Star History is a web application that visualizes GitHub repository star growth over time through interactive charts and embeddable SVG badges. The system consists of a Next.js 14 frontend with D3-based chart rendering, a Hono backend that generates SVG charts and Open Graph cards on demand, and a shared code library used by both. A data pipeline (gh/) fetches repository metadata from the GitHub API and weekly activity stats from BigQuery (GitHub Archive), stores them in SQLite, and exports JSON files consumed by the frontend for leaderboards, weekly rankings, and star count pyramids. The application also includes a blog with curated open-source project roundups, a sponsor system, and a Chrome extension for quick chart viewing. Architecturally, the frontend uses URL hash-based state management to persist chart configurations (repos, timeline mode, log scale) and supports embedding via iframe or SVG. The backend caches star data, SVG charts, and OG cards using LRU caches with TTL and size limits, and rotates GitHub API tokens with cooldown to handle rate limits. The shared chart utilities (axis drawing, labels, legend, watermark) are used both server-side (via JSDOM) and client-side, demonstrating a clean separation of rendering logic. External dependencies include the GitHub REST API, BigQuery for weekly stats, GH Archive for event data, D3.js for chart rendering, JSDOM and Satori for server-side SVG/OG card generation, and SVGO for SVG optimization. The project is deployed with Docker containers (frontend on Cloudflare Pages, backend on GKE) and uses GitHub Actions for weekly data refreshes and deployments. While primarily an application, the repo has a strong data pipeline component that ingests, transforms, and exports structured data about GitHub repositories. This hybrid nature is evident in the gh/ directory, which functions as an ETL pipeline, and the blog content that analyzes open-source trends using star history data.
This repository is a collection of Claude Code skills and Python scripts that automate daily paper recommendation and note-taking for researchers. It fetches papers from HuggingFace Daily Papers, HuggingFace Trending, and arXiv API, scores them against user-defined keywords, enriches metadata (authors, affiliations, figures, method names), and generates a tiered daily recommendation markdown file (must-read, worth reading, skip). For must-read papers, it invokes a paper-reader skill that produces detailed structured notes with figures, formulas, tables, and concept wiki entries, all saved into an Obsidian vault. It also fetches GitHub Trending repositories and writes notes. The system includes a web viewer (FastAPI + vanilla JS) for browsing the vault content, with full-text search, wikilink resolution, and a Claude chat interface. The architecture is a three-step pipeline (fetch, review, notes) orchestrated by Claude Code skills, with Python scripts handling data fetching, scoring, enrichment, and file management. The codebase demonstrates solid engineering: async HTTP with fallback chains, robust HTML parsing, heuristic text extraction, and clean modular design. It is primarily an application (a set of tools) rather than a data research project, though it involves significant data processing.
This repository is a curated archive of weekly GitHub trending project rankings published by the OpenGithub community. It contains over 200 markdown files organized by date, each listing the top 20 open-source projects by star growth with metrics like total stars, weekly growth, and descriptions. The data is manually compiled and presented in a consistent template. While the early entries appear genuine, later entries (2026) contain synthetic or fictional projects with implausible growth metrics, reducing reliability. The repository has no application code, scripts, or automation; it serves purely as a static content archive. It is not suitable for automated data pipelines without significant parsing and validation.