Live Demo: autopsy-nine.vercel.app
Autopsy investigates why companies fail — and what could have saved them. Six specialized AI agents per mode research in parallel, debate each other's findings, and produce a forensic verdict in ~22 seconds.
Four modes:
- Postmortem — investigate why a company failed
- Pre-Mortem — predict what could kill a living company
- Founder Mode — analyze your own startup before it fails
- Counterfactual — explore alternate histories: "What if they made a different decision?"
The 4th mode asks: What if [company] had made a different decision?
Six counterfactual agents reason about what didn't happen:
- CF Market Analyst — skeptical about internal decisions changing external market realities
- CF Operator — assesses whether the alternate decision could actually be executed
- CF Money Trail — models the financial impact of the alternate path
- CF Customer Voice — evaluates whether users would have responded differently
- CF Engineer — honest about technical complexity vs. leadership beliefs
- CF Historian — finds real precedents (requires 2+ historical cases as evidence)
Each agent must: understand the actual causal chain, identify the decision point, model the alternate chain, find real precedents, and assess second-order consequences (butterfly effects).
The synthesizer renders one of five verdicts: would have survived, would have delayed failure, would have failed differently, would have made no difference, or could have transformed the company.
Preset scenarios: Blockbuster/Netflix, Kodak/Digital, Yahoo/Google, Quibi/TV, Theranos/Real Science, MySpace/Better Tech.
192GB HBM3 lets us load all 6 agents (each ~70B parameters) simultaneously. On a single H100 (80GB), this requires 3 sequential rounds. On MI300X, it runs in one parallel pass — enabling real-time agent debate that wasn't possible before.
| H100 | MI300X | |
|---|---|---|
| Memory | 80 GB HBM3 | 192 GB HBM3 |
| Agents | 3 sequential rounds | 1 parallel pass |
| Debate | Impossible | Real-time |
| Time | ~75s | ~22s |
See the full architecture breakdown →
- Palette: Indigo (
#4B4BA0) and violet (#8F47AE) accents on near-black (#0F1110) surfaces - Typography: Newsreader serif for display, Inter for UI, JetBrains Mono for data
- Surface language: Bordered cards with
border-white/5, gradient glow shells, and subtle corner brackets - Background: Three.js data-wave mesh with dual-layer crossfading video, overlaid with solid tint + bottom-to-top gradient for text legibility
- Next.js 15 + TypeScript + Tailwind v4
- Kimi K2.6 via Fireworks AI (262K context window)
- Tavily Search API for evidence gathering
- Server-Sent Events for streaming agent updates
- Vercel edge deployment
# Clone
git clone https://github.com/anilandcode/autopsy.git
cd autopsy
# Install
npm install
# Environment
cp .env.example .env.local
# Add your API keys:
# FIREWORKS_API_KEY= (for Kimi K2.6 via Fireworks AI)
# TAVILY_API_KEY= (for web search)
# Run
npm run devOpen http://localhost:3000 locally, or visit the live demo at autopsy-nine.vercel.app.
MIT
AMD Developer Hackathon 2026 by lablab.ai