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Super excited about HydraFusion in GitHub Copilot, and what it shows about the shift from model selection to model orchestration. By bringing together multiple models to plan, build, critique, and complete coding tasks, it can deliver outcomes at up to 67% lower cost. It’s a great example of the...

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Satya Nadella

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220,226 görüntüleme • 19 saat önce •via X (Twitter)

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🆕 Kimi.ai's Kimi K2.7 Code is now generally available in GitHub Copilot. This is the first open-weight model offered as a selectable option in the Copilot model picker. 🎉 Early testing shows Kimi K2.7 is a lower-cost option with strong performance comparable to highly popular frontier models. Try it out now in Visual Studio Code and look out for it in more GitHub Copilot surfaces. 👇
0:55

🆕 Kimi.ai's Kimi K2.7 Code is now generally available in GitHub Copilot. This is the first open-weight model offered as a selectable option in the Copilot model picker. 🎉 Early testing shows Kimi K2.7 is a lower-cost option with strong performance comparable to highly popular frontier models. Try it out now in Visual Studio Code and look out for it in more GitHub Copilot surfaces. 👇

GitHub

200,393 görüntüleme • 2 ay önce

📣 Kimi.ai's Kimi K3, an open-weight model, is now generally available and rolling out in GitHub Copilot. The model shows frontier-level abilities on agentic coding with highly cost-effective pricing. It is hosted by Fireworks. Learn more. 👇
1:05

📣 Kimi.ai's Kimi K3, an open-weight model, is now generally available and rolling out in GitHub Copilot. The model shows frontier-level abilities on agentic coding with highly cost-effective pricing. It is hosted by Fireworks. Learn more. 👇

GitHub

235,928 görüntüleme • 29 gün önce

Our new Gemini 3.5 Flash is our strongest agentic and coding model yet, delivering frontier-level performance at 4x the speed of comparable frontier models at less than half the cost. We had Tulsee Doshi break down what you need to know about it.
1:24

Our new Gemini 3.5 Flash is our strongest agentic and coding model yet, delivering frontier-level performance at 4x the speed of comparable frontier models at less than half the cost. We had Tulsee Doshi break down what you need to know about it.

Google

77,764 görüntüleme • 3 ay önce

Heard about the new GitHub Copilot coding agent? 👀 Here's how it can free you up to focus on more creative and high-impact tasks. ⬇️
0:39

Heard about the new GitHub Copilot coding agent? 👀 Here's how it can free you up to focus on more creative and high-impact tasks. ⬇️

GitHub

32,149 görüntüleme • 1 yıl önce

Today we're shipping Nemotron 3 Ultra. A 550B MoE frontier-intelligence open model built for long-running agents. It delivers 5x faster inference and lowers the cost of complex agentic tasks by up to 30% versus other open frontier models.
2:59

Today we're shipping Nemotron 3 Ultra. A 550B MoE frontier-intelligence open model built for long-running agents. It delivers 5x faster inference and lowers the cost of complex agentic tasks by up to 30% versus other open frontier models.

NVIDIA AI

1,328,821 görüntüleme • 3 ay önce

Introducing Smart Routing in Unity AI Gateway. Stop overpaying for AI. Smart Routing matches each coding task to the right model and harness based on what the task needs, so higher-cost models can differentiate on intelligence while lower-cost models differentiate on cost and performance. Match frontier quality and cut task costs by 30%+.
0:37

Introducing Smart Routing in Unity AI Gateway. Stop overpaying for AI. Smart Routing matches each coding task to the right model and harness based on what the task needs, so higher-cost models can differentiate on intelligence while lower-cost models differentiate on cost and performance. Match frontier quality and cut task costs by 30%+.

Databricks

125,402 görüntüleme • 22 gün önce

Introducing Critique, a new multi-model deep research system in M365 Copilot. You can use multiple models together to generate optimal responses and reports.
0:50

Introducing Critique, a new multi-model deep research system in M365 Copilot. You can use multiple models together to generate optimal responses and reports.

Satya Nadella

1,393,030 görüntüleme • 5 ay önce

Sometimes one model gets stuck on a bug. Instead of trying the same path again, Evan Boyle shows how to use GitHub Copilot CLI to run multiple models in parallel and explore different possible root causes.
0:55

Sometimes one model gets stuck on a bug. Instead of trying the same path again, Evan Boyle shows how to use GitHub Copilot CLI to run multiple models in parallel and explore different possible root causes.

Microsoft Developer

21,820 görüntüleme • 5 ay önce

Introducing NVIDIA Nemotron 3 Ultra. A frontier smart open model built for long-running agents that need to plan, reason, use tools and keep working across complex coding, research and enterprise workflows. Up to 5x faster inference and up to 30% lower cost for agentic tasks. Learn more:
0:24

Introducing NVIDIA Nemotron 3 Ultra. A frontier smart open model built for long-running agents that need to plan, reason, use tools and keep working across complex coding, research and enterprise workflows. Up to 5x faster inference and up to 30% lower cost for agentic tasks. Learn more:

NVIDIA

228,735 görüntüleme • 3 ay önce

Agent Categories and Costs are now in the Agent Leaderboard! Two questions guide model selection: task-specific performance and cost to completion. Based on analysis of 1.7M+ real-world Agent Mode sessions, the leaderboard now lets users: - Filter the leaderboard by Code, Work, and Chat to compare model performance by task type - View real cost-per-task for each model, including the Pareto frontier of the most cost-efficient options at each performance level
0:38

Agent Categories and Costs are now in the Agent Leaderboard! Two questions guide model selection: task-specific performance and cost to completion. Based on analysis of 1.7M+ real-world Agent Mode sessions, the leaderboard now lets users: - Filter the leaderboard by Code, Work, and Chat to compare model performance by task type - View real cost-per-task for each model, including the Pareto frontier of the most cost-efficient options at each performance level

Arena.ai

29,227 görüntüleme • 18 gün önce

Larry Ellison says Oracle is building nuclear reactors to power "acres" of GPU clusters and it will cost companies $100 billion in the next 3 years to build a frontier AI model and stay in the race
1:09

Larry Ellison says Oracle is building nuclear reactors to power "acres" of GPU clusters and it will cost companies $100 billion in the next 3 years to build a frontier AI model and stay in the race

Tsarathustra

1,017,223 görüntüleme • 2 yıl önce

Former Google CEO Eric Schmidt says a frontier AI model costs up to $500 million to build and is worth about six months: "Are the scaling laws stopping?" "And the answer right now is no, which is unbelievable." "A model costs $200 to $500 million, you release it, and it has a life expectancy of about six months." "And then there's another one." "And the competition between the leading players is so brutal, right, that they literally count in the number of weeks of value of these things, which costs hundreds of millions of dollars." Schmidt is talking about the build cost. There is a second cost underneath it, which is re-proving each replacement, because whatever testing cleared one version does not carry over to the next. A company that standardizes on a frontier model this year is signing up to do that re-approval work twice a year. - Eric Schmidt (Eric Schmidt), former CEO of Google (Google) and now Chair and CEO of Relativity Space, with Alan Blinder at Princeton's Griswold Center (Princeton University).
0:34

Former Google CEO Eric Schmidt says a frontier AI model costs up to $500 million to build and is worth about six months: "Are the scaling laws stopping?" "And the answer right now is no, which is unbelievable." "A model costs $200 to $500 million, you release it, and it has a life expectancy of about six months." "And then there's another one." "And the competition between the leading players is so brutal, right, that they literally count in the number of weeks of value of these things, which costs hundreds of millions of dollars." Schmidt is talking about the build cost. There is a second cost underneath it, which is re-proving each replacement, because whatever testing cleared one version does not carry over to the next. A company that standardizes on a frontier model this year is signing up to do that re-approval work twice a year. - Eric Schmidt (Eric Schmidt), former CEO of Google (Google) and now Chair and CEO of Relativity Space, with Alan Blinder at Princeton's Griswold Center (Princeton University).

Karl Mehta

492,962 görüntüleme • 26 gün önce

To build more housing, we need to lower the cost of building new homes–reduce fees, lower the cost of capital, speed up permitting, and streamline regulations. We’re already doing it in San Jose. We can do it for every Californian.
0:24

To build more housing, we need to lower the cost of building new homes–reduce fees, lower the cost of capital, speed up permitting, and streamline regulations. We’re already doing it in San Jose. We can do it for every Californian.

Mahan HQ

44,503 görüntüleme • 7 ay önce

Introducing Entelligence AI Model Router, dynamic turn based routing trained on engineering tasks that cuts costs by over 50% Automatically route every coding turn to the model with the best balance of quality, latency, and cost. Escalate to frontier models when they’re actually needed Balanced mode reduces spend by 46% per session Eco mode helps teams save > 76%
1:00

Introducing Entelligence AI Model Router, dynamic turn based routing trained on engineering tasks that cuts costs by over 50% Automatically route every coding turn to the model with the best balance of quality, latency, and cost. Escalate to frontier models when they’re actually needed Balanced mode reduces spend by 46% per session Eco mode helps teams save > 76%

Aiswarya Sankar

84,305 görüntüleme • 1 ay önce

Nvidia has just announced Alpamayo 2 Super, an open 34 billion parameter reasoning vision-language-action model designed to accelerate the development of autonomous vehicles. This new model combines the NVIDIA Cosmos 3 Super reasoning model with a 2 billion parameter diffusion-based action expert model, and is post trained with reinforcement learning. The model can return multiple outputs: future trajectory plans, reasoning traces, grounded answers to questions about the scenes, and auto label generation. The model weights are now available for anyone to download on Hugging Face, and the inference code has been posted to GitHub. Distilled models can be deployed commercially without any further permission from Nvidia, and model outputs carry no license conditions. Automakers can distill down a compact version of this model that can run on the Nvidia computer in the car. Major kudos to Nvidia and Jensen Huang for advancing the state of the industry by releasing this as an open model with permissive licensing. Jensen isn't just paying lip service to the idea of open models, Nvidia is actually contributing to the ecosystem — and it's great for their business, because it helps sell more Thor computers that go in the car. Anyone can go download the model and play with it. If you do, let me know what you think. Personally I think it's so cool that we have open weights models that are this advanced, for anyone to download.
2:30

Nvidia has just announced Alpamayo 2 Super, an open 34 billion parameter reasoning vision-language-action model designed to accelerate the development of autonomous vehicles. This new model combines the NVIDIA Cosmos 3 Super reasoning model with a 2 billion parameter diffusion-based action expert model, and is post trained with reinforcement learning. The model can return multiple outputs: future trajectory plans, reasoning traces, grounded answers to questions about the scenes, and auto label generation. The model weights are now available for anyone to download on Hugging Face, and the inference code has been posted to GitHub. Distilled models can be deployed commercially without any further permission from Nvidia, and model outputs carry no license conditions. Automakers can distill down a compact version of this model that can run on the Nvidia computer in the car. Major kudos to Nvidia and Jensen Huang for advancing the state of the industry by releasing this as an open model with permissive licensing. Jensen isn't just paying lip service to the idea of open models, Nvidia is actually contributing to the ecosystem — and it's great for their business, because it helps sell more Thor computers that go in the car. Anyone can go download the model and play with it. If you do, let me know what you think. Personally I think it's so cool that we have open weights models that are this advanced, for anyone to download.

Whole Mars Catalog

45,595 görüntüleme • 1 ay önce

I joined Anthropic last week! It’s obvious to me that we’re bottlenecked not by model capabilities, but by creativity and understanding. I’ll be building demos and prototypes that highlight new capabilities and share what we learn about building using these models at Anthropic. Excited to build with the best team and the best models.
0:25

I joined Anthropic last week! It’s obvious to me that we’re bottlenecked not by model capabilities, but by creativity and understanding. I’ll be building demos and prototypes that highlight new capabilities and share what we learn about building using these models at Anthropic. Excited to build with the best team and the best models.

Thariq

165,430 görüntüleme • 1 yıl önce

How to use GitHub Actions to build a pipeline that tests your code, trains a model, and publishes a new version of it. Bonus: I let GitHub Workspace Copilot do a lot of the work. (This is the best "AI developer" I've tested so far by a mile.) Here is the full video:
18:00

How to use GitHub Actions to build a pipeline that tests your code, trains a model, and publishes a new version of it. Bonus: I let GitHub Workspace Copilot do a lot of the work. (This is the best "AI developer" I've tested so far by a mile.) Here is the full video:

Santiago

148,109 görüntüleme • 2 yıl önce

Think of Model Context Protocol as a USB port for your AI. 🔌 You can plug in documentation, tools, and context directly into Copilot CLI. Scott Hanselman 🌮 shows how to set it all up. 👇
4:07

Think of Model Context Protocol as a USB port for your AI. 🔌 You can plug in documentation, tools, and context directly into Copilot CLI. Scott Hanselman 🌮 shows how to set it all up. 👇

GitHub

44,392 görüntüleme • 7 ay önce

What a week at #GoogleCloudNext in Las Vegas ✨ From sessions to showfloor demos to networking events, we were excited to partner with Google Cloud to advance how the AI ecosystem can build, deploy, and innovate in the cloud. Looking forward to working together to advance AI agents, physical AI, and beyond.
0:29

What a week at #GoogleCloudNext in Las Vegas ✨ From sessions to showfloor demos to networking events, we were excited to partner with Google Cloud to advance how the AI ecosystem can build, deploy, and innovate in the cloud. Looking forward to working together to advance AI agents, physical AI, and beyond.

NVIDIA

32,762 görüntüleme • 4 ay önce

Apple built a large foundation model and fine-tuned it on multiple tasks. But they are doing something very clever: They load a single model in memory and use different adapters to specialize the model on the fly. I recorded a video to show you how to write the code to do the same thing Apple is doing. I explain everything step by step. Here is what I'll show you in the video: 1. We'll load two datasets 2. Then load a large model 3. Then, we'll fine-tune the model on both datasets I'll use LoRA to fine-tune the model. This process creates two small adapters, each specializing in solving one of the datasets. The base model's original parameters will remain unchanged. From here: 4. We'll generate a list of tasks 5. We'll load the correct adapter to solve each task The large model I'm using needs 346 MB of memory, but I only need to load it once. Each adapter is only 2.7 MB. I only need to load the base model once and pair it with any of the fine-tuned adapters. Minimum memory footprint and I can solve multiple tasks. Hope this helps!
38:02

Apple built a large foundation model and fine-tuned it on multiple tasks. But they are doing something very clever: They load a single model in memory and use different adapters to specialize the model on the fly. I recorded a video to show you how to write the code to do the same thing Apple is doing. I explain everything step by step. Here is what I'll show you in the video: 1. We'll load two datasets 2. Then load a large model 3. Then, we'll fine-tune the model on both datasets I'll use LoRA to fine-tune the model. This process creates two small adapters, each specializing in solving one of the datasets. The base model's original parameters will remain unchanged. From here: 4. We'll generate a list of tasks 5. We'll load the correct adapter to solve each task The large model I'm using needs 346 MB of memory, but I only need to load it once. Each adapter is only 2.7 MB. I only need to load the base model once and pair it with any of the fine-tuned adapters. Minimum memory footprint and I can solve multiple tasks. Hope this helps!

Santiago

84,747 görüntüleme • 2 yıl önce