Writing
Essays on agent infrastructure: memory layers, review gates, and search tooling. The numbers in them come from my own production work.
Every agent behind one seam: what A2A orchestration actually bought me
A conversational advisor that fans out to four agents per turn, deliberates, and composes a reply. The decision that mattered was not the model or the prompt. It was refusing to let any node import an agent directly. Here are two production bugs, one cost ceiling, and what that seam paid back.
The three-tier memory layer I built for my coding agent
Most agent memory designs either forget everything between sessions or fill with noise until they are useless. This one runs three tiers: a Karpathy-pattern LLM wiki, an Obsidian second brain, and a JSONL session knowledge graph. Each tier has its own auditor and its own cleanup cycle.
Code search tool benchmark: rg, ast-grep, MCP, and what the data actually says
A benchmark across four production codebases. rg wins on hit rate everywhere. ast-grep wins on one narrow class of query, structural patterns, where rg returns nothing and reports no error. The answer is to route by query class instead of switching tools.
Iron-Law Gates: How I Turned My PR Review History Into an AI Pipeline
I read more than 100 PR review comments across two production codebases over three months. Sixteen failure categories kept recurring. Each one became a gate in an AI-assisted development pipeline that the agent cannot skip. The gates exist because specific reviewers kept catching specific failures.