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Chat+Build is launching Living Brain which decouples memory from the model. One persistent brain that works across ChatGPT, Claude, Cursor, Codex, and custom agents. Write in one, retrieve in any. Models become interchangeable compute; the memory stays with you. 92.2% on LoCoMo. 464ms median retrieval. Up to 22.6x fewer... show more

Documenting AGI

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17,737 görüntüleme • 3 gün önce •via X (Twitter)

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Mike profil fotoğrafı

Mike3 gün önce

Decoupling memory, but what's the latency hit on ChatGPT

Muhammad Erwan profil fotoğrafı

Muhammad Erwan3 gün önce

Decoupling memory from the model like Chat+Build's Living Brain.

John K profil fotoğrafı

John K3 gün önce

Decoupling memory makes sense, though handling write conflicts when multiple agents update the same state concurrently is usually where shared stores get messy.

Oscar profil fotoğrafı

Oscar3 gün önce

this decoupling of memory from the model reminds me of cursor's wild scaling days with direct effect!

离岸工具包🎒 profil fotoğrafı

离岸工具包🎒3 gün önce

Big deal when memory's untethered from the model. Real breakthrough is when your AI brain spans platforms, not just apps. Interchangeable compute, unified memory? It's a step toward a seamless, holistic AI ecosystem.

Entiane Lynn'หลิน... profil fotoğrafı

Entiane Lynn'หลิน...3 gün önce

@grok wie funktioniert das mit den 92 Modellen von Chat+Build?

Benzer Videolar

UP TO 22.6X FEWER TOKENS PER QUESTION 🤯 Christel Buchanan was one of the first on Twitter's international team. today she's launching Living Brain persistent memory across ChatGPT and Claude. one to watch 👀

UP TO 22.6X FEWER TOKENS PER QUESTION 🤯 Christel Buchanan was one of the first on Twitter's international team. today she's launching Living Brain persistent memory across ChatGPT and Claude. one to watch 👀

Charly Wargnier ♨️

24,511 görüntüleme • 3 gün önce

One memory. Every agent. Lighthouse gives AI agents a persistent, encrypted brain that travels with you across ChatGPT, Claude and the rest. Saved once, owned by you, verifiable on decentralized storage. Lighthouse ($LLL) is now live Robinhood Crypto via Virtuals Protocol {Link in comments}

One memory. Every agent. Lighthouse gives AI agents a persistent, encrypted brain that travels with you across ChatGPT, Claude and the rest. Saved once, owned by you, verifiable on decentralized storage. Lighthouse ($LLL) is now live Robinhood Crypto via Virtuals Protocol {Link in comments}

Lighthouse

36,141 görüntüleme • 24 gün önce

New course: Agent Memory: Building Memory-Aware Agents, built in partnership with Oracle and taught by Richmond Alake and Nacho Martínez. Many agents work well within a single session but their memory resets once the session ends. Consider a research agent working on dozens of papers across multiple days: without memory, it has no way to store and retrieve what it learned across sessions. This short course teaches you to build a memory system that enables agents to persist memory and thereby learn across sessions. You'll design a Memory Manager that handles different memory types, implement semantic tool retrieval that scales without bloating the context, and build write-back pipelines that let your agent autonomously update and refine what it knows over time. Skills you'll gain: - Build persistent memory stores for different agent memory types - Implement a Memory Manager that orchestrates how your agent reads, writes, and retrieves memory - Treat tools as procedural memory and retrieve only relevant ones at inference time using semantic search Join and learn to build agents that remember and improve over time!

New course: Agent Memory: Building Memory-Aware Agents, built in partnership with Oracle and taught by Richmond Alake and Nacho Martínez. Many agents work well within a single session but their memory resets once the session ends. Consider a research agent working on dozens of papers across multiple days: without memory, it has no way to store and retrieve what it learned across sessions. This short course teaches you to build a memory system that enables agents to persist memory and thereby learn across sessions. You'll design a Memory Manager that handles different memory types, implement semantic tool retrieval that scales without bloating the context, and build write-back pipelines that let your agent autonomously update and refine what it knows over time. Skills you'll gain: - Build persistent memory stores for different agent memory types - Implement a Memory Manager that orchestrates how your agent reads, writes, and retrieves memory - Treat tools as procedural memory and retrieve only relevant ones at inference time using semantic search Join and learn to build agents that remember and improve over time!

Andrew Ng

163,155 görüntüleme • 7 ay önce

Introducing MemCurrent, a persistent memory architecture designed to help production agents stop starting from zero. MemCurrent combines episodic memory with derived memory across three access patterns: • Plugins to log and load context automatically • Tools the agent calls on demand • An offline “dreaming” pipeline that consolidates memory in the background Agents carry forward user preferences, prior corrections, metric definitions and successful workflows instead of rediscovering them every session. On our internal benchmark built from real enterprise data, MemCurrent triples the agent's pass rate while using 31% fewer tokens and running 26% faster. It's built into Agent Bricks' Memory offering and already powers memory in Genie. Read the research:

Introducing MemCurrent, a persistent memory architecture designed to help production agents stop starting from zero. MemCurrent combines episodic memory with derived memory across three access patterns: • Plugins to log and load context automatically • Tools the agent calls on demand • An offline “dreaming” pipeline that consolidates memory in the background Agents carry forward user preferences, prior corrections, metric definitions and successful workflows instead of rediscovering them every session. On our internal benchmark built from real enterprise data, MemCurrent triples the agent's pass rate while using 31% fewer tokens and running 26% faster. It's built into Agent Bricks' Memory offering and already powers memory in Genie. Read the research:

Databricks

44,883 görüntüleme • 18 gün önce

LLMs are stateless. We built Dria Mem Agent to change that: Making memory a first-class feature. A 4B agent with local interoperable memory across Claude, ChatGPT and LM Studio. It turns LLMs from stateless chat into stateful agents with persistent human-readable memory.

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LLMs are stateless. We built Dria Mem Agent to change that: Making memory a first-class feature. A 4B agent with local interoperable memory across Claude, ChatGPT and LM Studio. It turns LLMs from stateless chat into stateful agents with persistent human-readable memory.

Dria

170,847 görüntüleme • 1 yıl önce

What if your AI could remember everything, even when you switch models? Living Brain lets you carry your memory from ChatGPT to Claude, Cursor and other AI tools.

What if your AI could remember everything, even when you switch models? Living Brain lets you carry your memory from ChatGPT to Claude, Cursor and other AI tools.

Globe Eye News

28,811 görüntüleme • 3 gün önce

🚨 KEEP CALM AND AI - AUTONOMOUS AGENTS WITH INFINITE MEMORY We are launching the ability to create arbitrary agents that run on schedule and have access to a persistent and infinite memory The agents will be able to store, retrieve, and update information across sessions and perform repetitive tasks that require persistent memory Another step towards AGI and automating white-collar work 🚀🚀🚀

🚨 KEEP CALM AND AI - AUTONOMOUS AGENTS WITH INFINITE MEMORY We are launching the ability to create arbitrary agents that run on schedule and have access to a persistent and infinite memory The agents will be able to store, retrieve, and update information across sessions and perform repetitive tasks that require persistent memory Another step towards AGI and automating white-collar work 🚀🚀🚀

Bindu Reddy

13,082 görüntüleme • 9 ay önce

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.

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.

Movez

12,282 görüntüleme • 5 ay önce

just shipped thred!! context that carries your work forward. Claude → Codex → Cursor → whatever comes next. thred gives every agent one shared memory for decisions, revisions, evidence, and unfinished work — so the next agent starts where the last one stopped. built on MCP. powered by temporal memory with HydraDB. no more re-explaining the codebase. no more lost decisions between sessions. just save → switch → resume. built for Hack Hydra | Memory + Context Retrieval.

just shipped thred!! context that carries your work forward. Claude → Codex → Cursor → whatever comes next. thred gives every agent one shared memory for decisions, revisions, evidence, and unfinished work — so the next agent starts where the last one stopped. built on MCP. powered by temporal memory with HydraDB. no more re-explaining the codebase. no more lost decisions between sessions. just save → switch → resume. built for Hack Hydra | Memory + Context Retrieval.

nikhil · sys/quests

25,144 görüntüleme • 2 ay önce

Introducing Entelligence AI Memory: One shared memory for every coding agent on your team - Sessions, PRs, tickets and incidents in one graph - Agents have full memory access for all new sessions - 53% fewer repeat errors Available today for Claude Code, Codex and Cursor

Introducing Entelligence AI Memory: One shared memory for every coding agent on your team - Sessions, PRs, tickets and incidents in one graph - Agents have full memory access for all new sessions - 53% fewer repeat errors Available today for Claude Code, Codex and Cursor

Aiswarya Sankar

46,828 görüntüleme • 5 gün önce

We analyzed Anthropic’s memory architecture and built something better: a persistent, human-inspectable, and token-efficient memory layer that scales with your projects. Today, it’s OPEN-SOURCE. ByteRover CLI gives agents (like OpenClaw, Claude Code, and Hermes) persistent, structured memory. Built on the exact architecture that became the #1 memory system for OpenClaw (30,000+ downloads in a week), it lets developers curate project knowledge into a file-based hierarchy. This guarantees highly accurate, lightning-fast retrieval, even with lightweight models. 👉Highly Accurate: >92% retrieval accuracy across long-running sessions - the highest proven production accuracy on the market. ⚡Fast: ~1.6s average retrieval time. 💰Economic: Maintains >90% accuracy even with lightweight models, saving 50-70% on token costs. ☁ Portable: Runs locally by default, with cloud-sync to share memory across agents and teammates.

We analyzed Anthropic’s memory architecture and built something better: a persistent, human-inspectable, and token-efficient memory layer that scales with your projects. Today, it’s OPEN-SOURCE. ByteRover CLI gives agents (like OpenClaw, Claude Code, and Hermes) persistent, structured memory. Built on the exact architecture that became the #1 memory system for OpenClaw (30,000+ downloads in a week), it lets developers curate project knowledge into a file-based hierarchy. This guarantees highly accurate, lightning-fast retrieval, even with lightweight models. 👉Highly Accurate: >92% retrieval accuracy across long-running sessions - the highest proven production accuracy on the market. ⚡Fast: ~1.6s average retrieval time. 💰Economic: Maintains >90% accuracy even with lightweight models, saving 50-70% on token costs. ☁ Portable: Runs locally by default, with cloud-sync to share memory across agents and teammates.

andy nguyen

28,980 görüntüleme • 7 ay önce

Anthropic's Claude team just showed how to build an AI agent with real memory in under 30 minutes. 24-minutes. free. by the people who built Claude. one person + 10 agents with memory = a team that runs 24/7, remembers every customer improves itself. worth than $500 vibe-coding course.

Anthropic's Claude team just showed how to build an AI agent with real memory in under 30 minutes. 24-minutes. free. by the people who built Claude. one person + 10 agents with memory = a team that runs 24/7, remembers every customer improves itself. worth than $500 vibe-coding course.

Codez

1,626,612 görüntüleme • 5 ay önce

You probably don’t use just one AI agent anymore. Maybe Codex for coding. Claude Code for research. Cursor for quick edits. Hermes for the rest. Each one is great at something different. But every time you switch, you have to start over — re-explaining your project, preferences, and the way you like to work. Your agents know how to help you. They just don’t share a memory of you. memU just shipped an update that fixes this. One personal memory, shared across every agent and device you use. What Codex learned last night, Claude Code can remember this morning. 🧵

You probably don’t use just one AI agent anymore. Maybe Codex for coding. Claude Code for research. Cursor for quick edits. Hermes for the rest. Each one is great at something different. But every time you switch, you have to start over — re-explaining your project, preferences, and the way you like to work. Your agents know how to help you. They just don’t share a memory of you. memU just shipped an update that fixes this. One personal memory, shared across every agent and device you use. What Codex learned last night, Claude Code can remember this morning. 🧵

Hasan

50,678 görüntüleme • 3 ay önce

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

Adeniyi.sui

26,580 görüntüleme • 4 ay önce

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

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

Ronin

15,071 görüntüleme • 2 ay önce

i got tired of my agents living in six terminals with no memory of each other, so i built them a home. claude code, codex, kimi, grok, hermes, openclaw... > one house > one memory > cryptographic identity for each > continuity for all it's on mac, it's in beta, and it's free, b*tch. <3 first 100 get in early 👾

i got tired of my agents living in six terminals with no memory of each other, so i built them a home. claude code, codex, kimi, grok, hermes, openclaw... > one house > one memory > cryptographic identity for each > continuity for all it's on mac, it's in beta, and it's free, b*tch. <3 first 100 get in early 👾

Riley Coyote

29,382 görüntüleme • 1 ay önce

Introducing OpenAI Dots x Heybrain. Your always-on agents now have persistent, governed memory across sessions, tools, and agents. One agent can pick up where another left off, with the right company context and permissions already in place. Powered by Brain & GPT-6.1 Sol.

Introducing OpenAI Dots x Heybrain. Your always-on agents now have persistent, governed memory across sessions, tools, and agents. One agent can pick up where another left off, with the right company context and permissions already in place. Powered by Brain & GPT-6.1 Sol.

HeyBrain

36,894 görüntüleme • 1 ay önce

MICROSOFT QUIETLY BUILT ONE MEMORY MODULE THAT PLUGS INTO ANY AI AGENT AND CUTS ITS CONTEXT BY UP TO 100X no retraining. no task-specific redesign. you just plug it in Microsoft's insight: agents don't need to remember what happened. they need the knowledge pulled from it - the facts and the reusable skills, not the raw logs so PlugMem stops storing history. it compiles experience into a knowledge graph, then feeds the agent only what matters the result: > up to 1-2 orders of magnitude fewer tokens > highest decision-utility per token of any system tested > one unchanged module beats task-specific memory on chat QA, multi-hop search, and web agents more memory was never the answer. better memory is - and Microsoft just shipped it as a plug-in bookmark this. the agents that win won't remember more, they'll remember smarter.

MICROSOFT QUIETLY BUILT ONE MEMORY MODULE THAT PLUGS INTO ANY AI AGENT AND CUTS ITS CONTEXT BY UP TO 100X no retraining. no task-specific redesign. you just plug it in Microsoft's insight: agents don't need to remember what happened. they need the knowledge pulled from it - the facts and the reusable skills, not the raw logs so PlugMem stops storing history. it compiles experience into a knowledge graph, then feeds the agent only what matters the result: > up to 1-2 orders of magnitude fewer tokens > highest decision-utility per token of any system tested > one unchanged module beats task-specific memory on chat QA, multi-hop search, and web agents more memory was never the answer. better memory is - and Microsoft just shipped it as a plug-in bookmark this. the agents that win won't remember more, they'll remember smarter.

NO1ennn

227,956 görüntüleme • 3 ay önce

STANFORD BUILT AN LLM MEMORY THAT WORKS LIKE A BRAIN AND IT MADE STANDARD RAG LOOK ANCIENT no fine-tuning. no bigger model. just a smarter way to remember the insight: the brain doesn't store memories as isolated notes. it indexes them and links them - so one cue pulls back the whole memory so HippoRAG stops treating documents as separate chunks. it builds a knowledge graph, then spreads activation across it (Personalized PageRank) to pull a full answer from a single cue - the brain calls this "pattern completion" the result: > up to 20% better on hard multi-hop questions > 10-30x cheaper and 6-13x faster than iterative retrieval > connects facts split across documents in a single step most RAG still encodes every passage in isolation - so it breaks the moment the answer is scattered more retrieval was never the answer. brain-like memory is bookmark this. the next leap in AI agents is how they remember, not how big they get

STANFORD BUILT AN LLM MEMORY THAT WORKS LIKE A BRAIN AND IT MADE STANDARD RAG LOOK ANCIENT no fine-tuning. no bigger model. just a smarter way to remember the insight: the brain doesn't store memories as isolated notes. it indexes them and links them - so one cue pulls back the whole memory so HippoRAG stops treating documents as separate chunks. it builds a knowledge graph, then spreads activation across it (Personalized PageRank) to pull a full answer from a single cue - the brain calls this "pattern completion" the result: > up to 20% better on hard multi-hop questions > 10-30x cheaper and 6-13x faster than iterative retrieval > connects facts split across documents in a single step most RAG still encodes every passage in isolation - so it breaks the moment the answer is scattered more retrieval was never the answer. brain-like memory is bookmark this. the next leap in AI agents is how they remember, not how big they get

NO1ennn

32,454 görüntüleme • 3 ay önce

One model was never enough. So we built a living network. Minds that think. A hand that acts. A memory that knows you. Meet Nourva — one living intelligence built around you. One living system, devoted to one human life. Yours → 🜲

One model was never enough. So we built a living network. Minds that think. A hand that acts. A memory that knows you. Meet Nourva — one living intelligence built around you. One living system, devoted to one human life. Yours → 🜲

Nourva ai | نورفا

231,560 görüntüleme • 4 ay önce