OncBrain is a refreshingly specific project: a curated, AI-summarized digest of oncology meeting tweets. Major cancer conferences like ASCO, ESMO, and ASH generate a firehose of clinical commentary on social media — trial readouts, abstract reactions, hallway gossip about practice-changing data — and almost none of it ever makes it into a structured form that a busy clinician or researcher can scan in five minutes.

This repo takes a shot at solving exactly that problem. By scraping conference-related posts, clustering them by topic (drug, trial, indication), and running them through an LLM summarizer, it produces a digest that's far more useful than a raw timeline. The TypeScript stack suggests a web-deployable frontend, likely with scheduled background jobs pulling and processing tweet data between conference sessions.

Why this is interesting:

• It's a domain-specific application of LLMs that goes beyond "chatbot for X" — the value is in aggregation and synthesis, where AI summarization genuinely shines.
• Oncology Twitter (or whatever we're calling it now) is one of the most active medical specialty communities online, with KOLs livetweeting plenary sessions. Capturing that signal is real work.
• The pattern generalizes: cardiology meetings, infectious disease conferences, ML/AI research events — anywhere a niche community generates ephemeral expert commentary, this template applies.

Who would benefit: Practicing oncologists who can't attend every conference, pharma medical affairs teams tracking competitive readouts, science journalists, and patient advocates trying to keep up with rapidly-evolving treatment landscapes. Also useful as a reference architecture for anyone building domain-specific content-digest tools — the combination of social scraping, topic clustering, and LLM summarization is a reusable recipe.

A polished version of this could easily become a paid newsletter or a clinical decision-support side tool.