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Tool UI is a React framework for conversation-native UIs. Tools return JSON, and Tool UI renders it as inline, narrated, referenceable surfaces inside messages, optimized for chat width and scroll. It’s schema-first: every surface is driven by a serializable schema with stable IDs, so results stay consistent and reusable.... show more

Tw93

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66,900 次观看 • 5 个月前 •via X (Twitter)

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The AI party just got a major upgrade. 🦞✨ Today, we’re launching AgentHub by PIN AI — the interaction layer where agents finally talk to each other. Let’s be real: ❌ Siloed agents = Garbage out. ✅ Connected agents = Compounding output. We’re not just building a tool; we’re building the Open Agent Network. It’s time to stop the isolation and start the collaboration. Repost if you’re ready for the era of Personal Intelligence Network! 🚀

The AI party just got a major upgrade. 🦞✨ Today, we’re launching AgentHub by PIN AI — the interaction layer where agents finally talk to each other. Let’s be real: ❌ Siloed agents = Garbage out. ✅ Connected agents = Compounding output. We’re not just building a tool; we’re building the Open Agent Network. It’s time to stop the isolation and start the collaboration. Repost if you’re ready for the era of Personal Intelligence Network! 🚀

PIN AI

16,079 次观看 • 5 个月前

just vibe-coded an internal search tool that finds any file on my computer and answers questions related to them. it's a custom Raycast extension powered by Anthropic's MCP (thanks Alex Albert & Marc Klingen for showing me!)—but it works with any LLM for Q&A across my local files. given Raycast’s slick UX + deep integrations, I’ve always thought it’s perfectly positioned to solve internal search like Glean does with enterprises, so I built one. code is in the comments—curious to hear your thoughts

just vibe-coded an internal search tool that finds any file on my computer and answers questions related to them. it's a custom Raycast extension powered by Anthropic's MCP (thanks Alex Albert & Marc Klingen for showing me!)—but it works with any LLM for Q&A across my local files. given Raycast’s slick UX + deep integrations, I’ve always thought it’s perfectly positioned to solve internal search like Glean does with enterprises, so I built one. code is in the comments—curious to hear your thoughts

viet

104,019 次观看 • 1 年前

Turn complex docs into clean, LLM-ready data! Every AI company I've talked to is solving the same problem: how do you build systems that don't hallucinate and back up every answer with proper citations? Tensorlake is a tool that extracts custom-defined structured data from any unstructured document in 3 steps: ↳ Define your schema ↳ Enable citations ↳ Extract You get RAG-ready data with precise citations and bounding boxes. Feed this to your LLM, and you'll generate responses that are citation-backed and fully auditable. This is the difference between a demo and a production system. When your AI can show exactly where it got its information, you move from proof-of-concept to something people can actually trust and deploy. I've shared the Tensorlake GitHub repo in the replies!

Turn complex docs into clean, LLM-ready data! Every AI company I've talked to is solving the same problem: how do you build systems that don't hallucinate and back up every answer with proper citations? Tensorlake is a tool that extracts custom-defined structured data from any unstructured document in 3 steps: ↳ Define your schema ↳ Enable citations ↳ Extract You get RAG-ready data with precise citations and bounding boxes. Feed this to your LLM, and you'll generate responses that are citation-backed and fully auditable. This is the difference between a demo and a production system. When your AI can show exactly where it got its information, you move from proof-of-concept to something people can actually trust and deploy. I've shared the Tensorlake GitHub repo in the replies!

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Strategic Partnership Announcement | Jessie x Fetch.ai 💚 We’ve integrated Fetch.ai’s ASI-1, the world’s first Web3-native LLM, directly into the cognitive layer of Jessie OS. This enables: — On-device reasoning without prompts — Multi-modal agent workflows — Native support for decentralized AI execution With Mixture of Models (MoM) + Mixture of Agents (MoA) architecture, ASI-1 Mini transforms Jessie OS into the first modular AI Operating System capable of executing real-life tasks with precision, context, and autonomy. AI is no longer just a tool — it’s the OS. → Learn more:

Strategic Partnership Announcement | Jessie x Fetch.ai 💚 We’ve integrated Fetch.ai’s ASI-1, the world’s first Web3-native LLM, directly into the cognitive layer of Jessie OS. This enables: — On-device reasoning without prompts — Multi-modal agent workflows — Native support for decentralized AI execution With Mixture of Models (MoM) + Mixture of Agents (MoA) architecture, ASI-1 Mini transforms Jessie OS into the first modular AI Operating System capable of executing real-life tasks with precision, context, and autonomy. AI is no longer just a tool — it’s the OS. → Learn more:

Jessie

99,680 次观看 • 1 年前

🛑 A powerful OSINT tool for exploring global sanctions data If you ever need to check sanctions across different authorities, this tool brings everything together in one place and it’s FREE to use. It’s called Sanctions Explorer, and it provides: ✅ Current + historical sanctions data 📚 ✅ Coverage from major bodies like the UN, EU, OFAC, and more ✅ Clean filtering to track individuals, companies, and entities ✅ A unified view instead of searching each authority separately 🌍 The project was initiated by Archer (a Berkeley-based nonprofit focused on human rights) and later fully developed by the C4ADS Data & Technology team known for their evidence-based research on global conflict and transnational security. If sanctions research is part of your OSINT workflow, this tool is worth bookmarking. And in the next post, I’ll share a quick OSINT tip for checking phone numbers on Telegram Check it out here: __________ P.S. ♻️ Repost if you found this helpful. If you liked this post and would like to learn more methods and techniques to discover information about people, check out my OSINT Mastery course

🛑 A powerful OSINT tool for exploring global sanctions data If you ever need to check sanctions across different authorities, this tool brings everything together in one place and it’s FREE to use. It’s called Sanctions Explorer, and it provides: ✅ Current + historical sanctions data 📚 ✅ Coverage from major bodies like the UN, EU, OFAC, and more ✅ Clean filtering to track individuals, companies, and entities ✅ A unified view instead of searching each authority separately 🌍 The project was initiated by Archer (a Berkeley-based nonprofit focused on human rights) and later fully developed by the C4ADS Data & Technology team known for their evidence-based research on global conflict and transnational security. If sanctions research is part of your OSINT workflow, this tool is worth bookmarking. And in the next post, I’ll share a quick OSINT tip for checking phone numbers on Telegram Check it out here: __________ P.S. ♻️ Repost if you found this helpful. If you liked this post and would like to learn more methods and techniques to discover information about people, check out my OSINT Mastery course

CyberSudo

28,075 次观看 • 6 个月前

LangGraph. CrewAI. Agno. Which one to pick? The good news is that this will not matter soon! Finally, we have a full picture of how the industry is solving this with just three open protocols that work across ALL frameworks. It's not about picking the best framework. Instead, it's about understanding how protocols create interoperability. The Agent Protocol Landscape shows how three complementary protocols are creating a universal language for Agents: > AG-UI (Agent-User Interaction): - The bi-directional connection between agentic backends and frontends. - This is how agents become truly interactive inside your apps, not just as chatbots, but collaborative co-workers. > MCP (Model Context Protocol): - The standard for how agents connect to tools, data, and workflows. > A2A (Agent-to-Agent): - The protocol for multi-agent coordination. - How agents delegate tasks and share intent across systems. These aren't competing standards. They're layers of the same stack and have handshakes with each other. So instead of building point-to-point integrations, you build to protocols. Moreover, you can integrate LangGraph, CrewAI, or Agno into the same frontend, without rewriting your UI logic. These protocols let everything work together. For instance: - Your LangGraph agent pulls data via MCP. - It delegates analysis to a CrewAI agent via A2A. - Results stream to your React app via AG-UI. - Users see real-time collaboration in your interface. This way, you can focus on building agent capabilities instead of integration mechanics. The protocols handle interoperability automatically. CopilotKit unifies this entire stack into one framework so you can build "Cursor for X" style apps without implementing each protocol from scratch. It gives you all three protocols, generative UI support, and production-ready infrastructure in one framework. I have shared this playbook in the replies! It breaks down handshakes, misconceptions, and real examples and shows exactly how to start building.

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Future Stacked

183,095 次观看 • 5 个月前

I found an AI tool that turns a product image into a viral ugc ad Just upload a single product photo → type a description → Hit generate. What came out looked like a $1000 studio shoot. The feature is called "Product Showcase" and it’s available in Arcads. Here's the exact workflow DTC founders are using: → Go to and open composer → Click "See more" → Select "Product Showcase" → Pick any conversion-optimized template → Upload your product image → Add a short description and hit generate These templates are already optimized for 9:16 TikTok and Meta ads. Ready to publish the second they render. While competitors wait 2 weeks for influencer content and $500/clip, smart advertisers are testing 10 variations before lunch for pennies. Same quality. 1% of the cost. Zero delays. I am giving away a step-by-step guide to build an n8n automation that can create unlimited viral ads with Arcads. To get access, like + comment “arcads” and I’ll send it to you.

I found an AI tool that turns a product image into a viral ugc ad Just upload a single product photo → type a description → Hit generate. What came out looked like a $1000 studio shoot. The feature is called "Product Showcase" and it’s available in Arcads. Here's the exact workflow DTC founders are using: → Go to and open composer → Click "See more" → Select "Product Showcase" → Pick any conversion-optimized template → Upload your product image → Add a short description and hit generate These templates are already optimized for 9:16 TikTok and Meta ads. Ready to publish the second they render. While competitors wait 2 weeks for influencer content and $500/clip, smart advertisers are testing 10 variations before lunch for pennies. Same quality. 1% of the cost. Zero delays. I am giving away a step-by-step guide to build an n8n automation that can create unlimited viral ads with Arcads. To get access, like + comment “arcads” and I’ll send it to you.

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67,120 次观看 • 6 个月前

Agentic traces contain perfect information about an agent’s behavior with every plan, action, and retry. But that information gets lost in a sea of JSON. So we built AgentPrism: open source React components that turn traces into visual diagrams for debugging AI agents. You can plug in your OpenTelemetry data and see your agent’s process unfold: messages, tool calls, retries. Quotient AI automatically monitors, analyzes, and improves AI agents. Being able to review traces quickly is paramount for their research, and for their customers. “Dealing with agent traces was one of the biggest frustrations for our researchers and a huge time sink. All of that has gone away since adding AgentPrism, and we’re excited to bring that functionality to our users.” — Julia Neagu

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Evil Martians

101,372 次观看 • 9 个月前

The startup world runs on iMessage. The problem is, the Mac messages app is not designed for productivity and there’s no alternative online. If you’re responding to 100+ texts a day, it’s painful and messages get lost easily. As a weekend project, I used Fable to vibecode an AI-native iMessage wrapper. It has: - AI autodrafting that continually learns from your edits - Time sensitive message detection as a separate section - The ability to archive messages so you can get to inbox zero. It reads from the local chat.db on your Mac, and sends messages via AppleScript, so it’s fully secure since it still uses the iMessage protocol on your computer. At Trajectory, we’re all about using AI to accelerate our company as much as possible as possible, and natively bake in CL into all of our processes. This is just one example of how we operate. If there’s enough interest, I’ll clean up the code and send the GH link!

The startup world runs on iMessage. The problem is, the Mac messages app is not designed for productivity and there’s no alternative online. If you’re responding to 100+ texts a day, it’s painful and messages get lost easily. As a weekend project, I used Fable to vibecode an AI-native iMessage wrapper. It has: - AI autodrafting that continually learns from your edits - Time sensitive message detection as a separate section - The ability to archive messages so you can get to inbox zero. It reads from the local chat.db on your Mac, and sends messages via AppleScript, so it’s fully secure since it still uses the iMessage protocol on your computer. At Trajectory, we’re all about using AI to accelerate our company as much as possible as possible, and natively bake in CL into all of our processes. This is just one example of how we operate. If there’s enough interest, I’ll clean up the code and send the GH link!

Ronak Malde

47,278 次观看 • 18 天前

We’ve rebuilt everything. The website. The experience. The way UOMI speaks to the world. The new website is finally live, redesigned to show exactly what we’re building, how it works, and why it matters. UOMI is the first Layer 1 built for autonomous AI agents, and the new site is your gateway to understanding how this new kind of infrastructure will power the next generation of intelligent applications. From validators to agents, from use cases to docs, everything is now clearer, faster, and easier to navigate, with real applications, working demos, and the full stack of what makes UOMI different. Faster. Smarter. Bolder. Every detail reflects what UOMI is and what it’s becoming. Be part of the community. LIVE NOW.

We’ve rebuilt everything. The website. The experience. The way UOMI speaks to the world. The new website is finally live, redesigned to show exactly what we’re building, how it works, and why it matters. UOMI is the first Layer 1 built for autonomous AI agents, and the new site is your gateway to understanding how this new kind of infrastructure will power the next generation of intelligent applications. From validators to agents, from use cases to docs, everything is now clearer, faster, and easier to navigate, with real applications, working demos, and the full stack of what makes UOMI different. Faster. Smarter. Bolder. Every detail reflects what UOMI is and what it’s becoming. Be part of the community. LIVE NOW.

Uomi

105,855 次观看 • 1 年前

THIS FREE BROWSER TOOL LETS YOU BUILD REAL-TIME DITHER ANIMATIONS AND EXPORT THEM AS VIDEO dither is everywhere right now (Spotify, websites, album covers) but every tool for making it is either a Photoshop plugin or some after effects rabbit hole. this one runs in your browser and gives you full control over the motion. what you can do: > choose from Bayer 4x4, 8x8, halftone, blue noise, crosshatch, diamond, spiral, and ASCII patterns > animate with speed, intensity, scale, and FPS controls > add text or 3D rotation with auto-spin and flatten > tune foreground, background, and accent colors or pick from palettes > post-processing effects layered on top > export as PNG, WebM, MP4, or embed HTML to drop on a site > save presets to reuse your favorite looks resolution presets go up to 1920x1080 for landscape and 1080x1920 for portrait, plus custom sizes. most generative art tools online are single-style gimmicks. this is a full-blown animation studio for one specific aesthetic, and it looks like a real product. link in replies 👇

THIS FREE BROWSER TOOL LETS YOU BUILD REAL-TIME DITHER ANIMATIONS AND EXPORT THEM AS VIDEO dither is everywhere right now (Spotify, websites, album covers) but every tool for making it is either a Photoshop plugin or some after effects rabbit hole. this one runs in your browser and gives you full control over the motion. what you can do: > choose from Bayer 4x4, 8x8, halftone, blue noise, crosshatch, diamond, spiral, and ASCII patterns > animate with speed, intensity, scale, and FPS controls > add text or 3D rotation with auto-spin and flatten > tune foreground, background, and accent colors or pick from palettes > post-processing effects layered on top > export as PNG, WebM, MP4, or embed HTML to drop on a site > save presets to reuse your favorite looks resolution presets go up to 1920x1080 for landscape and 1080x1920 for portrait, plus custom sizes. most generative art tools online are single-style gimmicks. this is a full-blown animation studio for one specific aesthetic, and it looks like a real product. link in replies 👇

Creators Toolbox

28,982 次观看 • 3 个月前

React Native now has its own shadcn/ui equivalent — introducing 𝗡𝗮𝘁𝗶𝘃𝗲𝗨𝗜. If you love the flexibility of copying customisable components directly into your project (avoiding heavy, dependency-laden packages), NativeUI is designed for you. 𝗡𝗮𝘁𝗶𝘃𝗲𝗨𝗜 offers beautifully crafted, accessible components tailored for React Native, following the same copy-paste philosophy as shadcn/ui. Built with 𝗡𝗮𝘁𝗶𝘃𝗲𝗪𝗶𝗻𝗱 for fast, declarative, and flexible styling optimised for React Native. ➡️ 𝗖𝗼𝗽𝘆 𝗰𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁 𝗰𝗼𝗱𝗲 𝗱𝗶𝗿𝗲𝗰𝘁𝗹𝘆 𝗶𝗻𝘁𝗼 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 — no black-box dependencies required. ➡️ 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 𝗮𝗿𝗲 𝗮𝗰𝗰𝗲𝘀𝘀𝗶𝗯𝗹𝗲 𝗯𝘆 𝗱𝗲𝗳𝗮𝘂𝗹𝘁, supporting screen readers and keyboard navigation, and designed to align with native iOS and Android UX patterns. ➡️ 𝗙𝘂𝗹𝗹 𝗰𝗼𝗻𝘁𝗿𝗼𝗹 𝗼𝘃𝗲𝗿 𝘆𝗼𝘂𝗿 𝗨𝗜 without rebuilding common elements like buttons, inputs, or sliders from scratch. ➡️ 𝗖𝗼𝗺𝗽𝗮𝘁𝗶𝗯𝗹𝗲 𝘄𝗶𝘁𝗵 𝗘𝘅𝗽𝗼 𝗮𝗻𝗱 𝘃𝗮𝗻𝗶𝗹𝗹𝗮 𝗥𝗲𝗮𝗰𝘁 𝗡𝗮𝘁𝗶𝘃𝗲 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀, but not yet integrated with Tamagui’s styling system (future support may be planned). ➡️ 𝗦𝘂𝗽𝗽𝗼𝗿𝘁𝘀 𝘁𝗵𝗲𝗺𝗶𝗻𝗴 𝘃𝗶𝗮 𝗡𝗮𝘁𝗶𝘃𝗲𝗪𝗶𝗻𝗱 — though you’ll need to wire it up manually using Tailwind variables, context providers, and config files. Note: The term “install” in the documentation refers to using the shadcn CLI (e.g., npx shadcn@latest add component) to fetch and copy component code into your project, not adding a package to your dependencies. NativeUI isn’t a plug-and-play library; it’s a lightweight toolbox that empowers you to shape your UI with precision and control. 𝗪𝗵𝗮𝘁’𝘀 𝘆𝗼𝘂𝗿 𝗽𝗿𝗲𝗳𝗲𝗿𝗲𝗻𝗰𝗲: npm install a pre-built UI kit for speed, or copy/paste NativeUI components for ultimate customisation? #ReactNative #KeyboardUX #MobileDev #OpenSource #JSDev #Performance #iOSDev #KeyboardExtensions #ReactNativeKeyboard #UIUX #shadcn #nativeui

React Native now has its own shadcn/ui equivalent — introducing 𝗡𝗮𝘁𝗶𝘃𝗲𝗨𝗜. If you love the flexibility of copying customisable components directly into your project (avoiding heavy, dependency-laden packages), NativeUI is designed for you. 𝗡𝗮𝘁𝗶𝘃𝗲𝗨𝗜 offers beautifully crafted, accessible components tailored for React Native, following the same copy-paste philosophy as shadcn/ui. Built with 𝗡𝗮𝘁𝗶𝘃𝗲𝗪𝗶𝗻𝗱 for fast, declarative, and flexible styling optimised for React Native. ➡️ 𝗖𝗼𝗽𝘆 𝗰𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁 𝗰𝗼𝗱𝗲 𝗱𝗶𝗿𝗲𝗰𝘁𝗹𝘆 𝗶𝗻𝘁𝗼 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 — no black-box dependencies required. ➡️ 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 𝗮𝗿𝗲 𝗮𝗰𝗰𝗲𝘀𝘀𝗶𝗯𝗹𝗲 𝗯𝘆 𝗱𝗲𝗳𝗮𝘂𝗹𝘁, supporting screen readers and keyboard navigation, and designed to align with native iOS and Android UX patterns. ➡️ 𝗙𝘂𝗹𝗹 𝗰𝗼𝗻𝘁𝗿𝗼𝗹 𝗼𝘃𝗲𝗿 𝘆𝗼𝘂𝗿 𝗨𝗜 without rebuilding common elements like buttons, inputs, or sliders from scratch. ➡️ 𝗖𝗼𝗺𝗽𝗮𝘁𝗶𝗯𝗹𝗲 𝘄𝗶𝘁𝗵 𝗘𝘅𝗽𝗼 𝗮𝗻𝗱 𝘃𝗮𝗻𝗶𝗹𝗹𝗮 𝗥𝗲𝗮𝗰𝘁 𝗡𝗮𝘁𝗶𝘃𝗲 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀, but not yet integrated with Tamagui’s styling system (future support may be planned). ➡️ 𝗦𝘂𝗽𝗽𝗼𝗿𝘁𝘀 𝘁𝗵𝗲𝗺𝗶𝗻𝗴 𝘃𝗶𝗮 𝗡𝗮𝘁𝗶𝘃𝗲𝗪𝗶𝗻𝗱 — though you’ll need to wire it up manually using Tailwind variables, context providers, and config files. Note: The term “install” in the documentation refers to using the shadcn CLI (e.g., npx shadcn@latest add component) to fetch and copy component code into your project, not adding a package to your dependencies. NativeUI isn’t a plug-and-play library; it’s a lightweight toolbox that empowers you to shape your UI with precision and control. 𝗪𝗵𝗮𝘁’𝘀 𝘆𝗼𝘂𝗿 𝗽𝗿𝗲𝗳𝗲𝗿𝗲𝗻𝗰𝗲: npm install a pre-built UI kit for speed, or copy/paste NativeUI components for ultimate customisation? #ReactNative #KeyboardUX #MobileDev #OpenSource #JSDev #Performance #iOSDev #KeyboardExtensions #ReactNativeKeyboard #UIUX #shadcn #nativeui

The React Native Rewind

118,414 次观看 • 11 个月前

HTML enters 3D! Or vice versa? With the new HTML in Canvas by WICG, we can finally put native DOM elements directly into WebGL/WebGPU scenes. It is experimental for now, but the possibilities for 3D interfaces and special effects are huge. This demo was built using Three.js and Omma AI (tool by Spline ) It’s a fun new way to explore what the web can do! Are you interested in seeing the demo?

HTML enters 3D! Or vice versa? With the new HTML in Canvas by WICG, we can finally put native DOM elements directly into WebGL/WebGPU scenes. It is experimental for now, but the possibilities for 3D interfaces and special effects are huge. This demo was built using Three.js and Omma AI (tool by Spline ) It’s a fun new way to explore what the web can do! Are you interested in seeing the demo?

Gábor Pribék

176,065 次观看 • 3 个月前

today’s experiment: chimera. a little tool that let’s you realtime morph through a design system matrix generated by 4 reference images. this one was inspired by listening to techbimbo talk about her process on Dive Club with Ridd 🤿 and How I AI with claire vo 🖤. her approach to choosing moodboards over prompts made me wonder what might be possible if we applied that same approach to generative UI. and after a few dead ends, the idea turned into chimera. the results are very generic at the moment but I might tune it up if people seem interested. the big reminder for me is how much more inspiring a tool feels when you can explore in realtime instead of waiting for results every time you make a change. lots more things to try in that direction.

today’s experiment: chimera. a little tool that let’s you realtime morph through a design system matrix generated by 4 reference images. this one was inspired by listening to techbimbo talk about her process on Dive Club with Ridd 🤿 and How I AI with claire vo 🖤. her approach to choosing moodboards over prompts made me wonder what might be possible if we applied that same approach to generative UI. and after a few dead ends, the idea turned into chimera. the results are very generic at the moment but I might tune it up if people seem interested. the big reminder for me is how much more inspiring a tool feels when you can explore in realtime instead of waiting for results every time you make a change. lots more things to try in that direction.

River Marchand

201,820 次观看 • 4 个月前

DEMO IS LIVE! Create your Agents, chat, ask questions, and get ready to tokenize them soon. Negotiate with our businessman and grab some cool stuff 👀 What’s the AI Agent Layer? 👇 It’s a dynamic ecosystem of autonomous AI agents. On the platform, you can create AI Agents using data from X and user inputs. Coming soon: Each AI Agent will be tokenized and linked to our native token ($AIFUN). Once the bonding curve reaches 100%, tokens become tradable on DEX. Stay Tuned for our TGE 👆

DEMO IS LIVE! Create your Agents, chat, ask questions, and get ready to tokenize them soon. Negotiate with our businessman and grab some cool stuff 👀 What’s the AI Agent Layer? 👇 It’s a dynamic ecosystem of autonomous AI agents. On the platform, you can create AI Agents using data from X and user inputs. Coming soon: Each AI Agent will be tokenized and linked to our native token ($AIFUN). Once the bonding curve reaches 100%, tokens become tradable on DEX. Stay Tuned for our TGE 👆

AI Agent Layer | AIFUN

73,622 次观看 • 1 年前

🧠 Tired of AI tokens that overpromise and underdeliver? Problem: Many AI tokens look promising on paper — with bold visions and future plans. But in reality, most focus on potential, not delivery. Solution? $GTAI GTAI is built differently. It’s a utility token that powers real, working AI tools inside GT Protocol — already live, already delivering value. Here’s what you actually get as a holder: ✅ AI trading agents that analyze markets and trade for you ✅ Smart staking with dynamic rewards and multiplier boosts ✅ Token rankings driven by real-time AI backtesting ✅ Voice-interactive smart contracts, not just theory, but live 👉 Every feature is unlocked through holding and staking GTAI.

🧠 Tired of AI tokens that overpromise and underdeliver? Problem: Many AI tokens look promising on paper — with bold visions and future plans. But in reality, most focus on potential, not delivery. Solution? $GTAI GTAI is built differently. It’s a utility token that powers real, working AI tools inside GT Protocol — already live, already delivering value. Here’s what you actually get as a holder: ✅ AI trading agents that analyze markets and trade for you ✅ Smart staking with dynamic rewards and multiplier boosts ✅ Token rankings driven by real-time AI backtesting ✅ Voice-interactive smart contracts, not just theory, but live 👉 Every feature is unlocked through holding and staking GTAI.

GT Protocol

33,028 次观看 • 1 年前

A little over a year ago, was just a spark of an idea. Today, I’m blown away by what our team and community have built together. We’ve shipped a full-blown decentralized cloud stack—Ray, Kubernetes, Bare Metal, CaaS—with VMs and Deployment APIs on the horizon. It’s now a real contender alongside the biggest players out there. Over 300K GPUs have powered this network, with 6K+ active today and 5K+ hooked up through SuperAgg APIs. Our backbone is rock-solid and growing stronger every single day. IO Intelligence is live, serving up top open-source models and in-house tool agents, already handling 3B+ tokens a month. RAG is up and running, and agentic workflows are next in line. We’ve delivered $14M+ in compute, and the future is already fueled by 1K+ H200s and 800+ H100s serving enterprise-grade workloads. This is just the beginning—step one of a thousand. Grateful for this community and what’s to come. Let’s keep beaming forward. 🚀

A little over a year ago, was just a spark of an idea. Today, I’m blown away by what our team and community have built together. We’ve shipped a full-blown decentralized cloud stack—Ray, Kubernetes, Bare Metal, CaaS—with VMs and Deployment APIs on the horizon. It’s now a real contender alongside the biggest players out there. Over 300K GPUs have powered this network, with 6K+ active today and 5K+ hooked up through SuperAgg APIs. Our backbone is rock-solid and growing stronger every single day. IO Intelligence is live, serving up top open-source models and in-house tool agents, already handling 3B+ tokens a month. RAG is up and running, and agentic workflows are next in line. We’ve delivered $14M+ in compute, and the future is already fueled by 1K+ H200s and 800+ H100s serving enterprise-grade workloads. This is just the beginning—step one of a thousand. Grateful for this community and what’s to come. Let’s keep beaming forward. 🚀

Basem

13,611 次观看 • 1 年前

OpenAI's AgentKit will be so insane, build every step of agents on one platform. These visual agent builders make the whole process of iterating and launching agents far more efficient. It sits on top of the Responses API and unifies the tools that were previously scattered across SDKs and custom orchestration. It lets developers create agent workflows visually, connect data sources securely, and measure performance automatically without coding every layer by hand. The core of AgentKit is the Agent Builder, a drag-and-drop canvas where each node represents an action, guardrail, or decision branch. Developers can link these nodes into multi-agent workflows, preview results instantly, and version each setup. It supports inline evaluation so that developers can see how changes affect output before deploying. The Connector Registry is a single admin panel that manages how data and tools connect across the OpenAI ecosystem. It centralizes integrations like Google Drive, SharePoint, Dropbox, and Microsoft Teams. Large organizations can govern access and flow of data between agents securely under one global console. ChatKit provides a ready-to-use chat interface for embedding agents inside apps or websites. It manages streaming, message threads, and model reasoning displays automatically. Developers can skin the interface to match their product without writing custom front-end code. Under the hood, all these blocks use the same execution core that runs agent reasoning through OpenAI’s APIs. Workflows in Agent Builder compile down to structured instructions for the Responses API, which handles model calls, tool use, and context passing. Connector Registry handles authentication and routing for external tools, while Evals and RFT provide feedback loops that improve agents over time. This integration means developers no longer need to handle orchestration logic, model evaluation pipelines, or safety layers separately. Everything runs natively within OpenAI’s control plane with managed security, automatic versioning, and built-in testing. In short, AgentKit standardizes the entire life cycle of an AI agent—from visual design to deployment and performance tuning—inside a single unified system.

OpenAI's AgentKit will be so insane, build every step of agents on one platform. These visual agent builders make the whole process of iterating and launching agents far more efficient. It sits on top of the Responses API and unifies the tools that were previously scattered across SDKs and custom orchestration. It lets developers create agent workflows visually, connect data sources securely, and measure performance automatically without coding every layer by hand. The core of AgentKit is the Agent Builder, a drag-and-drop canvas where each node represents an action, guardrail, or decision branch. Developers can link these nodes into multi-agent workflows, preview results instantly, and version each setup. It supports inline evaluation so that developers can see how changes affect output before deploying. The Connector Registry is a single admin panel that manages how data and tools connect across the OpenAI ecosystem. It centralizes integrations like Google Drive, SharePoint, Dropbox, and Microsoft Teams. Large organizations can govern access and flow of data between agents securely under one global console. ChatKit provides a ready-to-use chat interface for embedding agents inside apps or websites. It manages streaming, message threads, and model reasoning displays automatically. Developers can skin the interface to match their product without writing custom front-end code. Under the hood, all these blocks use the same execution core that runs agent reasoning through OpenAI’s APIs. Workflows in Agent Builder compile down to structured instructions for the Responses API, which handles model calls, tool use, and context passing. Connector Registry handles authentication and routing for external tools, while Evals and RFT provide feedback loops that improve agents over time. This integration means developers no longer need to handle orchestration logic, model evaluation pipelines, or safety layers separately. Everything runs natively within OpenAI’s control plane with managed security, automatic versioning, and built-in testing. In short, AgentKit standardizes the entire life cycle of an AI agent—from visual design to deployment and performance tuning—inside a single unified system.

Rohan Paul

178,460 次观看 • 9 个月前

The Visual Studio Code insiders version that just shipped and will ship in the next few days will come with an insane amount of new capabilities. A few highlights: - You can now run sub-agents in parallel. Yes, really. I even attached a video. - Major UX improvements for sub agents, especially visible in the chat window - A new search tool wrapped as a sub-agent that iteratively runs multiple search tools: semantic_search, file_search, grep_search Which connects nicely to the point above: multiple searches running in parallel, efficiently and fast - Anthropic’s Message API is now enabled by default - You can choose the model for the cloud agent (three available, all premium) - Extended thinking support when using the Claude cloud agent This is part of the broader multi-vendor cloud support under AgentsHQ I wrote about a few weeks ago - Tasks sent to the background agent (basically the CLI tool) now always run in isolation, each with its own git worktree - In a multi-repo workspace, assigning a task to a cloud agent prompts you to choose the target repo Same behavior when opening an empty workspace with no repo - Support for building an external index for files not supported by GitHub’s default indexing - UI/UX improvements for starting new sessions and switching between local / background / cloud agents - Skills are now first-class citizens, just like prompt files, with better UX indicating when a skill is loaded - Improved API for dynamic contribution of prompt files New V2 includes skills as part of the model. Curious to see the extensions that will leverage this - Finally, initial support for showing context usage percentage per session - Skills are enabled by default - Resizable chat window and session view. Small thing, but it was driving me crazy 😁 - A new integrated browser meant to replace the old simple browser Maybe the beginning of real browser use? - Better UI/UX for token streaming in chat - Ability to index external files not supported by GitHub There’s a lot more. Some of it hasn’t fully landed yet, but everything that has is already in Insiders. The next stable release should drop in early February. As usual, I’m just shocked by the volume of features this team ships every month. After the holiday slowdown, this one is shaping up to be a wild release.

The Visual Studio Code insiders version that just shipped and will ship in the next few days will come with an insane amount of new capabilities. A few highlights: - You can now run sub-agents in parallel. Yes, really. I even attached a video. - Major UX improvements for sub agents, especially visible in the chat window - A new search tool wrapped as a sub-agent that iteratively runs multiple search tools: semantic_search, file_search, grep_search Which connects nicely to the point above: multiple searches running in parallel, efficiently and fast - Anthropic’s Message API is now enabled by default - You can choose the model for the cloud agent (three available, all premium) - Extended thinking support when using the Claude cloud agent This is part of the broader multi-vendor cloud support under AgentsHQ I wrote about a few weeks ago - Tasks sent to the background agent (basically the CLI tool) now always run in isolation, each with its own git worktree - In a multi-repo workspace, assigning a task to a cloud agent prompts you to choose the target repo Same behavior when opening an empty workspace with no repo - Support for building an external index for files not supported by GitHub’s default indexing - UI/UX improvements for starting new sessions and switching between local / background / cloud agents - Skills are now first-class citizens, just like prompt files, with better UX indicating when a skill is loaded - Improved API for dynamic contribution of prompt files New V2 includes skills as part of the model. Curious to see the extensions that will leverage this - Finally, initial support for showing context usage percentage per session - Skills are enabled by default - Resizable chat window and session view. Small thing, but it was driving me crazy 😁 - A new integrated browser meant to replace the old simple browser Maybe the beginning of real browser use? - Better UI/UX for token streaming in chat - Ability to index external files not supported by GitHub There’s a lot more. Some of it hasn’t fully landed yet, but everything that has is already in Insiders. The next stable release should drop in early February. As usual, I’m just shocked by the volume of features this team ships every month. After the holiday slowdown, this one is shaping up to be a wild release.

Oren Melamed

29,555 次观看 • 6 个月前