01

Coop, a personal AI operating system

Flagship

The system that runs my operations. Its hardest problem was not capability. It was that broken things kept reporting success.

Coop is a single-user operating layer that runs the recurring, deadline-bound decisions I would otherwise miss, and keeps a dated record of whether that helped. It is a Node codebase on one Mac: 165 files, 20,632 tracked lines, and no runtime dependencies of any kind. State lives in markdown and JSONL files a human can open, so the system can be audited by reading it.

The split is not across managed services. It runs on one axis: can this piece of work write to disk. Tier one is the Mac under launchd, where five scheduled jobs produce every durable artifact. Tier two is scheduled cloud tasks for work that needs the open web, running with no path to the filesystem. Tier three is a private git remote holding the code and the measurement files that cannot be regenerated.

Model routing turns on one question, and it is not difficulty. It is whether the output will be checked by something other than a model. Work that passes through an invariant, a parser, or a threshold runs on a cheaper tier, because a wrong answer gets caught mechanically. Judgment that nothing downstream validates gets the expensive tier. Every numeric result is computed by the engine, never by a model.

Known limit

The context caps are documented at roughly 20k tokens for monitoring and 50k for execution, and nothing enforces them. Searching the codebase for a token budget or a spend limit returns nothing. It is a written policy with no mechanism, in a system whose whole thesis is that mechanisms beat intentions.

Read the full teardown

02

Builds

Every card links to proof
01

Echo AI

A local-first meeting recorder that transcribes, identifies speakers, and answers questions across an audio archive on-device.

The hard parts were speaker identity and privacy guarantees: correcting merged voice clusters without cascading edits, and proving cloud calls never fire for flagged recordings.

Known limit. Diarization merges speakers in noisy in-person audio, so those recordings still need manual correction. The n8n integrations were cut.

02

Flo

A macOS menu bar app that turns held-key speech into cleaned-up text, entirely on-device, using Whisper and a local LLM.

The hard bugs were all silent failures, so most of the work was making the system report its own state.

Built on Bjepp77/walnut, an MIT-licensed local dictation tool

Known limit. After about two weeks of uptime the first dictation takes 20 to 30 seconds, because the models page out to swap and nothing keeps them warm.

03

Wolves Hub

A Minnesota Timberwolves news and stats hub: live multi-source news ingestion with story clustering, standings, a morning brief, and an installable PWA.

The fun part is the plumbing: ingestion that clusters the same story across outlets, and a daily sync job that keeps a free-tier Supabase project from pausing.