Weather Watching

Hacker News

Weather Watching

I was walking around New York last month during some light rain and noticed about half the people had umbrellas open. When the rain picked up a few minutes later, that number jumped closer to 80%. It got me thinking it'd be cool to track this somehow, so I built a website! I am taking a sidewalk livestream, feeding it into a YOLO model for people tracking, then sending a frame of each detected person to Gemini 2.0 Flash, which returns structured JSON about each person's clothing and if they're holding an umbrella. I also had fun making the site look like a TV weather channel. I showed some friends this project and someone mentioned how the legendary Tasks xkcd comic ( https://xkcd.com/1425 ) is out of date now. If you want to check whether a photo has birds in it (or if someone is holding an umbrella), you can just ask an inexpensive vision model for JSON.

Visit product View on Hacker NewsJun 23, 2025

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

82%82% 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: model, new, tasks · Missing: mac, agents, macos

71%71% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

52%52% 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

49%49% 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

30%30% 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

15%15% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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