Mustel
Non-AI static analysis, built for AI coding agents
What it does
Mustel is a local-first static analysis layer built specifically for AI coding agents like Cursor, Claude Code, and Windsurf. It runs Ruff, Bandit, and pip-audit under the hood, then compresses the output into a single sub-200-char agent_prompt field agents can act on directly - no wasted turns re-reading files or parsing raw linter noise. Dev Mode scans in under 30ms via mtime+size caching; Audit Mode runs deeper checks in CI. Auto-registers as an MCP server across major editors.
Mustel is a fully open-source, MIT-licensed Python CLI linter and MCP server on PyPI that prevents AI coding agents (Cursor, Claude Code, Windsurf) from hallucinating or missing deterministic bugs.
Mustel is a local, non-AI static analysis layer that intercepts editor save loops. It formats and compresses code diagnostics into ultra-short prompts to guide coding agents without context bloat. Standard coding agents transfer thousands of lines of raw, duplicate linter logs on every save loop. These verbose payloads clog context windows, increase latency, and inflate your API bills. Out-of-the-box rule configurations covering 26 core packages, cloud SDKs, web frameworks, and system utilities. Mustel executes linter checks, deduplicates overlap reports, and runs custom YAML pattern checks locally. It formats all issues and creates a sub-200 character summary to instruct AI agents clearly.…from mustel.vercel.app
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I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
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Launched alongside, August 2026
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I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
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