正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

Tacos anyone? 🌮😋 This video was created using two agents: Motion control 🕺 Lets you add your own movement and guide how the scene animates Multi-angle photo shoot 📸 Used to generate forced-perspective-style angles that can be animated into each other Agents + tutorial below 👇

GLIF

19,488 subscribers

94,722 次观看 • 5 个月前 •via X (Twitter)

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

Quick breakdown of how this sequence was created using WAN 2.2 Animate agent and Multi-Angle Fashion Shoot agent One changes your appearance and location, the other generates camera movements and transitions. Short tutorial on blending both techniques Links to these agents below 👇

Quick breakdown of how this sequence was created using WAN 2.2 Animate agent and Multi-Angle Fashion Shoot agent One changes your appearance and location, the other generates camera movements and transitions. Short tutorial on blending both techniques Links to these agents below 👇

GLIF

64,815 次观看 • 7 个月前

Introducing Character Motion Control for Video Diffusion Models Creators currently lack control over how characters move and act in the videos they generate with AI. Today, we’re changing that. With Character Motion Control, you can act it out and bring your exact vision to life. How it works: 1️⃣ Describe the scene with a prompt 2️⃣ Upload an acting video to guide character movement 🎥 Generate a video where character motion is driven by the acting video. Register for Early Access via the link in the comments.

Introducing Character Motion Control for Video Diffusion Models Creators currently lack control over how characters move and act in the videos they generate with AI. Today, we’re changing that. With Character Motion Control, you can act it out and bring your exact vision to life. How it works: 1️⃣ Describe the scene with a prompt 2️⃣ Upload an acting video to guide character movement 🎥 Generate a video where character motion is driven by the acting video. Register for Early Access via the link in the comments.

Kinetix

97,871 次观看 • 1 年前

AI film agents are here! The new vid.fun tool is great for anyone who wants to create AI videos, their system uses AI agents to automatically generate complete film storyboards, images, video, VFX, and even music in seconds. How this works?! You just drop your idea into a box (can be a prompt, link, or even a PDF!) and the AI does the heavy lifting, researching, and gathering inspiration. Then you can pick from tons of style options or use your custom LoRA. The coolest part? You still maintain creative control! You can define and make adjustments to any part, without getting lost in complex editing software. For this work, I chose my Lora "Soft Sketch" and Luma for animation of course the agents automatically add soundtracks through Udio, or you upload your audio file. Has anyone else tried this yet?

AI film agents are here! The new vid.fun tool is great for anyone who wants to create AI videos, their system uses AI agents to automatically generate complete film storyboards, images, video, VFX, and even music in seconds. How this works?! You just drop your idea into a box (can be a prompt, link, or even a PDF!) and the AI does the heavy lifting, researching, and gathering inspiration. Then you can pick from tons of style options or use your custom LoRA. The coolest part? You still maintain creative control! You can define and make adjustments to any part, without getting lost in complex editing software. For this work, I chose my Lora "Soft Sketch" and Luma for animation of course the agents automatically add soundtracks through Udio, or you upload your audio file. Has anyone else tried this yet?

Amira Zairi

12,015 次观看 • 1 年前

Learn how to receive Bitcoin (BTC) directly to your Best Wallet! 🙌 This step-by-step video guide will show you how to deposit BTC into your multi-chain wallet quickly and securely. Send from Coinbase, Kraken, Binance, and other wallets with ease. Watch the full guide below! 👇

Learn how to receive Bitcoin (BTC) directly to your Best Wallet! 🙌 This step-by-step video guide will show you how to deposit BTC into your multi-chain wallet quickly and securely. Send from Coinbase, Kraken, Binance, and other wallets with ease. Watch the full guide below! 👇

Best Wallet

12,880 次观看 • 1 年前

This is what AI was meant to be 🧠 Agents that think ⚡️ Tools that act 🤖 Workflows that run on autopilot We just dropped a full tutorial on how to build your own autonomous AI agent on Skillful Anyone can do it, just drag, drop and deploy

This is what AI was meant to be 🧠 Agents that think ⚡️ Tools that act 🤖 Workflows that run on autopilot We just dropped a full tutorial on how to build your own autonomous AI agent on Skillful Anyone can do it, just drag, drop and deploy

Skillful AI

13,200 次观看 • 1 年前

For a long time, software was limited by how fast people could write code, and how good that code was. As models have improved, that constraint has largely disappeared. Now the bottleneck is access: what surfaces can your agents actually reach? Those interaction layers sit on top of coding agents, the kernel that turns prompts into real-world impact. With Notion's custom agents, we pushed this further. They adapt to your work style as you collaborate with them, using the same deterministic logic that powers coding agents. Simon Last sarah guo

For a long time, software was limited by how fast people could write code, and how good that code was. As models have improved, that constraint has largely disappeared. Now the bottleneck is access: what surfaces can your agents actually reach? Those interaction layers sit on top of coding agents, the kernel that turns prompts into real-world impact. With Notion's custom agents, we pushed this further. They adapt to your work style as you collaborate with them, using the same deterministic logic that powers coding agents. Simon Last sarah guo

Notion Developers

25,065 次观看 • 3 个月前

Did you know you can create captivating motion sync videos on Yapper Follow the steps below and push your creativity boundaries Step 1: Go to on your browser step 2: Click on videos and select Motion Sync Step 3: Select Base Video The video's movement will be used to animate the reference image. Step 4: Add Reference Image Upload an image of a character to be animated by the base video. Step 5: Describe Your Vision Write a prompt that guides the background of the output video. Step 6: Generate Select your model and the output video will be generated in real-time

Did you know you can create captivating motion sync videos on Yapper Follow the steps below and push your creativity boundaries Step 1: Go to on your browser step 2: Click on videos and select Motion Sync Step 3: Select Base Video The video's movement will be used to animate the reference image. Step 4: Add Reference Image Upload an image of a character to be animated by the base video. Step 5: Describe Your Vision Write a prompt that guides the background of the output video. Step 6: Generate Select your model and the output video will be generated in real-time

Kuria | AI

46,344 次观看 • 2 个月前

I just tested Motion Sync on Yapper and I like the cinematic Video made in minutes. Let me show you how you can do it 👇 Step 1: Open Step 2: Click on video and select Motion Sync. Then, ✅️Select base video Pick a video with the movement you want — this will drive the animation. ✅️Add reference image Upload your character or image you want to bring to life. ✅️Generate video Hit generate and watch your idea turn into a smooth motion sync clip. Simple, fast, and actually fun to experiment with.

I just tested Motion Sync on Yapper and I like the cinematic Video made in minutes. Let me show you how you can do it 👇 Step 1: Open Step 2: Click on video and select Motion Sync. Then, ✅️Select base video Pick a video with the movement you want — this will drive the animation. ✅️Add reference image Upload your character or image you want to bring to life. ✅️Generate video Hit generate and watch your idea turn into a smooth motion sync clip. Simple, fast, and actually fun to experiment with.

Leonardo

50,931 次观看 • 2 个月前

New short course: Building Code Agents with Hugging Face smolagents! Learn how to build code agents in this course, created in collaboration with Hugging Face, and taught by Thomas Wolf, its co-founder and CSO, and m_ric, Hugging Face’s Project Lead on Agents. Tool-calling agents use LLMs to generate multiple function calls sequentially to complete a complex sequence of tasks. They generate one function call, execute it, observe, reason, and decide what to do next. Code agents take a different approach. They consolidate all these calls into a single block of code, letting the LLM lay out an entire action plan at once, which can be executed efficiently to provide more reliable results. You’ll learn how to code agents using smolagents, a lightweight agentic framework from Hugging Face. Along the way, you’ll learn how to run LLM-generated code safely and develop an evaluation system to optimize your code agent for production. In detail, you’ll learn: - How agentic systems have evolved, gaining greater levels of agency over time—and why code agents are a next step. - How code agents write their actions in code. - When code agents outperform function-calling agents. - How to run code agents safely in your system using a constrained Python interpreter and sandboxing using E2B. - To trace, debug, and assess the code agent to optimize its behaviours for complex requests. - How to build a research multi-agent system that can find information online and organize it into an interactive report. By the end of this course, you’ll know how to build and run code agents using smolagents, and deploy them safely with a structured evaluation system in your projects. Please sign up here!

New short course: Building Code Agents with Hugging Face smolagents! Learn how to build code agents in this course, created in collaboration with Hugging Face, and taught by Thomas Wolf, its co-founder and CSO, and m_ric, Hugging Face’s Project Lead on Agents. Tool-calling agents use LLMs to generate multiple function calls sequentially to complete a complex sequence of tasks. They generate one function call, execute it, observe, reason, and decide what to do next. Code agents take a different approach. They consolidate all these calls into a single block of code, letting the LLM lay out an entire action plan at once, which can be executed efficiently to provide more reliable results. You’ll learn how to code agents using smolagents, a lightweight agentic framework from Hugging Face. Along the way, you’ll learn how to run LLM-generated code safely and develop an evaluation system to optimize your code agent for production. In detail, you’ll learn: - How agentic systems have evolved, gaining greater levels of agency over time—and why code agents are a next step. - How code agents write their actions in code. - When code agents outperform function-calling agents. - How to run code agents safely in your system using a constrained Python interpreter and sandboxing using E2B. - To trace, debug, and assess the code agent to optimize its behaviours for complex requests. - How to build a research multi-agent system that can find information online and organize it into an interactive report. By the end of this course, you’ll know how to build and run code agents using smolagents, and deploy them safely with a structured evaluation system in your projects. Please sign up here!

Andrew Ng

124,382 次观看 • 1 年前

New Course: ACP: Agent Communication Protocol Learn to build agents that communicate and collaborate across different frameworks using ACP in this short course built with IBM Research's BeeAI, and taught by Sandi Besen, AI Research Engineer & Ecosystem Lead at IBM, and Nicholas Renotte, Head of AI Developer Advocacy at IBM. Building a multi-agent system with agents built or used by different teams and organizations can become challenging. You may need to write custom integrations each time a team updates their agent design or changes their choice of agentic orchestration framework. The Agent Communication Protocol (ACP) is an open protocol that addresses this challenge by standardizing how agents communicate, using a unified RESTful interface that works across frameworks. In this protocol, you host an agent inside an ACP server, which handles requests from an ACP client and passes them to the appropriate agent. Using a standardized client-server interface allows multiple teams to reuse agents across projects. It also makes it easier to switch between frameworks, replace an agent with a new version, or update a multi-agent system without refactoring the entire system. In this course, you’ll learn to connect agents through ACP. You’ll understand the lifecycle of an ACP Agent and how it compares to other protocols, such as MCP (Model Context Protocol) and A2A (Agent-to-Agent). You’ll build ACP-compliant agents and implement both sequential and hierarchical workflows of multiple agents collaborating using ACP. Through hands-on exercises, you’ll build: - A RAG agent with CrewAI and wrap it inside an ACP server. - An ACP Client to make calls to the ACP server you created. - A sequential workflow that chains an ACP server, created with Smolagents, to the RAG agent. - A hierarchical workflow using a router agent that transforms user queries into tasks, delegated to agents available through ACP servers. - An agent that uses MCP to access tools and ACP to communicate with other agents. You’ll finish up by importing your ACP agents into the BeeAI platform, an open-source registry for discovering and sharing agents. ACP enables collaboration between agents across teams and organizations. By the end of this course, you’ll be able to build ACP agents and workflows that communicate and collaborate regardless of framework. Please sign up here:

New Course: ACP: Agent Communication Protocol Learn to build agents that communicate and collaborate across different frameworks using ACP in this short course built with IBM Research's BeeAI, and taught by Sandi Besen, AI Research Engineer & Ecosystem Lead at IBM, and Nicholas Renotte, Head of AI Developer Advocacy at IBM. Building a multi-agent system with agents built or used by different teams and organizations can become challenging. You may need to write custom integrations each time a team updates their agent design or changes their choice of agentic orchestration framework. The Agent Communication Protocol (ACP) is an open protocol that addresses this challenge by standardizing how agents communicate, using a unified RESTful interface that works across frameworks. In this protocol, you host an agent inside an ACP server, which handles requests from an ACP client and passes them to the appropriate agent. Using a standardized client-server interface allows multiple teams to reuse agents across projects. It also makes it easier to switch between frameworks, replace an agent with a new version, or update a multi-agent system without refactoring the entire system. In this course, you’ll learn to connect agents through ACP. You’ll understand the lifecycle of an ACP Agent and how it compares to other protocols, such as MCP (Model Context Protocol) and A2A (Agent-to-Agent). You’ll build ACP-compliant agents and implement both sequential and hierarchical workflows of multiple agents collaborating using ACP. Through hands-on exercises, you’ll build: - A RAG agent with CrewAI and wrap it inside an ACP server. - An ACP Client to make calls to the ACP server you created. - A sequential workflow that chains an ACP server, created with Smolagents, to the RAG agent. - A hierarchical workflow using a router agent that transforms user queries into tasks, delegated to agents available through ACP servers. - An agent that uses MCP to access tools and ACP to communicate with other agents. You’ll finish up by importing your ACP agents into the BeeAI platform, an open-source registry for discovering and sharing agents. ACP enables collaboration between agents across teams and organizations. By the end of this course, you’ll be able to build ACP agents and workflows that communicate and collaborate regardless of framework. Please sign up here:

Andrew Ng

105,343 次观看 • 1 年前

Give your voice agents a face. Introducing Lemon Slice-2 – the world's first interactive talking AI video model Think of it as a face layer for your voice agents, available today in production as an API or an embeddable widget. What makes Lemon Slice-2 great: → Easy: All you need is 1 photo → Expressive: hands and full body motion → Any character and style: if it has a face, Lemon Slice-2 can animate it Under the hood: we trained a custom, 20B-parameter video diffusion transformer, streaming at 20fps on a single GPU. Infinite-length video generation with no error accumulation. Talk to the demo avatars on our homepage. Or, add your own interactive avatar to your website. Companies and creators are already building video agents that are: → Customer support agents who screen-share and show you where to click → Lead qualification agents that replace forms with real conversations → AI personal shoppers who try on clothes and guide purchases People don't engage with chat bubbles. They engage with faces. And now you can have a 24/7 spokesperson that’s the face of your brand. We’ve partnered with , Daily, and Modal to make this happen. As part of this launch, we’re giving away a guide: “How to Add a High-Converting Video Agent to Your Website in under 10 Minutes” Retweet and comment "LEMON SLICE" below and I'll send it over 🍋

Lina Colucci

1,162,861 次观看 • 7 个月前

🎬 Want to create cinematic AI videos in seconds? Here’s exactly how I do it using Seedance 2.0 on Yapper: Most people get this wrong… your results depend on how well you *direct* the AI. Here’s the workflow 👇 ✅ Step 1: Open Yapper Go to Create → Video ✅ Step 2: Choose your model Select Seedance 2.0 This model delivers smooth motion, dynamic camera angles, realistic lighting, and strong scene consistency. ✅ Step 3: Write a strong prompt Your prompt is your director. Be specific: Camera movement (pan, zoom, tracking shot) Lighting (golden hour, neon, soft shadows) Environment (city, desert, luxury interior) ✅ Step 4: Add a reference image (optional) Use this to guide style, composition, or subject for more control and consistency. ✅ Step 5: Hit Generate In seconds, your idea turns into a cinematic sequence. 🚀 Pro tip: If it’s not perfect, tweak your prompt — small changes = big difference. From idea → cinematic video in seconds.

🎬 Want to create cinematic AI videos in seconds? Here’s exactly how I do it using Seedance 2.0 on Yapper: Most people get this wrong… your results depend on how well you direct the AI. Here’s the workflow 👇 ✅ Step 1: Open Yapper Go to Create → Video ✅ Step 2: Choose your model Select Seedance 2.0 This model delivers smooth motion, dynamic camera angles, realistic lighting, and strong scene consistency. ✅ Step 3: Write a strong prompt Your prompt is your director. Be specific: Camera movement (pan, zoom, tracking shot) Lighting (golden hour, neon, soft shadows) Environment (city, desert, luxury interior) ✅ Step 4: Add a reference image (optional) Use this to guide style, composition, or subject for more control and consistency. ✅ Step 5: Hit Generate In seconds, your idea turns into a cinematic sequence. 🚀 Pro tip: If it’s not perfect, tweak your prompt — small changes = big difference. From idea → cinematic video in seconds.

Kuria | AI

43,685 次观看 • 2 个月前

Imagine if your way of thinking - your edge, your taste, your strategy - could be turned into a high-performance worker. Not a copy of you. Something better. An agent that acts on your judgment at scale, powered by superintelligent systems and refined through real-world results. That’s what Fraction AI makes possible. It launches today on Base mainnet. The core idea is simple: You create AI agents based on your own way of approaching problems. These agents compete on live tasks - writing, coding, finance, whatever - get feedback, learn from their performance, and improve over time. The better they get, the more they win. And so do you. No code required. Just your insight. Why now? Until now, building agents like this took huge teams and even bigger budgets. But with Fraction, anyone can do it. You can test ideas instantly. You can iterate fast. You can build a fleet of smart workers that evolve through competition. And it works. 30M+ sessions on testnet 320K users 1.2M agents already competing How it works? Agents join sessions within a Space - a domain like finance, writing, or games. Each session runs as a series of competitive rounds. In every round, agents try to generate the best solution to a task. Their outputs are scored by a decentralized network of AI judges trained to evaluate quality for that domain. The top agents in each round earn rewards from the pooled entry fees. The losers get to learn. Feedback from each round helps them adjust and improve, and every session becomes a training loop. What it means? Fraction is a decentralized intelligence economy - a system where your ideas become agents, and agents earn by proving they work. You don’t need credentials or code. Just a clear point of view. If your thinking holds up under pressure, your agents will rise. This kind of AI used to live in corporate labs, built by PhDs with massive compute. Now anyone with a smart idea and an internet connection can build agents that compete, learn, and earn on their behalf.

Imagine if your way of thinking - your edge, your taste, your strategy - could be turned into a high-performance worker. Not a copy of you. Something better. An agent that acts on your judgment at scale, powered by superintelligent systems and refined through real-world results. That’s what Fraction AI makes possible. It launches today on Base mainnet. The core idea is simple: You create AI agents based on your own way of approaching problems. These agents compete on live tasks - writing, coding, finance, whatever - get feedback, learn from their performance, and improve over time. The better they get, the more they win. And so do you. No code required. Just your insight. Why now? Until now, building agents like this took huge teams and even bigger budgets. But with Fraction, anyone can do it. You can test ideas instantly. You can iterate fast. You can build a fleet of smart workers that evolve through competition. And it works. 30M+ sessions on testnet 320K users 1.2M agents already competing How it works? Agents join sessions within a Space - a domain like finance, writing, or games. Each session runs as a series of competitive rounds. In every round, agents try to generate the best solution to a task. Their outputs are scored by a decentralized network of AI judges trained to evaluate quality for that domain. The top agents in each round earn rewards from the pooled entry fees. The losers get to learn. Feedback from each round helps them adjust and improve, and every session becomes a training loop. What it means? Fraction is a decentralized intelligence economy - a system where your ideas become agents, and agents earn by proving they work. You don’t need credentials or code. Just a clear point of view. If your thinking holds up under pressure, your agents will rise. This kind of AI used to live in corporate labs, built by PhDs with massive compute. Now anyone with a smart idea and an internet connection can build agents that compete, learn, and earn on their behalf.

Fraction AI

67,782 次观看 • 1 年前

i built a tool that lets you clone ANY video with AI saw a movie scene you want to recreate? a commercial? a music video? just upload it to this tool and get the exact structured prompt to generate it in sora 2 you no longer need to guess how to describe what you want to generate. show the tool the reference and it engineers the prompt for you. the tool analyzes: - camera movements and angles - lighting and color grading - scene composition and timing then converts everything into sora-optimized prompt structure this is how you recreate any visual style without prompt engineering experience RT + reply 'TOOL' and i'll send you access (must follow so i can dm)

i built a tool that lets you clone ANY video with AI saw a movie scene you want to recreate? a commercial? a music video? just upload it to this tool and get the exact structured prompt to generate it in sora 2 you no longer need to guess how to describe what you want to generate. show the tool the reference and it engineers the prompt for you. the tool analyzes: - camera movements and angles - lighting and color grading - scene composition and timing then converts everything into sora-optimized prompt structure this is how you recreate any visual style without prompt engineering experience RT + reply 'TOOL' and i'll send you access (must follow so i can dm)

Miko

42,909 次观看 • 9 个月前

Introducing Buzzy Canvas -- Your AI co-director The first infinite Canvas which is truly agentic. Unlike other canvas, where you spend 4 hours building one video, Buzzy agents inspire detailed storyline, build moodboards, and draft masterpiece in one click. you can: - keep unlimited subjects consistent 🤯 - switch view-angle, control lighting seamlessly - generate 20 variation in seconds - all the top AI models like Seedance, GPT image, Kling, Veo, Nano Banana in one canvas Detailed tutorial below:

Introducing Buzzy Canvas -- Your AI co-director The first infinite Canvas which is truly agentic. Unlike other canvas, where you spend 4 hours building one video, Buzzy agents inspire detailed storyline, build moodboards, and draft masterpiece in one click. you can: - keep unlimited subjects consistent 🤯 - switch view-angle, control lighting seamlessly - generate 20 variation in seconds - all the top AI models like Seedance, GPT image, Kling, Veo, Nano Banana in one canvas Detailed tutorial below:

Buzzy Now

1,916,480 次观看 • 1 个月前

New agentic short course! Multi AI Agent Systems with crewAI, built with CrewAI's founder and CEO João Moura. In this course, you'll learn how to break down complex tasks into subtasks for multiple AI agents, each playing a specialized role, to execute. For example, to generate a research report, you might have researcher, writer, and quality assurance agents collaborate. You'll define the roles, expectations, and interactions between the agents—like a manager organizing a team. You'll work with key agentic AI techniques like role-playing, tool use, memory, guardrails, and cross-agent collaboration. And you'll build your own multi-agent systems that can tackle complex tasks. I think you'll find it both productive and fun to design agents and watch them collaborate to get things done. Let me know what you think! I believe multi-agent architectures will drive significant progress in AI systems. Please sign up here!

New agentic short course! Multi AI Agent Systems with crewAI, built with CrewAI's founder and CEO João Moura. In this course, you'll learn how to break down complex tasks into subtasks for multiple AI agents, each playing a specialized role, to execute. For example, to generate a research report, you might have researcher, writer, and quality assurance agents collaborate. You'll define the roles, expectations, and interactions between the agents—like a manager organizing a team. You'll work with key agentic AI techniques like role-playing, tool use, memory, guardrails, and cross-agent collaboration. And you'll build your own multi-agent systems that can tackle complex tasks. I think you'll find it both productive and fun to design agents and watch them collaborate to get things done. Let me know what you think! I believe multi-agent architectures will drive significant progress in AI systems. Please sign up here!

Andrew Ng

349,977 次观看 • 2 年前

Ali's Wan 2.2 is absolutely insane 🤯 Map your voice and motion into a video clip featuring anyone with a single photo. In this video I shape-shift between an avatar woman, Matthew Mcconaughey, Anne Hathaway, Martin Luther King Jr., and Bane. Tools used: - Wan 2.2 for video gen - FreePik for image gen - ElevenLabs for voice cloning and voice changing Want the full prompting guide and a step-by-step tutorial video where I walk through exactly how I built it? Like, RT, and comment "REALITY", and I'll DM it to you. (must be following so I can dm you)

David Roberts

126,130 次观看 • 8 个月前

I tried to create product showcase using invideo "Money Shot" trend... Here is how you can do this: - Get 4-8 images of your product - Go to invideo -> Agents & Models -> Trends -> Choose Money shot - Select different templates accordingly - Just type one line prompt and hit Generate PS:) Like, REPOST, and bookmark I have used this for 4 templates (See the full VIDEO)

I tried to create product showcase using invideo "Money Shot" trend... Here is how you can do this: - Get 4-8 images of your product - Go to invideo -> Agents & Models -> Trends -> Choose Money shot - Select different templates accordingly - Just type one line prompt and hit Generate PS:) Like, REPOST, and bookmark I have used this for 4 templates (See the full VIDEO)

Beginnersblog

29,753 次观看 • 7 个月前

Don't just learn how to build AI agents. Instead, learn how to build... ...full-stack AI agents that actually work inside apps. Yet, the main challenge is transitioning them from local setups to user-facing apps. Here are some key factors to consider: - How do you embed a seamless UI? - How do you enable user control when necessary? - How do you unify the agent’s state and actions with the app? CoAgents by CopilotKit🪁 solves this. It is the only framework that offers all the essential Agentic front-end building blocks to set up full-stack agentic apps. And most recently, they have extended full-stack development support to Agents built with CrewAI ✨. This includes both regular Agentic Crews and Flows. Check their GitHub repo it's 100% open-source, I have shared link in the next tweet! With this update, here's what you can do with Agents built using CrewAI: ↳ Chat: Add chat components for interaction. ↳ Human-in-the-loop: Real-time human approvals. ↳ Predictive state updates: Preview upcoming changes. ↳ Agentic UI: Agents generate UI components dynamically. This way, you can give your CrewAI Agents a frontend brain—so they don’t just work behind the scenes, they become an integral part of the user experience. The video below showcases a full-stack CrewAI Agent built in 20 minutes using CoAgents. Impressive, right?

Don't just learn how to build AI agents. Instead, learn how to build... ...full-stack AI agents that actually work inside apps. Yet, the main challenge is transitioning them from local setups to user-facing apps. Here are some key factors to consider: - How do you embed a seamless UI? - How do you enable user control when necessary? - How do you unify the agent’s state and actions with the app? CoAgents by CopilotKit🪁 solves this. It is the only framework that offers all the essential Agentic front-end building blocks to set up full-stack agentic apps. And most recently, they have extended full-stack development support to Agents built with CrewAI ✨. This includes both regular Agentic Crews and Flows. Check their GitHub repo it's 100% open-source, I have shared link in the next tweet! With this update, here's what you can do with Agents built using CrewAI: ↳ Chat: Add chat components for interaction. ↳ Human-in-the-loop: Real-time human approvals. ↳ Predictive state updates: Preview upcoming changes. ↳ Agentic UI: Agents generate UI components dynamically. This way, you can give your CrewAI Agents a frontend brain—so they don’t just work behind the scenes, they become an integral part of the user experience. The video below showcases a full-stack CrewAI Agent built in 20 minutes using CoAgents. Impressive, right?

Akshay 🚀

27,153 次观看 • 1 年前