Knowledge infra for your coding agents.

Traditional RAG is doomed. Period.

Why AI Memory Systems Are Just Copying the Human Brain — Layer by Layer ~8 min read · AI Memory · Neuroscience · Cognitive Science ·…

Learn how to choose a PDF parser API for AI agents and RAG: accuracy, layout structure, latency, cost, and integration trade-offs that matter in…

Knowhere Notebook and MCP let agents in Cursor, Claude, and Codex search a shared cloud document Brain.

Production RAG quality is mostly evaluation and data/state work—not model swaps.

Knowhere is post-parse agent memory—tree hierarchy, multimodal assets, graph, and agentic retrieval—not a MinerU replacement.

A history of OCR from mechanical templates through deep learning to multimodal large models.

Reflecting on Microsoft SkillOpt: is editing skill.md real skill learning or prompt optimization?

An explainer of Microsoft Research’s SkillOpt: treat skill.md as trainable text parameters.

Applying Agent = Model + Harness to RAG failures rooted in ingestion and structure loss.

Flat fixed-window chunking causes many so-called hallucinations; structured document memory helps.

Knowhere open-source launch: tree-like parsing, table fidelity, OpenClaw plugin, SaaS and self-host.

Knowhere API as structured ingestion: parse, semantic chunk, and ship RAG-ready manifests.