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Were Cursor Wrong to Build Their Own Models: " I don't think so. We need to frame what their aspirations are with these models. There are generalists and there are specialists. Cursor's going to build specialized, coding models that are going to serve specialized coding tasks, especially for enterprise... users. They don't need for their models to be good at poetry or teach you how to make an apple pie. Their models are there for professional coders to do professional work That's very powerful and will continue to make the product experience really differentiated." Miles Clements Love to hear your thoughts on this Chamath Palihapitiya Eiso Kant Scott Wu Max Junestrand and the decision of build own or leverage others?show more

Harry Stebbings

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"Closed models are cheaper. The reason you need open models is that they give you control". - Nvidia CEO Jensen Huang "One of the things that people misunderstand about open models is this: yes, you can host them yourself, and you can build your own computer. But most people use computers in the cloud. Frankly, I think closed models are cheaper. If you do not build everything yourself, you avoid the significant cost and expertise required to train, fine-tune, maintain, guardrail, evaluate, and keep the model safe. You would also need to build or obtain the computers required to host it. So, there is nothing cheap about doing all of that yourself. The reason you need open models is that they give you control. You can adapt them for your own highly specialized use cases. I think there is a lot of misunderstanding about closed models versus open models. We felt it was important for people to understand that there is a place for both." ---- From "Bloomberg Podcasts" YouTube channel, (full video link in comment)

"Closed models are cheaper. The reason you need open models is that they give you control". - Nvidia CEO Jensen Huang "One of the things that people misunderstand about open models is this: yes, you can host them yourself, and you can build your own computer. But most people use computers in the cloud. Frankly, I think closed models are cheaper. If you do not build everything yourself, you avoid the significant cost and expertise required to train, fine-tune, maintain, guardrail, evaluate, and keep the model safe. You would also need to build or obtain the computers required to host it. So, there is nothing cheap about doing all of that yourself. The reason you need open models is that they give you control. You can adapt them for your own highly specialized use cases. I think there is a lot of misunderstanding about closed models versus open models. We felt it was important for people to understand that there is a place for both." ---- From "Bloomberg Podcasts" YouTube channel, (full video link in comment)

Rohan Paul

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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.”

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.”

Patrick OShaughnessy

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LEAKED AUDIO: In an all-hands meeting on April 30, Mark Zuckerberg tells employees that he's training AI on them ahead of mass layoffs. "The AI models learn from watching really smart people do things... The average intelligence of the people who are at this company is significantly higher than the average set of people that you can get to do tasks. So if we're trying to teach the models coding, for example, then having people internally build tools or solve tasks that help teach the model how to code, we think is going to dramatically increase our model's coding ability faster than what others in the industry have the capability to do, who don't have thousands and thousands of extremely strong engineers at their company."

LEAKED AUDIO: In an all-hands meeting on April 30, Mark Zuckerberg tells employees that he's training AI on them ahead of mass layoffs. "The AI models learn from watching really smart people do things... The average intelligence of the people who are at this company is significantly higher than the average set of people that you can get to do tasks. So if we're trying to teach the models coding, for example, then having people internally build tools or solve tasks that help teach the model how to code, we think is going to dramatically increase our model's coding ability faster than what others in the industry have the capability to do, who don't have thousands and thousands of extremely strong engineers at their company."

More Perfect Union

4,563,216 Aufrufe • vor 3 Monaten

$Enterprise AI is in a wildly paradoxical state 🤔, and we’re reaching the inflection point that will resolve it. 💥 Enterprises want to differentiate with AI, yet rent the same intelligence as their competitors. Their workflows and expertise are highly specialized, yet they rely on generic models built to be good at everything. They worry about AI costs, yet pay premium prices for massive models where only a fraction (1%) of the intelligence is relevant to their task. 💸 And they demand control and sovereignty, yet rent the intelligence becoming core to their business. This is not a sustainable equilibrium. The next era of enterprise AI is specialized intelligence companies build, own, and compound. And we are at the inflection point of this transition. That’s the bet we made when we started Oumi two years ago. Today we’re closing the loop: Oumi can now not only automatically build your specialized AI models, but also deploy them into production, learn from their production experience, and continuously improve them. The intelligence that your business runs on, becomes your differentiator. Your compounding advantage. The winners of the next AI era will turn their own data, expertise, and experience into specialized intelligence that nobody else can rent. Don’t rent your AI. Build it. Own it. Compound it.$

Enterprise AI is in a wildly paradoxical state 🤔, and we’re reaching the inflection point that will resolve it. 💥 Enterprises want to differentiate with AI, yet rent the same intelligence as their competitors. Their workflows and expertise are highly specialized, yet they rely on generic models built to be good at everything. They worry about AI costs, yet pay premium prices for massive models where only a fraction (1%) of the intelligence is relevant to their task. 💸 And they demand control and sovereignty, yet rent the intelligence becoming core to their business. This is not a sustainable equilibrium. The next era of enterprise AI is specialized intelligence companies build, own, and compound. And we are at the inflection point of this transition. That’s the bet we made when we started Oumi two years ago. Today we’re closing the loop: Oumi can now not only automatically build your specialized AI models, but also deploy them into production, learn from their production experience, and continuously improve them. The intelligence that your business runs on, becomes your differentiator. Your compounding advantage. The winners of the next AI era will turn their own data, expertise, and experience into specialized intelligence that nobody else can rent. Don’t rent your AI. Build it. Own it. Compound it.

Manos Koukoumidis

994,313 Aufrufe • vor 16 Tagen

I figured I'd share this to give an idea of how big the kitsunes are at max size, at the moment they have no AI or coding and this was just their models imported thanks to Rachel the Raichu using MCreator

I figured I'd share this to give an idea of how big the kitsunes are at max size, at the moment they have no AI or coding and this was just their models imported thanks to Rachel the Raichu using MCreator

Thatonebrasschronodent

16,329 Aufrufe • vor 1 Jahr

We skipped our own wedding and flew to SF. LLMs are the last piece of software that application developers still can’t easily build themselves. We’re letting every developer build their own LLMs in TypeScript, not Python. It’s Next.js + Vercel, but for building LLMs. In the last 7 days, users made 80k+ calls to models they fine-tuned. 3 devtool companies are running Arkor in their production pipelines to serve task-specific models with lower latency. Even users with PhDs in ML love the convenience of handling the entire training + deployment flow in Node.js. Meet Arkor. The best TypeScript framework for fine-tuning & deploying open models.

We skipped our own wedding and flew to SF. LLMs are the last piece of software that application developers still can’t easily build themselves. We’re letting every developer build their own LLMs in TypeScript, not Python. It’s Next.js + Vercel, but for building LLMs. In the last 7 days, users made 80k+ calls to models they fine-tuned. 3 devtool companies are running Arkor in their production pipelines to serve task-specific models with lower latency. Even users with PhDs in ML love the convenience of handling the entire training + deployment flow in Node.js. Meet Arkor. The best TypeScript framework for fine-tuning & deploying open models.

Hina

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Andrej Karpathy called the current AI models as "slop" "I do think that overall the models are not there yet. I feel like the industry is making too big of a jump and trying to pretend that this is amazing, but it's not. It's slop, and I think they are not coming to terms with it. Maybe they are trying to fundraise or something like that, I'm not sure what's going on. We are at this intermediate stage. The models are amazing, but they still need a lot of work for now. Autocomplete is my sweet spot." --- On the latest Dwarkesh Patel podcast.

Andrej Karpathy called the current AI models as "slop" "I do think that overall the models are not there yet. I feel like the industry is making too big of a jump and trying to pretend that this is amazing, but it's not. It's slop, and I think they are not coming to terms with it. Maybe they are trying to fundraise or something like that, I'm not sure what's going on. We are at this intermediate stage. The models are amazing, but they still need a lot of work for now. Autocomplete is my sweet spot." --- On the latest Dwarkesh Patel podcast.

Rohan Paul

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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.

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 Aufrufe • vor 2 Jahren

.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

33,251 Aufrufe • vor 5 Monaten

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 Aufrufe • vor 1 Monat

REPORTER: There are many Palestinians who are going to want to go back to Gaza and feel like that it is their right to do that TRUMP: We don't think that's gonna happen. We think people are gonna be very happy, thrilled

REPORTER: There are many Palestinians who are going to want to go back to Gaza and feel like that it is their right to do that TRUMP: We don't think that's gonna happen. We think people are gonna be very happy, thrilled

Aaron Rupar

248,810 Aufrufe • vor 1 Jahr

We are opening up the beta of our new AI writing tool to Exclusive Models. If you're exclusive to JFF, try it out and let us know your thoughts. At our Anniversary Conference, lots of models said they needed help making their posts more creative. This is a tool to help models take what they write and help them make better posts. Does this replace a human? No! In fact, if you are an erotic writer, apply to be in our Business Directory so models can hire you to help them out!

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We are opening up the beta of our new AI writing tool to Exclusive Models. If you're exclusive to JFF, try it out and let us know your thoughts. At our Anniversary Conference, lots of models said they needed help making their posts more creative. This is a tool to help models take what they write and help them make better posts. Does this replace a human? No! In fact, if you are an erotic writer, apply to be in our Business Directory so models can hire you to help them out!

JustForFans

15,325 Aufrufe • vor 3 Jahren

💡Why Vana? “Ultimately, AI models are only as good as their training data. So if you want to build the best AI, you need the best data.” shares the vision behind Vana’s mission, centered on DataDAOs that aggregate specific datasets, reward data owners for their contributions and level up AI models with high-quality data🏆

💡Why Vana? “Ultimately, AI models are only as good as their training data. So if you want to build the best AI, you need the best data.” shares the vision behind Vana’s mission, centered on DataDAOs that aggregate specific datasets, reward data owners for their contributions and level up AI models with high-quality data🏆

vana

108,377 Aufrufe • vor 1 Jahr

Earlier this week at GTC, we announced our partnership with Nvidia. We will work with Nvidia to build strong, American open-source models that are at the frontier of scientific reasoning. These models will be essential for the US to compete with China on science in the coming decades. Jensen is committing to spend tens of billions of dollars developing open-source models, and we are excited to be a partner with them in figuring out how to benchmark, train and use those agents to accelerate scientific research. We have already open-sourced some of the work we have done with them, and are looking forward to open-sourcing more. There are few things today that are more important. See our blog post below, and watch the video to learn more, narrated by the man himself.

Earlier this week at GTC, we announced our partnership with Nvidia. We will work with Nvidia to build strong, American open-source models that are at the frontier of scientific reasoning. These models will be essential for the US to compete with China on science in the coming decades. Jensen is committing to spend tens of billions of dollars developing open-source models, and we are excited to be a partner with them in figuring out how to benchmark, train and use those agents to accelerate scientific research. We have already open-sourced some of the work we have done with them, and are looking forward to open-sourcing more. There are few things today that are more important. See our blog post below, and watch the video to learn more, narrated by the man himself.

Sam Rodriques

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"One of the biggest misconceptions" Cerebras CFO Bob Komin pushes back on the small-models narrative. "We serve all models, and there is no limit to the size of the models that we can serve. Today, we're serving trillion parameter models. We're serving trillion parameter models that are internal for OpenAI today. We are currently running OpenAI 5.4 and 5.5 with them."

"One of the biggest misconceptions" Cerebras CFO Bob Komin pushes back on the small-models narrative. "We serve all models, and there is no limit to the size of the models that we can serve. Today, we're serving trillion parameter models. We're serving trillion parameter models that are internal for OpenAI today. We are currently running OpenAI 5.4 and 5.5 with them."

Deirdre Bosa

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