Treasure Guessr – A map-based web game

Hacker News

Hi HN, I recently launched Treasure Guessr, a small web game I made with a friend: https://play.treasureguessr.com You're shown a zoomed-in "treasure map" (a cropped view from somewhere in the world), and your goal is to find that same spot on a global map. It’s inspired by the treasure maps in FFXIV, and a bit by GeoGuessr, of course. We built it mostly for fun, and might explore a Steam version with more content for a few bucks if there’s enough interest. Fun fact: the backend is just a shared Google Sheet queried directly from the React app. Not sure how sustainable that is, but it did the trick for the prototype. Feedback welcome! Antoine aka teuteuf (also made Worldle a few years ago: https://news.ycombinator.com/item?id=30367906 )

Visit product View on Hacker NewsJul 30, 2025

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

63%63% 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.

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

53%53% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntUnlikely to reach the leaderboard · Strong signals: google, new · Missing: mac, agents, macos

48%48% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: google · Missing: mobile apps, ios, personal

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

16%16% 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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