Загрузка видео...

Не удалось загрузить видео

На главную

Today ReUI Pro goes live! 🔥 Design-forward shadcn/ui platform with MCP fluency for agents. AI made UI generation instant. It didn't make it production-ready. You still spend hours picking blocks, adjusting details, and fine-tuning the last mile. We spent six months building the agent-ready layer for shadcn/ui that closes...

reui_io's profile picture

ReUI

7,743 subscribers

40,748 просмотров • 25 дней назад •via X (Twitter)

Наука и технологииОбразованиеИскусство
LIVE

Anya Rossi• Live Now

Private livecam show

Комментарии: 0

Нет доступных комментариев

Здесь появятся комментарии из оригинального поста

Похожие видео

The wait is over, Guys! 😌 Finally, we've shipped Shadcn Studio 🎉 An open-source registry with copy-and-paste components, blocks, and templates! - Paired with a powerful theme editor to craft, customize, and ship faster. 🚀 - Shadcn Theme Editor & Component variants ✅ - Theme Import/Export ✅ - Save Shadcn Themes ✅ - Shadcn Component Variants ⌛ - Shadcn Figma UI Kit ⌛ - Shadcn Blocks & Templates ⌛ - Figma Plugin, AI, MCP, LLM and more.. ⌛ ➡️ Check it out here: Huge thanks to Anand Patel, Heet Bhalodiya & Aayush Makwana for their incredible work on this! 🙌 Special thanks to shadcn Sahaj 🫱🏼‍🫲🏼 We’d love to hear your thoughts and feedback from the community! Let us know what you think. 😀
1:42

The wait is over, Guys! 😌 Finally, we've shipped Shadcn Studio 🎉 An open-source registry with copy-and-paste components, blocks, and templates! - Paired with a powerful theme editor to craft, customize, and ship faster. 🚀 - Shadcn Theme Editor & Component variants ✅ - Theme Import/Export ✅ - Save Shadcn Themes ✅ - Shadcn Component Variants ⌛ - Shadcn Figma UI Kit ⌛ - Shadcn Blocks & Templates ⌛ - Figma Plugin, AI, MCP, LLM and more.. ⌛ ➡️ Check it out here: Huge thanks to Anand Patel, Heet Bhalodiya & Aayush Makwana for their incredible work on this! 🙌 Special thanks to shadcn Sahaj 🫱🏼‍🫲🏼 We’d love to hear your thoughts and feedback from the community! Let us know what you think. 😀

Ajay Patel

85,908 просмотров • 1 год назад

We are witnessing a fundamental shift in how software is built. Over the decades, UI was static and predictable. But AI is changing the fundamental nature of the interface. We are moving away from fixed UI and toward a new standard: Generative UI. Generative UI lets agents participate in the UI, not just the conversation. It’s a dynamic participant in the user experience. Depending on the complexity of your app, Generative UI generally falls into three categories: 1. Static (AG-UI): UI is chosen from a fixed set of hand-built components 2. Open-Ended (MCP Apps): agent sends arbitrary content like raw HTML or iframes for total flexibility. 3. Declarative (A2UI): agent and frontend communicate via a structured UI spec for dynamic widgets and forms. The AG-UI Protocol by CopilotKit is the real breakthrough. AG-UI acts as the universal transport layer. It provides the integrations and middleware for every Gen-UI spec, making it trivial to bring dynamic components into your existing app. CopilotKit provides the go-to ecosystem for building agentic applications and is the React client of AG-UI. It is designed to be deeply integrated, allowing developers to bring their own frameworks or protocols to the table. Play around with different types of Generative UI here → Check out the video attached to see Generative UI in action. Appreciate the CopilotKit team for collaborating with me on this deep dive.
0:33

We are witnessing a fundamental shift in how software is built. Over the decades, UI was static and predictable. But AI is changing the fundamental nature of the interface. We are moving away from fixed UI and toward a new standard: Generative UI. Generative UI lets agents participate in the UI, not just the conversation. It’s a dynamic participant in the user experience. Depending on the complexity of your app, Generative UI generally falls into three categories: 1. Static (AG-UI): UI is chosen from a fixed set of hand-built components 2. Open-Ended (MCP Apps): agent sends arbitrary content like raw HTML or iframes for total flexibility. 3. Declarative (A2UI): agent and frontend communicate via a structured UI spec for dynamic widgets and forms. The AG-UI Protocol by CopilotKit is the real breakthrough. AG-UI acts as the universal transport layer. It provides the integrations and middleware for every Gen-UI spec, making it trivial to bring dynamic components into your existing app. CopilotKit provides the go-to ecosystem for building agentic applications and is the React client of AG-UI. It is designed to be deeply integrated, allowing developers to bring their own frameworks or protocols to the table. Play around with different types of Generative UI here → Check out the video attached to see Generative UI in action. Appreciate the CopilotKit team for collaborating with me on this deep dive.

Pratham

33,920 просмотров • 6 месяцев назад

Introducing primeui. Build sites fast. Fear no code. Available today • 200+ handcrafted marketing UI components and templates • Sitemap planner • Prototype + experiment on an infinite canvas • Clean export to Next.js, Tailwind + shadcn/ui compatible • Not just landing pages: build Blog, Docs, Pricing, and more Coming soon • MCP • Skills • AI Designer We started Prime UI because we believe site builders that do not output real code end up slowing you down. So instead of building another “app builder,” we focused on marketing sites, the work we know best after 10+ years of designing and building high-end websites at Pixel Point. Human expertise combined with AI scalability is what we believe in. Prime UI isn’t here (yet) to fully replace your stack, but it is already capable of saving you hundreds of hours in prototyping and the early phases of your next site project. What’s next: MCP: continuously add new pages to your site from Prime UI, aligned with your project setup and style. Skills: an AI-agent-friendly concentrate of our years of website design and development expertise. It is time to ditch old habits. Fear no code and build sites fast, ready for the AI era.
0:30

Introducing primeui. Build sites fast. Fear no code. Available today • 200+ handcrafted marketing UI components and templates • Sitemap planner • Prototype + experiment on an infinite canvas • Clean export to Next.js, Tailwind + shadcn/ui compatible • Not just landing pages: build Blog, Docs, Pricing, and more Coming soon • MCP • Skills • AI Designer We started Prime UI because we believe site builders that do not output real code end up slowing you down. So instead of building another “app builder,” we focused on marketing sites, the work we know best after 10+ years of designing and building high-end websites at Pixel Point. Human expertise combined with AI scalability is what we believe in. Prime UI isn’t here (yet) to fully replace your stack, but it is already capable of saving you hundreds of hours in prototyping and the early phases of your next site project. What’s next: MCP: continuously add new pages to your site from Prime UI, aligned with your project setup and style. Skills: an AI-agent-friendly concentrate of our years of website design and development expertise. It is time to ditch old habits. Fear no code and build sites fast, ready for the AI era.

Alex Barashkov

72,100 просмотров • 5 месяцев назад

Anthropic's most viral feature is now open-source! Until now, Anthropic's Generative UI capabilities only existed inside its own products. CopilotKit🪁 just shipped Open Generative UI, an open-source implementation of Claude Artifacts that works in any app. The agent generates HTML/SVG at runtime, and CopilotKit streams it token-by-token into a sandboxed iframe inside the app's chat. So the user can watch the UI assemble itself in real time, not after the full response is ready. The sandbox is fully isolated with no access to the parent app, the DOM, or user data. So if the agent hallucinates broken markup or unexpected JavaScript, nothing leaks outside the iframe. Under the hood, the agent does not select from pre-built components. Instead, it generates arbitrary visuals from scratch every time. The output is unconstrained by default, but you can shape it by defining prompt-based skills that teach the agent specific visual formats or guidelines. For instance, a skill prompt can guide the agent toward producing a Chart.js dashboard with proper axis labels and responsive sizing, or an interactive 3D model with rotation controls. The video below shows this in action, and the output quality you see actually comes from the skills layer. Open Generative UI runs on AG-UI, so it works out of the box with LangGraph, CrewAI, Mastra, Google ADK, AWS Strands, and more. It also ships with a standalone MCP server that plugs into Claude Code, Cursor, or any MCP-compatible client. And the entire stack is built on top of CopilotKit, the open-source frontend framework for agents and generative UI. 30k+ GitHub stars, with SDKs for React, Next.js, Angular, and Vue. I have shared the GitHub repo and a live playground in the replies!
0:53

Anthropic's most viral feature is now open-source! Until now, Anthropic's Generative UI capabilities only existed inside its own products. CopilotKit🪁 just shipped Open Generative UI, an open-source implementation of Claude Artifacts that works in any app. The agent generates HTML/SVG at runtime, and CopilotKit streams it token-by-token into a sandboxed iframe inside the app's chat. So the user can watch the UI assemble itself in real time, not after the full response is ready. The sandbox is fully isolated with no access to the parent app, the DOM, or user data. So if the agent hallucinates broken markup or unexpected JavaScript, nothing leaks outside the iframe. Under the hood, the agent does not select from pre-built components. Instead, it generates arbitrary visuals from scratch every time. The output is unconstrained by default, but you can shape it by defining prompt-based skills that teach the agent specific visual formats or guidelines. For instance, a skill prompt can guide the agent toward producing a Chart.js dashboard with proper axis labels and responsive sizing, or an interactive 3D model with rotation controls. The video below shows this in action, and the output quality you see actually comes from the skills layer. Open Generative UI runs on AG-UI, so it works out of the box with LangGraph, CrewAI, Mastra, Google ADK, AWS Strands, and more. It also ships with a standalone MCP server that plugs into Claude Code, Cursor, or any MCP-compatible client. And the entire stack is built on top of CopilotKit, the open-source frontend framework for agents and generative UI. 30k+ GitHub stars, with SDKs for React, Next.js, Angular, and Vue. I have shared the GitHub repo and a live playground in the replies!

Akshay 🚀

86,515 просмотров • 2 месяцев назад

Anthropic's in trouble, again! They spent years building what's now fully open-source. What made Claude feel different from a normal app is that the agent could act inside the interface instead of only talking in a chat box. For instance, Claude Artifacts let an agent render real UI, charts, dashboards, and interactive components that assemble live inside the response. Every major AI product tried to replicate it. But the problem was that unlike reasoning, planning, tool-calling, etc., none of it shipped natively with LangGraph, CrewAI, or Google ADK. So teams started building an owned version that required engineering the entire interface layer from scratch. Most teams, however, just settled for shipping the agent as a backend API in a chat box since rendering the UI is only one piece of it. To actually make it work, the interface layer also needed real-time streaming, state kept in sync between agent and UI, conversations that persist across sessions, and reconnection when a user refreshes mid-run. CopilotKit🪁 is now the only open-source framework that actually lets you build your own full-stack Claude-like apps. It decouples the agent from the interface, talking over AG-UI (an open protocol for agent-to-user communication). Being a standard protocol, the frontend never needs to know whether it is talking to a LangGraph or a CrewAI agent. You can change the backend anytime and the UI will never notice. In practice, CopilotKit's interface layer gives several pre-implemented React building blocks that wire the agent directly into the app, like: - generative UI, so the agent renders real components instead of text - chat windows, sidebars, and popups, or a fully headless setup - shared state, so the agent and app stay in sync - human-in-the-loop approvals, where the agent waits before acting - persistent threads that store the whole session, including the agent-user interactions and generated UI, not just text And because that full history is captured, those interactions can feed a self-learning layer that also improves the agent from real usage over time. The interface layer that Anthropic spent years engineering in-house is now literally available to any developer/team. CopilotKit is open-source with 30k+ GitHub stars, and AG-UI, the protocol underneath, is already supported across every major agent framework: LangGraph, CrewAI, Mastra, Google ADK, and more. CopilotKit GitHub repo → (don't forget to star it ⭐ ) If you want to go deeper, I found a detailed breakdown by Shubham Saboo recently on the three Generative UI patterns, with implementation. Read it below.
0:23

Anthropic's in trouble, again! They spent years building what's now fully open-source. What made Claude feel different from a normal app is that the agent could act inside the interface instead of only talking in a chat box. For instance, Claude Artifacts let an agent render real UI, charts, dashboards, and interactive components that assemble live inside the response. Every major AI product tried to replicate it. But the problem was that unlike reasoning, planning, tool-calling, etc., none of it shipped natively with LangGraph, CrewAI, or Google ADK. So teams started building an owned version that required engineering the entire interface layer from scratch. Most teams, however, just settled for shipping the agent as a backend API in a chat box since rendering the UI is only one piece of it. To actually make it work, the interface layer also needed real-time streaming, state kept in sync between agent and UI, conversations that persist across sessions, and reconnection when a user refreshes mid-run. CopilotKit🪁 is now the only open-source framework that actually lets you build your own full-stack Claude-like apps. It decouples the agent from the interface, talking over AG-UI (an open protocol for agent-to-user communication). Being a standard protocol, the frontend never needs to know whether it is talking to a LangGraph or a CrewAI agent. You can change the backend anytime and the UI will never notice. In practice, CopilotKit's interface layer gives several pre-implemented React building blocks that wire the agent directly into the app, like: - generative UI, so the agent renders real components instead of text - chat windows, sidebars, and popups, or a fully headless setup - shared state, so the agent and app stay in sync - human-in-the-loop approvals, where the agent waits before acting - persistent threads that store the whole session, including the agent-user interactions and generated UI, not just text And because that full history is captured, those interactions can feed a self-learning layer that also improves the agent from real usage over time. The interface layer that Anthropic spent years engineering in-house is now literally available to any developer/team. CopilotKit is open-source with 30k+ GitHub stars, and AG-UI, the protocol underneath, is already supported across every major agent framework: LangGraph, CrewAI, Mastra, Google ADK, and more. CopilotKit GitHub repo → (don't forget to star it ⭐ ) If you want to go deeper, I found a detailed breakdown by Shubham Saboo recently on the three Generative UI patterns, with implementation. Read it below.

Avi Chawla

457,604 просмотров • 2 месяцев назад

Announcing the Style Guide in primeui. Customize your project theme with a shadcn-compatible token system. Preview it live with as many variants as you need on the canvas. Export clean, production-ready Next.js code in minutes. Prime UI gives you a strong foundation for your website, whether you build it yourself or with AI. It includes 200+ UI components, all crafted by humans with pixel-perfect quality, a solid design system, and clean code. Plan your site with the Sitemap tool. Explore content, layout ideas, and storytelling in Prototype. Shape the look and feel in Style Guide, then export everything to Next.js. You get more than just landing pages. You get a consistent foundation for your entire site, including Blog, Docs, Pricing, and more, without having to worry about architecture, content blocks, SEO, or performance. Available today.
0:25

Announcing the Style Guide in primeui. Customize your project theme with a shadcn-compatible token system. Preview it live with as many variants as you need on the canvas. Export clean, production-ready Next.js code in minutes. Prime UI gives you a strong foundation for your website, whether you build it yourself or with AI. It includes 200+ UI components, all crafted by humans with pixel-perfect quality, a solid design system, and clean code. Plan your site with the Sitemap tool. Explore content, layout ideas, and storytelling in Prototype. Shape the look and feel in Style Guide, then export everything to Next.js. You get more than just landing pages. You get a consistent foundation for your entire site, including Blog, Docs, Pricing, and more, without having to worry about architecture, content blocks, SEO, or performance. Available today.

Alex Barashkov

32,330 просмотров • 4 месяцев назад

🤯 Building computer-vision applications is never going to be the same again. As Andrej Karpathy says, "English is going to be the new programming-language", and we just made that happen for programming visual AI. 👨‍💻 This past month, the VLM Run team cooked up something magical that I'm especially proud of - a natural-language interface to build and run computer vision workflows (object-detection, OCR, parsing, and so much more) – all via our new VLM Run MCP server. 🔌 It works with any MCP-compatible AI agent (Claude, OpenAI, Cursor, etc.) and abstracts complex visual workflows into simple English commands. No need to write Python or fiddle with bespoke computer-vision code – just say what you want your agent to do, and it does it. Here’s a glimpse into what you can build today ( ☑️ Image AI agents – run detection, tagging, blurring, cropping, and more traditional CV tasks with English ☑️ Document AI agents – extract totals from invoices, search visual content, or act on what’s found ☑️ Video AI agents – understand scene content, trim clips, add subtitles, and create ready-to-share edits ☑️ And best-of-all, it's fully programmable with English. 🧑‍🍳 MCP Showcase: 📚 MCP Cookbooks: 📚 MCP Docs: #computervision #ai #agents #llm #vlm #mcp #vibecoding #genai
1:49

🤯 Building computer-vision applications is never going to be the same again. As Andrej Karpathy says, "English is going to be the new programming-language", and we just made that happen for programming visual AI. 👨‍💻 This past month, the VLM Run team cooked up something magical that I'm especially proud of - a natural-language interface to build and run computer vision workflows (object-detection, OCR, parsing, and so much more) – all via our new VLM Run MCP server. 🔌 It works with any MCP-compatible AI agent (Claude, OpenAI, Cursor, etc.) and abstracts complex visual workflows into simple English commands. No need to write Python or fiddle with bespoke computer-vision code – just say what you want your agent to do, and it does it. Here’s a glimpse into what you can build today ( ☑️ Image AI agents – run detection, tagging, blurring, cropping, and more traditional CV tasks with English ☑️ Document AI agents – extract totals from invoices, search visual content, or act on what’s found ☑️ Video AI agents – understand scene content, trim clips, add subtitles, and create ready-to-share edits ☑️ And best-of-all, it's fully programmable with English. 🧑‍🍳 MCP Showcase: 📚 MCP Cookbooks: 📚 MCP Docs: #computervision #ai #agents #llm #vlm #mcp #vibecoding #genai

Sudeep Pillai

17,789 просмотров • 1 год назад

AG-UI makes building agentic applications dramatically easier. Here's how it works. This is a model for a simple chatbot: User → LLM → Response But interactive agents that render UI, pause for approvals, and ask users for input need a much more complex model. When building these agents, a response from the LLM will include a series of state changes as the agent runs: • Agent started a task • Agent called a tool • Agent updated its state • Agent streams these tokens • Agent is waiting on a human • Agent is resuming the task The Agent-User Interaction Protocol (AG-UI) treats the LLM response as a stream of events rather than a text endpoint. In practice, here is what you get as an agent runs: 1. Lifecycle events so your UI knows where the agent is. 2. Text messages that stream tokens. 3. Tool calls so your UI can prefill a form with any required arguments. 4. State updates that keep your UI in sync with the agent. 5. Special events for human approvals, rich media, and custom needs. All of these events travel over standard transports (SSE, WebSockets, or plain HTTP) as JSON. As a result, you can build a frontend that stays in sync with the agent's progress without having to invent a custom process to make this happen. For example, building a human-in-the-loop workflow becomes an off-the-shelf component you can integrate rather than build from scratch. CopilotKit🪁 is the creator of AG-UI, and you can use it when building frontend applications pretty much anywhere: • React • Angular • Vue • React Native • Slack • Teams • Discord • WhatsApp • Telegram Here is the link for you to check it out: Thanks to the CopilotKit team for partnering with me on this post.
0:22

AG-UI makes building agentic applications dramatically easier. Here's how it works. This is a model for a simple chatbot: User → LLM → Response But interactive agents that render UI, pause for approvals, and ask users for input need a much more complex model. When building these agents, a response from the LLM will include a series of state changes as the agent runs: • Agent started a task • Agent called a tool • Agent updated its state • Agent streams these tokens • Agent is waiting on a human • Agent is resuming the task The Agent-User Interaction Protocol (AG-UI) treats the LLM response as a stream of events rather than a text endpoint. In practice, here is what you get as an agent runs: 1. Lifecycle events so your UI knows where the agent is. 2. Text messages that stream tokens. 3. Tool calls so your UI can prefill a form with any required arguments. 4. State updates that keep your UI in sync with the agent. 5. Special events for human approvals, rich media, and custom needs. All of these events travel over standard transports (SSE, WebSockets, or plain HTTP) as JSON. As a result, you can build a frontend that stays in sync with the agent's progress without having to invent a custom process to make this happen. For example, building a human-in-the-loop workflow becomes an off-the-shelf component you can integrate rather than build from scratch. CopilotKit🪁 is the creator of AG-UI, and you can use it when building frontend applications pretty much anywhere: • React • Angular • Vue • React Native • Slack • Teams • Discord • WhatsApp • Telegram Here is the link for you to check it out: Thanks to the CopilotKit team for partnering with me on this post.

Santiago

17,438 просмотров • 1 месяц назад

We are excited to announce OctoMCP (beta) — our Solana Programs Model Context Protocol (MCP) that allows anyone to build, test, and deploy Solana programs within minutes. Solana With OctoMCP, users can easily download all essential files including idl.json (for frontend integration), program .so (for deployment), and the full source code. After that, you can generate a custom UI using just a couple of prompts. In the coming days, we’ll be releasing detailed tutorials on how to use OctoMCP and the UI generator to quickly build powerful Solana programs and dApps. We’ll also be hosting dApp Generation Competitions, where users can showcase their creativity and win exciting rewards. Huge thanks to the boltdiy team for making the Stackblitz open-source version — our UI generator is built on top of boltdiy.
1:19

We are excited to announce OctoMCP (beta) — our Solana Programs Model Context Protocol (MCP) that allows anyone to build, test, and deploy Solana programs within minutes. Solana With OctoMCP, users can easily download all essential files including idl.json (for frontend integration), program .so (for deployment), and the full source code. After that, you can generate a custom UI using just a couple of prompts. In the coming days, we’ll be releasing detailed tutorials on how to use OctoMCP and the UI generator to quickly build powerful Solana programs and dApps. We’ll also be hosting dApp Generation Competitions, where users can showcase their creativity and win exciting rewards. Huge thanks to the boltdiy team for making the Stackblitz open-source version — our UI generator is built on top of boltdiy.

OctonetAI

434,501 просмотров • 1 год назад

🙌HERE WE GO - 8 minutes of our vision in plain sight! Three years of bootstrapping, hard work, and a dream come true, all to get our project to a place where you can see that we are building something incredible. Our game is finally ready for our crowdfunding campaign!! 🔮 Operation Safe Place Defense is more than just a game – it’s a mission. Enter a world torn apart by The Ripping, where dimensions collide and heroes rise to defend the innocent. Battle through the In-Between, face off against powerful forces, and experience a story like never before. 💥 🔥 Key Features: ☑️Tactical tower defense with customizable turrets 🔧 ☑️Epic boss fights with dynamic AI 🎮 ☑️Fully Conversational AI NPCs that react to your choices and shape the story 💬 ☑️Third-Person and RTS modes for total control over your gameplay 👾 ☑️A world driven by The Ripping and the conflict within The In-Between ⚔️ ☑️NFTs that unlock exclusive in-game rewards 🎁 ☑️Single-player and Multiplayer modes for personalized or team-based experiences 🎮 ☑️Cross-platform MMO to play with friends across all devices 🌍 ☑️MOBA mode for intense, competitive battles and strategy 🏆 ☑️Blockchain-powered rewards and in-game assets for true ownership 🔗 ☑️Disruptive AI-driven game mechanics that adapt to player strategies 🧠 ☑️Real-time procedural world generation that makes every playthrough unique 🌍 ☑️Augmented reality integration for an immersive experience beyond the screen 📱 💥 JUMP INTO OUR PLEDGE SITE! 💥 We’re launching our Pledge Store, and YOU have the chance to help bring Operation Safe Place Defense to life. Your support unlocks epic rewards, exclusive NFTs, and helps us fight back against the dark forces invading our world through The Ripping and The In-Between. 🌍 💬 Watch the video, explore the features, and pledge today! Let’s make history — together. 💥 LOVE YOU!!
7:58

🙌HERE WE GO - 8 minutes of our vision in plain sight! Three years of bootstrapping, hard work, and a dream come true, all to get our project to a place where you can see that we are building something incredible. Our game is finally ready for our crowdfunding campaign!! 🔮 Operation Safe Place Defense is more than just a game – it’s a mission. Enter a world torn apart by The Ripping, where dimensions collide and heroes rise to defend the innocent. Battle through the In-Between, face off against powerful forces, and experience a story like never before. 💥 🔥 Key Features: ☑️Tactical tower defense with customizable turrets 🔧 ☑️Epic boss fights with dynamic AI 🎮 ☑️Fully Conversational AI NPCs that react to your choices and shape the story 💬 ☑️Third-Person and RTS modes for total control over your gameplay 👾 ☑️A world driven by The Ripping and the conflict within The In-Between ⚔️ ☑️NFTs that unlock exclusive in-game rewards 🎁 ☑️Single-player and Multiplayer modes for personalized or team-based experiences 🎮 ☑️Cross-platform MMO to play with friends across all devices 🌍 ☑️MOBA mode for intense, competitive battles and strategy 🏆 ☑️Blockchain-powered rewards and in-game assets for true ownership 🔗 ☑️Disruptive AI-driven game mechanics that adapt to player strategies 🧠 ☑️Real-time procedural world generation that makes every playthrough unique 🌍 ☑️Augmented reality integration for an immersive experience beyond the screen 📱 💥 JUMP INTO OUR PLEDGE SITE! 💥 We’re launching our Pledge Store, and YOU have the chance to help bring Operation Safe Place Defense to life. Your support unlocks epic rewards, exclusive NFTs, and helps us fight back against the dark forces invading our world through The Ripping and The In-Between. 🌍 💬 Watch the video, explore the features, and pledge today! Let’s make history — together. 💥 LOVE YOU!!

ᴜɴᴄʟᴇ ꜰᴜɴᴋ | OSP/Citadel

14,660 просмотров • 1 год назад

Don't just learn how to build AI agents. Instead, learn how to build full-stack AI agents that actually work inside apps. Today, we have reliable tools to create decent agentic workflows. 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? This is where I would like to introduce CoAgents by CopilotKit🪁, they offer all the essential building blocks for full-stack agentic apps. The best part, they're completely open-source with an MIT Licence ✨ (link to their GitHub repo in the next tweet) The latest updates to CoAgents include: ↳ Human-in-the-loop: Real-time human approvals. ↳ LangGraph integration: Supports LG Studio, Platform, and self-hosted. ↳ Agentic UI: Agents generate UI components dynamically. ↳ Predictive state updates: Preview upcoming changes. ↳ Enhanced documentation: Clear, step-by-step guidance. The video below showcases a full-stack Travel Planner app built in 20 minutes using CoAgents. Impressive, right? Big thanks to CopilotKit for partnering on today's post and showing what's possible with their powerful Agent-development framework.
1:11

Don't just learn how to build AI agents. Instead, learn how to build full-stack AI agents that actually work inside apps. Today, we have reliable tools to create decent agentic workflows. 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? This is where I would like to introduce CoAgents by CopilotKit🪁, they offer all the essential building blocks for full-stack agentic apps. The best part, they're completely open-source with an MIT Licence ✨ (link to their GitHub repo in the next tweet) The latest updates to CoAgents include: ↳ Human-in-the-loop: Real-time human approvals. ↳ LangGraph integration: Supports LG Studio, Platform, and self-hosted. ↳ Agentic UI: Agents generate UI components dynamically. ↳ Predictive state updates: Preview upcoming changes. ↳ Enhanced documentation: Clear, step-by-step guidance. The video below showcases a full-stack Travel Planner app built in 20 minutes using CoAgents. Impressive, right? Big thanks to CopilotKit for partnering on today's post and showing what's possible with their powerful Agent-development framework.

Akshay 🚀

90,819 просмотров • 1 год назад

Finally got a Vision Pro, and now I can test all the prototypes that I made for VisionOS last year 🥳 My overall impression of Vision Pro: a piece of art. The user experience is very smooth and solid. Developing for VisionOS is a pure joy. No glitches and bugs so far. The real-time interactive updates in the preview are *chef kiss*. People who have been in XR for a while are not really spoiled by things that just work. In this regard, the Vision Pro experience feels magical. We are always happy when a system's behaviour matches our expectations (a little dopamine reward to celebrate the accuracy of our predictions). The greatest features for Spatial Designers: UI panel material highlights, UI panel shadow, and UI panel translucency. It finally makes spatial transformations meaningful (for free!). You see how your UI engages with the environment using visual affordances that people get used to. And yeah, as you can see, I recorded this video at night, and it worked great. So it's excellent news for all-night hackers 😁 #handtracking #directmanipulation #spatialcomputing #spatialdesign #visionpro #visionos #swiftui #mixedreality
0:39

Finally got a Vision Pro, and now I can test all the prototypes that I made for VisionOS last year 🥳 My overall impression of Vision Pro: a piece of art. The user experience is very smooth and solid. Developing for VisionOS is a pure joy. No glitches and bugs so far. The real-time interactive updates in the preview are *chef kiss*. People who have been in XR for a while are not really spoiled by things that just work. In this regard, the Vision Pro experience feels magical. We are always happy when a system's behaviour matches our expectations (a little dopamine reward to celebrate the accuracy of our predictions). The greatest features for Spatial Designers: UI panel material highlights, UI panel shadow, and UI panel translucency. It finally makes spatial transformations meaningful (for free!). You see how your UI engages with the environment using visual affordances that people get used to. And yeah, as you can see, I recorded this video at night, and it worked great. So it's excellent news for all-night hackers 😁 #handtracking #directmanipulation #spatialcomputing #spatialdesign #visionpro #visionos #swiftui #mixedreality

Oleg Frolov

156,622 просмотров • 2 лет назад

🔥 New LangChain Academy Course: LangChain Essentials (Python & TypeScript) 🔥 Learn the basics of LangChain – our open source framework that makes it easy to start building agents with any model provider. Last week, we released LangChain 1.0. We’ve completely rewritten LangChain to be opinionated, focused, and powered by LangGraph’s runtime. It includes a new `create_agent` abstraction to build agents quickly, middleware for flexibility, and standard content blocks that work across any model provider. In this quickstart course, you'll learn how to: - Build an agent with the `create_agent` abstraction - Use LangChain’s core building blocks: Models, Messages, Memory, and Tools - Customize your agent with middleware - Debug and test your agent with LangSmith Observability & Evaluation Enroll for free ➡️
1:37

🔥 New LangChain Academy Course: LangChain Essentials (Python & TypeScript) 🔥 Learn the basics of LangChain – our open source framework that makes it easy to start building agents with any model provider. Last week, we released LangChain 1.0. We’ve completely rewritten LangChain to be opinionated, focused, and powered by LangGraph’s runtime. It includes a new `create_agent` abstraction to build agents quickly, middleware for flexibility, and standard content blocks that work across any model provider. In this quickstart course, you'll learn how to: - Build an agent with the `create_agent` abstraction - Use LangChain’s core building blocks: Models, Messages, Memory, and Tools - Customize your agent with middleware - Debug and test your agent with LangSmith Observability & Evaluation Enroll for free ➡️

LangChain

50,475 просмотров • 9 месяцев назад

Introducing the Knowledge Agent Template Vercel dropped the Chat SDK so we built a full template on top of it that you can deploy in one click. Here's what's inside: • Add your sources: GitHub repos, YouTube, etc. directly from the UI • File-based agent using grep/find/cat • Powered by Vercel Workflow + Sandbox • Multi-platform: web chat, GitHub bot, Discord bot, and more • Comes with skills to edit your project, add sources, create agents Deploy, add a source, ask questions. Your knowledge base is live and always in sync. This is just scratching the surface. There's way more packed in this template, and I'll be breaking down the features all week. Links in the below 👇
0:29

Introducing the Knowledge Agent Template Vercel dropped the Chat SDK so we built a full template on top of it that you can deploy in one click. Here's what's inside: • Add your sources: GitHub repos, YouTube, etc. directly from the UI • File-based agent using grep/find/cat • Powered by Vercel Workflow + Sandbox • Multi-platform: web chat, GitHub bot, Discord bot, and more • Comes with skills to edit your project, add sources, create agents Deploy, add a source, ask questions. Your knowledge base is live and always in sync. This is just scratching the surface. There's way more packed in this template, and I'll be breaking down the features all week. Links in the below 👇

Hugo

54,481 просмотров • 5 месяцев назад

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?
0:25

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 просмотров • 1 год назад

Today, Sierra is releasing Ghostwriter, our agent for building agents. With Ghostwriter, you can create an AI agent for your customer experience — one that can chat, pick up the phone, speak dozens of languages, take action on your systems of record, and be protected with industry-leading guardrails — simply by having a conversation. No clicking, no forms, no menus. Codex and Claude Code have transformed how we build software, making it possible for software engineers to orchestrate and review the work rather than doing all the work themselves. We think the same transformation will happen for all software. Rather than every enterprise app having a web app for humans and an API for automation, every software platform’s UI will be an agent that can do the work on your behalf. I recorded a demo of my building and optimizing an agent with Ghostwriter so you can see how powerful and easy it is to use. It’s completely changed the way our early adopters build agents, and it’s changed the way I think about the software industry. Let me know what you think, and, if you’re interested in trying it out at your business, please reach out directly.
10:43

Today, Sierra is releasing Ghostwriter, our agent for building agents. With Ghostwriter, you can create an AI agent for your customer experience — one that can chat, pick up the phone, speak dozens of languages, take action on your systems of record, and be protected with industry-leading guardrails — simply by having a conversation. No clicking, no forms, no menus. Codex and Claude Code have transformed how we build software, making it possible for software engineers to orchestrate and review the work rather than doing all the work themselves. We think the same transformation will happen for all software. Rather than every enterprise app having a web app for humans and an API for automation, every software platform’s UI will be an agent that can do the work on your behalf. I recorded a demo of my building and optimizing an agent with Ghostwriter so you can see how powerful and easy it is to use. It’s completely changed the way our early adopters build agents, and it’s changed the way I think about the software industry. Let me know what you think, and, if you’re interested in trying it out at your business, please reach out directly.

Bret Taylor

1,061,349 просмотров • 4 месяцев назад

New course: MCP: Build Rich-Context AI Apps with Anthropic. Learn to build AI apps that access tools, data, and prompts using the Model Context Protocol in this short course, created in partnership with Anthropic Anthropic and taught by Elie Schoppik Elie Schoppik, its Head of Technical Education. Connecting AI applications to external systems that bring rich context to LLM-based applications has often meant writing custom integrations for each use case. MCP is an open protocol that standardizes how LLMs access tools, data, and prompts from external sources, and simplifies how you provide context to your LLM-based applications. For example, you can provide context via third-party tools that let your LLM make API calls to search the web, access data from local docs, retrieve code from a GitHub repo, and so on. MCP, developed by Anthropic, is based on a client-server architecture that defines the communication details between an MCP client, hosted inside the AI application, and an MCP server that exposes tools, resources, and prompt templates. The server can be a subprocess launched by the client that runs locally or an independent process running remotely. In this hands-on course, you'll learn the core architecture behind MCP. You’ll create an MCP-compatible chatbot, build and deploy an MCP server, and connect the chatbot to your MCP server and other open-source servers. Here’s what you’ll do: - Understand why MCP makes AI development less fragmented and standardizes connections between AI applications and external data sources - Learn the core components of the client-server architecture of MCP and the underlying communication mechanism - Build a chatbot with custom tools for searching academic papers, and transform it into an MCP-compatible application - Build a local MCP server that exposes tools, resources, and prompt templates using FastMCP, and test it using MCP Inspector - Create an MCP client inside your chatbot to dynamically connect to your server - Connect your chatbot to reference servers built by Anthropic’s MCP team, such as filesystem, which implements filesystem operations, and fetch, which extracts contents from the web as markdown - Configure Claude Desktop to connect to your server and others, and explore how it abstracts away the low-level logic of MCP clients - Deploy your MCP server remotely and test it with the Inspector or other MCP-compatible applications - Learn about the roadmap for future MCP development, such as multi-agent architecture, MCP registry API, server discovery, authorization, and authentication MCP is an exciting and important technology that lets you build rich-context AI applications that connect to a growing ecosystem of MCP servers, with minimal integration work. Please sign up here!
2:43

New course: MCP: Build Rich-Context AI Apps with Anthropic. Learn to build AI apps that access tools, data, and prompts using the Model Context Protocol in this short course, created in partnership with Anthropic Anthropic and taught by Elie Schoppik Elie Schoppik, its Head of Technical Education. Connecting AI applications to external systems that bring rich context to LLM-based applications has often meant writing custom integrations for each use case. MCP is an open protocol that standardizes how LLMs access tools, data, and prompts from external sources, and simplifies how you provide context to your LLM-based applications. For example, you can provide context via third-party tools that let your LLM make API calls to search the web, access data from local docs, retrieve code from a GitHub repo, and so on. MCP, developed by Anthropic, is based on a client-server architecture that defines the communication details between an MCP client, hosted inside the AI application, and an MCP server that exposes tools, resources, and prompt templates. The server can be a subprocess launched by the client that runs locally or an independent process running remotely. In this hands-on course, you'll learn the core architecture behind MCP. You’ll create an MCP-compatible chatbot, build and deploy an MCP server, and connect the chatbot to your MCP server and other open-source servers. Here’s what you’ll do: - Understand why MCP makes AI development less fragmented and standardizes connections between AI applications and external data sources - Learn the core components of the client-server architecture of MCP and the underlying communication mechanism - Build a chatbot with custom tools for searching academic papers, and transform it into an MCP-compatible application - Build a local MCP server that exposes tools, resources, and prompt templates using FastMCP, and test it using MCP Inspector - Create an MCP client inside your chatbot to dynamically connect to your server - Connect your chatbot to reference servers built by Anthropic’s MCP team, such as filesystem, which implements filesystem operations, and fetch, which extracts contents from the web as markdown - Configure Claude Desktop to connect to your server and others, and explore how it abstracts away the low-level logic of MCP clients - Deploy your MCP server remotely and test it with the Inspector or other MCP-compatible applications - Learn about the roadmap for future MCP development, such as multi-agent architecture, MCP registry API, server discovery, authorization, and authentication MCP is an exciting and important technology that lets you build rich-context AI applications that connect to a growing ecosystem of MCP servers, with minimal integration work. Please sign up here!

Andrew Ng

142,010 просмотров • 1 год назад

Introducing SuperDesign.Dev The first open-source Design Agent that lives inside Cursor, Windsurf, or any IDE: 👉 Instantly spin up 10 different design in parallel 👉 Explore, fork, and iterate 10x faster 👉 Create new or iterate on your existing UI — all fully local JackJack and I shipped this as a quick weekend experiment to validate a hunch: design is the killer use case for parallel agents. Currently at V0.005 — a simple customization of the Claude Code SDK + static HTML generation. (We might be the first product powered by the Anthropic Claude Code SDK!) More coming soon: web app, mobile app, SVG generation, and more. GitHub: Try it out: ----- Upvote on Hacker News ❤️:
0:55

Introducing SuperDesign.Dev The first open-source Design Agent that lives inside Cursor, Windsurf, or any IDE: 👉 Instantly spin up 10 different design in parallel 👉 Explore, fork, and iterate 10x faster 👉 Create new or iterate on your existing UI — all fully local JackJack and I shipped this as a quick weekend experiment to validate a hunch: design is the killer use case for parallel agents. Currently at V0.005 — a simple customization of the Claude Code SDK + static HTML generation. (We might be the first product powered by the Anthropic Claude Code SDK!) More coming soon: web app, mobile app, SVG generation, and more. GitHub: Try it out: ----- Upvote on Hacker News ❤️:

Jason Zhou

49,435 просмотров • 1 год назад

We put 1000s of hours into building open-source camera glasses And now we're giving away everything we learned for free. Checkout this in-depth teardown of the Mentra Live smart glasses. As an OS company, our goal is to spread our OS. That goal encourages us to do crazy things, like build the world's best AI glasses for business and then show you how we made them... all in the name of spreading the OS. It also convinced us to make it 100% open source, so adoption is faster and easier. We're at risk of our competitors easily copypasting our code now... all in the name of spreading our OS. Now we've built the glasses for you, and we've taken the risk, and we've released an open SDK. Now your AI apps and agents can enter the real world on smart glasses. So now, what will you build?
13:19

We put 1000s of hours into building open-source camera glasses And now we're giving away everything we learned for free. Checkout this in-depth teardown of the Mentra Live smart glasses. As an OS company, our goal is to spread our OS. That goal encourages us to do crazy things, like build the world's best AI glasses for business and then show you how we made them... all in the name of spreading the OS. It also convinced us to make it 100% open source, so adoption is faster and easier. We're at risk of our competitors easily copypasting our code now... all in the name of spreading our OS. Now we've built the glasses for you, and we've taken the risk, and we've released an open SDK. Now your AI apps and agents can enter the real world on smart glasses. So now, what will you build?

cayden 凯登

20,460 просмотров • 2 месяцев назад

$VET, #VeFam. In this video, I demonstrate in less than 4:30 minutes how to create an AI agent on veworld(.)ai. Watch me build a Mr. Robot Monologue Writer agent. If you haven't seen Mr. Robot, I suggest you watch it! This is just early bird access. The options for tools and integrations and such are limited, but what exists is already working quite well. The process is easy peasy. The UI is simple, but effective. It asks you for... 1. Role & Purpose 2. Voice & Style 3. Behavior 4. Rules 5. Tags 6. Avatar image 7. Welcome text. 8. Test drive before publication. ... and that's about it. This free version lets you have at most 3 agents, I am told. This implies that there is also a paid version. I'm all for it, because it sounds to me like VeChain is ready to do real business! I am providing feedback to Jérôme Grillères in order to help improve VeChain's AI agent marketplace. I didn't have to set up anything. The web UI is all I needed! The agent is running on Claude Sonnet 3.7. I did not have to provide a Claude API key. We seem to be riding along on VeChain's. I hope there'll be a choice for more models, including ChatGPT, in the future. This is so user friendly, that I can easily imagine that this would take off in a big, big way. I'm definitely building on this, when it goes into production with full features. Even if my own AI agents aren't successful, then I'm sure others' will be. And that means the $VET / $VTHO / $B3TR flywheel is going to take off in a big, big way. I, for one, am here for it. (See the reply below for the listing of the AI agent I just created.)
4:26

$VET, #VeFam. In this video, I demonstrate in less than 4:30 minutes how to create an AI agent on veworld(.)ai. Watch me build a Mr. Robot Monologue Writer agent. If you haven't seen Mr. Robot, I suggest you watch it! This is just early bird access. The options for tools and integrations and such are limited, but what exists is already working quite well. The process is easy peasy. The UI is simple, but effective. It asks you for... 1. Role & Purpose 2. Voice & Style 3. Behavior 4. Rules 5. Tags 6. Avatar image 7. Welcome text. 8. Test drive before publication. ... and that's about it. This free version lets you have at most 3 agents, I am told. This implies that there is also a paid version. I'm all for it, because it sounds to me like VeChain is ready to do real business! I am providing feedback to Jérôme Grillères in order to help improve VeChain's AI agent marketplace. I didn't have to set up anything. The web UI is all I needed! The agent is running on Claude Sonnet 3.7. I did not have to provide a Claude API key. We seem to be riding along on VeChain's. I hope there'll be a choice for more models, including ChatGPT, in the future. This is so user friendly, that I can easily imagine that this would take off in a big, big way. I'm definitely building on this, when it goes into production with full features. Even if my own AI agents aren't successful, then I'm sure others' will be. And that means the $VET / $VTHO / $B3TR flywheel is going to take off in a big, big way. I, for one, am here for it. (See the reply below for the listing of the AI agent I just created.)

₿lackthorne AI

16,562 просмотров • 1 месяц назад