Auto-aggregated global tech articles · 196 posts
The line came out of an essay about the app Cairn, at the end of a paragraph about visual restraint, and it was meant to stay there. The walls don't talk. Cairn is a room; you walk through it and notice what's there, and the walls carry color and texture and they don't address you. I wrote it to des
Every AI code review metric you track measures the hour before merge, and that is precisely the hour when the least information exists. Acceptance rate, test pass rate, and review approval all describe how a patch looked in isolation, not how it behaves under real traffic. Revert rate is the only si
A while ago I posted openGym here a self-hosted gym and body-weight tracker built with React + Node. It runs in two Docker containers, supports passkeys, has an Android APK, 14 languages and is AGPL licensed. The most common request I got since then was basically: "can you add some kind of coaching?
A developer needed a small notification service. GitHub pushes had to become short messages in a private chat. The budget was zero, and the tolerance for maintenance was lower than the budget. The obvious options were a hosted function or a cheap VPS. Both cost something, and both came with account
I've been building AI agents for a while now. Not chatbots. Not RAG demos. Real agents that take actions, make decisions, and run autonomously. Here's what nobody tells you. The gap between demo and production Every AI agent framework shows you a 5-line demo that works perfectly. Then y
I honestly am so confused rn as the ml community is overburst with people only caring about building rag modules and agentic ai for larger corporations. I have a passion for machine learning but honestly it feels really confusing as to what really counts today. I would love some advice on what exact
Our in-product RAG chatbot has two halves. The first is retrieval — hybrid BM25 + bge-m3 + a fine-tuned reranker, running locally as native .NET/ONNX, with zero API surface to anyone. The second is generation — turning the retrieved chunks into an answer, or into a proposed metadata change. That sec
Almost every AI lab now hands out a free tier. Google, Groq, Cerebras, Mistral, Cohere, NVIDIA, Cloudflare, OpenRouter, and a couple dozen more. Each one on its own is small. A few million tokens a month, a few thousand requests a day. Stacked together, they turn into something you can actually prot
Ryan chats with Greg Jennings, VP of Engineering for AI Products at Anaconda, about what it takes to build a secure-by-default AI coding agent, why prompts shouldn't be treated as strict security guardrails, and how Anaconda is using strategic acquisitions to secure the AI software supply chain.
I’m the author of a new preprint on repeated-query auditing of LLM brand recommendations, and the founder of Rankfor.AI. The practical question: how many times should we repeat a prompt before comparing results? The paper applies generalizability theory: estimate variance components from a pilot, th
FastDB is an (persistent) in-memory key/value store in Go. I made some updates to comments I've got (A big thank you to ShotgunPayDay) Test coverage : 96.54% https://codeberg.org/marcelloh/fastdb A test does the following: - Populate 1,000 items with rdom.Intn(1000000). - Inside for range b.N, exec
METR has been tracking one number since 2019, and it is not a benchmark score. It is a duration: the length of a task, measured in how long a human expert needs, that a frontier agent can finish on its own. The headline result from their NeurIPS 2025 paper is that this duration has been doubling ro