Olelo Foil - NACA Airfoil Sim

Hacker News

Hi HN! A while back, I started exploring ways to make aerodynamic simulation more interactive and visual for the web. I wanted something that felt immediate—intuitive enough for students, fast enough for hobbyists, and hackable enough for engineers. That’s how Olelo Foil was born. Foil is a browser-based airfoil simulator written in JavaScript using Three.js and WebGL. It lets you interactively explore how airfoils behave under different conditions, all rendered in real time. Right now, it uses simplified fluid models, but I’m working toward integrating Navier-Stokes for more accurate simulations—and I’d love help from anyone interested in fluid dynamics, GPU compute, or numerical solvers. I’m also building Olelo Honua, an educational platform focused on Hawaiian STEM content and digital tools. Foil is one piece of that larger vision—bringing STEM education into the browser with open, accessible tools. Check it out, and if you're interested in collaborating (especially on the physics side), I’d love to connect!

Visit product View on Hacker NewsMay 20, 2025

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting

86%86% predicted probability of success on Indie Hackers, 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

72%72% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

65%65% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

61%61% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: real time · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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