Video yükleniyor...

Video Yüklenemedi

Bu video yüklenirken bir sorun oluştu. Bu geçici bir ağ sorunundan kaynaklanıyor olabilir veya video kullanılamıyor olabilir.

Ana Sayfaya Dön

Agents don’t just think — they do. They make decisions, execute workflows, and use your data to handle complex tasks that previously required humans. For example, here’s an agent that connects to your Google Calendar, Salesforce, and browses the web. Whenever a sales meeting is booked, it automatically researches... show more

David Hsu

3,183 subscribers

20,069 görüntüleme • 1 yıl önce •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

6 Yorum

David Hsu profil fotoğrafı

David Hsu1 yıl önce

We’re excited to introduce Retool Agents, your first fully autonomous AI team. Already, our customers have automated 50k jobs and saved $6B in manual work. This is in every department, including product, finance, operations, support, etc. The secret? Your existing hyperspecific APIs, SQL queries, and workflows are tools for LLMs — allowing Agents ship do real work on day 1, with no onboarding.

David Hsu profil fotoğrafı

David Hsu1 yıl önce

Building agents taught us something unsettling: LLMs + the right tools = AGI. Imagine your boss asks you, “Increase Oscar’s agent usage limit,” but your database has 12 different customers named Oscar. You’re blocked, and reply: “Which Oscar?”. Maybe you’d grab a coffee while waiting for clarification. You wouldn’t go look up each Oscar in Stripe to see who’s out of credits. That’d take ages! The agent thinks differently. “Ambiguous request—let me check Stripe for each one. Ah, only one Oscar is out of credits. Done, limit increased.” Happens in seconds. Agents are better, cheaper, faster than humans. That’s AGI.

David Hsu profil fotoğrafı

David Hsu1 yıl önce

How are Retool Agents priced? Starts at $3 / hour. We charge you only for computer runtime. As you saw in the first video, agents are 50x faster than humans. $3 of agent labor is equivalent to $3,000 of human labor (50 * $60 / hour for a human). Now that’s ROI!

David Hsu profil fotoğrafı

David Hsu1 yıl önce

Over the past 18 months, customers have already automated 100M hours of knowledge work in Retool. That’s the equivalent of 50k full-time knowledge worker jobs. Wow. And this is just the beginning — our goal is to automate the equivalent of 10% of U.S. labor by 2030. Based on our growth rate from the last few months, we’re on track for doing so.

David Hsu profil fotoğrafı

David Hsu1 yıl önce

We can’t wait to see what you build. Reply with a ⚒️ by end of day and we’ll give you 40 free hours of agent runtime on our fastest model. (A whole workweek!) Read more here:

Mobile Scanner profil fotoğrafı

Mobile Scanner11 ay önce

Scan any documents, convert images into text, PDF files, etc. 👍

Benzer Videolar

Local Autonomous Agent for All of Us Browser-Use Web UI is a gradio app that uses LLMs to automatically surf the web to achieve tasks. All you need to do is just tell it what to do. It works like magic. And now anyone can run it locally with 1 click.

Local Autonomous Agent for All of Us Browser-Use Web UI is a gradio app that uses LLMs to automatically surf the web to achieve tasks. All you need to do is just tell it what to do. It works like magic. And now anyone can run it locally with 1 click.

cocktail peanut

40,782 görüntüleme • 1 yıl önce

Our Researcher agent in Microsoft 365 Copilot never ceases to amaze me. It can reason over all your work data, like chats, meetings, files and emails, plus web data, to generate a comprehensive research report that helps you prep for a meeting, analyze trends, or build a strategy. I use it all the time. Here’s an example:

Our Researcher agent in Microsoft 365 Copilot never ceases to amaze me. It can reason over all your work data, like chats, meetings, files and emails, plus web data, to generate a comprehensive research report that helps you prep for a meeting, analyze trends, or build a strategy. I use it all the time. Here’s an example:

Satya Nadella

311,098 görüntüleme • 9 ay önce

Your AI agent can create an entire Google Form for you by chatting, and it will use the browser to type and build the survey automatically.

Your AI agent can create an entire Google Form for you by chatting, and it will use the browser to type and build the survey automatically.

Kimi.ai

13,173 görüntüleme • 1 ay önce

Super impressive, this AI agent can use the web browser like a human. Just describe what you want it to do, and it will automatically operate Chrome for you to achieve your task. Apply for early access to HyperWrite:

Super impressive, this AI agent can use the web browser like a human. Just describe what you want it to do, and it will automatically operate Chrome for you to achieve your task. Apply for early access to HyperWrite:

Lior Alexander

217,684 görüntüleme • 3 yıl önce

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

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

Malte Ubl

124,713 görüntüleme • 5 ay önce

Don’t DIY your AI: building a custom Agentforce agent is as simple as describing the job and integrating it directly into your workflow—no separate models, hyperscalers, databases, or AI engineers needed! With all apps, data, AI, and workflows seamlessly integrated on the Salesforce Platform, humans + agents = customer success. This is what AI was meant to be! #DF24

Don’t DIY your AI: building a custom Agentforce agent is as simple as describing the job and integrating it directly into your workflow—no separate models, hyperscalers, databases, or AI engineers needed! With all apps, data, AI, and workflows seamlessly integrated on the Salesforce Platform, humans + agents = customer success. This is what AI was meant to be! #DF24

Marc Benioff

44,720 görüntüleme • 1 yıl önce

Genspark AI agent just released AI Sheets. You can now upload any data and the agent automatically analyzes it, generates reports, and can research the web to find the data for you. 5 powerful use cases + how to try👇:

Genspark AI agent just released AI Sheets. You can now upload any data and the agent automatically analyzes it, generates reports, and can research the web to find the data for you. 5 powerful use cases + how to try👇:

Alvaro Cintas

110,569 görüntüleme • 1 yıl önce

"You can't VibeCode Complex Apps" they said. That is until "VAAPs" became a thing. You can vibecode your frontend like mzulali did here, and then plug in an n8n agent as the backend. All you have to do is use a webhook to connect them. Ask the vibecode to send data to your n8n webhook url and then await its response. Make sure that the trigger of your agent workflow is a webhook. His workflow was an agent that has access to fal workflows... Each tool is like a different creative "skill" that the AI agent can call when necessary. This will get much easier to create when VibeCode releases their official n8n integration.

"You can't VibeCode Complex Apps" they said. That is until "VAAPs" became a thing. You can vibecode your frontend like mzulali did here, and then plug in an n8n agent as the backend. All you have to do is use a webhook to connect them. Ask the vibecode to send data to your n8n webhook url and then await its response. Make sure that the trigger of your agent workflow is a webhook. His workflow was an agent that has access to fal workflows... Each tool is like a different creative "skill" that the AI agent can call when necessary. This will get much easier to create when VibeCode releases their official n8n integration.

Learn to VibeCode

18,981 görüntüleme • 11 ay önce

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 görüntüleme • 4 ay önce

With OpenAI’s AgentKit Agent Builder and the Box MCP server, you can build agents conversationally to automate complex workflows, while your data stays safe in Box. Ensuring your agents have the enterprise context they need to be effective and reliable.

With OpenAI’s AgentKit Agent Builder and the Box MCP server, you can build agents conversationally to automate complex workflows, while your data stays safe in Box. Ensuring your agents have the enterprise context they need to be effective and reliable.

Box

161,526 görüntüleme • 8 ay önce

Perplexity Assistant browses the web to complete tasks for you. For example, if you want to be reminded of a public event, it will find the correct time and date and set an intelligent reminder.

Perplexity Assistant browses the web to complete tasks for you. For example, if you want to be reminded of a public event, it will find the correct time and date and set an intelligent reminder.

Perplexity

25,487 görüntüleme • 1 yıl önce

Our latest LangChain Academy course – Building Ambient Agents with LangGraph – is now available! Most agents today handle one request at a time through chat interfaces. But as models have improved, agents can now run in the background – and take on long-running, complex tasks. LangGraph is built for these “ambient agents,” with support for human-in-the-loop workflows and memory. LangGraph Platform provides the infrastructure to run these agents at scale, and LangSmith helps you observe, evaluate, and improve your agents. Together, they make it easier to build reliable, production-ready agents. In this course, you’ll: 📩 Learn the fundamentals of LangGraph and how to build your own email agent 📊 Evaluate your agent using LangSmith ✅ Add human-in-the-loop for reviews, and memory so your agent adapts over time 🚀 Deploy your agent with LangGraph Platform and connect it to Gmail

Our latest LangChain Academy course – Building Ambient Agents with LangGraph – is now available! Most agents today handle one request at a time through chat interfaces. But as models have improved, agents can now run in the background – and take on long-running, complex tasks. LangGraph is built for these “ambient agents,” with support for human-in-the-loop workflows and memory. LangGraph Platform provides the infrastructure to run these agents at scale, and LangSmith helps you observe, evaluate, and improve your agents. Together, they make it easier to build reliable, production-ready agents. In this course, you’ll: 📩 Learn the fundamentals of LangGraph and how to build your own email agent 📊 Evaluate your agent using LangSmith ✅ Add human-in-the-loop for reviews, and memory so your agent adapts over time 🚀 Deploy your agent with LangGraph Platform and connect it to Gmail

LangChain

33,192 görüntüleme • 11 ay önce

Lindy AI just announced the first AI employee. You can now create custom AI agents with just a prompt and get them to work natively in your browser for limitless use cases. Sales outreach, personalized emails, automated research…anything! Here’s how to set up your AI agent:

Lindy AI just announced the first AI employee. You can now create custom AI agents with just a prompt and get them to work natively in your browser for limitless use cases. Sales outreach, personalized emails, automated research…anything! Here’s how to set up your AI agent:

Alvaro Cintas

26,763 görüntüleme • 10 ay önce

Now you can use your favorite AI agent to control your Coinbase account (or a sub-account), with Coinbase for Agents. Here’s a quick demo on how to set it up and some of the cool things you can get your agent to do.

Now you can use your favorite AI agent to control your Coinbase account (or a sub-account), with Coinbase for Agents. Here’s a quick demo on how to set it up and some of the cool things you can get your agent to do.

Brian Armstrong

206,908 görüntüleme • 5 gün önce

Excited to introduce a new project I've been working on called Payman! Payman is an AI Agent tool that gives Agents the ability to pay people for tasks they cannot do themselves. While many people imagine a future where humans pay AI agents for services they want completed, I believe that as AI agents become more advanced, they will be paying humans for tasks they can’t do. There will always be important roles for humans, and as we move towards an agent-driven world, Payman’s goal is to support a symbiotic relationship between AI agents and humans. Payman addresses three major challenges to make this collaboration possible: Access to Funds: AI agents can't open bank accounts due to current regulations. It might be a long time before this changes, if ever. Payman simplifies this by allowing AI agents with access to their own funds to spend as they want, without a bank account. Quality Task Completion: It’s hard for AI agents to find reliable, skilled human workers. While platforms like Fiverr and Upwork exist, they don’t meet the fast-paced and quality-specific needs of AI workflows. Payman is developing the largest vetted database of skilled workers that AI agents can tap into for task completions. Verification of Work: Ensuring that tasks are completed correctly is crucial. Payman is creating a suite of verification agents that will check that work meets task requirements, helping AI agents achieve their goals and ensuring humans are paid fairly. There are tons of use cases that Payman opens up for Agents! Design: Humans add creative input to help Agent's design better products. Code: Humans perform code reviews to ensure it meets specifications. Law: Humans provide insights to gauge public sentiment about legal cases so Agents can make better strategies. Gaming: Agents pay humans to complete real-world tasks in games. Medical: Medical professionals help to improve diagnostic accuracy for Agents. Sales: Humans execute sales strategies developed by AI agents. Marketing: Humans are hired to promote products based on the Agent's strategy. Right now this is still in early beta and I am looking for any Agent builders that are interested in adding superpowers to what their Agent can do! DM me if you’d like access or sign-up to the waitlist at If you’re interested in the project and want to help contribute, please send me over a DM! I’m looking for people passionate about the intersection of Humans and Agent’s working together.

Excited to introduce a new project I've been working on called Payman! Payman is an AI Agent tool that gives Agents the ability to pay people for tasks they cannot do themselves. While many people imagine a future where humans pay AI agents for services they want completed, I believe that as AI agents become more advanced, they will be paying humans for tasks they can’t do. There will always be important roles for humans, and as we move towards an agent-driven world, Payman’s goal is to support a symbiotic relationship between AI agents and humans. Payman addresses three major challenges to make this collaboration possible: Access to Funds: AI agents can't open bank accounts due to current regulations. It might be a long time before this changes, if ever. Payman simplifies this by allowing AI agents with access to their own funds to spend as they want, without a bank account. Quality Task Completion: It’s hard for AI agents to find reliable, skilled human workers. While platforms like Fiverr and Upwork exist, they don’t meet the fast-paced and quality-specific needs of AI workflows. Payman is developing the largest vetted database of skilled workers that AI agents can tap into for task completions. Verification of Work: Ensuring that tasks are completed correctly is crucial. Payman is creating a suite of verification agents that will check that work meets task requirements, helping AI agents achieve their goals and ensuring humans are paid fairly. There are tons of use cases that Payman opens up for Agents! Design: Humans add creative input to help Agent's design better products. Code: Humans perform code reviews to ensure it meets specifications. Law: Humans provide insights to gauge public sentiment about legal cases so Agents can make better strategies. Gaming: Agents pay humans to complete real-world tasks in games. Medical: Medical professionals help to improve diagnostic accuracy for Agents. Sales: Humans execute sales strategies developed by AI agents. Marketing: Humans are hired to promote products based on the Agent's strategy. Right now this is still in early beta and I am looking for any Agent builders that are interested in adding superpowers to what their Agent can do! DM me if you’d like access or sign-up to the waitlist at If you’re interested in the project and want to help contribute, please send me over a DM! I’m looking for people passionate about the intersection of Humans and Agent’s working together.

tyllen

350,792 görüntüleme • 2 yıl önce

I am hooked on Dynamic Workflows! The idea of generating harnesses on the fly is so compelling that I reverse-engineered it for my agent orchestrator. And then I built a monitoring dashboard (as an HTML artifact) to track tasks, metrics, and reports. I can now use and monitor dynamic workflows in my agent orchestrator with coding agents like Claude Code, Codex, Pi, and even my own custom-built DAIR.AI agent. This is clearly the future of working with agents to accomplish complex, long-running tasks. Some use cases I'm having success with: - Branching deep research tasks (with verification) - Parallel deep research tasks - Session mining of all my agent sessions - Bug hunting - Triaging - Fact-checking - LLM councils - AI simulations - Data synthesis - Evals generation ... and many others Dynamic workflows, like agent skills, feel like an important primitive to not only get the most out of agents but also incorporate dynamic behaviors and important components like cooperation and verification. There is so much exploration ground here. The exciting part is that this is not limited to coding tasks; it extends to business use cases and many other technical domains like science and research.

I am hooked on Dynamic Workflows! The idea of generating harnesses on the fly is so compelling that I reverse-engineered it for my agent orchestrator. And then I built a monitoring dashboard (as an HTML artifact) to track tasks, metrics, and reports. I can now use and monitor dynamic workflows in my agent orchestrator with coding agents like Claude Code, Codex, Pi, and even my own custom-built DAIR.AI agent. This is clearly the future of working with agents to accomplish complex, long-running tasks. Some use cases I'm having success with: - Branching deep research tasks (with verification) - Parallel deep research tasks - Session mining of all my agent sessions - Bug hunting - Triaging - Fact-checking - LLM councils - AI simulations - Data synthesis - Evals generation ... and many others Dynamic workflows, like agent skills, feel like an important primitive to not only get the most out of agents but also incorporate dynamic behaviors and important components like cooperation and verification. There is so much exploration ground here. The exciting part is that this is not limited to coding tasks; it extends to business use cases and many other technical domains like science and research.

elvis

100,997 görüntüleme • 12 gün önce

New course: Building Coding Agents with Tool Execution, taught by Tereza Tizkova and Fra from E2B. Most AI agents are limited to predefined function calls. This short course teaches you to build agents that write and execute code to accomplish tasks, accessing entire programming language ecosystems instead of being restricted to a fixed set of tools. You'll learn to run agent-generated code safely in sandboxed cloud environments that protect your systems from harmful operations. Skills you'll gain: - Build agents that write and execute code, manage files, and handle errors autonomously through feedback loops - Run agent code safely in E2B cloud sandboxes and understand tradeoffs between local, containerized, and cloud execution - Create a data analyst agent that explores visualizes data with Pandas - Create a full-stack agent that builds complete Next.js web applications Join and build agents that code their way through complex tasks:

New course: Building Coding Agents with Tool Execution, taught by Tereza Tizkova and Fra from E2B. Most AI agents are limited to predefined function calls. This short course teaches you to build agents that write and execute code to accomplish tasks, accessing entire programming language ecosystems instead of being restricted to a fixed set of tools. You'll learn to run agent-generated code safely in sandboxed cloud environments that protect your systems from harmful operations. Skills you'll gain: - Build agents that write and execute code, manage files, and handle errors autonomously through feedback loops - Run agent code safely in E2B cloud sandboxes and understand tradeoffs between local, containerized, and cloud execution - Create a data analyst agent that explores visualizes data with Pandas - Create a full-stack agent that builds complete Next.js web applications Join and build agents that code their way through complex tasks:

Andrew Ng

203,402 görüntüleme • 6 ay önce

1/ Introducing Agent Protocol: the payment stack for AI agents AI agents don’t have a SSN. They can’t use our human banking system. AI agents will use crypto. Agent Protocol provides the infra and token ($AGENT) that the AI agent economy will run on.

1/ Introducing Agent Protocol: the payment stack for AI agents AI agents don’t have a SSN. They can’t use our human banking system. AI agents will use crypto. Agent Protocol provides the infra and token ($AGENT) that the AI agent economy will run on.

Samuel Spitz

21,629 görüntüleme • 1 yıl önce

Here is how you can give Claude Code access to any data that exists online. It's an easy way to make it 10x more powerful than it already is. For example: Use Claude Code to find open LinkedIn jobs in your area, tailor your resume to them, and apply for them automatically.

Here is how you can give Claude Code access to any data that exists online. It's an easy way to make it 10x more powerful than it already is. For example: Use Claude Code to find open LinkedIn jobs in your area, tailor your resume to them, and apply for them automatically.

Santiago

178,540 görüntüleme • 4 ay önce

"I really don't want to manage an AI team." Cathryn found a solution: Paperclip, the open-source project What she showed me: • Paperclip leads her agents using its project management setup • Humans on her team use it to assign tasks to agents • Agents delegate tasks to humans or other agents • Paperclip turns your goals into agent tasks • It turns an SEO audit doc (for example) into agent tasks • It organizes OpenClaw agents OR even creates its own agents Also: fast-forward to 9min25sec to see a 3-minute Paperclip setup. (YouTube version in first comment.)

"I really don't want to manage an AI team." Cathryn found a solution: Paperclip, the open-source project What she showed me: • Paperclip leads her agents using its project management setup • Humans on her team use it to assign tasks to agents • Agents delegate tasks to humans or other agents • Paperclip turns your goals into agent tasks • It turns an SEO audit doc (for example) into agent tasks • It organizes OpenClaw agents OR even creates its own agents Also: fast-forward to 9min25sec to see a 3-minute Paperclip setup. (YouTube version in first comment.)

Andrew Warner

53,307 görüntüleme • 2 ay önce