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NVIDIA drop a 3B vision-language model for fast, high-quality visual grounding in real time accurately. - Parallel box decoding - 10× faster than Qwen3-VL - Trained on 138M queries/785M boxes - GUI, OCR, and document layout, dense detection - Open source Useful for computer-use agents and Physical AI,

Md Ismail Šojal 🕷️

57,104 subscribers

131,447 görüntüleme • 4 gün önce •via X (Twitter)

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Md Ismail Šojal 🕷️ profil fotoğrafı

Md Ismail Šojal 🕷️4 gün önce

Worth a look if you work on agents, robotics, or document AI. -

falky profil fotoğrafı

falky4 gün önce

shit is 4 months old, fucking clickbait

Fajar M Reza profil fotoğrafı

Fajar M Reza4 gün önce

Does the 10× speedup hold on GUI workloads, or mainly dense grounding?

Samurai.AI profil fotoğrafı

Samurai.AI4 gün önce

@skalskip92

SHK profil fotoğrafı

SHK4 gün önce

@grok find this model to download

Dylan Colquhoun profil fotoğrafı

Dylan Colquhoun4 gün önce

Whats the optimal packing of 17 boxes in a square?

Derek Chia profil fotoğrafı

Derek Chia4 gün önce

Parallel box decoding sounds like the real trick. How does it hold up on dense GUI screens versus Qwen3-VL?

CEO del Socialismo de Mercado🌹🕊️ 市场社会主义CEO profil fotoğrafı

CEO del Socialismo de Mercado🌹🕊️ 市场社会主义CEO4 gün önce

Locate Anything?

Pies Avalon profil fotoğrafı

Pies Avalon4 gün önce

The final form of this will be an api service like jev.

Jim Wallace profil fotoğrafı

Jim Wallace4 gün önce

That scene in The Matrix Reloaded could use some DLSS5 treatment tho

Artzy profil fotoğrafı

Artzy4 gün önce

The throughput on that box decoding is wild

安叫兽|Bird🕊️ 🔶 BNB profil fotoğrafı

安叫兽|Bird🕊️ 🔶 BNB4 gün önce

3B 还能跑这么快，GUI 场景应该挺香，等实测了

Antonio Rocha profil fotoğrafı

Antonio Rocha4 gün önce

É mais rapido que o YOLO 26?

Ramesh Devasi profil fotoğrafı

Ramesh Devasi4 gün önce

why i am not able to get simple 4 corner flat planer tracker which can run in web

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NVIDIA's LocateAnything is a new vision model for grounding and detection. Very performant and accurate! > 10x faster than Qwen3-VL > 138M queries + 785M boxes > GUI, OCR, docs, dense detection > Free & open source

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A toolkit for building agents that watch, listen, and understand video. Low latency by design. Open source. Production ready. Vision Agents lets you build real time video AI that works with your models and your edge layer. Supports YOLO, Moondream, Cartesia, Deepgram, ElevenLabs, HeyGen, Gemini, OpenAI, and more. Quick model switching. Easy to use API. Perfect for coaching tools, collaboration apps, avatars, and robotics.

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Pascale Fung

90,144 görüntüleme • 9 ay önce

Okay so Jev can actually do computer use really well Without any screenshots, or LLMs and no Pixels leave my mac I dont even read the Dom elements A local CoreML model segments every button and UI element on screen. On-device OCR reads the labels. That text is all Jev gets. It returns a probability across those elements and tells me the best one to click. Then it clicks, re-runs detection, and decides again. In a loop until the goal is done. ~90ms per decision. Faster than any LLM computer use I've tried. Blazing fast computer use, without any latency TypeSafe AI is building something really interesting

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Introducing Phoenix Deep Research, a vertical AI agent for in-depth, multi-step research and real-time knowledge. Phoenix Deep Research is blazingly fast with average response output time of 30 to 90 seconds, much faster than industry averages and is built on top of an accelerated Deepseek R1 utilizing SkyNet High Performance Cluster consisting of the newest Huawei Ascend and Nvidia A100, and A10 GPUs. Phoenix DR combines the most cutting-edge open source LLM, industry-specific agentic workflows, real-time AI search, and novel decentralized compute infrastructure into a differentiated and versatile tool previously unseen in the decentralized AI space. Phoenix DR will be open for Beta whitelist and testing early next week for community users and partners. Stay tuned. #Phoenix $PHB #DeepResearch

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A big milestone for AI on Windows: gpt‑oss‑20b is now available in Windows AI Foundry and Azure AI Foundry! You can now run this open-source, frontier-class model locally on a high-performance Windows PC. It’s fast, capable, and built for real-world use. Brought to life through collaboration across Windows, Azure, and OpenAI teams. We can’t wait to see what you create. 🚀

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New LLMs that control UIs! ByteDance Research releases UI-TARS, fine-tuned GUI agent that integrates reasoning, and action capabilities into a single vision-language model. Think of computer use but open. 👀 TL;DR; 3️⃣ Available in 3 sizes: 2B, 7B, and 72B parameters 🧠 Trained Qwen2-VL models with SFT & DPO 🥇 72B version achieves 82.8% on VisualWebBench (beating GPT-4 and Claude) 🏆 Achieves state-of-the-art results on 10+ GUI agent benchmarks 💡 Reasons before taking an action 🧑🏻‍💻 Can Click, Long Press, type, scroll, open app, navigate back/home, wait 🤗 Released under Apache 2.0 on Hugging Face

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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

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

Milind S

38,361 görüntüleme • 4 ay önce

Smart Turn v2: open source, native audio turn detection in 14 languages. New checkpoint of the open source, open data, open training code, semantic VAD model on Hugging Face, fal, and Pipecat AI. - 3x faster inference (12ms on an L40) - 14 languages (13 more than v1, which was english-only) - New synthetic data set `chirp_3_all` with ~163k audio samples - 99% accuracy on held out `human_5_all` test data Good turn detection is critical for voice agents. This model "understands" both semantic and audio patterns, and mitigates the voice AI trade-off between unwanted turn latency vs the agent interrupting people before they are finished speaking. Training scripts for both Modal and local training are in the repo. We want to make it as easy as possible to contribute to or customize this model! Here's a demo running the smart-turn model with default settings, aimed at generally hitting 400ms total turn detection time. You can tune things to be faster, too. You can help by contributing data, doing architecture expermints, or cleaning open source data! Keep reading ...

Smart Turn v2: open source, native audio turn detection in 14 languages. New checkpoint of the open source, open data, open training code, semantic VAD model on Hugging Face, fal, and Pipecat AI. - 3x faster inference (12ms on an L40) - 14 languages (13 more than v1, which was english-only) - New synthetic data set `chirp_3_all` with ~163k audio samples - 99% accuracy on held out `human_5_all` test data Good turn detection is critical for voice agents. This model "understands" both semantic and audio patterns, and mitigates the voice AI trade-off between unwanted turn latency vs the agent interrupting people before they are finished speaking. Training scripts for both Modal and local training are in the repo. We want to make it as easy as possible to contribute to or customize this model! Here's a demo running the smart-turn model with default settings, aimed at generally hitting 400ms total turn detection time. You can tune things to be faster, too. You can help by contributing data, doing architecture expermints, or cleaning open source data! Keep reading ...

kwindla

42,246 görüntüleme • 1 yıl önce

LiteParse is the best model-free, open-source document parser for AI agents. It now gets a first-class landing page on our website 💫 Our company mission is building the world's best agentic document processing platform, and liteparse is the central pillar behind our OSS efforts. It's blazing fast (and getting faster soon!), supports 50+ file formats, and is one-shot installable as an agent skill. Webpage: Come check it out:

LiteParse is the best model-free, open-source document parser for AI agents. It now gets a first-class landing page on our website 💫 Our company mission is building the world's best agentic document processing platform, and liteparse is the central pillar behind our OSS efforts. It's blazing fast (and getting faster soon!), supports 50+ file formats, and is one-shot installable as an agent skill. Webpage: Come check it out:

Jerry Liu

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