SaveNeighbor – food delivery through your own personal network

Hacker News

Hey everyone, the idea came to me when I got a food delivery from a woman who mentioned she had her children with her. I thought, I wish I could request her every time I get delivery, just for the feeling of knowing just who my tips are supporting. That was 4 years ago, being non-technical I didn't see myself getting it done. But thanks to Cursor and Chatgpt, SaveNeighbor is born. I know the model adds friction and the convenience is low until you build up a network, but that's kinda the point. Think of Ubers 'Wait and save', if you can be flexible you can save big on fees. And being able to choose who your money goes to is the biggest benefit in my opinion. Shoot me all your questions, I can't wait to hear your feedback. Thanks.

Visit product View on Hacker NewsMay 25, 2026

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Actual performance

2points

5comments

Did not reach leaderboard

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

89%89% 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: cursor, model, chatgpt · Missing: mac, agents, macos

61%61% predicted probability of success on Product Hunt, 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

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.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

14%14% 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

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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