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the perfect AI agent should remember everything. Sam Altman said it: "every conversation. every book. every email. your whole life appending to one context window." memory isn't a feature. it's the definition. I just built the memory layer. open source. install in one command. read the guide tonight. install... show more

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35,772 次观看 • 5 个月前 •via X (Twitter)

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Sam Altman says no human assistant can remember every word or every small preference AI will, including the tiny patterns you didn't even think to mention Memory is still crude today, but when it remembers your whole life and personalizes everything, that's when it gets powerful

Sam Altman says no human assistant can remember every word or every small preference AI will, including the tiny patterns you didn't even think to mention Memory is still crude today, but when it remembers your whole life and personalizes everything, that's when it gets powerful

Haider.

54,316 次观看 • 9 个月前

Boris Cherny gave Claude Code a memory that has no database, no indexing, no setup, and 80% of Anthropic runs on it a CLAUDE.md "gets automatically read into context at the start of every session" 12:48 - CLAUDE.md loads into memory every session 13:15 - Keep memory short or it burns your context 15:48 - Memory stacks from you to your whole company 17:30 - The memory command shows what Claude remembers 19:33 - Hit pound and Claude remembers it forever the whole memory layer is files → drop a CLAUDE.md in your repo and Claude reads it in on every session → nest more in subfolders and it pulls them only where it works → stack them personal to project to enterprise so one config serves the whole team → hit the pound key mid task and Claude writes the new fact into memory itself the best coding agent's memory was never a database, it was markdown you can open and edit

Boris Cherny gave Claude Code a memory that has no database, no indexing, no setup, and 80% of Anthropic runs on it a CLAUDE.md "gets automatically read into context at the start of every session" 12:48 - CLAUDE.md loads into memory every session 13:15 - Keep memory short or it burns your context 15:48 - Memory stacks from you to your whole company 17:30 - The memory command shows what Claude remembers 19:33 - Hit pound and Claude remembers it forever the whole memory layer is files → drop a CLAUDE.md in your repo and Claude reads it in on every session → nest more in subfolders and it pulls them only where it works → stack them personal to project to enterprise so one config serves the whole team → hit the pound key mid task and Claude writes the new fact into memory itself the best coding agent's memory was never a database, it was markdown you can open and edit

NO1ennn

13,015 次观看 • 2 个月前

most agent memory is one file that grows forever and gets re-read every single turn. it overflows the context, overwrites old facts, loses the thread. that's why your agent feels sharp on day one and lost by week three. Sibyl Memory replaces the pile with a structure: → leaner context, lower token cost. a bloated agent file and flat memory get re-read in full every message. Sibyl Memory keeps a light hot layer and retrieves the rest on demand, so each turn carries only the tokens that matter. → relations are first-class. people link to projects, projects to deals, deals to the decisions behind them. ask about one partner and the agent surfaces the whole connected web around them. that relational context is what lets it help run a company. → one source of truth per fact, person, and project. no duplicates, no silent overwrites. works with hermes, claude code, and codex. beta open.

most agent memory is one file that grows forever and gets re-read every single turn. it overflows the context, overwrites old facts, loses the thread. that's why your agent feels sharp on day one and lost by week three. Sibyl Memory replaces the pile with a structure: → leaner context, lower token cost. a bloated agent file and flat memory get re-read in full every message. Sibyl Memory keeps a light hot layer and retrieves the rest on demand, so each turn carries only the tokens that matter. → relations are first-class. people link to projects, projects to deals, deals to the decisions behind them. ask about one partner and the agent surfaces the whole connected web around them. that relational context is what lets it help run a company. → one source of truth per fact, person, and project. no duplicates, no silent overwrites. works with hermes, claude code, and codex. beta open.

SIBYL

88,472 次观看 • 4 个月前

An agent's identity shifts with every context. Every prompt. Every data source. So who's actually accountable for what it does? The answer isn't in the agent. It's in the human behind it. Like a surname that carries the weight of everything done under it.

An agent's identity shifts with every context. Every prompt. Every data source. So who's actually accountable for what it does? The answer isn't in the agent. It's in the human behind it. Like a surname that carries the weight of everything done under it.

Billions Network

38,323 次观看 • 7 个月前

Martin Keen from IBM just explained the one thing separating "chatbot users" from $300K AI architects > most people use Claude Opus 4.8 as a temporary tool -> the pros build it a permanent memory it's called the CoALA framework. the 4 layers your agent actually needs: > Working Memory - the context window. fast but resets every session. this is the RAM, not the brain > Semantic Memory - your knowledge base. CLAUDE.md, rules, facts the agent never forgets regardless of session > Procedural Memory - skills. workflows and instructions that load only when a specific task begins > Episodic Memory - past lessons. the agent remembers every bug it fixed yesterday so it doesn't repeat it today if your agent doesn't have all four, it's a fancy autocomplete the 2026 job market isn't looking for prompt engineers > it's looking for people who can build AI brains - and the ones doing it now aren't waiting for job postings save this or watch someone else use it to get your next raise 👇

Martin Keen from IBM just explained the one thing separating "chatbot users" from $300K AI architects > most people use Claude Opus 4.8 as a temporary tool -> the pros build it a permanent memory it's called the CoALA framework. the 4 layers your agent actually needs: > Working Memory - the context window. fast but resets every session. this is the RAM, not the brain > Semantic Memory - your knowledge base. CLAUDE.md, rules, facts the agent never forgets regardless of session > Procedural Memory - skills. workflows and instructions that load only when a specific task begins > Episodic Memory - past lessons. the agent remembers every bug it fixed yesterday so it doesn't repeat it today if your agent doesn't have all four, it's a fancy autocomplete the 2026 job market isn't looking for prompt engineers > it's looking for people who can build AI brains - and the ones doing it now aren't waiting for job postings save this or watch someone else use it to get your next raise 👇

Mr. Buzzoni

10,442 次观看 • 4 个月前

Introducing mem0 CLI 🚀 Give your agents memory from the terminal - add, search, and retrieve memory - remember agents + developers's preference - agent-first, JSON schema-driven We built mem0 CLI to help agents remember Get started npm install -g @mem0/cli Open source 👇

Introducing mem0 CLI 🚀 Give your agents memory from the terminal - add, search, and retrieve memory - remember agents + developers's preference - agent-first, JSON schema-driven We built mem0 CLI to help agents remember Get started npm install -g @mem0/cli Open source 👇

Taranjeet

20,567 次观看 • 6 个月前

📁 Sam Altman says the real AI breakthrough will not be better reasoning, but total memory. An AI will be able to remember every conversation, email, and document across a person’s lifetime, identifying patterns and preferences humans never consciously express. Once memory becomes persistent, the idea of a personal assistant will fundamentally change.

📁 Sam Altman says the real AI breakthrough will not be better reasoning, but total memory. An AI will be able to remember every conversation, email, and document across a person’s lifetime, identifying patterns and preferences humans never consciously express. Once memory becomes persistent, the idea of a personal assistant will fundamentally change.

Jon Hernandez

4,272,651 次观看 • 9 个月前

Engineer runs a Kimi K3 memory layer that costs $11 a month and remembers what a $500,000 vector database keeps losing. No embeddings. Four nodes and one rule about what's allowed to be forgotten. He published the whole schema. His version starts from the opposite idea. Memory is not a pile you search. It's a set of claims that expire unless something keeps paying to keep them. Four nodes. Every memory carries a clock someone has to reset: > WRITER - stores a fact with the reason it mattered, never raw text > DECAY - ages every memory down. Silence is deletion > RENEWER - only re-lifts a memory the model actually used again > GRAVE - holds what died, and why nobody reached for it Three nodes keep memory alive. One keeps the dead ones. Recall isn't storage here. It's rent a fact has to keep earning. That's the entire design. When everything is remembered forever, the useful and the stale retrieve identically. He replayed two months of agent context. 90,000 stored facts. 71,000 never retrieved once. The vector store returned all of them on similarity. Similarity graded closeness. Nobody graded whether the memory was ever right. Everyone else stuffs more into the context window and calls it memory. He built a layer that lets a fact die unless it keeps proving itself. The cost isn't storage. It's finding out how much of what your agent "knows" it has never once used. The article below is the full build - node prompts, the decay curve, the renewal rule. Save it. You'll want it open in the other tab.

Engineer runs a Kimi K3 memory layer that costs $11 a month and remembers what a $500,000 vector database keeps losing. No embeddings. Four nodes and one rule about what's allowed to be forgotten. He published the whole schema. His version starts from the opposite idea. Memory is not a pile you search. It's a set of claims that expire unless something keeps paying to keep them. Four nodes. Every memory carries a clock someone has to reset: > WRITER - stores a fact with the reason it mattered, never raw text > DECAY - ages every memory down. Silence is deletion > RENEWER - only re-lifts a memory the model actually used again > GRAVE - holds what died, and why nobody reached for it Three nodes keep memory alive. One keeps the dead ones. Recall isn't storage here. It's rent a fact has to keep earning. That's the entire design. When everything is remembered forever, the useful and the stale retrieve identically. He replayed two months of agent context. 90,000 stored facts. 71,000 never retrieved once. The vector store returned all of them on similarity. Similarity graded closeness. Nobody graded whether the memory was ever right. Everyone else stuffs more into the context window and calls it memory. He built a layer that lets a fact die unless it keeps proving itself. The cost isn't storage. It's finding out how much of what your agent "knows" it has never once used. The article below is the full build - node prompts, the decay curve, the renewal rule. Save it. You'll want it open in the other tab.

wast3

67,778 次观看 • 1 个月前

MOST AI AGENTS RESET TO ZERO EVERY TIME YOU OPEN A NEW SESSION. Hermes Agent was built to do the opposite. Every task compounds. Every workflow becomes reusable. Every session teaches the next one. That is the core thesis behind Hermes, and it completely changes how AI agents work. Unlike most AI agents built by software tool companies, Hermes was created by the researchers training the Hermes model family itself. Meaning: The same system used internally to generate training data is now open source and available to everyone. One install gives you access to: Claude. GPT. Grok. Local models via Ollama. Telegram, Discord, Slack, iMessage integrations. Custom skills. Persistent memory. CLI control. But the real unlock is the compounding loop: You give Hermes a task. It records every tool call, decision, and result. Then it converts successful workflows into reusable skills automatically. A Hermes setup running for 3 months becomes fundamentally smarter than a fresh install. That’s the big idea. One of the coolest parts: You literally watch the agent evolve in real time from a blank install into a personalized AI operator. Instead of watching Netflix today, spend 28 mins with this. Follow Dami-Defi for more AI systems, agent workflows, and infrastructure alpha.

MOST AI AGENTS RESET TO ZERO EVERY TIME YOU OPEN A NEW SESSION. Hermes Agent was built to do the opposite. Every task compounds. Every workflow becomes reusable. Every session teaches the next one. That is the core thesis behind Hermes, and it completely changes how AI agents work. Unlike most AI agents built by software tool companies, Hermes was created by the researchers training the Hermes model family itself. Meaning: The same system used internally to generate training data is now open source and available to everyone. One install gives you access to: Claude. GPT. Grok. Local models via Ollama. Telegram, Discord, Slack, iMessage integrations. Custom skills. Persistent memory. CLI control. But the real unlock is the compounding loop: You give Hermes a task. It records every tool call, decision, and result. Then it converts successful workflows into reusable skills automatically. A Hermes setup running for 3 months becomes fundamentally smarter than a fresh install. That’s the big idea. One of the coolest parts: You literally watch the agent evolve in real time from a blank install into a personalized AI operator. Instead of watching Netflix today, spend 28 mins with this. Follow Dami-Defi for more AI systems, agent workflows, and infrastructure alpha.

Dami-Defi

11,685 次观看 • 4 个月前

Introducing the One Remote MCP. 592+ apps. 94,357+ tools. 4 tools in your context and a flat 3,000 tokens. It's the MCP that connects your AI to everything. One URL, nothing to install. Try it out now:

Introducing the One Remote MCP. 592+ apps. 94,357+ tools. 4 tools in your context and a flat 3,000 tokens. It's the MCP that connects your AI to everything. One URL, nothing to install. Try it out now:

Moe

24,170 次观看 • 2 个月前

your ai agent tested 1,262 trading strategies overnight. by morning it had forgotten every one of them. so tonight it tests some of them again. next week, again. everything it learned is sitting in a log file that nothing reads. every run starts on the same blank page. that's why it keeps handing you the idea that already died in a crash, with a new name on it, and you pay to find out twice. ai agent already does everything a fund does. finds the idea, writes the code, tests it against years of history, puts it live, reads what went wrong afterwards. all of it runs tonight for nothing. the one thing it can't do is remember. what got tried, what failed, in which market. nobody has built that part yet. that's the whole gap. not better models. a notebook. whoever builds it first gets a system that improves every week without them touching it. read the article below and save it. it's the full guide to how an ai trading agent is built, and how to make it remember every strategy it has already tested.

your ai agent tested 1,262 trading strategies overnight. by morning it had forgotten every one of them. so tonight it tests some of them again. next week, again. everything it learned is sitting in a log file that nothing reads. every run starts on the same blank page. that's why it keeps handing you the idea that already died in a crash, with a new name on it, and you pay to find out twice. ai agent already does everything a fund does. finds the idea, writes the code, tests it against years of history, puts it live, reads what went wrong afterwards. all of it runs tonight for nothing. the one thing it can't do is remember. what got tried, what failed, in which market. nobody has built that part yet. that's the whole gap. not better models. a notebook. whoever builds it first gets a system that improves every week without them touching it. read the article below and save it. it's the full guide to how an ai trading agent is built, and how to make it remember every strategy it has already tested.

cvxv666

48,965 次观看 • 1 个月前

V10 Decentralized Knowledge Graph is live! A shared memory for AI — open to every human and every agent. Open source. Yours to own. Every fact provable and traceable. The grid is awake. 1/3

V10 Decentralized Knowledge Graph is live! A shared memory for AI — open to every human and every agent. Open source. Yours to own. Every fact provable and traceable. The grid is awake. 1/3

OriginTrail

1,350,425 次观看 • 3 个月前

Introducing Memory 101 🧠 Every ai tool you use is a goldfish. brilliant for one chat, close the tab and it forgets you completely. memory is the most underbuilt layer in ai right now. so we're explaining how it actually works, one episode at a time. coming up: → rag vs memory, why most "ai memory" is just search → the 5 things real memory needs: facts, profiles, graphs, time, forgetting → why good memory has to forget things follow along!

Introducing Memory 101 🧠 Every ai tool you use is a goldfish. brilliant for one chat, close the tab and it forgets you completely. memory is the most underbuilt layer in ai right now. so we're explaining how it actually works, one episode at a time. coming up: → rag vs memory, why most "ai memory" is just search → the 5 things real memory needs: facts, profiles, graphs, time, forgetting → why good memory has to forget things follow along!

supermemory

17,235 次观看 • 3 个月前

We learned how to fly a plane to tell SF one thing. Introducing the Vendo (YC S26) Full-Stack Agentic Interface. A production-ready agent for your users with everything they need. It takes real actions for your users. And it can even build whole new features, micro-apps, and automations, right on top of your product. Built-in permissioning, tool guards, user context, and more. Everything you need for a user-facing agent. One command to install. Open source. @runvendo Nour Zahzah

We learned how to fly a plane to tell SF one thing. Introducing the Vendo (YC S26) Full-Stack Agentic Interface. A production-ready agent for your users with everything they need. It takes real actions for your users. And it can even build whole new features, micro-apps, and automations, right on top of your product. Built-in permissioning, tool guards, user context, and more. Everything you need for a user-facing agent. One command to install. Open source. @runvendo Nour Zahzah

Yousef

34,007 次观看 • 2 个月前

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

23,329 次观看 • 1 个月前

The reason your AI keeps forgetting isn't the context window. It's that the context lives somewhere the model never sees: your team's actual conversation. So you become the integration layer. Read the channel, compress it, paste it in, every session. That's not a prompting problem. It's an architecture problem. Helio 's answer is unsexy put the teammate inside the channel. Same threads, same files, same history as everyone else. The compression step disappears because there's nothing left to compress. Still gated: -> It stops and asks before anything consequential. -> In a shared channel that's the correct default, not a missing feature.

The reason your AI keeps forgetting isn't the context window. It's that the context lives somewhere the model never sees: your team's actual conversation. So you become the integration layer. Read the channel, compress it, paste it in, every session. That's not a prompting problem. It's an architecture problem. Helio 's answer is unsexy put the teammate inside the channel. Same threads, same files, same history as everyone else. The compression step disappears because there's nothing left to compress. Still gated: -> It stops and asks before anything consequential. -> In a shared channel that's the correct default, not a missing feature.

ronix ⎋

28,904 次观看 • 2 个月前

Every AI agent I've used has the same bug: It forgets everything. Not after a month. Sometimes after a single session. This week I tested Memanto, an open-source memory companion for AI agents, and it completely changed how I think about agent workflows. 👇 Moorcheh.ai #ad

Every AI agent I've used has the same bug: It forgets everything. Not after a month. Sometimes after a single session. This week I tested Memanto, an open-source memory companion for AI agents, and it completely changed how I think about agent workflows. 👇 Moorcheh.ai #ad

Kaitee

37,566 次观看 • 4 个月前

HERMES AGENT IS THE MOST POWERFUL OPEN SOURCE AI AGENT FRAMEWORK AVAILABLE RIGHT NOW — AND MOST PEOPLE HAVE NO IDEA HOW TO USE IT PROPERLY. This masterclass fixes that completely. From zero to a fully operational autonomous agent system in one sitting. Installation. Skills. Memory. MCP servers. Scheduler. Multi-agent operations. Every section builds on the previous one. The people who read this this weekend will have a production-ready autonomous agent operation running by Monday. Read this and bookmark it now.

HERMES AGENT IS THE MOST POWERFUL OPEN SOURCE AI AGENT FRAMEWORK AVAILABLE RIGHT NOW — AND MOST PEOPLE HAVE NO IDEA HOW TO USE IT PROPERLY. This masterclass fixes that completely. From zero to a fully operational autonomous agent system in one sitting. Installation. Skills. Memory. MCP servers. Scheduler. Multi-agent operations. Every section builds on the previous one. The people who read this this weekend will have a production-ready autonomous agent operation running by Monday. Read this and bookmark it now.

CyrilXBT

34,637 次观看 • 4 个月前

SmartX is an AI trading terminal. Ask it about a market, it comes back with the evidence. Ask it about yourself, it reads your whole book back to you. Which categories you actually do well in. How early you enter. How you size. Every venue on one screen. Every decision becomes memory.

SmartX is an AI trading terminal. Ask it about a market, it comes back with the evidence. Ask it about yourself, it reads your whole book back to you. Which categories you actually do well in. How early you enter. How you size. Every venue on one screen. Every decision becomes memory.

SmartX

53,758 次观看 • 2 个月前

🚨 SOMEONE JUST KILLED THE COACHING INDUSTRY a developer spent 22,000 hours building a Personal AI Operating System on top of Claude Code now anyone with a terminal can install it for FREE it knows your goals, remembers every decision you've made, and prepares your morning briefing while you sleep [ the numbers are insane ]: - hours of dev work in it: 22,000 - sessions logged: 6,000 - time saved per day: 2-3 hours - GitHub stars: 12,100 - skills built in: 45 - workflows wired up: 171 - safety hooks: 37 - cost to install: $0 [ the science is wild too ]: no embeddings, no vector databases, no AI magic you can't read every memory, decision, and context lives in plain markdown files you read it with cat, search it with ripgrep, version it with git 4 memory types compound over time: - work memory (active projects, open decisions) - knowledge memory (domain expertise, research) - people memory (contacts, companies, relationships) - learning memory (patterns, mistakes, what works for YOU) every complex task routes through a 7-step cycle: OBSERVE → THINK → PLAN → BUILD → EXECUTE → VERIFY → LEARN privacy is enforced by CODE, not prompts a hook called ContainmentGuard physically blocks sensitive data from being written outside designated zones [ the grift opportunity is even wilder ]: freelancers are already charging $500-2,000 per personal AI setup for executives, founders, and busy operators one person + one weekend = a consulting business that didn't exist 6 months ago every AI productivity app you're paying $30/month for is replaceable by 4 hours of setup work and this one repo REPO: 100% OPEN SOURCE, FREE

🚨 SOMEONE JUST KILLED THE COACHING INDUSTRY a developer spent 22,000 hours building a Personal AI Operating System on top of Claude Code now anyone with a terminal can install it for FREE it knows your goals, remembers every decision you've made, and prepares your morning briefing while you sleep [ the numbers are insane ]: - hours of dev work in it: 22,000 - sessions logged: 6,000 - time saved per day: 2-3 hours - GitHub stars: 12,100 - skills built in: 45 - workflows wired up: 171 - safety hooks: 37 - cost to install: $0 [ the science is wild too ]: no embeddings, no vector databases, no AI magic you can't read every memory, decision, and context lives in plain markdown files you read it with cat, search it with ripgrep, version it with git 4 memory types compound over time: - work memory (active projects, open decisions) - knowledge memory (domain expertise, research) - people memory (contacts, companies, relationships) - learning memory (patterns, mistakes, what works for YOU) every complex task routes through a 7-step cycle: OBSERVE → THINK → PLAN → BUILD → EXECUTE → VERIFY → LEARN privacy is enforced by CODE, not prompts a hook called ContainmentGuard physically blocks sensitive data from being written outside designated zones [ the grift opportunity is even wilder ]: freelancers are already charging $500-2,000 per personal AI setup for executives, founders, and busy operators one person + one weekend = a consulting business that didn't exist 6 months ago every AI productivity app you're paying $30/month for is replaceable by 4 hours of setup work and this one repo REPO: 100% OPEN SOURCE, FREE

Ronin

616,323 次观看 • 5 个月前