amosblomqvist
05f465942a
The README becomes the storefront for the public repo — what it is, why it exists, BYOK/local/inspectable values, download, quick start — written from the canonical articulation (owner doc, 2026-10-05): the dependency structure of understanding, measure-first teaching, one reasoning step at a time, the accumulating world model. All developer content (commands, env, architecture, mechanisms, testing, lineage) moves verbatim to docs/development.md (stale suite count and a private machine path dropped on the way). Owner will edit before the release; this is the base coat.
48 lines
2.9 KiB
Markdown
48 lines
2.9 KiB
Markdown
# LearnHarness
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A desktop AI tutor that teaches by questioning — one reasoning step at a time — and keeps everything you learn in a growing knowledge graph.
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It runs on your machine, with your own API key. Your data stays in plain files you can open.
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## The idea
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Understanding has a dependency structure: some things require other things first. A lesson that ignores what you already hold is wasted on you; a lesson that outruns what you hold is lost on you. There is exactly one optimal path — the smallest set of steps from what you know to what you want to know — and finding it requires knowing precisely what you know.
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So LearnHarness measures first. It questions you to bracket the edge of your understanding, plans the shortest path from that edge to your goal, then teaches it one reasoning step at a time — checking that each step lands before building on it. Every step is small enough to feel obvious on arrival.
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And it remembers. Everything taught accumulates into a single persistent structure — propositions and the reasoning chains that connect them — rendered as the graph that is the app's home view. The next session starts from what the last one built: no re-probing what you already hold, questions aimed at the actual boundary of your knowledge, learning that compounds.
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Teaching installs understanding; practice makes it available when you need it. Separate practice sessions draw problems from the same graph — retrieval at the edge of your ability, not review — because being taught something and having it come to mind in the right situation are different states.
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## Yours, locally
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A system whose premise is that it can be trusted is built so that it can be checked:
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- **Your keys.** Bring your own ([OpenRouter](https://openrouter.ai/) works out of the box). No subscription, no markup — a live cost meter shows what each session actually spent.
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- **Your machine.** Chats, graph, settings — plain files under your home directory. Nothing is sent anywhere except the model calls you configure.
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- **Inspectable.** Every system prompt is visible and tunable. The graph is stored as reasoning chains — the structure of what you were taught, not a pile of text.
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## Download
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**[Releases](https://github.com/amosblomqvist/learnharness/releases)** — macOS on Apple Silicon, signed and notarized. Windows and Linux are planned.
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## Quick start
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1. Download the DMG and drag the app to Applications.
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2. Open Settings, paste your OpenRouter API key, and pick your models.
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3. Start a session and say what you want to understand.
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## Development
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```bash
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npm install
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npm run dev # http://localhost:3000
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npm test # deterministic, no LLM, no network
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```
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The architecture spec and full development guide live in [`docs/`](docs/) — start with [`docs/architecture.md`](docs/architecture.md) and [`docs/development.md`](docs/development.md).
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## License
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[AGPL-3.0](LICENSE)
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