new-term-radar

new-term-radar catches the names of new AI models, products and repos the day they surface, before keyword tools list them. It reads X accounts you pick, Hugging Face trending, fast-rising GitHub repos and AI directory sitemaps, then a small model filters out common words. Built for SEO sites, newsletters and directories. The code is MIT.

Setup is a clone, a config file, one API key and python -m radar daily. A bundled GitHub Actions workflow runs it on a schedule. The three agent skills and MCP server are wrappers around that CLI.

The code is free; the data isn't. X posts, Google Trends, search volumes and the judging model all run through AIsa, a pay-per-call reseller, and the README estimates about $15 a month on the example config. Without the key you get Hugging Face, GitHub and sitemaps, unsorted. Solo and small teams doing new-term SEO: worth $15. Larger teams on Semrush or Exploding Topics: this finds terms earlier, not instead.

The catch: the README's AIsa links carry the author's referral code (disclosed), and your pipeline depends on a reseller keeping X and Google feeds alive. One signal is already broken upstream. Hit rates are unproven; the author says so.