Geopolitical Pattern Intelligence System
Local AI that finds patterns in geopolitical events
What it does
GPIS analyzes geopolitical events by finding recurring patterns in 100,000+ real historical records. No API keys. No cloud. No data leaving your computer. You describe a current event. The system retrieves similar historical situations from the GDELT corpus, runs a self-refining agentic loop (3–7 iterations), critiques its own analysis by searching for counterexamples, validates every claim against the corpus, and generates a professional PDF report with charts and confidence scores.
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I simulated closing the Strait of Hormuz on real oil trade dataJul 2026 · globaloilnetwork.staffinganalytics.io · ▲244OP here: I created this visualization tool as the byproduct of a supply chain class I taught at Columbia. The pedagogical exercise grew into a full blown visualization and paper about global oil trade. The model: The mechanics are the same as the financial network Eisenberg-Noe: Instead of banks, every country consumes oil interconnected via bilateral trading. Shocks propagate throughout the network, depleting oil reserves when bottleneck nodes (such as the Strait of Hormuz) are blocked. Insights: The interesting part is the mechanics of how the crisis unfolds: for example, France receives 0…
- SASoros – AI for geopolitical macro investingMar 2026 · asksoros.com · ▲26
Hi HN! We are Anshuman and Karén, the co-founders of Lookback Labs and the co-designers of Soros (https://www.asksoros.com/). Soros is a compound AI system built carefully from the ground up to trace a path (multiple paths, really) from a description of a geopolitical event all the way to capital market implications. * Here's how we set it up: Given a description of a given geopolitical event (can be a couple of words; the demo literally has "US-Iran conflict" as the entire string), Soros will - (1) first analyze and perform deep research on it, running scores of searches in…
- TTText to 3D simulation on a map (does history pretty well)2025 · mused.com · ▲72
Simulate anything on a map from a text prompt -- and conduct risk analysis against LiveUA map's global realtime data points from social media and news sources. I trained a GPT-2-size model on historical incident data used to predict things that will go wrong. As historian Benjamin Breen mentions, the leading language models are good historians, so the application will simulate historical events pretty well also. I include a Multi-Agent RL Urban Mobility model in progress displayed on the map as small white cubes representing traffic and pedestrians. Around SF, it uses real census data and…
- HUHistoryGPT – Unraveling History with GPT Insights2023 · historygpt.art · ▲35
More ai this month
the category →
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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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…
AI · 27d ago · cactuscompute.com
Launched alongside, May 2026
the whole month →
Parallel agents, diff reviewer, and multi-model comparisons
Dev tools · May 2026 · kilo.ai
- NW
Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
Life & fun · May 2026 · github.com
- FM
Dev tools · May 2026 · github.com