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Release Day 🎉 Meet OpenApps — a pure-Python, open-source ecosystem for stress-testing UI agents at scale. Runs on a single CPU. Generates thousands of unique UI variations. And it reveals just how fragile today’s SOTA agents are. (Yes, even GPT-4 and Claude struggle.)

Dr. Karen Ullrich

5,166 subscribers

10,079 次观看 • 8 个月前 •via X (Twitter)

Anya Rossi• Live Now

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🤯ByteDance just Open Sourced UI-TARS - 2 SOTA models (7B & 72B) + a PC/MacOS app to control your computer with vLMS And they are not messing around, beating GPT-4o and Claude, SOTA across 10 benchmarks Will you be installing this on your pc?

🤯ByteDance just Open Sourced UI-TARS - 2 SOTA models (7B & 72B) + a PC/MacOS app to control your computer with vLMS And they are not messing around, beating GPT-4o and Claude, SOTA across 10 benchmarks Will you be installing this on your pc?

Alex Volkov

69,738 次观看 • 1 年前

We’ve been working on exploring building a library of open source UI patterns for agents on mobile, starting with Live Activities for observability. It’s available on GitHub and compatible with 🦞OpenClaw.

We’ve been working on exploring building a library of open source UI patterns for agents on mobile, starting with Live Activities for observability. It’s available on GitHub and compatible with 🦞OpenClaw.

Aaron Abentheuer

190,140 次观看 • 5 个月前

1/4 We’re releasing MAI-UI—a family of foundation GUI agents. It natively integrates MCP tool use, agent user interaction, device–cloud collaboration, and online RL, establishing state-of-the-art results in general GUI grounding and mobile GUI navigation, surpassing Gemini-2.5-Pro, Seed1.8, and UI-Tars-2 on AndroidWorld. To meet real-world deployment constrains, MAI-UI includes a full-spectrum of sizes, including 2B, 8B, 32B and 235B-A22B variants. We are publicly releasing two models: MAI-UI-2B and MAI-UI-8B.

1/4 We’re releasing MAI-UI—a family of foundation GUI agents. It natively integrates MCP tool use, agent user interaction, device–cloud collaboration, and online RL, establishing state-of-the-art results in general GUI grounding and mobile GUI navigation, surpassing Gemini-2.5-Pro, Seed1.8, and UI-Tars-2 on AndroidWorld. To meet real-world deployment constrains, MAI-UI includes a full-spectrum of sizes, including 2B, 8B, 32B and 235B-A22B variants. We are publicly releasing two models: MAI-UI-2B and MAI-UI-8B.

Tongyi Lab

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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 the gap. Inside: ☑️ Free Hosted MCP with Agent Skills. ☑️ 1,019 open-source shadcn/ui components, free forever. ☑️ 17 open-source ReUI primitives(Data Grid, Filters,File Upload, Stepper and more). ☑️ 485 Pro blocks across over 50 categories. ☑️ 562 hand-crafted interactive icons in 4 styles. ☑️ Default Base UI and legacy Radix by WorkOS versions. Real credit to shadcn for the ecosystem! The registry model changed how we all build and ship UI, and ReUI stands on that foundation. Below is a thread on the 10 things we're most proud of👇

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 the gap. Inside: ☑️ Free Hosted MCP with Agent Skills. ☑️ 1,019 open-source shadcn/ui components, free forever. ☑️ 17 open-source ReUI primitives(Data Grid, Filters,File Upload, Stepper and more). ☑️ 485 Pro blocks across over 50 categories. ☑️ 562 hand-crafted interactive icons in 4 styles. ☑️ Default Base UI and legacy Radix by WorkOS versions. Real credit to shadcn for the ecosystem! The registry model changed how we all build and ship UI, and ReUI stands on that foundation. Below is a thread on the 10 things we're most proud of👇

ReUI

42,577 次观看 • 1 个月前

Framer Agents are really good at turning a picture into functional web UI. Just show the section you want, add a bit of context, and it’ll rebuild it into a fully functional section you can keep editing.

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Framer

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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.

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.

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Suno just got replaced entirely🤯 Someone built ACE-Step UI, a Spotify-style interface for ACE-Step 1.5 that generates full songs with vocals up to 4+ minutes, completely locally on your own GPU. 100% Free. Open-Source and Local

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68,047 次观看 • 16 天前

Today, we’re excited to announce and launch Reflex Cloud on Product Hunt! Reflex is an open-source framework for building and deploying data and AI web apps in pure Python. Frontend and Backend in Pure Python: No JavaScript required! With Reflex Cloud, you can now deploy, manage, and scale your Python apps with just a single command! If you're a Python developer, an upvote or a share would mean a lot to us :)💪❤️

Today, we’re excited to announce and launch Reflex Cloud on Product Hunt! Reflex is an open-source framework for building and deploying data and AI web apps in pure Python. Frontend and Backend in Pure Python: No JavaScript required! With Reflex Cloud, you can now deploy, manage, and scale your Python apps with just a single command! If you're a Python developer, an upvote or a share would mean a lot to us :)💪❤️

Reflex

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If you use Claude Code, install this immediately. It's called Claude Code Templates. It adds an entire ecosystem of AI tools to Claude. Here's what you get: 1. Agent Teams Visual dashboards showing how multiple AI agents collaborate on code. 2. Specialized Agents Front-end developers, backend engineers, QA testers, marketers. 3. MCP Integrations Connect Claude to external tools and workflows. 4. Real-Time Monitoring Track AI development sessions and performance analytics. 5. Automation Scheduling Run tasks automatically every day. Example: You can schedule an agent team to run daily at 5AM. It researches trends, generates content, and builds workflows. All automatically. The best part? Everything is free and open source.

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107,310 次观看 • 5 个月前

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We're on the precipice of a ChatGPT moment for AI agents on-chain. Thousands of agents interacting at scale. Blockchain and AI are the two most disruptive, highest-growth areas of technology going forward. We're deeply focused on their intersection. The combined growth is even stronger. Cosmo Jiang Jay Yu 🐟 The Rollup Andy

Pantera Capital

10,480 次观看 • 4 个月前

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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!

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86,515 次观看 • 3 个月前

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Claude Code creator: "Opus 5 can run for days, weeks, even months. It just doesn’t stop. You don’t even need scaffolding. Claude now maintaining itself. 30 routines running every day. Hundreds of agents sometimes thousands." In this 30-minute talk, Boris Cherny at Y Combinator reveals the latest setup used by the Claude team Worth more than a $500 AI course on the internet. Watch it today, then read the article on graph engineering with Claude below.

Codez

106,704 次观看 • 16 天前

Anthropic's Head of Claude Code: "I don't have a to-do list anymore. Claude just builds everything" he runs a tree of thousands of agents. agents prompting agents prompting agents one engineer set up a routine that auto-fixes any bug report not responded to in 5 hours. merges the easy ones himself Boris shipped a feature with an edge case. before he could fix it - another engineer's routine already had a PR up "Claude tells me this all the time. someone else has already fixed it. there's always another Claude working on it" 17 minutes. free. the most honest look at how the Claude Code team actually works bookmark & watch ↓

Anthropic's Head of Claude Code: "I don't have a to-do list anymore. Claude just builds everything" he runs a tree of thousands of agents. agents prompting agents prompting agents one engineer set up a routine that auto-fixes any bug report not responded to in 5 hours. merges the easy ones himself Boris shipped a feature with an edge case. before he could fix it - another engineer's routine already had a PR up "Claude tells me this all the time. someone else has already fixed it. there's always another Claude working on it" 17 minutes. free. the most honest look at how the Claude Code team actually works bookmark & watch ↓

Carnage

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I built a Three.js game director skill system for Codex & Claude Code to help agents create more polished playable browser games. It guides gameplay loops, graphics, HUD/UI, debugging, QA, and optional Tripo ElevenLabs Nano Banana 2 Lite 3D/image/audio assets with API keys. Open source, get the skills in this thread 👇

I built a Three.js game director skill system for Codex & Claude Code to help agents create more polished playable browser games. It guides gameplay loops, graphics, HUD/UI, debugging, QA, and optional Tripo ElevenLabs Nano Banana 2 Lite 3D/image/audio assets with API keys. Open source, get the skills in this thread 👇

Majid Manzarpour

144,639 次观看 • 1 个月前

49 x402 integrations. 157 ERC-8004 agents registered. One hackathon. 🦞 At "OpenClaw on Bitcoin" in SF, a full house of builders used GOAT Network to do something we haven't seen at scale before: agents actively hiring other agents, paying each other autonomously, and building reputation onchain - all in a single day. This is what the agentic economy on Bitcoin looks like - and it's just getting started. Still building? Our virtual hackathon is still open:

49 x402 integrations. 157 ERC-8004 agents registered. One hackathon. 🦞 At "OpenClaw on Bitcoin" in SF, a full house of builders used GOAT Network to do something we haven't seen at scale before: agents actively hiring other agents, paying each other autonomously, and building reputation onchain - all in a single day. This is what the agentic economy on Bitcoin looks like - and it's just getting started. Still building? Our virtual hackathon is still open:

GOAT Network 🟡

18,378 次观看 • 5 个月前

I tested how a team of four agents could work together to build something For this example, I had 4 agents take on the roles of product manager, designer, QA, and engineer It was surprisingly fun to watch them work through the problem together and challenge each other along the way. I found the QA agent especially useful, since that extra pass at the end caught a lot of small UI bugs. Bloome

I tested how a team of four agents could work together to build something For this example, I had 4 agents take on the roles of product manager, designer, QA, and engineer It was surprisingly fun to watch them work through the problem together and challenge each other along the way. I found the QA agent especially useful, since that extra pass at the end caught a lot of small UI bugs. Bloome

Ian Zelbo

51,339 次观看 • 1 个月前

Claude Skills might be the biggest upgrade to AI agents so far! Some say it's even bigger than MCP. I've been testing skills for the past 3-4 days, and they're solving a problem most people don't talk about: agents just keep forgetting everything. In this video, I'll share everything I've learned so far. It covers: > The core idea (skills as SOPs for agents) > Anatomy of a skill > Skills vs. MCP vs. Projects vs. Subagents > Building your own skill > Hands-on example Skills are the early signs of continual learning, and they can change how we work with agents forever! Here's everything you need to know:

Claude Skills might be the biggest upgrade to AI agents so far! Some say it's even bigger than MCP. I've been testing skills for the past 3-4 days, and they're solving a problem most people don't talk about: agents just keep forgetting everything. In this video, I'll share everything I've learned so far. It covers: > The core idea (skills as SOPs for agents) > Anatomy of a skill > Skills vs. MCP vs. Projects vs. Subagents > Building your own skill > Hands-on example Skills are the early signs of continual learning, and they can change how we work with agents forever! Here's everything you need to know:

Akshay 🚀

286,218 次观看 • 9 个月前

This Chinese developer launched 6 agents under 1 orchestrator, and they run his UI design agency at $32,000 a month on their own. He built a system of 6 agents on Claude Sonnet 4.6 that single-handedly runs his agency for UI auditing and redesign for SaaS startups and e-commerce. No contractors, no project manager, and no team. Just him, a MacBook, and 1 API key. Traditional design agencies out of Shenzhen keep teams of 8 people on salaries for the same volume, while he keeps only API tokens. 6 agents work through a single orchestrator on Claude Code Router. Usage is about 4 million tokens a day, the average API bill is just $480 a month. All 6 go through MCP servers and write shared state to the file system, without shared state in memory and without race conditions. And here is the system prompt he gave the orchestrator before launch: "you are the orchestrator of a one-man UI agency. you delegate read-only research tasks to 5 sub-agents and own all writes. sub-agents: // Hunter (finds SaaS and e-commerce sites with outdated UI) // Auditor (runs each site through Lighthouse, accessibility, and design system checks) // Pitcher (writes cold outreach and redesign proposals with before/after screenshots) // Splitter (breaks accepted projects into typed milestones) // Designer (generates Figma mockups and Tailwind components) // Checker (runs evals on every artifact before it leaves the harness). you never let 2 sub-agents touch 1 file. you stop and request human approval only when an invoice exceeds $5,000 or when the design system eval score drops below 0.88." Meaning the system knows exactly what it is and within what boundaries it operates. It knows it is supposed to find clients on its own. It knows it is supposed to write proposals with screenshots and mockups without intervention. It knows the human only plugs in when the amounts go above $5,000 or when the design system eval does not converge. → The system runs 24 hours a day → Hunter finds about 200 sites with outdated UI a day → Auditor runs each one through Lighthouse and WCAG → Pitcher prepares about 28 personalized proposals with before/after screenshots → Splitter breaks 3 accepted projects per week into milestones → Designer generates mockups and components, Checker runs evals on every artifact And only when the invoice breaks $5,000 or the eval drops below 0.88 does the orchestrator wake the human. Here is what the system outputs in his log during 1 of the sessions: "hunter report, tuesday: 213 sites found, 31 with last redesign before 2020, 14 with Lighthouse score below 65, 6 with active redesign RFP. passing top 6 to auditor." "pitcher: 27 cold outreach sent with before/after screenshots, 5 replies, 3 discovery calls scheduled. passing to splitter." "designer: milestone 2 of Lotus Tea Co redesign complete. Figma frames exported to /Users/dev/agency/clients/lotus/v2. checker running design system evals." "eval flag: proposal for $6,800 exceeds the approved limit of $5,000. sending for manual review." He has no remote server. No separate backend. Just a local file sandbox in /Users/dev/agency, an MCP router, and an API key to Claude. Out of everything I have seen this year, this is the cleanest one-person UI design agency: $480 in, about $32,000 out, and between them 6 prompts and 1 file system.

This Chinese developer launched 6 agents under 1 orchestrator, and they run his UI design agency at $32,000 a month on their own. He built a system of 6 agents on Claude Sonnet 4.6 that single-handedly runs his agency for UI auditing and redesign for SaaS startups and e-commerce. No contractors, no project manager, and no team. Just him, a MacBook, and 1 API key. Traditional design agencies out of Shenzhen keep teams of 8 people on salaries for the same volume, while he keeps only API tokens. 6 agents work through a single orchestrator on Claude Code Router. Usage is about 4 million tokens a day, the average API bill is just $480 a month. All 6 go through MCP servers and write shared state to the file system, without shared state in memory and without race conditions. And here is the system prompt he gave the orchestrator before launch: "you are the orchestrator of a one-man UI agency. you delegate read-only research tasks to 5 sub-agents and own all writes. sub-agents: // Hunter (finds SaaS and e-commerce sites with outdated UI) // Auditor (runs each site through Lighthouse, accessibility, and design system checks) // Pitcher (writes cold outreach and redesign proposals with before/after screenshots) // Splitter (breaks accepted projects into typed milestones) // Designer (generates Figma mockups and Tailwind components) // Checker (runs evals on every artifact before it leaves the harness). you never let 2 sub-agents touch 1 file. you stop and request human approval only when an invoice exceeds $5,000 or when the design system eval score drops below 0.88." Meaning the system knows exactly what it is and within what boundaries it operates. It knows it is supposed to find clients on its own. It knows it is supposed to write proposals with screenshots and mockups without intervention. It knows the human only plugs in when the amounts go above $5,000 or when the design system eval does not converge. → The system runs 24 hours a day → Hunter finds about 200 sites with outdated UI a day → Auditor runs each one through Lighthouse and WCAG → Pitcher prepares about 28 personalized proposals with before/after screenshots → Splitter breaks 3 accepted projects per week into milestones → Designer generates mockups and components, Checker runs evals on every artifact And only when the invoice breaks $5,000 or the eval drops below 0.88 does the orchestrator wake the human. Here is what the system outputs in his log during 1 of the sessions: "hunter report, tuesday: 213 sites found, 31 with last redesign before 2020, 14 with Lighthouse score below 65, 6 with active redesign RFP. passing top 6 to auditor." "pitcher: 27 cold outreach sent with before/after screenshots, 5 replies, 3 discovery calls scheduled. passing to splitter." "designer: milestone 2 of Lotus Tea Co redesign complete. Figma frames exported to /Users/dev/agency/clients/lotus/v2. checker running design system evals." "eval flag: proposal for $6,800 exceeds the approved limit of $5,000. sending for manual review." He has no remote server. No separate backend. Just a local file sandbox in /Users/dev/agency, an MCP router, and an API key to Claude. Out of everything I have seen this year, this is the cleanest one-person UI design agency: $480 in, about $32,000 out, and between them 6 prompts and 1 file system.

Blaze

56,062 次观看 • 3 个月前

The person who built Claude Code just explained how he is using it "Now I just have an army of agents that are doing stuff" Only 18 minutes, the best Claude guide on the internet right now. Boris Cherny covered: > how Claude Code is moving beyond just engineers > routines for CI, code review, and daily workflows > why the loop is the next big leap > working with hundreds of agents at once everything is already running through agents, ignoring this is not an option that's exactly why I wrote a full guide on how to build one yourself with Claude Code article below

The person who built Claude Code just explained how he is using it "Now I just have an army of agents that are doing stuff" Only 18 minutes, the best Claude guide on the internet right now. Boris Cherny covered: > how Claude Code is moving beyond just engineers > routines for CI, code review, and daily workflows > why the loop is the next big leap > working with hundreds of agents at once everything is already running through agents, ignoring this is not an option that's exactly why I wrote a full guide on how to build one yourself with Claude Code article below

Anatoli Kopadze

16,858 次观看 • 2 个月前

Introducing the Generative UI Research Canvas ✨ Every agent needs a powerful UI Combine LangChain, Tavily, Tako and CopilotKit🪁 and you get a LangChain-based research agent with a Generative UI-driven frontend. When agents can explore the underlying data, research becomes more trustworthy and useful. How does this work? - LangChain manages the agent workflow and tool orchestration. - Tako converts retrieved data into structured, visual components - Tavily provides agentic search - CopilotKit renders those components in the UI and keeps the user in the loop while the agent is working So instead of a boring and static report, the output is an interactive research canvas that evolves as the agent runs. 2026 is the year where users want to see the output, not read it... Generative UI makes this possible for agentic applications. Tutorial: Open-Source Repo: Docs:

Introducing the Generative UI Research Canvas ✨ Every agent needs a powerful UI Combine LangChain, Tavily, Tako and CopilotKit🪁 and you get a LangChain-based research agent with a Generative UI-driven frontend. When agents can explore the underlying data, research becomes more trustworthy and useful. How does this work? - LangChain manages the agent workflow and tool orchestration. - Tako converts retrieved data into structured, visual components - Tavily provides agentic search - CopilotKit renders those components in the UI and keeps the user in the loop while the agent is working So instead of a boring and static report, the output is an interactive research canvas that evolves as the agent runs. 2026 is the year where users want to see the output, not read it... Generative UI makes this possible for agentic applications. Tutorial: Open-Source Repo: Docs:

Atai Barkai

11,997 次观看 • 6 个月前