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rednote's AI lab, dots studio dots studio, just released a new open-weight model: dots3-note Preview → 280B parameters / 16B active → 512K context → Text + vision + speech Built for something most agents still struggle with: tasks that run for hours, days, or even weeks Here’s how:

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Meet dots3-note preview — an open-weight multimodal model built for long-horizon agency in real life. → 280B total / 16B active parameters → 512K context window → Native text, vision, and speech understanding Designed to reason, use tools, self-evaluate, and adapt as tasks evolve over time. dots studio

Meet dots3-note preview — an open-weight multimodal model built for long-horizon agency in real life. → 280B total / 16B active parameters → 512K context window → Native text, vision, and speech understanding Designed to reason, use tools, self-evaluate, and adapt as tasks evolve over time. dots studio

Alamin

56,808 views • 8 days ago

This is dots3-note preview from dots studio in a puzzle game it hasn't seen before. No game-specific prompt. It has to work out the controls, objective and new mechanics from the screen. It starts pressing buttons. Open-weight: 280B total / 16B active, 512K context.

This is dots3-note preview from dots studio in a puzzle game it hasn't seen before. No game-specific prompt. It has to work out the controls, objective and new mechanics from the screen. It starts pressing buttons. Open-weight: 280B total / 16B active, 512K context.

Khushboo Verma

37,624 views • 3 days ago

Most AI models are optimized for one-shot answers. rednote’s AI lab, dots studio dots studio, built dots3-note Preview around a harder question: Can an agent keep working when a task lasts for hours, new information arrives, and the environment changes along the way? Here’s what stood out to me. 🧵

Most AI models are optimized for one-shot answers. rednote’s AI lab, dots studio dots studio, built dots3-note Preview around a harder question: Can an agent keep working when a task lasts for hours, new information arrives, and the environment changes along the way? Here’s what stood out to me. 🧵

Jay

77,598 views • 3 days ago

A road trip plan is never just a simple route. Learn more: Api free now: Weather shifts. Highways close. Strategies must adapt. Real-world challenges demand AI that thrives in uncertainty. This is exactly what dots studio's dots3-note preview is designed to solve. It is an open-weight multimodal model boasting 280B total parameters, 16B active parameters, and a 512K context window. The true differentiator is TEMPO. During long-horizon tasks, agents require more than just memory — they must constantly verify they are still tracking toward the correct objective. Leveraging an Actor-Critic approach, TEMPO critically evaluates choices mid-action, course-correcting strategies long before the task concludes. Combined with: 🌍 Multimodal understanding — tackling real-world problems by processing text, images, and audio as one cohesive input. 🌱 Learning in deployment — adapting to unfamiliar environments through ongoing exploration and active feedback. From mapping a dynamic journey to navigating intricate problems, dots3-note preview marks a new era of AI agency. This exact vision fuels rednote, showcasing raw reasoning capabilities that hit IMO gold medal-level performance.

A road trip plan is never just a simple route. Learn more: Api free now: Weather shifts. Highways close. Strategies must adapt. Real-world challenges demand AI that thrives in uncertainty. This is exactly what dots studio's dots3-note preview is designed to solve. It is an open-weight multimodal model boasting 280B total parameters, 16B active parameters, and a 512K context window. The true differentiator is TEMPO. During long-horizon tasks, agents require more than just memory — they must constantly verify they are still tracking toward the correct objective. Leveraging an Actor-Critic approach, TEMPO critically evaluates choices mid-action, course-correcting strategies long before the task concludes. Combined with: 🌍 Multimodal understanding — tackling real-world problems by processing text, images, and audio as one cohesive input. 🌱 Learning in deployment — adapting to unfamiliar environments through ongoing exploration and active feedback. From mapping a dynamic journey to navigating intricate problems, dots3-note preview marks a new era of AI agency. This exact vision fuels rednote, showcasing raw reasoning capabilities that hit IMO gold medal-level performance.

SOUL AI

10,365 views • 3 days ago

Qwen3.8-Max is built for more than just answering prompts. Alibaba’s newest flagship model brings: • 2.4T total parameters • 1M-token context • Native vision + text reasoning • 95B active parameters through Sparse MoE The interesting part is what it can actually do. From autonomous coding and long-running tasks to analyzing 200+ page documents and massive streams of information, Qwen3.8-Max is designed to work through complex tasks, not just generate text. Now accessible globally through Alibaba Cloud Model Studio. Alibaba Group Qwen Qwen Studio: API: Blog: Alizila:

Qwen3.8-Max is built for more than just answering prompts. Alibaba’s newest flagship model brings: • 2.4T total parameters • 1M-token context • Native vision + text reasoning • 95B active parameters through Sparse MoE The interesting part is what it can actually do. From autonomous coding and long-running tasks to analyzing 200+ page documents and massive streams of information, Qwen3.8-Max is designed to work through complex tasks, not just generate text. Now accessible globally through Alibaba Cloud Model Studio. Alibaba Group Qwen Qwen Studio: API: Blog: Alizila:

Samaira AI

81,746 views • 22 days ago

Progress in open models is keeping Big AI labs up at night, and I'm here for it! We have a brand new open-weight multimodal model optimized for long-horizon tasks. This model is really good at something: it can work on tasks that keep evolving over time. • 280B total parameters, but only 16B active • 512K context window • Understands text, images, and audio • Strong reasoning, coding, and tool use But the best of all: the model learns and adapts to new information! Imagine you start running an agent today to solve a problem, and while it's working, you get new information that changes the initial conditions, or you change your mind. The agents you run today don't have issues with short tasks and goals that don't change, but reality is messy, and that makes it hard for long-horizon agents to succeed. The new dots3-note Preview model introduces TEMPO. TEMPO is a new reinforcement learning technique that lets the model periodically pause and critique its own progress. Basically, from time to time, the agent asks itself: "Am I getting closer to the goal, or am I wasting my time?" The same model switches between actor and critic. The actor works on the problem. The critic looks at the current state, reasons about how much progress it has made, and determines what should happen next. TEMPO gives the model feedback along the way. This is huge for any agent that can work on long-horizon tasks without wasting its time.

Santiago

79,311 views • 8 days ago

Google just launched Gemini 3.5 Transcribe, its most precise speech-to-text model yet. I built this app using Gemini 3.5 Transcribe and have been playing with the model for quite some time. Thanks to the Google DeepMind team for the early access. It’s already available in the Gemini API, AI Studio, and Antigravity, and is now in public preview.

Google just launched Gemini 3.5 Transcribe, its most precise speech-to-text model yet. I built this app using Gemini 3.5 Transcribe and have been playing with the model for quite some time. Thanks to the Google DeepMind team for the early access. It’s already available in the Gemini API, AI Studio, and Antigravity, and is now in public preview.

AshutoshShrivastava

14,668 views • 1 day ago

Introducing 1-Click Export to AI Studio. 🚀 No more manual copy-pasting HTML or dragging images. Just hit the export button, and Stitch automatically opens AI Studio with your full context (HTML + Screens) attached and ready to go. Yesterday: AI Studio gets Gemini 3. Today: Stitch gets 1-Click Export to AI Studio. 📅 It's the perfect storm. You can now easily build your beautiful designs with the most capable model available. It was our most requested feature for a reason. Go build something real, fast.

Introducing 1-Click Export to AI Studio. 🚀 No more manual copy-pasting HTML or dragging images. Just hit the export button, and Stitch automatically opens AI Studio with your full context (HTML + Screens) attached and ready to go. Yesterday: AI Studio gets Gemini 3. Today: Stitch gets 1-Click Export to AI Studio. 📅 It's the perfect storm. You can now easily build your beautiful designs with the most capable model available. It was our most requested feature for a reason. Go build something real, fast.

Stitch by Google

410,730 views • 9 months ago

FULL INTERVIEW: Engineers Edward Coristine (Edward Coristine) and Tai Groot (Tai Groot 🐧) just released an ML model called Rampart for the National Design Studio. It's a local-first, open-source AI privacy model that runs entirely in the browser. They joined TBPN to explain the model and why the National Design Studio launched it. 0:25 – National Design Studio launches Rampart 1:40 – Why Rampart is open source 2:40 – How NDS trained the model 3:55 – Use cases for the model 5:25 – How NDS thinks about side quests 6:25 – Team size, workflow, and why they work seven days a week 8:00 – Recruiting top talent to the National Design Studio

FULL INTERVIEW: Engineers Edward Coristine (Edward Coristine) and Tai Groot (Tai Groot 🐧) just released an ML model called Rampart for the National Design Studio. It's a local-first, open-source AI privacy model that runs entirely in the browser. They joined TBPN to explain the model and why the National Design Studio launched it. 0:25 – National Design Studio launches Rampart 1:40 – Why Rampart is open source 2:40 – How NDS trained the model 3:55 – Use cases for the model 5:25 – How NDS thinks about side quests 6:25 – Team size, workflow, and why they work seven days a week 8:00 – Recruiting top talent to the National Design Studio

TBPN

116,392 views • 1 month ago

WOW: Kimi K3 just killed the AI subscription era 🇨🇳 On July 27, Moonshot publishes the full K3 weights under a modified MIT license. ▫️ 2.8 trillion parameters ▫️ 1M token context ▫️ Open weights, yours to run "The frontier becomes a file." The closed labs charge you monthly for access. Moonshot just made the frontier a download. Does open weight end the subscription model, or do the big labs still win on polish?

WOW: Kimi K3 just killed the AI subscription era 🇨🇳 On July 27, Moonshot publishes the full K3 weights under a modified MIT license. ▫️ 2.8 trillion parameters ▫️ 1M token context ▫️ Open weights, yours to run "The frontier becomes a file." The closed labs charge you monthly for access. Moonshot just made the frontier a download. Does open weight end the subscription model, or do the big labs still win on polish?

Ruben

94,908 views • 1 month ago

NVIDIA just released a new open source transcription model, Nemotron Speech ASR, designed from the ground up for low-latency use cases like voice agents. Here's a voice agent built with this new model. 24ms transcription finalization and total voice-to-voice inference time under 500ms. This agent actually uses *three* NVIDIA open source models: - Nemotron Speech ASR - Nemotron 3 Nano 30GB in a 4-bit quant (released in December) - A preview checkpoint of the upcoming Magpie text-to-speech model These models are all truly open source: weights, training data, training code, and inference code. This is a big deal! Jensen said in the CES keynote yesterday that he expects open source models to catch up to proprietary models this year in a number of categories. NVIDIA is putting their weight behind making this happen. (As Alan Kay said, the best way to predict the future is to invent it.) The code for this agent is open source too, of course. You can deploy it to production with Modal and Pipecat AI cloud, or run locally on an NVIDIA DGX Spark or RTX 5090.

NVIDIA just released a new open source transcription model, Nemotron Speech ASR, designed from the ground up for low-latency use cases like voice agents. Here's a voice agent built with this new model. 24ms transcription finalization and total voice-to-voice inference time under 500ms. This agent actually uses three NVIDIA open source models: - Nemotron Speech ASR - Nemotron 3 Nano 30GB in a 4-bit quant (released in December) - A preview checkpoint of the upcoming Magpie text-to-speech model These models are all truly open source: weights, training data, training code, and inference code. This is a big deal! Jensen said in the CES keynote yesterday that he expects open source models to catch up to proprietary models this year in a number of categories. NVIDIA is putting their weight behind making this happen. (As Alan Kay said, the best way to predict the future is to invent it.) The code for this agent is open source too, of course. You can deploy it to production with Modal and Pipecat AI cloud, or run locally on an NVIDIA DGX Spark or RTX 5090.

kwindla

274,679 views • 7 months ago

Introducing Docker 🐳 AI Models for local + private LLMs in VSCode 🤩 You can now use Docker as a local model provider in VSCode, JetBrains, Cursor, and soon Visual Studio Enterprise. With Docker Model Runner (v4.40+), run AI models locally on your machine... no data sharing, no cloud dependency 👏 Just update Docker, open CodeGPT, select the model, click "Download", and enjoy 👉 Here’s how to update Docker:

Introducing Docker 🐳 AI Models for local + private LLMs in VSCode 🤩 You can now use Docker as a local model provider in VSCode, JetBrains, Cursor, and soon Visual Studio Enterprise. With Docker Model Runner (v4.40+), run AI models locally on your machine... no data sharing, no cloud dependency 👏 Just update Docker, open CodeGPT, select the model, click "Download", and enjoy 👉 Here’s how to update Docker:

Daniel San

65,228 views • 1 year ago

DESIGN.md is how we work with agents now, but a text file is a terrible way for a human to make a visual decision. So we built our preview to give some of that control back: edit the file as text or pick colors visually, and the two stay in sync. And swatches sit on the actual light and dark backgrounds because color only means something in the context you'll see it in.

DESIGN.md is how we work with agents now, but a text file is a terrible way for a human to make a visual decision. So we built our preview to give some of that control back: edit the file as text or pick colors visually, and the two stay in sync. And swatches sit on the actual light and dark backgrounds because color only means something in the context you'll see it in.

Chloe Park

86,462 views • 2 months ago

With Kimi K3 Day-0 on vLLM: Open Frontier Intelligence for Everyone 🚀 At 2.8 trillion parameters, Moonshot AI's Kimi K3 is one of the most powerful open-weight models ever released. Starting today, you can serve it on vLLM the moment the weights are public. What K3 brings: 🧠 2.8T-parameter Mixture-of-Experts (16 of 896 experts active per token) 📚 1M-token context window 🛠️ Native multimodal understanding, including vision ⚡ Kimi Delta Attention: a hybrid of linear and full attention that makes million-token context affordable Huge thank you to Kimi.ai AI for the model release and partnership, Inferact for leading the vLLM optimizations, and to our partners at NVIDIA, AMD, and the broader vLLM community. 1/6

With Kimi K3 Day-0 on vLLM: Open Frontier Intelligence for Everyone 🚀 At 2.8 trillion parameters, Moonshot AI's Kimi K3 is one of the most powerful open-weight models ever released. Starting today, you can serve it on vLLM the moment the weights are public. What K3 brings: 🧠 2.8T-parameter Mixture-of-Experts (16 of 896 experts active per token) 📚 1M-token context window 🛠️ Native multimodal understanding, including vision ⚡ Kimi Delta Attention: a hybrid of linear and full attention that makes million-token context affordable Huge thank you to Kimi.ai AI for the model release and partnership, Inferact for leading the vLLM optimizations, and to our partners at NVIDIA, AMD, and the broader vLLM community. 1/6

vLLM

103,062 views • 1 month ago

Voicebox: Text-Guided Multilingual Universal Speech Generation at Scale blog: Large-scale generative models such as GPT and DALL-E have revolutionized natural language processing and computer vision research. These models not only generate high fidelity text or image outputs, but are also generalists which can solve tasks not explicitly taught. In contrast, speech generative models are still primitive in terms of scale and task generalization. In this paper, we present Voicebox, the most versatile text-guided generative model for speech at scale. Voicebox is a non-autoregressive flow-matching model trained to infill speech, given audio context and text, trained on over 50K hours of speech that are neither filtered nor enhanced. Similar to GPT, Voicebox can perform many different tasks through in-context learning, but is more flexible as it can also condition on future context. Voicebox can be used for mono or cross-lingual zero-shot text-to-speech synthesis, noise removal, content editing, style conversion, and diverse sample generation. In particular, Voicebox outperforms the state-of-the-art zero-shot TTS model VALL-E on both intelligibility (5.9% vs 1.9% word error rates) and audio similarity (0.580 vs 0.681) while being up to 20 times faster.

Voicebox: Text-Guided Multilingual Universal Speech Generation at Scale blog: Large-scale generative models such as GPT and DALL-E have revolutionized natural language processing and computer vision research. These models not only generate high fidelity text or image outputs, but are also generalists which can solve tasks not explicitly taught. In contrast, speech generative models are still primitive in terms of scale and task generalization. In this paper, we present Voicebox, the most versatile text-guided generative model for speech at scale. Voicebox is a non-autoregressive flow-matching model trained to infill speech, given audio context and text, trained on over 50K hours of speech that are neither filtered nor enhanced. Similar to GPT, Voicebox can perform many different tasks through in-context learning, but is more flexible as it can also condition on future context. Voicebox can be used for mono or cross-lingual zero-shot text-to-speech synthesis, noise removal, content editing, style conversion, and diverse sample generation. In particular, Voicebox outperforms the state-of-the-art zero-shot TTS model VALL-E on both intelligibility (5.9% vs 1.9% word error rates) and audio similarity (0.580 vs 0.681) while being up to 20 times faster.

AK

429,355 views • 3 years ago

Excited about the launch of Amazon Nova Sonic, our new speech-to-speech model that helps make AI voice applications feel remarkably natural. It's designed to understand not just what people say, but how they say it – working with tone, style, and conversation flow including pauses and interruptions. Nova Sonic delivers speech understanding and generation through a single, unified model, making it easier for builders to develop voice applications that maintain important context and nuance for customer service, AI agents, and other use cases across industries. It’s available in Amazon Bedrock now. Look forward to seeing what teams build with Nova Sonic!

Excited about the launch of Amazon Nova Sonic, our new speech-to-speech model that helps make AI voice applications feel remarkably natural. It's designed to understand not just what people say, but how they say it – working with tone, style, and conversation flow including pauses and interruptions. Nova Sonic delivers speech understanding and generation through a single, unified model, making it easier for builders to develop voice applications that maintain important context and nuance for customer service, AI agents, and other use cases across industries. It’s available in Amazon Bedrock now. Look forward to seeing what teams build with Nova Sonic!

Andy Jassy

155,882 views • 1 year ago

Google just killed every paid coding tool. Their new AI Studio destroys Cursor completely. Here's how to build React apps instantly: → Open Google AI Studio Build Mode → Type what you want to build → Watch it code everything automatically → Deploy to GitHub with one click → Share working apps instantly Features that crush the competition: • Angular + React support ✔ • GitHub integration ✔ • Real-time code preview ✔ • Gemini API built-in ✔ • Enterprise AI for $0 ✔ Save this thread, you'll build faster apps. Want the SOP? DM me. 💬

Google just killed every paid coding tool. Their new AI Studio destroys Cursor completely. Here's how to build React apps instantly: → Open Google AI Studio Build Mode → Type what you want to build → Watch it code everything automatically → Deploy to GitHub with one click → Share working apps instantly Features that crush the competition: • Angular + React support ✔ • GitHub integration ✔ • Real-time code preview ✔ • Gemini API built-in ✔ • Enterprise AI for $0 ✔ Save this thread, you'll build faster apps. Want the SOP? DM me. 💬

Julian Goldie SEO

188,249 views • 11 months ago

🤯 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

🤯 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 views • 1 year ago

The coming merger of AI agents with the physical world. @iddris, founder/CEO of Open Matter and I have a wide-ranging conversation about how the next platform shift may not be just AI chat, robotics, smart glasses, or autonomous cars separately. It may be the connective tissue between all of them. This video teaches viewers that the next wave of AI agents will not just operate inside apps, browsers, calendars, and spreadsheets. The real frontier is agents that can understand, coordinate, and act in the physical world. Iddris explains a vision for “agentic infrastructure for atoms”: systems that let AI agents manage physical objects, deliveries, storage, location, identity, logistics, robots, vehicles, and real-world context.

The coming merger of AI agents with the physical world. @iddris, founder/CEO of Open Matter and I have a wide-ranging conversation about how the next platform shift may not be just AI chat, robotics, smart glasses, or autonomous cars separately. It may be the connective tissue between all of them. This video teaches viewers that the next wave of AI agents will not just operate inside apps, browsers, calendars, and spreadsheets. The real frontier is agents that can understand, coordinate, and act in the physical world. Iddris explains a vision for “agentic infrastructure for atoms”: systems that let AI agents manage physical objects, deliveries, storage, location, identity, logistics, robots, vehicles, and real-world context.

Robert Scoble

17,484 views • 3 months ago

Today, we’re launching Gemma 4, our most intelligent open models to date. Built with the same breakthrough technology as Gemini 3, Gemma 4 brings advanced reasoning to your personal hardware and devices. Here’s what Gemma 4 unlocks for developers: — Intelligence-per-parameter: Our 31B (Dense) and 26B (MoE) models deliver state-of-the-art performance for their size, outcompeting models 20x their size on Arena.ai — Commercial flexibility: Released under a permissive Apache 2.0 license for complete developer flexibility and digital sovereignty — Agentic workflows: Native support for function-calling and structured JSON output allows you to build reliable, autonomous agents — Multimodal edge AI: The E2B and E4B models bring native vision, audio, and low latency to mobile and IoT devices — Long-context reasoning: Up to 256K context windows allow you to process entire repositories or large documents in a single prompt Whether you're building global applications in 140+ languages or local-first AI code assistants, Gemma 4 is built to be your foundation. Explore in Google AI Studio or download the weights on Hugging Face, Kaggle, and ollama.

Today, we’re launching Gemma 4, our most intelligent open models to date. Built with the same breakthrough technology as Gemini 3, Gemma 4 brings advanced reasoning to your personal hardware and devices. Here’s what Gemma 4 unlocks for developers: — Intelligence-per-parameter: Our 31B (Dense) and 26B (MoE) models deliver state-of-the-art performance for their size, outcompeting models 20x their size on Arena.ai — Commercial flexibility: Released under a permissive Apache 2.0 license for complete developer flexibility and digital sovereignty — Agentic workflows: Native support for function-calling and structured JSON output allows you to build reliable, autonomous agents — Multimodal edge AI: The E2B and E4B models bring native vision, audio, and low latency to mobile and IoT devices — Long-context reasoning: Up to 256K context windows allow you to process entire repositories or large documents in a single prompt Whether you're building global applications in 140+ languages or local-first AI code assistants, Gemma 4 is built to be your foundation. Explore in Google AI Studio or download the weights on Hugging Face, Kaggle, and ollama.

Google AI

1,670,338 views • 4 months ago