Mvndvs TERRARUM – A Renaissance-style 3D world map

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In this fast-paced, AI-exploding era, I felt a strong desire for a slower, more deliberate pace. I built a 3D interactive world map: https://world.gjlmotea.com/ Lately, time seems to be flying by. I've been building a lot of quirky, experimental projects, but honestly, my mood has been quite restless and low. There are just too many options and new technologies emerging every day—choice paralysis sets in before I can even figure out what I truly want to build. But this project is different. It’s more grounded, human, and carries a sense of warmth. I wanted to create an immersive experience with a Renaissance, antique parchment aesthetic. Features: Explore: Browse different countries and click on nations or architecture for detailed information. Data Layers: Toggle between various global map modes including Architecture, Culture, Academia, Commerce, and Military. Themes: Multiple visual themes to switch between. 3D Architecture: Scale the models of buildings (like Taipei 101) to your liking. Global Enterprises: It maps the top 100 global commercial empires across Finance, Industry, Food, and Tech—from TSMC, MediaTek, and Foxconn to everyday chains like McDonald's. The Macro Perspective: As you zoom out from the map, the perspective shifts: First, you can observe constellations and celestial bodies. Keep zooming, and you'll see the 8 planets of our Solar System. Zoom out even further, and you are met with a beautiful view of the Milky Way.

Visit product View on Hacker NewsApr 28, 2026

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

89%89% 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: mac, model, new · Missing: agents, macos, agent

79%79% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Strong signals: way, para · Missing: mobile apps, ios, personal

51%51% 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

35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: arr, active · Missing: mrr, revenue, profit

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.

Incorrect prediction on native model

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