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The Timeline of Everything – see the history of any topic

Hacker News

The Timeline of Everything – see the history of any topic

When learning about history, I like to visualize when certain events happened relative to other events to better understand the context. I started working on this idea of using AI to take an input topic and generate an interactive timeline of important events within the topic. Some topics to try: "Tell me about the history of space exploration" "When were important fossils discovered?" "Show me when all the nintendo consoles were released" For now the generated events must be "known" by the LLM. One idea to improve event generation is to hook the AI up to a Wikipedia MCP server or something. Also interested in adding features like 'expand on this event' or 'show more events between these two' Help build it: https://github.com/MichaelMilstead/timeline-of-everything

Visit productView on Hacker NewsMay 22, 2025
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Product HuntOn track for Day 1 leaderboard · Strong signals: mcp, context, visual · Missing: mac, agents, macos
80%80% predicted probability of success on Product Hunt, 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: ide, io · Missing: https docs, excited, just released
59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: visualize · Missing: mobile apps, ios, personal
47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Strong signals: started · Missing: supports, reddit linkedin, podcasting
45%45% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · 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 · 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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