Your brain on chat GPT looks like your muscles after 6 months on the couch. Weak, flabby, and shriveling by the day. Scientists at MIT just proved it with brain scans.And yes, you should be worried. I'm worried. The more people used AI, the less their neurons fired.Memory gone. Creative thinking flatlined. Original thoughts, what are those.We're witnessing the first generation in history voluntarily outsourcing consciousness to AI. But wait, we've been here before and the story never ends the way you think ...

That said, KV cache also takes a lot of memory.Llama3-70B has:- total layers = 80- hidden size = 8k- max output size = 4kHere:- Every token takes up ~2.5 MB in KV cache.- 4k tokens will take up 10.5 GB.More users → more memory.I'll cover KV optimization soon. https://t.co/VjnyLa6aLa ...

[Music] I'm here today to tell you that there's no onesizefits all memory. Um, and why you need to model your memory after your business domain. So, if you saw me a little bit earlier and I was talking about Graffiti, Zep's open-source temporal graph framework, um, you might have seen me just speak to how you can build custom entities and edges in the graffiti graph for your particular business domain.So, business objects from your business domain. What I'm going to demo today is actually how Zep implements ...

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Core Viewpoint - The article discusses the limitations of large language models (LLMs) regarding their context window and memory management, emphasizing the need for improved memory systems to enhance their long-term interaction capabilities [5][6][9]. Context Window Evolution - Modern LLMs typically have a limited context window, with early models like GPT-3 handling around 2,048 tokens, while newer models like Meta's Llama 4 Scout claim to manage up to 10 million tokens [2][4]. Memory Management in LLMs - LLMs face an inherent "memory defect" due to their limited context window, which hampers their ability to maintain consistency in long-term interactions [5][6]. - Recent research has focused on memory management systems like MemOS, which treat memory as a critical resource alongside computational power, allowing for continuous updates and self-evolution of LLMs [9][49]. Long Context Processing Capabilities - Long context processing capabilities are crucial for LLMs, encompassing: - Length generalization ability, which allows models to extrapolate on sequences longer than those seen during training [12]. - Efficient attention mechanisms to reduce computational and memory costs [13]. - Information retention ability, which refers to the model's capacity to utilize distant information effectively [14]. - Prompt design to maximize the advantages of long context [15]. Types of Memory in LLMs - Memory can be categorized into: - Event memory, which records past interactions and actions [18]. - Semantic memory, encompassing accessible external knowledge and understanding of the model's capabilities [19]. - Procedural memory, related to the operational structure of the system [20]. Methods to Enhance Memory and Context - Several methods to improve LLM memory and context capabilities include: - Retrieval-augmented generation (RAG), which enhances knowledge retrieval for LLMs [27][28]. - Hierarchical summarization, which recursively summarizes content to manage inputs exceeding model context length [31]. - Sliding window inference, which processes long texts in overlapping segments [32]. Memory System Design - Memory systems in LLMs are akin to databases, integrating lifecycle management and persistent representation capabilities [47][48]. - Recent advancements include the development of memory operating systems like MemOS, which utilize a layered memory architecture to manage short-term, medium-term, and long-term memory [54][52]. Innovative Memory Approaches - New memory systems such as MIRIX and Larimar draw inspiration from human memory structures, enhancing LLMs' ability to update and generalize knowledge rapidly [58][60]. - These systems aim to improve memory efficiency and model inference performance by employing flexible memory mechanisms [44].

Context Engineering Overview - Context engineering is defined as the art and science of filling the context window with the right information at each step of an agent's trajectory [2][4] - The industry categorizes context engineering strategies into writing context, selecting context, compressing context, and isolating context [2][12] - Context engineering is critical for building agents because they typically handle longer contexts [10] Context Writing and Selection - Writing context involves saving information outside the context window, such as using scratch pads for note-taking or memory for retaining information across sessions [13][16][17] - Selecting context means pulling relevant context into the context window, including instructions, facts, and tools [12][19][20] - Retrieval-augmented generation (RAG) is used to augment the knowledge base of LLMs, with code agents being a large-scale application [27] Context Compression and Isolation - Compressing context involves retaining only the most relevant tokens, often through summarization or trimming [12][30] - Isolating context involves splitting up context to help an agent perform a task, with multi-agent systems being a primary example [12][35] - Sandboxing can isolate token-heavy objects from the LLM context window [39] Langraph Support for Context Engineering - Langraph, a low-level orchestration framework, supports context engineering through features like state objects for scratchpads and built-in long-term memory [44][45][48] - Langraph facilitates context selection from state or long-term memory and offers utilities for summarizing and trimming message history [50][53] - Langraph supports context isolation through multi-agent implementations and integration with sandboxes [55][56]

AI Agents and Memory - The presentation focuses on the importance of memory in AI agents, emphasizing that memory is crucial for making agents reflective, interactive, proactive, reactive, and autonomous [6] - The discussion highlights different forms of memory, including short-term, long-term, conversational entity memory, knowledge data store, cache, and working memory [8] - The industry is moving towards AI agents and agentic systems, with a focus on building believable, capable, and reliable agents [1, 21] MongoDB's Role in AI Memory - MongoDB is positioned as a memory provider for agentic systems, offering features needed to turn data into memory and enhance agent capabilities [20, 21, 31] - MongoDB's flexible document data model and retrieval capabilities (graph, vector, text, geospatial query) are highlighted as key advantages for AI memory management [25] - MongoDB acquired Voyage AI to improve AI systems by reducing hallucination through better embedding models and re-rankers [32, 33] - Voyage AI's embedding models and re-rankers will be integrated into MongoDB Atlas to simplify data chunking and retrieval strategies [34] Memory Management and Implementation - Memory management involves generation, storage, retrieval, integration, updating, and forgetting mechanisms [16, 17] - Retrieval Augmented Generation (RAG) is discussed, with MongoDB providing retrieval mechanisms beyond just vector search [18] - The presentation introduces "Memoriz," an open-source library with design patterns for various memory types in AI agents [21, 22, 30] - Different memory types are explored, including persona memory, toolbox memory, conversation memory, workflow memory, episodic memory, long-term memory, and entity memory [23, 25, 26, 27, 29, 30]

also, today we are making a lightweight version of memory available to the free tier of chatgpt!memory has probably become my favorite feature in chatgpt; excited for us to improve this a lot over time. ...

San Francisco, April 28, 2025 (GLOBE NEWSWIRE) -- San Francisco, California - Krispy, a rising voice in literary speculative fiction, has released their debut novel The Version Who Stayed on Amazon, marking the first book in an emotionally intelligent and thematically rich series titled The Mirror Archive. The novel introduces readers to Auren Solven, a character who will serve as a narrative anchor across Krispy's works—sometimes a protagonist, sometimes an observer, but always in a state of personal evol ...