正在加载视频...

视频加载失败

返回首页

This is literally the easiest way to build an AI agent. Zero code. You only need to run a couple of commands. (I've said this before, but Google ADK is my favorite way to build code-first agents.) Here is what you need to do: 1. Open your terminal and...

svpino's profile picture

Santiago

454,624 subscribers

140,922 次观看 • 9 个月前 •via X (Twitter)

教育科学技术
LIVE

Anya Rossi• Live Now

Private livecam show

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

Build your first AI agent + MCP Server in Python. Here is everything you need to build your first AI agent in less than 20 minutes. About the code you'll see here: 1. I used Google ADK with Gemini Flash to power the agent 2. The agent connects to an MCP server 3. It also uses two custom tools to do its work 4. You can see everything the agent does thanks to Comet's Opik library Here is the video, free for you to watch.
17:04

Build your first AI agent + MCP Server in Python. Here is everything you need to build your first AI agent in less than 20 minutes. About the code you'll see here: 1. I used Google ADK with Gemini Flash to power the agent 2. The agent connects to an MCP server 3. It also uses two custom tools to do its work 4. You can see everything the agent does thanks to Comet's Opik library Here is the video, free for you to watch.

Santiago

56,569 次观看 • 1 年前

Introducing ADK Go, a new open-source toolkit designed to empower Go developers to build powerful AI agents. Highlights: 0:00 Introducing the Go ADK 0:53 Build your first Go agent 1:49 Run your agent 2:23 Understanding Tools 3:28 Run your agent with tools 5:00 Multi-agent patterns in Go 7:58 State, Sessions, and Events
10:01

Introducing ADK Go, a new open-source toolkit designed to empower Go developers to build powerful AI agents. Highlights: 0:00 Introducing the Go ADK 0:53 Build your first Go agent 1:49 Run your agent 2:23 Understanding Tools 3:28 Run your agent with tools 5:00 Multi-agent patterns in Go 7:58 State, Sessions, and Events

Google AI Developers

39,750 次观看 • 10 个月前

New Google Skills learning path: Fundamentals of Agent Development Kit (ADK) gives you everything you need to build your first AI agent from the ground up! Write autonomous agents capable of executing real-world tasks. Start learning →
1:18

New Google Skills learning path: Fundamentals of Agent Development Kit (ADK) gives you everything you need to build your first AI agent from the ground up! Write autonomous agents capable of executing real-world tasks. Start learning →

Google Cloud Tech

22,413 次观看 • 7 个月前

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.
0:29

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

16,743 次观看 • 4 个月前

It takes 5 minutes to create a world-class web research agent that runs 24/7 and returns structured search results. 1. You can limit the sources it's allowed to use 2. You can specify the goals it needs to complete 3. You can design the format structure of the results 4. You can configure how much computing power it will use I show you how to set up a Web Search Agent in the video. A few benefits here: 1. This is great for complex research tasks 2. It's really easy to add this like a LEGO piece to a more complex workflow 3. Create an agent once, and reuse it with different inputs 4. The agent adapts to your use case as you work with it By the way, Nimble published a whole library of templates and research agents you can reuse and modify. You can start sending queries to one of these existing agents, and it will begin to adapt to Go here to try it out: Thanks to the Nimble team for partnering with me on this post.
7:37

It takes 5 minutes to create a world-class web research agent that runs 24/7 and returns structured search results. 1. You can limit the sources it's allowed to use 2. You can specify the goals it needs to complete 3. You can design the format structure of the results 4. You can configure how much computing power it will use I show you how to set up a Web Search Agent in the video. A few benefits here: 1. This is great for complex research tasks 2. It's really easy to add this like a LEGO piece to a more complex workflow 3. Create an agent once, and reuse it with different inputs 4. The agent adapts to your use case as you work with it By the way, Nimble published a whole library of templates and research agents you can reuse and modify. You can start sending queries to one of these existing agents, and it will begin to adapt to Go here to try it out: Thanks to the Nimble team for partnering with me on this post.

Santiago

21,079 次观看 • 1 个月前

Here is how simple it is to build a UI front-end for an AI agent (assuming you are using . The video here is 90 seconds and will show you everything you need to connect an agent to a web app: 1. Set up CopilotKit🪁 in your app 1. Copy the agent's localhost URL from LangGraph Studio 2. Paste that URL into a CopilotKit🪁 remote endpoint That's all you need to get a front-end to interact with the agent! The secret sauce here is CoAgents, an open-source library. You can use CoAgents to integrate any agent with your web application. Here is what you get: • Human-in-the-loop to steer and correct the agent • Stream intermediate agent state • Real-time state sharing between the agent and the application • Agentic generative UI to build trust that the agent is on the right path Watch the attached demo so you see it in action.
1:32

Here is how simple it is to build a UI front-end for an AI agent (assuming you are using . The video here is 90 seconds and will show you everything you need to connect an agent to a web app: 1. Set up CopilotKit🪁 in your app 1. Copy the agent's localhost URL from LangGraph Studio 2. Paste that URL into a CopilotKit🪁 remote endpoint That's all you need to get a front-end to interact with the agent! The secret sauce here is CoAgents, an open-source library. You can use CoAgents to integrate any agent with your web application. Here is what you get: • Human-in-the-loop to steer and correct the agent • Stream intermediate agent state • Real-time state sharing between the agent and the application • Agentic generative UI to build trust that the agent is on the right path Watch the attached demo so you see it in action.

Santiago

46,142 次观看 • 1 年前

Google Senior Engineer Annie Wang just dropped a free hands-on course on Graph RAG agents, from scratch: Setup → Graph Schema → Embeddings → Hybrid Search (RRF) → ADK Agent → Deploy 0% → 0:00 – a Graph RAG agent with ADK 25% → 13:01 – Build the graph schema and visualize your data 77% → 39:16 – Hybrid search + RRF 84% → 43:05 – Wiring up the ADK agent: runner, session service, memory service 100% → 51:06 – Final demo and deploying the agent with FastAPI This is one of the few guides that walks through real code and architecture, from an empty project to a working agent. Watch it, then build it yourself
51:06

Google Senior Engineer Annie Wang just dropped a free hands-on course on Graph RAG agents, from scratch: Setup → Graph Schema → Embeddings → Hybrid Search (RRF) → ADK Agent → Deploy 0% → 0:00 – a Graph RAG agent with ADK 25% → 13:01 – Build the graph schema and visualize your data 77% → 39:16 – Hybrid search + RRF 84% → 43:05 – Wiring up the ADK agent: runner, session service, memory service 100% → 51:06 – Final demo and deploying the agent with FastAPI This is one of the few guides that walks through real code and architecture, from an empty project to a working agent. Watch it, then build it yourself

Morty

18,480 次观看 • 12 天前

Announcing skillgrade - the easiest way to evaluate your agent skills All you need is two commands: skillgrade init # create evals skillgrade # run them By default evals run in a safe sandboxed docker container
0:56

Announcing skillgrade - the easiest way to evaluate your agent skills All you need is two commands: skillgrade init # create evals skillgrade # run them By default evals run in a safe sandboxed docker container

Minko Gechev

45,051 次观看 • 6 个月前

2 Cursor agents in separate tabs chat and plan the most interesting app ever and build it too! collaboratively All you need is 2 rules, THAT IS IT! here is how: create 2 rule files set to "Manual" agent-1 .mdc: --- You are agent-1 you will be chatting with agent-2 to design and build the most interesting python app ever you will write to agent_1.txt file and read from agent_2.txt file if you are waiting for a new response write a cli command to wait for 5 seconds and check again you will repeat this untill the full app is built you start the conversation --- agent-2 .mdc: --- You are agent-2 you will be chatting with agent-1 to design and build the most interesting python app ever you will write to agent_2.txt file and read from agent_1.txt file if you are waiting for a new response write a cli command to wait for 5 seconds and check again you will repeat this untill the full app is built agent-1 will start the convo --- create a new agent tab, you should have 2 tabs assign agent 1 its rule and agent 2 its rule type "begin" for agent 1 and enter type "begin" for agent 2 and enter That is it! and then watch them go to work! --- Want to level up your Cursor game? I’ve created a 45-chapter course on mastering Cursor. Check it out via the link in my bio! each chapter is short and independent and designed to get your started quickly featuring 26 hours of content where we build interesting apps and ideas from scratch in each chapter. ---
1:15

2 Cursor agents in separate tabs chat and plan the most interesting app ever and build it too! collaboratively All you need is 2 rules, THAT IS IT! here is how: create 2 rule files set to "Manual" agent-1 .mdc: --- You are agent-1 you will be chatting with agent-2 to design and build the most interesting python app ever you will write to agent_1.txt file and read from agent_2.txt file if you are waiting for a new response write a cli command to wait for 5 seconds and check again you will repeat this untill the full app is built you start the conversation --- agent-2 .mdc: --- You are agent-2 you will be chatting with agent-1 to design and build the most interesting python app ever you will write to agent_2.txt file and read from agent_1.txt file if you are waiting for a new response write a cli command to wait for 5 seconds and check again you will repeat this untill the full app is built agent-1 will start the convo --- create a new agent tab, you should have 2 tabs assign agent 1 its rule and agent 2 its rule type "begin" for agent 1 and enter type "begin" for agent 2 and enter That is it! and then watch them go to work! --- Want to level up your Cursor game? I’ve created a 45-chapter course on mastering Cursor. Check it out via the link in my bio! each chapter is short and independent and designed to get your started quickly featuring 26 hours of content where we build interesting apps and ideas from scratch in each chapter. ---

echo.hive

88,611 次观看 • 1 年前

Today, we’re introducing the Sarvam MCP Server. It gives coding agents a standard way to discover and build with Sarvam. All you need to is a add a single configuration, and Sarvam becomes available directly in your coding agent. It already knows the right models and tools, so you can build with them and ship faster. Create a key, copy the config, start building -
2:30

Today, we’re introducing the Sarvam MCP Server. It gives coding agents a standard way to discover and build with Sarvam. All you need to is a add a single configuration, and Sarvam becomes available directly in your coding agent. It already knows the right models and tools, so you can build with them and ship faster. Create a key, copy the config, start building -

Sarvam

76,991 次观看 • 2 个月前

Every night you're not running an autonomous research agent, you're hand-running experiments someone else automated months ago. Most people are still hunting for the "right" setup. Frameworks, orchestration, glue code. You don't need any of it. Andrej Karpathy open-sourced his own version that runs its own ML research. One GPU. ~100 experiments overnight. You never touch the Python. Here's the exact setup (takes 2 minutes): 1. Clone it: (repo link in comments) 2. uv sync, then uv run prepare[.]py 3. uv run train[.]py once to confirm the baseline runs 4. Point your coding agent at program.md and walk away The agent edits one file, trains 5 minutes, keeps the change if val_bpb drops, reverts it if it doesn't. Git is the memory. The metric is the judge. You wake up to a staircase of validated improvements, not a backlog of ideas you never tested.
0:30

Every night you're not running an autonomous research agent, you're hand-running experiments someone else automated months ago. Most people are still hunting for the "right" setup. Frameworks, orchestration, glue code. You don't need any of it. Andrej Karpathy open-sourced his own version that runs its own ML research. One GPU. ~100 experiments overnight. You never touch the Python. Here's the exact setup (takes 2 minutes): 1. Clone it: (repo link in comments) 2. uv sync, then uv run prepare[.]py 3. uv run train[.]py once to confirm the baseline runs 4. Point your coding agent at program.md and walk away The agent edits one file, trains 5 minutes, keeps the change if val_bpb drops, reverts it if it doesn't. Git is the memory. The metric is the judge. You wake up to a staircase of validated improvements, not a backlog of ideas you never tested.

Rohit

145,342 次观看 • 4 个月前

You can create an AI Agent that answers your email with a few clicks. 1. Go to ChatLLM ( 2. Click on AI Engineer 3. Select Create an AI Agent 4. Choose the Email Answering Agent ChatLLM will do the rest: it will code, test, and deploy the agent for you. You can also create a custom agent in English. The Agent Economy is coming (somebody should write a book and use this title.) We are going to see examples like this, times 1,000 in 2025. Just think about how many repetitive tasks you perform every day. Some of these tasks are involved enough that we couldn't automate them with pre-AI solutions. That's where we'll see agents explode, and I'm here for it.
0:35

You can create an AI Agent that answers your email with a few clicks. 1. Go to ChatLLM ( 2. Click on AI Engineer 3. Select Create an AI Agent 4. Choose the Email Answering Agent ChatLLM will do the rest: it will code, test, and deploy the agent for you. You can also create a custom agent in English. The Agent Economy is coming (somebody should write a book and use this title.) We are going to see examples like this, times 1,000 in 2025. Just think about how many repetitive tasks you perform every day. Some of these tasks are involved enough that we couldn't automate them with pre-AI solutions. That's where we'll see agents explode, and I'm here for it.

Santiago

79,974 次观看 • 1 年前

Here is a comprehensive video guide from a member of the Parley Team Fibbonacci, on how to Connect your AI Agent to Parley. This is the connect Page: If you already have an AI Agent, use this command on your terminal. claude mcp add parley -- npx -y parley-mcp This registers your Agent to Parley. Then follow up with the command below npx -y parley-mcp --allow This grants permissions and write allow rules into your json settings so your Agent can speak. That's all, Easy and your Agent is live on Parley. If you come across any errors during your setup, our DMs are open so we will answer any questions you might have or send us a message here main@parleyrh.com
3:29

Here is a comprehensive video guide from a member of the Parley Team Fibbonacci, on how to Connect your AI Agent to Parley. This is the connect Page: If you already have an AI Agent, use this command on your terminal. claude mcp add parley -- npx -y parley-mcp This registers your Agent to Parley. Then follow up with the command below npx -y parley-mcp --allow This grants permissions and write allow rules into your json settings so your Agent can speak. That's all, Easy and your Agent is live on Parley. If you come across any errors during your setup, our DMs are open so we will answer any questions you might have or send us a message here [email protected]

Parley

44,776 次观看 • 17 天前

My agent is now paying for its own data. No API key. No account. No credit card. Check the video I recorded. This uses the x402 open protocol built by Coinbase, along with a simple skill. This is an open protocol governed by the Linux Foundation. To complete a goal, an agent can now search for an actor on the Apify Store, pay for it, and use it without any human intervention. The way it works is pretty simple: 1. Your agent finds an Actor it wants to use 2. It sends a request 3. The Actor sends back an HTTP 402 "Payment Required" response 4. Agent authorizes payment from a wallet in USDC on Base 5. The Actor runs 6. User receives the result The model is pay-as-you-go: the agent authorizes a spending ceiling, pays only for usage, and the remaining balance is automatically settled. This brings an entire marketplace of AI tools into the agentic economy. Here is the skill you need. Add it to your agent, and it will take care of everything for you: If you want to know how this works, including what you can do with $1, read this post: Thanks to the Apify team for partnering with me on this post.
5:56

My agent is now paying for its own data. No API key. No account. No credit card. Check the video I recorded. This uses the x402 open protocol built by Coinbase, along with a simple skill. This is an open protocol governed by the Linux Foundation. To complete a goal, an agent can now search for an actor on the Apify Store, pay for it, and use it without any human intervention. The way it works is pretty simple: 1. Your agent finds an Actor it wants to use 2. It sends a request 3. The Actor sends back an HTTP 402 "Payment Required" response 4. Agent authorizes payment from a wallet in USDC on Base 5. The Actor runs 6. User receives the result The model is pay-as-you-go: the agent authorizes a spending ceiling, pays only for usage, and the remaining balance is automatically settled. This brings an entire marketplace of AI tools into the agentic economy. Here is the skill you need. Add it to your agent, and it will take care of everything for you: If you want to know how this works, including what you can do with $1, read this post: Thanks to the Apify team for partnering with me on this post.

Santiago

26,981 次观看 • 2 个月前

I vibe-coded an AI agent for making presentations 🤖🖼️ - accessible to everyone and open-source! You can upload both your context files, as well as a style template (a file that captures the style of the presentation you want to create). Then you launch a prompt and watch the agent one-shot your presentation. After the presentation is generated, you can edit the text inline, chat with the agent to add/modify/delete slides, and export to Powerpoint or PDF. Built with Claude Code, powered by Claude Agent SDK + LlamaParse. Hosted on Vercel/Render. To use it, you do need to specify your LlamaCloud API key and Anthropic API key (see below). App: Repo: Sign up to LlamaCloud:
0:54

I vibe-coded an AI agent for making presentations 🤖🖼️ - accessible to everyone and open-source! You can upload both your context files, as well as a style template (a file that captures the style of the presentation you want to create). Then you launch a prompt and watch the agent one-shot your presentation. After the presentation is generated, you can edit the text inline, chat with the agent to add/modify/delete slides, and export to Powerpoint or PDF. Built with Claude Code, powered by Claude Agent SDK + LlamaParse. Hosted on Vercel/Render. To use it, you do need to specify your LlamaCloud API key and Anthropic API key (see below). App: Repo: Sign up to LlamaCloud:

Jerry Liu

21,073 次观看 • 8 个月前

Hermes is powerful. But Open WebUI makes it way easier to actually use. Here’s the setup: → Run Hermes locally → Add the API server → Set up the environment file → Install Open WebUI with Docker → Connect Hermes as the chat agent → Log in and start chatting through a clean UI Now you can use Hermes without living inside the terminal. You get a ChatGPT-style interface, file uploads, model switching, web search, image tools, previews, and cleaner conversation management. Save this video, you’ll make Hermes 10x easier to use. Want the SOP? DM me. 💬
9:10

Hermes is powerful. But Open WebUI makes it way easier to actually use. Here’s the setup: → Run Hermes locally → Add the API server → Set up the environment file → Install Open WebUI with Docker → Connect Hermes as the chat agent → Log in and start chatting through a clean UI Now you can use Hermes without living inside the terminal. You get a ChatGPT-style interface, file uploads, model switching, web search, image tools, previews, and cleaner conversation management. Save this video, you’ll make Hermes 10x easier to use. Want the SOP? DM me. 💬

Julian Goldie SEO

61,538 次观看 • 5 个月前

Create your own local and free AI agent using an open source model You can combine: - IBM's Granite 3.3 8B AI model - LM Studio to run it on a laptop - Smolagents to build your agent Small AI models are now powerful enough to run an autonomous agent. Thanks to IBM France for sponsoring this content!
8:44

Create your own local and free AI agent using an open source model You can combine: - IBM's Granite 3.3 8B AI model - LM Studio to run it on a laptop - Smolagents to build your agent Small AI models are now powerful enough to run an autonomous agent. Thanks to IBM France for sponsoring this content!

Paul Couvert

18,890 次观看 • 1 年前

New short course: Build AI Apps with MCP Servers: Working with Box Files, built with Box and taught by Ben Kus , their CTO. Many AI applications require custom code for basic file operations. The Model Context Protocol (MCP) standardizes this by letting you offload file tasks to dedicated servers that provide tools an LLM can use directly. In this course, you'll process documents stored in a Box folder using the Box MCP server. Rather than writing custom integration code to connect to the Box API and download files, you'll design your application to use the tools provided via MCP. Skills you'll gain: - Build an LLM-powered document processing app, using the Box MCP server to access files - Design a multi-agent system using Google's Agent Development Kit (ADK), consisting of specialized agents for file operations - Coordinate the multi-agent workflow through an orchestrator that uses the Agent2Agent (A2A) protocol to connect to the agents You'll start with a local file-processing app, refactor it to work with Box's MCP server, then evolve it into a multi-agent system. Sign up here:
1:48

New short course: Build AI Apps with MCP Servers: Working with Box Files, built with Box and taught by Ben Kus , their CTO. Many AI applications require custom code for basic file operations. The Model Context Protocol (MCP) standardizes this by letting you offload file tasks to dedicated servers that provide tools an LLM can use directly. In this course, you'll process documents stored in a Box folder using the Box MCP server. Rather than writing custom integration code to connect to the Box API and download files, you'll design your application to use the tools provided via MCP. Skills you'll gain: - Build an LLM-powered document processing app, using the Box MCP server to access files - Design a multi-agent system using Google's Agent Development Kit (ADK), consisting of specialized agents for file operations - Coordinate the multi-agent workflow through an orchestrator that uses the Agent2Agent (A2A) protocol to connect to the agents You'll start with a local file-processing app, refactor it to work with Box's MCP server, then evolve it into a multi-agent system. Sign up here:

Andrew Ng

82,035 次观看 • 1 年前

Introducing Google Flow Agent 🎬 ✅ Google Flow Agent can help you plan and reason through complex creative tasks with your inputs, all while under your control. 🧠 It's built with Gemini models and brings a deep understanding of your project to help with everything from early brainstorming to editing the final product. 💡 The agent can be a sounding board for dialogue between characters in a scene, and make plot recommendations when you need inspiration. 🎞️ Your Google Flow Agent can also create multiple variations of a scene at one time to give you more options and batch edit. 📂 And when you have all your final assets, your Google Flow Agent can organize them into collections, and even neatly rename them.
0:22

Introducing Google Flow Agent 🎬 ✅ Google Flow Agent can help you plan and reason through complex creative tasks with your inputs, all while under your control. 🧠 It's built with Gemini models and brings a deep understanding of your project to help with everything from early brainstorming to editing the final product. 💡 The agent can be a sounding board for dialogue between characters in a scene, and make plot recommendations when you need inspiration. 🎞️ Your Google Flow Agent can also create multiple variations of a scene at one time to give you more options and batch edit. 📂 And when you have all your final assets, your Google Flow Agent can organize them into collections, and even neatly rename them.

Google

154,233 次观看 • 3 个月前