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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 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.