I use AI coding agents every day. I believe they are reshaping how we build software, and I think the teams that adopt them deliberately will outperform those that don't. I am not writing this to warn you away from AI-assisted development. I am writing this because the loudest voices in the AI enthusiasm camp are also the most allergic to discussing what can go wrong. And that worries me more than
Fixed-length chunking requires no external services, yet semantic chunking absolutely needs an Embedding API — why? The core idea of semantic chunking is to split text at semantic boundaries. Determining whether "two pieces of text belong to the same topic" requires converting text into vectors and computing similarity — that's exactly what the Embedding API does. Dimension Fixed-Length / Recur
On Second Thought — Episode 06 The ORM hides the SQL. The cache hides the ORM. The service mesh hides the services. The operator hides the YAML, which already hid the kubelet, which already hid the container, which already hid the process. By Tuesday, nobody quite remembers what the original problem was. They are too busy configuring its sixth wrapper. This is the post about that wrapper. When som
Every team experiences incidents. The teams that grow stronger from them are the ones that take postmortems seriously — not as blame sessions, but as structured learning opportunities. Yet most postmortems end up as a wall of text nobody reads twice, filed away and forgotten until the same incident happens again six months later. This guide walks you through writing postmortems that genuinely chan
Why Does Switching Embedding Models Make Such a Huge Difference? In the first four articles, we built the RAG pipeline, tuned parameters, and mastered chunking strategies. But there's one question we haven't dived into: After your documents are chunked, how do they become vectors? This process is called Embedding. It transforms human-readable text into machine-computable vectors. The choice of E