wassname
ee4e9a5caa
Gather debugging folklore from more practitioners, each a verbatim quote
checked against a cached source copy (footnoted with line numbers):
- koaning (Vincent Warmerdam), "Bad Labels": benchmark labels are often wrong;
find them with confidence-sorted errors.
- gwern, the tank-detection legend: the canonical data-leakage parable, plus
the scout-mindset twist that it's a likely-unsourced urban legend.
- Patrick Kidger, "Just Know Stuff": why research code is buggy ("kludge ...
bugs that don't cripple things only because some other bug stops them") and
"never accept the kludge". Plus a one-line jaxtyping pointer for shape bugs.
- nanochat (Karpathy): BOS-alignment fake metric improvement; all-ranks must
clip on inf (a multi-GPU bug single-GPU testing hides).
- cleanrl "37 Implementation Details of PPO" -> RL sub-skill, as the canonical
proof that reference-impl details (not ideas) decide whether PPO works.
Trim the lucidrains item to one quote (it had ballooned). Add wassname credit
+ companion-gist link. All 20 footnotes resolve.
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
-
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/.
-
docs/evidence/ -- frozen local copies of source material (blog posts, talks, papers, reddit threads). Claims in SKILL.md link back to exact quotes here.