FlyOnTime, a Chrome Extension that helps you book better flights

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Once I was in the airport with a delayed flight and realized: this absolutely sucks. I paid $xyz for a 6PM flight and what I received in return was a 7:45PM flight plus a 1h45min waste of time at the airport. Frustrated, I began to analyze the data. Do some airlines perform, on average, better than others? What about airports? What about days of the week? I found that the answer to all of the above questions is "yes". Then I thought, wouldn't it be great to have this information *at the time of booking*? Because that's when it really matters. Am I paying for a flight, or a delayed flight? Is it really worth paying over *twice as much* for American over Spirit (which is, contrary to popular belief, barely worse than American with its on-time performance). So I built one for US flightsL https://www.flyontime.app And if you want to read about the methodology: https://flyontime.substack.com/p/flyontimes-methodology Right now it is 100% free. Plan is to try to reach a couple thousand users organically and then think about a premium version with more advanced analytics that I can offer to executive assistants and travel agencies.

Visit product View on Hacker NewsFeb 24, 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.

AppSumoStrong fit for a featured deal · Strong signals: plus, users · Missing: platform, intuitive, reviews

54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: user · Missing: mac, agents, macos

45%45% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal

44%44% predicted probability of success on TrustMRR, 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

40%40% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

22%22% 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

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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