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Tooling

16 posts on tooling.

Every Ulric site. Talk to it.

Coming soon to every UlricCMS build. Connect the AI assistant you already use to your own website, and run it by chat. Nothing publishes without your OK.

ADU Builder: a backyard house from the drawing to the last coat

Five small house plans for a Eugene lot, built up one construction stage at a time, with a materials list and a loan estimate.

Jev Odds, Setup Score and Ask: market tools that show their work

Three small apps for odds questions. The chance a stock reaches a price by a date, a setup score with its inputs and backtest on the page, and a just-for-fun page for the silly questions.

The tool bench a one-person studio builds for itself

A private bench of ten apps, no accounts, no servers, nobody to support. Here is what is on it, how the slide proofer works out where a platform will crop before the file leaves my hands, and what happened when I ran a slide I had already posted through my own guides.

MiniMax H3: the Hailuo image-to-video model, in practice

H3 sits third on the image-to-video arena, generates its own stereo audio in the same pass, and turns a client photograph into five seconds of video for forty cents. It also ships open weights under a licence that excludes the United States by name. Both of those are true, and a studio has to hold both.

A status file per bot: how a small office of agents reports in

I run a small office of bots across two runtimes, and I can see every one of them at once: no message bus, no database, one small JSON file each. A dashboard reads that folder every ten seconds and draws the studio as a floor of desks. Here is the whole mechanism, including the freshness rules and the route I had to close on the day I opened it.

Which AI video model should a small studio use in 2026

OpenAI removes Sora from its API on 24 September. I re-priced the whole field off vendor rate cards rather than blog posts: what five seconds of video costs on each model, where the arena actually ranks them, and the licence clauses that decide it before the quality does.

World Labs Atlas: what a world model means for AI and physics

World Labs announced Atlas on 1 September 2026, a model trained on 3D Gaussian splats alongside text, images and video. Who built it, what a splat actually is, what the physics benchmarks say, and what a small studio can do with the parts that already ship.

A social composer that exports in the browser

The Ulric Composer designs Instagram posts and reels and renders the finished file in the page: a real PNG off a 2D canvas, a reel recorded frame by frame, no server round trip. How the export works, what WebKit refuses to paint, and the one number the browser and ffmpeg both have to agree on.

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.

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.

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.

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