“Garry Tan introduced GBrain, an MIT-licensed open-source retrieval layer for AI agents—acting like a Postgres-style, just-in-time “3-book librarian”—and it’s already in use by thousands of developers.”

#9 𝕏 Garry Tan introduced GBrain, an MIT-licensed open-source retrieval layer for AI agents—acting like a Postgres-style, just-in-time “3-book librarian”—and it’s already in use by thousands of developers.

“Summary: Garry Tan unveiled GBrain, a modular “company brain” framework that structures work via scoped AI agents organized into client pods.”

#12 𝕏 Summary: Garry Tan unveiled GBrain, a modular “company brain” framework that structures work via scoped AI agents organized into client pods. This detailed agent company architecture standardizes workflows and scales cross-functional collaboration.

“Garry Tan open-sourced GBrain (MIT-licensed) on GitHub and outlines a 30-minute setup using his 350k-page markdown LLM wiki plus an OpenClaw/Hermes agent that automates most tasks.”

#6 𝕏 Garry Tan open-sourced GBrain (MIT-licensed) on GitHub and outlines a 30-minute setup using his 350k-page markdown LLM wiki plus an OpenClaw/Hermes agent that automates most tasks.

“Garry Tan launched GBrain, an MIT-licensed, state-of-the-art retrieval engine for agents—built for OpenClaw and Hermes but with full MCP server support to plug into almost any agent harness.”

#6 𝕏 Garry Tan launched GBrain, an MIT-licensed, state-of-the-art retrieval engine for agents—built for OpenClaw and Hermes but with full MCP server support to plug into almost any agent harness. #10 𝕏 Garry Tan rolled out the latest GBrain update, which adds synthesized answers to your queries instead of just basic retrieval. An A/B test of GBrain Search vs. GBrain Think shows it improving in accuracy every single day.

“Garry Tan released GBrain—an MIT-licensed OSS retrieval and memory system that achieves SOTA on LongMemEval, beating all known open-source repos by over 1% without any LLM query rewriting.”

#12 𝕏 Garry Tan released GBrain—an MIT-licensed OSS retrieval and memory system that achieves SOTA on LongMemEval, beating all known open-source repos by over 1% without any LLM query rewriting. He’s already running it on his own 100k-page OpenClaw/Hermes Agent brain.

“#6 𝕏 Garry Tan announces that GBrain now ships with ZeroEntropy as its recommended default embedding and re-ranking engine, replacing OpenAI and Voyage AI.”

#6 𝕏 Garry Tan announces that GBrain now ships with ZeroEntropy as its recommended default embedding and re-ranking engine, replacing OpenAI and Voyage AI.

“#5 𝕏 Garry Tan launched GBrain, an open-source knowledge system (not RAG in a box) with eight memory-enhancing layers that make agents like OpenClaw and Hermes feel clairvoyant about you, paving the way for personal AI.”

Today's top 13 insights for PM Builders, ranked by relevance from X, Blogs, and LinkedIn. Why LLM features need end-to-end observability metrics #1 𝕏 Boris Cherny upgraded /usage to show personalized token usage by plugin, skill, and parallel agent, so you can pinpoint high-consumption drivers and maximize your doubled rate limits. #2 𝕏 xAI integrates X Premium subscriptions into Hermes Agent and equips it with native search across X posts. #3 📝 PromptLayer Blog A deep dive into LLM observability tools - Discusses the need for observability when shipping LLM-powered features, since models can return confidently wrong answers while logs show successful API responses. Argues observability must connect inputs, outputs, latency, cost, and quality to diagnose real production issues. #4 𝕏 Sebastian Raschka presents a visual overview of recent LLM architectures—from Gemma 4 to DeepSeek V4—showcasing long-context efficiency tweaks. He dives into innovations like KV sharing, per-layer embeddings, layer-wise attention budgets, compressed attention, and mHC. #5 𝕏 Garry Tan launched GBrain, an open-source knowledge system (not RAG in a box) with eight memory-enhancing layers that make agents like OpenClaw and Hermes feel clairvoyant about you, paving the way for personal AI.

“𝕏 Garry Tan suggests Gbrain should leverage git history to fetch context on demand, avoiding redundant inputs and adhering to the DRY (“don’t repeat yourself”) principle.”

#11 𝕏 Garry Tan suggests Gbrain should leverage git history to fetch context on demand, avoiding redundant inputs and adhering to the DRY (“don’t repeat yourself”) principle.

“Garry Tan built a GBrain eval harness using 145 queries over an Opus‐generated corpus and a hybrid retrieval stack (graph, vector, grep).”

#1 𝕏 Garry Tan built a GBrain eval harness using 145 queries over an Opus‐generated corpus and a hybrid retrieval stack (graph, vector, grep).

“#17 𝕏 Garry Tan announced that GBrain now supports multiple repos per brain, paving the way to store your GStack code transcripts, plans, and Claude Code artifacts directly in GBrain.”

#17 𝕏 Garry Tan announced that GBrain now supports multiple repos per brain, paving the way to store your GStack code transcripts, plans, and Claude Code artifacts directly in GBrain.