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By Emma Yanyang Kong , JJ Tan , Ishan Gupta , Lars Olds , Claire Campbell , David Fagnan , Ratna Kavuri , Rohan Gosain , Veli Balin , Minsu Jang , Louis Garcia Introduction Any team generating text with an LLM at scale hits the same problem. You cannot evaluate everything manually. You don't know which outputs are…
GitHub’s context aware secret classifier improves detection quality, raises weighted acceptance by 11.5%, and reduces median scan execution time by 86%. The post GitHub’s model for secret detection: Finding more secrets with less noise appeared first on The GitHub Blog .
Once AI is in production, code arrives faster than it can be reviewed, token costs rise, evals have to be good enough to act on, and agents need permissions and guardrails. QCon AI New York 2026 (December 15 16) has confirmed sessions from engineers sharing how they handle these constraints. By Artenisa Chatziou
Ryan is joined by Yanbing Li, Chief Product Officer at Datadog, to talk about applying observability to non deterministic AI agents, blurring the boundaries between software development and production workflows, and navigating emerging challenges in AI security and tokenomics.
A maturity model for taking agents from an impressive demo to a system people can depend on — with a self assessment and the map to a deep dive on each layer.
Your service can be 100% up and still quietly approving the wrong things, burning its budget, or failing over into untested quality. Level 5 is the infrastructure that lets you see your decisions, bound your spend, route and fail over between models, kill bad behavior in seconds — and the platform that makes all of…
We’re launching ReviewBench, a benchmark for code review agents built on representative GitHub pull requests, multi source ground truth, calibrated evaluation, and production aligned metrics. The post ReviewBench: An open benchmark for AI code review appeared first on The GitHub Blog .
TL;DR We integrated Helion into vLLM’s linear backend to explore how an autotuned, high level kernel DSL can improve LLM inference performance while reducing kernel implementation complexity. A single Helion general... The post Building a High Performance and Portable vLLM Linear Backend with Helion appeared first on…
Posted by Simona Milanovic, Developer Relations Engineer As Android developers, you have many choices when it comes to the agents, LLMs, tools, and command line interfaces (CLI) you use for app development. Our goal is to help you build beautiful, high quality Android apps, no matter how you choose to build. We’re…
It's simply impossible to not hear about Jev. Seemingly everyone is tinkering with it in some way, from using it to make trading decisions (what could possibly go wrong?) to generating UIs with it (maybe we're onto something here!) . Jev is a new kind of AI model. You feed it data and ask it a set of multiple choice…
Petr Viktorin gives an update on the Developer in Residence role and asks Python core developers for projects to prioritize
Hello! Recently I needed bike lights for my bike. And I remembered that I already had rechargeable bike lights that I bought ten years ago, that I hadn’t tried in a long time. I tried to recharge them, but after fully charging them, they only worked for maybe 5 minutes before they turned off again. I don’t know much…
How we built zero config Agent Tracing for Laravel AI, from AI code review to matching LLM calls with HTTP requests.
Posted by Matthew McCullough, VP, Product Management, Android Developer When we first launched Android Bench, we built a rigorous foundation for evaluating how large language models (LLMs) assist developers with real world Android tasks. As AI models and agents rapidly evolve, we’ve been updating our methodology,…
Part 1: Parsing, chunking, and vectorization Some time ago, we set out to build the best semantic code search platform we could: a RAG pipeline that gives LLM agents precise, citable evidence from real repositories instead of whatever grep happens to surface. The eventual solution was Air Context. We got it working,…
See how Sentry engineers used AI Conversations to debug a natural language search assistant and fix a tricky query generation bug.
Adding model tokens, chat Projects, MCP controls, and more.
We are allowing people to deploy code on Railway without an account, read how we made this new development happen.
Keep AI agents useful without combining their riskiest capabilities.
MDN's MCP server brings MDN's documentation and browser compatibility data directly into your editor or IDE, giving your LLM or coding agent access to accurate, up to date web platform information.
