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An open-source agent that never forgets you just crossed 200,000 stars, and it runs entirely on your laptop. I mapped every part worth stealing: 00:37 - the bot that lives inside your local files 03:00 - SOUL.md, the file that gives it a personality 04:31 - the self-improving loop... show more

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30,255 просмотров • 1 месяц назад •via X (Twitter)

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Anthropic Claude Code engineer: "The moment you're reviewing every line Claude writes, you became its tester. You weren't hired for that." this 36 min talk is the clearest breakdown of how to get yourself out of the loop entirely the shift is simple: you stop prompting and start building agents that work instead of you agents that run, check themselves, and keep going while your laptop is closed watch it, then build your own with the step-by-step guide below

Anthropic Claude Code engineer: "The moment you're reviewing every line Claude writes, you became its tester. You weren't hired for that." this 36 min talk is the clearest breakdown of how to get yourself out of the loop entirely the shift is simple: you stop prompting and start building agents that work instead of you agents that run, check themselves, and keep going while your laptop is closed watch it, then build your own with the step-by-step guide below

Anatoli Kopadze

28,042 просмотров • 1 месяц назад

Anthropic engineer: «You're not supposed to sit and re-prompt Claude all day. You build one loop, and it does the work while you sleep.» • 00:00 - Build your first agent that runs on its own • 05:02 - The loop that fixes its own mistakes • 16:43 - Why Claude forgets you between chats, and the fix You can learn it in one afternoon. 30 minutes here replace what most people spend a year figuring out. Watch it today, then read the guide on building loops below.

Anthropic engineer: «You're not supposed to sit and re-prompt Claude all day. You build one loop, and it does the work while you sleep.» • 00:00 - Build your first agent that runs on its own • 05:02 - The loop that fixes its own mistakes • 16:43 - Why Claude forgets you between chats, and the fix You can learn it in one afternoon. 30 minutes here replace what most people spend a year figuring out. Watch it today, then read the guide on building loops below.

Raytar

28,046 просмотров • 1 месяц назад

In the future, you’ll be able to accomplish a goal by just giving Claude an outcome and a budget. That’s the direction Anthropic is building in with its new Managed Agents features, announced at this week’s Code with Claude developer event. The basic idea: Claude, wrapped in a computer in the cloud, that you can spin up, scale, and manage as needed. Anthropic is taking on the infrastructure that kills most agent products, and making sure that it scales to meet the needs of agents running 24/7. On this week’s AI & I from Every 📧, I talk with Angela Jiang (Angela Jiang), head of product for the Claude platform, and Katelyn Lesse (Katelyn Lesse), head of engineering for the Claude platform, about what Anthropic is building and what it takes to make agents reliable in production. We get into: - Why the "build a generic harness, hot-swap any model behind it" playbook is already outdated. Angela points to eval data on Memory where the same task across different harnesses performed drastically differently. - The infrastructure wall every team hits in production—and why Katelyn thinks “my sandbox died and took the agent with it” is the real reason internal agents don't ship. - Why Anthropic is so bullish on using file systems and skills within Claude, including Angela's argument that those early design choices can compound for years. This is a must-watch for anyone trying to take an agent past the demo and into production. Watch below! Timestamps: How the Claude platform evolved from API to agents: 00:01:48 The primitives that make up Claude Managed Agents: 00:04:09 Why the harness and the model are becoming a single unit: 00:10:37 The infrastructure wall that kills most agent projects in production: 00:18:49 Why team agents need a different shape than individual productivity tools: 00:24:49 How Anthropic's legal team uses an agent to review marketing copy: 00:26:36 Using multi-agent orchestration for advisor strategies, adversarial pairs, and swarms: 00:34:24 How to measure agent success with outcome and budget as the end state: 00:35:50 What the platform looks like a year from now, when Claude writes its own harness: 00:39:11

In the future, you’ll be able to accomplish a goal by just giving Claude an outcome and a budget. That’s the direction Anthropic is building in with its new Managed Agents features, announced at this week’s Code with Claude developer event. The basic idea: Claude, wrapped in a computer in the cloud, that you can spin up, scale, and manage as needed. Anthropic is taking on the infrastructure that kills most agent products, and making sure that it scales to meet the needs of agents running 24/7. On this week’s AI & I from Every 📧, I talk with Angela Jiang (Angela Jiang), head of product for the Claude platform, and Katelyn Lesse (Katelyn Lesse), head of engineering for the Claude platform, about what Anthropic is building and what it takes to make agents reliable in production. We get into: - Why the "build a generic harness, hot-swap any model behind it" playbook is already outdated. Angela points to eval data on Memory where the same task across different harnesses performed drastically differently. - The infrastructure wall every team hits in production—and why Katelyn thinks “my sandbox died and took the agent with it” is the real reason internal agents don't ship. - Why Anthropic is so bullish on using file systems and skills within Claude, including Angela's argument that those early design choices can compound for years. This is a must-watch for anyone trying to take an agent past the demo and into production. Watch below! Timestamps: How the Claude platform evolved from API to agents: 00:01:48 The primitives that make up Claude Managed Agents: 00:04:09 Why the harness and the model are becoming a single unit: 00:10:37 The infrastructure wall that kills most agent projects in production: 00:18:49 Why team agents need a different shape than individual productivity tools: 00:24:49 How Anthropic's legal team uses an agent to review marketing copy: 00:26:36 Using multi-agent orchestration for advisor strategies, adversarial pairs, and swarms: 00:34:24 How to measure agent success with outcome and budget as the end state: 00:35:50 What the platform looks like a year from now, when Claude writes its own harness: 00:39:11

Dan Shipper 📧

66,339 просмотров • 3 месяцев назад

in 9 minutes on the Tokyo stage, Angela Jang, head of product for the Claude platform, said the part most builders still haven't figured out: "a model is only as good as the context that you actually give it." that's the whole game now. not the prompt. the context. what your agent remembers. what skills it can pull. what it learns from its own past runs. anthropic now ships all three: memory, skills, and dreaming. agents that inspect their own trajectories and self-improve. demoed live this week. the prompt was never the bottleneck. the context was. most people are still tweaking words in a chat box. the ones who get this engineer the context their agents run on. watch the 9 minutes. then read the full breakdown below ⬇️

in 9 minutes on the Tokyo stage, Angela Jang, head of product for the Claude platform, said the part most builders still haven't figured out: "a model is only as good as the context that you actually give it." that's the whole game now. not the prompt. the context. what your agent remembers. what skills it can pull. what it learns from its own past runs. anthropic now ships all three: memory, skills, and dreaming. agents that inspect their own trajectories and self-improve. demoed live this week. the prompt was never the bottleneck. the context was. most people are still tweaking words in a chat box. the ones who get this engineer the context their agents run on. watch the 9 minutes. then read the full breakdown below ⬇️

Avid

34,405 просмотров • 1 месяц назад

Hermes agent just left the terminal. 𝗛𝗲𝗿𝗺𝗲𝘀 𝗗𝗲𝘀𝗸𝘁𝗼𝗽 dropped yesterday. native app for macOS, Windows, and Linux. for months Hermes was the agent that learned your projects, wrote its own skills, and built a model of who you are. all of it buried in terminal logs. now it has a window. the important part is that it's not a wrapper. it runs the same agent core, the same sessions, memory, and skills as the CLI. you can start a task in the terminal and finish it in the app without anything resetting. the state is shared across every interface, not copied between them. what the GUI actually adds: → streaming chat that shows live tool calls and inline reasoning instead of a spinner → a preview rail that renders pages, code, and images right beside the conversation → an artifacts panel that collects every file the agent has ever produced → remote gateway mode, so you can point the app at a VPS and run the heavy work elsewhere → skills, cron, profiles, and gateways managed point-and-click instead of through YAML → voice mode, drag-drop files, and inline image generation remote gateway mode is the one worth slowing down on. the agent runs 24/7 on a $5 server while you control it from your laptop like a local app. other agent UIs are chatboxes with a logo. this one shows the autonomy instead of hiding it, so you watch the skills load, the tools fire, and the artifacts pile up as it works. it was teased in Jensen's GTC keynote. MIT licensed, local-first, no telemetry. if you already run Hermes, download it and everything is already there. your chats, memory, and skills carry straight over. i wrote a full masterclass on Hermes Agent that walks through the SOUL. md identity layer, the three-tier memory system, the self-evolving skills loop, and how to run three specialized agents 24/7. desktop is the interface that finally does all of it justice. the article is quoted below.

Akshay 🚀

51,474 просмотров • 2 месяцев назад

Anthropic engineer just dropped a full walkthrough on building AI agents from scratch this single video is worth more than any $600 AI agents course here's what she covers: > what Agent, Environment, and Session actually mean > how to stream live events from your agent > wiring up custom tools from scratch > the server-side loop that makes agents actually work > roadmap to subagents, vaults, and memory 37 minutes that will answer every question you have about building agents the people who understand what Claude can actually do are building things everyone else thinks requires a whole team most of them aren't smarter, they just know features that most people have never heard of full breakdown on every Claude feature worth knowing in the article below

Anthropic engineer just dropped a full walkthrough on building AI agents from scratch this single video is worth more than any $600 AI agents course here's what she covers: > what Agent, Environment, and Session actually mean > how to stream live events from your agent > wiring up custom tools from scratch > the server-side loop that makes agents actually work > roadmap to subagents, vaults, and memory 37 minutes that will answer every question you have about building agents the people who understand what Claude can actually do are building things everyone else thinks requires a whole team most of them aren't smarter, they just know features that most people have never heard of full breakdown on every Claude feature worth knowing in the article below

Anatoli Kopadze

38,627 просмотров • 2 месяцев назад

The guy who kicked off the entire "loop engineering" wave Peter Steinberger: "You shouldn't be prompting coding agents anymore. You should be designing loops that prompt your agents." One post. 6.5M views in a week. In this talk he walks the real stack: the agent loop, a verifier that fails its own work and retries, and a loop that rewrites the agent while he sleeps. Worth more than any $500 vibe-coding course. Watch it, then read the full breakdown of the 4 loops below.

The guy who kicked off the entire "loop engineering" wave Peter Steinberger: "You shouldn't be prompting coding agents anymore. You should be designing loops that prompt your agents." One post. 6.5M views in a week. In this talk he walks the real stack: the agent loop, a verifier that fails its own work and retries, and a loop that rewrites the agent while he sleeps. Worth more than any $500 vibe-coding course. Watch it, then read the full breakdown of the 4 loops below.

dunik

219,932 просмотров • 1 месяц назад

Anthropic just dropped 5 workshops that take you from zero to a full app with AI agents. Useful at any level, just pick the timecode where you're at: 00:00 - The model choice nobody explains 31:14 - All you need to know about prompting 1:04:42 - Claude Code features most people never find 1:51:34 - From a prompt to a working product 2:19:10 - Building your first app with Claude Watch today, then go to the guide that explains everything you have to know about loops.

Anthropic just dropped 5 workshops that take you from zero to a full app with AI agents. Useful at any level, just pick the timecode where you're at: 00:00 - The model choice nobody explains 31:14 - All you need to know about prompting 1:04:42 - Claude Code features most people never find 1:51:34 - From a prompt to a working product 2:19:10 - Building your first app with Claude Watch today, then go to the guide that explains everything you have to know about loops.

Anatoli Kopadze

54,349 просмотров • 1 месяц назад

Don't waste 2 years learning to build self-improving AI agents. Stanford just dropped a 3.5 hour course on building them from scratch. 0:00 - self-improving agent basics 1:06:29 - multi-step reasoning in agents 2:20:08 - agents learning from feedback look at the third one, because that is where the whole thing turns. Attempt → Signal → Correction → Better attempt an agent cannot improve from its own opinion of its work. the signal has to come from outside it, or the loop just reinforces whatever it already believed. self-review is the old workflow. external correction is the new one. that is the difference between a system that gets better every week and one that gets more confident every week. Anthropic pays $750,000 a year to engineers who can build this. bookmark and watch it today, then read the eval setup below

Don't waste 2 years learning to build self-improving AI agents. Stanford just dropped a 3.5 hour course on building them from scratch. 0:00 - self-improving agent basics 1:06:29 - multi-step reasoning in agents 2:20:08 - agents learning from feedback look at the third one, because that is where the whole thing turns. Attempt → Signal → Correction → Better attempt an agent cannot improve from its own opinion of its work. the signal has to come from outside it, or the loop just reinforces whatever it already believed. self-review is the old workflow. external correction is the new one. that is the difference between a system that gets better every week and one that gets more confident every week. Anthropic pays $750,000 a year to engineers who can build this. bookmark and watch it today, then read the eval setup below

Hanako

52,087 просмотров • 3 дней назад

this video is the CLEAREST explanation of how claude skills + AI agents work and how to use them most people set up an AI agent and wonder why it keeps disappointing them. the context window is everything context is what the model assembles before it takes any action. think of it like everything the agent needs to read before it does anything. the quality of what goes in determines the quality of what comes out. the models are genuinely really good right now. claude and gpt are exceptional. the variable is almost always the context you give them. 1. agent.md files are mostly unnecessary every single line you put in an agent.md file gets added to every single conversation you have with your agent. a 1000 line file is around 7000 tokens burning on every run. the model already knows to use react. it can read your codebase. save the agent.md for proprietary information specific to your company that the model genuinely cannot know on its own. 2. skills are the actual unlock a skill.md file works differently. what loads into context is only the name and description, around 50 tokens. the full instructions only appear when the agent recognizes it needs that skill. so instead of 7000 tokens on every run you have 50. and the agent stays sharp because the context window stays lean. the closer you get to filling the context window the worse the agent performs, same way you perform worse when someone dumps 10 things on you at once. 3. here is how to actually build a skill the right way most people identify a workflow and immediately try to write the skill. what you want to do instead is run the workflow by hand with the agent first. walk it through every single step. tell it what to check, what good looks like, what bad looks like. correct it in real time. once you have had a full successful run from start to finish, tell the agent to review everything it just did and write the skill itself. it writes a better skill than you will because it has the full context of what actually worked in practice not in theory. 4. recursively building skills is how you go from frustrated to reliable when the skill breaks, and it will break, ask the agent exactly why it failed. it will tell you specifically what went wrong. fix it together in that same conversation. then tell it to update the skill file so that failure mode never happens again. ross mike did this five times with his youtube report generator. it now pulls from eight different data sources and runs flawlessly every single time without him touching it. 5. sub agents are something you earn not something you set up on day one start with one agent. build one workflow. turn it into one skill. once that works add another. ross mike has five sub agents now covering marketing, business, personal and more. it took months to get there and every single one exists because a workflow proved it deserved to exist. the people who set up 15 sub agents on day one and wonder why nothing works skipped all the steps that make the thing actually run. 6. your workflow is the thing the model cannot get anywhere else the model has been trained on everything. it knows more than you about most things. what it does not have is your specific process, your taste, your way of doing things. that is what skills capture. that is what makes your agent actually useful versus a generic one. downloading someone else's skill means downloading their context onto your setup and it will not work the way you want it to because it was never built around how you work. this is the clearest explanation of how agents actually work i have heard. Micky runs this stuff every single day and the results show it. full episode is now live on The Startup Ideas Podcast (SIP) 🧃 where you get your pods people charge for this sorta stuff i give away the sauce for free i just want you to win watch

this video is the CLEAREST explanation of how claude skills + AI agents work and how to use them most people set up an AI agent and wonder why it keeps disappointing them. the context window is everything context is what the model assembles before it takes any action. think of it like everything the agent needs to read before it does anything. the quality of what goes in determines the quality of what comes out. the models are genuinely really good right now. claude and gpt are exceptional. the variable is almost always the context you give them. 1. agent.md files are mostly unnecessary every single line you put in an agent.md file gets added to every single conversation you have with your agent. a 1000 line file is around 7000 tokens burning on every run. the model already knows to use react. it can read your codebase. save the agent.md for proprietary information specific to your company that the model genuinely cannot know on its own. 2. skills are the actual unlock a skill.md file works differently. what loads into context is only the name and description, around 50 tokens. the full instructions only appear when the agent recognizes it needs that skill. so instead of 7000 tokens on every run you have 50. and the agent stays sharp because the context window stays lean. the closer you get to filling the context window the worse the agent performs, same way you perform worse when someone dumps 10 things on you at once. 3. here is how to actually build a skill the right way most people identify a workflow and immediately try to write the skill. what you want to do instead is run the workflow by hand with the agent first. walk it through every single step. tell it what to check, what good looks like, what bad looks like. correct it in real time. once you have had a full successful run from start to finish, tell the agent to review everything it just did and write the skill itself. it writes a better skill than you will because it has the full context of what actually worked in practice not in theory. 4. recursively building skills is how you go from frustrated to reliable when the skill breaks, and it will break, ask the agent exactly why it failed. it will tell you specifically what went wrong. fix it together in that same conversation. then tell it to update the skill file so that failure mode never happens again. ross mike did this five times with his youtube report generator. it now pulls from eight different data sources and runs flawlessly every single time without him touching it. 5. sub agents are something you earn not something you set up on day one start with one agent. build one workflow. turn it into one skill. once that works add another. ross mike has five sub agents now covering marketing, business, personal and more. it took months to get there and every single one exists because a workflow proved it deserved to exist. the people who set up 15 sub agents on day one and wonder why nothing works skipped all the steps that make the thing actually run. 6. your workflow is the thing the model cannot get anywhere else the model has been trained on everything. it knows more than you about most things. what it does not have is your specific process, your taste, your way of doing things. that is what skills capture. that is what makes your agent actually useful versus a generic one. downloading someone else's skill means downloading their context onto your setup and it will not work the way you want it to because it was never built around how you work. this is the clearest explanation of how agents actually work i have heard. Micky runs this stuff every single day and the results show it. full episode is now live on The Startup Ideas Podcast (SIP) 🧃 where you get your pods people charge for this sorta stuff i give away the sauce for free i just want you to win watch

GREG ISENBERG

193,219 просмотров • 4 месяцев назад

Anthropic's Claude team just showed how to give your AI agents real memory in under 18 minutes. Free. From the people who built it. You've been building agents that forget everything between sessions. This fixes that tonight. One person + 10 agents with memory + dreaming = a team that runs 24/7, learns from every task, and improves itself overnight. Worth more than any $500 AI agent course. Bookmark it. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward.

Anthropic's Claude team just showed how to give your AI agents real memory in under 18 minutes. Free. From the people who built it. You've been building agents that forget everything between sessions. This fixes that tonight. One person + 10 agents with memory + dreaming = a team that runs 24/7, learns from every task, and improves itself overnight. Worth more than any $500 AI agent course. Bookmark it. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward.

Himanshu Kumar

11,191 просмотров • 2 месяцев назад

Claude Code creator, Boris Cherny: "Forget everything you learned about computer science theory, now you just explain to the model what you need and it does the rest." 32 minutes of the head of Claude Code showing exactly how he builds now. He gave Claude one prompt two weeks ago and it's still running, spawning thousands of agents on its own. The people winning right now aren't writing more code, they're building loops that run without them. Watch it, then go deeper with the graph engineering guide that combines agents and loops below.

Claude Code creator, Boris Cherny: "Forget everything you learned about computer science theory, now you just explain to the model what you need and it does the rest." 32 minutes of the head of Claude Code showing exactly how he builds now. He gave Claude one prompt two weeks ago and it's still running, spawning thousands of agents on its own. The people winning right now aren't writing more code, they're building loops that run without them. Watch it, then go deeper with the graph engineering guide that combines agents and loops below.

Anatoli Kopadze

47,071 просмотров • 13 дней назад

Anthropic Research Lead: "99% of our engineers run swarms of 300+ self-improving agents" "Close the loop, give the model a way to verify its own output" In a 20-minute session, an Anthropic Team member breaks down how to build agents that improve themselves The real setup is Claude running through loops, plan mode, and dynamic workflows Better than most $300 agent courses Bookmark and watch the talk Then read the article below

Anthropic Research Lead: "99% of our engineers run swarms of 300+ self-improving agents" "Close the loop, give the model a way to verify its own output" In a 20-minute session, an Anthropic Team member breaks down how to build agents that improve themselves The real setup is Claude running through loops, plan mode, and dynamic workflows Better than most $300 agent courses Bookmark and watch the talk Then read the article below

rari

381,391 просмотров • 1 месяц назад

The entire AI industry is racing to build the smartest model. Satya Nadella just admitted that is not where the money is. The model is not the product. The harness is. That is the exact line. And it changes what Microsoft is actually competing on. OpenAI, Anthropic, Google, xAI, Meta every frontier lab is pouring hundreds of billions into training compute, chasing the next capability jump. Each betting that raw model intelligence is the moat. Microsoft is doing the opposite. It is building the harness the orchestration layer that sits above the model, connecting it to tools, data, permissions, sub-agents, and enterprise workflows. And it is letting OpenAI, Anthropic, and MAI compete to plug into it. "You need the model. But the model is not the product. The harness is." So do the math on what a harness actually does. A raw model dropped into an enterprise answers questions. That is a chatbot. A harness turns that same model into an agent that reads the SharePoint, edits the ERP entry, pulls the GitHub PR, updates Salesforce, and files the Excel report with the right permissions, the right audit trail, and the right sub-agent for each sub-task. The model provides the intelligence. The harness converts intelligence into work. Now here's where it gets interesting. "Even the best model in the world will feel broken without a great harness. And an okay model with a great harness can feel like magic." If that is true, the enterprise buyer is not buying model quality. The enterprise buyer is buying the harness. Which means model quality becomes a commodity input over time, and harness quality becomes the sustainable moat. Compare that to the strategy the entire frontier lab industry is executing. Everyone else is chasing the numerator raw intelligence. Almost nobody at scale is racing to build the denominator the orchestration layer that determines whether that intelligence can actually be deployed profitably inside a real company. The frontier model race has a 10 to 20 percent chance of producing a single dominant winner. Nadella just told the industry he does not need to be that winner. If OpenAI wins, Microsoft wins. If Anthropic wins, Microsoft wins. If MAI wins, Microsoft wins. If someone Microsoft has never heard of trains a better model in 2027, Microsoft still wins. Because the compute they train on, the harness they get plugged into, the enterprise contracts they get delivered through, and the products they sit inside are all Microsoft. He is not building the best AI model. He is building the layer that the best AI model has to run on to make anyone money. I wonder which position looks more valuable in ten years.

The entire AI industry is racing to build the smartest model. Satya Nadella just admitted that is not where the money is. The model is not the product. The harness is. That is the exact line. And it changes what Microsoft is actually competing on. OpenAI, Anthropic, Google, xAI, Meta every frontier lab is pouring hundreds of billions into training compute, chasing the next capability jump. Each betting that raw model intelligence is the moat. Microsoft is doing the opposite. It is building the harness the orchestration layer that sits above the model, connecting it to tools, data, permissions, sub-agents, and enterprise workflows. And it is letting OpenAI, Anthropic, and MAI compete to plug into it. "You need the model. But the model is not the product. The harness is." So do the math on what a harness actually does. A raw model dropped into an enterprise answers questions. That is a chatbot. A harness turns that same model into an agent that reads the SharePoint, edits the ERP entry, pulls the GitHub PR, updates Salesforce, and files the Excel report with the right permissions, the right audit trail, and the right sub-agent for each sub-task. The model provides the intelligence. The harness converts intelligence into work. Now here's where it gets interesting. "Even the best model in the world will feel broken without a great harness. And an okay model with a great harness can feel like magic." If that is true, the enterprise buyer is not buying model quality. The enterprise buyer is buying the harness. Which means model quality becomes a commodity input over time, and harness quality becomes the sustainable moat. Compare that to the strategy the entire frontier lab industry is executing. Everyone else is chasing the numerator raw intelligence. Almost nobody at scale is racing to build the denominator the orchestration layer that determines whether that intelligence can actually be deployed profitably inside a real company. The frontier model race has a 10 to 20 percent chance of producing a single dominant winner. Nadella just told the industry he does not need to be that winner. If OpenAI wins, Microsoft wins. If Anthropic wins, Microsoft wins. If MAI wins, Microsoft wins. If someone Microsoft has never heard of trains a better model in 2027, Microsoft still wins. Because the compute they train on, the harness they get plugged into, the enterprise contracts they get delivered through, and the products they sit inside are all Microsoft. He is not building the best AI model. He is building the layer that the best AI model has to run on to make anyone money. I wonder which position looks more valuable in ten years.

Vikram M

21,463 просмотров • 1 месяц назад

Karpathy gave 100,000 people the LLM Wiki idea. 1 guy actually built it into a Claude + Obsidian brain that never forgets you. Every AI tool forgets you the second you close the tab. ChatGPT, Claude, Codex. All gone. He killed that. A shared memory layer your AI builds and maintains on its own. 3 layers. Raw, Wiki, Schema. You drop a source, Claude reads it, pulls the concepts, links them, cites the original, never touches your raw notes. Scales to 1,000s of notes without breaking. Then it goes deeper. An agentic firewall locks agents out of your other vaults. A local model running free and private, zero cloud. 2 videos walk through it all. 1 on why. 1 on the build, start to finish. Most people will keep pasting the same context into 6 tabs for the next 3 years. Build it once. Never explain yourself to a model again.

Karpathy gave 100,000 people the LLM Wiki idea. 1 guy actually built it into a Claude + Obsidian brain that never forgets you. Every AI tool forgets you the second you close the tab. ChatGPT, Claude, Codex. All gone. He killed that. A shared memory layer your AI builds and maintains on its own. 3 layers. Raw, Wiki, Schema. You drop a source, Claude reads it, pulls the concepts, links them, cites the original, never touches your raw notes. Scales to 1,000s of notes without breaking. Then it goes deeper. An agentic firewall locks agents out of your other vaults. A local model running free and private, zero cloud. 2 videos walk through it all. 1 on why. 1 on the build, start to finish. Most people will keep pasting the same context into 6 tabs for the next 3 years. Build it once. Never explain yourself to a model again.

West Lord

75,208 просмотров • 1 месяц назад

Amazon AI engineer just explained how to build agentic loops from scratch in under 10-minutes. this is one of the clearest explanations of AI agentic loops you’ll find anywhere. Loop + model + agent + hooks + MCP = an autonomous, self-improving system. People are paying $500 for courses that teach less than this. Watch it, then read the step-by-step guide below on building loops for your own agents.

Amazon AI engineer just explained how to build agentic loops from scratch in under 10-minutes. this is one of the clearest explanations of AI agentic loops you’ll find anywhere. Loop + model + agent + hooks + MCP = an autonomous, self-improving system. People are paying $500 for courses that teach less than this. Watch it, then read the step-by-step guide below on building loops for your own agents.

Codez

26,868 просмотров • 1 месяц назад

The creator of High Bandwidth Memory (HBM) put a number on the AI build that should stop every infra investor cold. A cluster of a million GPUs runs at roughly 10-20% utilization (Save this). Kim Jung-ho spent thirty years building what feeds the GPU, and his claim is that the GPU is barely working. Here is what is actually happening. Every time a model generates output, the data has to be read out of memory, computed, and written back. The read and the write swallow almost the entire cycle. While that data moves, the GPU does nothing. It sits there, fully powered, fully paid for, waiting. By Kim's estimate the memory is doing only about 30 percent of the work it needs to do. The processor idles the rest. So a million installed GPUs run at 10 to 20 percent. You are not compute constrained. You are memory constrained, and the expensive part is standing around. Adding more GPUs does not fix this. It gives you more processors starving for the same data. Here is the part that decides the next decade. Memory can grow. When a cell cannot shrink any further, you stack it into a high-rise, layer on layer. A GPU cannot be stacked. It runs too hot and needs a cooler bolted to its back, so the one move that rescues memory is closed to the processor. The thing that can keep stacking compounds. The thing that cannot plateaus. The marginal dollar in an AI build now buys more by fixing the memory path than by bolting on another idle GPU. Which is why the companies that control memory bandwidth and supply are not suppliers to the AI trade. They are the AI trade.

The creator of High Bandwidth Memory (HBM) put a number on the AI build that should stop every infra investor cold. A cluster of a million GPUs runs at roughly 10-20% utilization (Save this). Kim Jung-ho spent thirty years building what feeds the GPU, and his claim is that the GPU is barely working. Here is what is actually happening. Every time a model generates output, the data has to be read out of memory, computed, and written back. The read and the write swallow almost the entire cycle. While that data moves, the GPU does nothing. It sits there, fully powered, fully paid for, waiting. By Kim's estimate the memory is doing only about 30 percent of the work it needs to do. The processor idles the rest. So a million installed GPUs run at 10 to 20 percent. You are not compute constrained. You are memory constrained, and the expensive part is standing around. Adding more GPUs does not fix this. It gives you more processors starving for the same data. Here is the part that decides the next decade. Memory can grow. When a cell cannot shrink any further, you stack it into a high-rise, layer on layer. A GPU cannot be stacked. It runs too hot and needs a cooler bolted to its back, so the one move that rescues memory is closed to the processor. The thing that can keep stacking compounds. The thing that cannot plateaus. The marginal dollar in an AI build now buys more by fixing the memory path than by bolting on another idle GPU. Which is why the companies that control memory bandwidth and supply are not suppliers to the AI trade. They are the AI trade.

Fireside Alpha

38,370 просмотров • 1 месяц назад

ANTHROPIC LEAD ENGINEER MAKING $2.3M/YEAR JUST LEAKED A 12-PAGE DOCUMENT - AND GOT FIRED 15 MINUTES AFTER PUBLISHING most developers build loops - but almost every loop breaks in one of five ways, and each failure has a symptom visible from the outside without reading a single line of code > Blind loop - the agent waits for a human to hand it work every morning - automating execution but not discovery > Tangled loop - parallel agents share one directory and overwrite each other parallelism that destroys instead of multiplies > Nodding loop - the agent grades its own work and approves it every time a loop that has never said no to itself is broken > Amnesiac loop - results live only in the context window every morning the system wakes up with no memory of yesterday > Manual loop - no trigger a human presses the button to start it that is not a loop that is a script waiting for a person a real loop finds its own work - remembers what it did has something that can say no never lets two agents touch the same file and fires itself on a timer remove any one of the five - and the loop either breaks or never starts 12 pages that changed how I build agentic systems today

ANTHROPIC LEAD ENGINEER MAKING $2.3M/YEAR JUST LEAKED A 12-PAGE DOCUMENT - AND GOT FIRED 15 MINUTES AFTER PUBLISHING most developers build loops - but almost every loop breaks in one of five ways, and each failure has a symptom visible from the outside without reading a single line of code > Blind loop - the agent waits for a human to hand it work every morning - automating execution but not discovery > Tangled loop - parallel agents share one directory and overwrite each other parallelism that destroys instead of multiplies > Nodding loop - the agent grades its own work and approves it every time a loop that has never said no to itself is broken > Amnesiac loop - results live only in the context window every morning the system wakes up with no memory of yesterday > Manual loop - no trigger a human presses the button to start it that is not a loop that is a script waiting for a person a real loop finds its own work - remembers what it did has something that can say no never lets two agents touch the same file and fires itself on a timer remove any one of the five - and the loop either breaks or never starts 12 pages that changed how I build agentic systems today

Sprytix

153,575 просмотров • 1 месяц назад

Watch what Nadella did on the Microsoft earnings call tonight. An analyst asked how Microsoft benefits from enterprises adopting open models when it carries all that frontier lab exposure. Instead of defending the lab relationship, Nadella laid out an architecture: "You've got to keep your harness separate from the model. The harness will ensure that your memory, your context, all of that is external. That means any given model at any given time is swappable." And then the part that should worry anyone underwriting model moats: use frontier models where they earn it, low-cost models where they don't, "and in fact, train your own model when you don't want to use any external model itself because after all, you have all the outputs, you have all the traces, you have all the context." The firm keeps the harness. The models compete for slots inside it. Jensen Huang said most companies will be built on harnesses at the LangChain fireside on July 8. I published the full framework on July 12, five launches in three days, all converging on the same architecture. Tonight the largest enterprise software company on earth made it the official pitch on an earnings call. The moat question in enterprise AI just moved from who has the best weights to who owns the loop around them. And if the completed task is the unit everyone now competes on, someone has to price it for the buyer. That is the next thing we are building at BEP Research: a cost per task tool for enterprises. More on that soon. I also took the paywall off the full framework piece tonight, so the whole thing is free to read:

Watch what Nadella did on the Microsoft earnings call tonight. An analyst asked how Microsoft benefits from enterprises adopting open models when it carries all that frontier lab exposure. Instead of defending the lab relationship, Nadella laid out an architecture: "You've got to keep your harness separate from the model. The harness will ensure that your memory, your context, all of that is external. That means any given model at any given time is swappable." And then the part that should worry anyone underwriting model moats: use frontier models where they earn it, low-cost models where they don't, "and in fact, train your own model when you don't want to use any external model itself because after all, you have all the outputs, you have all the traces, you have all the context." The firm keeps the harness. The models compete for slots inside it. Jensen Huang said most companies will be built on harnesses at the LangChain fireside on July 8. I published the full framework on July 12, five launches in three days, all converging on the same architecture. Tonight the largest enterprise software company on earth made it the official pitch on an earnings call. The moat question in enterprise AI just moved from who has the best weights to who owns the loop around them. And if the completed task is the unit everyone now competes on, someone has to price it for the buyer. That is the next thing we are building at BEP Research: a cost per task tool for enterprises. More on that soon. I also took the paywall off the full framework piece tonight, so the whole thing is free to read:

Ben Pouladian

53,187 просмотров • 11 дней назад

an OpenAI engineer just showed how he gets agents to do his whole job: code, debug and more, using loops 29 minutes from the engineer who coined "harness engineering" he writes the rules, agents write the code, a reviewer agent loops until it's right the winners won't have the smartest model, they'll have the best loop around it watch it, then read the full guide on loops below

an OpenAI engineer just showed how he gets agents to do his whole job: code, debug and more, using loops 29 minutes from the engineer who coined "harness engineering" he writes the rules, agents write the code, a reviewer agent loops until it's right the winners won't have the smartest model, they'll have the best loop around it watch it, then read the full guide on loops below

Anatoli Kopadze

176,492 просмотров • 1 месяц назад