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#RAG

2026-05 7 posts

  1. Harness as Environment

    A vector DB full of experience does not make an agent adapt. That takes a harness: what may change, how success is judged, how far a failure rolls back.

    AI·Agent #28

  2. Memory as Adaptation

    Long-running agents need more than a RAG store: why memory split into working, episodic, semantic, and procedural layers that ground policy adaptation.

    AI·Agent #27

  3. Comparing Four LightRAG Variants — Same Root, Different Production Strategies

    Three LightRAG derivatives read at source level: RAG-Anything adds multimodal, ApeRAG becomes a platform without multi-hop, EdgeQuake rewrites it in Rust.

    AI·RAG #25

  4. PDF to Markdown — Five Tools Compared

    Five PDF-to-Markdown converters on one academic paper, 100-point rubric: mineru scored 92 with formulas and tables intact; markitdown scored 14.

    AI·RAG #17

  5. LightRAG Without Apache AGE — Graph Storage in Recursive CTE

    Implementing LightRAG's BaseGraphStorage on plain PostgreSQL with RCTE — why a 1-hop-dominant retrieval pattern fits flat SQL

    AI·RAG #14

  6. Should Databases Handle AI Too?

    Oracle 23ai, EDB AIDB, PostgresML, Timescale pgai, pg_aidb — five systems and the abstraction-boundary discussion

    DB #9

  7. Replacing Elasticsearch with PostgreSQL

    Korean hybrid search on PostgreSQL alone (MeCab, BM25, pgvector, in-DB RRF) over eight phases: Elasticsearch-level quality, 2–5x faster on one node.

    DB #7

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