WebGPU + TypeScript Slime Mold Simulation

Hacker News

I made this slime mold simulation to learn more about WebGPU and compute shaders. It's essentially a recreation of Sebastian Lague's coding adventure on the web with TypeScript. I've linked a few video demos on the GitHub readme, and there is a playable demo on my website. Using WebGPU has been great so far. The iteration loop (with Vite.js) is much faster than when working with Unity. This helps with quick prototyping, though Unity's editor scripts allow you to build more interesting user input tools. I was also surprised by how good the WebGPU error logs were. They helped me debug most of my shader bugs pretty quickly.

Visit product View on Hacker NewsDec 29, 2024

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

92%92% 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: ide, io · Missing: https docs, excited, just released

63%63% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

50%50% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

15%15% 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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