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Project overview

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.

Project overview

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.