Bo

Book flights for 1/10 of the cost upfront

Hacker News

Book flights for 1/10 of the cost upfront

I built a flight booking app/website that lets you book flights immediately and pay for them over time. http://bliss.flights I got frustrated after being unable to afford an important trip ($1000+) while ass-broke in college and realized it’s a big problem for other young people who don’t own credit cards. Besides financing payments, Bliss has "GTFO Deals” similar to Scott’s Cheap Flights, where we notify you whenever we see unusually cheap flights flying out of your city (i.e. $250 roundtrip from New York to Madrid). Would you use it? Any feedback?

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
81%81% 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 NewsMay not resonate with HN audience · Strong signals: ide, 000 · Missing: https docs, excited, just released
48%48% 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: new · Missing: mac, agents, macos
44%44% 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
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
30%30% 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
15%15% 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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