Not a model problem. We built baselane because the thing that decides whether an agent writes good code isn't which model you use — it's whether your repo and your team share a standard. Almost none do.
Every team now ships AI-written code. Very few have made their team write it well, or consistently. That gap is the whole reason baselane exists.
An agent fails in a repo that never tells it how to build, test, and conform — regardless of how good the model is. Give it that context and the same model writes code that fits. The leverage isn't the model; it's the standard around it.
Today every developer reinvents their own prompts, rules and agent configs. Five hundred developers, five hundred setups. The org ends up with uneven, hard-to-review AI code and no way to roll anything out — while the tooling landscape shifts every month and no leader can track it.
Generating config is already an open, commoditizing problem. The hard parts are deciding what fits this codebase, distributing and governing it across every repo and developer, and continuously curating a fast-moving field so a bad practice never lands everywhere at once. We build on the open ecosystem and compete on those three.
The industry's first instinct was to measure AI by tokens spent. That's the wrong number. baselane captures the insights from AI work — what shipped, what got reviewed, what was caught before production — so the metric your team is judged on is value created, not consumption.
Standards arrive as pull requests your team reviews. Never silent, never mandatory at the machine level.
Everything we produce is plain files in your repos, across every tool — not another walled garden.
Curation is editorial. We'd rather ship one practice that fits than ten that look impressive.
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