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Abhisek987
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reacted to Quazim0t0's post with 👍 5 days ago
I built/building a small neural Raytracing/Projective-dynamics (PD)/Physics engine from scratch The idea I wanted to test: in a projective-dynamics solver, could you replace each hand-derived local constraint projection with a learned one, while keeping the analytic parts (rotations, the global solve) exactly as they are? It's one tiny network, shared across every element and across constraint types through material tokens. A new material isn't a new network, just a new token row. Fluids fall out of the same idea, with water treated as one more token. A few things held up in testing: one tied projector matched five separate per-material solutions, the neural fluid tracked the exact analytic solver closely on a dam-break sim, and a learned warm-start trimmed solver iterations without touching correctness. Try it here: (Projective-dynamics) Dam Break Demo: https://quazim0t0-neural-physics-engine-demo.hf.space/ FPS Shooter Demo: https://quazim0t0-neural-combat-evolved.hf.space/ (Raytracing) Voxel World: https://quazim0t0-neural-world.static.hf.space/index.html (Rigid Body) https://quazim0t0-ashendepths.static.hf.space/ Model Repo: https://huggingface.co/Quazim0t0/neural-physics-engine https://huggingface.co/Quazim0t0/neural-raytracing https://huggingface.co/Quazim0t0/physgait-weights
upvoted a collection 16 days ago
DepDoctor
repliedto their post 16 days ago
Every Python developer has hit this: you upgrade numpy or pandas, and code that worked yesterday breaks today. I built an open dataset for exactly this problem. DepDoctor is 6,204 examples of Python code broken by a dependency upgrade, each paired with the fix and a short note on the API change that caused it. It is a mixture of real cases mined from public GitHub commits and synthetic cases generated from a database of known breaking changes. A few things I tried to get right: - 935 "leave it alone" examples, to teach a model restraint, not just what to change. - Honest evaluation: a fine-tuned Qwen2.5-Coder-7B gets 62% of fixes fully correct. I report that, not just the 97% text-similarity score that hides the truth. - The main failure mode, over-editing, is measured and explained rather than buried. Dataset, fine-tuned model, and a live demo are all open in one place: https://huggingface.co/collections/Abhisek987/depdoctor Feedback welcome, especially from anyone working on code repair or API migration.
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