mirror of https://github.com/wassname/ml-debug.git synced 2026-09-26 14:00:26 +08:00

T

wassnameandClaudypoo 779beee03e refactor(ml_debug): tidy ordering/emphasis on the new top sections

Three targeted polishes to the rewritten skill:
- Reframe Part 1's "The hierarchy (work in order...)" -> "What 'collect clues'
  looks like": it's the catalog the loop's clue-collection step draws on, not a
  second master-procedure competing with "the debugging loop" 40 lines above.
- Reorder: lead straight into calibrate -> loop -> read-impl; relocate the
  2017-2021 caveat + LLM-pretraining pointers into a "Scope and modern pointers"
  block after the action sections, so the behaviour-changing content is the
  first screen instead of provenance.
- Emphasis: give the "priors are a starting weight, not a verdict" line a
  concrete clause (traceback / loss-metric misalignment / right init-loss
  override the data prior) -- the weakest comprehension dim in the quiz.

Before-vs-after panel A/B (6 cold readers): tie on ordering/clarity/
conciseness/focus, each leaning slightly positive, no regression.

Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>

2026-06-01 10:15:41 +08:00

docs

docs(ml_debug): annotate EMNLP 2018 NLP code tutorial; note sparse Adam embedding bug

2026-03-10 05:48:36 +08:00

pinn

name

2026-04-09 05:09:25 +08:00

refs

feat(ml_debug): expand nanochat evidence, add lec4 diagnostics file

2026-03-10 05:38:33 +08:00

name

2026-04-09 05:09:25 +08:00

.gitignore

chore: fix .gitignore (dlbooks path, *_log.md pattern)

2026-03-06 12:22:22 +08:00

README.md

feat(ml_debug): add Karpathy recipe + nanochat evidence, update-ratio diagnostic

2026-03-10 05:32:37 +08:00

SKILL.md

refactor(ml_debug): tidy ordering/emphasis on the new top sections

2026-06-01 10:15:41 +08:00

README.md

ML Debugging Folklore

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. Parts 1-5 are reference knowledge; Part 6 is a runnable triage protocol (grep patterns, diagnostic snippets, decision tree); Part 7 is debugging mental models and practitioner priors.
docs/evidence/ -- frozen local copies of source material (blog posts, talks, papers, reddit threads). Claims in SKILL.md link back to exact quotes here.