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I built an AI audio guide you can use at any museum

Hacker News

I built an AI audio guide you can use at any museum

Pre-recorded museum audio guides are usually limited — they only cover a few “highlight” pieces, or aren’t available at all. Human-led tours are great, but they’re only offered at fixed times and with limited slots. At the same time, if you try to read all the label text thoroughly, you often end up spending more time reading than actually looking at the exhibit — something I’d rather do on Wikipedia at home. Plus, museum fatigue sets in quickly. Or you skip the labels, hop from item to item, and walk away without really knowing what you were looking at or why it mattered. That’s why, as a museum fan, I built a web app that lets you generate an instant audio guide for any exhibit — just snap a photo of the label (or type it in), and you get an AI-generated narration you can listen to while looking at the piece. To be honest, it’s probably not especially technical by HN standards — this is my first serious coding project. But I think it’s interesting at the intersection of tech, culture, and how we experience learning in informal spaces like museums. Curious to hear thoughts — feedback much appreciated. Check it out here: https://www.museumwhisper.com

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best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: coding · Missing: mac, agents, macos
54%54% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: plus · Missing: platform, intuitive, reviews
42%42% 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
38%38% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
29%29% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: audio · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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