Ex

Explore fun and Noteworthy flights with airgeek

Hacker News

Explore fun and Noteworthy flights with airgeek

Being an aviation enthusiast and airplane spotter/photographer for years, I have been looking for a tool that can tell me some fun/spot-worthy aircrafts in real-time or in advance. I have relied on a combination of FlightRadar24 + FlightAware + other websites and got tired. So I finally decided to build a simpler tool myself. Introducing ( http://airgeek.link ). Simply input a specific time and airport, and instantly access a curated list of “fun” and noteworthy aircrafts. As I built the tool, I got intrigued by other aviation random facts so I expanded the toolbox to discover a flight's past gates and explore all canceled flight of an airport. The journey doesn't stop here. My future feature list is long! Visit the "( http://airgeek.link/next )" page for a list of upcoming features. Your feedback is invaluable as I strive to enhance ( http://airgeek.link ) for everyone.

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Actual performance

2points
6comments
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
74%74% 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: lua, ide, io · Missing: https docs, excited, just released
65%65% 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
46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos
46%46% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
27%27% 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
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.

Incorrect prediction on native model

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