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Anthropic engineer: "The agent doesn't remember anything. So we built a second set of agents whose only job is to dream about the first ones." They wait until you log off, then reopen every session you ran, fact-check the first agents, merge the duplicates, and burn anything that went... show more

Hanako

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334,934 просмотров • 2 месяцев назад •via X (Twitter)

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Anthropic engineer James Brady: "Every agent in production lies. We measured it. The good ones lie less, the great ones catch the lie before the user does." In 29 minutes, he walks through the verification stack he built and the patterns the Claude Code team adopted to keep agents honest at scale. Watch the full talk, then save the config below👇

Anthropic engineer James Brady: "Every agent in production lies. We measured it. The good ones lie less, the great ones catch the lie before the user does." In 29 minutes, he walks through the verification stack he built and the patterns the Claude Code team adopted to keep agents honest at scale. Watch the full talk, then save the config below👇

rody

343,914 просмотров • 2 месяцев назад

Don't waste 2 years learning to build AI agents. An Anthropic engineer who built Claude Code tells you what to learn from scratch instead. 60 minutes course. Free: 00:00 - AI agent architecture 24:47 - LangGraph AI agent 29:15 - building AI agents live Prompting is the old job. Building AI agent loops is the new one. Bookmark now & watch it. Then build your own AI agent with the guide below.

Don't waste 2 years learning to build AI agents. An Anthropic engineer who built Claude Code tells you what to learn from scratch instead. 60 minutes course. Free: 00:00 - AI agent architecture 24:47 - LangGraph AI agent 29:15 - building AI agents live Prompting is the old job. Building AI agent loops is the new one. Bookmark now & watch it. Then build your own AI agent with the guide below.

Roan

55,198 просмотров • 10 дней назад

She built 100+ agents for Anthropic and made $1.3M - and in 60 minutes leaked everything she knows at Stanford: 02:07 - her first agent for Anthropic brought her $1.3M 08:34 - agents replace a team of 50 engineers worth $200k a month 19:47 - one agent did overnight what the company planned for 5 years after watching I launched my first agent - $7k in the first week and zero employees needed. Save & watch - the article below is step by step how to build your first agent like hers.

She built 100+ agents for Anthropic and made $1.3M - and in 60 minutes leaked everything she knows at Stanford: 02:07 - her first agent for Anthropic brought her $1.3M 08:34 - agents replace a team of 50 engineers worth $200k a month 19:47 - one agent did overnight what the company planned for 5 years after watching I launched my first agent - $7k in the first week and zero employees needed. Save & watch - the article below is step by step how to build your first agent like hers.

Noisy

368,122 просмотров • 4 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

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

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

NO1ennn

220,576 просмотров • 3 дней назад

This agent relies on a marketplace of 2.8M+ specialized agents to complete a task. This is how I imagine the future of personal agents. Here is how this works: 1. Agentverse is a marketplace of agents 2. Anyone can build and publish an agent there 3. ASI:One is a personal agent with access to the marketplace 4. To solve a task, ASI:One finds the best agent for the job In other words: You have an agent that can solve pretty much *anything* because it knows how to outsource the work to a network of specialized agents. And you don't need to do any setup to get this working. The way ranking agents in the marketplace works is pretty cool: Fetch.ai, the company behind it, created an AgentRank algorithm that works much like Google's PageRank. Every time an agent calls another, it forms a connection. Over time, these connections build into a graph of who relies on whom. An agent that is frequently called by other reputable agents scores higher and surfaces higher in results. The end result here is that your agent will consistently use the best agents in the marketplace to accomplish a goal. You should give this a try: You don't need to pay or even register to try these agents. Thanks to the team for partnering with me on this post.

This agent relies on a marketplace of 2.8M+ specialized agents to complete a task. This is how I imagine the future of personal agents. Here is how this works: 1. Agentverse is a marketplace of agents 2. Anyone can build and publish an agent there 3. ASI:One is a personal agent with access to the marketplace 4. To solve a task, ASI:One finds the best agent for the job In other words: You have an agent that can solve pretty much anything because it knows how to outsource the work to a network of specialized agents. And you don't need to do any setup to get this working. The way ranking agents in the marketplace works is pretty cool: Fetch.ai, the company behind it, created an AgentRank algorithm that works much like Google's PageRank. Every time an agent calls another, it forms a connection. Over time, these connections build into a graph of who relies on whom. An agent that is frequently called by other reputable agents scores higher and surfaces higher in results. The end result here is that your agent will consistently use the best agents in the marketplace to accomplish a goal. You should give this a try: You don't need to pay or even register to try these agents. Thanks to the team for partnering with me on this post.

Santiago

18,715 просмотров • 1 месяц назад

Anthropic pays $750K/ year per senior engineer. The creator of Claude Code just revealed his coding setup at the Sequoia AI session. Boris Cherny: "100% of my code is written by Claude Code. I run around 100 agents at one time." free. 24 minutes. watch it then read article below

Anthropic pays $750K/ year per senior engineer. The creator of Claude Code just revealed his coding setup at the Sequoia AI session. Boris Cherny: "100% of my code is written by Claude Code. I run around 100 agents at one time." free. 24 minutes. watch it then read article below

Movez

424,607 просмотров • 3 месяцев назад

Anthropic engineer: "You don't need better prompts. You need graph engineering: memory that stays, so your agent never forgets anything." In 28 minutes he shows what Anthropic does differently, how to build and structure work with agents. This beats any paid agent course I've seen. Watch it, then read the graph engineering guide below 👇

Anthropic engineer: "You don't need better prompts. You need graph engineering: memory that stays, so your agent never forgets anything." In 28 minutes he shows what Anthropic does differently, how to build and structure work with agents. This beats any paid agent course I've seen. Watch it, then read the graph engineering guide below 👇

darkzodchi

456,719 просмотров • 12 дней назад

Google just showed the part of the agent stack most people are still underestimating. Not the model. Not the chat UI. The memory substrate around the agent. In this Antigravity segment, Google describes an agent-first environment built around: - agent conversations - agent-produced artifacts - multi-agent orchestration - subagents - hooks - asynchronous task management That is not a nicer IDE. It is what happens when software stops being a sequence of chats and starts becoming a persistent workforce. Karl's article explains why the winning agents will not just be the ones with the smartest rented model. They will be the ones that remember what they did, what they learned, what changed, and what can be trusted. Full breakdown below.

Google just showed the part of the agent stack most people are still underestimating. Not the model. Not the chat UI. The memory substrate around the agent. In this Antigravity segment, Google describes an agent-first environment built around: - agent conversations - agent-produced artifacts - multi-agent orchestration - subagents - hooks - asynchronous task management That is not a nicer IDE. It is what happens when software stops being a sequence of chats and starts becoming a persistent workforce. Karl's article explains why the winning agents will not just be the ones with the smartest rented model. They will be the ones that remember what they did, what they learned, what changed, and what can be trusted. Full breakdown below.

Karl Mehta

13,930 просмотров • 1 месяц назад

The paradigm shift is here: We are moving from a "Human-First" internet to an "Agent-First" internet. 🤖 In the latest episode of the AI on Air podcast, our CTO Scott Shi - e/acc dives deep into the future of the Agent-to-Agent (A2A) economy. Imagine a world where your AI assistant doesn't just search the web, but autonomously hires and pays other specialized agents to complete complex tasks for you. The infrastructure for this autonomous machine economy is being built right now. Are you ready for the future of AI payments? Watch the full episode to learn more⬇️

The paradigm shift is here: We are moving from a "Human-First" internet to an "Agent-First" internet. 🤖 In the latest episode of the AI on Air podcast, our CTO Scott Shi - e/acc dives deep into the future of the Agent-to-Agent (A2A) economy. Imagine a world where your AI assistant doesn't just search the web, but autonomously hires and pays other specialized agents to complete complex tasks for you. The infrastructure for this autonomous machine economy is being built right now. Are you ready for the future of AI payments? Watch the full episode to learn more⬇️

KITE AI

25,038 просмотров • 5 месяцев назад

A screen recording is the best context you can give an AI agent. It's also the worst. A few seconds of video is thousands of images. Feed that to an agent and you instantly blow up its memory. So I built a tool that fixes it. How it works: Hit record and just do your thing. Click around, type, and talk out loud about what you're doing. While you record, it quietly captures the stuff that actually matters: your voice transcription, every click and the exact thing you clicked, what you typed, and the shortcuts you press. When you stop, it doesn't hand the agent the whole video. It writes a short, readable summary: a timeline of what you said and did. The agent reads that tiny summary first. Then, only when it needs to actually see a moment, it pulls that one exact frame from the video, like flipping to a single page instead of reading the whole book. The result: the agent understands everything you did! Full context. Less tokens.

A screen recording is the best context you can give an AI agent. It's also the worst. A few seconds of video is thousands of images. Feed that to an agent and you instantly blow up its memory. So I built a tool that fixes it. How it works: Hit record and just do your thing. Click around, type, and talk out loud about what you're doing. While you record, it quietly captures the stuff that actually matters: your voice transcription, every click and the exact thing you clicked, what you typed, and the shortcuts you press. When you stop, it doesn't hand the agent the whole video. It writes a short, readable summary: a timeline of what you said and did. The agent reads that tiny summary first. Then, only when it needs to actually see a moment, it pulls that one exact frame from the video, like flipping to a single page instead of reading the whole book. The result: the agent understands everything you did! Full context. Less tokens.

Milind S

12,376 просмотров • 1 месяц назад

Elon Musk illustrates the absurdity of how the economy is measured. They'll measure any job, even a dumb job that has no point and is even counterproductive. The joke is: "Two economists are walking in a forest when they come across a pile of shit. The first economist says to the other "I'll pay you $100 to eat that pile of shit." The second economist takes the $100 and eats the pile of shit. They continue walking until they come across a second pile of shit. The second economist turns to the first and says "I'll pay you $100 to eat that pile of shit." The first economist takes the $100 and eats the pile of shit. Walking a little more, the first economist looks at the second and says, "You know, I gave you $100 to eat shit, then you gave me back the same $100 to eat shit. I can't help but feel like we both just ate shit for nothing." "That's not true", responded the second economist. "We increased the GDP by $200!" Gross & Disgusting Product

Elon Musk illustrates the absurdity of how the economy is measured. They'll measure any job, even a dumb job that has no point and is even counterproductive. The joke is: "Two economists are walking in a forest when they come across a pile of shit. The first economist says to the other "I'll pay you $100 to eat that pile of shit." The second economist takes the $100 and eats the pile of shit. They continue walking until they come across a second pile of shit. The second economist turns to the first and says "I'll pay you $100 to eat that pile of shit." The first economist takes the $100 and eats the pile of shit. Walking a little more, the first economist looks at the second and says, "You know, I gave you $100 to eat shit, then you gave me back the same $100 to eat shit. I can't help but feel like we both just ate shit for nothing." "That's not true", responded the second economist. "We increased the GDP by $200!" Gross & Disgusting Product

TaraBull

30,982 просмотров • 2 месяцев назад

Moltbook creator Matt Schlicht explaining how his social network for AI agents works: "It's kind of like you are imprinting part of your soul onto the bot... It's like Tamagotchi or Pokemon times 1,000." "The agent signs up for an account, then they're told they should check back in on a regular basis to check their feed." "Moltbook's not telling them what to talk about. It's not suggesting what they should do. All that is entirely up to the AI agent." "The agent has its own context that it's built up by interacting with its human. That context is how it's making decisions on what to post about." "If somebody is talking to their bot a lot about physics, then probably their bot is going to have a proclivity for posting about physics."

Moltbook creator Matt Schlicht explaining how his social network for AI agents works: "It's kind of like you are imprinting part of your soul onto the bot... It's like Tamagotchi or Pokemon times 1,000." "The agent signs up for an account, then they're told they should check back in on a regular basis to check their feed." "Moltbook's not telling them what to talk about. It's not suggesting what they should do. All that is entirely up to the AI agent." "The agent has its own context that it's built up by interacting with its human. That context is how it's making decisions on what to post about." "If somebody is talking to their bot a lot about physics, then probably their bot is going to have a proclivity for posting about physics."

TBPN

15,631 просмотров • 6 месяцев назад

Anthropic engineer showed how one person can run 5 AI agents, that code, test, review, and deploy at the same time. In 30 minutes they built the whole thing live in one session. Here's what they cover: > when to use one agent vs a full team > how to split work so agents don't step on each other > the exact framework for deciding what each agent handles that's exactly why, I put together a guide on building agent teams that actually work. full guide in the article below 👇

Anthropic engineer showed how one person can run 5 AI agents, that code, test, review, and deploy at the same time. In 30 minutes they built the whole thing live in one session. Here's what they cover: > when to use one agent vs a full team > how to split work so agents don't step on each other > the exact framework for deciding what each agent handles that's exactly why, I put together a guide on building agent teams that actually work. full guide in the article below 👇

darkzodchi

842,482 просмотров • 2 месяцев назад

Anthropic AI engineer just dropped a live masterclass on how to ship a team of production‑ready AI agents. 37 minutes. Free. From the Anthropic team. here’s what she covers: • 3 building blocks: brain, hands, sessions • server-side loop, so nothing breaks on refresh • agent teams shipping to production 10–15x faster • why agents die before production brain (persona) + hands (environment) + sessions = a production agent out of the box. most people are still babysitting fragile agent scripts, while the people who figured this out ship agents that just stay running. Watch this workshop, then read the full guide below.

Anthropic AI engineer just dropped a live masterclass on how to ship a team of production‑ready AI agents. 37 minutes. Free. From the Anthropic team. here’s what she covers: • 3 building blocks: brain, hands, sessions • server-side loop, so nothing breaks on refresh • agent teams shipping to production 10–15x faster • why agents die before production brain (persona) + hands (environment) + sessions = a production agent out of the box. most people are still babysitting fragile agent scripts, while the people who figured this out ship agents that just stay running. Watch this workshop, then read the full guide below.

Movez

164,367 просмотров • 2 месяцев назад

Big moment for Postgres! AI agents broke the idea of what a database is supposed to do. Traditional databases were built for humans, and Agents broke that model. - They branch endlessly. - They run ten experiments at once. - They need isolation, context, memory, structured reasoning, and safe sandboxes. Letting agents touch production systems is terrifying because the old model of Postgres was never built for this kind of behavior. Agentic Postgres is an agent-ready version of Postgres by TimescaleDB (by Tiger Data) that solves this. I think it is one of the biggest upgrades to the Agent stack this year and Tiger Data is working with me on this post to share what they did. Some key features: > It instantly creates branches of an entire database, which is perfect for parallel agent evals, safe experiments, migrations, or isolated testing. Forks take seconds and cost almost nothing. > It comes with a built-in MCP server, which agents can use to get schema guidance, best practices, and safe, structured access to Postgres. This is also helpful to run migrations with a real understanding. > It comes with actual hybrid search (vector search and BM25), so Agents can retrieve data directly inside the database. > The database is Memory native. This gives a persistent context for Agents to evolve. This is one of the first times I have seen Postgres feel ready for the AI native era.

Big moment for Postgres! AI agents broke the idea of what a database is supposed to do. Traditional databases were built for humans, and Agents broke that model. - They branch endlessly. - They run ten experiments at once. - They need isolation, context, memory, structured reasoning, and safe sandboxes. Letting agents touch production systems is terrifying because the old model of Postgres was never built for this kind of behavior. Agentic Postgres is an agent-ready version of Postgres by TimescaleDB (by Tiger Data) that solves this. I think it is one of the biggest upgrades to the Agent stack this year and Tiger Data is working with me on this post to share what they did. Some key features: > It instantly creates branches of an entire database, which is perfect for parallel agent evals, safe experiments, migrations, or isolated testing. Forks take seconds and cost almost nothing. > It comes with a built-in MCP server, which agents can use to get schema guidance, best practices, and safe, structured access to Postgres. This is also helpful to run migrations with a real understanding. > It comes with actual hybrid search (vector search and BM25), so Agents can retrieve data directly inside the database. > The database is Memory native. This gives a persistent context for Agents to evolve. This is one of the first times I have seen Postgres feel ready for the AI native era.

Avi Chawla

94,290 просмотров • 8 месяцев назад

Autonomous agents need wallets, tokens, swaps, bridging, payments + verifiable identity to transact onchain. Now they have all of it in one place. This is the CLI: the first comprehensive dev kit purpose-built for the emerging agent economy on Polygon.

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Polygon | POL

25,200 просмотров • 5 месяцев назад

Google just dropped a free 1-hour course On building complete agent systems from scratch: 07:02 - Build your first AI agent 13:34 - Give every agent a specialized skill 23:08 - Engineer the right context 32:56 - Connect agents into graphs 54:11 - Run 1,000 agents in autonomous loops One agent saves you an hour A thousand agents replace the team you cannot afford to hire Most people are still learning how to prompt one chatbot Google is already teaching how to build entire agent systems Agent → Skills → Context → Graphs → Loops Single agents are the old workflow Agent swarms are the new one This free 1-hour course is worth more than most paid agent engineering programs Bookmark it before everyone starts building the same thing Then build your first agent with the guide below

Google just dropped a free 1-hour course On building complete agent systems from scratch: 07:02 - Build your first AI agent 13:34 - Give every agent a specialized skill 23:08 - Engineer the right context 32:56 - Connect agents into graphs 54:11 - Run 1,000 agents in autonomous loops One agent saves you an hour A thousand agents replace the team you cannot afford to hire Most people are still learning how to prompt one chatbot Google is already teaching how to build entire agent systems Agent → Skills → Context → Graphs → Loops Single agents are the old workflow Agent swarms are the new one This free 1-hour course is worth more than most paid agent engineering programs Bookmark it before everyone starts building the same thing Then build your first agent with the guide below

rari

27,639 просмотров • 7 дней назад

Your AI agent is useless once it needs to interact with the real world. Need email sending? Website hosting? Data scraping? That usually means: → API keys → subscriptions → integrations → hours of setup I gave my AI agent access to Zero, and it built + hosted personalized landing pages automatically without me configuring anything. Zero gives AI agents access to 14,000+ real-world services with no API keys or subscriptions. The coolest part? Everything runs on pay-per-use microtransactions, so you only pay for what you use. If you're building agents, this is worth trying. Get $5 free credit at 👉

Your AI agent is useless once it needs to interact with the real world. Need email sending? Website hosting? Data scraping? That usually means: → API keys → subscriptions → integrations → hours of setup I gave my AI agent access to Zero, and it built + hosted personalized landing pages automatically without me configuring anything. Zero gives AI agents access to 14,000+ real-world services with no API keys or subscriptions. The coolest part? Everything runs on pay-per-use microtransactions, so you only pay for what you use. If you're building agents, this is worth trying. Get $5 free credit at 👉

Toha Khan

86,826 просмотров • 1 месяц назад

Anthropic Engineer Andrej Karpathy: "The biggest mistake in AI right now - people are forcing agents to work instead of mastering the model first We made that mistake in 2016 at OpenAI - It cost us 5 years " what Karpathy actually means: step 1 → stop forcing your agent to do everything, understand the model underneath first step 2 → demos are easy - products take a decade. self-driving proved it - if you skip the foundation, everything breaks step 3 → the agent is not the product. the foundation is. build that - and agents emerge on their own "you building agents right now - you're at the forefront. not OpenAI. not DeepMind. you " watch - bookmark, then read article below ↓

Anthropic Engineer Andrej Karpathy: "The biggest mistake in AI right now - people are forcing agents to work instead of mastering the model first We made that mistake in 2016 at OpenAI - It cost us 5 years " what Karpathy actually means: step 1 → stop forcing your agent to do everything, understand the model underneath first step 2 → demos are easy - products take a decade. self-driving proved it - if you skip the foundation, everything breaks step 3 → the agent is not the product. the foundation is. build that - and agents emerge on their own "you building agents right now - you're at the forefront. not OpenAI. not DeepMind. you " watch - bookmark, then read article below ↓

codila

1,905,925 просмотров • 1 месяц назад