Argus
The Problem
GDELT is a public database that monitors the world’s news in real time — 300+ sources, updated every 15 minutes, hundreds of thousands of events logged every day. The problem is that most of what it flags as conflict isn’t: crime reports, court cases, and sports stories that got mislabeled. A map built directly on that data is useless. It’s just noise.
What I Did
I needed to make that data actually usable, which meant deciding what to filter, how, and in what order. I designed a layered approach — drawing on Palantir’s open source documentation for the pipeline architecture — simple rules handle the obvious cases first (fast and free), then an AI reads the source article and scores genuinely ambiguous events before making a call. Sequencing it that way kept costs low and made every decision traceable — you can always see why something was included or rejected.
I also recognized that checking a media report against more media reports is a weak form of verification. So I brought in NASA satellite thermal data as a second, independent source. When a reported explosion lines up with a heat signature at the same location and time, that’s physical evidence — not just another headline saying the same thing. The interface labels every event by its evidence type so there’s no ambiguity about what you’re looking at.
I owned the full scope from problem definition to deployment. It runs on a 15-minute automated cycle and is live at argusosint.vercel.app.
