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Globe of History – Interactive 3D Map of 6k Years of Human Events

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Globe of History – Interactive 3D Map of 6k Years of Human Events

I built Globe of History — an interactive 3D visualization of 6,000 years of human events, mapped onto the Earth’s surface. It includes 5,000+ data points covering wars, inventions, assassinations, natural disasters, and philosophers — spanning 4000 BCE → 2025 CE. The main goal was to build a visual entry point into history — not a database, but a way to instantly sense density and continuity over time. Stack / Process Python for scraping structured event data from Wikipedia/Wikidata Gemini 2.0 Flash for AI-based enrichment and multi-stage verification Mapbox GL JS + React (Vite) for the interactive 3D globe Custom scripts for deduplication, temporal grouping, and category color logic Currently optimized for desktop only I’d love feedback from this community on: data-pipeline scaling / verification best practices improving performance with large point datasets in Mapbox ideas for better temporal exploration UX Source link: globeofhistory.com

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Indie HackersFits the IH revenue-focused audience · Strong signals: gemini · Missing: supports, reddit linkedin, podcasting
90%90% 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: visual, gemini · Missing: mac, agents, macos
81%81% 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, pipe, 000 · 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.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
19%19% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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