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3.1 Pro brings advanced reasoning to creative projects, too. 🎨 For example, we used it to build a portfolio based on the novel “Wuthering Heights.” More than just summarizing text, the model reasoned through the novel’s atmospheric tone to design a sleek UI, generating website-ready code that actually captures... show more

Google

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51,347 görüntüleme • 3 ay önce •via X (Twitter)

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Watch one of our favorite Veo 3.1 features in action ⬇️ With Veo 3.1, you can quickly and easily make creative videos in the Google Gemini and Google Flow based on just ingredient images and a prompt. It’s designed to give you more creative control while generating mobile-first videos. Have you tried Veo 3.1 yet?

Watch one of our favorite Veo 3.1 features in action ⬇️ With Veo 3.1, you can quickly and easily make creative videos in the Google Gemini and Google Flow based on just ingredient images and a prompt. It’s designed to give you more creative control while generating mobile-first videos. Have you tried Veo 3.1 yet?

Google

59,005 görüntüleme • 2 ay önce

Introducing Gemini 3.1 Pro 🚀 3.1 Pro represents a major step forward in core reasoning. It scored 77.1% (more than doubling 3 Pro’s score) on ARC-AGI-2, the benchmark that evaluates a model's ability to solve new logic patterns and work through challenges it hasn’t encountered before. This demo illustrates how the model can go beyond the prompt. Instead of rendering a video or static graphic, 3.1 Pro codes a full environment, integrating generative audio and providing UI controls.

Introducing Gemini 3.1 Pro 🚀 3.1 Pro represents a major step forward in core reasoning. It scored 77.1% (more than doubling 3 Pro’s score) on ARC-AGI-2, the benchmark that evaluates a model's ability to solve new logic patterns and work through challenges it hasn’t encountered before. This demo illustrates how the model can go beyond the prompt. Instead of rendering a video or static graphic, 3.1 Pro codes a full environment, integrating generative audio and providing UI controls.

Google AI

3,066,928 görüntüleme • 3 ay önce

With this new release, Gemini applies advanced reasoning to make sense of complex, changing information in real-time. 3.1 Pro can utilize advanced reasoning to bridge the gap between complex APIs and user-friendly design. For example, Gemini built a live aerospace dashboard, taking live telemetry from a public API and designing a front end to track the Space Station's orbit.

With this new release, Gemini applies advanced reasoning to make sense of complex, changing information in real-time. 3.1 Pro can utilize advanced reasoning to bridge the gap between complex APIs and user-friendly design. For example, Gemini built a live aerospace dashboard, taking live telemetry from a public API and designing a front end to track the Space Station's orbit.

Google Gemini

106,360 görüntüleme • 3 ay önce

Introducing Nano Banana Pro (Gemini 3 Pro Image), our new state-of-the-art image generation and editing model from Google DeepMind. It improves on the original model while adding new advanced capabilities, enhanced world knowledge and text rendering, allowing you to create and edit studio-quality, production-ready visuals.

Introducing Nano Banana Pro (Gemini 3 Pro Image), our new state-of-the-art image generation and editing model from Google DeepMind. It improves on the original model while adding new advanced capabilities, enhanced world knowledge and text rendering, allowing you to create and edit studio-quality, production-ready visuals.

Google

1,896,757 görüntüleme • 6 ay önce

LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models paper page: github: Recent advancements in text-to-image generation with diffusion models have yielded remarkable results synthesizing highly realistic and diverse images. However, these models still encounter difficulties when generating images from prompts that demand spatial or common sense reasoning. We propose to equip diffusion models with enhanced reasoning capabilities by using off-the-shelf pretrained large language models (LLMs) in a novel two-stage generation process. First, we adapt an LLM to be a text-guided layout generator through in-context learning. When provided with an image prompt, an LLM outputs a scene layout in the form of bounding boxes along with corresponding individual descriptions. Second, we steer a diffusion model with a novel controller to generate images conditioned on the layout. Both stages utilize frozen pretrained models without any LLM or diffusion model parameter optimization. We validate the superiority of our design by demonstrating its ability to outperform the base diffusion model in accurately generating images according to prompts that necessitate both language and spatial reasoning. Additionally, our method naturally allows dialog-based scene specification and is able to handle prompts in a language that is not well-supported by the underlying diffusion model.

LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models paper page: github: Recent advancements in text-to-image generation with diffusion models have yielded remarkable results synthesizing highly realistic and diverse images. However, these models still encounter difficulties when generating images from prompts that demand spatial or common sense reasoning. We propose to equip diffusion models with enhanced reasoning capabilities by using off-the-shelf pretrained large language models (LLMs) in a novel two-stage generation process. First, we adapt an LLM to be a text-guided layout generator through in-context learning. When provided with an image prompt, an LLM outputs a scene layout in the form of bounding boxes along with corresponding individual descriptions. Second, we steer a diffusion model with a novel controller to generate images conditioned on the layout. Both stages utilize frozen pretrained models without any LLM or diffusion model parameter optimization. We validate the superiority of our design by demonstrating its ability to outperform the base diffusion model in accurately generating images according to prompts that necessitate both language and spatial reasoning. Additionally, our method naturally allows dialog-based scene specification and is able to handle prompts in a language that is not well-supported by the underlying diffusion model.

AK

83,657 görüntüleme • 2 yıl önce

Used the updated Build Web Apps plugin that uses GPT-Image-2 to design a UI and assets to recreate Windows 3.1 incl. a virtual filesystem for the MS-DOS prompt. Naturally the next step was to use Browser Use to interact with it and play Minesweeper 😂 Note: Once the cursor is starting to move the video is unedited

Used the updated Build Web Apps plugin that uses GPT-Image-2 to design a UI and assets to recreate Windows 3.1 incl. a virtual filesystem for the MS-DOS prompt. Naturally the next step was to use Browser Use to interact with it and play Minesweeper 😂 Note: Once the cursor is starting to move the video is unedited

dominik kundel

37,219 görüntüleme • 1 ay önce

🏦 For a small taste of how good our new Gemini 2.5 Pro model is at generating UI elements: 👇Check out how well it was able to replicate a screenshot of an example bank statement - even creating a similar logo, and preserving all of the text: Google AI Studio

👩‍💻 Paige Bailey

16,052 görüntüleme • 1 yıl önce

🔥 Battle for the top reasoning LLM intensifies! The QwQ-32B-Preview is a very good reasoning LLM. Full video of my tests here: Summary of my findings and thoughts: It was able to solve a couple of hard math problems so it looks very promising for maths. It didn’t do so well on my coding task (generating bash script). By the results reported on the LiveCodeBench it has room for improvement. One thing that’s become very clear to me is that the reasoning capabilities of these LLMs are significantly closing the gap between the open and closed-sourced models. The competition is now going to be on a different level and it's going to be focused on which model produces the most efficient, optimized, accurate, and fastest reasoning steps beyond just accurate responses. That's what developers will care about. Traditional benchmarks are not going to be good enough for this. On that note, it's getting harder to assess these models, especially the consistency, efficiency, and quality of reasoning steps. After experimenting with this model, I realized that the reasoning paths are not fully optimized and there is a lot more optimization that needs to happen before these models are used in production settings. There might be a need to build some type of native and efficient self-assessment or self-reflection capability that prevents these reasoning LLMs to go in loops or produce unnecessary lengthy sequences. I also noticed that this model, at least from the HF demo, doesn’t separate the reasoning from the response. I think that actually hurts the performance of the model. On the other hand, o1 and R1 do that really well. In addition to that, I believe the training on reasoning is hurting the performance of the LLM in other areas such as helpfulness (check the code example in the video). Something that’s necessary at the moment is validating or evaluating the quality of the reasoning chains and figuring out a better strategy to optimize them. Current methods are probably not sufficient to solve this problem but that's where innovation will comes next. I recognize that this is a first effort so kudos to the Qwen team on this release. These issues highlight the importance of transparency with reasoning LLMs. We need to know how it was trained and with exact data or optimization strategy. Understanding that will enable researchers and developers to build better intuition and improve the reasoning capabilities and components at a faster rate. There is an opportunity for someone or a company to build a truly open-reasoning LLM. The race is on! I will continue to track the state-of-the-art in reasoning LLMs and report my takes and observations here. Stay tuned for more.

🔥 Battle for the top reasoning LLM intensifies! The QwQ-32B-Preview is a very good reasoning LLM. Full video of my tests here: Summary of my findings and thoughts: It was able to solve a couple of hard math problems so it looks very promising for maths. It didn’t do so well on my coding task (generating bash script). By the results reported on the LiveCodeBench it has room for improvement. One thing that’s become very clear to me is that the reasoning capabilities of these LLMs are significantly closing the gap between the open and closed-sourced models. The competition is now going to be on a different level and it's going to be focused on which model produces the most efficient, optimized, accurate, and fastest reasoning steps beyond just accurate responses. That's what developers will care about. Traditional benchmarks are not going to be good enough for this. On that note, it's getting harder to assess these models, especially the consistency, efficiency, and quality of reasoning steps. After experimenting with this model, I realized that the reasoning paths are not fully optimized and there is a lot more optimization that needs to happen before these models are used in production settings. There might be a need to build some type of native and efficient self-assessment or self-reflection capability that prevents these reasoning LLMs to go in loops or produce unnecessary lengthy sequences. I also noticed that this model, at least from the HF demo, doesn’t separate the reasoning from the response. I think that actually hurts the performance of the model. On the other hand, o1 and R1 do that really well. In addition to that, I believe the training on reasoning is hurting the performance of the LLM in other areas such as helpfulness (check the code example in the video). Something that’s necessary at the moment is validating or evaluating the quality of the reasoning chains and figuring out a better strategy to optimize them. Current methods are probably not sufficient to solve this problem but that's where innovation will comes next. I recognize that this is a first effort so kudos to the Qwen team on this release. These issues highlight the importance of transparency with reasoning LLMs. We need to know how it was trained and with exact data or optimization strategy. Understanding that will enable researchers and developers to build better intuition and improve the reasoning capabilities and components at a faster rate. There is an opportunity for someone or a company to build a truly open-reasoning LLM. The race is on! I will continue to track the state-of-the-art in reasoning LLMs and report my takes and observations here. Stay tuned for more.

elvis

14,740 görüntüleme • 1 yıl önce

We just released Gemma 4 — our most intelligent open models to date. Built from the same world-class research as Gemini 3, Gemma 4 brings breakthrough intelligence directly to your own hardware for advanced reasoning and agentic workflows. Released under a commercially permissive Apache 2.0 license so anyone can build powerful AI tools. 🧵↓

We just released Gemma 4 — our most intelligent open models to date. Built from the same world-class research as Gemini 3, Gemma 4 brings breakthrough intelligence directly to your own hardware for advanced reasoning and agentic workflows. Released under a commercially permissive Apache 2.0 license so anyone can build powerful AI tools. 🧵↓

Google

7,728,598 görüntüleme • 2 ay önce

Introducing ImagineArt 2.0 - The world's most realistic image generation model! 🧠 Reasoning-based prompt adherence 🎬 Cinematic photography ⚡ Concept to final in seconds 🔤 Text rendering that actually works This is what happens when an image model stops generating and starts reasoning.

Introducing ImagineArt 2.0 - The world's most realistic image generation model! 🧠 Reasoning-based prompt adherence 🎬 Cinematic photography ⚡ Concept to final in seconds 🔤 Text rendering that actually works This is what happens when an image model stops generating and starts reasoning.

ImagineArt

714,500 görüntüleme • 2 ay önce

Introducing the new Pixel 10a. It has everything you need — like a completely flat, durable design, more battery life, advanced AI tools and a best-in-class camera system* — for just $499. *Among smartphones under $500 (MSRP) in the U.S. Based on third-party evaluation of Pixel 10a.

Introducing the new Pixel 10a. It has everything you need — like a completely flat, durable design, more battery life, advanced AI tools and a best-in-class camera system* — for just $499. *Among smartphones under $500 (MSRP) in the U.S. Based on third-party evaluation of Pixel 10a.

Google

84,161 görüntüleme • 3 ay önce

this is getting weird 🤯 used Operator to access Google AI Studio used Gemini 2.0 to create instructions to build a website instructed Operator to use the instructions to create a portfolio website on replit THIS IS NOT A TOOL TO BOOK FLIGHTS!!! 🚨 THIS IS AN INTELLIGENT AGENT THAT CAN DO COGNITIVE WORK FOR HUMANS IN THE INFORMATION LAYER OF HUMANITY THAT WE CALL THE WEB.

this is getting weird 🤯 used Operator to access Google AI Studio used Gemini 2.0 to create instructions to build a website instructed Operator to use the instructions to create a portfolio website on replit THIS IS NOT A TOOL TO BOOK FLIGHTS!!! 🚨 THIS IS AN INTELLIGENT AGENT THAT CAN DO COGNITIVE WORK FOR HUMANS IN THE INFORMATION LAYER OF HUMANITY THAT WE CALL THE WEB.

Dan Mac

332,182 görüntüleme • 1 yıl önce

We used Gemini 3.1 Pro to build a realistic city planner app. 🏙️ Watch how the model tackles complex terrain, maps out infrastructure, and simulates traffic to generate a high-quality visualization.

We used Gemini 3.1 Pro to build a realistic city planner app. 🏙️ Watch how the model tackles complex terrain, maps out infrastructure, and simulates traffic to generate a high-quality visualization.

Google DeepMind

14,571,910 görüntüleme • 3 ay önce

Want to clone a website's design DNA? 🧬 MCP plugin (just added to MCP Marketplace) for Cline lets you: • Feed it any website for inspiration • Extract design patterns • Analyze content structure • Map navigation flows Real example: - Analyzed - Extracted typography choices - Mapped navigation patterns - Studied section layouts Result? A new website that captures the essence while maintaining unique identity. Think of it as design inspiration on autopilot: Input → Analysis → Adaptation → Creation Key insight: You're not copying—you're learning from the best and making it your own. Question: What website's DNA would you want to study?

Want to clone a website's design DNA? 🧬 MCP plugin (just added to MCP Marketplace) for Cline lets you: • Feed it any website for inspiration • Extract design patterns • Analyze content structure • Map navigation flows Real example: - Analyzed - Extracted typography choices - Mapped navigation patterns - Studied section layouts Result? A new website that captures the essence while maintaining unique identity. Think of it as design inspiration on autopilot: Input → Analysis → Adaptation → Creation Key insight: You're not copying—you're learning from the best and making it your own. Question: What website's DNA would you want to study?

Cline

77,732 görüntüleme • 1 yıl önce

Who let the DOGE 🐕 out? We did: ckDOGE brings new functionality to DOGE that enables it to be more than just a symbolic, passive asset. It can now become active capital: • used in DeFi • integrated into onchain apps • potentially generating yield Fun fact: ckDOGE transactions are 10–100× faster and cheaper than native Several ICP ecosystem projects are already working on integrations. In the meantime, here's a demo 👇

Who let the DOGE 🐕 out? We did: ckDOGE brings new functionality to DOGE that enables it to be more than just a symbolic, passive asset. It can now become active capital: • used in DeFi • integrated into onchain apps • potentially generating yield Fun fact: ckDOGE transactions are 10–100× faster and cheaper than native Several ICP ecosystem projects are already working on integrations. In the meantime, here's a demo 👇

DFINITY Foundation

20,434 görüntüleme • 2 ay önce

branching interface for writing a tighter creative feedback loop guided by a language model to weave through the possibility space of text

branching interface for writing a tighter creative feedback loop guided by a language model to weave through the possibility space of text

Azlen

51,705 görüntüleme • 1 yıl önce

The plot thickens. 🕵️ Use Gemini 2.5 Pro in the Google Gemini to turn random videos from your camera roll into a dramatic narrative. Try it yourself: 1️⃣ Go to Gemini and select “2.5 Pro” from the model dropdown. 2️⃣ Tap the plus icon and upload three videos from your camera roll. (Pro/Ultra subscribers, try using your Veo creations! 🎥) 3️⃣ Ask Gemini to write a short story based on the videos. Here’s our prompt to get you started: “Come up with a premise for an epic adventure based on these videos.”

The plot thickens. 🕵️ Use Gemini 2.5 Pro in the Google Gemini to turn random videos from your camera roll into a dramatic narrative. Try it yourself: 1️⃣ Go to Gemini and select “2.5 Pro” from the model dropdown. 2️⃣ Tap the plus icon and upload three videos from your camera roll. (Pro/Ultra subscribers, try using your Veo creations! 🎥) 3️⃣ Ask Gemini to write a short story based on the videos. Here’s our prompt to get you started: “Come up with a premise for an epic adventure based on these videos.”

Google

164,366 görüntüleme • 11 ay önce

Today we’re releasing a preview of Gemini 3.1 Pro and making it available to our users and developers. Very excited to bring the upgraded core we used in Deep Think to everyone. Learn more about Gemini 3.1 Pro:

Today we’re releasing a preview of Gemini 3.1 Pro and making it available to our users and developers. Very excited to bring the upgraded core we used in Deep Think to everyone. Learn more about Gemini 3.1 Pro:

koray kavukcuoglu

105,158 görüntüleme • 3 ay önce

Welcome to Shipmas Day 3 part 1… the big one. 🚢🎁 Today, we remove the ceiling on what you can generate. Starting today, our new default agent is Thinking with Gemini 3 Pro. This agent uses advanced reasoning to "think" through your design before writing a single line of code. The community has been calling Gemini 3 "too good at frontend" and "unmatched in visual reasoning." We agree. It handles complex dashboards, intricate DOM manipulation, and pixel-perfect CSS better than anything we've tested. All designs are backed by code and exportable to your favorite coding agent. The video below shows some single prompt examples to inspire you. Sound on for a preview of the new Stitch theme song 🔊

Welcome to Shipmas Day 3 part 1… the big one. 🚢🎁 Today, we remove the ceiling on what you can generate. Starting today, our new default agent is Thinking with Gemini 3 Pro. This agent uses advanced reasoning to "think" through your design before writing a single line of code. The community has been calling Gemini 3 "too good at frontend" and "unmatched in visual reasoning." We agree. It handles complex dashboards, intricate DOM manipulation, and pixel-perfect CSS better than anything we've tested. All designs are backed by code and exportable to your favorite coding agent. The video below shows some single prompt examples to inspire you. Sound on for a preview of the new Stitch theme song 🔊

Stitch by Google

192,180 görüntüleme • 6 ay önce

The latest Visual Studio Code release brings auto model selection (preview) - a way to have the best model picked for you based on current capacity and performance. Currently being rolled out to all GitHub Copilot users in Visual Studio Code starting with individuals. Learn more:

The latest Visual Studio Code release brings auto model selection (preview) - a way to have the best model picked for you based on current capacity and performance. Currently being rolled out to all GitHub Copilot users in Visual Studio Code starting with individuals. Learn more:

Visual Studio Code

67,645 görüntüleme • 9 ay önce