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Introducing: Walrus Memory. 🦭 A portable memory layer that lets your AI agents carry context across every app you run them in. No more starting from zero. No more being locked into one platform.…

Walrus

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40,457,970 görüntüleme • 3 ay önce •via X (Twitter)

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Your AI agent's memory is probably stuck inside one app. Walrus Memory (Walrus) fixes that with an MCP that creates memories as you chat, then can recall them anywhere - Claude Code, Pi, etc. Portable and actually owned by you!

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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

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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Everyone’s building more AI agents. Almost nobody is building a way for them to share your memory. MemU just did. Your AI now remembers you across every device and every agent, getting smarter after every conversation instead of starting from zero. This changes how personal AI actually works. 👇

Everyone’s building more AI agents. Almost nobody is building a way for them to share your memory. MemU just did. Your AI now remembers you across every device and every agent, getting smarter after every conversation instead of starting from zero. This changes how personal AI actually works. 👇

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Introducing the OpenMemory Chrome Extension! 🎉 Shared memory you can carry across ChatGPT, Claude, Perplexity, Grok, Gemini & more. Think universal context, synced across every AI assistant you use. It's free and opensource. Try it Now!

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AI can write a 10,000-word essay in seconds… but forgets you just told it your name yesterday. Engramme is trying to fix that. Memory that sticks. Context that follows you. Across every app. This might be the missing layer AI needed.

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agents with goldfish memory were getting kinda old so we shipped evolve one click and your agent reviews its past runs keeps the signals that actually mattered writes them back into memory then carries that context into the next run same agent more context less relearning the same lesson evolve is live on neotrade

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We just launched a new primitive for AI Walrus Memory gives your agents persistent, portable memory that you own, and it drops straight into the platforms you already build on. Anthropic Claude, OpenAI ChatGPT, and Google Gemini, you name it plus native MCP and SDKs, added in a few lines of code LIVE TODAY

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holy sh*t. this is f**king insane. langchain just documented the missing memory layer for agents that are supposed to improve after every run. instead of stuffing everything into one giant prompt, deep agents can keep long-term memory, reusable skills and lessons from previous tasks. [it takes a few minutes to understand the loop] 1/ run the task 2/ save what actually worked 3/ load that lesson into the next run that’s how an agent stops starting from zero every single time.

holy sh*t. this is f**king insane. langchain just documented the missing memory layer for agents that are supposed to improve after every run. instead of stuffing everything into one giant prompt, deep agents can keep long-term memory, reusable skills and lessons from previous tasks. [it takes a few minutes to understand the loop] 1/ run the task 2/ save what actually worked 3/ load that lesson into the next run that’s how an agent stops starting from zero every single time.

Gipp 🦅

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🚨 AI News | TestingCatalog

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Introducing MemSync, the first unified memory layer that works across all your apps and AI agents. We're backed by a16z crypto, SV Angel, Coinbase 🛡️ Ventures, and the co-creator of the Transformer architecture, Illia (root.near) (🇺🇦, ⋈). MemSync is like Plaid for memory, enabling you to carry your personal memory across every AI agent. How it works: 1. Learn. MemSync learns key facts about you from each conversation. 2. Lock. Those facts go into a vault that only you control — no one else touches them. 3. Sync. Next time you open a new chat, MemSync grabs the right facts and feeds them to the agent so you can pick up right where you left off. We built MemSync after hearing from 1M+ BitQuant users who faced 3 brutal pains: 1. Re-teaching every chatbot who you are. Now, your portable profile follows you into ChatGPT, Claude, and more. 2. Prompt history is easy to lose. MemSync surfaces every past gem with ⌘-I — no more digging through histories. 3. Amnesia. MemSync bridges token limits so long chats stay sharp (without blowing your token budget). If you’ve made it this far, we have a surprise for you. 👇 First 500 to comment "MemSync" get 30 days of Pro for free. (Link below)

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EverMe × Kimi Code Kimi.ai Kimi Developers Kimi Code now has shared memory. Connect Kimi Code to EverMe Memory with one prompt, carry context across sessions, agents, machines, and operating systems, and turn what it learns into reusable Skills. Less cold start. More continuity. EverMe: Kimi Code:

EverMe × Kimi Code Kimi.ai Kimi Developers Kimi Code now has shared memory. Connect Kimi Code to EverMe Memory with one prompt, carry context across sessions, agents, machines, and operating systems, and turn what it learns into reusable Skills. Less cold start. More continuity. EverMe: Kimi Code:

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i merged all my AI agents into one brain Claude Code, Codex, Gemini: one team, one memory, zero re-briefing result: 8+ hours a week back, and every agent already knows my projects, my clients and how i think here's the setup with Jarvix: 1. dump your context once: projects, clients, writing style, how you make decisions.. it builds your personal model from your actual AI interactions 2. switch agents mid-task with zero re-briefing: Claude Code builds, Codex reviews, Gemini researches, all reading the same context 3. it carries what YOU choose across sessions.. never start a chat from zero again 4. when it's time to act, it pulls the right agents together as one team instead of five separate assistants the unlock isn't another agent. it's that your context finally travels agents without shared memory are five interns on their first day, forever. agents with it are a team that's worked with you for years not more agents. one shared context that's the right bet P.S. thanks to the Jarvix team for the early access

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Anthropic AI engineer just showed how to give AI agents real memory in 4 steps - and it changes everything in 28 minutes he shows exactly how agents can remember across sessions, completely free worth more than any $500 AI engineering course here's what he covers: • why agents forget everything between sessions • memory stores - agents read, write across sessions • dreaming - agents that improve their own memory • 95% cache hit rate, so it stays cheap most people are still copy-pasting context into every new chat - while the people who figured this out are building agents that get smarter every single night watch full video then read article below

Anthropic AI engineer just showed how to give AI agents real memory in 4 steps - and it changes everything in 28 minutes he shows exactly how agents can remember across sessions, completely free worth more than any $500 AI engineering course here's what he covers: • why agents forget everything between sessions • memory stores - agents read, write across sessions • dreaming - agents that improve their own memory • 95% cache hit rate, so it stays cheap most people are still copy-pasting context into every new chat - while the people who figured this out are building agents that get smarter every single night watch full video then read article below

Movez

712,203 görüntüleme • 3 ay önce

300 agents just worked in parallel. the hard part starts when they’re done. The real unlock is the shared context graph. 100 entities already have 4,950 possible pairwise relationships. at 300, that jumps to 44,850. 300 agents → thousands of steps → shared entities → shared relationships → one connected memory layer Every run adds more structure instead of starting from zero. That means the next agent doesn’t just get more data. It gets a better map of how everything connects. That’s the part people are underestimating about multi-agent systems.

300 agents just worked in parallel. the hard part starts when they’re done. The real unlock is the shared context graph. 100 entities already have 4,950 possible pairwise relationships. at 300, that jumps to 44,850. 300 agents → thousands of steps → shared entities → shared relationships → one connected memory layer Every run adds more structure instead of starting from zero. That means the next agent doesn’t just get more data. It gets a better map of how everything connects. That’s the part people are underestimating about multi-agent systems.

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🚀 Global Launch: Jarvix, Your Context OS is now live! Meet the next generation of personalized AI built around your context, your taste, and your way of thinking. #JarvixOS transforms how you work with AI by understanding your patterns, carrying your context across agents, and helping you move from ideas to action. Step into a new era of AI collaboration: • Discover your AI-native archetype • Build your personal AI model from your evolving AI interactions • Never start from zero - your context moves with you across sessions • Coordinate Codex, Claude Code, Gemini, Hermes, OpenClaw, and more as one connected AI team. • Turn insights into your next move, with you always in control Instead of adapting yourself to every agent, Jarvix carries the context you choose, helps you understand what it reveals, and brings the right agents together when you decide to act. Your AI tools shouldn’t work like separate assistants. They should work like one team. That’s the future of AI: Not more agents. One shared context. One Jarvix.

🚀 Global Launch: Jarvix, Your Context OS is now live! Meet the next generation of personalized AI built around your context, your taste, and your way of thinking. #JarvixOS transforms how you work with AI by understanding your patterns, carrying your context across agents, and helping you move from ideas to action. Step into a new era of AI collaboration: • Discover your AI-native archetype • Build your personal AI model from your evolving AI interactions • Never start from zero - your context moves with you across sessions • Coordinate Codex, Claude Code, Gemini, Hermes, OpenClaw, and more as one connected AI team. • Turn insights into your next move, with you always in control Instead of adapting yourself to every agent, Jarvix carries the context you choose, helps you understand what it reveals, and brings the right agents together when you decide to act. Your AI tools shouldn’t work like separate assistants. They should work like one team. That’s the future of AI: Not more agents. One shared context. One Jarvix.

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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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Introducing Brain in Computer. Brain is a continuously learning memory system. Every task on Computer plugs into a context graph built by Brain. It makes Computer more stateful with every run. Available as a research preview for all Perplexity Max subscribers.

Introducing Brain in Computer. Brain is a continuously learning memory system. Every task on Computer plugs into a context graph built by Brain. It makes Computer more stateful with every run. Available as a research preview for all Perplexity Max subscribers.

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