BetaZero

A developer's AI twin, small enough to commit to the repo.

BetaZero is an SFT-only twin of a single developer: a LoRA adapter over SmolLM3-3B, trained on that developer's own git history, and served to coding agents as MCP tools through the TrueForge agent harness.

No RL, no reward model, no reinforcement stage. Supervised fine-tuning only — hence BetaZero. The whole twin is one ~29 MB file in this repository, so you can clone it and run it.

Built for the Agent Harness Hackathon.

The problem

When a developer leaves, the code stays and the reasoning goes with them. Git records what changed and when; it rarely records why.

Coding agents inherit that blind spot. They read code, see something that looks wrong, and "fix" it — reverting a deliberate decision nobody wrote down.

Related MCP server: CodeGraph

What BetaZero does

BetaZero exposes a developer's recorded voice to any agent as MCP tools:

A TrueForge agent about to refactor a file asks first, and revises its plan instead of undoing a decision it never knew about.

What it is trained on, and what that means

The twin in this repo is trained on D2, a developer with 443 commits to the public COBWEB-ca/cobweb2 repository (2008–2019). Each training row pairs a commit diff with the commit message that developer wrote.

Be clear about what that teaches. The targets are commit messages — a median of 49 characters, 85% under 80. So BetaZero learns the developer's voice and intent at commit time. It does not learn unwritten reasoning, because git history does not contain unwritten reasoning. Recovering that requires elicitation, not observation, and is out of scope here.

The corpus is derived entirely from public git history and is sanitised before publication — see scripts/sanitize_corpus.py, which redacts third-party contact details that appear in the upstream credits screen and refuses to run if a redaction would alter a training label.

Does it actually work?

An adapter that ships without evidence is a claim, not a result. This repo publishes the A/B: BetaZero against the untrained base model, averaged over repeated runs and judged against the paired standard error, with the sampling noise floor stated alongside the effect.

See docs/evaluation.md — including the honest answer if the effect turns out to be small.

Status

Early. See the issue tracker for planned work and CONTRIBUTING.md for how changes get made.

Qodo Code Review Evidence

This project is entered in the hackathon's Q Branch (code quality) track. Every substantive change reaches main through a pull request reviewed by Qodo. main is branch-protected; there are no direct pushes.

This section will link representative merged PRs, what Qodo surfaced, which findings were addressed or dismissed and why, and the follow-up review.

License

Apache License 2.0 — see LICENSE and NOTICE.

The cobweb2 corpus derives from a public repository that publishes no licence of its own; NOTICE records what is and is not claimed for it, and the sanitiser shows how it is prepared.