I Built a Gaming GeoGuessr

Hacker News

My friend and I made a fully featured gaming geoguessr - complete with 360 degree panoramas, movement, leaderboards, and multiplayer. We had made a Fortnite geoguessr a while back, and although this was well received, our goal always was to bring the worlds of all games to the browser. A much harder task considering the scale and scope of many open world games. We had to make a bunch of tools to be able to achieve this. World of Warcraft alone took us roughly 3,000,000 images to do (and will likely need another 1,000,000 for Dragonflight once it releases). We are finally at a point where we are happy to release this into the wild and let people try it, although we are still working hard on adding all the games that are listed as coming soon. If you're ever bored, and would like to test your gaming map knowledge - or would just like to explore the world in your browser akin to Google StreetView, you can check us out at https://lostgamer.io

Visit product View on Hacker NewsNov 16, 2022

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

77%77% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Product HuntOn track for Day 1 leaderboard · Strong signals: google, open · Missing: mac, agents, macos

75%75% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, 000, io · Missing: https docs, excited, just released

73%73% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Strong signals: soon · Missing: plus, platform, intuitive

60%60% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

43%43% 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

11%11% 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.

Correct prediction on native model

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