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Tooling

7 posts on tooling.

Procedural geometry vs Blender MCP: where I drew the line

I rebuilt the procedural world demo to settle an argument with myself: draw the geometry in code, or model it in Blender now that an agent can drive Blender over MCP. Four worlds of fences, trees and desks cost 19 to 53 draw calls a frame in code. One running dog cost a 0.91 MB file and three trips back to the prompt.

Anatomy of an agent harness: hooks, loops, memory, RAG

The model is the one part of an agentic system I did not build and cannot pin down. Everything around it is scaffolding I can test: hooks that block, a loop that verifies, files that remember, a ladder that routes, retrieval that has to stay true. Six components, what each one decides, and the failure that put it there.

28,000 listings on one map with MapLibre and no API bill

Every active listing in the state lands on one map, drawn by MapLibre GL JS on ordinary shared hosting, with nothing metering the page loads. One version-stamped GeoJSON off the sync cron feeds the pins, and a self-hosted PMTiles archive is measured and staged to answer for the basemap the free hosted style is drawing today. Measured numbers, the compliance furniture, and the version pin that a release note forced.

LLM model routing: a ladder, a cascade, and a kill switch

LLM model routing is two problems: a ladder that picks the model tier by what a wrong answer costs, and a cascade that answers when a provider dies. Here is how both are built, with the real chain, the real prices, and the paper that explains why the second one will not save you money.

RAG that stays current: knowledge that ships with the feature

Most retrieval systems do not fail at retrieval: they fail because the paragraph they found describes software that no longer exists. Keeping RAG up to date is a scheduling problem before it is an engineering one, so I made it a rule: an admin feature is not done until the migration that ships it has also written the chunks that describe it, and the embed step has run.

Claude Code hooks: the guardrails that never ask the model

A model can be talked out of things: worn down by a long context, swayed by a file it read, or simply wrong at one in the morning. A hook cannot: it is code that runs at a fixed point in the agent loop and returns a decision the model gets no vote on. The hook lifecycle, the exit code that actually blocks, and the two guards I run on every session.

Grok Bot, and the question every small business now has to answer

xAI shipped agents that get their own cloud computer and sign into your tools like you do. My feed called it god mode. The interesting part is not the army of bots. It is that renting one and owning one are different bets, and most businesses only need one of them.

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