Jeremy Olmsted-Thompson

I recently discussed with Adam Tornhill, Markus Borg and 🌀 Luca Rossi how we’ve been integrating the excellent CodeScene tooling into our agentic development workflows here at loveholidays. We see the outcomes which they predict in their most recent research. Our deployment cadence improves as ai adoption grows, along with improving code health. In addition, the quality of agentic commits made by our ai ready teams now starts to outperform their human-only commits. Video: https://lnkd.in/edcQ8cyf

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At face value the throughput acceleration AI enables feels like progress, faster pipelines and more commits shipped. When you look more closely however, the data reported in the 2025 DORA Report points to a harsher truth... we are moving faster with less control. The metrics behind instability, change fail rate, rework and recovery time are all trending upwards. More often than not, AI is simply automating in failure. The 2025 DORA Report surfaces four painful realities of embedding AI into your SDLC: - AI boosts throughput at the cost of higher deployment failure and rework. We are codifying instability into our pipelines. - Burnout remains flat. Productivity gains are absorbed by higher business expectations, not returned to developers. Faster delivery means faster deadlines. - Over 80% feel more productive with AI. Yet the research shows developers who slowed down by 19% still believed they were 20% faster. Perception of acceleration from AI usage is often just a placebo effect. - The AI Capabilities Model makes value contingent on seven systemic investments, including platforms, data ecosystems and strategy. For many organisations this means a multi-year transformation before they can get close to extracting real value from AI tooling. AI is not a silver bullet, it magnifies the strengths of already high-performing teams, while compounding the weaknesses of others. It is becoming clear that if your team and organisation are already practicing engineering excellence and have a strong, continuously improving product culture, you will benefit the most from AI adoption. If your organisations maturity is low, you are better off investing in foundational changes to your ways of working and culture first.

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For understanding the virtual thread state transitions... Specifically, thread pinning on Java 21 vs. Java 24 and when the thread waits on IO how the platform thread views the state compared against the virtual thread enabling interruption

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Proud to share this post from Akash Mittal, a brilliant engineer on our team! Learn how Verkada engineered a custom vector database from the ground up, our own inference engine at scale, and a very scalable pipeline to enable natural language search across more than a million cameras. It's awesome to look at all the challenges and innovations behind scaling our AI-powered search for real-world use cases we went through in less than a year! Also stay tuned for the next part coming soon: https://lnkd.in/g6S2zyip

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The Claude ecosystem is exploding faster than I expected. My newest favorite plugins: - Get Shit Done (orchestration layer that plans and builds apps phase by phase) - Ralph Wiggum Plugin ( iterative, self-referential AI development loops) - n8n-MCP (direct Claude integration with 1,000+ automation nodes) What's interesting is how quickly we've moved from simple AI coding assistants to full orchestration platforms. The shift feels similar to what happened with CI/CD tools a few years ago. First you had basic scripts, then comprehensive pipelines, now full DevOps orchestration. We're seeing the same evolution with AI coding tools, just compressed into months instead of years.  #AI #Claude #SoftwareDevelopment #AIAgents #DevTools #Automation

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Clay doubling down on increasing action costs while a lot of gtm-e world is laser focused on BYOK workflows with Claude Code is an interesting tension. In the last ~week Cargo shipped a CLI, Apollo.io released an MCP connector for Claude & most of my feed is Claude Code. The ecosystem energy feels entirely focused on terminal-first. I’ve loved Clay for orchestration but have increasingly felt that the UI/UX is slowing me down as I ramp up my terminal-first workflows. That said, terminal-first isn’t multiplayer yet and isn’t in it's final form - esp for larger companies where multiple humans benefit from a shared UI. What’s interesting is this is coming from the people who coined “GTM Engineer” in the first place. The early adopters Clay built the category for are the ones feeling the pull away from the orchestration layer Clay monetizes. The dispersion is probably temporary. But if Clay’s pricing move take them further from the early adopters who built the category, that’s a deliberate bet to cross the chasm to enterprise, while some subset of builders build elsewhere. Interesting to watch.

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Measuring developer productivity is complex. At Meta, the team has been taking a rigorous, data-driven approach and even building new infrastructure to support it. Kudos to Payam, Pavel, and the entire team for this impressive work and presentation. Over the past year, my main focus has been on building AI coding agents, so this really resonates. Check out the section about Devmate at 6:52 in the video to learn more on what I've been up to. A few highlights: - Thanks to Devmate, Meta’s developers are more productive - with Diffs per Developer per Month (DDM) improving by 6–12% (a conservative estimate!). - Devmate tracks AI code attribution at the character level, allowing Meta to know precisely which code was generated by Devmate vs. written by humans.

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Iteration is at the heart of innovation. We built global scale feature flagging system to support our innovation at the pace of 300M+ evals per second. Ben Congdon and team just wrote a new blog post describing the journey and why building in house was required for our environments. Link in comments

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Google Changed RAG with NEW Gemini File Search Tool (RAG Made Simple) 5 Days Ago, Google Took a Big Step Forward in the RAG Ecosystem Google has launched the File Search Tool, a fully managed Retrieval-Augmented Generation (RAG) system built directly into the Gemini API. This new capability abstracts away the complexity of retrieval pipelines, offering a simple, scalable, and developer-friendly way to ground Gemini models with your own data. Key Highlights: 1. Fully managed RAG system: No need to build or maintain custom retrievers or vector stores. 2. Integrated & scalable: Works seamlessly within the Gemini API. 3. Improved grounding: Delivers responses that are more accurate, relevant, and verifiable. 4. Cost-efficient billing: Embedding generation at query time is free; you only pay for initial file indexing at $0.15 per 1M tokens. This launch marks a major leap toward making enterprise-grade RAG systems accessible to all developers, empowering teams to build intelligent, data-grounded applications faster with precision and transparency. #GenAI #GoogleAI #Gemini #RAG #AIInnovation #LLMs #AIEngineering #GeminiFileSearchTool

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Ask AI how OpenClaw can automate workflows in your business, then send me a message and I'll get that all setup for you. Setting up an agentic system can be tricky. Northstar AI Labs has been deep in the weeds, developing AI solutions. If you have heard of AI automation, but you don't want to DIY, a hassle free installation is just 1 message away.

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Developing a large ecosystem with both open- and closed-source components comes with some interesting challenges. Back when we were bootstrapping CKEditor, my purist mindset pushed us to develop each of our 30+ packages in separate repositories. The goal was to sort of "force" higher modularity and more responsible architecture for every component. As you can probably guess 😅, it did not scale. Later, we consolidated most of the packages into two monorepos – one open source, one closed source – while keeping a few granular packages separate where it still made sense. This helped a lot, but it didn’t fully solve the problem. We were still working across multiple repositories and kept running into issues like dependency coordination and lockfiles. Recently, we took another step forward. We migrated to pnpm and redesigned our overall approach. In this post, Filip Sobol explains the new setup implemented by the CKEditor 5 Platform team and what we learned along the way: https://lnkd.in/eHS28qxe

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I don’t fully agree with framing this as “promotion-driven development.” That label feels reductive and misses where the real failure usually sits. Participation measures effort. Visibility measures the presence or comfort of being seen (or, worse, the tendency toward extraversion/meeting count tolerance). Artifacts measure activity. None of those reliably measures whether anything actually improved. Impact does, which is why it has to be the primary signal. The problem is that impact is not applied consistently as the primary metric at more senior levels, for both ICs and management. If you see five overlapping chat products inside one company, that’s not staff engineers chasing promos. That’s a failure of VP- and SVP-level leadership to converge on strategy, make hard calls, and stop duplication. Likewise, when a principal engineer builds a bespoke observability system instead of selecting an existing open-source framework, extending an internal platform, or influencing one to support the use case, that’s not “gaming the ladder.” That’s a miss on the principal-level impact, which explicitly includes leverage, reuse, and ecosystem improvement. If we applied that same impact lens evenly across all levels, a lot of what gets labeled as “promotion-driven development” would resolve itself.

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We shipped automatic snapshot expiration for R2 Data Catalog today. With this new feature you can define retention policies (age thresholds and minimum snapshots to keep) so old snapshots are cleaned up automatically, reducing metadata overhead and storage costs while keeping table performance healthy. https://lnkd.in/eAD5rKd8

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A lot shifted in 2025. AI made some things easier and other things more complicated, and it pushed teams to focus more on resilience. The 2025 State of the Internet Year in Review breaks down what stood out most this year. https://lnkd.in/eh2eZXfA

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With AI, according to all of the marketing materials from all of the companies, including Meta, SWE not required anymore and you can just scale your engineering up by using LLM models alone... Yet: - Meta cannot keep Desktop app for their own Facebook Messenger alive - https://lnkd.in/ekKTbEfG keeps growing and Google cannot continue to develop already released products - Windsurf cannot keep up and to have their IJ plugin for Remote Development in sync with their non-RD plugin https://lnkd.in/eeMjrZ_6 - Claude IJ plugin is also far from perfect What has happened to "You need to practice what you preach"?

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Platform engineering is the future 🚀 Developers are at the core of everything we do at PagerDuty 💚 Our latest collaboration with Spotify for Backstage brings PagerDuty’s industry-leading Incident Management directly into Backstage, the open platform reimagining developer experience. With the new PagerDuty Plugin for Backstage, devs can see incidents, on-call context, and service ownership right where they already work : Less dilution ; More focus. Let’s go 🎉 Read more 👇 https://lnkd.in/gnsq8Nr9

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How to disrupt an industry with AI? On the latest Hard Calls, Trisha Price sits down with Vantaca CEO Ben Currin to explore how they’re reshaping community management with AI-powered workflows.

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This is a huge update that anyone who cares about workload identity or agentic security should be paying attention to. Kong Mesh is the first and only service mesh to natively act as a SPIFFE identity issuer, to interoperate with an existing SPIRE deployment, or to federate between two. This vastly improves interoperability in hybrid and multi mesh deployments. Great work Kuma team! We'll be sharing more about how this can help solve for modern enterprise security challenges in a future post.

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