MonumentAI – Shazam for buildings (History without the boring parts)

Hacker News

Hi HN, I'm Ozan, the developer behind MonumentAI. I built this app because I enjoy traveling but find traditional audio guides and plaques incredibly boring. They usually focus on dates and architectural styles, skipping the interesting human stories—the scandals, exiles, and secrets. I wanted a "Shazam for Buildings" that feels like a local friend whispering the gossip in your ear, rather than a history textbook. How it works: 1. You take a photo of a landmark. 2. The app identifies the location using Google Gemini's vision capabilities. 3. It generates a short, engaging story focused on the "gossip" or hidden history (also via Gemini). It's currently an iOS app built with SwiftUI. The app is free to download (with a paid tier for unlimited scans), but you can try the core functionality without paying. App Store Link: https://apps.apple.com/us/app/monumentai-scan-explore/id6756... I'd love to hear your feedback on the UX and the quality of the stories. Thanks!

Visit product View on Hacker NewsDec 24, 2025

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Actual performance

1points

Did not reach leaderboard

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

96%96% 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: apple, google, apps · Missing: mac, agents, macos

68%68% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

50%50% 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

34%34% 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

26%26% 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

25%25% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: paid, audio · Missing: web3, chat, crypto

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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