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Monitors real-time signals from Docker Hub to track the distribution and adoption metrics of AI project containers. Collects real-time repository statistics, release summaries, and star momentum to track the development state and popularity of open-source AI projects. Pulls package download data and release information from the npm registry to compute momentum, hype ratios, and project tiers. Pulls package download data and release information from PyPI to compute momentum, hype ratios, and project tiers for Python-based AI projects. Ingests discussions from V2EX to monitor community feedback and track real-time signals regarding AI development trends.

PT-Edge — AI Infrastructure Intelligence

PT-Edge tracks 220,000+ AI repos across GitHub, PyPI, npm, Docker Hub, and HuggingFace, scores them daily on quality, and publishes the results as a directory site and via MCP tools and REST API.

Directory site: mcp.phasetransitions.ai — 165,000+ pages across 17 domains with 2,400 categories, updated daily.

Built by Phase Transitions

Directory Domains

Domain	Pages	Categories	Path
ML Frameworks	49,120	715	`/ml-frameworks/`
LLM Tools	26,982	346	`/llm-tools/`
AI Agents	18,934	198	`/agents/`
MCP Servers	12,551	178	`/`
NLP	12,023	236	`/nlp/`
RAG Tools	8,511	107	`/rag/`
Voice AI	6,703	125	`/voice-ai/`
Transformers	5,654	96	`/transformers/`
Generative AI	5,377	89	`/generative-ai/`
Embeddings	3,915	68	`/embeddings/`
Prompt Engineering	3,899	64	`/prompt-engineering/`
Diffusion Models	3,952	57	`/diffusion/`
AI Coding Tools	3,733	52	`/ai-coding/`
Vector Databases	2,847	48	`/vector-db/`
Computer Vision	382	9	`/computer-vision/`
Data Engineering	388	2	`/data-engineering/`
MLOps	94	2	`/mlops/`

Every project page includes a composite quality score (0-100) computed from four dimensions — maintenance, adoption, maturity, community — plus AI-generated technical summaries, live metrics paragraphs, risk flags, and structured data for search engines.

How It Works

Daily ingest pipeline pulls GitHub stats, package downloads, releases, HN posts, HuggingFace models/datasets, public API specs, and npm registry data
Quality scoring via materialized views: composite 0-100 score from maintenance (commits, push recency), adoption (stars, downloads, reverse deps), maturity (license, packaging, age), and community (forks, fork/star ratio)
AI summaries from READMEs via Claude Haiku — 2-3 sentences of technical depth beyond the GitHub description
Daily metric snapshots for all 220K repos — stars, forks, downloads, commits tracked over time
Embedding-based category discovery — 1536d embeddings + UMAP + HDBSCAN clustering + LLM labelling discovers 2,400 search-intent-aligned categories automatically
Static site generation via Jinja2 templates + Tailwind CSS, served from FastAPI alongside the MCP server and REST API
47 MCP tools for programmatic access via Claude Desktop, Claude.ai, and any MCP client
REST API with keyed access for B2B integrations

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)

Stack

Runtime: Python 3.11, FastAPI, FastMCP
Database: PostgreSQL 16 with pgvector
Embeddings: OpenAI text-embedding-3-large (256d)
LLM: Claude Haiku 4.5 (summaries, classification, enrichment)
Site: Jinja2 + Tailwind CSS (static, generated at startup)
Hosting: Render (web service + cron + managed Postgres)

Development

git clone https://github.com/grahamrowe82/pt-edge.git
cd pt-edge
cp .env.example .env  # Add your API keys
docker compose up -d  # Start database
alembic upgrade head  # Run migrations
uvicorn app.main:app --reload  # Start server
python scripts/ingest_all.py   # Run daily ingest
python scripts/generate_site.py --domain mcp --output-dir site  # Generate directory