FlyScape - A university hackathon product.

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Hi everyone, As part of their Innovative Learning Week, the School of Informatics at University of Edinburgh organized a hackathon and one of the sponsors happened to be Skyscanner (skyscanner.net). The team I was in decided to build a system which finds the cheapest way to fly to a number of cities you want to visit in a given month. It also suggests people who offer their couches (couchsurfing) in the cities you go to. Since I am not entirely sure if we can use the website publicly yet, here's a youtube video which quickly highlights the features of the system. I would be interested to find out if you think there could be a market for something like that? Thanks, Y

Visit product View on Hacker NewsFeb 25, 2013

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TrustMRRFits verified-revenue profile · Strong signals: video, month, way · Missing: mobile apps, ios, personal

63%63% predicted probability of success on TrustMRR, 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

63%63% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

52%52% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

50%50% 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

38%38% 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

20%20% 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.

Correct prediction on native model

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