Flybnb.io – Flights and Airbnbs in one smart search

Hacker News

Hi HN! I’m excited to share flybnb.io, a side project I built out of personal frustration while traveling full-time with my wife. We often searched for flights and Airbnb stays separately, only to realize that one was way out of budget after planning the other. I wanted a way to combine both searches and instantly see the total trip cost, so I built it myself. What flybnb.io can do: - Gets flights from SkyScanner and Airbnb prices in one go with total costs for each condition (ex: fast flight + cheap stay) - Smart Filters: Set minimum review counts and scores for stays to filter out unreliable listings that skew pricing. - Auto-categorize Airbnb listings into three price tiers (Economy, Standard, and Premium) for easy research/planning. - Generates links to directly search/book exact itinerary or listing on SkyScanner and Airbnb. (Note that sometimes SkyScanner results might differ in price from what is shown, working on a fix to have more accurate prices). Using this tool ourselves we were able to actual visit new destinations that had a pricier flight, but was much cheaper overall due to great value and low cost Airbnbs. In the past we would search cheap flights first, then find accommodation resulting in a pretty high total cost. But for long stays and vacations, accommodation quickly becomes the largest cost. For tech stack, I'm still new/beginner so using React with Material-UI, Supabase, and API's for the results. I’d love feedback from the community—especially on features you’d find helpful or ideas to improve discovery. This is a bootstrapped solo build, so I’m learning a lot as I go. Ask me anything!

Visit product View on Hacker NewsDec 20, 2024

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

95%95% 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 · Strong signals: new, using · Missing: mac, agents, macos

69%69% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: excited, ide, io · Missing: https docs, just released, exist

63%63% 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

50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: bootstrapped · 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 · Strong signals: smart · 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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