Inverse Kinematics in the Browser with Rust

Hacker News

I built a small web app as a way to teach myself inverse kinematics/controls concepts-- and to get more familiar with Rust and Web Assembly. My takeaways: - Redundant Inverse Kinematics is very complex. Treating it as an optimization problem has the simplest model but is very inefficient. Jacobian methods are much faster but have their own quirks and limitations. - Web Assembly is pretty slow actually (for my use case). Where I was seeing a 100x speedup on native Rust over js, I was only seeing a 4-6x speedup of WASM over js.

Visit product View on Hacker NewsJan 31, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: model · Missing: mac, agents, macos

75%75% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

67%67% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Strong signals: efficient · Missing: plus, platform, intuitive

55%55% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

34%34% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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