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wassname
38ec634ff3
Reorder around what's durable, per wassname's curation: - human-written intro up top; rename to "wassname's ML Debugging Folklore" - mindset first: calibrate -> mental models -> Part 1 general tricks (kept, they're well-based) -> read a working implementation when stuck - a Folklore section built from verbatim, source-checked quotes (Jones, Rahtz, Karpathy, Schulman, Henderson, Irpan, CS231n, Slavv, Goodfellow), each footnoted to the canonical URL + the cached copy with line numbers - LLM-agent babysitting (debugging loop, triage menu, anti-patterns) moved to the bottom where it belongs; triage reframed as a menu, not a flowchart - deeper one-off tricks split to refs/ (loss_surface, metric_stuck, sweeps), scrubbed of private tooling (wandb/just/SI/personal scripts) Quote integrity: every quote independently verified by fresh-eyes subagents against the cached sources; fixed a reformatted Schulman slide, a truncated Jones sentence, a reversed-order Rahtz stitch, a falsely-quoted Slavv phrase, and the 3e-4 line (now the real tweet, framed as the joke Karpathy confirmed it was, not gospel). lr_scheduler anti-pattern nuanced (warmup/cyclic matter). Remove superseded SKILL2.md draft. Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
20 lines
1.5 KiB
Markdown
20 lines
1.5 KiB
Markdown
# wassname's ML Debugging Folklore
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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.
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Practitioner knowledge for debugging ML systems, curated and synthesized by [wassname](https://github.com/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.
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## Use as a Claude skill
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```
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/skills add https://github.com/wassname/ml_debug
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```
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Or paste `SKILL.md` into your system prompt / context when debugging.
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## What's here
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- **[SKILL.md](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/](refs/).
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- **[docs/evidence/](docs/evidence/)** -- frozen local copies of source material (blog posts, talks, papers, reddit threads). Claims in SKILL.md link back to exact quotes here.
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