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Microsoft silently updated OmniParser on the hub 👀 60% faster than v1 - sub-second latency on a 4090! "OmniParser is a general screen parsing tool, which interprets/converts UI screenshot to structured format, to improve existing LLM based UI agent." Bonus: you can try it out for free!

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Vaibhav (VB) Srivastav

35,855 subscribers

95,750 görüntüleme • 1 yıl önce •via X (Twitter)

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Vaibhav (VB) Srivastav profil fotoğrafı
Vaibhav (VB) Srivastav1 yıl önce

Model checkpoint:

Vaibhav (VB) Srivastav profil fotoğrafı
Vaibhav (VB) Srivastav1 yıl önce

Try out the model for free here:

AssemblyAI profil fotoğrafı
AssemblyAI1 yıl önce

Our speech-to-text models are the most accurate on the market with top rankings across industry benchmarks. - The highest accuracy rates—up to 95% - Up to 30% fewer hallucinations than other leaders - Low latency—63 minutes converts in 35 seconds Try via API for free today 👇

Rohith profil fotoğrafı
Rohith1 yıl önce

Super hard to use omnitool and its very slow even on my 4080. Like taking 1min+ just for the Image parsing.

Vaibhav (VB) Srivastav profil fotoğrafı
Vaibhav (VB) Srivastav1 yıl önce

are you using the GPU, could be that you're running the op via CPU

moonbi 🇪🇺🪢 profil fotoğrafı
moonbi 🇪🇺🪢1 yıl önce

The problem is u need huge gpu till run it

Cybersphere AI profil fotoğrafı
Cybersphere AI1 yıl önce

Setup is a nightmare on Windows. The setup is: FUSE-based (horribly slow) Assumes Linux KVM is present (Not true on Windows) Not built with Windows users in mind (requiring modifications to scripts) The devs need to release a setup procedure optimised for Windows.

Shabah profil fotoğrafı
Shabah1 yıl önce

This is really interesting

Electe profil fotoğrafı
Electe1 yıl önce

@huggingface @huggingface, incredible improvements with OmniParser. Speed like this can truly enhance workflows and drive productivity. 🚀 #Innovation

Tenkaizen profil fotoğrafı
Tenkaizen1 yıl önce

That's impressive progress. Faster parsing opens up so many possibilities for UI agents

Korey 🧘🏾‍♂️ profil fotoğrafı
Korey 🧘🏾‍♂️1 yıl önce

such a project, want to build some code samples with this.

Benzer Videolar

Microsoft released a groundbreaking model that can be used for web automation, with MIT license 🔥👏 OmniParser is a state-of-the-art UI parsing/understanding model that outperforms GPT4V in parsing. 👏
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Microsoft released a groundbreaking model that can be used for web automation, with MIT license 🔥👏 OmniParser is a state-of-the-art UI parsing/understanding model that outperforms GPT4V in parsing. 👏

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Microsoft just released an impressive tool OmniParser V2 can turn any LLM into an agent capable of using a computer 🔥 You can enable GPT-4o, DeepSeek R1, Sonnet 3.5, Qwen... to understand what's on your screen and take actions. 100% free & open source
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Microsoft just released an impressive tool OmniParser V2 can turn any LLM into an agent capable of using a computer 🔥 You can enable GPT-4o, DeepSeek R1, Sonnet 3.5, Qwen... to understand what's on your screen and take actions. 100% free & open source

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OmniParser, the new screen parsing tool from Microsoft (and #1 trending model on Hugging Face), can now run 100% locally in your browser with Transformers.js! 🤯 Who's going to be the first to turn this into a browser extension? 👀 Endless possibilities! Demo & code below! 👇
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OmniParser, the new screen parsing tool from Microsoft (and #1 trending model on Hugging Face), can now run 100% locally in your browser with Transformers.js! 🤯 Who's going to be the first to turn this into a browser extension? 👀 Endless possibilities! Demo & code below! 👇

Xenova

64,553 görüntüleme • 1 yıl önce

I took Clicky and I made it 5x Faster So I saw that Farza 🇵🇰🇺🇸 was using uses Claude's vision to find UI elements on screen, send a screenshot, wait for coordinates back. It works, but it's slow. I replaced that with OmniParser V2 by Microsoft which is a local Object detection model trained specifically on UI elements. It runs on-device, detects every button, menu, and icon in 400ms, and gives me pixel-precise coordinates. No API call, no latency, no cost. The green highlights you see around the UI elements is the detection overlay and you can see as I am switching the screen it takes no time to detect and highlight which is pretty neat! With Local models improving by the day, Its the right direction for applications like these. Next up: video-synced tutorials where a YouTube tutorial pauses and waits for you to perform each action in the real app. I am not stopping!
2:53

I took Clicky and I made it 5x Faster So I saw that Farza 🇵🇰🇺🇸 was using uses Claude's vision to find UI elements on screen, send a screenshot, wait for coordinates back. It works, but it's slow. I replaced that with OmniParser V2 by Microsoft which is a local Object detection model trained specifically on UI elements. It runs on-device, detects every button, menu, and icon in 400ms, and gives me pixel-precise coordinates. No API call, no latency, no cost. The green highlights you see around the UI elements is the detection overlay and you can see as I am switching the screen it takes no time to detect and highlight which is pretty neat! With Local models improving by the day, Its the right direction for applications like these. Next up: video-synced tutorials where a YouTube tutorial pauses and waits for you to perform each action in the real app. I am not stopping!

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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.
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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.

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People are overlooking Google Gemini Realtime models for computer use It gave me sub 100ms latency with computer use. It has a much larger context window and is much cheaper as well Combine that with local OCR and local screen detection model based on Omniparser by Microsoft it works under 100ms action taking when combined with Cua I also put in a harness for Nous Research Hermes with it. You can access it all at your tip of your cursor. You can draw on your screen to give your agents a context And I am making it Open Source! Link in the Comments Sundar Pichai Min-Liang Tan Mojtaba Seyedhosseini
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People are overlooking Google Gemini Realtime models for computer use It gave me sub 100ms latency with computer use. It has a much larger context window and is much cheaper as well Combine that with local OCR and local screen detection model based on Omniparser by Microsoft it works under 100ms action taking when combined with Cua I also put in a harness for Nous Research Hermes with it. You can access it all at your tip of your cursor. You can draw on your screen to give your agents a context And I am making it Open Source! Link in the Comments Sundar Pichai Min-Liang Tan Mojtaba Seyedhosseini

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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:
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Introducing Shadify: Generative UI built on ShadCN Simply describe a UI and watch your LangChain agent compose from @ShadCN on the fly, using AG-UI and CopilotKit🪁. Then export it as React code. Repo below. Try it out here:

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From screenshot to functional UI in no time. Powered by Gemini 3 Flash, Antigravity can reproduce mockups with high fidelity. Just drop a screenshot, ask to rebuild as a Flutter UI, and watch it launch on your device.
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From screenshot to functional UI in no time. Powered by Gemini 3 Flash, Antigravity can reproduce mockups with high fidelity. Just drop a screenshot, ask to rebuild as a Flutter UI, and watch it launch on your device.

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How does Netflix know when you try to take a screenshot? Turns out it doesn’t. Rather than a simple piece of software recognizing a screenshot attempt and blacking out the screen, what’s going on is actually happening at the hardware level.
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How does Netflix know when you try to take a screenshot? Turns out it doesn’t. Rather than a simple piece of software recognizing a screenshot attempt and blacking out the screen, what’s going on is actually happening at the hardware level.

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Let's give this another go... 👀 Latest comparison of submenu performance for Base UI vs. bazza/ui <𝙼𝚎𝚗𝚞>. Updated to v1.2.0 — I was told v1.0.0 contained a temporary regression of this exact functionality. Base UI feels faster than before ⚡ Let me know what you think.
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Let's give this another go... 👀 Latest comparison of submenu performance for Base UI vs. bazza/ui <𝙼𝚎𝚗𝚞>. Updated to v1.2.0 — I was told v1.0.0 contained a temporary regression of this exact functionality. Base UI feels faster than before ⚡ Let me know what you think.

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I hated spending minutes trying to explain which element I meant in a long prompt, so I shipped something to fix it. Inspector’s new precision UI select tool connects the element on your screen to its exact source code, saving you time on prompting.
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I hated spending minutes trying to explain which element I meant in a long prompt, so I shipped something to fix it. Inspector’s new precision UI select tool connects the element on your screen to its exact source code, saving you time on prompting.

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This Claude 3.7 UI workflow is awesome And many vibe coders not aware of it: 1. Have Claude 3.7 to design whole UI screen by screen 2. Ask it to make a plan, break down into components 3. Turn into actual project By having Claude focus on just UI generation, it does a great job You can build beautiful UI without shadcn If you want to learn more about vibe coding beyond basics, join my community here: (also get free access to 10xCoder.Dev)
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This Claude 3.7 UI workflow is awesome And many vibe coders not aware of it: 1. Have Claude 3.7 to design whole UI screen by screen 2. Ask it to make a plan, break down into components 3. Turn into actual project By having Claude focus on just UI generation, it does a great job You can build beautiful UI without shadcn If you want to learn more about vibe coding beyond basics, join my community here: (also get free access to 10xCoder.Dev)

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Microsoft 🤝 AG-UI Protocol Microsoft Agent Framework is now AG-UI compatible! 🎉 Connect your MS Agent Framework .NET agents into fullstack applications with Python coming soon 👀 MS Agent Framework takes care of the agentic backend, AG-UI connects your agents to the frontend. Features include: - Chat UI - Generative UI - State synchronization - Human-in-the-loop - Frontend Tool Calls All the building blocks you need to take your agents from the backend and into a fullstack agentic application. Available in React & Angular via CopilotKit, and Rust, Kotlin, Rust, Java and .NET via community SDKs Big shoutout to the Microsoft & CopilotKit teams, who shipped this highly requested integration at breakneck speeds. More coming soon 👀 🙏 Javier Nelson, Joonseok Oh, Daniel Roth, Evan Mattson, Tyler Slaton, Brandon McConnell, Markus Ecker Read more about our collaboration: Get started: Check out the Dojo:

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