protect your health difficult to swipe away. It stays quiet in the menu bar, asks only during your active hours, keeps your data on your Mac, and costs nothing.

Onepin is an AI voice production agent that turns any script into voice that is ready to ship: model-matched, validated and auto-fixed. Rather than replacing the text-to-speech engines a team already relies on, Onepin works on top of them, connecting more than thirty providers — including ElevenLabs, OpenAI, Google, Microsoft, AWS, Rime, Inworld, Fish Audio, MiniMax, Deepgram, Naver Clova, Cartesia and Murf AI — behind a single account. It is built for teams shipping AI voiceover in several languages, such as product videos, dubbing and courses, and its stated promise is straightforward: human voice, right words, every language, so you do not have to listen to every line twice. The problem Onepin addresses is not voice quality. As the site puts it, AI voices sound human now, but they still guess names from the spelling, and nobody has time to listen to every line. Addresses, prices, dates and abbreviations make every TTS model stumble — "St." can mean Saint or Street, "NW" can mean NorthWest or North West — and mispronouncing a brand, a drug or a name makes the whole line unusable. That risk multiplies when a team ships in several languages at once, because the definition of correct has to stay the same in US English, Spanish, Japanese, Korean and German. Onepin exists to remove that listening burden and the manual re-rendering that follows it. The first step Onepin takes with a script is to clean the text. A normalizer expands and disambiguates the material that trips models up: addresses, prices, dates and abbreviations are fixed before synthesis, in every language. The site's own examples show "3042" rendered as "thirty forty-two", "St." resolved as either "Saint" or "Street", and "NW" expanded to "NorthWest". Onepin describes this as the most common cause of misreads, and states that because it happens before synthesis rather than after, roughly half of voice errors are gone. The value is easy to see in practice: the model never has to interpret raw, ambiguous text, so it reads the line the way it was meant to be read in the first place. The second step is pronunciation. Onepin checks names against a four-million-word pronunciation dictionary that teaches the system how to say the hard words right, in any language. The dictionary covers brand and product names — the site lists AbbVie, Versace, Sade, Siobhán, Måneskin, Hermès, Givenchy, Björk, Saoirse, Stromae, Balenciaga, Rammstein, Röyksopp, Moët, Ng, Xóchitl and Hoegaarden — as well as complex medical and scientific terms such as semaglutide, esomeprazole, hydroxychloroquine, esophagogastroduodenoscopy and electroencephalography. Onepin frames this as the first thing generic TTS gets wrong: one mispronounced brand, drug or name and the entire line is unusable, no matter how natural the surrounding audio sounds. Once voice is generated, every line comes back scored. Onepin validates four dimensions on every line — naturalness, word accuracy, clarity and pronunciation — and it scores against your bar, not its own, applying one bar across every language. The site illustrates the step with a single sentence, "Your order has shipped", rendered in US English, Spanish, Japanese, Korean and German, each version queued for scoring. The point of the validation stage is that quality becomes measurable rather than a matter of taste: a team decides what passes, and Onepin holds each line and each language to that same standard instead of asking someone to judge by ear. The fourth step is repair. When a line misses — the site's example is "Siobhan" misread in "Hold the gate, Siobhan" — Onepin either regenerates the line or fixes just the word that missed, using the same model with new settings. Fixes run themselves, and the stated outcome is that you always get the best take. This matters because re-rendering a whole take for one bad word is exactly the kind of manual work that makes multi-language voiceover expensive and slow; targeting the single missed word keeps the rest of the performance intact while still correcting the error. Behind those four steps sits a routing layer that puts every top TTS model in one place. A script flows into more than thirty models behind one account and is routed line by line. Onepin also benchmarks so customers do not have to: the company measures quality continuously and picks per run, on the stated premise that no model wins every language. Onepin presents this as the answer to shipping at scale, and it doubles as protection against vendor risk — if a provider hikes prices, deprecates a model or shuts down entirely, the pipeline does not move. A team keeps the same workflow while the engines underneath it change. Onepin also addresses the commercial side of voice production. Every output is assigned to you and cleared for commercial use, and the Enterprise tier adds a negotiated IP indemnity, which the site frames as what shipping at scale, and answering to legal, actually requires. Billing is pooled rather than fragmented: you buy one credit volume, allocate it across workspaces and reallocate it anytime, and seats are free. That structure means one organization can fund several teams' voice production from a single purchase instead of negotiating separate subscriptions for each group, with the flexibility to move budget between workspaces as priorities change. The use cases Onepin highlights are the workflows where a wrong word is most costly. Its examples span gaming ("Slough off the poison before it spreads"), audiobooks ("He read the minute inscription above Hawthorne's tomb, then wept quietly"), advertising ("Book your stay in Yosemite before the summer rush"), e-learning ("Today we'll cover the reign of Louis XIV") and film ("Subtlety was never his forte") — lines that hinge on words generic text-to-speech reads incorrectly. The product's entry point invites visitors to type a script of up to 100 characters and see results across categories including Product, L&D, Healthcare, Support, Travel and Sports, which are the verticals where brand names, medical terms, dates and prices appear routinely. Onepin is aimed at teams producing AI voiceover in several languages — product videos, dubbing and courses — and says it is trusted by teams shipping voice at scale, naming HeyGen, Hyundai Motor Group, Sierra, Bland and Johns Hopkins University. It is free to start with no credit card required, with a Pro plan for teams that are already shipping, and the site directs teams to start free or contact the company. The takeaway is that Onepin is not trying to be another voice model: it is the production agent that sits between your script and the many models that can read it, cleaning the text, fixing the pronunciations, scoring every line and repairing the misses automatically — so what ships is voice you do not have to listen to twice.

Maisters TalentIQ™ is an AI-powered recruitment platform designed for government agencies and public-sector organizations that need to modernize hiring without sacrificing transparency, fairness, or compliance.The platform automates time-consuming recruitment activities including candidate screening, qualification checks, structured first-round interviews, candidate scoring, interview documentation, and compliance recordkeeping. AI-powered structured interviews give every candidate consistent questions and evaluate responses against agency-defined competencies and scoring rubrics.TalentIQ™ is designed with government hiring requirements in mind. It provides explainable candidate scoring, bias monitoring, veteran preference handling, compliance documentation, configurable records retention, and complete audit trails that help HR teams prepare for reviews, public-records requests, and internal oversight.Hiring managers receive ranked candidate shortlists supported by interview transcripts, scoring details, evaluation criteria, and decision evidence. Human staff retain final hiring authority throughout the process.The platform also provides workforce analytics for monitoring time-to-fill, candidate sources, hiring patterns, pipeline diversity, and recruitment performance. It can integrate with existing applicant tracking systems, HRIS platforms, background screening providers, credential verification services, identity providers, and agency-specific workflows through APIs.Maisters TalentIQ™ helps public-sector HR teams reduce administrative workload, accelerate recruitment, improve consistency in candidate evaluation, and maintain documented, audit-ready hiring processes.

Opposable is computer use for phones. It gives Claude Code, Codex, Cursor, any MCP client, or the agent built into the app the ability to use a real Android phone the way a person does: reading the screen, tapping, typing, swiping and opening apps in every app you are signed into. The Android app is out now as a 43 MB APK and needs Android 11 or later. It is built for people who already drive an agent such as Claude Code or Codex and want that agent to reach the parts of their life that only exist on a handset — messaging, banking, rides, delivery, shopping, calendars and the constant stream of two-factor codes. The product's purpose is stated plainly on its own site: give your AI thumbs. Most of your life runs through phone apps, and none of them have an API. Banking, rides, delivery, messaging, and the two-factor prompt sitting in the middle of every workflow all live behind app interfaces designed for human thumbs rather than for software. An agent can write code, search the web or summarise a document, but it cannot open a banking app, check an amount and stop before paying. Reval Labs describes Opposable as the layer that lets agents use those apps, on the phone you own and on phones the company runs for you. Computer use is coming to phones, and Opposable is building the hands. At the centre of Opposable is an agent that runs on the phone itself. Where most phone agents send your screen to a model in a data centre, Opposable's own agent runs on the device it operates, so it keeps working with the laptop closed or with no signal at all, and your screen never leaves the phone. It is small enough to download once over Wi-Fi, and it gets faster with every release. Three levels of intelligence are described on the site. With no model at all it handles about 40 everyday tasks — alarms, reminders, calendar, calls and texts, camera, settings, volume, maps and conversions — in 0 to 15 seconds, checking its own result on the screen. A 1.4 GB screen model looks at the screen and taps like you in any app, and picks from the screen's labels in under a second when it can. A 0.7 GB answer model replies to questions from web results. On recent Snapdragon phones the screen model's vision step runs on the phone's NPU; on a Galaxy S25 the company measured it at about a quarter of a second, roughly 16 times faster than on a laptop CPU. Opposable does not replace your agent; it gives it hands. Anything that speaks MCP can drive the phone. For Claude Code it is a single claude mcp add --transport http command, and Codex takes a few lines in its config file; Cursor works the same way through MCP. The app shows its address and a token, and the phone becomes a tool in your next session. There is also a Claude and ChatGPT connector: paste the URL https://getopposable.com/mcp into a custom connector in Claude's settings, or turn on Developer mode in ChatGPT and add it there with OAuth sign-in. Either way you land on Opposable's page, sign in, pick your phone and allow access. The connector is free for 14 days per phone and then part of Pro. Opposable is model-neutral: Claude, Codex, OpenAI-compatible endpoints and the company's own on-device agent all drive the same surface. Two capabilities make the phone usable as a tool. It sees: Opposable returns a screenshot plus the screen as a list of labelled elements with their positions — text views, switches and their coordinates — which is cheap enough to read on every step. It acts: taps, swipes, typing in any language, system keys, opening apps and links, and every call reports what it actually did. The action surface is small and explicit — tap_element, swipe, type_text, launch_app, press_key — so an agent can chain a handful of calls into a finished task. Tasks can also be handed over whole: a REST call to the phone's /api/do endpoint with a task string returns a status, a result and the number of seconds it took, so the phone does the work locally and only the result comes back. Connectivity is deliberately boring. The phone can be reached over USB, over the same Wi-Fi network, or from anywhere through Opposable's hosted relay; the phone keeps one outbound connection, so there is no port to forward. If you have no spare phone, you can rent a private virtual Android phone in the cloud, sign into your apps once through a browser viewer and connect your agent to its private HTTPS endpoint with one command. Cloud phones start at $19 a month. If you are on an iPhone, Opposable for Mac pairs with the iPhone as a Bluetooth keyboard and pointer and sees its screen over a cable or AirPlay, so the agent can tap and type in every app with no jailbreak and nothing installed on the phone; on Windows or Linux a small Opposable Stick does the Bluetooth part. The benefits are practical rather than abstract. Your agent stops at the edge of the phone and starts working inside it, in the apps you are already signed into, with the sessions, the accounts and the history you already have. Because the agent runs on the phone, tasks keep running when the laptop is closed or the signal drops, and the screen never leaves the device for a data centre. Common tasks are handled without any model at all, which makes them fast and free. Payments and purchases always stop and wait for your Approve tap on every plan: the agent does the twenty taps, you make the one that matters. And because a task Claude does once can be saved as a recipe, the phone can replay it on its own in seconds the next time. The site describes a set of concrete scenarios. Messages: Opposable reads a 2FA text the moment it lands and types the code where it is needed — its own example shows the code 482 913 being copied from Messages and used to sign in to a bank, before the same phone replies to a chat and books a ride. Bank: it opens your banking app, checks the amount and stops for your OK before paying. Chat: it replies in your chat apps in your voice, with the whole thread in mind. Rides: it books the ride, picks the pickup point and sends you the driver's ETA. Shop: it finds last week's grocery order and puts it back in the basket. Inbox: it archives, replies and moves invites into your calendar. Recorded runs on the site show an agent turning on dark mode and setting a five-minute timer in 50 seconds and 17 tool calls, and looking up Apollo 11's launch date and crew through Chrome in 118 seconds for $0.44. Opposable is free to use; you pay for what is annoying to host yourself. The Free tier includes the phone app, its MCP server and REST API, the model-free common tasks, unlimited tasks and recipe replays over USB or the same Wi-Fi, and iPhone support through Opposable for Mac in beta. Pro costs $8 a month ($79 a year, with founding members at $5 a month for life) and adds the hosted relay so Claude Code can reach your phone on mobile data or any Wi-Fi, scheduled tasks and webhooks while your laptop is closed, payment approvals from the phone's notification, an audit log, and recipes backed up and shared across up to three phones. Power is $29 a month for up to 10 phones. Cloud phones run from $19 a month for a sleeping starter phone, $49 for an always-on phone and $149 for three always-on phones. The audience is developers and power users already running Claude Code, Codex or another MCP agent who want those agents to reach phone-only apps, plus teams that need several phones for parallel agents or separate accounts. Opposable's pitch is short: give your AI thumbs. It takes the computer-use idea that has been applied to browsers and desktops and points it at the device that actually holds your accounts, using an on-device agent, an MCP server, a relay for remote reach and cloud phones when there is no handset to spare. The agent does the endless tapping; you keep the one tap that matters.

OpenCharm is an open-source body for the AI agent you already run. It gives that agent a face, a voice and one key, and it works with Claude Code, Codex, Hermes Agent, OpenClaw or any agent that speaks ACP, the Agent Client Protocol, as well as any server with an OpenAI-compatible API. One package: one agent at a time. Today it lives at the top of your Mac or Windows screen as the desktop charm — its eyes sit on either side of the notch on a Mac, or above a small black pill at the top centre on Windows — and next it is designed to run on a small ESP32-S3 board you build yourself. It exists for people who already trust an agent and want a glanceable, hands-free companion for it. The question the product asks is a simple one: why not just an app? The page acknowledges that a phone app could do most of this. The rest is the reason OpenCharm is a small thing that sits by your laptop or hangs from your keys. Your phone lies face-down and locked, so checking on an agent means picking it up and getting pulled into your inbox; the charm sits by your keyboard and a glance tells you whether your agent is busy, stuck or done. It does one job, then goes quiet. The page also positions OpenCharm against earlier AI gadgets: the Humane AI Pin and Rabbit R1 both promised to replace your phone, and both were slow, missed the basics and asked you to learn a new way of doing things. OpenCharm replaces nothing. Your agent keeps its memory, skills and model; the charm is the part it was missing — eyes, a voice, and a key you can find without looking. The charm's face is deliberately simple and readable. It is built from two characters for eyes and one for a mouth, which makes it readable from across the room, cheap to draw, and easy for anyone to write a new one and share it as text. There are 22 moods across 13 agent states, ranging from Neutral, Happy, Joy, Cute, Wink and Loved through Listening, Thinking, Focused, Curious, Confused, Surprised, Doubtful and Unamused to Strain, Sad, Oops, Dizzy, Sleepy, Learned, Money and Needs You. The face is never static: it breathes all the time and more slowly when asleep, blinks quickly, doubly or slowly, glances around and wanders up while it thinks, follows your finger on the screen and squashes when the key goes down, its eyes swell with your voice while you hold the key, it greets you after you unlock it and is pleased after it helps, and it falls asleep after about four minutes alone, with z's. A poke gets a different reaction each time, and three quick pokes make it dizzy. Beyond the idle animation, the charm turns your agent's events into faces so you can see what it is doing without asking. It shows Hello when you pick it up or sit down for the first touch of the day; Listening while you hold the key and talk; Thinking while the agent works on an answer; Working while a tool or a long task is running; Needs You for an approval request or a clarifying question, with an orange ring and one sound; Done when a turn completes or a cron job finishes, with one line before it returns to the face; Stuck when three tool errors pile up in a row; Failed when a run breaks, saying what in one line; Learned when a skill is created or updated; Noted on a memory write such as user.md or memory.md; Cost when spend passes a limit you set; Asleep when the charm is face-down or during quiet hours while the agent keeps running; and Idle when nothing is going on, with slow blinks and glances. Interaction is built around a single key with three moves, so there is nothing to learn: hold, press, tap. Holding the key is like a walkie-talkie — you talk to it, and the microphone is on only while you hold it. Pressing the key does everything else: wake it, stop it talking, dismiss, or say no. When the orange ring is showing because your agent has a question or wants an approval, holding means yes and pressing means no. Tapping its face is just for a reaction; you never need a swipe. On the desktop charm the key is Option + Space (Ctrl + Alt + Space on Windows) and works in any app; you can change it in Settings. Speech and decisions each get their own simple representation on screen. When the charm answers, the words type out under the eyes or in a panel below the notch — long answers scroll like captions and nothing runs off the screen. When your agent needs a decision, an orange ring appears around the screen or the panel and the key says what it will do. The design rules stated on the page are strict: one thing per screen, glyphs on true black, orange only when it needs you, and a target reaction time of under 100 ms. You can make the charm your own without touching code. Pick its colour — White, Cobalt, Lime, Lilac, Sun or Coal, or your own, which lights the glyphs but should not be orange or too dark — give it a name of up to 12 letters, digits and spaces, and choose one of the 22 faces. The same settings can be made in the desktop charm's Settings or in your workspace's opencharm.json, and the notch follows them, as will the board, along with its greeting, voice and how calm it moves. On the board you print the shell in one of the six colours or any colour you like. On a real charm, your agent's state picks the face, and your agent can try on a new look when you ask. Nothing you pick on the page is saved or sent. Underneath, OpenCharm is deliberately thin. The charm holds no API keys and runs no model; it is a small body for a brain you already have. charmd, a small daemon that runs on your machine next to your agent, carries the charm's voice to your agent and brings back speech, faces and questions. It handles pairing and the PIN, speech on your machine or with your own key, and the charm's own tools for your agent. It can run on your laptop, a Mac mini or a server, and the desktop charm brings its own. Your agent — Claude Code, Codex, Gemini CLI, goose, Hermes Agent or OpenClaw, one at a time — keeps its memory, skills and model, and the charm asks you before it acts. Anything risky turns the face orange and waits for your key. The desktop charm is the version you can use today, without hardware. It lives at the top of your screen and is the same OpenCharm OS the board will run; it uses about 2% of one core at rest and is a 4 MB app. By the notch on a Mac, its eyes sit on either side of the notch, breathing and blinking, and holding the talk key in any app opens a panel below. On Windows it is a small black pill at the top centre. In Settings you choose the agent's folder, the agent (Claude Code, Codex, Gemini CLI, goose, Hermes, OpenClaw, any ACP command or an OpenAI-compatible server) and the voice: local on a Mac, OpenAI with the key in your keychain, or none. It runs its own charmd and pairs by itself. It is available for macOS and Windows, downloaded from GitHub Releases, built by the repository's own CI from the same code with checksums and build provenance, or you can build it yourself. If you want the physical version, the build is short: one board, one evening. The hardware is a ready-made Waveshare ESP32-S3-Touch-AMOLED-2.16 board with a battery, which already has the screen, microphones, speaker amp, battery and a case: a 2.16-inch 480 x 480 AMOLED, ESP32-S3, two mics, in a 46 x 46 x 22.5 mm case for $31.99. You need a USB-C cable that carries data, and optionally a printed shell — about 14 cm3 of matte PLA in your colour with a little of a contrast colour for the key, for roughly $1 — and any thin phone-charm strap for about $2, bringing the total to $32-35. The charm already runs on your computer as the desktop charm; flashing the board comes with the device port, and Waveshare's factory firmware can always be restored. There are three levels of making it yours. On level one, no code is needed: who the charm is and what it knows are text files in its workspace, and you can ask your coding agent to change them — for example a skill file that makes it wrap up your day in three lines when you say "wrap up". On level two you give it tools by adding MCP servers to its workspace; it already has the charm's own tools: show_face, ask, notify, say and set_look. On level three you change the body by forking OpenCharm OS, one C++ core for the desktop charm and the board, developed and tested in an emulator on your computer. A C++ ESP-IDF snippet on the page shows how an Ask server kind triggers the decision view. Whatever your agent can do, the charm does — it adds a face, a voice and one key, while the skills and tools are your agent's. Described examples include talking to your agent by holding the key and hearing one clear answer; approving what your agent wants to do, holding for yes and pressing for no; walking away from a long task and knowing from the face when it is done; telling your agent something once so it remembers it; five-minute quizzes that adapt to you and get a reaction on every answer; a morning brief with the one thing that matters today; hands-free cooking with one step at a time and timers, readable with flour on your hands; controlling lights, heating and the door through Home Assistant MCP; travel days with gate changes, walking time and the right platform; and winding down when it gets sleepy. On safety and privacy, the product is explicit. The microphone opens only while you hold the key and closes when you let go; there is no wake word, and the rule lives in OpenCharm OS itself so your agent cannot open the microphone on its own. A PIN locks the charm on every power-on and five wrong tries block it; you can lock or revoke it from your machine. The charm keeps no API keys and never stores its PIN — charmd keeps only hashes. Everything runs with you: your agent runs on your own machine or server with its own memory and model, and what you say is understood on your computer. By default replies are spoken with Microsoft's free voices, which means the text of each spoken reply goes to Microsoft; you can keep everything local with the local voice instead, or use OpenAI with your own key. The software, the desktop charm included, is free and open source; the board is about $32 before shipping, there is no subscription, and the project sells nothing. OpenCharm is therefore not a new assistant and not a phone. It is an open-source body for the agent you already run: a face made of typed glyphs, a voice, and one key, on your Mac or Windows screen today and on a board you build next. It replaces nothing — your agent keeps its memory, skills and model — and if the company were to disappear, nothing would happen, because your agent and charmd run on your machine and the code is yours.

Busabase is an open-source (MIT) database and workspace for AI agents, described in its own words as the general system of record for AI agents. It gives people and AI agents one shared base where business records, docs, skills, and apps live in the same place, so the next task can build on whatever the previous one produced. Instead of letting agent output sit in a chat window, Busabase keeps it as data, docs, skills, and apps that a team can reuse. It is aimed at teams that already work with agents such as Claude Code, Codex, Cursor, Hermes, OpenClaw, WorkBuddy, and Buda AI, and it can be run on your own machine, self-hosted inside your network, or used through Busabase Cloud. The problem Busabase addresses is that agent work keeps evaporating. A team already uses AI and the work does get done, but it scatters across personal chat windows: one person drafts a quote in ChatGPT, another cleans up a customer list in Claude, a third finishes a data cleanup script in Cursor, and someone else records a pricing decision in Gemini. The site states the consequences plainly: results stay in a chat log nobody finds again, every session starts from zero, and when someone leaves the company, their work goes too. Busabase takes the opposite position: the work lands in one workspace and stays after people leave, so the next agent reads what the last one wrote and the work stays with the company. Busabase organizes that shared work into bases. The Data Base holds customers, projects, and inventory in tables with fixed columns, and because the columns are typed, every agent reads and writes the same fields rather than inventing its own shape for the data. The Knowledge Base is described as the team's shared memory: answers you can find, with a source, that someone checked. A record there carries its body, its sources, and its review history, so a fact can be traced back to where it came from and to whoever verified it. Together these two bases give agents a stable place to read from and write to, and give people a place to check the result. Three more bases extend the same idea. The Apps Base holds internal tools, called AirApps, built on the same data, so every screen shows the latest version — the site shows Busa CRM, a pipeline with deals and follow-ups that reads the same records. The Skills Base lets you save a workflow once as a skill, and any agent can run it next time. Playbooks are where a team writes down how it does things: agents check the playbook before every task and follow it instead of improvising. The site's summary of the workspace is Data, Knowledge, Apps, Skills, Playbooks, and External Agents, all in one place for people and agents. Agents connect to Busabase from wherever they already run. You can talk to a connected agent right inside Busabase, or connect 40+ external agents over Agent Skills, MCP, and more — Claude Code, Codex, and OpenClaw are named. Either way, those agents read and write the same workspace. The connection options listed are Agent Skills, MCP, OpenAPI, and the CLI, and the FAQ broadens this to any agent that can call a REST API, naming Claude Code, Codex, Gemini CLI, n8n, Zapier, LangChain, or your own agent; for that route the instruction is to paste the SKILL.md and go. Because the same structured data sits underneath, an agent does not have to be told the shape of the business each time it starts. The workflow Busabase describes has three steps. First, connect your agents: nothing to switch, your agents keep working the way they do today and simply write here. Second, every write is on the record: each change carries who made it, what changed, and a full history, and for the writes that matter you can hold the change for a person to review. Third, reuse it next time: the next agent, a teammate, or an AirApp reads the same data, so nobody starts from zero. Underneath, Busabase is open source under the MIT license, and the same engine runs on your laptop, on your own server, or in Busabase Cloud. Busabase Desktop keeps data in a folder on your computer with no usage tracking and keeps working offline; the Docker image is deployed inside your company network so data never has to leave it; Cloud needs nothing installed. The benefits the site claims follow from that structure. Work that would otherwise sit in personal chat windows lands in tables, docs, and apps you can open tomorrow, and it stays with the company after people leave. Knowledge comes with a source that someone checked, rather than as an unsourced answer. Every change keeps its history, so the record remains explainable later — approve, request changes, and reject decisions all stay attached, and rejected work never enters the canonical data. And because the same data feeds agents, people, and internal apps, a new session does not begin from nothing. For teams with a security review coming up, Busabase points to its self-hosting documentation about keeping data inside your network. The website lists eight kinds of work you can start keeping today. Customer support stores Q&A and product facts so agents draft the reply and a person approves it. Marketing keeps CRM contacts, campaign records, social posts, and creative assets in one place. Content moves briefs, drafts, and published pieces through a single pipeline. Team memory captures decisions, meeting notes, and context the next agent can read. Operations tracks projects, to-dos, and supplier details. Research collects market signals and monitored sources, verified and adding up over time. Product catalog holds SKUs, specs, pricing, and relations, searchable and managed in one place. Compliance covers access reviews, vendor checks, and a complete audit log. A demo on the site shows a content board where scheduled posts carry visual, platform, copy, scheduled status, and read-from relations, with the price pulled from the Products base. Busabase is built for teams and individuals who want agent output to accumulate somewhere shared. Named agent integrations are Claude Code, Codex, Cursor, Hermes, OpenClaw, WorkBuddy, and Buda AI, with n8n, Zapier, LangChain, and Gemini CLI mentioned in the FAQ, and connection surfaces including Agent Skills, MCP, OpenAPI, webhooks, a CLI, and REST APIs. Pricing has three tiers. Free costs $0 for one person keeping what their agents produce, with 1 space you own, 3 seats per space, 5,000 records per space, 1 GB of attachment storage, and 1 self-hosted connection online. Plus is $10 per seat per month, billed $120 per seat annually, with 3 spaces you own, 10 seats per space, 20 GB of attachment storage, and OpenAPI, webhooks, and MCP. Pro is $20 per seat per month, billed $240 per seat annually, with unlimited spaces you own, unlimited seats, 100 GB of attachment storage, and priority support over WhatsApp, WeChat, or email. The open source engine is free forever, and Cloud starts free and grows per seat. Taken together, Busabase's value proposition is simple: give your agents a workspace worth building in. The work they produce becomes typed data, sourced docs, saved skills, and internal apps that the next agent, the next teammate, or an AirApp can read; every change keeps its source and its full history; and sensitive work can stay on your machine, on your own server, or in Busabase Cloud. You can start local for free and add Cloud when your team is ready.

Refs is described as the video reference library for agents: a collection of 3,032 films that moved people, each measured cut by cut and turned into blueprints and prompt recipes an AI agent can build from. The site frames the product with a simple premise — Claude is only as good as the references you give it — and positions Refs as the way to hand Claude better refs. It is aimed at people who plan and produce launch films, motion pieces and explainers and who want their agent to work from real, published work rather than a loose collection of inspiration. The archive is free to browse, while the Pro plan opens up every plate of every blueprint, a runnable recipe for every film, formulas that compare films of one format, and the Refs MCP connection. The problem it addresses is reference quality. An agent can only plan what it has been shown, and the site notes that Refs is deliberately not a mood board: it publishes measured cuts, timed beats and a recipe for each film, so that Claude copies the method rather than the footage. The archive is built from films studied at Apple, Google, OpenAI, Anthropic, Framer, Notion, Cursor, ElevenLabs, Raycast, Arc, Vercel, Linear and Perplexity, among others, gathered into a single searchable place. By the numbers shown on the site, the library contains 3,032 films, 67,695 shots measured and 28 formulas. Refs does not host the videos: every film plays from its original post and stays its maker's, while Refs publishes its own measurements and analysis and credits and links every source. Every cut is measured. The site explains that shot boundaries are found frame by frame, and that clicking a shot jumps straight to it. Each film carries a cut map with figures such as cuts per minute and average shot length — the example shown on the homepage is 36 cuts per minute with an average shot of 1.5 seconds — so the pacing of a film becomes a number a planner or an agent can reason about rather than something you have to eyeball. Playback is built for scanning: walk the archive, scroll to move through it, hover a film to play it, move to scrub and click to open, with tap-to-play on a phone. Search runs across the whole archive, so a query pulls matching films out of the 3,032 on record. Each film is taken apart into a 15-plate blueprint covering hook, beats, shots, type, colour, sound and script. The plates are designed to be flipped through: Format states what the film is in one breath; Card gives length, pace, shots and sound at a glance; Hook breaks down the first two seconds frame by frame; and Make it yours carries the instruction to swap the story and keep the method. The public FAQ describes the blueprint as containing hook, beats, shots, type, colour, sound and script, plus a prompt recipe you can hand to Claude. Free users see the first plates of every blueprint; Pro opens all 15 plates of every blueprint. A runnable recipe sits at the end of each blueprint. The recipe is written to be pasted into Claude Code and made your own — the example on the site is a prompt recipe asking Remotion to create an original 15-second, 60 fps motion-design showreel for a product, with timed beats spelled out second by second, from a point growing into the mark inside faint circular guides to a full-screen shape wipe and a warm-ivory process chart. Alongside the per-film material, Refs publishes formulas: shared patterns across the strong films of one format, 28 of them in total, with counts of how many films sit behind each. Named examples include The Music-Led Motion Showreel, The Dark Staged Product Reveal, The Live-Drawn Paradox, The Fifteen-Second Service Rescue, The Sonified Chart Relay, The Capability Proof Montage and The Price of Regret. The core mechanism is the Refs MCP. One command adds Refs to Claude Code, Codex or Cursor — the site shows a claude mcp add command pointing at the Refs MCP endpoint with an authorization header carrying a Refs key — after which Claude can search the archive, pull blueprints and formulas, and plan your film from real references. The demo on the site shows the agent calling a Refs search tool with a query and returning matching entries, such as the Lumi founder launch and product walkthrough, the Warp 2.0 founder-led launch film and the Bolt v2 founder-led product launch. The result, as the site puts it, is the same Claude with better references: ask for a film and Claude studies films that already worked, then plans and builds yours. The Refs MCP is listed as part of the Pro plan with 1,500 credits a month. Because the references are measured rather than merely admired, the output an agent produces can follow an evidenced method: the hook, the beat timings, the shot lengths, the sound and the pacing of films that demonstrably moved people. Users get a way to brief an agent with cut maps, numbers and recipes instead of adjectives, and they keep the connection to the original work, since Refs links and credits every source and films play from their original posts. The library is also browsable on its own, so the same archive works as a study tool for a person and as a retrieval source for an agent. Use cases described on the site revolve around planning and producing video. A person planning a launch film can ask Claude for a film and have it study the films that already worked before it plans and builds theirs. Someone building a motion design piece can pull a recipe such as the Remotion prompt for an original 15-second, 60 fps showreel and adapt it to their product. Searches in the demo return matches under labels like founder launch, kinetic type, silent demo, three.js and terminal, with results such as a founder-led product launch or a product walkthrough, which suggests how a specific style or format can be retrieved and studied. Others can work from the formulas to understand what the strong films of one format share, or from the cut maps to match a pacing pattern. The archive itself can be walked as a study resource, with films such as A Miniature Garden Becomes Life-Size sitting alongside dozens of others drawn from YouTube and X posts. Refs is built for people who make and commission product and brand films — launch films, motion pieces and explainers — and for the developers and designers who drive AI coding agents such as Claude Code, Codex and Cursor. Browsing is free: every film playable from its source, cut maps, hover-scrub and the numbers, the first plates of every blueprint, and search across the whole archive. Pro costs $10 a month billed yearly or $16 monthly, and includes all 15 plates of every blueprint, a runnable recipe for every film, formulas across films of one format, and the Refs MCP for Claude Code, Codex and Cursor with 1,500 credits a month. A launch offer on the site notes that the code PRODUCTHUNT takes 50% off a year of Pro. Taken together, Refs is a reference library that treats video as data an agent can use: 3,032 films measured shot by shot, 67,695 shots and 28 formulas, packaged into 15-plate blueprints and recipes and delivered to Claude Code, Codex or Cursor through the Refs MCP. Its promise is narrow and specific — give Claude better references, so the film it plans copies the method of work that already moved people, never the footage.

Odyssey-3 is Odyssey's most powerful foundation world model yet, described as a learned dynamical system implemented as an autoregressive diffusion transformer that predicts how objects move and interact through space and how situations evolve over time. It generates embodied environments from a prompt and predicts in real time how those environments change as a person or an agent takes actions or introduces events, using previous observations together with the latest inputs. The model is built for physical AI developers — teams building robots, humanoids, self-driving cars, drones, and other autonomous systems — who want to explore how foundation world models can accelerate their work. A research preview is available now, letting users prompt an environment, act within it, and observe how the world model responds, while developers interested in building with Odyssey-3 can get in touch for API access. The starting point for Odyssey-3 is a problem the founding team lived with for a decade. They spent that decade building driverless cars, where predicting the world was essential to determining what a car should do next. They founded Odyssey to pursue that idea far beyond the roads — to build a general-purpose technology that could bring learned world knowledge to all machines and tasks. With Odyssey-3, they believe this idea is now being realized. Physical accuracy is central to that goal, because a foundation model for physical intelligence must learn to predict how the world actually behaves rather than simply producing plausible-looking video. Odyssey-3 learns representations of physics, dynamics, and cause-and-effect from a broad dataset of visual observations, and developers use that knowledge both to simulate environments and to train policies for different physical systems. The first capability is real-time environment generation. Odyssey-3 generates embodied environments from a prompt and predicts in real time how they change as a person or an agent takes actions or introduces events, using previous observations and the latest inputs. You can move through the environment or introduce an event during generation and observe how the model responds. Today's preview provides first-person and third-person navigation alongside independent camera movement, giving you different ways to interact with and inspect the model's predictions. Physical accuracy is measured against public benchmarks. Odyssey-3 Pro sets a new state of the art on Physics-IQ Verified's video-to-video benchmark, achieving 66.1, the highest reported score. Physics-IQ tests physical behavior across fluid dynamics, optics, solid mechanics, magnetism, and thermodynamics, asking models to continue videos of real physical experiments and comparing their predictions with what actually happened. Odyssey-3 Pro also scores 54.7 in image-to-video, while Odyssey-3 improves the measured tradeoff between physical accuracy and generation cost, making it possible to generate more simulations within the same compute budget. On WorldMark, which measures control-following, visual quality, and world memory, Odyssey-3 ranks first in first-person stylized, third-person real, and third-person stylized environments, using the benchmark's own captions and the mean of its 13 reported metric scores. Odyssey-3 adapts to physical systems. Its learned world knowledge can be applied to different systems by training an action decoder or policy on paired observations and actions. These learned components translate that knowledge into the controls required by a particular machine, letting developers adapt the foundation to a new body or task. With only tens of hours of robot demonstrations, Odyssey-3 completed manipulation tasks and showed recovery behaviors absent from those demonstrations, including reorienting a gripper after a missed grasp and retrieving a dropped object in an unusual position. Flexion has built humanoid control policies on Odyssey-3 whose performance exceeded the tested VLA baselines under environmental changes and continued to perform tasks under lighting changes that caused those baselines to fail. Odyssey-3 was also adapted to drive a car on real roads in India, training a driving policy on just 20 hours of driving data while keeping the Odyssey-3 backbone frozen; the policy uses the model's visual representations to predict waypoints ahead of the car, allowing it to drive in closed loop. The model can additionally be adapted to generate observations for particular sensor arrangements — in an early experiment using the front three cameras of an autonomous-driving dataset, an Odyssey-3 training checkpoint produced driving sequences with three camera views generated together after just 100 training steps. Odyssey-3 can also help train agents. An agent is an AI system that pursues a goal by observing its surroundings, choosing actions, and using what happens to decide what to do next. A world model can provide the environment in which those decisions are made, giving a way to study how an agent responds to changing conditions and whether it can complete a task inside a world whose behavior is learned. In the task-completion demonstration, an agent receives a natural-language goal and pursues it inside Odyssey-3, observing the generated world as it works toward the task. How Odyssey-3 was built is described in the launch material. Its training data combines internet video with time-localized, schema-verified event annotations, gameplay recordings with time-aligned keyboard and mouse inputs, and simulated rigid-body interactions with captions and metadata. Together, these sources connect diverse observations with descriptions of what happens and, where available, the actions that produced it. The stated design goal was to enable dynamic, open-ended interactions with an environment that responds as the user acts. Odyssey-3 is built as a multi-step video diffusion transformer, using temporally resolved prompts and controls to guide how the world unfolds. It is then extended autoregressively through teacher forcing and causal masking, training it to continue from preceding observations and predict future states conditioned on action inputs. Finally, a post-training pipeline that combines distribution-matching and adversarial distillation produces a distilled variant of Odyssey-3 — a few-step model capable of real-time interaction. The measured results point to concrete benefits for users. Because physical accuracy and generation cost are traded off more efficiently, developers can generate more simulations within the same compute budget. Because the model can be adapted with small amounts of data — tens of hours of robot demonstrations, or 20 hours of driving data with a frozen backbone — developers can bring learned world knowledge to a new machine or task without training a world model from scratch. Because the distilled variant is a few-step model capable of real-time interaction, environments respond as the user acts rather than only after generation is complete. And because the model can provide the environment in which agents pursue natural-language goals, it offers a way to study agent behavior in worlds whose behavior is learned. Odyssey-3 appears in several concrete scenarios in the launch material. For robot arms, prompts such as "Pour the cereal into the bowl" and "Close the screwbox" were used with the model to perform manipulation. For humanoids, Flexion built control policies on Odyssey-3 and ran tasks such as "Open the blue container and take out the cardboard box" and "Move the plate to the center of the table and place the mug on top of it." For vehicles, prompts like "Take the first roundabout exit" and "Drive along the road" were used while the adapted policy drove autonomously on real roads in India. For multi-sensor data, Odyssey-3 generated driving sequences with three camera views produced together for an autonomous-driving dataset. For agents, an agent receives a natural-language goal and pursues it inside the generated world. And in the research preview, a user prompts an environment, navigates it in first or third person, and introduces events to see how the model responds. Odyssey-3 is aimed at physical AI developers — teams developing a robot, humanoid, self-driving car, drone, or any other autonomous system — as well as researchers studying world models and anyone who wants to try the research preview. Developers who want to build with Odyssey-3 are invited to get in touch for API access. The research preview, offered as "Try Odyssey-3 Flash," is available now through experience.odyssey.systems. Published evaluation details describe two model sizes: Odyssey-3 at 832×480 and Odyssey-3 Pro at 1280×720. The launch material presents Odyssey-3 as available today as a research preview, with API access offered on request; no pricing or plan details are published in the provided content. Taken together, Odyssey-3's primary value proposition is that it brings learned world knowledge to physical AI: a foundation world model that generates interactive environments from a prompt, predicts in real time how they evolve as someone acts, advances the reported state of the art in physical accuracy on Physics-IQ Verified, and can be adapted to control robot arms, humanoids, and vehicles or to serve as the environment in which agents learn and are evaluated.

OpenVids is an open-source, agent-first video editor for macOS and Windows. Its premise is simple: you direct, and agents edit. Instead of building an edit by hand, you describe the film you want, and AI agents build the chapters, cut the timeline, add motion and sound, and review the render. OpenVids is built for people who want to start from a description of the story rather than from an empty timeline, while keeping the result editable. Every change an agent makes lands on a timeline you can take over yourself at any point. The problem OpenVids was built around is that the timeline grows faster than the story. Every project starts with a story, but as footage accumulates the edit becomes a list of clips to manage rather than a story to tell. The creators of OpenVids describe losing the thread somewhere around clip two hundred. They wanted the story to stay visible the whole way through, so that the narrative — not the clip count — remains the organising principle of the project. That framing shapes the whole product: the editor is designed around chapters and a story structure rather than an undifferentiated sequence of clips. At the centre of OpenVids are its agents. You describe the film, and the agents work on your behalf: building the chapters, cutting the timeline, adding motion and sound, and reviewing the render. This is why OpenVids describes itself as an agent-first editor rather than an AI-assisted one — the agents are the primary way edits get made, and you direct them in plain language. The Product Hunt listing summarises the same idea as an open-source video editor you run by chatting with AI agents. The workflow is a conversation about the film rather than a series of manual trimming operations. OpenVids organises a project through named workspaces — Agents, Media, Story Graph, Edit, Motion, Sound, Render and Licenses — which the site presents as one continuous workflow rather than separate tools. The Story Graph is where the story structure lives: on the site's own demo project the chapters are listed as Cold open, The problem, Meet OpenVids, Feature tour, In the field and Outro, each with a timecode. The Media workspace tracks the assets in the project — the example shown lists 23 assets with 1 missing — so you can see what the edit depends on and what still needs to be supplied. The Edit, Motion and Sound workspaces cover the craft layers of the edit. The example project shows media assets such as an A-roll clip, B-roll footage and a motion asset, indicating that the editor works with different kinds of material — camera footage, supporting shots and motion pieces — inside the same project. Motion assets carry their own durations, and sound is handled as its own workspace, so an agent can add motion and sound to the cut as it builds. The Render workspace is where the finished edit is produced, and the agents also review the render, closing the loop between the description you gave and the video that comes out. The unique approach is the combination of agent-driven editing with a fully inspectable timeline. Agents do the work — cutting, adding motion and sound, and reviewing the render — but nothing is hidden: every change lands on a timeline you can take over. That means you can start with a description, let the agents assemble a first cut around a story structure of chapters, and then step in and edit directly when you want to. OpenVids is also free and open source, with the code available on GitHub and downloadable builds published as releases, so the editing engine is something you can inspect and run yourself. The payoff for users is that the story stays visible from the first line of your description to the final render. Rather than losing the thread in a growing pile of clips, you keep a chaptered structure that reflects what you are trying to say. Because agents handle the repetitive work of cutting the timeline and adding motion and sound, the first version of an edit arrives without a long manual pass. And because the result is a real timeline rather than a black box, you keep control: you can take over at any point instead of accepting whatever the agents produced. Concrete scenarios follow the demo project on the site. A creator describes a short film and the agents turn it into a chaptered edit with a cold open, a problem section, a product introduction, a feature tour, an in-the-field section and an outro. Editors working with mixed material can bring in A-roll and B-roll footage alongside motion assets such as a title reveal, and let the agents place motion and sound around them. Projects that depend on many assets benefit from the Media workspace view, which surfaces the full asset list and flags anything missing before render. Finally, anyone who wants to check the result can review the render with the agents, then open the timeline and make their own changes. OpenVids runs on macOS and Windows. The site offers a download for macOS on Apple Silicon and a download for Windows, both at version 0.5.4, and points to the source on GitHub. It is free and open source, and the site's own demo work references open movies from the Blender Foundation under CC BY, along with a Licenses workspace — so projects can track the provenance of the material they use. The Product Hunt entry categorises it under Design Tools, GitHub, YouTube and Video. It suits directors, editors and creators who work on macOS or Windows and want to direct an edit with agents while retaining a timeline they can take over. OpenVids' value proposition is straightforward: you direct, agents edit. By describing the film you want and letting agents build chapters, cut the timeline, add motion and sound, and review the render — all onto a timeline you can take over — OpenVids keeps the story, not the clip count, at the centre of the edit. It is free, open source, and available for macOS and Windows.

Pine Computer is a cloud computer built for AI to use. Rather than pointing an AI agent at an ordinary desktop, you hand Pine Computer a job — research, forms, spreadsheets, or portals that have no API — and the finished work comes back to your product. It exists so that software products can complete real work on the web rather than merely describe it. A single SDK lets you spin up a computer per customer, you can bring your own model or use Pine's, and a person can take over whenever a sign-in or approval is required. Pine Computer is invite-only while in beta. The starting point is that today's agents are usually run on computers built for humans. A human looking at a screen sees pixels; a human has to keep checking that screen for change; and a human works on one or two screens at a time. An AI agent driving that same machine inherits every one of those constraints, which makes long, real-world tasks across many apps slow and expensive. Pine Computer also targets a specific and widespread gap: the many portals, seller sites, city systems and insurer forms that have no API at all, so the only way to get work done on them is to actually use them. On the same long, real-world tasks across apps, Pine reports that its preliminary tests ran 2–5× faster than AI agents on ordinary computers, at 1/25 the model API cost. Two of Pine Computer's core ideas are reading structure and sensing change. Because humans see pixels but AI can read what each thing on a screen is and what it can do, Pine's AI works from the structure of an app rather than from images. In the examples shown on the site, that structure looks like a heading called Invoices, a field labelled Email that is empty, a field for PO number, a Submit button that can be clicked, a table with 12 rows and 3 columns, or a link that downloads a PDF. Working from that structure means the agent does not have to guess at coordinates or recognise pictures of buttons — it knows what a control is and what it can do with it. The second idea is sense: humans keep checking the screen, while Pine's AI is told what changed the moment it does. Notifications such as a completed download, or a window gaining focus, arrive as events rather than requiring the agent to poll and re-inspect the page. The third idea is seeing: humans work on one or two screens, but Pine's AI gets many screens, rendered in memory and only when it needs to look. That is what allows an agent to fill a basket at three shops at the same moment, or to renew permits on three different city portals at once, without needing physical displays to exist the whole time. The fourth idea is access: the computer is sealed off on its own and reached through your code, and your keys stay with you. Together these four ideas — read, sense, see, access — describe how Pine Computer differs from running an agent on a machine designed for a person. Pine Computer is designed to be embedded in your product. You spin up a computer per customer through one SDK, so each customer's work runs on its own machine rather than sharing a queue. You can bring your own model or use Pine's, which lets teams keep the model relationships, credentials and evaluation setups they already have. And a person can take over for sign-ins or approvals: where a portal asks for a login that only the account owner can provide, the human signs in on the live screen and hands the computer back to the AI. The site shows this in several examples — a property owner signing into a city portal for a permit, an analyst signing into a subscription research source, a shop owner signing into a marketplace, and a shopper approving a purchase before anything is spent. The developer experience starts with code. The Ruby example on the site creates a computer with a location and an ephemeral flag, opens a browser session on it, and then runs a job written in plain language: pull last month's invoices from the supplier portals into the ledger. From the outside, Pine Computer behaves like a machine your product can hand a job to: the product submits the task, the AI drives the apps it needs, and the finished work comes back. Because each computer is sealed off and reached through your code, the integration boundary stays inside your own system rather than in a shared browsing environment, and because each customer gets their own computer, work can run in parallel instead of one task waiting behind another. The stated benefits are speed and cost. In Pine's preliminary tests, on the same long, real-world tasks across apps, Pine Computer ran 2–5× faster than an ordinary computer driven by AI agents, and used 1/25 of the model API cost — model API cost only, with results varying by task. Beyond the headline numbers, the four design ideas produce practical outcomes: reading structure instead of pixels reduces the guessing that makes agents brittle; being told what changed removes the constant re-checking that burns time and tokens; virtual screens let one job fan out across several apps at once; and the sealed, code-reached design keeps access limited to you and your code. The site lists 26 use cases, grouped into sites with no API, research, documents, assistants, sales and marketing, and engineering. Six are shown in detail. Renew permits: a customer clicks Renew once, and every permit due this quarter is renewed on its own city's portal, with each city's receipt returning to its permit's row as a PDF. Research a market: an analyst names a market and gets a report in which every figure carries the number of its source, including both figures where two sources size the market differently. Fill PDF forms: each client's row on a broker's sheet becomes a filled copy of the insurer's current PDF form, read back field by field, and nothing goes to the insurer until the broker has read each copy. Compare and buy: an assistant checks three shops at once, fills a basket in each, stops before payment, returns a comparison with delivery included in every total, and only places the order once the person approves. Update marketplace listings: a spring range is updated on Larkspur Market, Harbour Lane and Ferrow in turn, with each change recorded once the live listing reads back right. Test a web app: users write test steps in plain English, and each run follows them in a real browser on a fresh computer and screenshots every step. Pine Computer is built for teams that are putting AI to work inside their own products — the examples describe property software, research software, broker software, an assistant, commerce software and a testing platform handing jobs to Pine. Access is currently invite-only while in beta; interested teams leave an email address, join the last step of an application, and request access. The site documents an SDK with a Ruby example, plus developer docs, and a create-computer call that takes a location such as country US and an ephemeral flag. There is no published pricing on the page, and no list of third-party integrations beyond the choice to bring your own model or use Pine's. The takeaway is that Pine Computer turns the hard, human-shaped parts of web work into something your product can delegate. Read structure, sense change, see many virtual screens, and stay sealed off behind your own code — you hand it a job, and the finished work comes back, 2–5× faster and at 1/25 the model cost in Pine's preliminary tests.

Phonable is a smarter voicemail app for iPhone that replaces your carrier's voicemail. When you cannot pick up a call, Phonable handles both sides of that missed call: your caller hears your greeting and then gets a short text back explaining why you could not answer, while you receive the voicemail as a recording, a full transcript and a one-line AI summary. It is designed for iPhone users who miss calls because they are in a meeting, on the road, at the gym or away for the weekend, and who want callers to know what happened without changing anything about their phone number. Phonable keeps your own number, your SIM and your phone, and only steps in for the calls you do not answer. Carrier voicemail has not changed in decades. You get a beep, a message you never quite get round to listening to, and a caller left wondering whether you received their call at all. As Phonable puts it, a missed call leaves both sides hanging. The caller does not know why you did not pick up or when you will get back to them, and you have to dial in, press 1 and sit through messages to find out what mattered. Phonable treats the missed call as a two-sided problem and fixes both ends of it: the person calling gets an immediate, polite explanation by text, and you get the substance of any voicemail delivered in seconds rather than minutes. The first half of the fix is the automatic text back. The moment you miss a call, Phonable sends a short text on your behalf, even if the caller hangs up without leaving a voicemail. That closes the loop for people who would otherwise never know their call landed. Phonable ships with templates for every moment — starting points such as "In a meeting", "Driving" or "Back on Monday" — and you can write your own wording if you prefer. Messages are considerate by default: each caller receives at most one text per hour, so someone who tries twice is not flooded with replies. Because each mode carries its own text, what callers read always matches where you are — driving, at the gym or away for the weekend. Texts use the SMS credits in your plan and are not sent to landlines or unsupported countries. The second half is visual voicemail with transcription and AI summarisation. Instead of dialling in and pressing 1, every voicemail appears in the app with the recording, a full transcript and a one-line AI summary, so you know what a message is about in seconds. The AI summary arrives as a notification, before you have even opened the app. From there you can read it or play it: skim the transcript quickly, or listen to the recording at whatever speed you like. If the message needs a response, you can reply in one tap, either calling back or sending a message straight from the voicemail screen. A voicemail stops being a chore you postpone and becomes something you can absorb at a glance. Greetings are the third piece. If you are not a fan of recording yourself, you can type what you would like to say, pick a voice and a tone, and Phonable speaks it naturally. The available voices are Sarah (soft and reassuring, American), George (warm and trustworthy, British), Laura (bright and upbeat, American), Lily (clear and polished, British), Brian (deep and composed, American) and Charlie (casual and friendly, Australian), and each can be delivered in a professional, friendly, calm or energetic tone. If you would rather sound like yourself, you can record your own greeting in seconds. Either way, callers hear something that fits the moment rather than a default carrier message. Modes, introduced in version 2.0, tie the greeting and the text reply together into a single switchable state. A mode pairs what callers hear with the text they get back, so you can move from Workday to Driving in one tap. Modes can be kept on for a while — an hour, three hours or until tomorrow morning — or set on a schedule by choosing the days and hours, after which Phonable switches automatically. The built-in examples show how specific they get. In Workday's "In a meeting" mode callers are told: "Sorry I missed your call. I'm in a meeting and will call you back within the hour." Driving's "On the road" mode says: "I'm driving right now and can't pick up. I'll get back to you as soon as I've parked." Gym's "Mid-workout" mode says: "Mid-workout, can't pick up! I'll call you back within the hour." And Weekend's "Away for the weekend" mode says: "Thanks for calling! I'm away until Monday. For anything urgent, send me an email." Phonable works through conditional call forwarding, a standard feature of mobile networks. After you sign up, the app shows you exactly what to dial and walks you through three quick steps to turn forwarding on with your carrier — a process that takes about a minute — and you can switch it back at any time, at which point missed calls return to your carrier's voicemail. Forwarding is conditional, so Phonable only takes over in the situations where you cannot answer: when a call rings out while you are busy, when you are already on a call or tap decline, or when you are unreachable because of flight mode, a flat battery or no signal. If you do pick up, nothing changes at all; the call connects as it always has and Phonable is not involved. Beyond the core loop, Phonable adds a set of thoughtful details. Every call and voicemail sits in one timeline organised by name, and if someone hung up without leaving a message you can still see that they received your text. Callers show up with the name from your contacts rather than a bare number. Privacy is handled deliberately: your contacts stay on your iPhone and are never uploaded, and the company states that it never sells your data. Pro subscribers can upgrade to a dedicated Phonable line that only handles their calls, rather than sharing a community line. The app also follows your iPhone's light and dark mode with a calm design. The overall benefit is simple: callers are never left guessing, and you never have to dial in and press 1 to find out what a voicemail said. The use cases follow the modes. If you are in a meeting, Workday mode tells callers you will call back within the hour instead of leaving them with silence. If you are driving, Driving mode explains that you cannot pick up and will respond once you have parked — without you touching the phone. If you are mid-workout, Gym mode does the same in a lighter tone. If you are away for the weekend, Weekend mode redirects urgent matters to email until Monday. Someone who simply misses a call while out of signal still benefits, because the caller gets a text and any message left is transcribed and summarised for later. Anyone who screens calls, works across time zones or is frequently in situations where answering is impossible can let a mode handle the explanation while they stay present. Phonable is an iPhone app available on the App Store, and it is aimed at anyone who misses calls regularly and wants callers treated well without switching numbers. Pricing starts free with Test Drive, which includes greetings, voicemail and text backs, a monthly allowance of voicemails and texts, up to two modes and a shared community line. Basic, billed monthly or yearly, adds more voicemails and texts every month, up to five modes and modes that switch on a schedule. Pro, also monthly or yearly, is described as everything Phonable does with room to spare: the most voicemails and texts, unlimited modes on a schedule, AI voice greetings and a dedicated line that is only yours. Prices are shown in the App Store in your local currency, yearly billing saves 20%, and you can cancel anytime in your App Store settings. Phonable's value proposition is that a missed call stops being a small failure on both sides. Callers get an immediate text explaining why you could not pick up and when to expect you back, and you get every voicemail transcribed and summarised by AI, delivered as a notification you can read in seconds. Nothing changes about your number, your SIM or your phone, forwarding takes about a minute to set up and can be turned off whenever you like, and you can start free before deciding whether Basic or Pro fits how many calls you miss. For iPhone users who cannot always answer, Phonable turns the calls you miss into handled, explained moments.

OpenPilot is an open-source desktop AI agent that runs locally and works with any large language model you choose. Instead of shipping a fixed, hosted model or forcing you through a vendor login, OpenPilot acts as a harness: you point it at any OpenAI-compatible API endpoint, add an API key, and the agent can then create files, scaffold projects, run real terminal commands, search the live web, and ship complete projects. It is built for people who want the agent itself—not the platform tax that usually comes with it—and who refuse to be locked into a single model provider. It is designed to be an agent that actually does the work, wiring filesystem tools, a live shell, web research, MCP, skills, and memory to the model you chose. The problem OpenPilot addresses is vendor lock-in. Most AI agents today tie you to one provider's account, one set of models, and one billing relationship, which means swapping models or moving to a local server requires rewriting your workflow. OpenPilot's answer is a harness, not a hosted model marketplace. There is no OpenPilot account and no vendor login required. You bring an OpenAI-compatible endpoint and API key—whether that is OpenAI, OpenRouter, a local server such as Ollama or LM Studio, or your own custom gateway—and your usage stays local to your keys. That makes the tool portable across the models you already pay for and keeps you in control of where your data and your spend go. The agent's filesystem tools are the foundation of its work. OpenPilot reads, writes, edits, greps, and globs across your workspace, so the agent can inspect a repository, understand its structure, and modify files in place. You can ask for a full project and watch the file tree fill in—for example package.json, src/index.ts, and src/routes/users.ts—and then refine the result with follow-up edits rather than starting over. Alongside file tools, the shell is a first-class capability. OpenPilot runs real terminal commands such as installing dependencies, starting servers, and running tests, streaming stdout back into the chat so you can see exactly what happened. This is not a pretend snippet; it is an actual shell connected to your machine, and you approve sensitive actions before they run. Because you explicitly open a workspace by pointing at a folder, the filesystem and shell access the agent receives is scoped to that workspace. When the answers you need live outside the repository, OpenPilot performs live web research. Using TinyFish, the agent searches and fetches pages in real time—for example looking up documentation, an API reference, or a specific error message—and then grounds its answer in what it actually read, returning a cited answer alongside the fix. The agent also supports skills and long-term memory. You can drop reusable playbooks into the workspace as skills, and persist preferences in a file such as AGENTS.md, including instructions like preferring TypeScript strict mode, using pnpm in a particular repository, or never committing .env files. OpenPilot loads these into context automatically and can update its memory when you ask, so preferences carry across sessions instead of being retyped every time you start working. For teams that need more than the built-in tools, OpenPilot connects to Model Context Protocol (MCP) servers, which can be flipped on per chat. MCP lets you extend the agent with the systems you already run—databases, browsers, or internal APIs—configured through an mcp.json file and enabled only when a conversation needs them. On the model side, configuration is deliberately simple: paste a base URL, an API key, and a model id, and you are connected. Each model can have its own custom base URL and key, so you can swap providers without rewriting your workflow. OpenPilot also shows session and lifetime token usage in-app, giving you visibility into consumption across the endpoints you use, both cloud APIs and local OpenAI-compatible servers. OpenPilot's overall approach is a three-step workflow. First, add a model by pasting your base URL, API key, and model id—no account with OpenPilot is required. Second, open a workspace by pointing the app at a folder; the agent then receives filesystem and shell access there. Third, describe the outcome you want: ask it to build, fix, research, or automate something, and then review the tool trail as it works. That tool trail is central to how OpenPilot operates. Rather than presenting an answer as a black box, the agent surfaces the sequence of tool calls it makes—file writes, edits, commands, searches—so you can follow along and intervene where needed. The benefits follow directly from that design. Because the agent runs locally as a desktop application and uses your own keys, there is no vendor login and no locked-in models; you keep usage local to your keys and stay portable across providers. Swapping models does not require rewriting your workflow, so the same working habits carry across a cloud API, a local server, or a custom gateway. Token tracking in-app makes spend visible at both the session and lifetime level. Skills and memory reduce repetition by carrying your preferences and playbooks into context automatically, and MCP support means the agent can reach the systems you already rely on. For developers who want an AI agent that works on real files and real terminals, OpenPilot delivers that capability without the platform tax. Concrete scenarios described in the content include scaffolding an entire project from a prompt and then iterating in place with follow-ups; installing dependencies, starting servers, and running a test suite such as npm test with the results streamed back into the chat; researching documentation, APIs, or errors that live outside the repo and receiving a cited answer plus a fix; and automating build, fix, or research tasks by simply describing the desired outcome. Users running local models through Ollama or LM Studio can keep everything on their own machine, while those using OpenAI, OpenRouter, or a custom gateway can move between cloud providers as needed. MCP connections extend these same workflows to databases, browsers, and internal APIs when extra tools are required. OpenPilot is aimed at developers and builders—people who want the agent rather than the platform tax and who refuse vendor lock-in. It ships as a local Electron desktop application under the MIT license, and pricing is free. Windows is available now with a 64-bit installer and a portable executable, requiring Windows 10 or later (x64); macOS builds for Apple silicon and Intel are releasing soon. The model layer is bring-your-own, using any OpenAI-compatible endpoint, including OpenAI, OpenRouter, local Ollama or LM Studio servers, or a custom base URL gateway. In short, OpenPilot is an open-source desktop AI agent that puts you in control of the model, the keys, and the machine. Own the harness, bring the model, and let the agent do the work—without vendor lock-in.

AgentSDR is an open-source, self-hosted AI SDR workspace for outbound sales teams. It runs email sequences, LinkedIn campaigns and WhatsApp calls from one place, and pairs them with an AI CRM that reads every reply, classifies it and drafts the answer. The product is described as the open-source AI SDR that replaces Clay, Smartlead, HubSpot and many more, and it is designed to run on your own server with your own model key. Its stated promise is to reach, reply and close from a single workspace: sequences on each channel, AI triage on every reply, and measurement of replies, meetings and customers in one view. It is built for teams that want to own their outbound stack instead of renting a hosted service. Outbound today is spread across a long list of separate tools. The AgentSDR landing page shows logos for Smartlead, Clay, Instantly, HubSpot, HeyReach, Attio, lemlist, Salesforce, Apollo.io, Pipedrive, Expandi, Close, Outreach, Lusha, Salesloft, Hunter, Waalaxy and folk, making the fragmentation of the modern sales stack visible. AgentSDR's answer is to collapse that stack into one self-hosted workspace where email, LinkedIn and WhatsApp run side by side and every reply lands in a single AI CRM. Because it is self-hosted, your leads, conversations and call recordings live in your own Postgres database and your own storage bucket, and there is no hosted AgentSDR service in the middle. Provider keys are encrypted at rest with AES-256-GCM, and the data and sending reputation stay yours. Email is the first channel. AgentSDR sends multi-step sequences from your own Google Workspace mailboxes, connected through a service account with domain-wide delegation. Any CSV or XLSX column can be used as a merge field, and the system supports {A|B} spin text. Each mailbox has its own daily cap — 30 by default — plus its own sending window and signature, and campaigns can share the same pool of mailboxes, which are assigned round-robin. The list tools include one-click unsubscribe and automatic bounce suppression. The email analytics view shows emails sent, leads contacted, replies, reply rate, bounced and unsubscribed, and it is explicit that opens and clicks are not recorded, so reply rate is the engagement metric to watch. A campaign table breaks those numbers down per campaign, and mailbox health is shown with near-limit warnings and connected versus failing accounts. LinkedIn campaigns run across several accounts through Unipile. A sequence consists of an invite, an accept message and three follow-ups. Safe pacing keeps the accounts inside LinkedIn's limits: 30 invites a day on premium accounts and 5 on free accounts, a randomised 30–60 second gap between invites, and sending only inside each account's working hours; an account that hits LinkedIn's own limit pauses for the day. Search batches can bring up to 400 leads a day per account into campaigns, and every LinkedIn reply appears in one thread view with an AI draft ready. WhatsApp adds calling and messaging. A Chrome extension, the AgentSDR Call Recorder, dials inside WhatsApp Web when you call a lead from AgentSDR, records both sides to your Cloudflare R2 bucket, and your chosen model writes the transcript; it relies on WhatsApp Web's English interface. Unanswered leads can be retried after 1, 2 and 4 days, and messages sync with a 24-hour warm-up for new numbers and a limit of 25 new chats a day per number. At the centre is the AI CRM and inbox. Every reply is classified as Interested, Customer, Not interested or Other, using your own stages. Grounded drafts are written from your knowledge base — pricing, FAQs, case studies and objections — and held for approval rather than sent automatically; reply sequences also have every follow-up step drafted for review. The analytics show how many drafts were sent, how many replies the AI classified, and the override rate for labels a person changed. In the sample data, 71% of sent drafts went out unedited (151 of 214). The inbox is keyboard-first: J and K move, Enter opens, and ⌘K jumps anywhere. An Action required queue centralises conversations waiting on you, follow-ups that are due, and drafts to review, alongside warnings such as failing mailboxes or disconnected LinkedIn accounts. Under every channel is one database of people and companies. You import CSV or XLSX, add your own columns of text, number, date or select, and duplicates match on email or LinkedIn. Enrichment tables let a column call an API, run a formula, ask your model or pull from Apollo, and a table can then become a campaign. The AI layer is bring-your-own-model: every AI call runs on your OpenRouter key, pinned to the provider you chose with fallbacks turned off, so your data only goes where you decided. Classification, reply drafts, transcription and AI table columns all run this way. Optional lead enrichment integrations for emails, phones and company data include Hunter, Lusha, RocketReach, Snov, FullEnrich, LeadMagic, Findymail, ZeroBounce and Apollo, plus six more. The overall method is expressed in three words: Reach, Triage and Measure. Reach covers sequences on email, LinkedIn and WhatsApp inside each channel's limits. Triage means every reply is classified by AI with the answer already drafted. Measure means replies, meetings and customers are tracked by channel in one view. Deployment follows the same self-hosted logic: clone the repository, copy the example environment file and set the database, auth and encryption secrets, then run docker compose up; the app is ready at localhost and each channel is connected from inside the interface. Guardrails apply everywhere: daily caps, sending windows, warm-up for new numbers, and Do Not Contact honoured on every channel. The AI proposes and a person approves — a confident classification can move a lead forward in the pipeline, but a backward move, a low-confidence call and every new Customer are held for a person. The benefit is ownership and control. You send from your own accounts, so sending reputation and data stay yours; you pay for your server and your own AI usage rather than seats, tiers or per-contact fees. Analytics turn conversations into a clear view of meetings and customers by channel. In the sample workspace, 512 replies produced 104 positive replies (27% of people who replied), 37 meetings and 6 customers, with a median first response of 38 minutes; channel reply rates were 4.1% for email, 9% for LinkedIn and 18% for WhatsApp. Because drafts wait for approval, the human stays in charge of what goes out, while low-confidence classifications are held for review. Concrete workflows run through the workspace daily. A team imports a list, builds an enrichment table, and creates a campaign from it. Email sequences pace themselves across a pool of Google Workspace mailboxes, respecting caps and windows. LinkedIn accounts send invites and follow-ups within their limits, and replies arrive in a thread with a draft ready. WhatsApp calls are dialled from AgentSDR, recorded, transcribed and retried if unanswered. Reps then work the triage queue: approving or editing AI drafts, clearing follow-ups that are due, and moving leads through stages such as Information Requested, Demo Requested, Meeting Requested, Meeting Done and Trial User. Analytics closes the loop by showing which channel brought the replies, meetings and customers, and a single deployment can hold several organisations with separate leads, inboxes and connected accounts. AgentSDR is built for sales teams, founders and agencies that run outbound and want a self-hosted alternative to a stack of subscriptions; the product explicitly supports whole teams, with owners, admins and members, and multiple organisations in one deployment. What you connect is your own accounts: Google Workspace mailboxes for email sequences, Unipile for LinkedIn and WhatsApp, OpenRouter for every AI step, and Cloudflare R2 for call recordings, with optional enrichment providers for table data. The stack is a TypeScript Next.js 16 application using React 19, Postgres, Drizzle, Tailwind 4, Bun and Docker, and it needs a machine that runs Docker plus PostgreSQL 16 or newer. Pricing is free: the project is open source, with no seats or per-contact fees, so the only costs are your server, your connected accounts and your AI usage. AgentSDR's core proposition is simple: one open-source, self-hosted workspace where email sequences, LinkedIn campaigns and WhatsApp calls sit beside an AI CRM that triages every reply and prepares the answer, with your data, your model key and your sending reputation kept on your own infrastructure. Teams reach, reply and close from a single place, with guardrails on every channel and a human approving the drafts.

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Hark Pro is a personal AI built to take things off your plate rather than add another chatbot to your day. The company describes it as a new kind of system for getting things done: whatever you need, let Hark handle it. Hark remembers what matters, thinks ahead about what you might need, and uses its own cloud computer to get things done across the web, covering everything from managing calendars and email to shopping, travel, bills, and everyday errands. Hark Pro is available as an app on browser, iOS, and Android. The product is positioned against the usual pattern of AI tools, which the company frames as adding another chatbot to your day rather than removing work from it. The tasks Hark takes on are the ones that quietly pile up: remembering that you put off getting home insurance quotes, comparing equivalent coverage, ordering from your usual pizza place, sorting email, submitting expenses from receipts, or finding a spot for an annual physical. Each of these normally means opening a browser, logging in somewhere, comparing options, filling in forms, and often making a payment. Hark's premise is that you should be able to state the outcome you want and let the AI carry out the steps. The core of the everyday experience is a single chat thread. Chat with Hark in a single thread for whatever you need, whether that is booking a table for a birthday dinner, submitting expenses from email receipts, or doing quick research. Hark has persistent memory, so it tailors responses to actually be useful to you rather than treating every request as a fresh start. That memory is what allows it to act on personal specifics, such as your usual order or the coverage you already have. For larger efforts, Projects keep bigger undertakings organized; the company names job searches and trip planning as examples of the kind of work that benefits from being grouped rather than scattered across messages. Hark's computer use agent is called Handoff. It can use the internet just like you would, and it can build websites, do research, place orders, and make slides, among other things. Crucially, it does this from its own secure computer with a full browser that is individual to you, rather than simply returning text suggestions. That means tasks which require navigating real websites, clicking through flows, and completing transactions can be carried out from start to finish instead of being described back to you. Because Hark asks you to connect accounts, save logins, and make payments, the company treats security as a first-class part of the product under the name Secured by Hark. It uses end-to-end encryption so your data stays safe, and describes the arrangement as a vault that absolutely no person has access to. Hark also checks in with you before doing something that requires your approval. The company states that your sensitive credentials stay encrypted, that Hark never sells your data or shares it with advertisers, and that you can watch it work. Action Buttons are Hark's answer to the to-do list. Hark suggests and prioritizes these actions based on what it knows you have on your plate, so the list is generated for you rather than written by you. All you have to do is click on one, and Hark will perform that action from start to finish. This turns the assistant into something closer to a queue of delegated work than a conversation you have to keep driving. Panels are customizable views you create to stay on top of whatever you like. You describe what you want and Hark builds mini-apps completely tailored to you. You can also ask your Panels questions to dig deeper into their data, unlocking even more from services you already use; Strava, Spotify, and Venmo are the examples named on the site. Where Action Buttons handle discrete tasks, Panels are about ongoing visibility into the things you care about, built on the fly from your own description. Taken together, Hark's approach is to combine a conversational front end, persistent memory, a proactive task layer, and an agent that can actually operate the web. Rather than a chat window that only produces answers, Hark pairs the conversation with Handoff's own secure browser, with end-to-end encryption protecting the accounts and payment details you connect, and with approval checkpoints before sensitive actions. Meanwhile Action Buttons and Panels let the assistant surface what it thinks you should do and give you customized views of your own data. The result is a single system that spans talking to an assistant, delegating to it, and monitoring what it has done. The stated benefits are practical. Hark is designed to take things off your plate, remember what matters, and think ahead about what you might need. It can handle calendars and email, shopping, travel, bills, and everyday errands on your behalf, and you can watch it work. With persistent memory, responses are tailored rather than generic; with Handoff, work that would otherwise require your own browsing can be completed end to end; with Action Buttons, you get a prioritized to-do list you can clear with a click; and with Panels, you can dig deeper into data from services you already use. The site illustrates the scope with example requests. Hark can be asked to book a trip to Mexico City next weekend, find the cheapest flights, and locate a nice hotel in Polanco, or to find the cheapest flight from SFO to JFK this weekend and book it. It can order a Margherita from your usual pizza place, or find a nice dinner for two in your neighborhood this Friday. It can find competitive home insurance quotes with equivalent coverage, show a comparison, and make the switch, or audit what you are spending the most on and help you save money. It can find a dentist who takes your insurance and book a checkup, or find a spot for an annual physical. It can help manage and sort your email, submit expenses from email receipts, and do quick research. And through Handoff it can build websites, do research, place orders, and make slides. Hark Pro is aimed at people who want their everyday logistics handled without adopting another tool to manage. The examples span travel, dining, insurance, spending, health appointments, and email, the kind of personal admin that any busy individual faces. It is offered through the Hark Pro app on browser, iOS, and Android, and sign-up is through Google or Apple, or by continuing directly; by continuing you agree to the Terms of Service and acknowledge that your data will be processed in accordance with the Privacy Policy. The company frames the product around building the most personal, human-like intelligence in the world, and points to enterprise-level security as part of that promise. In short, Hark Pro sets out to be the personal AI that handles life before you have to. It combines a persistent-memory chat, Projects for bigger goals, Handoff's secure browser-based agent, approval-gated encryption, Action Buttons, and custom Panels into one system, built to take things off your plate rather than add another chatbot to your day.

Paw-Paw is a free desktop pet for macOS. A small illustrated animal sits on your screen — in the bottom-right corner next to the Dock, or the bottom-left in Mirror Mode — and reacts as you type and click anywhere on your Mac. It is made for anyone who wants a little companion while they code, write or study, and it deliberately stays out of the way. There are no ads, no subscription and no account needed. Paw-Paw runs on any MacBook, iMac or Mac mini with macOS 13 or later, and you download it directly rather than through the App Store. Click your Paw-Paw to hear a short cheering line, or type fast to set it On Fire. Most of the day on a Mac is quiet and solitary: typing, clicking, switching windows. Paw-Paw turns that ordinary activity into something a bit cozier. Instead of adding another dashboard or task list, it makes the typing and clicking you already do count for something — key presses and clicks build XP, and XP levels your Paw-Paw up. The pet is designed to stay out of the way, so it never becomes another thing to manage. Paw-Paw needs macOS Accessibility access to notice when you type and click, but it counts key presses and clicks only, never what you type, and it never logs or transmits typed text. It checks its CDN for updates, and it sends usage analytics or crash diagnostics only if you opt in. The core loop is reactions and XP. You start with Shiba or Sheep, and as you type and click each season grows on its own track. Get into a rhythm and your Paw-Paw catches fire, setting it On Fire and earning 2× XP while it burns. You can choose which season or collection earns XP, so your progress goes where you want it. Leveling up unlocks more Paw-Paws, and both seasons — Season 1, the original Paw-Paws, and Season 2, which starts with Sheep — are free to discover. Clicking your Paw-Paw also makes it talk, offering little cheers for the next small step in speech bubbles. Paw-Paw is also a collection game. Type, click and work to earn random item drops: more than 100 free items, from common sprouts to legendary frog princes, and every one of them fits every Paw-Paw. You try an item on by clicking it or dragging it onto your Paw-Paw in the corner; click or tap it again to take it off. Gifts arrive as one of three gift balls — pick one and you can wear what is inside straight from the reveal — and Open All lets you open a pile of gifts at once. The Library window brings every Paw-Paw, item and achievement together in one place, with a page for each season, a category filter, a search field, and badges showing which season each item came from. Season 2 added achievements to earn, along with secret Paw-Paw and item pairings to find in the app. Achievements appear as pop-ups, such as First Spark and New Look. You can also make the pet yours: choose a Small, Medium or Large size and turn on Mirror Mode in Settings, then right-click your Paw-Paw, or its Dock icon, to switch Paw-Paws and items or set its opacity. Your Paw-Paw sits next to the Dock but can be dragged anywhere, and Reset Position puts it back. Paw-Paw also has its own Dock icon, with a right-click menu that includes Open Library, Change Paw-Paw and Click-Through, among other options. Paw-Paw's approach is deliberately lightweight. It is a native macOS app rather than a browser toy, shipped as a universal binary that runs on Apple silicon and Intel Macs. You download it directly, with no App Store and no sign-up, and the download is signed with Apple Developer ID and notarized by Apple. Accounts are optional: sign in only if you want to buy a collection or keep your progress in sync, and signing in on a new Mac brings your progress along in one step. Updates download in the background, the Library now opens on Home, and a short tour shows you around after setup. Compared with most desktop extras, there is very little to configure and nothing to maintain. The outcome is small but real: a desk that feels less empty during long sessions. Because Paw-Paw reacts to what you are already doing, using it costs no extra effort — you do not open it, configure it or check it, and XP, item drops and achievements simply accumulate while you type. The pet naps when you pause and returns when you start again, so it stays out of the way until you want to interact with it. Clicking it gives a quick moment of encouragement; leveling up gives you new Paw-Paws and items to look forward to. Everything is free with no ads and no subscription, so the fun does not come with an ongoing cost or a recurring bill to track. Day to day, Paw-Paw fits the way people already work on a Mac. During a long coding session it keeps you company and quietly turns keystrokes into XP; while drafting or writing it levels up on the same typing you would do anyway. Students and solo workers get a tiny companion in the corner that cheers them on when they click it. Collectors work through two seasons of Paw-Paws and 100+ items, hunt achievements and secret pairings, and try endless item combinations on their pet. Anyone who uses more than one Mac can sign in and carry their progress along. And because the pet can be resized, mirrored, made more or less opaque and dragged anywhere, it also serves as a small piece of desktop decoration you can arrange around your own setup. Paw-Paw is aimed at macOS users who want a cute, low-effort companion on their desktop, and at collectors who enjoy unlocking things over time. It requires macOS 13 Ventura or later, including macOS 14 Sonoma, macOS 15 Sequoia, macOS 26 Tahoe and macOS 27, and runs on Apple silicon and Intel Macs, from a MacBook Air to an iMac or Mac Studio. It is macOS only for now; a Windows version is planned for later in 2026, alongside multiplayer, Season 3, a winter holiday special and Japanese, Korean and Chinese translations. Pricing is simple: Paw-Paw is free, with both seasons, all of their Paw-Paws and items, and no ads or subscription. The one optional extra is the Illustrain Collection — 5 Paw-Paws and 57 items on a level track of their own, illustrated by Illustrain, bought inside the app for a one-time USD 2.99. In short, Paw-Paw turns the ordinary typing and clicking you already do on a Mac into a small, free, ongoing source of companionship and collection. It asks nothing of you — no account, no subscription, no ads, no keystroke recording — and gives back reactions, cheers, XP, level-ups, items and achievements, all from a tiny friend sitting quietly in the corner of your screen.

Clippo is a visual orchestration canvas for Windows that turns software development into a command room where you act as the tech lead and your AI coding agents work together in parallel. Instead of wrestling with dozens of lost terminal tabs or repeatedly copy-pasting the same briefings, Clippo gives you an infinite canvas where you assemble your AI dev team, delegate tasks, and monitor code progress in real time, arranging your workspace however you like. It is built for developers who already use AI coding agents day to day and want a single screen to coordinate them, watch what they are doing, and keep every piece of project context in one place. The problem Clippo addresses is fragmentation. When you run several AI coding agents at once, each one typically lives in its own terminal tab, and the moment you switch between them you lose sight of which agent is still coding and which one has finished. Briefings get copied and pasted again and again, and context that was explained to one agent is not available to the next. Clippo replaces that scattered, tab-driven workflow with a spatial one: agents, notes, browsers, and editors are all placed on one canvas and connected to each other, so the state of your project is visible at a glance rather than buried in a list of windows. Clippo's core capability is running your AI dev team in parallel. You can assign agents to different fronts at the same time — one building the backend API, another writing test suites, and another refactoring the frontend — and see everyone working side by side on a single screen. Because the agents are laid out visually, you know instantly who is coding and who is done without switching windows. This parallel approach matters because development work naturally splits into independent tracks, and keeping those tracks visible next to each other makes it far easier to spot a stalled agent or an overlapping change before it becomes a problem. The Project binder keeps your agents aligned through a structured set of notes placed directly on the canvas. It has three parts. Project captures the overall architecture, stack guidelines, and team conventions. Plan holds a step-by-step implementation roadmap for you to review before execution begins. Walkthrough produces an executive delivery report with diff summaries and verification checklists. The binder is not just documentation — you connect a cable from any note to a terminal, and the agent absorbs that context immediately, with no re-prompting needed. That single mechanism means architecture decisions, conventions, and roadmaps are shared with every agent you link, keeping a multi-agent effort pointed in the same direction. ClipSurf is an embedded browser that lives on the canvas and serves as web browsing and eyes for any agent. It is available to both you and your agents, and it is particularly valuable for terminal agents and CLI models that do not have built-in web access. With ClipSurf those agents can browse documentation, research solutions online, and test the local web app they just built, live in front of you. Getting external information into an agent normally means copying links and pasted text; ClipSurf removes that step and lets the agent look things up directly, while you watch the same view it is working from. Persistent memory with local AI addresses one of the most costly parts of working with coding agents: losing context between sessions. Clippo keeps a local memory that learns your project over time, storing architectural decisions, solved pitfalls, and coding standards. A lightweight local model retrieves relevant memories on demand, which cuts token usage and keeps context across sessions. The practical effect is that an agent starting a new session does not begin from zero — the decisions you already made and the problems you already solved are available to it, and because retrieval is local, that recall does not come with a token cost for re-explaining the project each time. Clippo IDE is a visual code editor built into the canvas, designed for inspecting and polishing code before Git. You can browse project files, review agent-generated code with syntax highlighting, inspect Git diffs, and make quick edits before committing with a single keystroke. Because the IDE sits on the same canvas as the agents, reviewing their output does not mean leaving the orchestration view. The point is control: agent-generated code is checked, adjusted, and only then committed, so you stay in full control of your repository rather than trusting output blindly. Clippo is built to be lightweight, fast, and 100% private. There are no bloated web wrappers that eat up your RAM; Clippo opens fast, stays light, and leaves your CPU and memory free for compiling and development. Your files, notes, and prompts never leave your PC, with zero account creation, zero login, and zero tracking. For developers working on proprietary code, that on-device design removes the friction and risk of sending project material to a third-party service just to coordinate the tools building it. Overall, Clippo works as a visual orchestration layer that sits alongside the AI tools you already use rather than replacing them. It is compatible with Claude Code, OpenAI Codex, Gemini CLI, OpenCode, Aider, Copilot CLI, and local engines like Ollama and LM Studio, and WSL and Docker are supported out of the box. The methodology is node-based: you place terminals, notes, browsers, and editors on an infinite canvas and link them with cables, so context flows from a note into an agent, and agents run in parallel on separate fronts of the same project. Everything — unlimited terminals, notes, Clippo IDEs, ClipSurfs, and Spaces — lives inside the workspace you arrange yourself. The benefits Clippo claims follow from that structure. You stop drowning in terminal tabs and stop copy-pasting briefings, because one canvas shows every agent's progress at once. You save tokens, because on-device memory recalls project context instead of forcing you to restate it. You keep your CPU and memory for compiling and development, because the app stays light. And you retain full control of your repository, because nothing is committed before you review it in the built-in IDE. The outcome is a workflow where you step up as tech lead of your own AI engineering team rather than acting as a dispatcher between disconnected windows. Concrete scenarios include splitting a feature across agents — one on the backend API, one writing test suites, one refactoring the frontend — while you watch all three on one screen. Another is giving a CLI model without web access the ability to browse docs and research a solution through ClipSurf, then test the local web app it just built in the same embedded browser. A third is reviewing agent-generated code, inspecting Git diffs, and making a quick edit before committing. Persistent memory supports ongoing projects where sessions restart regularly, and the Clippo Pro add-on covers developers working across multiple projects at once using unlimited workspaces. Clippo is aimed at developers who use AI coding agents such as Claude Code, OpenAI Codex, Gemini CLI, OpenCode, Aider, or Copilot CLI, and at anyone running local engines like Ollama or LM Studio. It runs on Windows 11, version 22H2 or higher, on x64. The free plan includes a complete workspace with unlimited terminals, notes, Clippo IDEs, ClipSurfs, and Spaces inside it. To work across multiple projects at once, the Clippo Pro add-on unlocks unlimited workspaces through a one-time purchase from the Microsoft Store. The app offers in-app purchases and is developed by Thiago Grião. Clippo's primary value proposition is simple: it gives Windows developers a visual command room for AI agents, turning a scattered collection of terminal tabs into one infinite canvas where agents run in parallel, project context is shared through linked notes, memory persists locally to cut token usage, and code is inspected before it ever reaches Git.

tide is a simple, self-hostable video conference service that positions itself as an alternative to Jitsi, Zoom and Google Meet. Rather than renting conferencing from a hosted provider, an organisation can run tide on its own infrastructure and host meetings at an address such as meet.custom.domain. The product's own tagline describes it as "A Jitsi alternative that does a few things really well," and the site lists the core capabilities as link sharing, screen sharing and server-side recording. It is for anyone who wants to own the video meeting stack rather than depend on someone else's servers, and the site offers both a live demo and deployment documentation as starting points. Most video conferencing today happens on someone else's infrastructure. Zoom and Google Meet are hosted services: the meeting runs on the vendor's servers, under the vendor's terms, and the media travels through networks the customer does not control. Jitsi offers a self-hostable route, and tide explicitly frames itself as an alternative to it, naming Zoom and Google Meet alongside. The problem tide addresses is therefore one of ownership: whose server carries the audio and video, who holds the recordings, and who decides the sign-in rules. The website states its answer in two headline bullets — "Media stays on your server" and "Server-side recording" — arguing that a conferencing service can stay simple while still keeping the media and the resulting files inside the operator's own environment. For teams with data-residency expectations, internal security policies or a preference for self-reliance, that distinction weighs more heavily than a long feature list. Deployment is presented as one of tide's defining traits. The site lists "One Go binary" as its first bullet and calls tide a simple, self-hostable video conference service. Two links carry the practical next steps: a demo instance at demo.tide.convex.works and a Deploy link pointing to tide.convex.works/docs. The single-binary pitch means the whole service can be distributed and run as one artifact rather than as a suite of separate components the operator has to assemble and keep in sync. Go is the one technology named directly on the page, and the claim of one binary follows from that. For anyone evaluating self-hosted video, the effect is a smaller surface to install, fewer moving parts to update, and a documented entry point at the docs URL. On the meeting side, tide covers the core workflow of getting people into a call. Link sharing is named among the core features, and the interface shown on the site includes a "New room" form with fields for a Name, a Link, and a calendar shortcut. The illustrated example is a "Weekly sync" with the link meet.example.com/m/chw-pnih-sxi, and the form includes an "Add to calendar" action plus a Date block with Starts and Ends times — 10/05/2026, 07:30 PM to 08:00 PM in the example. Two actions are offered once the room is configured: "Create" and "Create & join," so an organiser can either prepare a room or start it immediately. Screen sharing is listed explicitly as a core feature, and recording is handled server-side rather than on a participant's machine — the site states "Server-side recording" both as a headline bullet and in the Product Hunt description alongside link sharing and screen sharing. Sign-in is delegated to OIDC, the fourth headline point. Using OIDC means authentication can be tied to an identity provider the organisation already operates, instead of tide maintaining its own account system, which fits the self-hosted positioning: the operator keeps control over who is allowed in. The meeting view shown on the site displays named participants — Daniel, Maya, Marcus and Nikos, marked as "You" — with a participant count of four, illustrating a small call. The overall approach tide takes is stated in its own tagline: "A Jitsi alternative that does a few things really well." Rather than competing on breadth, the product concentrates on a short, explicit list — one Go binary, media that stays on your server, server-side recording and OIDC sign-in — and repeats that framing in the Product Hunt description: "A few things, done well." The same description notes that tide lets you host your own "meet.custom.domain" for "a simple video conferencing environment with all core features you expect." The methodology, in other words, is to be a focused self-hosted service: install one binary, point a domain at it, and run meetings with link sharing, screen sharing and server-side recording under your own roof. The benefits that follow from the page's stated facts are straightforward. Because media stays on your server, meeting traffic terminates in infrastructure the operator controls. Because recording happens server-side, call recordings are produced by the service itself rather than depending on a participant capturing their own local stream. Because sign-in uses OIDC, access control can follow whatever identity system the organisation already runs instead of a separate set of credentials. Because the service is a single Go binary, deployment and maintenance are framed as simple rather than as an ongoing platform project. And because the scope is deliberately small — a few things done well — the operator does not have to weigh a long list of optional modules to get a working conferencing environment. Concrete scenarios appear on the page. The "New room" example is a recurring team meeting — a "Weekly sync" scheduled for a specific date and time with a fixed start and end — and the presence of an "Add to calendar" action indicates the intended flow of setting up a meeting and adding the appointment to a calendar. The generated link, meet.example.com/m/chw-pnih-sxi, shows the pattern of a short meeting URL that can be shared with attendees, which matches the link-sharing feature. The "Create" and "Create & join" buttons serve both the organiser who wants to prepare a room ahead of time and the one who wants to start straight away. The demo at demo.tide.convex.works provides a way to experience a call before committing to a deployment, and the site's framing of "meet.custom.domain" points to organisations that want their meetings hosted on a domain of their own. Server-side recording suits meetings that need to be captured for people who cannot attend, and OIDC sign-in suits invitations that must be restricted to members of an existing directory. The intended audience can be read from the page's own vocabulary: the three alternatives invoked — Jitsi, Zoom and Google Meet — describe both the self-hosting tradition tide belongs to and the mainstream services it is offered against. Anyone who wants to run their own "meet.custom.domain" is the stated target. In terms of stack, the site names exactly one technology, Go, in the bullet "One Go binary," and describes OIDC for sign-in. Deployment documentation is linked at tide.convex.works/docs. The page itself does not state a price, plan or licensing model, and it does not describe native mobile or desktop applications; it presents a self-hosted service with a room creation form and a shareable meeting link. Taken together, tide makes a single, deliberate claim: video conferencing that you host yourself, built around a few things done really well. One Go binary to deploy, media that stays on your server, server-side recording and OIDC sign-in, wrapped around the familiar flow of creating a room, sharing a link, sharing your screen and joining the call. For teams that want their meetings — and the recordings they produce — to remain on infrastructure they control, tide offers a focused alternative to Jitsi, Zoom and Google Meet.

Udon is a control deck for a home server built on an Apple Silicon Mac. It turns a Mac mini, Mac Studio, MacBook, or iMac into a home for your files, photos, media, and apps, and lets you manage all of it from your browser. Udon installs with a single terminal command, and the Mac keeps working as a normal Mac. Any Apple Silicon Mac (M1 or newer) running macOS 26 or later is supported; Intel Macs are not. A Mac mini is the usual pick for an always-on home server because it is small, quiet, and draws a few watts at idle. An optional AI sysadmin can help you install apps, check the server, and investigate problems. The product is built for people who want to own their digital life and keep their data on hardware they own. macOS has no dashboard for running self-hosted apps, and Udon exists to add one. Without a control panel, running services on a Mac means working manually over SSH with a container tool such as Portainer, which puts everyday management out of reach for many people. The established alternatives are Unraid, TrueNAS, or Proxmox on dedicated x86 hardware, a Synology NAS, or Umbrel, CasaOS, or OpenMediaVault on a Raspberry Pi or spare PC. Each of those routes asks for extra hardware, extra power draw, or extra maintenance. Meanwhile a Mac mini idles at a few watts, runs near silent, and still has power to spare for media, photos, and a dozen containers, so the hardware is often already sitting on the desk. Udon provides the missing software layer that makes that machine manageable as a server. The AI sysadmin is Udon's most distinctive component. It is optional and stays off until you turn it on. When enabled, you can ask it to install apps, check your server, or investigate a problem, rather than working through each screen of the dashboard yourself. You choose the AI provider: connect an Anthropic or OpenAI API key, a coding CLI such as Claude Code or Codex, or a local model running on the same Mac. By default the assistant asks before every change, and it records its actions in an audit log, so you keep control over what happens to your machine. If you connect a cloud model, it sees what you ask the assistant to work on, which the website states plainly. Containers and packages are managed in one place. Udon runs standard images from Docker Hub, GitHub Container Registry, or any other registry, on Apple Container, Apple's native runtime, or on OrbStack, which is Docker-compatible. On OrbStack you can also build compose stacks in the visual Composer. Alongside containers, Udon manages Homebrew packages, so the software your Mac runs is chosen and tracked from a single interface instead of a mix of command-line tools. This matters because container and package management is usually the part of self-hosting that people find hardest to keep organised. The built-in App Store makes the first steps easier. You can install self-hosted apps with settings pre-filled, so building a photo library, streaming your media, or organising documents at home does not require configuring every service by hand. If the catalogue does not cover what you need, Udon points you to more apps on Docker Hub and Homebrew, and the documentation includes a guide on how to deploy a container. Terminal and remote desktop access are built into the dashboard. You can work on your Mac from any browser, with its terminal and its desktop within reach, and terminal sessions stay open between visits, so a long-running task is still there when you come back. Storage and sharing are handled in the same place. Your files stay on your own drives, and Udon lets you browse and preview them, check drive health, and share folders across your home network as SMB shares. Remote access is handled through Tailscale. Your files and apps live on your Mac, and you stay in control of them; Udon connects over your own Tailscale network with trusted HTTPS for the dashboard, so you can manage the server from your phone's browser at home or away without exposing it publicly. The Activity view shows how your Mac is doing at a glance: CPU, memory, network, and power use live, plus which processes are running. Together these two areas cover both reaching the server and understanding what it is doing. Udon's overall approach is to take a machine you already own and give it a browser-based control deck. Installation is a single terminal command, and after that the dashboard is reached at an address such as mac-mini.local:4443. Everything the product manages — containers and compose stacks, apps from the App Store, files and SMB shares, drive health, live system stats, Homebrew packages, a terminal, and the Mac's screen through remote desktop — is available from any browser. The Mac keeps working as a normal Mac throughout. A companion app for iPhone and Android is coming soon, and until then the dashboard works from a phone's browser. The benefits follow directly from that design. Your files, photos, and media stay on your Mac rather than in a cloud service, and the dashboard is served over HTTPS with remote access over your own Tailscale network. Udon is a one-time purchase rather than a subscription, so there is nothing to renew. Trying it does not require a card, and if you do not buy a licence after the free days, nothing is charged: the dashboard locks, but containers keep running and your files and settings stay exactly as they are. In practice, people use Udon to run the self-hosted services that would otherwise need a NAS or a separate server. Streaming media with Jellyfin or Plex, backing up photos with Immich, syncing files with Nextcloud, keeping passwords in Vaultwarden, blocking ads for the whole network, archiving documents with Paperless, and automating workflows with n8n are all named as things you can self-host on a Mac. Udon can also host your dev environments and coding agents, which you reach from a laptop or an iPad anywhere. Udon is available for Apple Silicon Macs running macOS 26 or later, with the dashboard managed from a web browser. It runs Docker images on Apple Container or OrbStack, manages Homebrew packages, and uses Tailscale for remote access. Pricing is a free 5-day trial with no card required, followed by a one-time payment: early supporters pay $29, reduced from $49, for a lifetime licence that includes every feature and updates and is tied to the Mac's serial number with no accounts. Every purchase comes with a 14-day money-back guarantee. Udon's core proposition is simple: own your digital life, start with your Mac. It takes an Apple Silicon Mac you already have, adds the dashboard macOS is missing, and brings self-hosted apps, containers, storage, monitoring, and an optional AI sysadmin together in one browser-based control deck.

offstage is a free, open-source tool that gives a computer-use coding agent its own Mac desktop instead of your screen. It works with Claude Code, Codex and opencode, and it exists for one purpose: any command an agent runs that could open a window or take focus on your Mac is sent to a second, logged-in macOS account instead. Simulators, Xcode UI tests, osascript calls and the app your agent just built all open on that helper account's desktop, which sits behind yours. Your windows, keyboard and mouse stay yours the whole time. offstage ships as an MCP server and a CLI, is MIT licensed, and runs on Apple Silicon Macs with Node 20 or newer. The problem it addresses is the collision between coding agents and your own computer. Agents increasingly do GUI work: they launch simulators, run Xcode UI tests, drive browsers and open the apps they just compiled. On a normal Mac all of that lands on the one screen you are using, taking focus, moving the mouse and covering your windows. offstage's answer is not to hide the GUI work but to give it a different desktop. Because the helper account is a real macOS account with its own desktop, window server and input, the agent can screenshot, decide and click in a place that never overlaps with what you are doing. The site states plainly why that second macOS account is the whole point: it has its own desktop, window server and input, and that is why offstage exists. The core of the product is the helper account. One setup command creates an ordinary account called computeruse and builds a small Swift daemon for it. If the terminal has Full Disk Access, that daemon is granted Screen Recording and Accessibility automatically; if not, offstage tells you which two toggles to flip. It prints the entire root script before running it. After a single login through the menu bar user menu, the account stays logged in behind yours until the Mac restarts. offstage session status reports where things stand and exits 0 when the session is ready. Nothing here is a virtual machine: it is a second user account on the Mac you already have, with no guest OS and nothing to boot. The trade-off is that both accounts share the Mac's CPU, memory and disk. Storage is where the difference is stark, because the helper account takes about 3 GB of disk, where the macOS VM image the team measured was a 68.8 GB download. offstage also decides where each command should go. The site describes it as reading every command before it runs and sending it to the cheapest place that keeps it off your display, and it defines four lanes. Commands such as xcodebuild test, xcrun simctl, XCUITests, open -a, osascript and a built .app run in the second macOS account. Commands that force a real browser window, including --headed, headless: false, cypress open, WebGL and GPU flags, run in a Linux container with a virtual display if Docker is installed; it is a real display, just not yours. Headless work such as npm test, vitest, headless Playwright and Puppeteer runs right where it is, because those never open a window and wrapping them would only cost time. Finally, installers, .pkg, .dmg and hdiutil are refused outright: both accounts share one machine, so offstage will not run them and no flag overrides that. Agents connect through MCP. The site provides copy-paste configuration for Claude Code (claude mcp add offstage -- npx -y --package=@viraatdas/offstage@latest offstage-mcp), for Codex in ~/.codex/config.toml, and for opencode in opencode.json. Claude Code can also use offstage as a plugin. The MCP surface includes offstage_route, which tells the agent which lane a command gets, and offstage_run, which runs it there, alongside session tools: offstage_session_launch, which waits until the app registers and returns its pid, offstage_session_screenshot, offstage_session_input and offstage_session_quit. The documented GUI loop is launch, screenshot, decide, input, then screenshot again to confirm. Coordinates are points, not pixels, so a pixel coordinate has to be divided by the screenshot's scale. For anything that cannot use MCP, the CLI runs the same code path through offstage route -- and offstage run -- . The reason offstage can promise your screen stays yours is the daemon. It posts input to its own session only, never to the global input stream that feeds your screen, and if its session is ever the one on the console it refuses to send input at all. In testing, the window server's own log showed every synthetic event landing in the helper session and none reaching the console. The site is equally explicit about files: the helper account is an ordinary second user, so it cannot write to your files, and it can read only what macOS lets any other local account read. If a run fails because the helper account cannot read your repository, offstage session share grants read-only access to one folder at a time, and unshare takes it back. Each run writes its output to its own artifacts folder. To make the behaviour stick, you paste a block of instructions into your agent, or into your project's AGENTS.md or CLAUDE.md, and the agent connects offstage itself so GUI work stays off your screen from then on. Two things only the user can do, and the site's rules tell the agent to ask rather than work around them: sharing a repository folder with the helper account, and running offstage session setup --create, which needs sudo. The agent is also told to never re-run a skipped or refused command outside offstage to get past it, because that would put it back on your screen. The practical outcome is that you can keep using your Mac while an agent runs an entire GUI test suite somewhere else. Nothing boots, nothing is virtualised, and the agent's windows, cursor and focus live on a desktop that is not yours. A skipped status means nothing ran anywhere, and refused means the command will not run on any lane. Concrete scenarios appear throughout the site. In one real pair of captures taken on a single Mac on 2026-09-22, Claude Code checks that the helper account is logged in, off your screen, with Screen Recording and Accessibility granted; a second Claude Code session is then testing a messaging app called i2Message on the computeruse desktop, with a search for the word coffee typed in through offstage_session_input. None of it appears on the console user's screen, and the console user was watching a video in a browser the whole time. Other workflows are the lanes themselves: running Xcode UI tests and simulators off-screen, driving a headed browser in a Docker container with a virtual display, leaving npm test or headless Playwright in place because they never open a window, and refusing installers so they never land on either account. offstage is aimed at developers on Apple Silicon Macs who run coding agents and want their own machine left alone during GUI-heavy work: Xcode and simulator workflows, UI tests, browser automation, and any project where an agent builds an app that then needs to be clicked. The agents it supports explicitly are Claude Code, Codex and opencode, through MCP, with Claude Code also usable as a plugin; anything else that can run a shell command can use the offstage CLI, which runs the same code. Requirements are an Apple Silicon Mac and Node 20 or newer, with Docker optional for the container lane. Pricing is free: the project is MIT licensed and open source on GitHub. The takeaway is that offstage solves a very specific, very annoying problem, an agent that needs a screen taking over the one you are using, by giving that agent a second, logged-in macOS account with its own desktop, window server and input. It routes each command to the cheapest place that keeps it off your display, refuses the commands that could touch the whole machine, and costs nothing but a folder share and one sudo command. Your agent gets its Mac desktop; you keep yours.

Liquid Inference is an LLM router that runs an instant auction for every AI request. You send an ordinary chat request, and providers compete to answer it at the lowest price that meets the rules you set. Instead of committing to one vendor's rate card, you describe what the request needs — model, region, speed and minimum quality — and the cheapest provider that satisfies those requirements answers. The product is built for developers and teams already working with large language models through coding agents, SDKs or direct API calls, and who want access to hundreds of open- and closed-weight models without managing a separate account, key and billing relationship for each one. Choosing a model provider has usually meant accepting a published, fixed rate and paying it whether or not cheaper capacity exists somewhere else at that exact moment. Inference capacity fluctuates with supply and demand, so providers often hold spare capacity they would be willing to sell at a lower price, but buyers have had no practical way to discover and capture that price request by request. Liquid Inference turns every prompt into a small auction to close that gap. Providers publish a price per token for each model and can change it at any time, lowering it when they have spare capacity. The result is that a buyer pays the lowest marginal cost available for that request while still getting the model, region, speed and quality constraints they specified. The auction runs in three steps. First, providers set their prices: each provider sets a price per token for each model and may change it whenever they like, so the offers on the book move with supply and demand. Second, the cheapest match answers: you set the rules — model, region, speed and minimum quality — and the cheapest provider that meets them answers your request. Third, you pay for what you use: before the model starts, you know the most a request can cost, and you are billed only for the tokens it produces. Because the cap is fixed before the first token is generated, the price of a request is known up front rather than discovered later on the invoice. Control over routing is explicit. A price limit is placed on every request before the first token, so you know the maximum a request can cost and pay only for the tokens produced. Routing rules let you choose the model, region, speed and minimum quality, and only providers that meet those requirements compete for the work. To support trust in the marketplace, Liquid Inference tests every provider regularly with standard benchmarks and publishes the results, so the speed and measured quality of providers are visible and the same rules apply to everyone. Billing is itemized, showing what every request cost line by line, and public price history lets you download live and past prices for every model so you can send work when prices are low. Liquid Inference covers hundreds of open- and closed-weight models, spanning families such as GPT, Qwen, Gemini, GLM, Deepseek, Gemma, Kimi, Grok, Claude, Mistral, O, Nemotron, Ernie, Mimo, Llama, Phi, Nova, Command, Hermes, Granite, Sonar and many more. Model counts per family are listed on the site alongside the lowest current offer in USD per million output tokens, which ranged from $0.01 for Nemotron to $4.62 for O at the time of capture. Compatibility is deliberately simple: one key covers both OpenAI Chat Completions and Anthropic Messages on a single base URL, so existing code works unchanged, and getting started requires changing one URL. The product also supports full multi-modal requests. The overall approach is to treat inference as a live market rather than a static catalogue. Providers compete dynamically on price for every single prompt, offering the lowest marginal cost at the moment the request is made. You can create routing rule presets or use the Auto routing algorithms to decide how requests are matched, and the rules you set determine which offers are allowed to compete. Because providers can raise or lower prices at will and stop quoting when they are full, the offers on the book reflect current capacity. The buyer's side of the market is equally concrete: a single base URL, one API key, and code that already speaks the OpenAI or Anthropic API continues to work. Practically, this means a lower cost per request for equivalent work, because you are matched to the cheapest provider that meets your stated requirements rather than paying a fixed published rate. It means more predictable spend, since a price limit is fixed before the first token and you are billed only for produced tokens, with itemized billing showing what each request cost. It gives you leverage over supplier choice — model, region, speed and quality are yours to specify — while independent benchmark results keep providers comparable. And because prices move with supply and demand, public price history gives you the information needed to time workloads when prices are low. Concrete workflows follow from those capabilities. A developer using an agentic coding tool such as Claude Code, Codex, Cursor, OpenCode, Cline or Pi can point the tool at Liquid Inference by changing the base URL, keeping their existing client while gaining access to every listed model. A team planning spend can pick a model and a monthly output volume in the cost estimator and see the lowest offer now and the estimated cost per month. A workload that is price-sensitive rather than latency-sensitive can be scheduled using public price history when prices are low. A user who needs multi-modal support can send those requests through the same single key. And anyone already running inference on their own models can join as a provider, post a price and be paid for the tokens they serve. For buyers, sign-up is free with email and the first 500 users receive $20 of free inference; referring others earns 20% of referred fees as free inference, with 10% for second-level referrals. Access is through an API key and the base URL https://router.inference.ai.exchange/v1. The documented list of compatible clients and SDKs includes Claude Code, Codex, Pi, Oh My Pi, Cursor, OpenCode, Cline, Roo Code, Kilo Code, Goose, Open WebUI, Cherry Studio, n8n, the OpenAI SDK, the Anthropic SDK, the Claude Agent SDK, the OpenAI Agents SDK, the Vercel AI SDK, LiteLLM, LangChain and curl. On the supply side, anyone running models can create an account, register the deployment they want to serve, have an operator review and list it, then connect their agent and start quoting. Registration as a provider is free, and Liquid Inference charges its fee to the customer, not to the provider. In short, Liquid Inference replaces fixed inference pricing with a per-prompt auction in which providers compete to serve your request at the lowest price that still satisfies your rules. You keep the models and tools you already use, gain a price cap before the first token, itemized and predictable billing, benchmark-backed provider trust and public price history, while providers gain a live market for their spare capacity. The value proposition is straightforward: complete your AI requests and pay the lowest offer that meets your requirements.