I

I built a fun AI tour guide into Google Street View

Hacker News

I built a fun AI tour guide into Google Street View

I love culture + travel, and one of my favorite pastimes is dropping into random streets on Google Street View. I just LOVE getting a tiny snippet into how people live, the architecture, and discovering hidden corners of places I'll probably never visit. I'm kinda weird like that. When AI came along, I started asking it to explain what I was looking at - and I learned a ton. I couldn't stop using this combo. So I built Streetwhip. It's kinda like reverse Geoguessr - instead of guessing where you are, you get educated about places as if you had a smart local guide with you. What used to be just "some random street in Buenos Aires" becomes a rich learning adventure about Argentine architecture, why things look the way you are, and stuff about the history and way of life. I've been having so much fun with it. I LOVE when I'm on an ordinary looking street and suddenly I find out there's a WILD historic story behind why the architecture is a certain way. A couple of tips: - make sure you log in! If you don't, things aren't really real-time (I had to do this because Street View's API costs can get expensive and this is just a fun hobby project for me). - play around with the categories - they're fun. I'm curating in more places over time. - the search is not perfect - when it works its magical (totally llm-driven). But sometimes it isn't. - don't use this on mobile, it kinda sucks Would love to get your thoughts on the concept and what you could see it turn into! :) P.S. Oh and one of my favorite features: radio! You get a live radio from a stream in the country (please mute it if you get annoyed - but I promise you it makes things a whole lot more immersive)

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
90%90% 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: google, tiny, using · Missing: mac, agents, macos
86%86% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: google, way · Missing: mobile apps, ios, personal
39%39% 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
38%38% predicted probability of success on AppSumo, 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
33%33% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: smart · 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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