Orbtx – A physics-based ΔV engine with real-time 3D visualization

Hacker News

Hi HN, I’m the creator of ORBTX. I built this because most tools in astrodynamics are either overly simplistic web calculators or heavyweight, expensive legacy suites that look like they were made in the 90s. I wanted to create a "middle ground": professional-grade orbital computation that is fast, visual, and API-first. The Tech Stack: Frontend: Next.js & React. 3D Engine: Three.js (React Three Fiber) for real-time trajectory rendering. Physics: Custom engine handling classical two-body mechanics (Hohmann and Bi-elliptic transfers). Key Challenges: One of the main hurdles was maintaining floating-point precision for astronomical distances within a browser environment while keeping the 3D visualization smooth at 60fps. I’m currently refining the propagator to handle more complex perturbations in future updates. The Goal: To move orbital mechanics away from "Excel-engineering" into a modern, developer-ready infrastructure. Think of it as a step toward "Figma for orbital mechanics." No sign-up required, it's open for testing. I’d love to hear your thoughts on the physics implementation, the API structure, or any edge cases you find!

Visit product View on Hacker NewsFeb 27, 2026

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

80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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

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

52%52% 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: way · Missing: mobile apps, ios, personal

33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

19%19% 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.

Incorrect prediction on native model

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