Av

Aviation calculators I built while learning

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Aviation calculators I built while learning

I'm a software developer from Argentina, currently working on my private pilot license. As I progressed through ground school, I kept building tools to help me understand and practice the concepts, and it turned into this. joseflys.com is a collection of aviation calculators that run entirely in the browser. Everything works offline as a PWA. I know most pilots use ForeFlight, SkyDemon, or similar apps, and I'm not trying to compete with those. This started as a learning project to really understand the math behind flight planning, and I figured others might find it useful too. Some things you can do: - Wind correction: Calculate heading and ground speed given wind. Useful for understanding the wind triangle. - Flight planning: Build multi-leg flight plans with fuel calculations, ETAs, waypoints, and climb/descent performance. Export to PDF or Excel. - TAS/ISA calculators: Convert between indicated and true airspeed, calculate density altitude. - Takeoff and V-stall: Performance calculators for go/no-go decisions and understanding stall margins. - LNAV Segments: An educational tool showing how FMS systems approximate great circle routes with constant-heading segments. Tech: Next.js, TypeScript, GeographicLib for WGS-84 geodesic calculations, World Magnetic Model for magnetic variation. All calculations happen client-side. The geodesic precision was an interesting challenge. Haversine isn't accurate enough for real navigation, especially at high latitudes or long distances. It's open source: https://github.com/jfromaniello/joseflys Happy to answer questions!

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
80%80% 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.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: open source, ide, io · Missing: https docs, excited, just released
43%43% 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 HuntUnlikely to reach the leaderboard · Strong signals: model, apps, open · Missing: mac, agents, macos
39%39% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps, way, education · Missing: mobile apps, ios, personal
35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: margin, margins · 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 · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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