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Greg Osuri built the case for decentralized AI in three layers in under 5 minutes. Layer one: access. Anthropic's Claude is the best coding model available. OpenClaw is the most successful open-source project in history. Anthropic just announced they're banning OpenClaw usage. The most powerful model cutting off the...

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The Rollup

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15,005 просмотров • 4 месяцев назад •via X (Twitter)

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Chris Camillo just made the most bullish case on $AMZN. Everyone worried when Amazon raised their capex to $200 billion Camillo believes they might make that $200 billion back on their Anthropic investment alone when Anthropic IPOs. What Amazon has done in the last 18 months is remarkable in scope. They didn't try to win the foundation model race against OpenAI and Google directly. They went a completely different direction, positioning themselves as the infrastructure underneath everything that is AI. Trainium chips, cloud buildout, advertising integration and equity stakes in two of the three most important AI companies on the planet. First Anthropic. Now $50 billion into OpenAI on top of that. And that OpenAI investment isn't just a financial bet. It ties OpenAI into Amazon's cloud ecosystem, probably permanently. Two of the big three AI labs now run on Amazon infrastructure. Google is the only one that doesn't. The market has spent two years debating who builds the best model. Amazon decided to own the plumbing those models run on and bought equity in the winners while they were at it.
1:25

Chris Camillo just made the most bullish case on $AMZN. Everyone worried when Amazon raised their capex to $200 billion Camillo believes they might make that $200 billion back on their Anthropic investment alone when Anthropic IPOs. What Amazon has done in the last 18 months is remarkable in scope. They didn't try to win the foundation model race against OpenAI and Google directly. They went a completely different direction, positioning themselves as the infrastructure underneath everything that is AI. Trainium chips, cloud buildout, advertising integration and equity stakes in two of the three most important AI companies on the planet. First Anthropic. Now $50 billion into OpenAI on top of that. And that OpenAI investment isn't just a financial bet. It ties OpenAI into Amazon's cloud ecosystem, probably permanently. Two of the big three AI labs now run on Amazon infrastructure. Google is the only one that doesn't. The market has spent two years debating who builds the best model. Amazon decided to own the plumbing those models run on and bought equity in the winners while they were at it.

Milk Road Macro

55,169 просмотров • 3 месяцев назад

Chris Camillo made the most bullish case on $AMZN. Everyone worried when Amazon raised their capex to $200 billion Camillo believes they might make that $200 billion back on their Anthropic investment alone when Anthropic IPOs. What Amazon has done in the last 18 months is remarkable in scope. They didn't try to win the foundation model race against OpenAI and Google directly. They went a completely different direction, positioning themselves as the infrastructure underneath everything that is AI. Trainium chips, cloud buildout, advertising integration and equity stakes in two of the three most important AI companies on the planet. First Anthropic. Now $50 billion into OpenAI on top of that. And that OpenAI investment isn't just a financial bet. It ties OpenAI into Amazon's cloud ecosystem, probably permanently. Two of the big three AI labs now run on Amazon infrastructure. Google is the only one that doesn't. The market has spent two years debating who builds the best model. Amazon decided to own the plumbing those models run on and bought equity in the winners while they were at it.
1:25

Chris Camillo made the most bullish case on $AMZN. Everyone worried when Amazon raised their capex to $200 billion Camillo believes they might make that $200 billion back on their Anthropic investment alone when Anthropic IPOs. What Amazon has done in the last 18 months is remarkable in scope. They didn't try to win the foundation model race against OpenAI and Google directly. They went a completely different direction, positioning themselves as the infrastructure underneath everything that is AI. Trainium chips, cloud buildout, advertising integration and equity stakes in two of the three most important AI companies on the planet. First Anthropic. Now $50 billion into OpenAI on top of that. And that OpenAI investment isn't just a financial bet. It ties OpenAI into Amazon's cloud ecosystem, probably permanently. Two of the big three AI labs now run on Amazon infrastructure. Google is the only one that doesn't. The market has spent two years debating who builds the best model. Amazon decided to own the plumbing those models run on and bought equity in the winners while they were at it.

Milk Road Stocks

41,351 просмотров • 1 месяц назад

Your data is your edge, but only if your AI is built on it. Rent a generic model and so can your competitor. The companies with an edge are deploying custom models that they own and improve over time. Our co-founder Rhythm Garg recently stopped by to share how companies are owning their intelligence with Applied Compute.
0:24

Your data is your edge, but only if your AI is built on it. Rent a generic model and so can your competitor. The companies with an edge are deploying custom models that they own and improve over time. Our co-founder Rhythm Garg recently stopped by to share how companies are owning their intelligence with Applied Compute.

Applied Compute

119,306 просмотров • 2 месяцев назад

FOR THE FIRST TIME, THE US GOVERNMENT PULLED A PUBLICLY DEPLOYED AI MODEL OFFLINE. THE MARKET SHOULD PAY ATTENTION. 🇺🇸 On June 12, the Commerce Department ordered Anthropic to block access to its newest models, Fable 5 and Mythos 5, for any foreign national. Anthropic had to disable them for everyone to comply. The stated reason was a security concern. Anthropic disputes it, calling the flagged technique a narrow jailbreak available in other models too. Whatever the truth, here's the macro takeaway: Frontier AI now sits close enough to national security that a government can switch it off overnight. If you don't control your own models and compute, you're one directive away from losing access. That's the case for open and local models, in one headline.
0:27

FOR THE FIRST TIME, THE US GOVERNMENT PULLED A PUBLICLY DEPLOYED AI MODEL OFFLINE. THE MARKET SHOULD PAY ATTENTION. 🇺🇸 On June 12, the Commerce Department ordered Anthropic to block access to its newest models, Fable 5 and Mythos 5, for any foreign national. Anthropic had to disable them for everyone to comply. The stated reason was a security concern. Anthropic disputes it, calling the flagged technique a narrow jailbreak available in other models too. Whatever the truth, here's the macro takeaway: Frontier AI now sits close enough to national security that a government can switch it off overnight. If you don't control your own models and compute, you're one directive away from losing access. That's the case for open and local models, in one headline.

Merlijn The Trader

47,996 просмотров • 2 месяцев назад

ANTHROPIC'S NEW AI MODEL "MYTHOS" IS SO POWERFUL THE COMPANY WON'T RELEASE IT TO THE PUBLIC YET Instead, they're giving it to 11 companies first through "Project Glasswing" to find cybersecurity vulnerabilities before bad actors can. The partners: Amazon, Apple, Broadcom, Nvidia, Google, and others. The model wasn't built for cybersecurity. It was built to be an automated AI researcher, an AI that helps build better AI. But those same capabilities make it a serious tool for both offensive and defensive cyber operations. OpenAI reportedly has a similar model codenamed "SPU" going through the same slow rollout process. Palisade Research: "Anthropic is a little ahead, but not overwhelmingly ahead. They don't necessarily have a permanent moat here."
6:38

ANTHROPIC'S NEW AI MODEL "MYTHOS" IS SO POWERFUL THE COMPANY WON'T RELEASE IT TO THE PUBLIC YET Instead, they're giving it to 11 companies first through "Project Glasswing" to find cybersecurity vulnerabilities before bad actors can. The partners: Amazon, Apple, Broadcom, Nvidia, Google, and others. The model wasn't built for cybersecurity. It was built to be an automated AI researcher, an AI that helps build better AI. But those same capabilities make it a serious tool for both offensive and defensive cyber operations. OpenAI reportedly has a similar model codenamed "SPU" going through the same slow rollout process. Palisade Research: "Anthropic is a little ahead, but not overwhelmingly ahead. They don't necessarily have a permanent moat here."

WOLF

80,158 просмотров • 4 месяцев назад

Jensen Huang just told the smartest founders in AI they’re building a commodity. Not a platform. Not a moat. A commodity. Huang: “AI is actually essentially a five-layer cake.” Energy. Chips. Cloud. Models. Applications. Five layers. The entire industry is crowded onto one. Huang: “This is where most people think AI is.” The model layer. Most money. Most talent. Most competition. Most competition always ends the same way. A commodity. The harder they compete, the faster they erase their own value. That’s not a failure of strategy. That’s the strategy working. Huang: “At the bottom is energy.” No algorithm solves a power grid. No fundraise builds a dam. For decades everyone assumed intelligence would be the scarce resource. AI proved the opposite. Intelligence scales. Energy doesn’t. The bottleneck was never thinking. It was always power. Huang: “This layer on top ultimately is where economic benefit will happen.” The top captures revenue. The bottom controls access. The middle converts one into the other. Hundreds of billions deployed between someone else’s power grid and someone else’s economy. Most funded layer in history. Least defensible layer in the stack. The most intelligent thing ever built sits in the one position where intelligence doesn’t matter.
1:00

Jensen Huang just told the smartest founders in AI they’re building a commodity. Not a platform. Not a moat. A commodity. Huang: “AI is actually essentially a five-layer cake.” Energy. Chips. Cloud. Models. Applications. Five layers. The entire industry is crowded onto one. Huang: “This is where most people think AI is.” The model layer. Most money. Most talent. Most competition. Most competition always ends the same way. A commodity. The harder they compete, the faster they erase their own value. That’s not a failure of strategy. That’s the strategy working. Huang: “At the bottom is energy.” No algorithm solves a power grid. No fundraise builds a dam. For decades everyone assumed intelligence would be the scarce resource. AI proved the opposite. Intelligence scales. Energy doesn’t. The bottleneck was never thinking. It was always power. Huang: “This layer on top ultimately is where economic benefit will happen.” The top captures revenue. The bottom controls access. The middle converts one into the other. Hundreds of billions deployed between someone else’s power grid and someone else’s economy. Most funded layer in history. Least defensible layer in the stack. The most intelligent thing ever built sits in the one position where intelligence doesn’t matter.

Dustin

20,739 просмотров • 1 месяц назад

In Ackman's latest interview he divides the AI market, stating that it is not a bubble at the foundational layer, but highly risky at the software/model layer. He sees "near infinite demand" for compute. He believes infrastructure providers such as Microsoft, Amazon, Meta, and even SpaceX will earn massive returns on their data center investments. He is highly skeptical of pure model layer businesses like OpenAI and Anthropic. He notes they face massive capital burn rates and an existential threat from open-source models such as Chinese model and SpaceX that are driving the cost of AI access effectively to zero. A business model reliant on charging for proprietary models will collapse if open-source alternatives are essentially free. $AMZN $META $MSFT $SPCX
3:09

In Ackman's latest interview he divides the AI market, stating that it is not a bubble at the foundational layer, but highly risky at the software/model layer. He sees "near infinite demand" for compute. He believes infrastructure providers such as Microsoft, Amazon, Meta, and even SpaceX will earn massive returns on their data center investments. He is highly skeptical of pure model layer businesses like OpenAI and Anthropic. He notes they face massive capital burn rates and an existential threat from open-source models such as Chinese model and SpaceX that are driving the cost of AI access effectively to zero. A business model reliant on charging for proprietary models will collapse if open-source alternatives are essentially free. $AMZN $META $MSFT $SPCX

CapexAndChill

138,008 просмотров • 1 месяц назад

The most dangerous thing a company can do right now is rent intelligence from the same place as its competitors (Save this). You cannot rent intelligence from the same place that rents it to your competitor as Chamath Palihapitiya points out. If every company in an industry is feeding their workflows into the same frontier model, they are all converging on the same outputs, the same decisions, the same product improvements. The model becomes the equalizer and everyone pays a premium to become more mediocre. This is happening exactly as Chamath predicted, and the evidence is now concrete. Anthropic and OpenAI have established what analysts are now openly calling an emerging model layer duopoly. Anthropic crossed $45 billion ARR in may 2026, more than tripling from $9 billion at the end of 2025, OpenAI was at roughly $24 to $33 billion ARR at the same time. Together, the two companies combined could hit $160 to $240 billion ARR by end of 2026 and Anthropic and OpenAI now control 88% of enterprise LLM spend. That concentration is the structural problem Chamath is pointing at. And Anthropic isn't just winning on merit because it's actively lobbying for regulatory outcomes that would make that duopoly permanent. Dario Amodei has explicitly framed open source models as unsafe, pushing a safety agenda that, if enshrined in regulation, would effectively make it illegal for enterprises to use the cheaper, private, sovereign alternatives locking them into a closed model dependency by government decree rather than by choice. So you have market forces producing a duopoly, and potential regulatory capture moving to enforce it from the top down. This is exactly why the Nvidia Palantir partnership is not just a product announcement but rather a strategic counter to that duopoly. The logic is straightforward from both sides because If you're Palantir, sitting at the application layer, the last thing you want is to be permanently beholden to Anthropic or OpenAI for the intelligence that powers your product. You want competitive model options, sovereignty and be able to tell enterprise customers they can run AI on their own infrastructure with their own data without any of it touching a frontier lab's servers. If you're Nvidia, sitting at the chip layer, an Anthropic-OpenAI duopoly is an existential concentration risk. Right now, Meta, Google, Microsoft, Amazon, and dozens of other companies buy Nvidia's hardware. If the model layer consolidates into two players, both of which are building their own chips Nvidia faces a monopsony where its best customers are building the tools to displace it. A healthy open source ecosystem where thousands of enterprises train, fine tune, and deploy their own models is Nvidia's ideal market structure. More buyers, more diversity, more demand, less pricing leverage from any single customer.
4:38

The most dangerous thing a company can do right now is rent intelligence from the same place as its competitors (Save this). You cannot rent intelligence from the same place that rents it to your competitor as Chamath Palihapitiya points out. If every company in an industry is feeding their workflows into the same frontier model, they are all converging on the same outputs, the same decisions, the same product improvements. The model becomes the equalizer and everyone pays a premium to become more mediocre. This is happening exactly as Chamath predicted, and the evidence is now concrete. Anthropic and OpenAI have established what analysts are now openly calling an emerging model layer duopoly. Anthropic crossed $45 billion ARR in may 2026, more than tripling from $9 billion at the end of 2025, OpenAI was at roughly $24 to $33 billion ARR at the same time. Together, the two companies combined could hit $160 to $240 billion ARR by end of 2026 and Anthropic and OpenAI now control 88% of enterprise LLM spend. That concentration is the structural problem Chamath is pointing at. And Anthropic isn't just winning on merit because it's actively lobbying for regulatory outcomes that would make that duopoly permanent. Dario Amodei has explicitly framed open source models as unsafe, pushing a safety agenda that, if enshrined in regulation, would effectively make it illegal for enterprises to use the cheaper, private, sovereign alternatives locking them into a closed model dependency by government decree rather than by choice. So you have market forces producing a duopoly, and potential regulatory capture moving to enforce it from the top down. This is exactly why the Nvidia Palantir partnership is not just a product announcement but rather a strategic counter to that duopoly. The logic is straightforward from both sides because If you're Palantir, sitting at the application layer, the last thing you want is to be permanently beholden to Anthropic or OpenAI for the intelligence that powers your product. You want competitive model options, sovereignty and be able to tell enterprise customers they can run AI on their own infrastructure with their own data without any of it touching a frontier lab's servers. If you're Nvidia, sitting at the chip layer, an Anthropic-OpenAI duopoly is an existential concentration risk. Right now, Meta, Google, Microsoft, Amazon, and dozens of other companies buy Nvidia's hardware. If the model layer consolidates into two players, both of which are building their own chips Nvidia faces a monopsony where its best customers are building the tools to displace it. A healthy open source ecosystem where thousands of enterprises train, fine tune, and deploy their own models is Nvidia's ideal market structure. More buyers, more diversity, more demand, less pricing leverage from any single customer.

Milk Road AI

34,385 просмотров • 1 месяц назад

The MiniMax M2 model is mind-blowing! It's open-source. It outperforms Gemini 2.5, Claude 4.1, and Qwen3 across coding and tool-use benchmarks. Right now, it's one of the world's top 5 models in intelligence! And here is the best part: Claude is one of the best models you can use today, and MiniMax M2 costs only 8% of that! It's smaller, faster, and cheaper. Extremely efficient at using tokens. Minimax M2's biggest strength: High agentic capabilities. The model can plan and execute complex multi-tool workflows. It's reliable and very robust at executing long-horizon tool chains. In summary: • Low latency • Very cheap • Excels at agentic tasks • Open-source The model currently powers the MiniMax Agent and is available for a free global trial. You can access MiniMax M2's API here: To access the agent: And here is the MiniMax website: Thanks to the MiniMax team for showing me the ropes and partnering with me on this post.
6:01

The MiniMax M2 model is mind-blowing! It's open-source. It outperforms Gemini 2.5, Claude 4.1, and Qwen3 across coding and tool-use benchmarks. Right now, it's one of the world's top 5 models in intelligence! And here is the best part: Claude is one of the best models you can use today, and MiniMax M2 costs only 8% of that! It's smaller, faster, and cheaper. Extremely efficient at using tokens. Minimax M2's biggest strength: High agentic capabilities. The model can plan and execute complex multi-tool workflows. It's reliable and very robust at executing long-horizon tool chains. In summary: • Low latency • Very cheap • Excels at agentic tasks • Open-source The model currently powers the MiniMax Agent and is available for a free global trial. You can access MiniMax M2's API here: To access the agent: And here is the MiniMax website: Thanks to the MiniMax team for showing me the ropes and partnering with me on this post.

Santiago

91,197 просмотров • 10 месяцев назад

Jensen Huang was asked whether AI's future is open source or proprietary the question the whole industry keeps fighting over. He didn't pick a side. He said something that quietly ends the fight. "models is a technology, not a product. Model is a technology, not a service" "For the vast majority of consumers, the horizontal layer, the general intelligence, I would really, really love not to go fine-tune my own" "all these industries, their domain expertise, their specialization has to be channeled, has to be captured in a way that they can control, and that it can only come from open models" "the open model industry we're contributing tremendously to it is near the frontier" Everyone else is arguing about which lab wins OpenAI or Anthropic, who has the smartest frontier model this quarter. Jensen is standing on stage saying that's the sideshow. The proprietary model is the convenience layer you rent when you don't care. The open model is the thing you own when the work is yours. Two different products for two different needs not two horses in one race. open models are the second-biggest in the room. Ahead of Anthropic. Behind only OpenAI.
2:51

Jensen Huang was asked whether AI's future is open source or proprietary the question the whole industry keeps fighting over. He didn't pick a side. He said something that quietly ends the fight. "models is a technology, not a product. Model is a technology, not a service" "For the vast majority of consumers, the horizontal layer, the general intelligence, I would really, really love not to go fine-tune my own" "all these industries, their domain expertise, their specialization has to be channeled, has to be captured in a way that they can control, and that it can only come from open models" "the open model industry we're contributing tremendously to it is near the frontier" Everyone else is arguing about which lab wins OpenAI or Anthropic, who has the smartest frontier model this quarter. Jensen is standing on stage saying that's the sideshow. The proprietary model is the convenience layer you rent when you don't care. The open model is the thing you own when the work is yours. Two different products for two different needs not two horses in one race. open models are the second-biggest in the room. Ahead of Anthropic. Behind only OpenAI.

Vikram M

12,078 просмотров • 1 месяц назад

🚨David Sacks: There is no White House decision to ban open source models. Trump is listening to a chorus of voices — and Sacks says it would be a tragic mistake to take action against the open source ecosystem. His argument: Anthropic is the fastest growing tech company at scale in history. $10B ARR at the start of the year. $70B now. This is not a company that needs government protection. And yet they've spent enormous energy trying to panic Washington into banning American developers from using Chinese open source models. If distillation by Chinese companies was really their primary concern — a genuine national security threat — they would push to ban Chinese access to American models. Instead they're pushing to ban American access to Chinese models. Because this helps Anthropic's competitive position. Sacks: "That's the way you know the whole distillation thing is fake." He went further. If distillation is happening at industrial scale, it's obvious to see. Anthropic has 90% gross margins. Why aren't they stopping it at the source? Answer: because KYC'ing their customers would slow their growth. So instead of fixing their own house, they're asking the government to kneecap their competitors. "The question should be on Anthropic to explain why it's doing such a bad job — not on the whole American open source ecosystem to be punished for Anthropic's failure." Trump's AI Czar on The All-In Podcast.
4:30

🚨David Sacks: There is no White House decision to ban open source models. Trump is listening to a chorus of voices — and Sacks says it would be a tragic mistake to take action against the open source ecosystem. His argument: Anthropic is the fastest growing tech company at scale in history. $10B ARR at the start of the year. $70B now. This is not a company that needs government protection. And yet they've spent enormous energy trying to panic Washington into banning American developers from using Chinese open source models. If distillation by Chinese companies was really their primary concern — a genuine national security threat — they would push to ban Chinese access to American models. Instead they're pushing to ban American access to Chinese models. Because this helps Anthropic's competitive position. Sacks: "That's the way you know the whole distillation thing is fake." He went further. If distillation is happening at industrial scale, it's obvious to see. Anthropic has 90% gross margins. Why aren't they stopping it at the source? Answer: because KYC'ing their customers would slow their growth. So instead of fixing their own house, they're asking the government to kneecap their competitors. "The question should be on Anthropic to explain why it's doing such a bad job — not on the whole American open source ecosystem to be punished for Anthropic's failure." Trump's AI Czar on The All-In Podcast.

KanekoaTheGreat

48,392 просмотров • 1 месяц назад

🚨 The Silvia team just announced our latest engineering advancement. Every business wants access to the highest level of intelligence, but at the lowest cost possible. The rise of LLMs has made intelligence abundant, yet one of the hardest problems across startups and corporate America is predicting the compute cost associated with this intelligence. I have been dealing with this personally as we build Silvia and the problem comes up in almost every conversation I have with CEOs, founders, and executives. Every business embraced AI about 18 months ago and things seemed great until the compute bills started to show up. The bills for internal compute usage were difficult to swallow, but things got outrageous if you had an AI product that allowed your users to consume compute without limits. I know this problem intimately because that is the situation that Silvia was in. Every question that was asked meant higher compute costs for our company. But we didn’t want to limit usage because users were getting genuine value out of the product. This challenge sent our team down a deep rabbit hole of cutting costs, while improving the experience for users. The second part was really important: we did not want to degrade the user experience by simply taking away access to the highest quality models. Thankfully, resource constraints breed innovation. We aren’t the biggest company, nor do we have the largest balance sheet, but we came up with a very novel solution that we are announcing today. The Silvia engineering team built a model router that cut costs by up to 29%, decreased latency, and improved the quality of answers for users. Trifecta! The way we do this is by reading the first 500 characters of a query and then predicting the level of effort that will be needed by a model to answer the query. The highest effort needs are routed to the most powerful models. The lowest effort needs are routed to different, better models for the query. A good example of this would be “what is the date?” You don’t need to use the latest Anthropic model to answer this query. In fact, sending a simple query like this to the most powerful model will make your compute costs increase and will actually increase the latency, which means a worse user experience for the Silvia user. By implementing the model router, the user gets a better experience and we get lower costs. Win-win. One of the interesting aspects of the implementation is that our model router runs on CPUs instead of GPUs. This allows us to read the query and predict the level of effort needed in less than 1 millisecond. This CPU implementation is why latency is not affected, nor is cost significantly increased by any potential additional GPU consumption. Another important point is that many of you have probably seen the news that OpenRouter is being purchased by Stripe for around $7 billion. This is a great outcome from what appears to be a very smart, capable team. Their model routing API is related (their product and our internal implementation both touch model routing), but you should think of OpenRouter as making it possible to do model routing for companies, while Silvia’s model router is a custom, intelligent system that specifically routes Silvia queries to the right model. They give access to the functionality of model routing to many companies, while our internal product does the real decision-making specific to our use case. Lastly, our implementation of a model router is a strategic bet that will allow us to become model-agnostic over time. We don’t care who created the different models, we just want to route a query to the model best positioned to answer. The large model labs will never allow their users to be model agnostic, but that would require the lab to potentially route a query to a competitor’s model. No bueno in their eyes. Instead, Silvia being an independent AI research lab gives us the power of being agnostic. We simply want the best experience for our users. Last week we announced that Silvia is now the most accurate AI tax product on the market, including beating OpenAI, Anthropic, Google, and xAI. Today we are announcing a custom, in-house model router that rivals the best technology anyone else has built. There will be many more engineering announcements to come. I truly believe we have assembled one of the best AI teams and we are currently the best AI research lab in finance. If you are interested in learning more about the technical details of the model router, you can read the engineering blog post here: Everyone wants the best intelligence and the lowest cost. Silvia just showed the world what is possible in this pursuit. I anticipate many other companies will build this custom solutions to achieve the same benefits.
2:50

🚨 The Silvia team just announced our latest engineering advancement. Every business wants access to the highest level of intelligence, but at the lowest cost possible. The rise of LLMs has made intelligence abundant, yet one of the hardest problems across startups and corporate America is predicting the compute cost associated with this intelligence. I have been dealing with this personally as we build Silvia and the problem comes up in almost every conversation I have with CEOs, founders, and executives. Every business embraced AI about 18 months ago and things seemed great until the compute bills started to show up. The bills for internal compute usage were difficult to swallow, but things got outrageous if you had an AI product that allowed your users to consume compute without limits. I know this problem intimately because that is the situation that Silvia was in. Every question that was asked meant higher compute costs for our company. But we didn’t want to limit usage because users were getting genuine value out of the product. This challenge sent our team down a deep rabbit hole of cutting costs, while improving the experience for users. The second part was really important: we did not want to degrade the user experience by simply taking away access to the highest quality models. Thankfully, resource constraints breed innovation. We aren’t the biggest company, nor do we have the largest balance sheet, but we came up with a very novel solution that we are announcing today. The Silvia engineering team built a model router that cut costs by up to 29%, decreased latency, and improved the quality of answers for users. Trifecta! The way we do this is by reading the first 500 characters of a query and then predicting the level of effort that will be needed by a model to answer the query. The highest effort needs are routed to the most powerful models. The lowest effort needs are routed to different, better models for the query. A good example of this would be “what is the date?” You don’t need to use the latest Anthropic model to answer this query. In fact, sending a simple query like this to the most powerful model will make your compute costs increase and will actually increase the latency, which means a worse user experience for the Silvia user. By implementing the model router, the user gets a better experience and we get lower costs. Win-win. One of the interesting aspects of the implementation is that our model router runs on CPUs instead of GPUs. This allows us to read the query and predict the level of effort needed in less than 1 millisecond. This CPU implementation is why latency is not affected, nor is cost significantly increased by any potential additional GPU consumption. Another important point is that many of you have probably seen the news that OpenRouter is being purchased by Stripe for around $7 billion. This is a great outcome from what appears to be a very smart, capable team. Their model routing API is related (their product and our internal implementation both touch model routing), but you should think of OpenRouter as making it possible to do model routing for companies, while Silvia’s model router is a custom, intelligent system that specifically routes Silvia queries to the right model. They give access to the functionality of model routing to many companies, while our internal product does the real decision-making specific to our use case. Lastly, our implementation of a model router is a strategic bet that will allow us to become model-agnostic over time. We don’t care who created the different models, we just want to route a query to the model best positioned to answer. The large model labs will never allow their users to be model agnostic, but that would require the lab to potentially route a query to a competitor’s model. No bueno in their eyes. Instead, Silvia being an independent AI research lab gives us the power of being agnostic. We simply want the best experience for our users. Last week we announced that Silvia is now the most accurate AI tax product on the market, including beating OpenAI, Anthropic, Google, and xAI. Today we are announcing a custom, in-house model router that rivals the best technology anyone else has built. There will be many more engineering announcements to come. I truly believe we have assembled one of the best AI teams and we are currently the best AI research lab in finance. If you are interested in learning more about the technical details of the model router, you can read the engineering blog post here: Everyone wants the best intelligence and the lowest cost. Silvia just showed the world what is possible in this pursuit. I anticipate many other companies will build this custom solutions to achieve the same benefits.

Anthony Pompliano 🌪

75,896 просмотров • 10 дней назад

Gavin Baker: "if you're a foundation model company and you do not have unique data and internet scale distribution, you are the fastest depreciating asset in human history - I think most of these companies are zeros, and there's like 10 of them" this is him explaining why the 100 largest buildings in the world will be data centers, why there are only three places left in America you can legally build one, and what he says is setting up the first real memory cycle since 1996 "in our lifetime there will be data centers that are bigger than Manhattan" "the two rate limiting factors for AI are no longer compute and GPUs - it's power, finding places to plug the GPUs in" "there's only three places in the United States you can put one of these - it's got to be near multiple nuclear reactors that are unregulated within 50 miles of each other" "if Apple doubles the memory in the iPhone and Google does the same for Android at the same time, you could have the first real memory cycle since 1996 - and in 1996 Micron was a 50 bagger" "the only hope most of these companies have is for Amazon to acquire them the way Microsoft did with Inflection" bookmark & watch the full conversation ↓
11:48

Gavin Baker: "if you're a foundation model company and you do not have unique data and internet scale distribution, you are the fastest depreciating asset in human history - I think most of these companies are zeros, and there's like 10 of them" this is him explaining why the 100 largest buildings in the world will be data centers, why there are only three places left in America you can legally build one, and what he says is setting up the first real memory cycle since 1996 "in our lifetime there will be data centers that are bigger than Manhattan" "the two rate limiting factors for AI are no longer compute and GPUs - it's power, finding places to plug the GPUs in" "there's only three places in the United States you can put one of these - it's got to be near multiple nuclear reactors that are unregulated within 50 miles of each other" "if Apple doubles the memory in the iPhone and Google does the same for Android at the same time, you could have the first real memory cycle since 1996 - and in 1996 Micron was a 50 bagger" "the only hope most of these companies have is for Amazon to acquire them the way Microsoft did with Inflection" bookmark & watch the full conversation ↓

Сarm1ne

274,201 просмотров • 27 дней назад

🚨🇺🇸🇻🇪 MADURO'S CAPTURE: BROUGHT TO YOU BY THE DISCOMBOBULATOR... AND CLAUDE AI? Just when you thought this operation couldn't be any wilder. The military deployed Claude during the raid, not just in planning. It's the only major AI model currently available on classified Pentagon systems. Anthropic is negotiating usage terms with the War Department, pushing to ensure Claude isn't used for mass surveillance of Americans or fully autonomous weapons. Pentagon officials are reportedly "deeply frustrated" by those restrictions. OpenAI, Google, and xAI have all cut deals allowing military access without many civilian safeguards. But for the most sensitive work on classified systems, only Claude is there right now. Source: Axios
0:57

🚨🇺🇸🇻🇪 MADURO'S CAPTURE: BROUGHT TO YOU BY THE DISCOMBOBULATOR... AND CLAUDE AI? Just when you thought this operation couldn't be any wilder. The military deployed Claude during the raid, not just in planning. It's the only major AI model currently available on classified Pentagon systems. Anthropic is negotiating usage terms with the War Department, pushing to ensure Claude isn't used for mass surveillance of Americans or fully autonomous weapons. Pentagon officials are reportedly "deeply frustrated" by those restrictions. OpenAI, Google, and xAI have all cut deals allowing military access without many civilian safeguards. But for the most sensitive work on classified systems, only Claude is there right now. Source: Axios

Mario Nawfal

240,321 просмотров • 6 месяцев назад

We deployed a fully private AI agent on NuNet in under 5 minutes 🚀 OpenClaw🦞 running Qwen through ollama , one of the hottest open source model families right now, entirely on decentralized compute. No cloud. No API keys. No data leaving the machine. This is what private AI looks like when you actually build it instead of just talking about it. Your model. Your hardware. Your rules. Full walkthrough showing exactly how it works: What should we deploy next?
12:59

We deployed a fully private AI agent on NuNet in under 5 minutes 🚀 OpenClaw🦞 running Qwen through ollama , one of the hottest open source model families right now, entirely on decentralized compute. No cloud. No API keys. No data leaving the machine. This is what private AI looks like when you actually build it instead of just talking about it. Your model. Your hardware. Your rules. Full walkthrough showing exactly how it works: What should we deploy next?

NuNet 🌐

87,604 просмотров • 5 месяцев назад

OpenClaw is the fastest growing open-source project in history, but it can be a pain to set up. We (Aethir ) built Aethir Claw so you can deploy your AI agent on our decentralized VPS infrastructure in minutes. No DevOps degree required. Hit play. I'll walk you through the full onboarding, start to finish in under 5 mins! This is just the beginning...
6:00

OpenClaw is the fastest growing open-source project in history, but it can be a pain to set up. We (Aethir ) built Aethir Claw so you can deploy your AI agent on our decentralized VPS infrastructure in minutes. No DevOps degree required. Hit play. I'll walk you through the full onboarding, start to finish in under 5 mins! This is just the beginning...

Mark

43,134 просмотров • 5 месяцев назад

The biggest limitation of AI coding isn't the model anymore. It's your laptop. RAM fills up. Terminals become a mess. You stop experimenting because every branch feels expensive. Ara just moved Claude into the cloud. Spin up 20 variations of the same feature in minutes. Compare every PR. Merge the best one. Delete the rest. Your machine stays free while the agents keep working. Feels much closer to how software will be built over the next few years.
0:37

The biggest limitation of AI coding isn't the model anymore. It's your laptop. RAM fills up. Terminals become a mess. You stop experimenting because every branch feels expensive. Ara just moved Claude into the cloud. Spin up 20 variations of the same feature in minutes. Compare every PR. Merge the best one. Delete the rest. Your machine stays free while the agents keep working. Feels much closer to how software will be built over the next few years.

Vyom

18,662 просмотров • 28 дней назад

We think #GenAI will transform virtually every customer experience. That’s why we’re doubling down on all three layers of what’s called the GenAI “stack.” What do we mean by stack? Simple. Three layers, bottom to top. The “bottom layer” means the infrastructure. Think chips, servers, networking – the software, hardware, and computing power in our data centers used to train, build, and deploy AI models and applications. The “middle layer” is the GenAI sandbox – all the tools, Large Language Models (LLMs), and Foundation Models (FMs) we’ve built into Amazon Bedrock. This lets customers pick the #AI models that best fit their needs to easily and securely customize GenAI applications specific to their business. The “top layer” means the popular agents and assistants that people actually interact with – now including Amazon Q. These are built from the models in the middle layer – in Amazon Bedrock. While other apps might help you write a quick email, or edit an image, Amazon Q’s meant for taking care of business. It’s been engineered to be the most capable GenAI-powered assistant for speeding up software dev and leveraging companies’ internal data. Are you as bullish on the future of GenAI as we are? Listen to our CEO Andy Jassy talk about our investment across the stack.
1:19

We think #GenAI will transform virtually every customer experience. That’s why we’re doubling down on all three layers of what’s called the GenAI “stack.” What do we mean by stack? Simple. Three layers, bottom to top. The “bottom layer” means the infrastructure. Think chips, servers, networking – the software, hardware, and computing power in our data centers used to train, build, and deploy AI models and applications. The “middle layer” is the GenAI sandbox – all the tools, Large Language Models (LLMs), and Foundation Models (FMs) we’ve built into Amazon Bedrock. This lets customers pick the #AI models that best fit their needs to easily and securely customize GenAI applications specific to their business. The “top layer” means the popular agents and assistants that people actually interact with – now including Amazon Q. These are built from the models in the middle layer – in Amazon Bedrock. While other apps might help you write a quick email, or edit an image, Amazon Q’s meant for taking care of business. It’s been engineered to be the most capable GenAI-powered assistant for speeding up software dev and leveraging companies’ internal data. Are you as bullish on the future of GenAI as we are? Listen to our CEO Andy Jassy talk about our investment across the stack.

Amazon

47,421 просмотров • 2 лет назад