- AIHOT is a site operation framework that collects materials from various news sources, has them evaluated twice by a large language model to select important content, groups similar news into one incident, and then delivers the latest issues on a daily basis.
- This repository publicly shares all prompts including the entire AIHOT website, backend, selection process, clustering and popularity algorithms, evaluation criteria, and thresholds so that anyone can customize signal sources and criteria to create a personalized issue site tailored to their industry.
- Built with universal IT technologies such as Node.js 24 version, PostgreSQL 17, and Docker Compose, it supports 6 types of signal sources including RSS, web, JSON, SNS, WeChat, and scripts. All processes including signal source classification, selection, summarization, clustering, and popularity aggregation can be tuned.
- Evaluation scores are independently assigned twice per signal source document, then selection is made based on document grade and signal source stage thresholds. Clustering determines whether news covers the same incident through recent 2-week vector similarity of similar titles and summaries along with model re-verification.
In AIHOT's clustering process, the dual verification method stands out, where the similarity over the recent 2 weeks is evaluated as vectors, and when the judgment is ambiguous, another model is asked to confirm again. It would be good to check how consistently this automated duplication and event integration is maintained, especially how reliable the results are when a user adjusts it to fit their own industry.