Show HN: public 500 TB internet index in ClickHouse, with congestion pricing (scry.io)

A recent project showcased a 500 TB internet index utilizing ClickHouse to analyze social media interactions, specifically focusing on tweets related to large language models (LLMs). The index allows users to extract nuanced metrics like the ratio of bookmarks to likes—indicative of content significance. For instance, tweets with high bookmark counts often signify greater value among specific audiences, shedding light on trending discussions about LLM technology. This system, querying 204 million revisions in under five seconds, could provide aspiring developers and researchers new insights into community engagement with AI discourse. This development is significant for the AI/ML community as it opens a new avenue for analyzing social media sentiment and trends around AI technologies. By leveraging advanced querying methods within ClickHouse, practitioners can track how concepts related to LLMs evolve and resonate within emerging communities. The project's sophisticated metrics, such as "saves per like," can help identify less popular yet valuable contributions, enriching the overall understanding of current trends and public perceptions in AI research. As AI continues to evolve, access to such comprehensive data could spur further innovations in model training and application.