grahamrowe82/pt-edge

PT-Edge is a precomputed reasoning cache for AI infrastructure decisions. It tracks 220,000+ AI repos across GitHub, PyPI, npm, Docker Hub, HuggingFace, and Hacker News, scores them daily on quality, and publishes the results as a 220,000+ page directory site.
The site serves two audiences: **AI agents** reading pages on behalf of humans (structured, front-loaded, machine-readable) and **humans** reading directly (navigable, trustworthy, original analysis). Every page is designed so an AI agent can land on it and walk away with a confident, citable recommendation in one pass.
Every major AI lab's crawl infrastructure treats the site as a primary data source. The access logs are themselves an intelligence layer — see Demand Radar below.
**Directory site:** mcp.phasetransitions.ai — 220,000+ pages across 17 domains with 2,400+ categories, updated daily.
**Built by Graham Rowe**
How It Works
1. **Ingest** — daily pipeline pulls GitHub stats, package downloads, releases, HN posts, HuggingFace models/datasets, and registry data
2. **Score** — composite quality score (0-100) from four dimensions: maintenance, adoption, maturity, community
3. **Enrich** — LLM-generated technical summaries, practitioner-focused assessments, and comparison analyses from READMEs
4. **Publish** — static site generation across 17 domains with structured data, internal linking, and freshness signals
5. **Observe** — bot traffic analysis reveals what the AI ecosystem values (Demand Radar)
The entire system runs on a single server instance for under $300/month.
Quality Scoring
| Dimension | Max | Signals |
|-----------|-----|---------|
| Maintenance | 25 | Commit activity (30d), push recency |
| Adoption | 25 | Stars (log scale), monthly downloads, reverse dependents |
| Maturity | 25 | License, PyPI/npm packaging, repo age |
| Community | 25 | Forks (log scale), fork-to-star ratio |
**Tiers:** Verified (70-100), Established (50-69), Emerging (30-49), Experimental (10-29)
Demand Radar
Every bot hit on the site is latent intelligence. The access logs carry three layers of signal:
- **Indexing bots** (Meta, Anthropic, Amazon, Google, Perplexity, OpenAI) — what AI companies think will be valuable in future model weights. Each bot has a distinct crawl strategy that reveals its parent company's priorities.
- **User-action bots** (ChatGPT-User, OAI-SearchBot, Perplexity-User) — what real humans are asking AI right now. Each hit represents a practitioner making a technology decision through an AI intermediary.
- **Human visitors** — what people find through search engines directly.
The Demand Radar extracts these signals and feeds them into content prioritisation — eventually via trained ML models rather than hand-tuned weights. See scratch/demand-radar/ for the full analysis.