analytics

Livebook lets teams write Markdown and Elixir code cells together in a browser, then run queries, charts, and other data tasks on demand. Notebooks are stored as versionable .livemd files and can connect to a fresh runtime, an existing Elixir node, or a project. Because users can execute code and access files on the host, deployed instances should be carefully protected.

Litlyx helps websites and web apps collect visits, real-time users, and custom events through a small script tag or JavaScript package. Data is viewed in its analytics dashboard, with a hosted service available for quick setup or Docker deployment for teams that want to run their own instance. Its cookie-free approach makes it a practical option for projects seeking straightforward analytics without committing entirely to a third-party cloud.

AI Hot Monitor is a hands-on teaching project built around a working trend-tracking application. Users define keywords, aggregate results from more than eight sources including Twitter, Bing, Hacker News, and Bilibili, then apply AI query expansion, authenticity checks, relevance scoring, and summaries. Results can be filtered and pushed through WebSockets or email, and the monitoring workflow is also packaged as an agent skill; the repository is primarily presented as a build-from-scratch course project rather than a hosted service.

Codex Usage Tracker reads the JSONL logs already stored on your machine, indexes aggregate call, thread, cache, and supporting evidence into SQLite, and exposes the results through a localhost dashboard, CLI, and MCP tools. You can filter costly or low-cache calls, inspect thread-level drain, and ask Codex to explain usage patterns. Indexed content remains local, while generated reports and support bundles omit it by default. The project is independent and not affiliated with OpenAI.