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Choose a model (any model) and build your application with it. Do not spend time swapping models early on. Do not try to optimize before you have a working system. This is one of the first recommendations I make to every new team I consult with. Eventually, it will...

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Santiago

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12,014 görüntüleme • 7 ay önce •via X (Twitter)

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The Microsoft AI model family expands in Microsoft Foundry with 7 new models. A complete multimodal stack: Text, image, transcription, and voice, ready for developers to build with under one set of governance and security controls. #MSBuild
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The Microsoft AI model family expands in Microsoft Foundry with 7 new models. A complete multimodal stack: Text, image, transcription, and voice, ready for developers to build with under one set of governance and security controls. #MSBuild

Microsoft

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Tune Studio is an end-to-end platform for developing applications using Large Language Models. So far, I haven't seen any other platform like this one. You can do everything here: 1. You can curate your data. 2. Use the playground to play with different models and try your ideas. 3. Fine-tune an open-source model on your data. 4. Deploy the model when you are done. This is awesome for anyone building generative AI applications. You can use Tune Studio to work with any of the open-source models out there. They were one of the few companies to host Llama 2 and Llama 3 before anyone else. Here is a link to check it out: One of their main selling points is that Tune Studio scales! You don't have to worry about serving your model to lots of users. They also have built-in user management, authentication, on-prem support, user context management, and pretty much everything you need to build generative AI applications. Thanks to the Tune team for collaborating with me on this post. We are living through the best years of development tools for AI developers. The field is unstoppable.
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Tune Studio is an end-to-end platform for developing applications using Large Language Models. So far, I haven't seen any other platform like this one. You can do everything here: 1. You can curate your data. 2. Use the playground to play with different models and try your ideas. 3. Fine-tune an open-source model on your data. 4. Deploy the model when you are done. This is awesome for anyone building generative AI applications. You can use Tune Studio to work with any of the open-source models out there. They were one of the few companies to host Llama 2 and Llama 3 before anyone else. Here is a link to check it out: One of their main selling points is that Tune Studio scales! You don't have to worry about serving your model to lots of users. They also have built-in user management, authentication, on-prem support, user context management, and pretty much everything you need to build generative AI applications. Thanks to the Tune team for collaborating with me on this post. We are living through the best years of development tools for AI developers. The field is unstoppable.

Santiago

39,101 görüntüleme • 2 yıl önce

Hosted Agents in Microsoft Foundry is now Generally Available! This brings agent native compute for your agents with any framework, any language, and any model 🚀 Build with the GitHub Copilot App, connect to Microsoft IQ, run in Foundry, securely interact with Microsoft Teams, govern in Agent 365, and optimize to compound your learnings continuously -- such a valuable combo. Check out the demo / blog below to see it in action!
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Hosted Agents in Microsoft Foundry is now Generally Available! This brings agent native compute for your agents with any framework, any language, and any model 🚀 Build with the GitHub Copilot App, connect to Microsoft IQ, run in Foundry, securely interact with Microsoft Teams, govern in Agent 365, and optimize to compound your learnings continuously -- such a valuable combo. Check out the demo / blog below to see it in action!

Jeff Hollan

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🧳 Bring Your Own AI Models to GitHub Copilot in VS Code Use your own Azure-hosted AI models with GitHub Copilot in VS Code. See how Bring Your Own Key (BYOK) lets you connect models from Microsoft Foundry, choose which models appear in Copilot, and use the right model for different coding tasks.
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🧳 Bring Your Own AI Models to GitHub Copilot in VS Code Use your own Azure-hosted AI models with GitHub Copilot in VS Code. See how Bring Your Own Key (BYOK) lets you connect models from Microsoft Foundry, choose which models appear in Copilot, and use the right model for different coding tasks.

Visual Studio Code

38,434 görüntüleme • 16 gün önce

Small Language Models (SML) are the future of AI. "Small" (SML) instead of "Large" (LLM). These small models are highly specialized models with superhuman abilities on specific tasks. Here are two techniques to build these models: • Spectrum • Model Merging I give you a short introduction in the attached video, but here is a quick summary: Spectrum helps us identify the most relevant layers to solve one specific task. We can ignore everything else and focus on fine-tuning these layers. Using Spectrum, we can fine-tune models in a heartbeat. Model Merging combines multiple models into a unique, much better model than any of the individual input models. You can also combine models specialized in different tasks and get a model with multiple abilities. This is the state of the art of productizing models. It's what Arcee.ai's platform does behind the scenes. Arcee collaborated with me on this post and is sponsoring it. There are three main steps to produce a model for your particular use case: 1. You create a dataset by uploading your data. 2. You train a model. At this step, Arcee uses Spectrum and Model Merging to produce a highly specialized model for your task. 3. You can deploy that model to any environment you want. Three important notes: • Training process is 2x faster and 2x cheaper than regular fine-tuning. • Resultant models are smaller and have higher accuracy. • They create these specialized models from open-source models. Check this site so you can fully appreciate how this works: If you want to fine-tune an open-source model, consider Arcee's platform. This is the state of the art.
2:52

Small Language Models (SML) are the future of AI. "Small" (SML) instead of "Large" (LLM). These small models are highly specialized models with superhuman abilities on specific tasks. Here are two techniques to build these models: • Spectrum • Model Merging I give you a short introduction in the attached video, but here is a quick summary: Spectrum helps us identify the most relevant layers to solve one specific task. We can ignore everything else and focus on fine-tuning these layers. Using Spectrum, we can fine-tune models in a heartbeat. Model Merging combines multiple models into a unique, much better model than any of the individual input models. You can also combine models specialized in different tasks and get a model with multiple abilities. This is the state of the art of productizing models. It's what Arcee.ai's platform does behind the scenes. Arcee collaborated with me on this post and is sponsoring it. There are three main steps to produce a model for your particular use case: 1. You create a dataset by uploading your data. 2. You train a model. At this step, Arcee uses Spectrum and Model Merging to produce a highly specialized model for your task. 3. You can deploy that model to any environment you want. Three important notes: • Training process is 2x faster and 2x cheaper than regular fine-tuning. • Resultant models are smaller and have higher accuracy. • They create these specialized models from open-source models. Check this site so you can fully appreciate how this works: If you want to fine-tune an open-source model, consider Arcee's platform. This is the state of the art.

Santiago

164,162 görüntüleme • 2 yıl önce

The Biggest Risk is Models Moving into the App Layer: " The models are becoming agentic. It's possible the model is all you need. Imagine if you wanted the model to update medical benefits information. If the model is agentic and it is integrated into the database; you don't need anything else in the middle." How do you stop yourself becoming a data repository which an agentic layer feeds off Nicolas Sharp Marc Benioff Brian Halligan Tuomo Riekki Keith Peiris
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The Biggest Risk is Models Moving into the App Layer: " The models are becoming agentic. It's possible the model is all you need. Imagine if you wanted the model to update medical benefits information. If the model is agentic and it is integrated into the database; you don't need anything else in the middle." How do you stop yourself becoming a data repository which an agentic layer feeds off Nicolas Sharp Marc Benioff Brian Halligan Tuomo Riekki Keith Peiris

Harry Stebbings

47,055 görüntüleme • 10 ay önce

Alex Finn reveals that you need to spend $20,000 to get the equivalent of Opus 4.6 on a local model "The advantages are that you aren’t paying for tokens, all you have to pay for is electricity.” "The fact you're not paying for tokens means you can run your model 24/7 365, you can't do that with Opus" “If you run it locally it’s a completely private system. Nothing you say is private if you're not on a local model”
1:36

Alex Finn reveals that you need to spend $20,000 to get the equivalent of Opus 4.6 on a local model "The advantages are that you aren’t paying for tokens, all you have to pay for is electricity.” "The fact you're not paying for tokens means you can run your model 24/7 365, you can't do that with Opus" “If you run it locally it’s a completely private system. Nothing you say is private if you're not on a local model”

Mikli

85,206 görüntüleme • 7 ay önce

Engram cofounder Jack Morris just raised $98M to build a new type of AI. He says models don't need to get smarter over time. Instead, they just need to know you better and better over time. Jack describes what he's building: "Our product is a new type of AI. We have a pretty different vision from a lot of the frontier labs, which are working on one model per lab, and trying to make that model smarter every month." "There's another way to think about it, which is that the model doesn't need to get smarter every month. It needs to know you better." "So we're working on a whole different stack, which is a way to train models that train themselves to know your world better and adjust to the things that you say." "So: new ways of training, new ways of running the models."
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Engram cofounder Jack Morris just raised $98M to build a new type of AI. He says models don't need to get smarter over time. Instead, they just need to know you better and better over time. Jack describes what he's building: "Our product is a new type of AI. We have a pretty different vision from a lot of the frontier labs, which are working on one model per lab, and trying to make that model smarter every month." "There's another way to think about it, which is that the model doesn't need to get smarter every month. It needs to know you better." "So we're working on a whole different stack, which is a way to train models that train themselves to know your world better and adjust to the things that you say." "So: new ways of training, new ways of running the models."

TBPN

70,635 görüntüleme • 3 ay önce

How to use 50+ API keys (models) for FREE on OpenClaw API??? - go to - login or register your account - click on "more models" - click on "use case" and select what you need it for - choose the model and open it - click on "view code" → "Generate API key" many models don't allow direct deploy, so use the "view code" button to generate API access basically Nvidia NIM gives you the ability to test almost any model from their list for FREE some of them are not worse than GPT 5.2 or Claude Opus 4.6, some might even perform better depending on the task how to understand if a model is efficient and compare it with others??? - go to - type the model name in search - click on "benchmarks" - you’ll see performance tests and rankings this way you can easily compare free models with paid ones of course there are RPM limits, on many models it’s around ~40 requests per minute each model is different, after generating the API key, RPM limits are shown in the top-right corner nothing stops you from using them, many work perfectly fine, super solid option for first tests and for learning OpenClaw or any other system where you need an AI API model
1:54

How to use 50+ API keys (models) for FREE on OpenClaw API??? - go to - login or register your account - click on "more models" - click on "use case" and select what you need it for - choose the model and open it - click on "view code" → "Generate API key" many models don't allow direct deploy, so use the "view code" button to generate API access basically Nvidia NIM gives you the ability to test almost any model from their list for FREE some of them are not worse than GPT 5.2 or Claude Opus 4.6, some might even perform better depending on the task how to understand if a model is efficient and compare it with others??? - go to - type the model name in search - click on "benchmarks" - you’ll see performance tests and rankings this way you can easily compare free models with paid ones of course there are RPM limits, on many models it’s around ~40 requests per minute each model is different, after generating the API key, RPM limits are shown in the top-right corner nothing stops you from using them, many work perfectly fine, super solid option for first tests and for learning OpenClaw or any other system where you need an AI API model

Ronin

58,974 görüntüleme • 7 ay önce

MICROSOFT JUST OPEN SOURCED THE ENTIRE AGENT RUNTIME, SO YOU NEVER HAVE TO BUILD ONE AGAIN a raw model only writes text. to make it a real agent it needs a loop, tools, memory, planning, approvals, and a way to know when it is done. Microsoft's Agent Harness is that whole layer, and it ships with everything on by default. you bring the model and the tools, it wires up the rest: > an automatic tool calling loop with iteration limits > history saved after every call, so a crash resumes instead of starting over > context compaction that summarizes when the window fills > a persistent todo list plus plan and execute modes > file memory that survives across turns > tool approval with standing rules and auto approval > OpenTelemetry tracing and web search built in > Agent Skills that load domain expertise on demand the mental model is simple: the model is the brain, the harness is the body around it. and it works with whatever you already run: > Azure OpenAI, OpenAI, Anthropic, and GitHub Copilot, and it hosts on Microsoft Foundry you spin one up in a single line: agent = create_harness_agent(client, name, instructions) open source, Python, .NET, and Go, stable since mid 2026. bookmark this. Microsoft already built the hard part of every AI agent, you just plug in the model.
0:23

MICROSOFT JUST OPEN SOURCED THE ENTIRE AGENT RUNTIME, SO YOU NEVER HAVE TO BUILD ONE AGAIN a raw model only writes text. to make it a real agent it needs a loop, tools, memory, planning, approvals, and a way to know when it is done. Microsoft's Agent Harness is that whole layer, and it ships with everything on by default. you bring the model and the tools, it wires up the rest: > an automatic tool calling loop with iteration limits > history saved after every call, so a crash resumes instead of starting over > context compaction that summarizes when the window fills > a persistent todo list plus plan and execute modes > file memory that survives across turns > tool approval with standing rules and auto approval > OpenTelemetry tracing and web search built in > Agent Skills that load domain expertise on demand the mental model is simple: the model is the brain, the harness is the body around it. and it works with whatever you already run: > Azure OpenAI, OpenAI, Anthropic, and GitHub Copilot, and it hosts on Microsoft Foundry you spin one up in a single line: agent = create_harness_agent(client, name, instructions) open source, Python, .NET, and Go, stable since mid 2026. bookmark this. Microsoft already built the hard part of every AI agent, you just plug in the model.

NO1ennn

69,163 görüntüleme • 1 ay önce

The entire AI industry is racing to build the smartest model. Satya Nadella just admitted that is not where the money is. The model is not the product. The harness is. That is the exact line. And it changes what Microsoft is actually competing on. OpenAI, Anthropic, Google, xAI, Meta every frontier lab is pouring hundreds of billions into training compute, chasing the next capability jump. Each betting that raw model intelligence is the moat. Microsoft is doing the opposite. It is building the harness the orchestration layer that sits above the model, connecting it to tools, data, permissions, sub-agents, and enterprise workflows. And it is letting OpenAI, Anthropic, and MAI compete to plug into it. "You need the model. But the model is not the product. The harness is." So do the math on what a harness actually does. A raw model dropped into an enterprise answers questions. That is a chatbot. A harness turns that same model into an agent that reads the SharePoint, edits the ERP entry, pulls the GitHub PR, updates Salesforce, and files the Excel report with the right permissions, the right audit trail, and the right sub-agent for each sub-task. The model provides the intelligence. The harness converts intelligence into work. Now here's where it gets interesting. "Even the best model in the world will feel broken without a great harness. And an okay model with a great harness can feel like magic." If that is true, the enterprise buyer is not buying model quality. The enterprise buyer is buying the harness. Which means model quality becomes a commodity input over time, and harness quality becomes the sustainable moat. Compare that to the strategy the entire frontier lab industry is executing. Everyone else is chasing the numerator raw intelligence. Almost nobody at scale is racing to build the denominator the orchestration layer that determines whether that intelligence can actually be deployed profitably inside a real company. The frontier model race has a 10 to 20 percent chance of producing a single dominant winner. Nadella just told the industry he does not need to be that winner. If OpenAI wins, Microsoft wins. If Anthropic wins, Microsoft wins. If MAI wins, Microsoft wins. If someone Microsoft has never heard of trains a better model in 2027, Microsoft still wins. Because the compute they train on, the harness they get plugged into, the enterprise contracts they get delivered through, and the products they sit inside are all Microsoft. He is not building the best AI model. He is building the layer that the best AI model has to run on to make anyone money. I wonder which position looks more valuable in ten years.
3:19

The entire AI industry is racing to build the smartest model. Satya Nadella just admitted that is not where the money is. The model is not the product. The harness is. That is the exact line. And it changes what Microsoft is actually competing on. OpenAI, Anthropic, Google, xAI, Meta every frontier lab is pouring hundreds of billions into training compute, chasing the next capability jump. Each betting that raw model intelligence is the moat. Microsoft is doing the opposite. It is building the harness the orchestration layer that sits above the model, connecting it to tools, data, permissions, sub-agents, and enterprise workflows. And it is letting OpenAI, Anthropic, and MAI compete to plug into it. "You need the model. But the model is not the product. The harness is." So do the math on what a harness actually does. A raw model dropped into an enterprise answers questions. That is a chatbot. A harness turns that same model into an agent that reads the SharePoint, edits the ERP entry, pulls the GitHub PR, updates Salesforce, and files the Excel report with the right permissions, the right audit trail, and the right sub-agent for each sub-task. The model provides the intelligence. The harness converts intelligence into work. Now here's where it gets interesting. "Even the best model in the world will feel broken without a great harness. And an okay model with a great harness can feel like magic." If that is true, the enterprise buyer is not buying model quality. The enterprise buyer is buying the harness. Which means model quality becomes a commodity input over time, and harness quality becomes the sustainable moat. Compare that to the strategy the entire frontier lab industry is executing. Everyone else is chasing the numerator raw intelligence. Almost nobody at scale is racing to build the denominator the orchestration layer that determines whether that intelligence can actually be deployed profitably inside a real company. The frontier model race has a 10 to 20 percent chance of producing a single dominant winner. Nadella just told the industry he does not need to be that winner. If OpenAI wins, Microsoft wins. If Anthropic wins, Microsoft wins. If MAI wins, Microsoft wins. If someone Microsoft has never heard of trains a better model in 2027, Microsoft still wins. Because the compute they train on, the harness they get plugged into, the enterprise contracts they get delivered through, and the products they sit inside are all Microsoft. He is not building the best AI model. He is building the layer that the best AI model has to run on to make anyone money. I wonder which position looks more valuable in ten years.

Vikram M

21,463 görüntüleme • 2 ay önce

You can now try Llama 3.1 405B for free (link below)! ​ This is the largest open-source model out there, and for the first time, an open model is competitive with closed models. ​ This time around, Meta did something new: Llama 3.1 has a license that allows developers to use it to enhance other models. For the first time, you can distill Llama 3.1 405B's capabilities into a smaller, more practical model for your use case. ​ First, here is the link where you can play with Llama 3.1 for free: ​ The model is hosted in Tune Studio, an end-to-end platform for developing applications using Large Language Models. They are sponsoring this post. ​ Take a look at the attached video. It will show you how you can fine-tune a simple model using Llama 3.1 without leaving the platform: ​ 1. You can create an empty dataset 2. Use the playground to generate and record interactions with Llama 3.1 3. Modify the dataset directly using the playground 4. Export the data and fine-tune a smaller model ​ Fast and easy! ​ As long as you have a web browser, you can start experimenting with fine-tuning and Llama 3.1. That's all it takes!
0:46

You can now try Llama 3.1 405B for free (link below)! ​ This is the largest open-source model out there, and for the first time, an open model is competitive with closed models. ​ This time around, Meta did something new: Llama 3.1 has a license that allows developers to use it to enhance other models. For the first time, you can distill Llama 3.1 405B's capabilities into a smaller, more practical model for your use case. ​ First, here is the link where you can play with Llama 3.1 for free: ​ The model is hosted in Tune Studio, an end-to-end platform for developing applications using Large Language Models. They are sponsoring this post. ​ Take a look at the attached video. It will show you how you can fine-tune a simple model using Llama 3.1 without leaving the platform: ​ 1. You can create an empty dataset 2. Use the playground to generate and record interactions with Llama 3.1 3. Modify the dataset directly using the playground 4. Export the data and fine-tune a smaller model ​ Fast and easy! ​ As long as you have a web browser, you can start experimenting with fine-tuning and Llama 3.1. That's all it takes!

Santiago

55,609 görüntüleme • 2 yıl önce

Satya Nadella was asked why Microsoft, with a $175 billion AI budget, still doesn't have a frontier model. Satya Nadella: "We are well on our way building our MAI models our goal is to basically hill climb from the bottom, by the way, not distilling anything." "My advice is more like use all but be independent of all. I would pull out a model and see whether I can retain the eval. If I can't, that means you really are dependent on something that may or may not be yours." "If you're a hyperscaler, you're not a supplier to two model companies, that's not a business we need more." "I worked on Windows interrupt with Unix first. In fact, it was counterintuitive, right ? We used to think, oh my god, this means we'll be less used, except we were more used we were able to penetrate the enterprise primarily because we did that interrupt work." If owning the infrastructure layer was the Microsoft plan and winning move, why is the same company now racing to build its own frontier model ?
3:41

Satya Nadella was asked why Microsoft, with a $175 billion AI budget, still doesn't have a frontier model. Satya Nadella: "We are well on our way building our MAI models our goal is to basically hill climb from the bottom, by the way, not distilling anything." "My advice is more like use all but be independent of all. I would pull out a model and see whether I can retain the eval. If I can't, that means you really are dependent on something that may or may not be yours." "If you're a hyperscaler, you're not a supplier to two model companies, that's not a business we need more." "I worked on Windows interrupt with Unix first. In fact, it was counterintuitive, right ? We used to think, oh my god, this means we'll be less used, except we were more used we were able to penetrate the enterprise primarily because we did that interrupt work." If owning the infrastructure layer was the Microsoft plan and winning move, why is the same company now racing to build its own frontier model ?

Vikram M

712,353 görüntüleme • 11 gün önce

Microsoft CEO Satya Nadella on why winning against ChatGPT, Gemini, and Claude was never the goal: The Hard Fork hosts ask him directly how Microsoft plans to overtake the competition in the AI model race. His answer reframes the entire question. "Our real goal is to get everyone across the ecosystem to the frontier." Satya explains the problem with how frontier models are currently built. You hill climb, you do reinforcement learning, and then you need data. But at this point, the world has essentially saturated publicly available data. So the only way to keep scaling is to pull data from everywhere. He asks: "What if you turn that around and said no, there's a base model that has reasoning, that has the agent loop, but you can bring it into your RL. Every company." This is where his thinking gets interesting. Satya Nadella argues that the future of the firm runs on human capital and token capital together: "If the future of the firm is human capital and token capital, I want every balance sheet, every income statement in every company to have both." AI becomes a financial asset sitting on a company's books the same way its people do. And Microsoft's role in this? To provide the best possible base model. One that companies build on top of with their own data, their own context, their own weights. One they can even replace. That last part is the striking bit. Satya is explicitly building a platform where customers are free to walk away. He frames it not as a risk, but as the whole point: "I always ask the question — why does Microsoft, or why does the world need Microsoft? And if we are successful, can the world around us be successful? This, I believe, is a more sustainable way to go at it."
1:21

Microsoft CEO Satya Nadella on why winning against ChatGPT, Gemini, and Claude was never the goal: The Hard Fork hosts ask him directly how Microsoft plans to overtake the competition in the AI model race. His answer reframes the entire question. "Our real goal is to get everyone across the ecosystem to the frontier." Satya explains the problem with how frontier models are currently built. You hill climb, you do reinforcement learning, and then you need data. But at this point, the world has essentially saturated publicly available data. So the only way to keep scaling is to pull data from everywhere. He asks: "What if you turn that around and said no, there's a base model that has reasoning, that has the agent loop, but you can bring it into your RL. Every company." This is where his thinking gets interesting. Satya Nadella argues that the future of the firm runs on human capital and token capital together: "If the future of the firm is human capital and token capital, I want every balance sheet, every income statement in every company to have both." AI becomes a financial asset sitting on a company's books the same way its people do. And Microsoft's role in this? To provide the best possible base model. One that companies build on top of with their own data, their own context, their own weights. One they can even replace. That last part is the striking bit. Satya is explicitly building a platform where customers are free to walk away. He frames it not as a risk, but as the whole point: "I always ask the question — why does Microsoft, or why does the world need Microsoft? And if we are successful, can the world around us be successful? This, I believe, is a more sustainable way to go at it."

Big Brain AI

11,770 görüntüleme • 2 ay önce

Codex doesn't lock you into OpenAI models. Tibo Tibo on why: "If you are part of this community and building an excellent coding harness, why would you couple it to your model? That felt quite disappointing, to make that decision. It didn't feel right. And it's open source in the first place. It would have been trivial for anyone to fork it. But then you're encouraging people to go and use that fork, and now suddenly you have overhead and the only reason you have a fork is because you wanted to change 10 lines of code to add support for another model provider. That feels very silly. So why not just support it in the first place? The other thing is we benefit a lot from being able to give optionality. Maybe today you love using OpenAI models and you're super productive with them, but tomorrow there's a new model that comes out and you want to try that. Why force you to completely change your setup just to try a new model? Optionality is very important to companies that we work with. And this is something that we absolutely lean into."
1:50

Codex doesn't lock you into OpenAI models. Tibo Tibo on why: "If you are part of this community and building an excellent coding harness, why would you couple it to your model? That felt quite disappointing, to make that decision. It didn't feel right. And it's open source in the first place. It would have been trivial for anyone to fork it. But then you're encouraging people to go and use that fork, and now suddenly you have overhead and the only reason you have a fork is because you wanted to change 10 lines of code to add support for another model provider. That feels very silly. So why not just support it in the first place? The other thing is we benefit a lot from being able to give optionality. Maybe today you love using OpenAI models and you're super productive with them, but tomorrow there's a new model that comes out and you want to try that. Why force you to completely change your setup just to try a new model? Optionality is very important to companies that we work with. And this is something that we absolutely lean into."

The Pragmatic Engineer

198,981 görüntüleme • 15 gün önce

OpenAI researcher David Holtz reveals workers who don't adapt their prompting to newer models leave 50% of the alpha on the table because half of AI's value comes from how you interact with it: "We ran a lab experiment. We give people the same task to do, but we give them models of different capabilities. How much of the improvement when we give people more capable models comes from the fact that we gave them a better model vs the fact that they actually update and adapt the way they prompt and interact with these models?" "For tasks where precision matters, where you have some output you're trying to get and you're trying to steer the model, which is different from a creative task like brainstorming." "For these steering-based tasks, when you give people a better model, about 50% of the improvement we see in how well they're able to complete the task comes from changes in how they interact with the model." "If you just kept the way you interact with the model static and just made the capabilities better and better, you'd leave 50% of the alpha basically on the table." OpenAI
2:59

OpenAI researcher David Holtz reveals workers who don't adapt their prompting to newer models leave 50% of the alpha on the table because half of AI's value comes from how you interact with it: "We ran a lab experiment. We give people the same task to do, but we give them models of different capabilities. How much of the improvement when we give people more capable models comes from the fact that we gave them a better model vs the fact that they actually update and adapt the way they prompt and interact with these models?" "For tasks where precision matters, where you have some output you're trying to get and you're trying to steer the model, which is different from a creative task like brainstorming." "For these steering-based tasks, when you give people a better model, about 50% of the improvement we see in how well they're able to complete the task comes from changes in how they interact with the model." "If you just kept the way you interact with the model static and just made the capabilities better and better, you'd leave 50% of the alpha basically on the table." OpenAI

MTS

15,590 görüntüleme • 1 ay önce

Today we’re taking a big step on the path toward AGI and releasing Gemini 3— our most intelligent model yet. With Gemini 3, you can bring any idea to life. It is state-of-the-art in reasoning, the best model in the world for multimodal understanding, and our best agentic and vibe coding model.
0:51

Today we’re taking a big step on the path toward AGI and releasing Gemini 3— our most intelligent model yet. With Gemini 3, you can bring any idea to life. It is state-of-the-art in reasoning, the best model in the world for multimodal understanding, and our best agentic and vibe coding model.

Google AI

493,436 görüntüleme • 10 ay önce

.Anish Acharya says the future of AI isn’t one model to rule them all—and explains why platforms that integrate multiple models will benefit the most: "I think we're going to need and rely on all of the models." "It's sort of like if you have a team of people... if you have five people, they could all do a basic set of things pretty capably." "But then they all have their specializations. Maybe one of them is really good at closing a customer who doesn't want to sign the deal, and one of them is really good at culture and getting the best out of the team." "There are some areas in which they are going to build apps, and that will be a threat to app companies. But there are many areas in which app companies are advantaged. Cursor and Krea are great examples of this—products where you benefit from being multi-model." "When you actually use a creative tool, you don't want to just use Nano Banana, you want to have access to OpenAI, Nano Banana, Kling—all of them—Qwen, you name it. So using a single interface to access all the models is powerful." Anish Acharya on BILLIONS with Guillaume Moubeche
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.Anish Acharya says the future of AI isn’t one model to rule them all—and explains why platforms that integrate multiple models will benefit the most: "I think we're going to need and rely on all of the models." "It's sort of like if you have a team of people... if you have five people, they could all do a basic set of things pretty capably." "But then they all have their specializations. Maybe one of them is really good at closing a customer who doesn't want to sign the deal, and one of them is really good at culture and getting the best out of the team." "There are some areas in which they are going to build apps, and that will be a threat to app companies. But there are many areas in which app companies are advantaged. Cursor and Krea are great examples of this—products where you benefit from being multi-model." "When you actually use a creative tool, you don't want to just use Nano Banana, you want to have access to OpenAI, Nano Banana, Kling—all of them—Qwen, you name it. So using a single interface to access all the models is powerful." Anish Acharya on BILLIONS with Guillaume Moubeche

a16z

33,291 görüntüleme • 6 ay önce