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Hacktron founder s1r1us on why frontier models are still far ahead of open models for real-world hacking: "The frontier models are insanely capable. There's a huge gap. You don't even want to use Kimi K3 when you have access to GPT 5.6." "We can't really use Opus 5 anymore.... show more

MTS

521,999 subscribers

15,129 次观看 • 8 天前 •via X (Twitter)

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GGUFzy 的头像

GGUFzy7 天前

@S1r1u5_ I want which hand hold step still fails on the open ones.

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SITUATION EXPLAINED: What happens to open source when the US restricts access to frontier AI models? We asked Adrian Dittmann: "China is releasing these models [open source] to gain international dominance, people will work on these things and say, 'Hey, maybe I can contribute to this if it's open source.' This is the thing that actually gets them talent." "If you're not allowed into the next GPT release or you're not allowed to use Fable, then what are you gonna do? You're stuck with the existing stuff, or you will have to use an open source model that might have subtly better capabilities." "The United States will catch up to open source in some form as well. They seem to just be maximizing product creation." "The problem with open source models is you have to be kind of a nerd in order to use them properly. Consumer facing, the effects will be minimal because the average person doesn't care about open source models. They only care whether or not they can do a quick search with Gemini or ChatGPT."

SITUATION EXPLAINED: What happens to open source when the US restricts access to frontier AI models? We asked Adrian Dittmann: "China is releasing these models [open source] to gain international dominance, people will work on these things and say, 'Hey, maybe I can contribute to this if it's open source.' This is the thing that actually gets them talent." "If you're not allowed into the next GPT release or you're not allowed to use Fable, then what are you gonna do? You're stuck with the existing stuff, or you will have to use an open source model that might have subtly better capabilities." "The United States will catch up to open source in some form as well. They seem to just be maximizing product creation." "The problem with open source models is you have to be kind of a nerd in order to use them properly. Consumer facing, the effects will be minimal because the average person doesn't care about open source models. They only care whether or not they can do a quick search with Gemini or ChatGPT."

MTS

14,406 次观看 • 3 个月前

Sam Altman on Open Source AI's Future⁣ ⁣ "Right now, what everybody wants is just the most capable frontier coding model they can have. The big frontier models, even if we made them open source, are hard to run." — #sama

Sam Altman on Open Source AI's Future⁣ ⁣ "Right now, what everybody wants is just the most capable frontier coding model they can have. The big frontier models, even if we made them open source, are hard to run." — #sama

AI Insights

19,607 次观看 • 3 个月前

Announcing the Open Model Series: a new series on open source AI models, with merve of Hugging Face. Hugging Face is the open model hub: it hosts model weights, datasets, evals, & demo apps, and maintains the open-source libraries (like transformers) used to run and train them. I think of it like GitHub, but for AI models. You can download open models and run them on your own machines. In this series we'll cover what open source and open weight models are, how they work, how to run them yourself, and why they're important. By the way - this video is edited entirely with Claude! I can make a tutorial on how I did it. If there's a question you want answered about open models, leave it in the comments. We will read them all. Episode 1 is out soon.

Announcing the Open Model Series: a new series on open source AI models, with merve of Hugging Face. Hugging Face is the open model hub: it hosts model weights, datasets, evals, & demo apps, and maintains the open-source libraries (like transformers) used to run and train them. I think of it like GitHub, but for AI models. You can download open models and run them on your own machines. In this series we'll cover what open source and open weight models are, how they work, how to run them yourself, and why they're important. By the way - this video is edited entirely with Claude! I can make a tutorial on how I did it. If there's a question you want answered about open models, leave it in the comments. We will read them all. Episode 1 is out soon.

Harper Carroll

30,392 次观看 • 1 个月前

Stop paying for three AI subscriptions just to use the best models. One private platform gives you all of them, and it never records your prompts. Use chat, images, video, even a video editor. Every frontier model, live on Part 6: The AI Talk

Stop paying for three AI subscriptions just to use the best models. One private platform gives you all of them, and it never records your prompts. Use chat, images, video, even a video editor. Every frontier model, live on Part 6: The AI Talk

OpenGradient (∇, ∇)

14,683 次观看 • 2 个月前

.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

.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 次观看 • 6 个月前

In between major AI model breakthroughs, new models still ship, but they’re not fundamentally expanding what you can do with them. Braintrust CEO Ankur Goyal says that’s exactly when open source starts to surge: "When new models come out, people forget about open source and forget about economics because it changes fundamentally what you can do." "Programming is a good example. The workflow today is completely different than it was a year ago. If you tried to use models from a year ago today, you wouldn't be able to do crazy stuff like OpenClaw or whatever people are building nowadays." "When models don't change at that speed, people optimize the performance on the use cases that they have." "If you look at usage on our platform, almost half the token usage is coming from open source models from a very small number of companies that have figured out how to optimize use cases really well."

In between major AI model breakthroughs, new models still ship, but they’re not fundamentally expanding what you can do with them. Braintrust CEO Ankur Goyal says that’s exactly when open source starts to surge: "When new models come out, people forget about open source and forget about economics because it changes fundamentally what you can do." "Programming is a good example. The workflow today is completely different than it was a year ago. If you tried to use models from a year ago today, you wouldn't be able to do crazy stuff like OpenClaw or whatever people are building nowadays." "When models don't change at that speed, people optimize the performance on the use cases that they have." "If you look at usage on our platform, almost half the token usage is coming from open source models from a very small number of companies that have figured out how to optimize use cases really well."

TBPN

16,294 次观看 • 7 个月前

BREAKING: Only ~1% of Bending Spoons’ AI requests & tokens goes to frontier models. 99% runs on self-hosted open-weight models. CEO Luca Ferrari on the shift to Sovereign AI: “We end up using open-weight models we self-host, so these are basically free. I mean, there is a little bit of cloud cost, but for 99% of the requests and tokens.” “And the frontier models, generally closed weight, through APIs for maybe 1% of the requests, only for the most complex tasks.” “Sometimes we use them automatically to check the work that got done by the slightly less intelligent models, as a more senior engineer would with a more junior engineer.”

BREAKING: Only ~1% of Bending Spoons’ AI requests & tokens goes to frontier models. 99% runs on self-hosted open-weight models. CEO Luca Ferrari on the shift to Sovereign AI: “We end up using open-weight models we self-host, so these are basically free. I mean, there is a little bit of cloud cost, but for 99% of the requests and tokens.” “And the frontier models, generally closed weight, through APIs for maybe 1% of the requests, only for the most complex tasks.” “Sometimes we use them automatically to check the work that got done by the slightly less intelligent models, as a more senior engineer would with a more junior engineer.”

Molly O’Shea

241,276 次观看 • 7 天前

Sriram Krishnan on why Chinese models can distill off American models but American startups can't: "First of all, every model we have today is distilled off of all human knowledge. If you go back to the original GPT or the original Claude, they all had to derive off crawling the internet, crawling off all of our blogs and tweets and content. They kind of consume human knowledge to bootstrap that." "I'm going to steal this from Dean Meyer of Sequoia, who had a fantastic post yesterday. The situation which is bad today is that some of these models from other countries can train off American models, whereas if you are an American open weight model, it may be really confusing or challenging on whether you can distill off of other American models." "We kind of have a really uneven ecosystem here, where if you're a Chinese model, you could probably get a bunch of reasoning traces. But if you are a new Valley startup and you want to use some reasoning traces, you don't know what the legal situation is." "If you look at any American open source model today, they are using Chinese models as a teacher, or in a way as a part of the fine-tuning process. How do we make sure that is protected and enshrined, so if you're an American model company, you have the same level playing field as the Chinese models?" Sriram Krishnan

Sriram Krishnan on why Chinese models can distill off American models but American startups can't: "First of all, every model we have today is distilled off of all human knowledge. If you go back to the original GPT or the original Claude, they all had to derive off crawling the internet, crawling off all of our blogs and tweets and content. They kind of consume human knowledge to bootstrap that." "I'm going to steal this from Dean Meyer of Sequoia, who had a fantastic post yesterday. The situation which is bad today is that some of these models from other countries can train off American models, whereas if you are an American open weight model, it may be really confusing or challenging on whether you can distill off of other American models." "We kind of have a really uneven ecosystem here, where if you're a Chinese model, you could probably get a bunch of reasoning traces. But if you are a new Valley startup and you want to use some reasoning traces, you don't know what the legal situation is." "If you look at any American open source model today, they are using Chinese models as a teacher, or in a way as a part of the fine-tuning process. How do we make sure that is protected and enshrined, so if you're an American model company, you have the same level playing field as the Chinese models?" Sriram Krishnan

MTS

71,286 次观看 • 2 个月前

.L3Harris says Palantir helped them beat frontier AI models in less than 2 days, at 95% lower cost: "When we fine-tuned open source models trained on our own data, we were able to outperform the frontier models in less than 48 hours." "The cost of our fine-tuned open source model was 95% lower than the frontier models we were using." "AI is a commodity. It's all about the data. We view our data as a corporate asset. It's our unique hard-earned knowledge." "We believe American defense companies should not be a vassal for frontier AI labs, handing over our data and institutional knowledge, hoping to rent back the intelligence it creates." " We own the model, we own the compute, we own the advantage."

.L3Harris says Palantir helped them beat frontier AI models in less than 2 days, at 95% lower cost: "When we fine-tuned open source models trained on our own data, we were able to outperform the frontier models in less than 48 hours." "The cost of our fine-tuned open source model was 95% lower than the frontier models we were using." "AI is a commodity. It's all about the data. We view our data as a corporate asset. It's our unique hard-earned knowledge." "We believe American defense companies should not be a vassal for frontier AI labs, handing over our data and institutional knowledge, hoping to rent back the intelligence it creates." " We own the model, we own the compute, we own the advantage."

Jawwwn

1,103,053 次观看 • 16 天前

We literally could have less than 5 years left. The companies who are pushing the frontier of these AI models need to immediately figure out a way to control them. I’m talking about you Open AI, Anthropic, and Google.

We literally could have less than 5 years left. The companies who are pushing the frontier of these AI models need to immediately figure out a way to control them. I’m talking about you Open AI, Anthropic, and Google.

Brian Krassenstein

29,511 次观看 • 17 天前

Fascinating to hear how openly Krishna talked about the trade-offs around how Anthropic allocates compute: "If you buy too much compute, you go out of business. If you buy too little, you can't serve your customers and you're not at the frontier. Same thing." "There's a level of compute for model development we will not go below, even if it means it's harder to serve customers." "If we said to our employees, you can't use our models anymore, we could serve billions of dollars of revenue with that compute." "But we want to take a long-term view [because] the returns to frontier intelligence are extremely high, especially in enterprise."

Fascinating to hear how openly Krishna talked about the trade-offs around how Anthropic allocates compute: "If you buy too much compute, you go out of business. If you buy too little, you can't serve your customers and you're not at the frontier. Same thing." "There's a level of compute for model development we will not go below, even if it means it's harder to serve customers." "If we said to our employees, you can't use our models anymore, we could serve billions of dollars of revenue with that compute." "But we want to take a long-term view [because] the returns to frontier intelligence are extremely high, especially in enterprise."

Patrick OShaughnessy

83,688 次观看 • 4 个月前

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

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 次观看 • 7 个月前

We had some talks about booth models because not everyone seems to understand how they work. Booth models are almost always custom unless they're public uploads. You can't buy the booth models I use, they're custom and not publicly uploaded. Most of the models I use on stream are custom, some are purchased to help promote an avatar creator.

ChaoticMadness 😺😈 Queen of Chaos «VCVNTS»

60,666 次观看 • 9 个月前

.Decagon co-founder and CTO Ashwin Sreenivas says the choice between expensive frontier models and cheaper, less capable ones is a false trade-off: "Even if you have a 'dumber model,' you can get it to higher performance on that specific task." "When we fine-tune smaller, dumber models, it's that they're just not as general purpose, but on the specific task we want them to do, they actually outperform the large, smart, state-of-the-art models." "So we end up getting all three things. It is better at the task, it is cheaper, and it is faster." Ashwin Sreenivas

.Decagon co-founder and CTO Ashwin Sreenivas says the choice between expensive frontier models and cheaper, less capable ones is a false trade-off: "Even if you have a 'dumber model,' you can get it to higher performance on that specific task." "When we fine-tune smaller, dumber models, it's that they're just not as general purpose, but on the specific task we want them to do, they actually outperform the large, smart, state-of-the-art models." "So we end up getting all three things. It is better at the task, it is cheaper, and it is faster." Ashwin Sreenivas

a16z

36,067 次观看 • 1 个月前

Databricks' Ali Ghodsi on why most companies wouldn't even notice if frontier AI models stopped advancing: "The models are smart enough, but they just don't have the context that exists inside any organization. They have not been in every meeting. They don't know what's in everybody's heads. They don't know all the processes." "There's always a couple of employees who know everything in every organization. You go tap on their shoulder, and everybody's like, 'Oh my God, what would happen if he or she quits?' [The models] don't have that context." "If you just fused that and gave that context to the AI models... I think there are so many productivity gains you could get for any organization on the planet. For that, we actually don't need smarter models." "We don't need a smarter model that can solve Navier-Stokes or conjectures or do better on Humanity's Last Exam." "If the frontier doesn't advance, it doesn't actually matter, I think, for the vast majority of organizations on the planet. They're just so far behind in the adoption curve of actually automating things and getting value out of this stuff." Ali Ghodsi

Databricks' Ali Ghodsi on why most companies wouldn't even notice if frontier AI models stopped advancing: "The models are smart enough, but they just don't have the context that exists inside any organization. They have not been in every meeting. They don't know what's in everybody's heads. They don't know all the processes." "There's always a couple of employees who know everything in every organization. You go tap on their shoulder, and everybody's like, 'Oh my God, what would happen if he or she quits?' [The models] don't have that context." "If you just fused that and gave that context to the AI models... I think there are so many productivity gains you could get for any organization on the planet. For that, we actually don't need smarter models." "We don't need a smarter model that can solve Navier-Stokes or conjectures or do better on Humanity's Last Exam." "If the frontier doesn't advance, it doesn't actually matter, I think, for the vast majority of organizations on the planet. They're just so far behind in the adoption curve of actually automating things and getting value out of this stuff." Ali Ghodsi

a16z

55,243 次观看 • 8 天前

David Sacks says companies are trapped paying OpenAI & Anthropic because they can't figure out how to use open source models "I think enterprise CTOs would like to shift their token consumption to cheaper models for the obvious reason that it would be more efficient. They are seeing compute costs or token costs skyrocket right now, so everyone's trying to figure this out." "You also have the AI sovereignty issue that Alex Karp talked about. They're worried about giving up the secret sauce or the alpha in their business to a frontier lab that may one day be competing with them. "The problem is, I think in most cases, they don't have the technical ability to do it. Coinbase figured out how to do it. DoorDash figured out how to do it. They built a token routing system that allows them to send frontier tasks to frontier models and non frontier tasks to more mundane models. But I don't think your average enterprise has the technical capability to do that." "This is why the share of wallet of closed models, it actually increased. I think that open source went from 19% last year to 11% this year. So open source as a share of enterprise spending is actually decreasing." "I don't think that means usage is decreasing. I think usage is skyrocketing. It also may be the case that because the whole point of using an open model is you just pay for the compute costs, you don't have to pay a lab, so it may be that it's hard to measure that usage in terms of spend." "But nonetheless, anyone who's saying that these closed models are going to lose or are somehow losing, you're just not seeing it in the data."

David Sacks says companies are trapped paying OpenAI & Anthropic because they can't figure out how to use open source models "I think enterprise CTOs would like to shift their token consumption to cheaper models for the obvious reason that it would be more efficient. They are seeing compute costs or token costs skyrocket right now, so everyone's trying to figure this out." "You also have the AI sovereignty issue that Alex Karp talked about. They're worried about giving up the secret sauce or the alpha in their business to a frontier lab that may one day be competing with them. "The problem is, I think in most cases, they don't have the technical ability to do it. Coinbase figured out how to do it. DoorDash figured out how to do it. They built a token routing system that allows them to send frontier tasks to frontier models and non frontier tasks to more mundane models. But I don't think your average enterprise has the technical capability to do that." "This is why the share of wallet of closed models, it actually increased. I think that open source went from 19% last year to 11% this year. So open source as a share of enterprise spending is actually decreasing." "I don't think that means usage is decreasing. I think usage is skyrocketing. It also may be the case that because the whole point of using an open model is you just pay for the compute costs, you don't have to pay a lab, so it may be that it's hard to measure that usage in terms of spend." "But nonetheless, anyone who's saying that these closed models are going to lose or are somehow losing, you're just not seeing it in the data."

dnap

110,354 次观看 • 2 个月前

Inferact Co-founder Simon Mo on the trajectory of open and closed models: "What really differentiates open-weight models from closed-weight models, right? In the end, there's not much differentiation. It's more about the distribution strategy and go-to-market strategy... Capability-wise, I don't really see a big gap, not even today." "It's not about just source data. It's not about where to get the data from. Rather, it's who can build the best environment and who can make the most optimization and algorithmic choices to leverage all this learning from this environment." "So the next year is all gonna be about that. It's about how open-weight model labs are differentiating and really getting the model to meet the real world." "Project out in a year, there's not gonna be any difference." Simon Mo

Inferact Co-founder Simon Mo on the trajectory of open and closed models: "What really differentiates open-weight models from closed-weight models, right? In the end, there's not much differentiation. It's more about the distribution strategy and go-to-market strategy... Capability-wise, I don't really see a big gap, not even today." "It's not about just source data. It's not about where to get the data from. Rather, it's who can build the best environment and who can make the most optimization and algorithmic choices to leverage all this learning from this environment." "So the next year is all gonna be about that. It's about how open-weight model labs are differentiating and really getting the model to meet the real world." "Project out in a year, there's not gonna be any difference." Simon Mo

a16z

33,782 次观看 • 1 个月前

Today’s a big day! We have something really exciting to share with the open-source community. We’re launching two open-weight language models: gpt-oss-120b and gpt-oss-20b. They’re incredible models, built for developers, trained for reasoning, efficiency, and real-world use.🧵

Today’s a big day! We have something really exciting to share with the open-source community. We’re launching two open-weight language models: gpt-oss-120b and gpt-oss-20b. They’re incredible models, built for developers, trained for reasoning, efficiency, and real-world use.🧵

Romain Huet

34,199 次观看 • 1 年前

Neil on open source v. frontier models: "The labs pay an immense premium to be 3-6 months ahead of everything else. I think that's probably still worth it. But I don't know that the premium is going to last that long. If you look at enterprise deployments, they don't move at three to six month speed. A lot of enterprises are probably still on Opus 4.6 or Opus 4.7. I don't think there's any way to call a winner in this race, and certainly I don't even think this is a race that can be decided ever. It's a continual process. I don't think open source ever goes away. If there's a vacuum because one leader steps out, a new leader will step in. It just gets easier every day to train a frontier class model."

Neil on open source v. frontier models: "The labs pay an immense premium to be 3-6 months ahead of everything else. I think that's probably still worth it. But I don't know that the premium is going to last that long. If you look at enterprise deployments, they don't move at three to six month speed. A lot of enterprises are probably still on Opus 4.6 or Opus 4.7. I don't think there's any way to call a winner in this race, and certainly I don't even think this is a race that can be decided ever. It's a continual process. I don't think open source ever goes away. If there's a vacuum because one leader steps out, a new leader will step in. It just gets easier every day to train a frontier class model."

Patrick OShaughnessy

32,180 次观看 • 1 个月前