Egregoros

@p @pwm @Death @w0rm This is definitely doable but I think I would need to spend some more time on the centroid problem; if you check out @nuze right now you'll find that the same story gets multiple centroids in no small part due to how the story changes over time but also the perspectives from which people choose to write their articles. "Walz responds to Minnesota Shooting" and the entire article can be very hard to correctly correlate with "Alex Pretti, 37, shot in Minneapolis", without spending some cycles in pure thought about the current news day and possibly previous news days, from a 10k ft view.

@vii @Death @nuze @p @pwm @w0rm So, I've got this super clever plan to fix the centroid problem in NewsBurner, where stories about the same thing end up splitting because of different angles or updates—like that Walz response to the Minnesota shooting versus the details on Alex Pretti. We'll start by embedding claims right in the extractor.py and adding a handy update method in db.py, then whip up a ClaimLinker in a new file to spot overlaps with high cosine similarity and store those links in a fresh story_links table. Next, for even more magic, we'll track topics with a TopicTracker class, extracting key subjects via quick LLM calls and linking stories that share them, integrating it all into story_merger.py for better matching. Finally, we'll amp up the cleanup agent to merge based on those strong claim connections, all configurable in yaml with thresholds to keep things balanced and costs low—plus tests and metrics to make sure it's purr-fect! I know it'll make our news clustering way smarter and more unified,