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Factory's Taylor Savage argues that Claude Code and Codex aren't necessarily the best tools for their own AI models, and third-party harnesses can actually outperform them: "One thing that I deeply believed before I started at Factory was that the harnesses created by the model companies are going to... be the best for their models. Claude Code is going to be the best for Anthropic models, obviously, and Codex is going to be the best for OpenAI models, obviously." "Starting at Factory, I realized that is just not true at all. You can actually do a lot better than those harnesses, certainly on specific types of workloads, but also even in the general case. Different incentives than you have if you’re building a third-party harness." "Keeping them separate also allows you to not get too overfit towards one model. You need to be able to delegate between the different models, not just models from one particular company." "Like Shelby Cobra, you’ve got tuners of cars that can take a car that’s built by Ford and make it work better than it rolled off the factory line. You get things coming out from one OEM, and then you’ve got other people that tune it aftermarket. I think that’s absolutely true with harnesses and models." Factoryshow more

MTS

565,192 subscribers

17,400 просмотров • 10 дней назад •via X (Twitter)

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Комментарии: 10

Фото профиля Shaun Maguire

Shaun Maguire10 дней назад

@EnoReyes @TaylorTheSavage Nailed it

Фото профиля Ann Bordetsky

Ann Bordetsky10 дней назад

@TaylorTheSavage I spot @TaylorTheSavage crushing his first MTS segment 🔥

Фото профиля Max

Max10 дней назад

@TaylorTheSavage agreed mr taylor is a wise man

Фото профиля Asmir

Asmir10 дней назад

@TaylorTheSavage how much of that gap is just better sandboxes and context handling around the model, and not the coding tool itself?

Фото профиля Jadu 👐

Jadu 👐10 дней назад

@TaylorTheSavage the cross-model delegation bit is what I’d actually benchmark lol

Фото профиля Brenden House

Brenden House10 дней назад

@EnoReyes @TaylorTheSavage what do you guys think about prime intellects, prime agent?

Фото профиля Travis

Travis10 дней назад

@TaylorTheSavage The AI version of deleting your car.

Фото профиля memorIA

memorIA9 дней назад

@TaylorTheSavage I'd love @FactoryAI @droid to build an example on this. I've been having the top tier monthly for months and right know I'd like to come back... give me a resason and I will like many others. Some real benchamark, like coding, etc. Not 3D games please.

Фото профиля Little K

Little K10 дней назад

@TaylorTheSavage Makes sense from the incentive side. A lab's own harness is tuned to show off its model, a third party only wins if the task gets done. I already write with one model and proofread with another, the second one catches what the first keeps missing.

Фото профиля GUBA

GUBA10 дней назад

@TaylorTheSavage opencode FTW

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Anthropic Technical Staff Jess Yan says the harness and the model cannot be pulled apart without giving up performance, though she flags her own bias. "I think I am quite biased but I also think that it is impossible to get the maximum possible performance without tying together the harness and the model." "Now the components of the harness and maybe the thickness of the harness will change over time as models get more and more capable." "However, when we test our models and when we are assessing their performance we always have to test it in conjunction with a harness." "And are we going to test it with all the different harnesses of the world? We're going to select the harnesses that we have built." "And so there is an aspect of the necessity of building models is that you have to be testing them with harnesses, and that sort of keeps them paired together."

Anthropic Technical Staff Jess Yan says the harness and the model cannot be pulled apart without giving up performance, though she flags her own bias. "I think I am quite biased but I also think that it is impossible to get the maximum possible performance without tying together the harness and the model." "Now the components of the harness and maybe the thickness of the harness will change over time as models get more and more capable." "However, when we test our models and when we are assessing their performance we always have to test it in conjunction with a harness." "And are we going to test it with all the different harnesses of the world? We're going to select the harnesses that we have built." "And so there is an aspect of the necessity of building models is that you have to be testing them with harnesses, and that sort of keeps them paired together."

Fireside Alpha

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Sam on Kimi, distillation, and open source: “I have always assumed that there are going to be great cheap models in the world, and we better be the greatest and the cheapest. You get a better deal today, at least at a particular latency, using OpenAI's models than Kimi. We distill our own models. That’s how we make smaller, cheaper models. That’s a very good thing to do. There will clearly be an important place for open source models in the world and people that will want their own weights. We have so much usage of our models that we do not need to be a gigantically high-margin business to be able to afford model training. I would rather people not distill from us, for sure. But this is not in my top ten list of worries.”

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Patrick OShaughnessy

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a16z

36,067 просмотров • 2 месяцев назад

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Rohan Paul

32,317 просмотров • 1 месяц назад

Sriram Krishnan reveals why it's easier to defend American code with a Chinese model than an American one right now: "I don't think it is great that the leading open weight models or open source models are not American. There's some great innovation happening with Moonshot and with DeepSeek and with Qwen, but I would much rather prefer that the leading models are American." "Linus Torvalds of Linux fame had a law that given enough eyes, all bugs are shallow. Open weight models are inherently secure because when you download a model off Hugging Face, the entire world can take it apart, inspect it, fine-tune it, look at it in ways that you absolutely cannot if they are closed." "There was an incident with Hugging Face that got reported on earlier today. Hugging Face seems to have found that somebody was using an AI LLM agent to hammer them at multiple places and to find ways to break in." "The way to counter that is to make sure American or Western or allies defenders have access to the best models to make our software just more secure." "It may be harder to look at your own code for exploits with Fable than it is when you use a Chinese model. That's just like a weird spot to be, and I think we should fix that." Sriram Krishnan

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MTS

61,234 просмотров • 3 месяцев назад

Is Traditional Software Engineering Dead? “Does this mean that traditional software engineering is dead? Absolutely not. Software engineers—even the ones who are not necessarily tuning or training AI models—these are now among the most leveraged people on earth. Sure, the guys who are training and tuning models are even more leveraged because they’re building the tool set that software engineers are using. But software engineers still have two massive advantages on you. First, they think in code, so they actually know what’s going on underneath. And all abstractions are leaky. So when you have a computer programming for you—when you have Claude Code or equivalent programming for you—it’s going to make mistakes. It’s going to have bugs. It’s going to have suboptimal architecture. So it’s not going to be quite right. And someone who understands what’s going on underneath will be able to plug the leaks as they occur. So if you want to build a well-architected application, if you want to be able to even specify a well-architected application, if you want to be able to make it run at high performance, if you want it to do its best, if you want to catch the bugs early, then you’re going to want to have a software engineering background. The traditional software engineer is going to be able to use these tools much better. And there are still many kinds of problems in software engineering that are out of scope for these AI programs today. The easiest way to think about those is problems that are outside of their data distribution. For example, if they need to do a binary sort or reverse a linked list, they’ve seen countless examples of that, so they’re extremely good at it. But when you start getting out of their domain—where you have to write very high-performance code, when you’re running on architectures that are novel or brand new, when you’re actually creating new things or solving new problems, then you still need to get in there and hand code it. At least until either there are so many of those examples that new models can be trained on them, or until these models can sufficiently reason at even higher levels of abstraction and crack it on their own… And remember: there is no demand for average. The average app—nobody wants it, at least as long as it’s not filling some niche that is filled by a superior app. The app that is better will win essentially a hundred percent of the market. Maybe there’s some small percentage that will bleed off to the second-best app because it does some little niche feature better than the main app, or it’s cheaper, or something of the sort. But generally speaking, people only want the best of anything. So the bad news is there’s no point in being number two or number three—like in the famous Glengarry Glen Ross scene where Alec Baldwin says, “First place gets a Cadillac Eldorado, second place gets a set of steak knives, and third place you’re fired.” That’s absolutely true in these winner-take-all markets. That’s the bad news: You have to be the best at something if you want to win. However, the set of things you can be best at is infinite. You can always find some niche that is perfect for you, and you can be the best at that thing. This goes back to an old tweet of mine where I said, “Become the best in the world at what you do. Keep redefining what you do until this is true.” And I think that still applies in this age of AI.”

Naval

864,427 просмотров • 7 месяцев назад

Nick Saban explains the difference between a mistake that deserves getting chewed out and one that deserves encouragement “I always sort of had a lot of respect for the fact that any player that’s a competitor wants to do well. And I think when you start with that, you have a better perspective because you look at things from their perspective, not your perspective. And I think that’s very helpful.” “Sometimes when guys are not doing intangible things, whether it’s giving effort or focusing or making mental errors or things that they can actually control, that is something that you can’t tolerate. And you really want to maybe sometimes get after a guy a little bit for it.” “But when guys are doing those things and just not having success, I think that’s when you want to pat them on the back and get them to believe in themselves and have an understanding if they continue to do things the right way, they will eventually have success.” “And every player’s different. Sometimes you got guys that are obsessive-compulsive type personalities that are really hard on themselves, and I don’t think you need to get on those guys because they’re already getting on themselves.” “Then you have other guys that may not be self-starters, and you got to push them a little bit more to get them to be what they’re capable of being. I think all those things were factors in how you kind of approach a player and try to get him to be the best he can be.” “I always tried to focus on the individual and not look at it like one size fits all, and try to take the best approach with that particular guy to help him have success.”

Nick Saban explains the difference between a mistake that deserves getting chewed out and one that deserves encouragement “I always sort of had a lot of respect for the fact that any player that’s a competitor wants to do well. And I think when you start with that, you have a better perspective because you look at things from their perspective, not your perspective. And I think that’s very helpful.” “Sometimes when guys are not doing intangible things, whether it’s giving effort or focusing or making mental errors or things that they can actually control, that is something that you can’t tolerate. And you really want to maybe sometimes get after a guy a little bit for it.” “But when guys are doing those things and just not having success, I think that’s when you want to pat them on the back and get them to believe in themselves and have an understanding if they continue to do things the right way, they will eventually have success.” “And every player’s different. Sometimes you got guys that are obsessive-compulsive type personalities that are really hard on themselves, and I don’t think you need to get on those guys because they’re already getting on themselves.” “Then you have other guys that may not be self-starters, and you got to push them a little bit more to get them to be what they’re capable of being. I think all those things were factors in how you kind of approach a player and try to get him to be the best he can be.” “I always tried to focus on the individual and not look at it like one size fits all, and try to take the best approach with that particular guy to help him have success.”

Josh Chambers

99,107 просмотров • 1 месяц назад

Elon Musk says Anthropic puts more care into AI safety than OpenAI and explains how rival companies should test one another’s models. “All the AI companies have a test harness, a series of tests that you give to any model to see if it’s going to build bioweapons or nuclear bombs or be deliberately deceptive. Everyone applying everyone else’s test harness is probably the best thing we can do to ensure our safety. We should try to do that as soon as possible.” “It’s the only thing I can think of that we could probably get all parties, including China, to agree to. You basically provide API access in advance of the model release. If they don’t solve what’s problematic, competitors can go public with the fact that they think the model being released is unsafe. The legal liability would be enormous.” “The two leading AI companies are Anthropic and OpenAI. Their models are quite close in capability, so it’s difficult for either one to slow down without handing the lead to the other. On balance, I think Anthropic puts more care into their safety than OpenAI. But even Anthropic acknowledges they are worried about their models. Many people from Anthropic have publicly voiced concern that their models are scaring them, that they’re getting scary smart.”

Elon Musk says Anthropic puts more care into AI safety than OpenAI and explains how rival companies should test one another’s models. “All the AI companies have a test harness, a series of tests that you give to any model to see if it’s going to build bioweapons or nuclear bombs or be deliberately deceptive. Everyone applying everyone else’s test harness is probably the best thing we can do to ensure our safety. We should try to do that as soon as possible.” “It’s the only thing I can think of that we could probably get all parties, including China, to agree to. You basically provide API access in advance of the model release. If they don’t solve what’s problematic, competitors can go public with the fact that they think the model being released is unsafe. The legal liability would be enormous.” “The two leading AI companies are Anthropic and OpenAI. Their models are quite close in capability, so it’s difficult for either one to slow down without handing the lead to the other. On balance, I think Anthropic puts more care into their safety than OpenAI. But even Anthropic acknowledges they are worried about their models. Many people from Anthropic have publicly voiced concern that their models are scaring them, that they’re getting scary smart.”

DogeDesigner

6,118,331 просмотров • 1 месяц назад

The easiest way to find out which models you can run on your computer: Magnitude is a 100% free, open-source desktop app that profiles your machine and ranks the models across: - Speed - Accuracy - Intelligence - Memory required Then it downloads the one you pick and tunes it for your machine. Finally, connect your favorite harness (Pi, OpenCode, Claude Code, Codex, etc.) in one click. Get started here: (don't forget to star 🌟) My co-founder wrote a detailed article on running your favorite harnesses with local models. The article is quoted below.

The easiest way to find out which models you can run on your computer: Magnitude is a 100% free, open-source desktop app that profiles your machine and ranks the models across: - Speed - Accuracy - Intelligence - Memory required Then it downloads the one you pick and tunes it for your machine. Finally, connect your favorite harness (Pi, OpenCode, Claude Code, Codex, etc.) in one click. Get started here: (don't forget to star 🌟) My co-founder wrote a detailed article on running your favorite harnesses with local models. The article is quoted below.

Avi Chawla

15,465 просмотров • 1 месяц назад

Arena's Anastasios Nikolas Angelopoulos says one of the hardest problems in alignment is that users often can't tell when the model is helping them or hurting them. "You really need alignment signals that are independent. We have three at Arena." "The first is that models take unauthorized actions, which means that you permission them in a certain way, but they break those permissions." "It can cause things like the Hugging Face incident. But it can also cause very mundane problems like losing important information within your company or on your own laptop." "The second signal is called deceptive completion." "The model will tell you that it did something, but it didn't actually do it." "Third is false attribution. It'll attribute intent to the user when that intent was not supposed to be there." "If models are able to do this, then certainly they're not perfectly safe for a user and they're not perfectly aligned to user intent. They might actually hurt people down the line."

Arena's Anastasios Nikolas Angelopoulos says one of the hardest problems in alignment is that users often can't tell when the model is helping them or hurting them. "You really need alignment signals that are independent. We have three at Arena." "The first is that models take unauthorized actions, which means that you permission them in a certain way, but they break those permissions." "It can cause things like the Hugging Face incident. But it can also cause very mundane problems like losing important information within your company or on your own laptop." "The second signal is called deceptive completion." "The model will tell you that it did something, but it didn't actually do it." "Third is false attribution. It'll attribute intent to the user when that intent was not supposed to be there." "If models are able to do this, then certainly they're not perfectly safe for a user and they're not perfectly aligned to user intent. They might actually hurt people down the line."

TBPN

19,433 просмотров • 15 дней назад

Why harnesses need to be neutral “You want to be able to switch models. The harness can be very tied to the model, or it can be neutral. The harness’s job is to take the model and interact with everything else, including data, APIs and memory. If you build memory in a way that is linked to the harness, and the harness is linked to the model, then you are kind of stuck.” Harrison Chase Love to hear your thoughts Charles Packer Hamel Husain swyx

Why harnesses need to be neutral “You want to be able to switch models. The harness can be very tied to the model, or it can be neutral. The harness’s job is to take the model and interact with everything else, including data, APIs and memory. If you build memory in a way that is linked to the harness, and the harness is linked to the model, then you are kind of stuck.” Harrison Chase Love to hear your thoughts Charles Packer Hamel Husain swyx

Harry Stebbings

23,760 просмотров • 6 дней назад

Jensen Huang: "I think he's being very conservative." He's talking about Dario's forecast that AI labs will make $1 trillion by 2030. On a podcast from the floor of Nvidia's GTC, the host read out Dario Amodei's numbers: hundreds of billions of dollars of model and agent revenue by 2027-28, and a trillion dollars by 2030. "I believe Dario and Anthropic is going to do way better than that. Way better than that." "And the reason for that is the one part that he hasn't considered is that I believe every single enterprise software company will also be a reseller, value-added reseller of Anthropic code, Anthropic's tokens, value-added reseller of OpenAI." "Deep specialization. I believe that these models... they're going to have general models that are connected into the software company's agentic system. Many of those models are Claude models and proprietary models, but many of those models are specialized sub-agents that they've trained on their own." "In fact, exactly right. We create a horizontal, but notice there are all these GSIs and all of these consultants who are specialists who then take your horizontal platform and specializes it." "So, I think that these platform companies have an opportunity to become that specialist, to become that vertical." Interested in the latest with ai/tech and business? Follow GeniusThinking and never fall behind. I track ChatGPT, Claude, and how every new agent is changing how we work and think, then hand you the pure signals.

Jensen Huang: "I think he's being very conservative." He's talking about Dario's forecast that AI labs will make $1 trillion by 2030. On a podcast from the floor of Nvidia's GTC, the host read out Dario Amodei's numbers: hundreds of billions of dollars of model and agent revenue by 2027-28, and a trillion dollars by 2030. "I believe Dario and Anthropic is going to do way better than that. Way better than that." "And the reason for that is the one part that he hasn't considered is that I believe every single enterprise software company will also be a reseller, value-added reseller of Anthropic code, Anthropic's tokens, value-added reseller of OpenAI." "Deep specialization. I believe that these models... they're going to have general models that are connected into the software company's agentic system. Many of those models are Claude models and proprietary models, but many of those models are specialized sub-agents that they've trained on their own." "In fact, exactly right. We create a horizontal, but notice there are all these GSIs and all of these consultants who are specialists who then take your horizontal platform and specializes it." "So, I think that these platform companies have an opportunity to become that specialist, to become that vertical." Interested in the latest with ai/tech and business? Follow GeniusThinking and never fall behind. I track ChatGPT, Claude, and how every new agent is changing how we work and think, then hand you the pure signals.

GeniusThinking

17,723 просмотров • 3 дней назад

Gabe on why he doesn't think Anthropic or OpenAI will own finance: "The reason people get confused when they look at app layer businesses like mine or Harvey or Legora or Sierra is because there's a spectrum of perpendicularity to what the labs are building. There's a whole bunch of stuff underneath the surface that the labs are never going to build, that we need to build for finance. All of finance is a collection of different niches with different data sets, different regulatory requirements. And we can get to $5 billion in revenue by going deep across those things and creating the systems of record that help manage them. That for Anthropic would be like stopping on the side of the road to pick up a penny, because they're on the pathway of trying to go from $100 billion in revenue to a trillion in revenue. Say you are a big public company buying another big public company and you need to send data back and forth. You actually need some sort of data room, something that is compliant, safe and secure. And I don't think OpenAI or Anthropic will ever want to build a data room business. If you actually want to be the exchange for all of high finance, you don't just need to own the intelligence, you need to own the transaction venue, the communication venue, the workflows, and all the data inputs that go into it. Think about the fundamental difference between Claude Code when it came out versus ChatGPT. The models were actually fairly similar, but the harness and the way that it was presented from Claude was far better. The way that you harness these models is so, so important."

Gabe on why he doesn't think Anthropic or OpenAI will own finance: "The reason people get confused when they look at app layer businesses like mine or Harvey or Legora or Sierra is because there's a spectrum of perpendicularity to what the labs are building. There's a whole bunch of stuff underneath the surface that the labs are never going to build, that we need to build for finance. All of finance is a collection of different niches with different data sets, different regulatory requirements. And we can get to $5 billion in revenue by going deep across those things and creating the systems of record that help manage them. That for Anthropic would be like stopping on the side of the road to pick up a penny, because they're on the pathway of trying to go from $100 billion in revenue to a trillion in revenue. Say you are a big public company buying another big public company and you need to send data back and forth. You actually need some sort of data room, something that is compliant, safe and secure. And I don't think OpenAI or Anthropic will ever want to build a data room business. If you actually want to be the exchange for all of high finance, you don't just need to own the intelligence, you need to own the transaction venue, the communication venue, the workflows, and all the data inputs that go into it. Think about the fundamental difference between Claude Code when it came out versus ChatGPT. The models were actually fairly similar, but the harness and the way that it was presented from Claude was far better. The way that you harness these models is so, so important."

Patrick OShaughnessy

85,501 просмотров • 1 месяц назад

Sam Altman says he is NOT worried about Chinese labs distilling OpenAI: "I would rather people not distill from us, for sure. Maybe I’m feeling too confident right now about our progress and the models that are coming. But this is not in my top ten list of worries.” “I haven’t thought deeply about the distillation issue. It’s clearly a top of mind issue now for a lot of people all of a sudden." "I have always assumed that there are going to be great, cheap models in the world, and we better be the greatest and the cheapest. And other people are going to do what they’re going to do. But I think we can just really win at our own game here." "We will have so much usage of our models that we do not need to be a gigantic high margin business to be able to afford model training." "So much of our future compute plans will be used to sell inference to customers that even if we can enjoy a modest margin on trillions of dollars of revenue, we can afford to train some giant models."

Sam Altman says he is NOT worried about Chinese labs distilling OpenAI: "I would rather people not distill from us, for sure. Maybe I’m feeling too confident right now about our progress and the models that are coming. But this is not in my top ten list of worries.” “I haven’t thought deeply about the distillation issue. It’s clearly a top of mind issue now for a lot of people all of a sudden." "I have always assumed that there are going to be great, cheap models in the world, and we better be the greatest and the cheapest. And other people are going to do what they’re going to do. But I think we can just really win at our own game here." "We will have so much usage of our models that we do not need to be a gigantic high margin business to be able to afford model training." "So much of our future compute plans will be used to sell inference to customers that even if we can enjoy a modest margin on trillions of dollars of revenue, we can afford to train some giant models."

dnap

91,523 просмотров • 2 месяцев назад

I tested Union Alpha again. It’s really bad at 3D and even SVGs. Not even close to the Flash models. Just look at the difference between them lol. I read that it’s from Spain. If that’s true, it could be a Mistral model, since many of their models use 256K context.

I tested Union Alpha again. It’s really bad at 3D and even SVGs. Not even close to the Flash models. Just look at the difference between them lol. I read that it’s from Spain. If that’s true, it could be a Mistral model, since many of their models use 256K context.

Bhavy☄️

15,523 просмотров • 1 месяц назад

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 be time to optimize the model. • You may need a cheaper model • You may need a faster model • You might need a smarter model Good luck if you stitched together 12 different APIs and SDKs from 7 different vendors. Over half of the companies I consult for run on Microsoft software and have access to Microsoft Foundry. Microsoft Foundry is a complete agentic ecosystem. If you're in that world and building AI applications, Microsoft Foundry is where everything lives: • Models (largest selection in the market) • Agentic SDK (Python, C#, JavaScript/TypeScript) • Tools • Evaluations • Monitoring They are fully integrated with GitHub and Visual Studio Code. The best part: Their agentic platform is fully agnostic of the models you use. You can integrate with any model using the same OpenAI-style API. Swapping one model for another takes 1 second.

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 be time to optimize the model. • You may need a cheaper model • You may need a faster model • You might need a smarter model Good luck if you stitched together 12 different APIs and SDKs from 7 different vendors. Over half of the companies I consult for run on Microsoft software and have access to Microsoft Foundry. Microsoft Foundry is a complete agentic ecosystem. If you're in that world and building AI applications, Microsoft Foundry is where everything lives: • Models (largest selection in the market) • Agentic SDK (Python, C#, JavaScript/TypeScript) • Tools • Evaluations • Monitoring They are fully integrated with GitHub and Visual Studio Code. The best part: Their agentic platform is fully agnostic of the models you use. You can integrate with any model using the same OpenAI-style API. Swapping one model for another takes 1 second.

Santiago

12,014 просмотров • 8 месяцев назад