I built a new 311 iOS App for SF

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Hi everyone! I've spent the last few months working on a new 311 app for San Francisco and it went live on the Apple App Store recently. This is my first big software project and the first personal project I've put in front of strangers. This project started because I was an avid user of the official 311 app, but I found I was not reporting as much as I wanted to because it took just a little too long. Right now you can report trash, graffiti, illegal parking, damaged public property, and encampments through my app in just a few seconds by taking a photo. There are some other features like an offline mode, browsing nearby reports, commenting, bookmarking, upvoting, notifications, tracking your stats... and more will be added. The city is actually pretty good at resolving issues - much better than I realized before I started being actively involved. I highly recommend submitting a few reports and seeing for yourself. Let me know your thoughts!

Visit product View on Hacker NewsFeb 21, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: apple, user, new · Missing: mac, agents, macos

84%84% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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.

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

62%62% 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

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

18%18% 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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