apoorva lal

@apoorvalal.com

causal inference, econometrics, ML, arsenal, loud music, unix, FOSS for scientific computing. opinions my own. apoorvalal.github.io (passively) maintains @paperposterbot.bsky.social

quite nice prototype built over telegram: apply finishing touches on your figures in a web-ui using the magic of vega json graphics and get back json/code that you can put back in your source-code [thereby maintaining reproducibility of output - usually my biggest bugbear with wysiwyg fig edits]

apoorva lal@apoorvalal.com · 4mo ago

prototype seems to work; source here github.com/apoorvalal/v... and deployed here lalten.org/vega-ui/ 1) get starter plot from altair and extract json; then paste into vega-ui 2) edit [fig1:every 'apply' button mutates the vega json directly] 3) when done, extract python/json [fig2]

Codex is great at Rust, i am not. I have strong preferences and tests for what a lean statistics package should do, it does not. I'm not crazy enough to try to patch out numpy deps but that really is it. Solid collaboration, now on pypi (uv add crabbymetrics) github.com/apoorvalal/c...

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bschool research is hilarious. Drop operations researchers into breaking bad and watch them begin a paper with "We show that, in general, the optimal meth-lab distribution problem is NP-hard, but here's a lagrangian relaxation that has bounded and shrinking regret."

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hobbyist programming to build tooling for hobbyist musicianship: glue library for mp3s->stems->midi->chords/tabs using small pre-trained models. 1) trimming is bulletproof since it's just ffmpeg 2) stemming is reasonable 3) midi is hit-or-miss 4) tabs are underwhelming github.com/apoorvalal/m...

GitHub - apoorvalal/mlodies: glues pretrained models to do stem separation and transcription to help learn music by ear.

glues pretrained models to do stem separation and transcription to help learn music by ear. - apoorvalal/mlodies

github.com