// Agent memory is a data system now // Great paper on long-term memory for LLM agents. (bookmark it) Agent memory has grown from simple retrieval into a full data-management layer with storage, retrieval, update, consolidation, and lifecycle governance. Yet most evaluations
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Brain reads threads and provides a complete deliverable
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Users can mention Brain on any task, doc, or discussion to read the entire thread, extract from connected apps like Google Drive, GitHub, and Slack via MCP, and then provide a complete deliverable. Try it.
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Give AI Agents Persistent Memory with LangGraph and MongoDB
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3/ Last → users HATE forgetful chatbots. You must give your agents a persistent brain. The 'Memory for AI Apps' module teaches you to use LangGraph and MongoDB to build stateful AI apps with isolated, long-term memory across sessions. Sweet. > https://
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Vector Search Perf module for slow AI retrieval diagnosis
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2/ Next problem → sluggish AI retrieval. Scaling it up is notoriously difficult. This Vector Search Perf module walks you through diagnosing slow queries with Atlas Metrics. PLUS actually managing memory sizing and quantization in full production > https://
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Unify vector search with Voyage AI retrieval pipelines
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1/ First: aren't you tired of juggling a separate vector DB just to make semantic search work? You can keep it all unified. The Voyage AI course shows exactly how to build advanced two-step retrieval pipelines where your original data already lives. > https://
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MongoDB offers free AI badges for essential foundations
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MOST AI STACKS ARE A COMPLETE MESS
— Charly Wargnier (@DataChaz) 24 juin 2026
🚨 @MongoDB just dropped 3 Free AI Skill Badges covering the AI foundations that ACTUALLY matter:
1/ a unified semantic search experience
2/ a scalable vector database
3/ a persistent memory state for agents
Because production AI doesn't need… pic.twitter.com/sFaQZoX3yZMOST AI STACKS ARE A COMPLETE MESS @MongoDB just dropped 3 Free AI Skill Badges covering the AI foundations that ACTUALLY matter: 1/ a unified semantic search experience
2/ a scalable vector database
3/ a persistent memory state for agents Because production AI doesn't need -
AI model can connect old unsolved cases to new papers
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4/ One detail explains why this works. A case can go unsolved, then a year later a research paper is published that finally connects a gene to a disease. No human re-reads every old case against every new paper. The model can.
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AI solves 18 medical cases of children abandoned by doctors
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Urgent: An AI just solved 18 medical cases that doctors had abandoned. All children with rare diseases that no one could identify. Here is how the AI did it:
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Claude Code as document processing agent preserving structure and reading order
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Turn Claude Code into a document processing agent! Traditional OCR extracts text but loses critical information. Table structures with merged cells disappear. Relationships between charts and captions break. Multi-column reading order gets scrambled. That's why most document
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Insights from running 350M GTM agents: caching, bounding, fairness
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At Interrupt, @Clay's Head of AI @jeffbarg shared insights from running 350m GTM agents a month.
— LangChain (@LangChain) 24 juin 2026
✅ Caching can cut LLM costs up to 70%
✅ Bounding tool calls often improves quality, not just cost
✅ Fairness queues matter once you have real multi-tenant load
Worth 12 minutes if… pic.twitter.com/2qbvyct3lxAt Interrupt, @Clay
's Head of AI @jeffbarg shared insights from running 350m GTM agents a month. Caching can cut LLM costs up to 70% Bounding tool calls often improves quality, not just cost Fairness queues matter once you have real multi-tenant load Worth 12 minutes if