At a window seat, learn about what you are flying over

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I always wonder what I am flying over on a plane, and beforehand, which side of the plane should I book for the best views. Lookoutwindow.com shows snippets about Points of Interest below, and each has a Wikipedia link that you can read more about. You can save them as favourites also, to read once you land. Currently, covers all routes from Ireland, due to the cost of API access for flight data. Works offline once the route is selected. If you have an RTL-SDR receiver, in time, it will track your live location from the plane's transponder (since GPS can be unavailable inside a plane). Been thinking of reaching out to Inflight Entertainment system providers/airlines with this tool. Or making it into a paid app, access per route or unlimited routes per month. Would this meet your threshold of something you would pay for? Any feedback/thoughts on the concept, I would love to hear.

Visit product View on Hacker NewsJul 17, 2024

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

84%84% 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 · Missing: mac, agents, macos

56%56% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

47%47% 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: month, way · Missing: mobile apps, ios, personal

44%44% 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

26%26% 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

18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: paid · Missing: web3, chat, crypto

5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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