Navcat – JavaScript 3D pathfinding for games and simulations

Hacker News

I built and open sourced navcat, a new JavaScript 3D pathfinding library. It helps you build web games, simulations, and creative websites that require pathfinding and navigation in 3D environments. It supports navigation mesh generation from 3D geometry, navigation mesh querying, and has some higher level APIs for simulating a crowd of agents on a navmesh. I built this after previously building recast-navigation-js, a webassembly port of a state of the art c++ library for navigation. But limitations with WASM <-> JavaScript interop and a desire to extend recastnavigation's functionality drove me to building navcat. Say hi to the cats - https://navcat.dev/ examples: https://navcat.dev/examples github: https://github.com/isaac-mason/navcat npm: https://npmjs.com/package/navcat

Visit product View on Hacker NewsOct 30, 2025

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

77%77% 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.

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

70%70% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

58%58% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · 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

16%16% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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