wassname
a602ea5a0e
Spinning Up as a Deep RL Researcher was only a bare code link; it's the canonical RL-researcher guide and its debugging advice is gold. Cache the rigour/debugging sections verbatim and quote the sharpest lines in the RL sub-skill: "broken RL code almost always fails silently", "if it doesn't work, assume there's a bug", "measure everything ... you can't tell it's broken if you can't see that it's breaking", and test on more than one env. Add to RL sources. Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
wassname's ML Debugging Folklore
In an attempt to upskill the ML debugging on AI coding assistants (and humans), I've collected high quality sources on ML debugging and the mindset and the "taste". When I started ML I went searching for discussions on best practices, and started a few discussions of my own and they helped me a lot, I hope they can help others. This intro is human written, and the below is AI written with human guidance.
Practitioner knowledge for debugging ML systems, curated and synthesized by wassname. Opinionated by source selection -- I picked sources I trust (Schulman, Goodfellow, CS231n, ...) and had an LLM extract the most relevant information for debugging ML systems.
Use as a Claude skill
/skills add https://github.com/wassname/ml_debug
Or paste SKILL.md into your system prompt / context when debugging.
What's here
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SKILL.md -- the main artifact. Load into an LLM agent's context as a debugging skill. Leads with the mindset (calibrate, mental models, general debugging tricks, and reading a working implementation when stuck), then a folklore section of sourced quotes, then an LLM-agent playbook (debugging loop, triage menu, anti-patterns). Deeper one-off tricks (loss-surface analysis, stuck-metric diagnosis, sweep reliability) live in refs/.
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docs/evidence/ -- frozen local copies of source material (blog posts, talks, papers, reddit threads). Claims in SKILL.md link back to exact quotes here.