Three steps for OCaml to crest the AI humps
summary
OCaml appears in only 0.003% in Stack v2, causing ~5x worse agent performance versus Python on some tasks; local 32B models like Qwen 3 approaching Claude Sonnet 3.7 narrow the gap but increase latency and cost. Practical steps: measure real use cases (Jupyter-first-year exercises), contribute those tasks to public suites (terminal-bench), and provide agent-facing infrastructure — markdown docs, Odoc/ODO-LLM + MCP tool wrappers, and Dune RPC integration.
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chapters (9)
- 0:00OCaml and AI performance gap
- 1:26Measuring agent deficiencies
- 6:23Contributing to benchmarks
- 9:52Building agent-friendly tools
- 15:27Call to community action
- 16:14Q&A: MCP and errors
- 20:08Q&A: Core hallucinations
- 22:14Q&A: Dune limitations
- 23:38Q&A: OCaml in training
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