Ian Swanson

The FireHydrant acquisition of Blameless signals a major shift in observability and incident management. My Partner Jaya and I just wrote about the looming "observability crisis"—how the explosion of data and tools has paradoxically made it harder to extract meaningful insights. This deal is a direct response to that crisis. By combining forces, FireHydrant and Blameless aim to build an end-to-end incident management platform to reimagine root cause analysis from the ground up. But to *truly* address the observability crisis, it will take more than consolidation. We need AI-driven systems that: → Provide natural language interfaces to query complex systems → Automatically generate and update incident response playbooks → Offer predictive maintenance by recognizing patterns across heterogeneous data sources → Synthesize post-mortem reports, distilling insights from terabytes of data Most critically, we need to ditch volume-based pricing models that incentivize data hoarding over problem-solving. Expect more moves as the market evolves to meet the demands of increasingly complex enterprise systems.

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Apple’s Private Cloud Compute and the business of privacy With Private Cloud Compute, Apple has gone to extraordinary lengths to safeguard user privacy with AI-related tasks. In doing so, Apple has surely also incurred an extraordinary expense. How can Apple justify the cost of providing PCC if it doesn’t directly monetize its Apple Intelligence features? Either through the improved brand perception captured in a broader privacy narrative or by monetizing PCC with other non-AI use cases. While the narrative explanation is clear, the more interesting theory about Apple’s motivation in building PCC is that it can be expanded to other use cases that can be directly monetized. The first alternative use case that springs to mind is advertising attribution. PCC sounds conceptually similar to a data clean room, albeit far more sophisticated than most implementations, which merely use IP addresses and other identifiers to match publisher-observed ad interactions with advertiser-observed conversions. While the PCC nodes seem hardware-optimized for processing AI-related tasks, other nodes could be built that aren’t.

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➰ APIContext's latest insights on cloud-based API performance have been featured on Nordic APIs! In the 2024 report, we delve into crucial aspects affecting API efficiency: 🔐 Authentication is key. Ensuring robust authentication mechanisms in APIs is crucial, yet offloading this function can introduce complex challenges. 📖 DNS configurations matter. How you handle DNS settings can significantly impact API performance, particularly when tied to authentication processes. 🌐 The role of third parties. Critical third-party services play a significant role in 🔗 API transactions, underscoring the need for careful integration and monitoring. ⛅ Not all clouds are created equal. Differences in cloud services can drastically affect API performance, an essential consideration as APIs account for over 80% of all internet traffic. #APIs #CloudComputing #TechInsights #APIContext

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We're launching the inaugural edition of Beyond Benchmarks, a deep dive into the metrics and trends observed across the early-stage enterprise cloud market. This is the first look at some of the business data around GenAI in SaaS. Some initial observations: 1) 60% of SaaS companies have already integrated GenAI into their offerings (60% of these as part of their current product and 40% as a new product), and an additional 20% are planning to do so this year. Almost half aren't monetizing these features. Those that are are primarily experimenting with both usage-based and flat fee pricing models. 2) Most companies are utilizing OpenAI as their primary LLM, but many are experimenting with multiple models - and we are noticing an early trend towards intelligently routing GenAI inference requests to different models based on cost, performance, and security. 3) Though we are in the initial innings of GenAI, we are seeing some positive signals - companies that implemented GenAI had 7% higher NDR than those that did not. Still early days, but we'll be doing this benchmarking report regularly to track and share how things progress in this new era. Link to report in comments.

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Worth reading: How the Apache Arrow Format Accelerates Query Result Transfer by Ian Cook One of the hidden problems of intersystem communication is transcription of data formats. Ian does a wonderful job of explaining the "why" of Apache Arrow as well as the "how" of its advantages. Read on...

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🚨 Is California’s AI Regulation Going Too Far? SB 1047 is making waves across Silicon Valley, and the entire tech world is paying attention. While it's clear we need safety regulations to keep AI from causing harm, is this bill overdoing it? 🤔 The big tech companies are raising alarms about the potential for increased legal liabilities ⚖️ and how these regulations could stifle innovation 🛑. They’re concerned that heavy-handed rules will slow down progress and put the U.S. behind in the global AI race. And it’s not just the tech giants feeling the squeeze—smaller startups are also on edge. For them, vague testing standards are like an added weight that could make it harder to innovate and compete. 🚀 Of course, there’s the other side. AI safety is no joke. I’m all for responsible AI, but regulations like SB 1047 seem like a step too far. Are we at risk of over-regulating AI in a way that might protect us now but choke creativity and progress in the future? What do you think? Should we push for more balanced regulation that keeps AI development alive and well while still protecting people? Drop your thoughts in the comments!👇 #AI #Innovation #SB1047 #TechRegulation #SiliconValley #AIsafety #AIregulation #CaliforniaAI #ResponsibleAI #AIethics #TechInnovation #AIoversight #AIrisk

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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

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Despite the disdain for "GPT-wrappers", LLM-driven apps is the layer where value is accruing. This is cuz evals are the differentiator & not easily replicable, unlike AI models. The reason why OpenAI pursues regulatory capture is cuz the moat is shallow, not cuz of existential risk of humanity. In Clay Christensen's lesser known "Law of Conservation of Modularity", he spells this out, summarized and outlined by Stratechery. https://lnkd.in/gt-2isr9 So if you don't want your lunch eaten. Get started on your evals today, and pick up a copy. https://lnkd.in/gxwnHVND

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We keep building the largest crypto exchange, powered by its community. 🚢 Proud of everything the dYdX Chain ecosystem has accomplished so far. Highlights: - Launched the only fully open-sourced perpetual DEX on a sovereign decentralized app chain - Decentralized the order book and matching engine - Open-sourced web app, iOS app, and Android app - Migrated users, liquidity, and volume from v3 to v4 - Shifted 77% of DYDX supply from Ethereum - Engaged community voting on 63 proposals - Doubled the number of markets in 6 months - Integrations with many partners The v5 upgrade is the biggest software upgrade since the launch of dYdX Chain: - Unlocked 800+ new isolated markets via Slinky - Isolated margin trading to keep risk in check - Go long or short on your favorite Solana tokens - New vaults primitive supporting liquidity in new markets - More collabs and partnerships coming in hot

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AI memory is fundamental for conversational AI and AI agents. It is critical for building personalized experiences and improving reasoning. Check out Letta’s course to understand how to use AI memory to enhance the customization and quality of your AI app.

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New Metrics Utilizing Universal Laws: Power Law https://lnkd.in/gvzr3Htx

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Hard-coded model routing takes away intelligence from your system instead of adding to it! Interesting read below on how Ema Unlimited's Fusion model is a potent solution for the heterogeneous multi-model future of AI in the enterprise.

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You down with MCP? Who’s down with MCP? Just came across the Model Context Protocol (MCP) from Anthropic. 😎 Think of it as OAuth for AI agents, defining and controlling how they access tools and data. Or maybe a new syslog for AI agents, logging every interaction for auditing and anomaly detection. Either way, feels like the start of something important in security. Here’s why I’m paying attention: 1️⃣ Momentum: Anthropic not only created MCP, but they’re spending big resources to define how “Agentic architectures” should work. 2️⃣ Structure: MCP gives us a framework to monitor and audit AI interactions, a critical need as these systems scale. 3️⃣ Transparency: By focusing on data connectivity, permissions, and access control, MCP could open the door to the next gen of AI security tools. 4️⃣ Consistency: Predictability matters in security, and MCP’s structured approach could make it easier to validate and trust these systems. What could people actually do with MCP? I dunno, maybe build an AI monitoring product, audit agents for compliance, use it to change permissions, see the data they're accessing, log activity for misuse, etc. It feels like one of those early signals of a new "inspection point", like eBPF for containers. Anyone in cybersecurity focusing on this layer?! https://lnkd.in/ey3X9eVV https://lnkd.in/e-82jcWy #cybersecurity #AI

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Thanks for sharing these insights. What I am seeing is that orgs test an idea using a proprietary #LLM like #OpenAI, then put it into production with an open source LLM using a development platform like #Llamaindex. That way, private data remains private.

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5️⃣ 🇪🇺 🚀 early stage rounds to pay attention to this week 👇 1️⃣ Dash0 🇩🇪 (Tech / Observability Platform) – Dash0 is redefining observability with its OpenTelemetry-native platform, providing transparent, predictable pricing and eliminating vendor lock-in for businesses aiming to monitor distributed systems efficiently. 💰 Raised $9.5 million in seed funding from Accel, Dig Ventures , and angel investors like Guy Podjarny and Guillermo Rauch. 2️⃣ Tranched 🇬🇧 (Fintech / Asset-Based Financing) – Tranched automates the securitization process using a blockchain-based platform, making asset-based financing more transparent and reducing traditional costs by up to 90%. 💰 Raised $1.96 million in seed funding from Blockwall, Kima Ventures, Speedinvest, and Andreessen Horowitz. 3️⃣ Pemo 🇦🇪 (Fintech / Spend Management for SMEs) – Pemo’s platform streamlines financial operations for SMEs across the MENAP region, integrating invoice payments, expense management, and corporate cards for real-time cash flow insights. 💰 Raised $7 million in a pre-Series A led by Augmentum Fintech and Shorooq. 4️⃣ Prelude 🇫🇷 (Tech / SMS Verification) – Prelude enhances user authentication by offering fraud-resistant SMS verification, reducing costs and improving security for businesses worldwide. 💰 Raised €7.6 million in seed funding led by Singular and Seedcamp. 5️⃣ In Parallel 🇫🇮 (Tech / AI Strategy Execution) – In Parallel’s AI-powered Intelligent Operating Model allows organizations to test, monitor, and adapt strategies in real-time, bridging the gap between strategy formulation and execution. 💰 Raised €2.8 million in seed funding led by Lifeline Ventures with participation from Notion Capital.

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Regulatory oversight can provide short-term stability and protection, but if not carefully planned, it can often stifle innovation. The SEC's regulation of #ICOs in 2018 is a prime example. The increased regulatory burden and uncertainty led to many ICOs excluding US investors and positioning themselves as foreign offerings to avoid SEC jurisdiction. This shift limited access to capital for US-based early-stage startups and created new challenges for companies seeking to raise funds through ICOs. Most importantly, the lack of clear guidance made startups hesitant to explore new financing models, fearing regulatory repercussions. It's ironic that a #gambler can lose their house in minutes in #Vegas without the same level of scrutiny and regulation on #casinos! And kudos to Office of California Governor Gavin Newsom for not getting too excited or bowing to pressure! If the government wants to be part of innovation, they should offer grants and non-dilutive funds to startups, just like #Europe and #Canada do with - SR&ED Tax Incentive Program - AgriScience - NRC-IRAP - EU Grants for Digital Health and Technology Startups - IIA Investment Programs and many more! This way, they can keep an eye on startups and disruptive solutions as they submit their progress each year to qualify for more funding. Let's support our startups the right way! #InnovationBeforeRegulation #AI PBS KQED NPR BBC CNN

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LLMs are not vast, infallible databases. As models grow larger so does their tendency to confidently state falsehoods (hallucinations). Matei Zaharia, co-founder & CTO of Databricks, warns against treating LLMs as internet-scale encyclopedias and proposes we focus on what they excel at:  → Reasoning → Interpreting language → Understanding context For founders building AI applications, treat LLMs as powerful reasoning engines and not fact repositories. Build your knowledge base separately, feeding the LLM with trusted up-to-date information through retrieval mechanisms and tool-calling. This approach lets you harness the LLM's strengths in language analysis and generation while sidestepping the risks of its unreliable memory. Building with AI? Share your approach to balancing LLM reasoning power with factual accuracy in the comments. Let's hear it!

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