mindfork is public

The repository is open, and the first public release is on the releases page.

mindfork is an AI chat that lives in your terminal. It runs local models through llama.cpp, or talks to OpenAI, Anthropic, Gemini and Grok — all four configured at once if you like, switching between them without losing anything. It is one native binary for Windows and Linux, it keeps its data in a folder next to itself, and it has no telemetry, no update check and no account.

What it is actually for

Most clients are a window onto a model. This one is built on a different premise: that a local Gemma or Qwen becomes more interesting to talk to when it is given room to remember and to act.

So it keeps a self-model — a summary, goals, traits and an observation narrative that the assistant maintains about itself and about you, and that you can read and wipe on one screen. It writes notes and links them into a graph where one note can supersede another. It builds a knowledge base out of your own files, retrieved semantically rather than by keyword. All of it per companion profile, all of it on your disk.

And it has tools: web search and page fetching, a Python sandbox with no access to your files, a file-access tool jailed to one directory you name, an attached code project it can read and change under a diff you approve, video understanding, and anything else you plug in over MCP. Every one of those is off until you turn it on, and an optional confirmation shows exactly what a call is about to do before it does it.

What a first run looks like

mindfork demo

Sample conversations, a scripted engine, nothing written outside a temporary folder. It takes a minute and answers the only question that matters early: whether you want this shape of thing at all.

After that, the install page has the downloads and the manual has the rest.

How it was built, and why the repository is worth a look

Two years of small reviewed tracks, each one a design document with its alternatives written down, a branch, tests beside the code, and — for anything touching a model — a run against a real one before it shipped. The suite is over three thousand tests, and the engineering log says what was measured and what was rejected, not just what was done.

Much of the code was written by AI models under review, and the history says so: every commit names the model that wrote it. That is not a disclaimer, it is the same honesty the rest of the project tries for — the journal is full of measurements that overturned the plan they were meant to confirm.

The code is MIT. The disclaimer says what shipping no model means for what appears on your screen, and the privacy policy says what leaves your machine — which is nothing you did not configure.

If you try it, issues are the place to say what broke.