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Graphon just launched Persistent Intelligence, giving AI agents memory across company documents and calls. Interesting to see a team with robot memory experience at $GOOGL, $META and $NVDA bring that expertise to enterprise AI.

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Shay Boloor

462,912 subscribers

21,749 görüntüleme • 5 gün önce •via X (Twitter)

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Justice U profil fotoğrafı
Justice U5 gün önce

Saving the old answer is easy. When I correct an AI reply, can it pick up the correction next time without pulling the old example back in? That’s what I need the memory for.

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Serena Pulcini5 gün önce

Memory sounds useful til privacy slips.

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Dario Amodei (CEO of Anthropic) : "powerful AI systems will need persistent memory to work over long horizons." spoiler: most agents today are just stateless demos pretending to be products this guy just shipped the exact memory playbook for building agents that actually remember users, tasks, and context over time bookmark this for the weekend

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Anthropic's Claude team just showed the exact playbook for AI agents with real memory. 24 minutes. free. by people who built Claude. 99% of "AI agents" reset every session. the 1% with memory - they're employees that never sleep. One person + 10 of them = a team that runs 24/7 and improves itself. worth more than any $500 vibe-coding course.
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our big bet with WALRUS MEMORY is that memory becomes a fully composable, transactable part of what ai agents need to do in the future and it's going to be an incredibly valuable asset and we've built a primitive that lets you fully own and carry it across every agent you use
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our big bet with WALRUS MEMORY is that memory becomes a fully composable, transactable part of what ai agents need to do in the future and it's going to be an incredibly valuable asset and we've built a primitive that lets you fully own and carry it across every agent you use

Adeniyi.sui

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Google just showed how AI engineers are moving from simple "RAG" to "graphs", memory and multimodal agents • 15:32 - setting up the production agent stack • 28:00 - turning disconnected data into a knowledge graph • 41:00 - Graph RAG with semantic + hybrid search • 58:00 - extracting graph context from images, text and video • 1:09:00 - orchestrating specialized agents with ADK • 1:20:00 - giving agents persistent memory across sessions 94-minute Google Cloud workshop, and it’s one of the clearest hands-on examples of the shift from -> RAG → Graph RAG → Memory → Multimodal Agent Graphs. The workshop uses Spanner Graph, Gemini, ADK and Memory Bank to build that stack. Watch it today, then read the full “From RAG to Context Graphs” roadmap below.

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