- engineering 21
- agents 14
- claude-code 12
- rag 10
- memory 7
- architecture 6
- operations 5
- migration 4
- security 4
- hooks 3
- vault 3
- durability 2
- honest-tradeoffs 2
- multi-agent 2
- query-planner 2
- queues 2
- self-hosting 2
- skills 2
- storage 2
- transactions 2
- afk 1
- chunking 1
- compliance 1
- concurrency 1
- context-engineering 1
- cost 1
- cost-tracking 1
- data 1
- decision-frameworks 1
- disaster-recovery 1
- evals 1
- firebase 1
- hardware 1
- incident 1
- intro 1
- isolation 1
- kubernetes 1
- learning 1
- manifesto 1
- mcp 1
- mongodb 1
- observability 1
- ops 1
- performance 1
- persistence 1
- pgvector 1
- postgres 1
- regression-testing 1
- sagas 1
- sdk 1
- search 1
- slash-commands 1
- stride 1
- threat-model 1
- tutorial 1
- vector 1
- wal 1
- MAY 2026
An MCP server for RedDB — one database, every agent CLI
The Model Context Protocol lets every modern agent CLI — Claude Code, Codex, Cursor, Gemini CLI — share the same toolbelt. Wrap RedDB once and you give all of them agent memory, document storage, and semantic search through a single endpoint. Manifest, server, wiring.
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Slash commands with memory — commands that learn between sessions
A Claude Code slash command is a markdown file with a shell expansion. Wire that shell call to RedDB and the command stops being a stateless macro — /remember, /forget, /recall become a tiny CRUD app the agent uses for itself.
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Skills as data — storing metadata, runs, and learned refinements in RedDB
Claude Code skills are static SKILL.md files today. Promote them to first-class data — metadata, execution traces, success rates — and the agent can pick the right skill for a new task with a SQL query instead of a keyword-match heuristic.
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Chunking inside the engine: when the DB owns segmentation
Most RAG stacks chunk in Python glue between Postgres and a vector store. The result is a second pipeline that drifts. This post walks through what it looks like when chunking is a declarative rule attached to a column, reranking is a query operator, and the engine — not the application — owns segmentation.
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Hybrid search done right: lexical, vector, and filter in one plan
A walk through the RedDB query planner fusing BM25, vector similarity, and a structured filter into a single execution plan — with EXPLAIN output, the cost model behind it, and what happens on the edge cases that trip up naive two-stage rerankers.
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Postgres + pgvector → RedDB: a migration playbook with wall-clock numbers
A step-by-step playbook for moving a production Postgres + pgvector workload to RedDB — dual-write, embedding backfill, query translation, cutover, rollback — with measured timings from a 12M-row test corpus and the three places teams trip over.
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RedDB as Claude Code's memory backend — beyond CLAUDE.md
Swap the static MEMORY.md file for a queryable, embeddable memory layer. A SessionStart hook reads top-K memories into context, a /remember slash command writes them, and RedDB stores rows, vectors, and audit log in one transaction.
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RAG without a second database
When your vectors live on the same row as the document they describe, the entire CDC-and-backfill layer of a RAG pipeline disappears — and a class of stale-retrieval bugs goes with it.
- APR 2026
The drift window: why your RAG retrieves stale chunks
A customer-visible failure mode unique to two-store RAG — source updates, queue lag, retrievals against the old embedding. Anatomy of one outage and how same-transaction writes eliminate the window.
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Building an agent memory layer on RedDB
A copy-pasteable schema for episodic, semantic, and procedural memory in a single database — with the queries that retrieve them and the bookkeeping that keeps them honest.