3D World Explorer and Location Guesser Game

Hacker News

TL;DR Google Earth meets GeoGuessr. I wanted to explore the idea of visualising my city Vilnius with exaggerated terrain since it mostly looks flat on any map. The resulting visual represents more what it "feels" like in real life where each hill you drive up becomes like a small mountain. Exploration can be done via text search or flying around both on mobile (touch drag or drone/joysticks mode) and desktop (spectator WASD). Afterwards, I slapped on some location presets + a location guessing game for some extras to support the project. The solution uses open source data glued by vibes over a weekend.

Visit product View on Hacker NewsSep 18, 2026

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Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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

65%65% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

65%65% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

60%60% predicted probability of success on Indie Hackers, 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.

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.

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

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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