How KBlip Works
Cluster-based deduplication for AI/LLM news, research, and releases
Which sources does KBlip monitor?
RSS feeds from major labs and outlets, Reddit communities, Hacker News, arXiv (new papers across the AI/ML categories), GitHub releases for widely-used AI tools, select YouTube channels, and X posts from official lab and tool accounts. New sources are added as the ecosystem grows; every source is visible on the thread it contributed to, never hidden behind an aggregate score.
How does cluster-based deduplication work?
Every incoming article, paper, or release is converted into a vector embedding (Voyage AI) capturing what it's actually about, not just its keywords. New items are compared against recent threads by cosine similarity; anything above the similarity threshold joins the existing thread as another source instead of spawning a duplicate. A verification pass (DeepSeek) then confirms the match is a genuine same-story duplicate — not just topically adjacent — before merging, and writes a fresh summary in KBlip's own words rather than republishing any single source.
How is content verified before publishing?
Thread titles and summaries are LLM-generated from the clustered sources, but every thread keeps a full, visible source trail — you can always click through to the original articles, papers, or repos. Summaries can contain errors, same as any automated system; the source trail is what makes that checkable rather than something you have to take on faith.
Who builds this?
KBlip is built and run by a small independent team, not a venture-funded product with an ads or data-resale business model. It carries no ads, no accounts requirement, and no paywall. See About for how content is sourced and how to reach us, or read the engineering deep-dive on the deduplication pipeline for the full technical detail.