Br

Browser-based WebGL terrain editor with authoring mode and camera

Hacker News

Browser-based WebGL terrain editor with authoring mode and camera

Hi HN, I’m building browser-based WebGL terrain editor powered by a custom Three.js engine. It’s a real-time 3D scene, not a static render. What’s new since the last iteration: Authoring mode UI integrated into the left navigation (clean tool separation, no ad-hoc overlays) Camera Mode vs Explore Mode (orbit/zoom author camera with a clear mode switch) Visual modes for authoring (production parity vs simplified white “workspace” mode) Live demo: https://playzafiro.com/isle-lab/ Short context: I’m doing this solo. The long-term goal is a lightweight world-building tool that can evolve into a broader editor/workflow. I’d love feedback on: - usability (does the navigation/tooling feel intuitive?) - performance on your machine - what feature would make this immediately useful for you (even as a prototype) Thanks!

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, new, context · Missing: agents, macos, agent
88%88% 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.
Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
56%56% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: intuitive · Missing: plus, platform, reviews
51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
44%44% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: para · Missing: mobile apps, ios, personal
43%43% 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
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
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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