Langfuse is an open-source LLM observability and evaluation platform. It provides tracing, prompt management, evaluations, datasets, experiments, monitors, and a public API for integrating with AI applications. The architecture is a monorepo with pnpm workspaces and Turborepo, consisting of a Next.js frontend (web), a background worker (Express + BullMQ), a shared package with types and server utilities, and an enterprise edition. Data flows from ingestion (REST API, OpenTelemetry, SDKs) into ClickHouse for high-volume analytics and PostgreSQL for relational metadata. Background jobs handle evaluations, batch exports, cloud billing, and integrations. The codebase is well-structured with extensive testing, a custom ESLint plugin, and reusable UI components. It is actively maintained with frequent migrations and feature additions.
This repository provides a Python CLI tool and Makefile pipeline to scrape all essays from Paul Graham's website (paulgraham.com), convert them to Markdown, and produce an EPUB ebook. The core logic resides in a single script (`graham.py`) that uses `requests`, `BeautifulSoup`, `html2text`, and `htmldate` to download, parse, and date-stamp each essay. A `Makefile` orchestrates the full build: creating a virtual environment, fetching essays, merging them with `pandoc`, and generating the final EPUB with a custom CSS and an iBooks dark-mode patch. The CI workflow (GitHub Actions) runs daily, detects new essays by comparing article counts, and automatically creates a release with the updated EPUB and metadata CSV. The architecture is straightforward—no web server, database, or tests—but the pipeline is robust and well-documented for its single purpose. While the scraper is tightly coupled to Paul Graham's site structure, several components (the iBooks patcher, the footnote conversion function, and the EPUB stylesheet) are cleanly isolated and reusable in other EPUB generation projects.