Automated keyword monitoring and alerts for new ML papers on arXiv

Executive Summary

Researchers and engineers miss relevant ML papers because arXiv publishes ~10,000 papers monthly. Provide continuous keyword and semantic alerts, AI summaries, and team integrations so users see only high priority new papers daily. Research output growth and high daily frequency - the source cites ~10,000 new arXiv papers monthly, creating chronic discovery pain. Advances in open embedding models and low-cost LLM summarization make semantic matching and on-demand abstracts practical for real time alerts. Also, researchers already use paid workarounds and daily workflows, demonstrating willingness to pay and habit formation that can be captured now with improved tooling. Target ML researchers and engineers with daily habit value by combining precise keyword + semantic filters, LLM powered one paragraph summaries, and lightweight team integrations into Slack/Notion. Evidence from the source shows ~10,000 new arXiv papers per month and daily recurrence of discovery work, so a product that reduces daily triage time and funnels only high priority papers into existing developer workflows can win. Speed to market is high because arXiv provides APIs and embeddings plus open models enable rapid implementation of semantic matching and summarization without heavy custom data needs. The product can differentiate by supporting boolean keyword grammars tailored to ML, per-user relevance learning from click/save signals, and team alert routing for paid plans.