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Context memory essentially unlocks Agentic AI Much needed for Opus 4.6's "multi-agent swarms" In this SemiDoped pod, Vikram Sekar talks to Val Bercovici from Weka about context storage. - How token warehouses save inference costs - A new networking tier? Context Storage Network! - High Bandwidth Flash for context?...

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Semi Doped

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12,796 просмотров • 4 месяцев назад •via X (Twitter)

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Breaking the Memory Wall: Micron’s Strategy for the AI Era Ben Bajarin and Jay Goldberg 顾忠南 talk micron's strategy for the AI era with Jeremy Werner SVP and GM of Micron’s Core Data Center Business Unit. Chapters: (00:00) Introduction and Guest Welcome (01:12) How the AI Cycle Differs from Past Volatility (04:23) Anticipating the Explosive Growth of AI (06:40) The Long-Term Sustainability of the AI Market (10:07) The "Memory Wall" in AI Inference (12:05) Explaining KV Cache and Context Windows (15:07) The Memory Hierarchy: From HBM to SSDs (20:21) Scaling for Billions of Simultaneous Users (23:05) Solving the Power and Efficiency Bottleneck (26:27) Why AI is Driving a Storage Shortage (29:57) Personal AI Agents and Persistent Memory Needs (34:13) Innovations in Ultra-High-Capacity SSDs (39:00) Accelerating the Pace of Engineering Innovation (43:50) Market Misunderstandings and the Future Potential of AI (47:38) The Global Race to Build New Fabs (51:34) Closing Remarks

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93,925 просмотров • 1 месяц назад

Introducing HydraDB. The graph native context infrastructure for agents. Purpose built to deliver precise context & observability into why agents act the way they do. We've always believed graphs are the best way to manage AI context, but they've been too expensive to scale or impractical for storing full context. Until now. HydraDB combines in memory, NVMe, and object storage into a single graph layer, making context delivery faster, cheaper, and more precise. We want context delivery to be extremely fast, 1000x cheap, and highly precise. Give your agents a brain.
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Introducing HydraDB. The graph native context infrastructure for agents. Purpose built to deliver precise context & observability into why agents act the way they do. We've always believed graphs are the best way to manage AI context, but they've been too expensive to scale or impractical for storing full context. Until now. HydraDB combines in memory, NVMe, and object storage into a single graph layer, making context delivery faster, cheaper, and more precise. We want context delivery to be extremely fast, 1000x cheap, and highly precise. Give your agents a brain.

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2,281,094 просмотров • 14 дней назад

New short course: LLMs as Operating Systems: Agent Memory, created with Letta, and taught by its founders Charles Packer and Sarah Wooders. An LLM's input context window has limited space. Using a longer input context also costs more and results in slower processing. So, managing what's stored in this context window is important. In the innovative paper MemGPT: Towards LLMs as Operating Systems, its authors (which include the instructors) proposed using an LLM agent to manage this context window. Their system uses a large persistent memory that stores everything that could be included in the input context, and an agent decides what is actually included. Take the example of building a chatbot that needs to remember what's been said earlier in a conversation (perhaps over many days of interaction with a user). As the conversation's length grows, the memory management agent will move information from the input context to a persistent searchable database; summarize information to keep relevant facts in the input context; and restore relevant conversation elements from further back in time. This allows a chatbot to keep what's currently most relevant in its input context memory to generate the next response. When I read the original MemGPT paper, I thought it was an innovative technique for handling memory for LLMs. The open-source Letta framework, which we'll use in this course, makes MemGPT easy to implement. It adds memory to your LLM agents and gives them transparent long-term memory. In detail, you’ll learn: - How to build an agent that can edit its own limited input context memory, using tools and multi-step reasoning - What is a memory hierarchy (an idea from computer operating systems, which use a cache to speed up memory access), and how these ideas apply to managing the LLM input context (where the input context window is a "cache" storing the most relevant information; and an agent decides what to move in and out of this to/from a larger persistent storage system) - How to implement multi-agent collaboration by letting different agents share blocks of memory This course will give you a sophisticated understanding of memory management for LLMs, which is important for chatbots having long conversations, and for complex agentic workflows. Please sign up here!
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157,168 просмотров • 1 год назад

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holy, competition is heating up a lot Anthropic introduces a memory feature that lets users transfer their context and preferences from other AI tools into Claude by copying a generated prompt and pasting the result into Claude’s memory settings. This allows Claude to immediately continue conversations with retained context, available for all paid plans.

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📈 NVIDIA tops AI leaderboards and benchmarks with open models driven by extreme co-design across compute, networking, memory, storage, and software. This includes models for biology, AI physics, agentic AI, physical AI, robotics, and autonomous vehicles. By being vertically integrated and horizontally open, NVIDIA enables its ecosystem to scale agentic workflows at every layer of the stack with the lowest cost per token.
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📈 NVIDIA tops AI leaderboards and benchmarks with open models driven by extreme co-design across compute, networking, memory, storage, and software. This includes models for biology, AI physics, agentic AI, physical AI, robotics, and autonomous vehicles. By being vertically integrated and horizontally open, NVIDIA enables its ecosystem to scale agentic workflows at every layer of the stack with the lowest cost per token.

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Satya Nadella says the 3 capabilities needed for AI agents are now in place and improving exponentially: a universal interface of multimodality; the neural algebra of reasoning and planning; and long-term memory, rich context and tool use

Tsarathustra

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MemOS now has a plugin for OpenClaw that enables your AI agents to work on the common memory layer and cut down token usage. - Multiple agents read/write the same memory — no manual context handoff - 72% lower token costs (15.6M → 4.4M on LOCOMO dataset) - Cross-session and long-term memory that actually works Sponsored
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MemOS now has a plugin for OpenClaw that enables your AI agents to work on the common memory layer and cut down token usage. - Multiple agents read/write the same memory — no manual context handoff - 72% lower token costs (15.6M → 4.4M on LOCOMO dataset) - Cross-session and long-term memory that actually works Sponsored

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41,003 просмотров • 4 месяцев назад

Andrew meets with John, where they discuss Supermicro and VΛST Data's collaboration on exciting storage projects such as the CNode-X AI Data Platform. Learn more about Supermicro and VAST Data's AI and Storage solutions: #Supermicro #VASTData #NVIDIA
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Andrew meets with John, where they discuss Supermicro and VΛST Data's collaboration on exciting storage projects such as the CNode-X AI Data Platform. Learn more about Supermicro and VAST Data's AI and Storage solutions: #Supermicro #VASTData #NVIDIA

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.Juan Benet from Protocol Labs shares that Filecoin is now the world’s largest decentralized storage network. It reduces storage costs while enabling new capabilities like smart contracts and AI agent integration.

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13,377 просмотров • 1 год назад

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Shay Boloor

400,708 просмотров • 4 месяцев назад

"What happens when we can access each other's AI memory in enterprises?" scott belsky, investor/founder of Behance: "If I was working as an intern for years, and the context window is accessible to the enterprise, can the enterprise (keep prompting) and asking me what I'd think or do, after I leave?" "Every company right now is trying to build this AI memory for each of its products and users. A lot of them are building connectors to form a collective memory." "In the social/consumer world, imagine your girlfriend saying she wants access to your context window or memory." "There are all kinds of weird questions and implications that arise from this."
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"What happens when we can access each other's AI memory in enterprises?" scott belsky, investor/founder of Behance: "If I was working as an intern for years, and the context window is accessible to the enterprise, can the enterprise (keep prompting) and asking me what I'd think or do, after I leave?" "Every company right now is trying to build this AI memory for each of its products and users. A lot of them are building connectors to form a collective memory." "In the social/consumer world, imagine your girlfriend saying she wants access to your context window or memory." "There are all kinds of weird questions and implications that arise from this."

TBPN

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Demis Hassabis says bigger context windows are still a brute force answer to memory. The human brain does something stranger. During sleep, it replays what matters and folds new knowledge into what it already knows. AI does not need infinite context. It needs the right memory at the right moment.
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Demis Hassabis says bigger context windows are still a brute force answer to memory. The human brain does something stranger. During sleep, it replays what matters and folds new knowledge into what it already knows. AI does not need infinite context. It needs the right memory at the right moment.

vitrupo

80,388 просмотров • 1 месяц назад