Farah Ali

Anthropic's latest Claude AI model is taking human-AI interaction to a whole new level! 🖥️🤖 The new Claude 3.5 Sonnet can now use computers just like a human would - by looking at the screen, moving the cursor, clicking buttons, and typing text. This is a major leap forward in AI capabilities. Key features: Operates based on screenshots, emulating human computer use Can perform complex multi-step tasks across different applications Self-corrects and retries when encountering obstacles2 Accessible via API for developers to build with Why it's game-changing: This opens up incredible possibilities for AI assistance in data entry, form filling, software testing, and automating repetitive computer tasks13. Imagine having an AI assistant that can seamlessly work alongside you, handling tedious computer work while you focus on higher-level tasks. While still in beta, this technology demonstrates how AI is rapidly advancing toward more human-like interaction with digital interfaces. As it develops further, it could revolutionize how we work with computers and boost productivity across industries. The future of human-AI collaboration just got a lot more exciting! What do you think about this new capability? #AIInnovation #FutureOfWork Here is a video about it: https://lnkd.in/gh8aNYEu

6

Reimagining Healthcare Workforce Platfforms: Turning Capital Into Labor The future isn’t just about filling shifts. It’s about scaling labor intelligently, driving better outcomes for systems, staff, and patients alike. Andreessen Horowitz and Alastair (Alex) Rampell reinforced this Vision in a recent post and it applies to the next phase of healthcare workforce evolution. Healthcare workforce marketplaces plus AI are at the tipping point of transformation, evolving into systems of record that don’t just manage labor—they optimize it. Much like EHRs centralized patient care data, these marketplaces can centralize workforce operations, aligning talent supply with clinical demand. But there’s a bigger opportunity here: turning capital into labor. Here’s what I mean. Historically, industries (including healthcare) have focused on enterprise computing tasks—streamlining data entry, compliance, and back-office functions. While valuable, this approach often stops short of transforming the real-world, repetitive processes that make up the bulk of work in healthcare. Think beyond enterprise software. The next frontier lies in leveraging embodied AI and workforce marketplaces to automate and enhance labor-driven processes, transforming capital investments into productive labor outputs. For healthcare, this means turning staffing costs into scalable, dynamic systems that deliver value far beyond filling shifts: 1. Dynamic Labor Matching: Capital invested in marketplace platforms enables real-time matching of labor supply with patient demand, replacing outdated scheduling models with predictive, automated systems. 2. Workflow Automation: From credentialing to compliance and shift bidding, embodied AI can streamline traditionally manual tasks, freeing human resources for higher-value work. 3. Labor as a Strategic Asset: Workforce marketplaces shift the focus from static staffing budgets to proactive labor deployment, ensuring that every dollar invested in labor drives outcomes and efficiency. It is about the billions of repetitive, planned tasks that AI can improve. In healthcare, that means automating everything from staffing logistics to clinical task assignments, creating a horizontal system of engagement for labor (think Zendesk) that integrates seamlessly with vertical, workflow-specific platforms (think Toast for staffing operations). The result? Hundreds of billions in untapped market potential—and a healthcare workforce model that transforms how care is delivered. By turning capital into labor through smarter marketplaces and automation, we don’t just solve staffing challenges; we redefine what’s possible in healthcare operations and ShiftMed is leading the way with health systems moving beyond tired models. #capitalintolabor #systemsofrecord #healthworkforce #aiworkforce #healthsystems #hospitals #cno #hospitalceo #healthcareinnovation

11

2 Comments

Some great Roblox insights on this recent Deconstructor of Fun podcast: https://lnkd.in/gjMvFPUQ A few key points surfaced when talking to Voldex CPO Andrew Rose: 1) Genre innovation hits different on Roblox: we're inherently social, and genre mashups that lean into social interactions tend to retain players better. 2) The power of Roblox's community driven development to help any creator get started. 3) Roblox is multi-platform by design, and games that can easily be experienced across mobile, pc, console (and even VR) thrive. Great podcast if you're curious what's driving success on Roblox. #roblox #gamedevelopment

24

Hey everyone, I am an investor and advisor to Katanemo.... They are reinventing the infrastructure primitives for GenAI and launched Arch - https://lnkd.in/gCrbbkEV - an intelligent prompt gateway. Give the project a look on Github, and star the project if it resonates

88

3 Comments

First story on what we have been up to at Augment. Much more to come! https://lnkd.in/gC6gbADA

370

64 Comments

Fantastic progress Hadi Javeed and RevelAi Health team. Your aspiration to realize fully autonomous AI agents is great. Healthcare ecosystems are complex. Even after developing these agents, establishing confidence in them would take a while. Human-in-the loop, continuous monitoring, and revisions would be indispensable. You have rightly called out in your blog "By involving healthcare professionals in our iterative processes, maintaining a human-in-the-loop approach, and building strong feedback mechanisms, we aim to de-risk our systems while maximizing the value they bring." #healthcareTech #innovation #AI/ML

2

1 Comment

Enums enhance code readability and clarity. They define named values for model attributes, making them ideal for representing statuses, stages, and states. An article from the team at reinteractive explores the powerful features of enums, particularly how they make code readable and self-explanatory … https://buff.ly/4cKGWiu #RubyOnRails #Programming #Enums #reinteractive

1

The most downloaded datasets from the California HHS site? If you follow me on LinkedIn, you've probably guessed it - the Medi-Cal enrolled provider list and Suspended and Ineligible list are the top two, followed by other datasets by a wide margin. The California Health & Human Services Agency (Cal HHS) has a nice data hosting site that resembles data.cms.gov. The site looks slick and provides interesting insights, including visitor statistics and download metrics. It's fascinating to see how people are interested in provider eligibility and exclusion data for Medi-Cal billing. Looking at how organizations use these public datasets, compliance emerges as the dominant use case. This makes sense - when accessing government data portals, users are typically verifying facts related to government programs. The user base spans health plans, hospitals, IPAs, medical groups, and even commercial payers, as Medicaid suspensions often trigger reviews across the healthcare ecosystem. What's interesting is that this pattern extends beyond healthcare. On data.ca.gov, which hosts all kinds of government data, the most downloaded dataset is the directory of certified electricians. This shows a consistent theme - whether it's healthcare providers or electricians, people come to government sites primarily to verify professional credentials and eligibility. This raises an intriguing observation about how government data is used. The way organizations use these datasets - downloading lists to verify credentials and eligibility - is functionally similar to what modern Identity Provider (IdP) systems do, but through a much more manual process. What are your thoughts on the future of government data in healthcare identity verification? #Healthcare #DigitalTransformation #Compliance #HealthTech #GovTech #Identity

24

Crazy to think how fast interfaces will evolve: Screens → Voice → Brainwaves https://lnkd.in/gd_V9YqG

2

1 Comment

Come check out this talk if you want to learn how we built distributed queries for Iceberg + Python!

4

The revoultion in Storytelling coming to us via Generative AI is two-fold. One, we are soon to be able to assemble small, and very talented creative teams (writers, artists, directors, and actors) and create vast universes and intricate stories that while still driven from their center by human hearts, minds, and hands, have the ability to explode into being and even evolve over time at the highest fidelity and in myriad or even blended styles. Two, these narratives will become increasingly personalized, immersive, but still social shared experiences that sustain engagement in ways that give each of us what we love most without compromising the integrity of the storytelling universe, succumbing to the weight of insurmountable costs, or being hindered by the production timeline realities of today. It will be a disruptive shift, but one that can be ultimately democratizing in ways that will eventually encourage and empower artists again and give rise to new talents and new stories of all kinds because the gap between the audience and the creator will close to the point of intimacy. #media #generativeai #storytelling #ai #artists #streaming

11

Clear and rich information on RAG variations. I think this will be useful to most practitioners who have gone beyond the intro and demo workflows, and now need to consider how to refine their flows.

10

Hey folks! Garrett Milster and I have only posted about Sacramento Labs once, so we figured it's time for an update 😊 **What is Sacramento Labs?** We’re a small team focused on launching software products that solve real-world problems. Think of us as a startup lab—constantly testing ideas, learning from feedback, and building tools that matter, and having an impact while doing it. **How do we decide what to build?** We like to build products that: -         Solve real-world problems in mostly ignored industries -         Are small, highly maintainable, and scalable codebases -         Make the world a better place directly, or by donating a percentage of profits to a relevant charity in that space You can learn more about our philosophy at sacramentolabs.com **What you can expect from us** We’ll be posting product ideas that are in various stages of our pipeline: Early Concept, Customer Development, In Progress, Launched, Scaling. We’d love to hear your feedback! **Our latest concept: Self-Hosted Security** Here’s the problem: - Businesses often use third-party URL shorteners to share internal tools, resources, or campaigns, but these services introduce serious risks. Enterprise companies like Google, Microsoft, and Stripe have built their own internal URL shorteners for this reason – but most businesses don’t have the resources to do that. - Phishing attacks are rampant, and links from external services can be spoofed or abused. - Branding and security controls are limited, leaving sensitive information exposed. - You can’t track link activity—who’s clicking what, when, or why. - When employees leave, their shared links still work, leaving potential vulnerabilities. Our proposed solution: Self-Hosted Security. It’s a plug-and-play solution for businesses to host their own branded URL shorteners with: - SSO for secure access tied to your company’s directory. - Link expirations to ensure old links don’t stay active indefinitely. - Audit logs to track activity and improve visibility into link usage. 📢 We need your input! Would this solve a problem for you? What features would make this indispensable? Check out the landing page and share your thoughts: selfhostedsecurity.com Want to chat? Email me at [email protected] 😊 💡 And here’s something we’re passionate about: a percentage of profits from Self-Hosted Security will go to the Electronic Frontier Foundation (EFF). Your feedback not only helps us create better solutions, but also contributes to making a positive impact. Thanks for being part of the journey!

58

6 Comments

I just realized: we should start saying "build memoization" rather than "build caching." We aren't really talking about least-recently-used or time-to-live algorithms, we're talking about "don't bother recompiling this piece if none of its inputs changed." (It has been puzzling me that some people are so deeply fearful of "build cache" as a concept, and honestly I think it's mostly because most build systems have non-hermetic state scattered all over the place. Maybe a slightly more precise term for the type of caching we're concerned with would clarify the semantics in question.)

83

9 Comments

A good use case for using context-caching, specially if you are trying to build RAG on a large cluster of documents. https://lnkd.in/gTEaG_ET

3

At the core of effective engineering leadership is the ability to stay connected with your team. This is perhaps the most challenging aspect because retaining the respect of your people is difficult unless you remain technically involved yourself. In my journey as an engineering leader, staying true to the individual contributor role that originally earned their respect has kept me close. This approach extends beyond technical expertise; it's about maintaining a deep connection with the core activities that propel product development forward at Gearflow. One of my biggest professional fears is becoming irrelevant or disconnected from my team and our work. This fear motivates me to continually reinvent and retool in response to market demands. Next month, I'll be sharing about my adventures generating synthetic data recently. And while I'm no expert when I start preparing for these talks, the process is incredibly valuable. It's less about delivering a perfect talk and more about how the preparation expands my thinking and exposes me to new capabilities. This helps me connect the dots to opportunities at Gearflow that bring real innovation to our customers. Speaking regularly holds me accountable and pushes me to learn ahead of the curve, ensuring I remain relevant and impactful day-to-day. Every talk is a journey of discovery, helping me to lead with humility, communicate clearly, and inspire a culture of innovation through a deep, technical understanding of our work.

20

Hey folks! I'd like to more formally introduce and talk about a few of the projects we're working on towards improving large-scale bioinformatics analyses (one of which was developed during St. Jude's KIDS24 Biohackathon)! If you're not aware, my team started a project called St. Jude Rust Labs (https://lnkd.in/eXY_9dJ8) where we're rewriting a more foundation for bioinformatics analysis in Rust. This cuts across more than just workflow execution, but, recently, we've been primarily focused on improving the experience of working with the Workflow Definition Language (WDL) at scale. To that end, we've released a number of projects, including: - We've written a complete lexer/parser for WDL v1.2 language (https://lnkd.in/eGWHSM2g). Anyone can use this foundation to build tools for WDL. - We've written a VSCode plugin that includes a language server protocol (LSP) for linting and validation directly in your editor (https://lnkd.in/euApG9tF). - And, announcing today, we've started down the road of writing our own WDL execution engine spread across the Crankshaft (https://lnkd.in/et_RFkA3) and Sprocket (https://lnkd.in/eUTuPE68) projects. Crankshaft was prototyped during the St. Jude Biohackathon last week—it's a _headless_ workflow execution engine, meaning that, in theory, others could come along and write drivers for Crankshaft built on NextFlow, CWL, Snakemake, etc. As I said earlier, our team is really focused on WDL specifically, so we're going to continue building out a "head unit" for Crankshaft using WDL. That being said, I would love to see other community projects popping up and using the core machinery for other workflow languages. Thanks to all of the individuals who participated on our Biohackathon team (Kevin Benton, suchitra chavan, Braden Everson, Andrew Frantz, Michael Gattas, Peter Huene, and John McGuigan)!

76

1 Comment

I'm so excited about the problems "traditional" software companies are tackling right now. LLMs let software teams move faster, make sense of larger domains, and built much more complex tech than we were able to even five years ago. I believe the impacts in healthtech, govtech, and fintech will be large. Simultaneously, I'm amazed at all the ways one can lose money investing in AI companies - especially the foundational models. There's no part of this market that isn't trending toward commoditization. This has been true for a while but the release of llama3.1 makes it especially clear: Most workflows can retrain on this open-weight 405B model with no change to their budgets (AWS Bedrock charges *at max* the same as OpenAI) and then find ways to negotiate/optimize that cost way down while not sending their data away to a 3rd party. Even the B2C story is commoditized — what AI wrappers have believable ROI if I can run this one-liner and the hard part is accomplished for a fraction of a cent?: aws bedrock-runtime converse --model-id meta.llama3-1-405b-instruct-v1:0 --messages '[{"role":"user","content":[{"text":"Write me a post enumerating how investments in LLMs may yield no competitive advantage."}]}]' As I look past the flashy parts of AI I see: * Building one-off tools against novel APIs is now (and forever) instant * Analyzing requirements for most tasks and understanding new domains is an order of magnitude faster * GitHub Copilot yearns to write React for us * LLMs give the same boost as pair programming at zero cost I haven't been this excited to build complex products in a long time.

37

1 Comment

LLMs aren't problem solvers, they're eloquent search engines. I've been heavily leveraging AI coding assistants in a personal project for the last few months. In recent weeks, I've been working heavily on a web frontend, an area where this lifelong infrastructure engineer is way out of his depth. Overall, I feel 10x as productive with an assistant as without. But it's far from perfect. I've started to notice a pattern in the good and bad answers I get from the assistant. "Make this text bounce" → awesome. "Make a 4-column layout for these divs" → very good start. "Why is there a scrollbar here?" → terrible. "Explain React's component lifecycle" → awesome. "Why isn't React picking up the updated value for this state variable?" → terrible. "Explain how 'flex' works in CSS" → awesome. "What are popular frontend frameworks and what should I know about them?" → awesome. "Write a test for this component" → medium. "This call generates [compiler error]" → solid. The theme is that it's really good with "knowledge" and it's really bad at "thinking." When my question is just "what CSS property controls this?" it nails it. It doesn't just know the answer (which "old tech" like Google or StackOverflow could help with), but it can contextualize that to my program and integrate it with no trouble. Brilliant. Similarly, if I need a lecture, like "tell me how React works," it's perfect. It gives accurate info, contextualized to my current scope, and can effortlessly handle my follow-up questions so I learn at my own speed. Where it fails is with problem-solving skills. Its contextualization helps it fake a degree of problem solving, but over time it becomes clear that it only really solves problems that it has seen before. When things get truly tricky, it absolutely founders. "Make this scroll bar go away" just gives me random stabs in the dark, which either change nothing or make it worse. It feels like it's basically just saying, "I found 30 half-relevant threads on StackOverflow, let's just try the same stuff they tried until it works!" It's garbage. Contextualizing is not the same as thinking. The corollary is that these LLMs are not prepared to make real problem solvers—i.e. good software engineers—obsolete. They can turbocharge a good problem solver with encyclopedic access to contextualized information and much faster typing speed. But you can't really solve hard problems without a professional problem solver in the room.

903

76 Comments