grahamrowe82/pt-edge
🐍 ☁️ 🍎 🪟 🐧 - AI project intelligence MCP server. Tracks 300+ open-source AI projects across GitHub, PyPI, npm, HuggingFace, Docker Hub, and Hacker News. 47 MCP tools for discovery, comparison, trend analysis, and semantic search across AI repos, HuggingFace models/datasets, and public APIs.
Quick Install
{
"mcpServers": {
"grahamrowe82-pt-edge": {
"command": "npx",
"args": [
"-y",
"grahamrowe82-pt-edge"
]
}
}
}Using an AI coding agent (Claude Code, Cursor, etc.)? Copy a ready-made prompt that tells it to fetch the setup instructions and install this server for you.
Documentation Overview
PT-Edge — AI Infrastructure Intelligence
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
- Ingest — daily pipeline pulls GitHub stats, package downloads, releases, HN posts, HuggingFace models/datasets, and registry data
- Score — composite quality score (0-100) from four dimensions: maintenance, adoption, maturity, community
- Enrich — LLM-generated technical summaries, practitioner-focused assessments, and comparison analyses from READMEs
- Publish — static site generation across 17 domains with structured data, internal linking, and freshness signals
- 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.
Stack
Python, FastAPI, PostgreSQL + pgvector, LLM enrichment (multiple providers), static site generation via Jinja2 + Tailwind CSS. Hosted on Render. MCP tools and REST API for programmatic access.
Development
This is a production system with no staging environment. The database is a live 1GB+ PostgreSQL instance — queries hit real data. See docs/development.md for setup notes and safety rules.
Documentation
docs/strategy.md— strategic positioning and reasoningdocs/roadmap.md— phased build plandocs/design/worker-architecture.md— task queue and worker designdocs/development.md— development setup and database safetyscratch/demand-radar/— access log intelligence analysis
License
MIT — see LICENSE.