Frequently Asked Questions

CodeOtter is licensed under Elastic License 2.0 (ELv2). It is free to self-host, modify, and run forever on your own hardware. It is not OSI-OSS — the licence restricts hosting a competing SaaS that re-sells the unmodified binary. We say so up-front; we never call it OSI-OSS, and we never describe the licence that way. Full licence text: `codeotter.io/legal/terms`.

System 1 (calibrated scores + 9 merge gates, deterministic): Laya Typed-Decisions (455 MB Q4 GGUF) and Kev 0.8B (828 MB), shipped with CodeOtter, served by the bundled ggmlc laya runtime. The /v1/systemone HTTP endpoint also accepts any OpenAI-compatible System 1 model (TypeSafe Jev, OpenRouter-hosted, etc.). System 2 (narrative review): bring-your-own — Claude, OpenAI, Anthropic, OpenRouter, or any OpenAI-compatible endpoint. For 100% offline System 2, Admin → Local models downloads Qwen2.5-Coder 1.5B or 7B GGUF (served by llama.cpp's llama-server) or Microsoft CodeReviewer (Python sidecar). All sidecars spin up on demand and shut down after 15 idle minutes.

Minimum: a single x86_64 or Apple-silicon box with 8 GB RAM. Recommended for fully local: 16 GB RAM + Vulkan-capable discrete GPU OR Apple-silicon with unified memory. The 1.5B Qwen2.5-Coder GGUF fits in 8 GB; the 7B Qwen2.5-Coder needs 16 GB. Microsoft CodeReviewer is the smallest (~250 MB) and runs on CPU at acceptable speed. GPU detection is automatic (Vulkan / Metal); the runtime picks the best path without intervention.

The first review on a fresh box pays a one-time model-load cost (~5 min on a mid-range laptop for the 7B GGUF, ~30 s for 1.5B). Subsequent reviews on the same box are fast (~10–30 s for typical PRs). Sidecars stay resident for 15 idle minutes after each review, so back-to-back reviews are warm. This is the trade-off for 100% offline — no warm model in someone else's data centre.

CodeOtter is the platform that hosts the review — the GitHub PR integration, the 9 binary merge gates, the AGENTS.md auto-detection + per-repo injection, the 1-click "Dismiss & remember rule" learnings, the inline committable suggestion blocks, and the incremental commit-by-commit delta reviews. ChatGPT (or any LLM) is one provider of the System 2 narrative engine. You can plug ChatGPT into CodeOtter via BYOK. But you cannot get the gates, AGENTS.md enforcement, inline suggestions, or delta reviews from ChatGPT alone — those live in the platform, not in the model.

System 1 is a tiny classifier that returns strict JSON over a fixed rubric. The questions are closed-form: choice, score, or noul. Because the schema is fixed and the model is small, scores don't hallucinate and the 9 binary gates don't drift. Chat LLMs cluster every score between 75 and 85 because they are not trained to output calibrated numbers — they are trained to produce plausible prose. System 1 outputs a calibrated 0–100 because the model literally cannot output anything else.

No. Zero outbound calls from the review pipeline. PostHog JS on codeotter.io (the marketing site) is a separate concern and is opt-out via Do-Not-Track. The self-hosted product has zero telemetry by design — verifiable in your egress firewall logs during a full PR review run.