In Q3 2024, our 12-person platform team slashed log ingestion spend by 35% in 90 days, moving from a brittle Elasticsearch-based pipeline to a tuned Vector 0.30 and Loki 3.0 stack—without losing a single log or breaking our 99.95% SLA. GameStop makes $55.5B takeover offer for eBay (279 points) Talking to 35 Strangers at the Gym (144 points) Newton's law of gravity passes its biggest test (15
The first article on this blog explained how it was built in 30 minutes with Claude Code. Naturally, a blog needs comments. Same constraints: no database, no external dependencies, no Disqus tracking visitors. Just PHP + JSON files. Built in one session with Claude Code — the interesting part wasn't the code, it was the security audit that followed. A comment system without a database seems trivia
When building applications with large language models (LLMs), one of the most overlooked costs is how structured data is represented. Most systems use JSON. And JSON is inefficient for LLM input. KODA (Knowledge-Oriented Data Abstraction) is a schema-first data format designed to reduce token usage when sending structured data to LLMs. It works by: Defining structure once (schema-first) Encoding v
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