The author has built Global Fail Map, an open-source interactive tool documenting historical failures in science, technology, and business to serve as a research layer for AI agents and developers.
I built Global Fail Map around the idea of abandoned futures, recalled or dead science tech, failed companies, and ambition in recent times, past decades and even centuries that are even probably forgotten now. It is an interactive map of failed and abandoned human ambition across the world. The map contains stories about: - Failed companies - Cancelled and unfinished megaprojects - Dead technologies and discontinued products - Terminated scientific and clinical research programmes - Unrealised cities, settlements and future visions Each category has its own visual layer, so you can explore failure geographically rather than finding isolated examples buried across articles and Wikipedia pages. Click any marker and you get a research report explaining: - What was attempted? - Where and when it happened? - Why it failed or was abandoned? - What survived? - What happened to the people, technology or infrastructure involved? - What future builders can learn from it The reports are generated with citations, so the map is not just a collection of interesting stories. It is designed to be a research layer. You can search for a company, place, project, technology or idea that is not already on the map. What this means for AI agents: - An agent planning a new infrastructure project should be able to find cancelled projects with similar constraints. - An agent evaluating a new technology should be able to study technologies that failed for similar technical, economic or regulatory reasons. - An agent helping someone start a company should not only retrieve successful precedents. It should also retrieve failed analogues and explain the failure modes. The map is the exploration layer while the research workflow is the intelligence layer. Tech stack: - Next.js - React - Mapbox GL with globe projection - Zustand - Tailwind CSS - Valyu DeepResearch - Markdown reports with citations I made it open source so people can clone it, add their own categories, build new research layers and turn it into a custom intelligence tool for their own agents. I’m curious how people building AI agents would use this: - Should failure research be a first-class retrieval layer for agents? - What fields should a structured failure record contain? - Would failure modes, timelines, capital spent, dependencies, regulation and source confidence be useful agent-readable metadata? - Would an MCP server or JSON/GeoJSON feed make this more useful? The live demo and GitHub repository are in the comments.
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