# Argus

- **Event:** [National Security Hackathon (by Army xTech)](https://cerebralvalley.ai/e/3rd-annual-natsec-hackathon)
- **When:** May 2 at 9:00 AM – May 3 at 4:00 PM (PDT)
- **Where:** Shack15, San Francisco, CA
- **Team:** [Jeffrey Wang](https://cerebralvalley.ai/u/jeffreywangcf), [Myles Pember](https://cerebralvalley.ai/u/Mylespember)
- **GitHub:** https://github.com/jeffreywangcf/argus
- **Demo video:** https://youtu.be/huIZMSoXKfg
- **Gallery:** https://cerebralvalley.ai/e/3rd-annual-natsec-hackathon/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/3rd-annual-natsec-hackathon/hackathon/gallery/54

ARGUS ingests real-time Telegram signals from verified battlefield channels, enriches each event using an LLM to extract structured intelligence — actors, locations, equipment, casualties, faction attribution — and cross-verifies against GDELT media signals to produce a confidence-weighted event stream.
The system exposes a unified command dashboard where analysts can scrub through historical battlespace evolution, watching events appear and transition from unverified to confirmed in real time as corroboration builds. A natural language query layer allows commanders to ask plain English questions — "what Russian equipment was reported near Kharkiv in the last six hours?" — and receive both the raw data and a structured SITREP in seconds.
Every event is weighted by source trust, snapped to frontline position, and stored with full audit trail. The result is a living operational picture that compresses hours of manual intelligence synthesis into a continuously updated, queryable battlespace model.

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Markdown version of https://cerebralvalley.ai/e/3rd-annual-natsec-hackathon/hackathon/gallery/54. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt