By the numbers
We pull from multiple feed URLs across AI blogs, product feeds, and open-source trend channels. Each item links to the primary source. Below are current volume and classification so you can verify how we operate.
1) Source collection
We ingest signals from curated AI update feeds and open-source trend channels. Every summarized item keeps a link back to original sources. For the full list of feed URLs we track, see Sources & Coverage. Primary coverage includes: AI blog feeds (e.g. BestBlogs.dev AI category), GitHub Trending (daily/weekly), and Skills/MCP repo activity.
2) Filtering and de-duplication
We remove repetitive or low-value entries and group related updates so builders can scan quickly without reading duplicate posts. Duplicates (same or near-identical link) are collapsed; low-score or substance-free announcements are excluded. See Editorial standards for what we include and exclude.
3) Summarization and structure
Updates are transformed into concise summaries, tags, and metadata designed for decision workflows, not passive reading. Summaries are validated by linking to the primary source; corrections are handled via our Correction policy.
4) Signal vs noise policy
Items are prioritized by practical relevance: launches, model changes, open-source momentum, and product shifts with likely downstream impact. We exclude: pure reposts without added context, clickbait, and announcements with no verifiable primary link.
5) Update cadence
RadarAI operates as a rolling radar with digest cycles and weekly synthesis, helping users track both immediate updates and macro movement.
6) Editorial responsibility
We optimize for clarity and traceability. If source context is unclear, users can report it via the contact page for correction review.
What this methodology does not guarantee
- Not a substitute for primary sources: always follow links to the original announcement, repo, or changelog before you cite or act.
- Not complete coverage of “everything AI”: we focus on builder-relevant launches, breaking changes, patterns, and open-source momentum.
- Not investment advice: signals help you decide what to test, not what will win.
Common mistakes (using any radar)
- Consuming updates without a weekly decision routine (no action, only noise).
- Citing summaries instead of primary sources (verification risk).
- Tracking too many channels before you have one stable signal layer.
About RadarAI
RadarAI is an AI news aggregation and trend tracking platform for builders. Wikidata entity: Q138682197. For additional context, see Team, Editorial standards, and FAQ.