WorthDoing.ai
Google finds what exists. WorthDoing finds what should.
---
**WorthDoing.ai** is an agentic web application that continuously discovers, investigates, challenges, and ranks things genuinely worth doing. It answers a different question from search engines:
> **What should exist, be built, researched, tested, funded, or pursued that does not exist yet — or is not being pursued enough?**
It is not a thin LLM wrapper and not a fixed `query → search → scrape → summarize` pipeline. It is a real multi-step autonomous investigation agent: Claude repeatedly decides what it knows, what remains uncertain, what to search next, which sources to inspect, whether to reject hypotheses, and when the evidence suffices to conclude.
## How an investigation works
1. **Scout** — the agent maps a domain with varied Firecrawl searches and forms *falsifiable hypotheses*.
2. **Investigate** — targeted searches (`verify` / `market` / `technical` / `competition`), page scrapes, and evidence saved as near-verbatim quotes linked to hypotheses.
3. **Skeptic** — mandatory falsification: every high-confidence hypothesis gets adversarial `falsify` searches. Hypotheses weaken, branch, or die. *A rejected hypothesis is useful progress.*
4. **Synthesize** — surviving hypotheses become **opportunities** with a seven-dimension, evidence-cited **Worth Score** (demand, neglectedness, feasibility, why-now, impact, competition, risk) — with evidence confidence reported separately, never blended in.
5. **Report** — a streamed, citation-grounded report per opportunity; citations resolve mechanically to saved evidence, never invented.
Every UI event corresponds to a real backend `AgentEvent` streamed over SSE — the live timeline never fabricates progress.
## Screenshots
| Discover | Live investigation |
| --- | --- |
|  |  |
| Opportunity report | Mobile (390 px) |
| --- | --- |
|  | 
|
*Real output: the agent investigated "Find things worth doing in local AI" — 21 steps, 14 searches, 17 evidence items, one hypothesis rejected and one weakened by adversarial checks, one opportunity (Worth Score 74 at 69% evidence confidence) with a fully cited report.*
## Architecture
```
Next.js 16 (App Router, port 3001)
├── src/lib/agent/ the investigation engine
│ ├── engine.ts explicit tool loop (Claude decides → backend executes)
│ ├── tools.ts 11 tools, Zod-validated, strict schemas
│ ├── executors.ts budget enforcement, dedup, persistence, events
│ ├── state.ts InvestigationState + compact digest (state compression)
│ ├── synthesis.ts streamed reports with mechanical citations
│ └── scoring.ts evidence-weighted Worth Score
├── src/lib/firecrawl/ search / scrape / crawl / extract behind an adapter
│ └── cache.ts canonical URLs, sha-256 content hashes, 24h freshness
├── src/lib/anthropic/ Claude adapter (streaming, cost telemetry)
├── src/lib/db/ Drizzle schema — 17 tables (Opportunity-Graph-ready)
└── src/app/ Discover · live investigation · reports · explore · history
```
**Hard guarantees**
- Budgets are hard bounds (30 steps, 20 searches, 60 scrapes, 3 crawls, wall-time) with structured stop reasons.
- No conclusion without traceable evidence; every score dimension carries `{score, confidence, reasoning, evidenceIds}`.
- Scraped content is untrusted data, never instructions (prompt-injection defense in the system prompt and `` wrapping).
- Investigations are resumable: state lives in Postgres, not in the model's context.
## Getting started
```bash
cp .env.example .env # fill in ANTHROPIC_API_KEY, FIRECRAWL_API_KEY, DATABASE_URL
pnpm install
pnpm db:migrate
pnpm dev # http://localhost:3001
pnpm test # unit tests (scoring, dedup, tool contracts)
```
## Deployment
Runs on node `m3u96a` behind an ngrok tunnel mapped to **https://www.worthdoing.ai** (see `deploy/`). SSE routes are proxy-safe: `Cache-Control: no-cache`, `X-Accel-Buffering: no`, per-event flush, `Last-Event-ID` replay on reconnect.
---
## Author
**Simon-Pierre Boucher**
📧 [contact@spboucher.ai](mailto:contact@spboucher.ai)
*Deployment: node `m3u96a` via ngrok → https://www.worthdoing.ai*