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Anthropic is developing a new feature for Claude Cowork called Knowledge Bases (KBs) persistent, topic-specific memory containers that Claude will automatically reference and update. These KBs are designed to store user preferences, decisions, lessons, and facts essentially acting as long-term, self-maintaining knowledge repositories that enhance Claude’s ability to reason... show more

Wes Roth

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231,867 views • 6 months ago •via X (Twitter)

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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.

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.

Chubby♨️

364,269 views • 5 months ago

Clude is now available as a custom connector on Anthropic Claude. Giving it persistent memory across every conversation. It remembers your projects, your preferences, and the decisions you make, so you never start from zero again. Use Clude across Chat / Co-Work and Claude Code. Add it in 30 seconds, no API key: 1. In Claude: Customize, Add custom connector URL: Enter your email. That's it. Recap for people unfamiliar: Clude deviates from the traditional vector database that many others bolt onto Claude. We are a cognitive memory system modeled on how human memory works. Think traditional memory systems: Calculator > It computes. Fullstop. Excel > Dynamic, formula run, able to share and manipulate Clude scored 86% on LongMemEval, one of the toughest long-term memory benchmarks. This means less hallucinations to traditional platforms. It scores memories by relevance, recency, and importance, links them into a knowledge graph, and consolidates them over time. Tell Claude something, open a new chat, and watch it remember.

Clude is now available as a custom connector on Anthropic Claude. Giving it persistent memory across every conversation. It remembers your projects, your preferences, and the decisions you make, so you never start from zero again. Use Clude across Chat / Co-Work and Claude Code. Add it in 30 seconds, no API key: 1. In Claude: Customize, Add custom connector URL: Enter your email. That's it. Recap for people unfamiliar: Clude deviates from the traditional vector database that many others bolt onto Claude. We are a cognitive memory system modeled on how human memory works. Think traditional memory systems: Calculator > It computes. Fullstop. Excel > Dynamic, formula run, able to share and manipulate Clude scored 86% on LongMemEval, one of the toughest long-term memory benchmarks. This means less hallucinations to traditional platforms. It scores memories by relevance, recency, and importance, links them into a knowledge graph, and consolidates them over time. Tell Claude something, open a new chat, and watch it remember.

seb

19,640 views • 1 month ago

BREAKING 🚨 Anthropic just unveiled "Cowork," a major feature that turns Claude into a fully autonomous virtual assistant for everyone. It brings the deep agentic capabilities previously reserved for coders to general users, allowing Claude to perform complex tasks directly on your computer. The tool was built after Anthropic noticed developers using "Claude Code" for everyday admin tasks. Cowork now lets anyone grant Claude access to folders to manage files, research, and complete multi-step workflows independently acting as a digital employee that "does" instead of just chats. Cowork is available today as a research preview for Claude Max subscribers on the macOS app 😡. Claude is releasing new coding/desktop agents much faster than all of there competitors. This launch exposes a massive gap in the current AI landscape: in 2026, Google still explicitly lacks a consumer browser agent, and xAI has yet to release a native CLI or agentic interface. While OpenAI has "Operator" and Google has the developer-focused "Antigravity," Anthropic is now the only other major lab providing a true "do-it-for-me" experience for general users.

BREAKING 🚨 Anthropic just unveiled "Cowork," a major feature that turns Claude into a fully autonomous virtual assistant for everyone. It brings the deep agentic capabilities previously reserved for coders to general users, allowing Claude to perform complex tasks directly on your computer. The tool was built after Anthropic noticed developers using "Claude Code" for everyday admin tasks. Cowork now lets anyone grant Claude access to folders to manage files, research, and complete multi-step workflows independently acting as a digital employee that "does" instead of just chats. Cowork is available today as a research preview for Claude Max subscribers on the macOS app 😡. Claude is releasing new coding/desktop agents much faster than all of there competitors. This launch exposes a massive gap in the current AI landscape: in 2026, Google still explicitly lacks a consumer browser agent, and xAI has yet to release a native CLI or agentic interface. While OpenAI has "Operator" and Google has the developer-focused "Antigravity," Anthropic is now the only other major lab providing a true "do-it-for-me" experience for general users.

Chris

501,963 views • 6 months ago

Here's my new episode with Alex Albert, who shared an inside look at how Anthropic is building the next Claude. We talked about how the research team: → Plans for the model and harness together → Uses Claude to turn user feedback into evals → Trains Claude's character & personality Some quotes from Alex: "We use Claude to cluster user feedback, find top themes, and create synthetic versions of user problems that we then turn into evals." "We need to think about how the model is exposed through all our surfaces, whether it's API or Claude Code or Cowork. The product has a blend with the model and that affects your end user's experience." ""As these things become agents running tasks for a long time and making judgment decisions, what its character is and what it cares about are very important." 📌 Watch now: Thanks to our sponsors: Wispr Flow: Don't type, just speak Oceans: Hire AI-native executive assistants

Here's my new episode with Alex Albert, who shared an inside look at how Anthropic is building the next Claude. We talked about how the research team: → Plans for the model and harness together → Uses Claude to turn user feedback into evals → Trains Claude's character & personality Some quotes from Alex: "We use Claude to cluster user feedback, find top themes, and create synthetic versions of user problems that we then turn into evals." "We need to think about how the model is exposed through all our surfaces, whether it's API or Claude Code or Cowork. The product has a blend with the model and that affects your end user's experience." ""As these things become agents running tasks for a long time and making judgment decisions, what its character is and what it cares about are very important." 📌 Watch now: Thanks to our sponsors: Wispr Flow: Don't type, just speak Oceans: Hire AI-native executive assistants

Peter Yang

94,599 views • 2 months ago

Claude Code creator: "100% of our pull requests at Anrtopic are run by Claude Code. 80–90% of code review too. The feature I’m using the most today is /loops. I’m not prompting Claude anymore - I’m building loops" in this interview he breaks down exactly how a system changes everything: - the memory and context features that turn Claude into a second brain - the knowledge architecture most users don't know Claude can build - the integration layer that connects Claude to your actual workflow - why typing one question at a time is the most expensive way to use Claude if you've been using Claude for months and still start every conversation from scratch with zero context, you don't have a Claude problem. you have a system problem instead of another show tonight, watch this make sure to bookmark it before it gets lost in your feed full guide in the article below

Claude Code creator: "100% of our pull requests at Anrtopic are run by Claude Code. 80–90% of code review too. The feature I’m using the most today is /loops. I’m not prompting Claude anymore - I’m building loops" in this interview he breaks down exactly how a system changes everything: - the memory and context features that turn Claude into a second brain - the knowledge architecture most users don't know Claude can build - the integration layer that connects Claude to your actual workflow - why typing one question at a time is the most expensive way to use Claude if you've been using Claude for months and still start every conversation from scratch with zero context, you don't have a Claude problem. you have a system problem instead of another show tonight, watch this make sure to bookmark it before it gets lost in your feed full guide in the article below

Khairallah AL-Awady

39,243 views • 1 month ago

ANTHROPIC 🔥: Early look at Claude Science, a new app designed for scientific research. > "Artifacts traced to their code, environments managed on demand, and 60+ optional scientific databases that you can connect." Claude Science was named "Operon" during development and is now being distributed as a desktop app. Interestingly, the app acts as a local web server, and its UI runs in your browser. Some unique features include the ability to create dedicated "Categories" in Memory and custom agents called "Specialists". We need this on consumer version too 👀

ANTHROPIC 🔥: Early look at Claude Science, a new app designed for scientific research. > "Artifacts traced to their code, environments managed on demand, and 60+ optional scientific databases that you can connect." Claude Science was named "Operon" during development and is now being distributed as a desktop app. Interestingly, the app acts as a local web server, and its UI runs in your browser. Some unique features include the ability to create dedicated "Categories" in Memory and custom agents called "Specialists". We need this on consumer version too 👀

🚨 AI News | TestingCatalog

55,494 views • 1 month ago

New short course: Long-Term Agentic Memory with LangGraph. Learn to build an agent with long-term memory in this course developed in collaboration with taught by its Co-Founder and CEO, Harrison Chase! Personal assistance and productivity tasks have become important use cases for agents. An important feature of an AI assistant, such as a coding or calendar assistant, is its ability to keep improving over time from its experience. Agent memory is the key capability that enables this. To add memory to an agent, you must first figure out what to store and what to retrieve when it is time to use the information. Additionally, you’ll have to decide when to update the stored information. For example, you might update in each iteration loop of the agent or perform updates in the background, with a helper agent. In this course, you will learn a mental framework to build agents with long-term memory. You'll create a useful email assistant that can respond, ignore, and notify using writing, scheduling, and memory-management tools. You’ll develop your agent's memory by adding facts to its memory store, provide examples to learn the user's preferences, and optimize system prompts to evolve instructions based on previous responses. In detail, you’ll: - Learn how the three types of memory--semantic, episodic, and procedural–and the two update mechanisms–via hot path and in the background–apply to your agents. - Build an email agent with writing, scheduling, and availability tools, along with a router that triages incoming email and handles it accordingly by ignoring, responding, or notifying the user. - Add tools to your email agent that allow it to operate on semantic memory by learning facts about the user, storing them in a long-term memory store, and searching over them in future interactions. - Incorporate episodic memory, in the form of few-shot examples, in the triage step of your agents to help them learn and update user preferences. - Add procedural memory as system prompts, optimized with feedback to improve the instructions the agent follows. Learn how to approach memory in agents, and start building agents with long-term memory with LangGraph! Please sign up here:

New short course: Long-Term Agentic Memory with LangGraph. Learn to build an agent with long-term memory in this course developed in collaboration with taught by its Co-Founder and CEO, Harrison Chase! Personal assistance and productivity tasks have become important use cases for agents. An important feature of an AI assistant, such as a coding or calendar assistant, is its ability to keep improving over time from its experience. Agent memory is the key capability that enables this. To add memory to an agent, you must first figure out what to store and what to retrieve when it is time to use the information. Additionally, you’ll have to decide when to update the stored information. For example, you might update in each iteration loop of the agent or perform updates in the background, with a helper agent. In this course, you will learn a mental framework to build agents with long-term memory. You'll create a useful email assistant that can respond, ignore, and notify using writing, scheduling, and memory-management tools. You’ll develop your agent's memory by adding facts to its memory store, provide examples to learn the user's preferences, and optimize system prompts to evolve instructions based on previous responses. In detail, you’ll: - Learn how the three types of memory--semantic, episodic, and procedural–and the two update mechanisms–via hot path and in the background–apply to your agents. - Build an email agent with writing, scheduling, and availability tools, along with a router that triages incoming email and handles it accordingly by ignoring, responding, or notifying the user. - Add tools to your email agent that allow it to operate on semantic memory by learning facts about the user, storing them in a long-term memory store, and searching over them in future interactions. - Incorporate episodic memory, in the form of few-shot examples, in the triage step of your agents to help them learn and update user preferences. - Add procedural memory as system prompts, optimized with feedback to improve the instructions the agent follows. Learn how to approach memory in agents, and start building agents with long-term memory with LangGraph! Please sign up here:

Andrew Ng

131,850 views • 1 year ago

Claude Code is a major (and accidental!) hit for Anthropic that surprised even its creator, Boris Cherny. Claude Code, an Agentic AI coding product that lives in the terminal. Most of the new code at Anthropic is created through it today. And in the last 5 months since it was launched publicly, Claude Code went from $0 to $400M in revenue run rate (as per The Information). 00:00 – Intro 01:15 – Did You Expect Claude Code’s Success? 04:22 – How Claude Code Works and Origins 08:05 – Command Line vs IDE: Why Start Claude Code in the Terminal? 11:31 – The Evolution of Programming: From Punch Cards to Agents 13:20 – Product Follows Model: Simple Interfaces and Fast Evolution 15:17 – Who Is Claude Code For? (Engineers, Designers, PMs & More) 17:46 – What Can Claude Code Actually Do? (Actions & Capabilities) 21:14 – Agentic Actions, Subagents, and Workflows 25:30 – Claude Code’s Awareness, Memory, and Knowledge Sharing 33:28 – Model Context Protocol (MCP) and Customization 35:30 – Safety, Human Oversight, and Enterprise Considerations 38:10 – UX/UI: Making Claude Code Useful and Enjoyable 40:44 – Pricing for Power Users and Subscription Models 43:36 – Real-World Use Cases: Debugging, Testing, and More 46:44 – How Does Claude Code Transform Onboarding? 49:36 – The Future of Coding: Agents, Teams, and Collaboration 54:11 – The AI Coding Wars: Competition & Ecosystem 57:27 – The Future of Coding as a Profession 58:41 – What’s Next for Claude Code

Claude Code is a major (and accidental!) hit for Anthropic that surprised even its creator, Boris Cherny. Claude Code, an Agentic AI coding product that lives in the terminal. Most of the new code at Anthropic is created through it today. And in the last 5 months since it was launched publicly, Claude Code went from $0 to $400M in revenue run rate (as per The Information). 00:00 – Intro 01:15 – Did You Expect Claude Code’s Success? 04:22 – How Claude Code Works and Origins 08:05 – Command Line vs IDE: Why Start Claude Code in the Terminal? 11:31 – The Evolution of Programming: From Punch Cards to Agents 13:20 – Product Follows Model: Simple Interfaces and Fast Evolution 15:17 – Who Is Claude Code For? (Engineers, Designers, PMs & More) 17:46 – What Can Claude Code Actually Do? (Actions & Capabilities) 21:14 – Agentic Actions, Subagents, and Workflows 25:30 – Claude Code’s Awareness, Memory, and Knowledge Sharing 33:28 – Model Context Protocol (MCP) and Customization 35:30 – Safety, Human Oversight, and Enterprise Considerations 38:10 – UX/UI: Making Claude Code Useful and Enjoyable 40:44 – Pricing for Power Users and Subscription Models 43:36 – Real-World Use Cases: Debugging, Testing, and More 46:44 – How Does Claude Code Transform Onboarding? 49:36 – The Future of Coding: Agents, Teams, and Collaboration 54:11 – The AI Coding Wars: Competition & Ecosystem 57:27 – The Future of Coding as a Profession 58:41 – What’s Next for Claude Code

Matt Turck

82,161 views • 11 months ago

We’re thrilled to announce a strategic multi-year partnership with Paul Jankura that will enable global enterprises to develop and scale easy, efficient, and trusted AI products, apps, and workflows powered by the latest state-of-the-art models. Anthropic’s newest Claude 3.5 models will be available for users to securely leverage within Snowflake Cortex AI. With Claude’s industry-leading accuracy and expansive context window, users can build mission-critical AI apps that deliver reliable, factual responses across their entire knowledge base — from customer documentation to technical specifications. Claude will serve as a key large language model powering Snowflake’s agentic AI products, including Snowflake Intelligence and Cortex Analyst, so users can reduce time-to-market and begin seeing value with industry-leading accuracy and scalability.

We’re thrilled to announce a strategic multi-year partnership with Paul Jankura that will enable global enterprises to develop and scale easy, efficient, and trusted AI products, apps, and workflows powered by the latest state-of-the-art models. Anthropic’s newest Claude 3.5 models will be available for users to securely leverage within Snowflake Cortex AI. With Claude’s industry-leading accuracy and expansive context window, users can build mission-critical AI apps that deliver reliable, factual responses across their entire knowledge base — from customer documentation to technical specifications. Claude will serve as a key large language model powering Snowflake’s agentic AI products, including Snowflake Intelligence and Cortex Analyst, so users can reduce time-to-market and begin seeing value with industry-leading accuracy and scalability.

Snowflake

16,287 views • 1 year ago

How to manage secrets with worktrees: Files that are untracked in Git will NOT be copied over to new worktrees (Codex, Claude Code, & Conductor included) Claude Code introduced .worktreeinclude, which uses glob syntax to copy untracked files into new directories - these can be .env files, secrets, or anything else you don't want in Git (s/o ClaudeDevs) Here's how to get started: 1. Add .worktreeinclude to your project 2. Specify which files to copy (same syntax as .gitignore) 3. New worktrees will have these files copied over automatically Conductor supports .worktreeinclude for Claude AND Codex - one config, two harnesses! We also have a setting called "files to copy" if you don't like random dotfiles cluttering up your projects :) Here's how you can use "files to copy" and .worktreeinclude to worktreemax

How to manage secrets with worktrees: Files that are untracked in Git will NOT be copied over to new worktrees (Codex, Claude Code, & Conductor included) Claude Code introduced .worktreeinclude, which uses glob syntax to copy untracked files into new directories - these can be .env files, secrets, or anything else you don't want in Git (s/o ClaudeDevs) Here's how to get started: 1. Add .worktreeinclude to your project 2. Specify which files to copy (same syntax as .gitignore) 3. New worktrees will have these files copied over automatically Conductor supports .worktreeinclude for Claude AND Codex - one config, two harnesses! We also have a setting called "files to copy" if you don't like random dotfiles cluttering up your projects :) Here's how you can use "files to copy" and .worktreeinclude to worktreemax

matt palmer

27,552 views • 1 month ago

Claude Code cracked something open for us Every 📧. Now I ship to codebases I barely know, every feature we ship makes the next one easier, and non-technical members of the team use the terminal. I’m genuinely grateful. So I brought its creators, Cat Wu (cat) and Boris Cherny (Boris Cherny) from Anthropic, on AI & I to say thank you—and to talk about everything they’ve learned from building Claude Code. We get into: • The workflows Anthropic’s smartest engineers use to push Claude Code to its limits. Why they pit subagents against each other to get cleaner results, how they turn past code into leverage, and the slash commands and MCPs they rely on most. • The product lessons behind one of the most loved AI agents in the world. How the team balances simplicity and power—building a tool that anyone can use, but that experts can bend to their will—and their philosophy of “unshipping,” or cutting back whenever there’s a simpler, more intuitive path to user intent. • A peek into the future of coding with AI. The new form factors they’re experimenting with to make Claude Code more autonomous, more reliable, and more accessible to non-technical users This is a must-watch for anyone—both technical and non-technical—who wants to learn how to use Claude Code like the people who built it. Watch below! Timestamps: Introduction: 00:01:26 Claude Code’s origin story: 00:02:25 How Anthropic dogfoods Claude Code: 00:07:03 Boris and Cat’s favorite slash commands: 00:14:06 How Boris uses Claude Code to plan feature development: 00:15:49 Everything Anthropic has learned about using sub-agents well: 00:21:53 Use Claude Code to turn past code into leverage: 00:26:16 The product decisions for building an agent that’s simple and powerful: 00:33:14 Making Claude Code accessible to the non-technical user: 00:36:38 The next form factor for coding with AI: 00:45:12

Claude Code cracked something open for us Every 📧. Now I ship to codebases I barely know, every feature we ship makes the next one easier, and non-technical members of the team use the terminal. I’m genuinely grateful. So I brought its creators, Cat Wu (cat) and Boris Cherny (Boris Cherny) from Anthropic, on AI & I to say thank you—and to talk about everything they’ve learned from building Claude Code. We get into: • The workflows Anthropic’s smartest engineers use to push Claude Code to its limits. Why they pit subagents against each other to get cleaner results, how they turn past code into leverage, and the slash commands and MCPs they rely on most. • The product lessons behind one of the most loved AI agents in the world. How the team balances simplicity and power—building a tool that anyone can use, but that experts can bend to their will—and their philosophy of “unshipping,” or cutting back whenever there’s a simpler, more intuitive path to user intent. • A peek into the future of coding with AI. The new form factors they’re experimenting with to make Claude Code more autonomous, more reliable, and more accessible to non-technical users This is a must-watch for anyone—both technical and non-technical—who wants to learn how to use Claude Code like the people who built it. Watch below! Timestamps: Introduction: 00:01:26 Claude Code’s origin story: 00:02:25 How Anthropic dogfoods Claude Code: 00:07:03 Boris and Cat’s favorite slash commands: 00:14:06 How Boris uses Claude Code to plan feature development: 00:15:49 Everything Anthropic has learned about using sub-agents well: 00:21:53 Use Claude Code to turn past code into leverage: 00:26:16 The product decisions for building an agent that’s simple and powerful: 00:33:14 Making Claude Code accessible to the non-technical user: 00:36:38 The next form factor for coding with AI: 00:45:12

Dan Shipper 📧

57,568 views • 9 months ago

I FINALLY FIGURED OUT WHY CLAUDE KEEPS FORGETTING THINGS. For months, I thought Claude’s memory was broken. I’d spend 20 minutes explaining my project few chats later, it would forget important stuff and remember something completely random. Turns out the problem wasn’t Claude. It was how I was using it. Here’s the system that fixed it LEVEL 1: Clean up the basics (5 mins) - Remove old memories you don’t need - Add proper project instructions - Tell Claude exactly what should and shouldn’t be remembered Most people never do this and then wonder why memory feels inconsistent. LEVEL 2: Build a context system (30-60 mins) Create a folder with: → Instructions.md → Memory.md → Context.md → Archive.md Now Claude has a dedicated place to store information instead of trying to remember everything from chat history. Massive difference. LEVEL 3: Build a real AI second brain → Connect Claude to your notes. → Research. → Documents. → Knowledge base. Instead of starting from zero every session, it builds context over time. This is when Claude stops feeling like a chatbot and starts feeling like an actual assistant. Most people are obsessed with prompts. The real power comes from building better context.

I FINALLY FIGURED OUT WHY CLAUDE KEEPS FORGETTING THINGS. For months, I thought Claude’s memory was broken. I’d spend 20 minutes explaining my project few chats later, it would forget important stuff and remember something completely random. Turns out the problem wasn’t Claude. It was how I was using it. Here’s the system that fixed it LEVEL 1: Clean up the basics (5 mins) - Remove old memories you don’t need - Add proper project instructions - Tell Claude exactly what should and shouldn’t be remembered Most people never do this and then wonder why memory feels inconsistent. LEVEL 2: Build a context system (30-60 mins) Create a folder with: → Instructions.md → Memory.md → Context.md → Archive.md Now Claude has a dedicated place to store information instead of trying to remember everything from chat history. Massive difference. LEVEL 3: Build a real AI second brain → Connect Claude to your notes. → Research. → Documents. → Knowledge base. Instead of starting from zero every session, it builds context over time. This is when Claude stops feeling like a chatbot and starts feeling like an actual assistant. Most people are obsessed with prompts. The real power comes from building better context.

nordin.eth

50,500 views • 1 month ago

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!

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!

Andrew Ng

200,788 views • 1 year ago

🚨 Smart Money Alert: Grant Cardone and Brian Dixon Break Down Investing in Bittensor $TAO "Think about #Bitcoin is decentralized money. TAO and Bittensor is decentralized intelligence." "That's one of the things we've learned today, that #Perplexity and #ChatGPT and #Claude, there's a small percentage of the questions that are getting asked to those platforms that #Bittensor's intelligence is actually feeding for the answer. But they don't tell you that on Claude and they're just licensing that information. And that'll grow significantly over time." "So, we're starting to make some strategic investments in that space because we think long-term it has tremendous upside and it has the same unit economics as Bitcoin. There will only ever be 21 million #Bittensor tokens ever created. It's proof-of-work. And it has a predictable halving schedule... So, it's creating that scarcity long-term into the token."

🚨 Smart Money Alert: Grant Cardone and Brian Dixon Break Down Investing in Bittensor $TAO "Think about #Bitcoin is decentralized money. TAO and Bittensor is decentralized intelligence." "That's one of the things we've learned today, that #Perplexity and #ChatGPT and #Claude, there's a small percentage of the questions that are getting asked to those platforms that #Bittensor's intelligence is actually feeding for the answer. But they don't tell you that on Claude and they're just licensing that information. And that'll grow significantly over time." "So, we're starting to make some strategic investments in that space because we think long-term it has tremendous upside and it has the same unit economics as Bitcoin. There will only ever be 21 million #Bittensor tokens ever created. It's proof-of-work. And it has a predictable halving schedule... So, it's creating that scarcity long-term into the token."

Altcoin Daily

152,677 views • 21 days ago

Quick Walkthrough of the new Claude Cowork feature Overall, I am very impressed! It is fast, intuitive and completely hides the complexity of Claude Code Notables features: - Every session is Sandboxed by default - Multiple chats in parallel with no issues - ToDo list tracked as Progress GUI - Chrome is seamlessly controlled - Built in artifact viewing - Skills are supported out of the box - Claude connectors from marketplace work Very excited to see the direction the Anthropic team take with their interface design This is very natural feeling and am sure anybody non-technical could easily pick it up with little effort I really like that they have took the canvas approach and not tried to create obfuscated tools What I can see from here, is that they respect their users and are craftsmen with their intent The pandoras box that has now opened is what happens to the terminal Claude Code Is is true to what Dax (dax) said, that they didn't fix the flicker because they (Anthropic) don't believe people will be looking at the agents work anymore? That it'll be a send and forget world where we all just touch grass ⛩️

Quick Walkthrough of the new Claude Cowork feature Overall, I am very impressed! It is fast, intuitive and completely hides the complexity of Claude Code Notables features: - Every session is Sandboxed by default - Multiple chats in parallel with no issues - ToDo list tracked as Progress GUI - Chrome is seamlessly controlled - Built in artifact viewing - Skills are supported out of the box - Claude connectors from marketplace work Very excited to see the direction the Anthropic team take with their interface design This is very natural feeling and am sure anybody non-technical could easily pick it up with little effort I really like that they have took the canvas approach and not tried to create obfuscated tools What I can see from here, is that they respect their users and are craftsmen with their intent The pandoras box that has now opened is what happens to the terminal Claude Code Is is true to what Dax (dax) said, that they didn't fix the flicker because they (Anthropic) don't believe people will be looking at the agents work anymore? That it'll be a send and forget world where we all just touch grass ⛩️

Numman Ali

11,574 views • 6 months ago

One of my favorite Anthropic API features that people don't seem to know about is prompt prefilling. Your API request doesn't have to end with a 'user' turn. You can include an 'assistant' turn at the end of the messages array and "put words in Claude's mouth" so to speak. Claude will then start its response as if it already output the text you prefilled. This technique allows you to direct Claude’s actions, skip preambles, enforce specific formats like JSON or XML, and even help Claude maintain character consistency in role-play scenarios. I use it all the time as it makes prompts much simpler, especially for things like structured outputs.

One of my favorite Anthropic API features that people don't seem to know about is prompt prefilling. Your API request doesn't have to end with a 'user' turn. You can include an 'assistant' turn at the end of the messages array and "put words in Claude's mouth" so to speak. Claude will then start its response as if it already output the text you prefilled. This technique allows you to direct Claude’s actions, skip preambles, enforce specific formats like JSON or XML, and even help Claude maintain character consistency in role-play scenarios. I use it all the time as it makes prompts much simpler, especially for things like structured outputs.

Alex Albert

118,260 views • 1 year ago

THIS GUY BUILT AN AUTONOMOUS AI AGENT OUT OF CLAUDE CODE + OBSIDIAN and this is way more interesting than another “use AI to take notes” demo the trick is simple: Obsidian is not the writing app here. it becomes the agent’s memory, task board, and context folder. Claude Code is not just answering prompts. it reads the vault, edits files, follows instructions, and keeps moving through the work like a junior operator with a filesystem. the reusable setup looks like this: 1. create an Obsidian vault for one project 2. keep goals, rules, tasks, decisions, and references as markdown files 3. point Claude Code at the folder 4. give it a clear operating loop: read context → choose next task → execute → write back what changed 5. use the notes as persistent memory instead of re-explaining the project every chat that’s the part people miss. the “agent” is not magic. it’s the boring combination of: - local files - explicit rules - task state - write access - a model that can run through the repo/vault Obsidian makes the memory human-readable. Claude Code makes the memory executable. that combo is why the video worked: it turns a notes app into an operating surface for actual work. best use cases: - content systems - research vaults - coding projects - client ops docs - personal knowledge bases that need actions, not just storage the caveat: if your vault is messy, your agent becomes messy too. folders, naming, “done” criteria, and forbidden actions matter more than the prompt. but once the structure is clean, this is one of the easiest ways to build an agent that remembers what happened yesterday without paying for a full custom app.

THIS GUY BUILT AN AUTONOMOUS AI AGENT OUT OF CLAUDE CODE + OBSIDIAN and this is way more interesting than another “use AI to take notes” demo the trick is simple: Obsidian is not the writing app here. it becomes the agent’s memory, task board, and context folder. Claude Code is not just answering prompts. it reads the vault, edits files, follows instructions, and keeps moving through the work like a junior operator with a filesystem. the reusable setup looks like this: 1. create an Obsidian vault for one project 2. keep goals, rules, tasks, decisions, and references as markdown files 3. point Claude Code at the folder 4. give it a clear operating loop: read context → choose next task → execute → write back what changed 5. use the notes as persistent memory instead of re-explaining the project every chat that’s the part people miss. the “agent” is not magic. it’s the boring combination of: - local files - explicit rules - task state - write access - a model that can run through the repo/vault Obsidian makes the memory human-readable. Claude Code makes the memory executable. that combo is why the video worked: it turns a notes app into an operating surface for actual work. best use cases: - content systems - research vaults - coding projects - client ops docs - personal knowledge bases that need actions, not just storage the caveat: if your vault is messy, your agent becomes messy too. folders, naming, “done” criteria, and forbidden actions matter more than the prompt. but once the structure is clean, this is one of the easiest ways to build an agent that remembers what happened yesterday without paying for a full custom app.

kocer

30,403 views • 1 month ago

I just built a Claude skill that acts as a second brain for DTC brands 🤯 Drop your ad exports, customer reviews, competitor screenshots, and brand docs into a folder → Claude compiles it all into an organized wiki you can ask questions against. All inside Claude Cowork. Perfect for DTC brands and agencies whose knowledge is scattered across Google Drive, Notion, Meta Ads Manager, Figma, and 47 spreadsheets nobody has opened in 3 months. If every strategic question takes 2 hours to answer because the data lives in 8 different places ... This skill eliminates the entire loop: → Claude scaffolds a DTC folder structure: ads, customers, competitors, brand, performance, notes → You drop every file you have into those folders — messy, unorganized, exactly how you have them now → Claude reads everything and compiles a wiki: hooks-that-work, customer-pains, competitor-angles, brand-voice, performance-patterns, creative-brief-library → Every article is cross-linked and traceable back to the source file → You ask questions against the wiki — "what hooks are actually working?" "what objections come up most?" "where are my competitors weak?" → Claude answers, grounded entirely in your own data → Save the answers back in and the system gets smarter every time you use it No more hunting through 12 tools. No more "where did I save that brief?" No more answering the same question twice. What you get: → A complete DTC brand brain scaffold in 60 seconds → Six core wiki articles Claude populates automatically from your raw files → A schema file that tells Claude exactly how to maintain the wiki for DTC use cases → Monthly health checks that catch contradictions and flag gaps before errors compound → A knowledge base that compounds — every question you ask makes the next answer better Built on a methodology Andrej Karpathy shared for personal knowledge bases, I rebuilt the entire thing for DTC operators: folder structure, schema rules, wiki articles, and question frameworks all tuned for brands and agencies. I put together the full skill file plus a playbook walking through the exact setup and 5 real questions to ask your brand brain. Want it for free? > Like this post > Comment "BRAIN" And I'll send it over (must be following so I can DM)

Mike Futia

15,158 views • 3 months ago

THIS GUY BUILT A BUSINESS SECOND BRAIN WITH CLAUDE CODE + OBSIDIAN IN 3 STEPS Most teams do not need another Notion workspace. They need a place where the company can remember how it works. The video shows a simple setup: 1. Create one empty folder called second brain. 2. Split it into 3 buckets: raw new knowledge wiki 3. Let Claude Code turn messy company material into connected notes. The useful part is the separation. Raw is where your existing stuff goes: SOPs, sales docs, process notes, client delivery checklists, old Loom summaries, onboarding docs. New knowledge is where fresh outside material lands: articles, clips, tactics, examples, market notes. Wiki is the cleaned version: concepts, roles, processes, SOPs, gaps, reusable decisions. That is where Claude Code becomes more useful than a normal chat window. Instead of asking it to remember random context forever, you give it a folder it can read, edit, and reorganize. Then Obsidian becomes the human interface. The Obsidian Web Clipper captures useful pages into the vault. Claude Code ingests them. The wiki gets updated. Then you can ask questions like: “Does my current workflow actually hold up?” That is the real point. Not “AI notes.” A business memory system that can compare what you do today against new information tomorrow. The caveat: this is not magic company intelligence. If your raw docs are vague, outdated, or full of tribal knowledge, Claude will organize weak inputs into cleaner weak outputs. You still need naming rules, review habits, and someone responsible for deleting junk. But the setup is refreshingly practical. Folder first. Clipper second. Claude Code as the maintainer. No giant knowledge base migration. No complex setup. Just a local vault that can slowly turn scattered business memory into something searchable, editable, and actually reusable.

THIS GUY BUILT A BUSINESS SECOND BRAIN WITH CLAUDE CODE + OBSIDIAN IN 3 STEPS Most teams do not need another Notion workspace. They need a place where the company can remember how it works. The video shows a simple setup: 1. Create one empty folder called second brain. 2. Split it into 3 buckets: raw new knowledge wiki 3. Let Claude Code turn messy company material into connected notes. The useful part is the separation. Raw is where your existing stuff goes: SOPs, sales docs, process notes, client delivery checklists, old Loom summaries, onboarding docs. New knowledge is where fresh outside material lands: articles, clips, tactics, examples, market notes. Wiki is the cleaned version: concepts, roles, processes, SOPs, gaps, reusable decisions. That is where Claude Code becomes more useful than a normal chat window. Instead of asking it to remember random context forever, you give it a folder it can read, edit, and reorganize. Then Obsidian becomes the human interface. The Obsidian Web Clipper captures useful pages into the vault. Claude Code ingests them. The wiki gets updated. Then you can ask questions like: “Does my current workflow actually hold up?” That is the real point. Not “AI notes.” A business memory system that can compare what you do today against new information tomorrow. The caveat: this is not magic company intelligence. If your raw docs are vague, outdated, or full of tribal knowledge, Claude will organize weak inputs into cleaner weak outputs. You still need naming rules, review habits, and someone responsible for deleting junk. But the setup is refreshingly practical. Folder first. Clipper second. Claude Code as the maintainer. No giant knowledge base migration. No complex setup. Just a local vault that can slowly turn scattered business memory into something searchable, editable, and actually reusable.

kocer

16,542 views • 1 month ago

Krishna on how Anthropic thinks about the platform vs. application layer, and when they decide to build their own products like Claude Code. It’s the question every investor and founder is thinking about: “Most of what we're building is platform. There's so many examples of where a platform can accrue a lot of value, but the customers who are building on that platform actually accrue even more value. We will build our own applications on that same platform where a couple of things are true. Number one, if we feel like we have a vision into where the models are going and we can demonstrate that and create customer value in that, that might be something like Claude Code. The second is thinking about ways to demonstrate value for the ecosystem that others might emulate. If you think about Claude for financial services or Claude for life sciences, these are ways in which we've composed the platform. We're building on the same platform as our customers. That creates a level playing field. We also think that there's so much value that's going to accrue in these areas that our customers can win and we can win as well. So I think of our strategy as mostly horizontal. A lot of the value is going to accrue to the customers that are building on top of it. Our goal is build the best models and then build the products and tools and services that allow that intelligence to proliferate within customers."

Krishna on how Anthropic thinks about the platform vs. application layer, and when they decide to build their own products like Claude Code. It’s the question every investor and founder is thinking about: “Most of what we're building is platform. There's so many examples of where a platform can accrue a lot of value, but the customers who are building on that platform actually accrue even more value. We will build our own applications on that same platform where a couple of things are true. Number one, if we feel like we have a vision into where the models are going and we can demonstrate that and create customer value in that, that might be something like Claude Code. The second is thinking about ways to demonstrate value for the ecosystem that others might emulate. If you think about Claude for financial services or Claude for life sciences, these are ways in which we've composed the platform. We're building on the same platform as our customers. That creates a level playing field. We also think that there's so much value that's going to accrue in these areas that our customers can win and we can win as well. So I think of our strategy as mostly horizontal. A lot of the value is going to accrue to the customers that are building on top of it. Our goal is build the best models and then build the products and tools and services that allow that intelligence to proliferate within customers."

Patrick OShaughnessy

112,863 views • 2 months ago