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This AI doesn’t just automate. It uses your computer like a human clicks, types, navigates all from a simple prompt. Meet Simular Pro, the first production-grade computer-use agent. It runs thousands of steps, 24/7, so you don’t have to. How? Most agents flake. Simular Pro doesn’t. It combines two... show more

FELIX

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76,011 次观看 • 1 年前 •via X (Twitter)

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1/ Wait, Bigfoot figured out how to run a startup without drowning in multitasking 👀 It found 𝗦𝗶𝗺𝘂𝗹𝗮𝗿 𝗣𝗿𝗼, the world’s first production-grade, computer-use agent that runs thousands of steps without a hiccup - working 24/7 so he didn’t have to. So how does Simular Pro work? Most agents use LLMs - great explorers, but flaky when # steps grows; RPAs are stable but rigid. Simular Pro uses 2 agents, maximizing generality & repeatability: - Neural: explores - Symbolic: executes Every action is editable, deterministic code.

1/ Wait, Bigfoot figured out how to run a startup without drowning in multitasking 👀 It found 𝗦𝗶𝗺𝘂𝗹𝗮𝗿 𝗣𝗿𝗼, the world’s first production-grade, computer-use agent that runs thousands of steps without a hiccup - working 24/7 so he didn’t have to. So how does Simular Pro work? Most agents use LLMs - great explorers, but flaky when # steps grows; RPAs are stable but rigid. Simular Pro uses 2 agents, maximizing generality & repeatability: - Neural: explores - Symbolic: executes Every action is editable, deterministic code.

Simular

2,257,119 次观看 • 1 年前

Alibaba just dropped an AI that doesn’t just write code. It runs it. Tests it. Fixes it. Until it actually works. Here’s what makes Qwen 3 Coder Next different: → Executes code, not just autocomplete → Runs tests automatically → Iterates until all tests pass → Handles multi-file repositories → Works fully offline This is a real coding agent, not a text generator. Save this video, you’ll rethink how coding gets done. Want the SOP? DM me. 💬

Alibaba just dropped an AI that doesn’t just write code. It runs it. Tests it. Fixes it. Until it actually works. Here’s what makes Qwen 3 Coder Next different: → Executes code, not just autocomplete → Runs tests automatically → Iterates until all tests pass → Handles multi-file repositories → Works fully offline This is a real coding agent, not a text generator. Save this video, you’ll rethink how coding gets done. Want the SOP? DM me. 💬

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🚀 Introducing 𝐀𝐠𝐞𝐧𝐭 𝐒3, the most advanced computer-use agent, now 𝐚𝐩𝐩𝐫𝐨𝐚𝐜𝐡𝐢𝐧𝐠 𝐡𝐮𝐦𝐚𝐧-𝐥𝐞𝐯𝐞𝐥 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞🧠💻 Just one year ago, Agent S scored ~20% on OSWorld: SOTA then, but far from human 72%. Today, Agent S3 reaches 6̳9̳.̳9̳%̳ (⬆10% over prior SOTA), nearly matching humans on computer use. Few imagined this gap could close so fast, but with steady progress and 𝐟𝐢𝐫𝐬𝐭-𝐩𝐫𝐢𝐧𝐜𝐢𝐩𝐥𝐞 simplicity, Simular did it in just one year. 🧪 The s̵e̵c̵r̵e̵t̵ 𝐨𝐩𝐞𝐧 𝐫𝐞𝐜𝐢𝐩𝐞: → 𝐁𝐞𝐡𝐚𝐯𝐢𝐨𝐫 𝐁𝐞𝐬𝐭-𝐨𝐟-𝐍 — scaling agents 𝘵𝘩𝘦 𝘳𝘪𝘨𝘩𝘵 𝘸𝘢𝘺 → Simpler design, stronger baseline → 100% 𝐨𝐩𝐞𝐧 𝐬𝐨𝐮𝐫𝐜𝐞 (as always) Learn more at & 👇

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Google just unveiled a new project that shows how AI Agents are coming for full browser and computer use. This opens up AI to any of the laborious knowledge tasks that we do today. Most importantly, it lets us automate work that we never had even contemplated before.

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Aaron Levie

145,442 次观看 • 1 年前

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GREG ISENBERG

375,365 次观看 • 4 个月前

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Bash is all you need! Which is why I'm introducing my holiday project: just-bash just-bash is a pretty complete implementation of bash in TypeScript designed to be used as a bash tool by AI agents. Because it turns out agents love exploring data via shell scripts, even beyond coding. It comes with grep, sed, awk and the 99th percentile features that an agent like Claude Code or Cursor would use. In fact, Claude Code can use it for secure bash execution. In the package - A bash-tool for AI SDK - A binary for use by yourself or your coding agents - An overlay filesystem to feed files to your agent securely - A Vercel Sandbox compatible API, so you can quickly upgrade to a real VM if you need to run binaries - An example AI agent that explores the just-bash code base using just-bash - I imported the Oils shell bash compatibility suite and just-bash passes a very good chunk What is interesting about this codebase: It was essentially entirely written by Opus 4.5. Coding agents love bash and they are good at reproducing it. They are also great at text-book recursive descent parsers and AST tweet-walk interpreters. That said, it is, like, a lot of code and I didn't read it all 😅. This is very much a hack, but it also seems to be _really_ useful. I haven't really found anything agents want to use that it doesn't support and it's fast and secure (caveats apply). It doesn't have write access to your computer and the filesystem is given a root that the agent cannot escape from. Find it at Related: Our recent blog post how we migrated our data analysis agent to bash tools and achieved incredible quality improvements The video shows the example agent investigating the just-bash code base

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124,713 次观看 • 7 个月前

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Amira Zairi

11,039 次观看 • 1 年前

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I love the game of Boggle. This demo showcases our Gemini 2.0 Pro model’s coding abilities in AI Studio. It is mind boggling to think that it can write the full piece of code, including all the right data structures and search algorithms to find all valid words on a Boggle board from a relatively simple prompt asking it to do so. As a computer scientist, I'm also happy that it got its data structures right on the first trie. Discombobulating!

Jeff Dean

122,248 次观看 • 1 年前

Imagine a computer where you don’t need to learn 10 apps to get work done. You just tell it what you want, and it adapts to how you work. I tested Happycapy with a real use case and created an image and a video. No coding, no complex software. Do this: - Build automations that run on schedule - Deploy agent teams that work for you AI molds how you work, not the other way around. If you can use a computer, you can make anything happen. Start your first Automation and Agent teams today.

Imagine a computer where you don’t need to learn 10 apps to get work done. You just tell it what you want, and it adapts to how you work. I tested Happycapy with a real use case and created an image and a video. No coding, no complex software. Do this: - Build automations that run on schedule - Deploy agent teams that work for you AI molds how you work, not the other way around. If you can use a computer, you can make anything happen. Start your first Automation and Agent teams today.

Aaliya

17,042 次观看 • 5 个月前

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This little device lets your agents do what they do best, whilst making sure you approve every important action. This is a Ledger Nano Gen5, a hardware signer that keeps your accounts secure. You can install a CLI and a set of skills in your projects. These will enable your agents to send their plan to the signer, so you can approve it. This is awesome: 1. You can have an automated agentic workflow 2. Your agents can't make costly mistakes Check the video I recorded to see how this works. You can run all of this in two commands: 1. Install the CLI 2. Install the skills for your agent From here, the agent can query my Ethereum accounts, check balances, and even initiate transactions as long as my Ledger Nano Gen5 is plugged into the computer. My keys are never stored in my computer and never shared with the agent. This is huge! Also, while the agent can set up transactions, it can't execute them unless a human approves them using the device. Pretty awesome! Thanks to the Ledger team for partnering with me on this post.

Santiago

44,296 次观看 • 1 个月前

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Anthropic dropped a free 59-minute Claude Code course From autocomplete to real agents: 00:00 - From autocomplete to agents 04:50 - How the agentic loop works 14:07 - CLAUDE.md as project memory 26:53 - Why Plan Mode comes first 33:31 - Live task from brief to commit 54:46 - Skills vs CLAUDE.md Most people still use Claude Code like a smarter autocomplete This shows how Anthropic uses it to plan, call tools, test, and ship real work Worth more than 99% of paid Claude Code tutorials Bookmark and watch it later

rari

118,190 次观看 • 21 天前

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J-Cal’s framework for how to be "AI first". It’s not just a slogan 1. Every task… Ask AI first. “How can I do this better.” 2. Every repeated task… Let your agents take care of it so you can do MORE. 3. Give the agent your last 10 outputs, let it score them, run it daily, and do deep-research weekly on what the best are doing differently.

This Week in Startups

45,723 次观看 • 2 个月前

The next wave of great AI devs won’t be prompt engineers. They’ll be system designers. Most coding agents are like sharp tools with no blueprint. You send a prompt, you get back something “close.” But close doesn’t ship. I’ve tested Claude, Copilot, Cursor, Roocode, Cline, they’re all capable. But without structure, they break. That’s where Traycer comes in. It scopes the build like a tech lead, diagrams what needs to happen, and then sends clean tasks to your agents. I still use all the same models. I just don’t babysit them anymore. Here’s what I learned after orchestrating 100+ agent tasks: →Planning before prompting multiplies quality →Splitting strategy from execution keeps context clean →One plan is better than 10 retries →A structured system beats a clever prompt every time AI won’t replace devs. But it will replace devs who don’t know how to design systems.

The next wave of great AI devs won’t be prompt engineers. They’ll be system designers. Most coding agents are like sharp tools with no blueprint. You send a prompt, you get back something “close.” But close doesn’t ship. I’ve tested Claude, Copilot, Cursor, Roocode, Cline, they’re all capable. But without structure, they break. That’s where Traycer comes in. It scopes the build like a tech lead, diagrams what needs to happen, and then sends clean tasks to your agents. I still use all the same models. I just don’t babysit them anymore. Here’s what I learned after orchestrating 100+ agent tasks: →Planning before prompting multiplies quality →Splitting strategy from execution keeps context clean →One plan is better than 10 retries →A structured system beats a clever prompt every time AI won’t replace devs. But it will replace devs who don’t know how to design systems.

Robin Delta

39,363 次观看 • 1 年前

🚨 Secure OpenClaw - Message Your Custom AI Agent From Telegram We are excited for our first release of personal agents. You can now use Abacus AI's DeepAgent to vibe code a personal agent on Telegram Create and simply message the agent! - do tasks like respond to emails - schedule meetings - use the browser to book tickets - connect to your internal systems The agent runs on a secure environment with constraints and you can pretty much customize it exactly like you want it when you are building it This is just v1 - we are going to be launching something WAY more exciting soon

🚨 Secure OpenClaw - Message Your Custom AI Agent From Telegram We are excited for our first release of personal agents. You can now use Abacus AI's DeepAgent to vibe code a personal agent on Telegram Create and simply message the agent! - do tasks like respond to emails - schedule meetings - use the browser to book tickets - connect to your internal systems The agent runs on a secure environment with constraints and you can pretty much customize it exactly like you want it when you are building it This is just v1 - we are going to be launching something WAY more exciting soon

Bindu Reddy

10,702 次观看 • 5 个月前

Hermes Agent just got a free plugin nobody is talking about. And it finally fixes the biggest problem with AI. Right now, your AI agents are a black box. You give them a job. They go quiet. You cross your fingers and hope they do it right. If they fail, you have no clue why. Hermes Labyrinth changes the game. It works like a flight recorder for your AI. It maps out every single move your agent makes. Every prompt it writes. Every web search it tries. Every time it switches to a smarter model. You see the exact path it took from start to finish. It is totally read-only, so it cannot break your system. It even hides your secret passwords automatically. Setup takes exactly two lines of code. Drop it in your plugins folder and restart your dashboard. Stop guessing what your AI is doing behind your back.

Hermes Agent just got a free plugin nobody is talking about. And it finally fixes the biggest problem with AI. Right now, your AI agents are a black box. You give them a job. They go quiet. You cross your fingers and hope they do it right. If they fail, you have no clue why. Hermes Labyrinth changes the game. It works like a flight recorder for your AI. It maps out every single move your agent makes. Every prompt it writes. Every web search it tries. Every time it switches to a smarter model. You see the exact path it took from start to finish. It is totally read-only, so it cannot break your system. It even hides your secret passwords automatically. Setup takes exactly two lines of code. Drop it in your plugins folder and restart your dashboard. Stop guessing what your AI is doing behind your back.

Julian Goldie SEO

16,351 次观看 • 3 个月前

Yesterday I met with a $100M brand completely swamped trying to build AI agents. They were duct taping together N8n, Gumloop, Make, Lindy and Zapier hoping it would help automate. Here’s the thing: Those tools are great but only if your data is clean, connected, and actually usable by AI. For example you can’t build a real media buying agent if it doesn’t know: – What’s in stock – What your customers LTV – How your sales change by season Triple Whale brings all of your data into one place. So when you ask our AI a question, it doesn’t guess because it already has the data. And it knows how to use it. We’ve spent 920 days and hired 72 full-time engineers to build the most advanced data layer in ecom designed for AI from day one. Wallaroo Media figured it out👇 They dropped every other platform for Triple Whale. ❌ No more scattered agents ❌ No more platform-hopping ✅ Better answers, better automations, better results Shout out to Justin Doyle and the whole Wallaroo team for the incredible partnership!

Maxx Blank 🐳

47,001 次观看 • 1 年前

Our first short course with Anthropic! Building Towards Computer Use with Anthropic. This teaches you to build an LLM-based agent that uses a computer interface by generating mouse clicks and keystrokes. Computer Use is an important, emerging capability for LLMs that will let AI agents do many more tasks than were possible before, since it lets them interact with interfaces designed for humans to use, rather than only tools that provide explicit API access. I hope you will enjoy learning about it! This course is taught by Anthropic's Head of Curriculum, Colt_Steele. You'll learn to apply image reasoning and tool use to "use" a computer as follows: a model processes an image of the screen, analyzes it to understand what's going on, and navigates the computer via mouse clicks and keystrokes. This course goes through the key building blocks, and culminates in a demo of an AI assistant that uses a web browser to search for a research paper, downloads the PDF, and finally summarizes the paper for you. In detail, you’ll: - Learn about Anthropic's family of models, when to use which one, and make API requests to Claude - Use multi-modal prompts that combine text and image content blocks, and also work with streaming responses - Improve your prompting by using prompt templates, using XML to structure prompts, and providing examples - Implement prompt caching to reduce cost and latency - Apply tool-use to build a chatbot that can call different tools to respond to queries - See all these building blocks come together in Computer Use demo Please sign up here:

Our first short course with Anthropic! Building Towards Computer Use with Anthropic. This teaches you to build an LLM-based agent that uses a computer interface by generating mouse clicks and keystrokes. Computer Use is an important, emerging capability for LLMs that will let AI agents do many more tasks than were possible before, since it lets them interact with interfaces designed for humans to use, rather than only tools that provide explicit API access. I hope you will enjoy learning about it! This course is taught by Anthropic's Head of Curriculum, Colt_Steele. You'll learn to apply image reasoning and tool use to "use" a computer as follows: a model processes an image of the screen, analyzes it to understand what's going on, and navigates the computer via mouse clicks and keystrokes. This course goes through the key building blocks, and culminates in a demo of an AI assistant that uses a web browser to search for a research paper, downloads the PDF, and finally summarizes the paper for you. In detail, you’ll: - Learn about Anthropic's family of models, when to use which one, and make API requests to Claude - Use multi-modal prompts that combine text and image content blocks, and also work with streaming responses - Improve your prompting by using prompt templates, using XML to structure prompts, and providing examples - Implement prompt caching to reduce cost and latency - Apply tool-use to build a chatbot that can call different tools to respond to queries - See all these building blocks come together in Computer Use demo Please sign up here:

Andrew Ng

170,425 次观看 • 1 年前

Anthropic's Claude Ai Agents Team just Educated how to build production AI agents in under 30 mins. For Free. From the engineers who built the stack. CANCEL Your Weekend Plans, and Learn to Build AI Agents Today. Bookmark it. Watch it. Build your first production agent this weekend. $5,000/month. $7,000/month. $12,000/month. People are building agents for clients and charging $$$ as Beginners. You're still stuck in the thinking about AI phase. This video fixes that tonight. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward. ↓ Ivan Nardini runs Developer Relations for AI at Google Cloud. He just gave away the entire production agent stack in 30 minutes. This is the talk that separates people deploying AI agents that actually scale from people whose agents break the moment they leave localhost. Here's everything inside. I break down a production AI video like this every week. Follow Himanshu Kumar. ↓ The 4-part agent stack that actually scales. Most devs are duct-taping frameworks together and calling it an "AI agent." Ivan lays out the real stack: Agent Development Kit (ADK): open-source, code-first framework for building, evaluating, and deploying agents. Supports Claude models through Vertex AI directly. Model Context Protocol (MCP): lets your agent talk to any tool or data source with one standard. Vertex AI Agent Engine: managed platform for deploying, monitoring, and scaling agents in production. No DevOps headaches. Agent-to-Agent Protocol: open protocol so agents built on different frameworks can actually work together. This is the stack replacing every hacky agent setup in production right now. Full MCP + Claude breakdowns drop weekly on Himanshu Kumar. ↓ Building your first real agent. Ivan builds a birthday planner agent live. LLM Agent class. Name it. Define instructions. Pick the model. He uses Claude 3.7 Sonnet. You could use Opus 4.7 for better reasoning. Full agent built in minutes. Not weeks. Watch the build once and you'll never structure an agent the wrong way again. I post agent architectures people pay $500 courses to learn. Himanshu Kumar. ↓ Multi-agent systems without the chaos. Single agents are easy. Multi-agent systems are where 99% of builders fail. Ivan extends the birthday planner by: Adding a calendar service through MCP tools Creating an orchestrator agent to route requests between agents Handling state and context across agent handoffs This is production multi-agent architecture. Clean. Scalable. Debuggable. Most tutorials hand-wave this part. This one shows you every step. Multi-agent orchestration content drops weekly on Himanshu Kumar. ↓ Deployment without the DevOps nightmare. This is where most AI projects die. You build a cool agent locally. It works. You try to deploy it. Everything breaks. Vertex AI Agent Engine fixes this: Minimal code deployment Automatic monitoring of latency, CPU, and memory Built-in observability and logging No infrastructure setup needed You provide config and requirements. The platform handles the rest. This is how agents actually get to production. Deployment guides for Claude agents post every week. Himanshu Kumar. ↓ Agent-to-Agent Protocol: the future nobody's talking about. Most people don't know this exists yet. The A2A Protocol lets agents built in different frameworks communicate seamlessly. Your Claude agent. My LangChain agent. Someone else's CrewAI agent. All talking to each other. All solving parts of the same problem. All without custom integration code. This is the infrastructure layer of the coming AI economy. Getting in early on A2A Protocol is like getting in early on HTTP in 1995. A2A deep dive coming soon. Himanshu Kumar. ↓ 30 minutes from the team shipping this in production. You'll learn more from this than from 6 months of YouTube tutorials made by people who've never deployed an agent past localhost. People who watch this understand production AI agents at the architect level. People who skip it keep hacking together frameworks that break every time an API updates. Save the video. Watch it tonight. Build a real agent this weekend. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward.

Anthropic's Claude Ai Agents Team just Educated how to build production AI agents in under 30 mins. For Free. From the engineers who built the stack. CANCEL Your Weekend Plans, and Learn to Build AI Agents Today. Bookmark it. Watch it. Build your first production agent this weekend. $5,000/month. $7,000/month. $12,000/month. People are building agents for clients and charging $$$ as Beginners. You're still stuck in the thinking about AI phase. This video fixes that tonight. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward. ↓ Ivan Nardini runs Developer Relations for AI at Google Cloud. He just gave away the entire production agent stack in 30 minutes. This is the talk that separates people deploying AI agents that actually scale from people whose agents break the moment they leave localhost. Here's everything inside. I break down a production AI video like this every week. Follow Himanshu Kumar. ↓ The 4-part agent stack that actually scales. Most devs are duct-taping frameworks together and calling it an "AI agent." Ivan lays out the real stack: Agent Development Kit (ADK): open-source, code-first framework for building, evaluating, and deploying agents. Supports Claude models through Vertex AI directly. Model Context Protocol (MCP): lets your agent talk to any tool or data source with one standard. Vertex AI Agent Engine: managed platform for deploying, monitoring, and scaling agents in production. No DevOps headaches. Agent-to-Agent Protocol: open protocol so agents built on different frameworks can actually work together. This is the stack replacing every hacky agent setup in production right now. Full MCP + Claude breakdowns drop weekly on Himanshu Kumar. ↓ Building your first real agent. Ivan builds a birthday planner agent live. LLM Agent class. Name it. Define instructions. Pick the model. He uses Claude 3.7 Sonnet. You could use Opus 4.7 for better reasoning. Full agent built in minutes. Not weeks. Watch the build once and you'll never structure an agent the wrong way again. I post agent architectures people pay $500 courses to learn. Himanshu Kumar. ↓ Multi-agent systems without the chaos. Single agents are easy. Multi-agent systems are where 99% of builders fail. Ivan extends the birthday planner by: Adding a calendar service through MCP tools Creating an orchestrator agent to route requests between agents Handling state and context across agent handoffs This is production multi-agent architecture. Clean. Scalable. Debuggable. Most tutorials hand-wave this part. This one shows you every step. Multi-agent orchestration content drops weekly on Himanshu Kumar. ↓ Deployment without the DevOps nightmare. This is where most AI projects die. You build a cool agent locally. It works. You try to deploy it. Everything breaks. Vertex AI Agent Engine fixes this: Minimal code deployment Automatic monitoring of latency, CPU, and memory Built-in observability and logging No infrastructure setup needed You provide config and requirements. The platform handles the rest. This is how agents actually get to production. Deployment guides for Claude agents post every week. Himanshu Kumar. ↓ Agent-to-Agent Protocol: the future nobody's talking about. Most people don't know this exists yet. The A2A Protocol lets agents built in different frameworks communicate seamlessly. Your Claude agent. My LangChain agent. Someone else's CrewAI agent. All talking to each other. All solving parts of the same problem. All without custom integration code. This is the infrastructure layer of the coming AI economy. Getting in early on A2A Protocol is like getting in early on HTTP in 1995. A2A deep dive coming soon. Himanshu Kumar. ↓ 30 minutes from the team shipping this in production. You'll learn more from this than from 6 months of YouTube tutorials made by people who've never deployed an agent past localhost. People who watch this understand production AI agents at the architect level. People who skip it keep hacking together frameworks that break every time an API updates. Save the video. Watch it tonight. Build a real agent this weekend. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward.

Himanshu Kumar

228,270 次观看 • 3 个月前