Factori MCP
Talk to the real world with Factori MCP
What it does
Ask your AI agent about foot traffic, demographics, or competitor density anywhere on earth, and get a structured, data-backed answer in seconds. Factori MCP connects Claude, Cursor, and other AI agents directly to 12 layers of location intelligence spanning 150+ countries and 200M+ POIs. No dashboards, no SQL, no waiting on analysts. Just ask, and let the world's data answer back.
Ask your AI agent about foot traffic, demographic and competitive density anywhere in the world and get a structured, data-backed answer in seconds using Factori MCP.
Large language models made the internet fully queryable. They unlocked the ability to reason across vast amounts of text, code, and knowledge in seconds. But that understanding has always been limited to what exists online. The physical world operates differently. It is dynamic, constantly changing, and largely unstructured. It is defined by how people move, where demand emerges, and how places evolve over time. These signals do not live on the internet, and as a result, they have remained outside the scope of how AI systems understand context. We built the first MCP server for real-world data, so any AI agent can now query the physical world the way it queries the internet. By structuring…from factori.ai
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MCP-Builder.ai11d ago · mcp-builder.ai · ▲174The fastest way to connect your data with your AI Tools.





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Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
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Hello HN! I found that picking out plausible but diverse skin tones for my digital art and game development projects was kind of difficult, and I got curious about if there was a way to define a color space that made it easy. I've built a color picker and procedural generation algorithm based on the space as well as a bunch of other fun js features and demos throughout the page that use the equations. If you find it interesting, I have lots of explanations of how I built it and what properties the space has. The methodology might be a bit shaky, but hopefully the result is as helpful for…
Life & fun · Aug 2026 · toneyalexander.github.io


I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
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