A-RAG: Scaling Agentic Retrieval-Augmented Generation via Hierarchical Retrieval Interfaces · HackerPapers discussionshttps://hackerpaper.kaipfister.de/p/2602.03442/Collected HN comments and submissions linking this paper.enMon, 02 Nov 2026 11:27:26 GMTA-RAG: Scaling Agentic Retrieval-Augmented Generation via Hierarchical Retrieval Interfaces — HN submissionhttps://news.ycombinator.com/item?id=46986940urn:hackerpapers:submission:2602.03442:46986940Thu, 12 Feb 2026 10:11:31 GMT<p>Show HN: SuperLocalMemory– Local-first AI memory for Claude, Cursor and 16+tools</p><p>The Problem AI assistants have amnesia. Every new Claude/ChatGPT/Cursor session starts from zero. You waste hours re-explaining your project architecture, coding preferences, and previous decisions. Existing solutions (Mem0, Zep, Letta) are cloud-based, cost $40-50+/month, and your private code goes to their servers. Stop paying → lose all your data. My Solution: Local-First, Free Forever Built a universal memory system that stores everything on YOUR machine, works with 16+ AI tools simultaneously, requires zero API keys, costs nothing. 10-Layer Architecture Each layer enhances but never…</p><p>1 point; 0 discussion replies on HN. <a href="https://hackerpaper.kaipfister.de/p/2602.03442/">Paper and source history</a>. A submission is not an endorsement.</p>cs.CL