More rules should mean better output. That's the intuition. I spent weeks building a comprehensive CLAUDE.md — 200 lines covering naming conventions, security rules, error handling, architectural patterns, import ordering, type safety requirements, and more. I was proud of it. I'd thought through every scenario. Then I scored the output. 79.0 / 100. My carefully crafted documentation was actively
At 100 million 768-dimensional embeddings, the gap between top-tier vector search tools isn't just measurable—it's existential. In our 6-month benchmark across 12 hardware configurations, FAISS 1.9 delivered 4.2x lower p99 latency than Chroma 0.6, while Pinecone 1.6 cost 11x more than self-hosted FAISS for equivalent throughput. Here's what the numbers actually say. What Chromium versions are ma
If you’ve ever waited 12 seconds for a git clone of a 5GB monorepo behind a corporate firewall, you know the cost of poor Git server performance: $47k annual productivity loss for a 50-person engineering team, per our 2024 internal benchmark. For 15 years, I’ve tuned Git infrastructure for teams from 4-person startups to 10k+ engineer orgs, and the debate between lightweight Gitea and feature-heav
Benchmark CI/CD in Docker 25 vs Cilium: What You Need to Know Modern CI/CD pipelines demand high performance, low latency, and reliable networking. Two technologies often at the center of containerized workflow discussions are Docker 25 (the latest major release of the ubiquitous container runtime) and Cilium (the eBPF-powered CNI plugin for Kubernetes). While they operate at different layers of
Have you ever looked at code you wrote six months ago and thought: "Who wrote this monster?"? Relax, it happens to all of us. In software engineering, writing code that a machine understands is the easy part. The real challenge is writing code that other humans (including your future self) can understand, maintain, and scale. This is exactly where Software Design Principles come into play. In this
Part 1 of 5 in The New Engineering Contract — what it means to lead engineers when AI is doing more of the coding. SWE-CI tested 18 AI models across 71 consecutive commits. Most broke something on commit 47 they'd already broken on commit 1. That's not an intelligence problem. That's a learning system that isn't learning. A paper made me uncomfortable this month. Not because of what it found about