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Deal, HyperLocal Buying & Selling

Hacker News

Deal, HyperLocal Buying & Selling

Hi HackerNews, I'm Chris and I'd like to show you my new free app called: Deal It works like this, you list the things you want to Buy &/or Sell When you walk past people in the High Street or at a Market, Your lists are compared automatically. If there is a match, you’re alerted & you can chat to each other to organize a Deal. Video: http://vimeo.com/52853298 Download link: https://itunes.apple.com/us/app/deal/id333252543?ls=1&mt=8 Website link: http:/deal.gameweaver.com Scenarios: “The last shopping day before Christmas” It’s the last shopping day before Christmas and all of the shops have sold out of “SuperMega Toy” - the must have toy for Christmas. You’ve been looking everywhere for it. It may be the case that some people might be selling the “SuperMega Toy” you are after, they might have brought it and changed their mind etc. You enter “SuperMega Toy” in the App as “Buy” whilst the seller enters “SuperMega Toy” in the App with “Sell”, If the two people are sitting down having lunch at the same fast-food place, or walk buy each other in the street. Their lists will be compared, there’s a match, they chat to each other to work out a Deal and Christmas is saved. “Market” You’re a collector of “My Little Pony” memorabilia, you visit Markets/Yard Sales/other events in your search for “My Little Pony” items. Market stall sellers enter a list of the items they are selling and you as the buyer enter the “My Little Pony” items you are buying. As you walk around the different stalls, your lists are compared, saving you time to search for the items you want. Hopefully you get a match and find the items you want to buy.

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Actual performance

4points
3comments
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting
75%75% 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 HuntUnlikely to reach the leaderboard · Strong signals: apple, new · Missing: mac, agents, macos
43%43% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: ios, video · Missing: mobile apps, personal, entrepreneurs
41%41% predicted probability of success on TrustMRR, 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
39%39% 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
36%36% 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
13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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