# Delphi

- **Event:** [Google I/O Hackathon](https://cerebralvalley.ai/e/google-io-hackathon)
- **When:** Sat, May 23 at 9:00 AM – 10:00 PM (PDT)
- **Where:** Shack15, San Francisco, CA
- **Team:** [Sankaranarayanan Subbayya](https://cerebralvalley.ai/u/Sankar)
- **GitHub:** https://github.com/SankarSubbayya/delphi
- **Demo video:** https://youtu.be/FObF2xpgBAw
- **Gallery:** https://cerebralvalley.ai/e/google-io-hackathon/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/google-io-hackathon/hackathon/gallery/11

Delphi · synthetic populations as a computational primitive.

You ask any question — Will the Fed cut rates in Q3? Pretest this tagline. Stress-test this decision — and a swarm of Gemini 3.5 Flash sub-agents, each role-playing a different American persona generated from real US Census demographic axes and grounded in live web, reasons in parallel.

In ~60 seconds you get back: a probabilistic forecast with confidence interval, the strongest reasons for and against drawn from agents' own reasoning, the demographic axes where groups diverged most, and a striking outlier quote — all synthesized into a Wall Street Journal–style summary by a final Gemini call.

Live-validated on stage at N=500 with a +66.2 percentage point shock response (13.3% → 79.5% when CPI surprised). Validation across 27 automated tests, an adversarial persona-stability harness (4.92/5 in-character, 0% drift to centrist mean), and a cross-model comparison that showed Gemini 3.5 Flash holds 100% per-agent success where Gemini 2.5 Flash collapses to 12.5% on identical prompts.

Until Gemini 3.5 Flash made hundreds of parallel grounded reasoning agents economical at conversational latency, this category did not exist. Forecasting is the wedge — the same primitive powers marketing pretests, policy war-gaming, synthetic juries, and behavioral pre-mortems.

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Markdown version of https://cerebralvalley.ai/e/google-io-hackathon/hackathon/gallery/11. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt