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"100 million words context window is already possible, which is roughly what a human hears in a lifetime. Inference support is the only bottleneck to achieve it. And AI Models actually do learn during the context window, without changing the weights." ~ Anthropic CEO Dario Amodei This is from... show more

Rohan Paul

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407,212 Aufrufe • vor 28 Tagen •via X (Twitter)

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Based on a staggering leap from 3% to 50% on professional benchmarks in mere months, Anthropic CEO Dario Amodei states that human-level AI is no longer a distant concept. This exponential progress suggests it is now a near-term reality, arriving not in decades, but likely within months, shattering all conventional predictions. The age of AGI is at our doorstep.

Based on a staggering leap from 3% to 50% on professional benchmarks in mere months, Anthropic CEO Dario Amodei states that human-level AI is no longer a distant concept. This exponential progress suggests it is now a near-term reality, arriving not in decades, but likely within months, shattering all conventional predictions. The age of AGI is at our doorstep.

Camus

11,080 Aufrufe • vor 11 Monaten

I have deep respect for Dario Amodei & Anthropic and I also get why every major AI lab CEO must downplay the open source threat. But his argument in Big Technology podcast to Alex Kantrowitz against open source models feels surprisingly weak - 'someone still has to run inference' completely misses the core economics. Makes me wonder: is there actually no good counter-argument to open source commoditization risk?

I have deep respect for Dario Amodei & Anthropic and I also get why every major AI lab CEO must downplay the open source threat. But his argument in Big Technology podcast to Alex Kantrowitz against open source models feels surprisingly weak - 'someone still has to run inference' completely misses the core economics. Makes me wonder: is there actually no good counter-argument to open source commoditization risk?

Jikku Jose

22,574 Aufrufe • vor 1 Jahr

Launched by popular demand. Qwen3-32B is now live on GroqCloud. Supports over 100 languages and dialects with a 131k context window. It is not incremental. It is a leap. Only Groq delivers this level of context with real-time speed. Demo video below to spark your next build. GitHub link in the comments. ⬇️

Launched by popular demand. Qwen3-32B is now live on GroqCloud. Supports over 100 languages and dialects with a 131k context window. It is not incremental. It is a leap. Only Groq delivers this level of context with real-time speed. Demo video below to spark your next build. GitHub link in the comments. ⬇️

Groq Inc

26,498 Aufrufe • vor 1 Jahr

Dario Amodei directly argues that the US Department of War is misrepresenting the law to claim power that the government simply does not have. CBS News asked "Do you think Anthropic can survive this as a business?" Dario effectively says that the Department of War is using social media to trick the public into believing they have powers that Congress never actually gave them. Dario Amadoui confidently states that the impact of the "supply chain risk" designation is actually very limited and will not stop their success, and that Anthropic will be just fine. ---- Video from 'CBS News + CBS Evening News + CBS Mornings' YT channel (link in comment)

Dario Amodei directly argues that the US Department of War is misrepresenting the law to claim power that the government simply does not have. CBS News asked "Do you think Anthropic can survive this as a business?" Dario effectively says that the Department of War is using social media to trick the public into believing they have powers that Congress never actually gave them. Dario Amadoui confidently states that the impact of the "supply chain risk" designation is actually very limited and will not stop their success, and that Anthropic will be just fine. ---- Video from 'CBS News + CBS Evening News + CBS Mornings' YT channel (link in comment)

Rohan Paul

30,664 Aufrufe • vor 5 Monaten

Anthropic' Dario Amodei on delaying Mythos release. "We have suffered enormously commercially from not releasing this model. This model has incredibly accelerated research within Anthropic and production in next models. It would do the same in the outside world if we were to release it. This has hurt us enormously commercially." ---- From "Bloomberg Originals" YouTube channel, (link in comment)

Anthropic' Dario Amodei on delaying Mythos release. "We have suffered enormously commercially from not releasing this model. This model has incredibly accelerated research within Anthropic and production in next models. It would do the same in the outside world if we were to release it. This has hurt us enormously commercially." ---- From "Bloomberg Originals" YouTube channel, (link in comment)

Rohan Paul

41,022 Aufrufe • vor 1 Monat

The Bible in its context is not a Christian book. The Bible in its nature is a catholic book which was edited and given to us in the context of what it is. - Joshua Maponga

The Bible in its context is not a Christian book. The Bible in its nature is a catholic book which was edited and given to us in the context of what it is. - Joshua Maponga

Typical African

12,891 Aufrufe • vor 4 Monaten

New short course: LLMs as Operating Systems: Agent Memory, created with Letta, and taught by its founders Charles Packer and Sarah Wooders. An LLM's input context window has limited space. Using a longer input context also costs more and results in slower processing. So, managing what's stored in this context window is important. In the innovative paper MemGPT: Towards LLMs as Operating Systems, its authors (which include the instructors) proposed using an LLM agent to manage this context window. Their system uses a large persistent memory that stores everything that could be included in the input context, and an agent decides what is actually included. Take the example of building a chatbot that needs to remember what's been said earlier in a conversation (perhaps over many days of interaction with a user). As the conversation's length grows, the memory management agent will move information from the input context to a persistent searchable database; summarize information to keep relevant facts in the input context; and restore relevant conversation elements from further back in time. This allows a chatbot to keep what's currently most relevant in its input context memory to generate the next response. When I read the original MemGPT paper, I thought it was an innovative technique for handling memory for LLMs. The open-source Letta framework, which we'll use in this course, makes MemGPT easy to implement. It adds memory to your LLM agents and gives them transparent long-term memory. In detail, you’ll learn: - How to build an agent that can edit its own limited input context memory, using tools and multi-step reasoning - What is a memory hierarchy (an idea from computer operating systems, which use a cache to speed up memory access), and how these ideas apply to managing the LLM input context (where the input context window is a "cache" storing the most relevant information; and an agent decides what to move in and out of this to/from a larger persistent storage system) - How to implement multi-agent collaboration by letting different agents share blocks of memory This course will give you a sophisticated understanding of memory management for LLMs, which is important for chatbots having long conversations, and for complex agentic workflows. Please sign up here!

New short course: LLMs as Operating Systems: Agent Memory, created with Letta, and taught by its founders Charles Packer and Sarah Wooders. An LLM's input context window has limited space. Using a longer input context also costs more and results in slower processing. So, managing what's stored in this context window is important. In the innovative paper MemGPT: Towards LLMs as Operating Systems, its authors (which include the instructors) proposed using an LLM agent to manage this context window. Their system uses a large persistent memory that stores everything that could be included in the input context, and an agent decides what is actually included. Take the example of building a chatbot that needs to remember what's been said earlier in a conversation (perhaps over many days of interaction with a user). As the conversation's length grows, the memory management agent will move information from the input context to a persistent searchable database; summarize information to keep relevant facts in the input context; and restore relevant conversation elements from further back in time. This allows a chatbot to keep what's currently most relevant in its input context memory to generate the next response. When I read the original MemGPT paper, I thought it was an innovative technique for handling memory for LLMs. The open-source Letta framework, which we'll use in this course, makes MemGPT easy to implement. It adds memory to your LLM agents and gives them transparent long-term memory. In detail, you’ll learn: - How to build an agent that can edit its own limited input context memory, using tools and multi-step reasoning - What is a memory hierarchy (an idea from computer operating systems, which use a cache to speed up memory access), and how these ideas apply to managing the LLM input context (where the input context window is a "cache" storing the most relevant information; and an agent decides what to move in and out of this to/from a larger persistent storage system) - How to implement multi-agent collaboration by letting different agents share blocks of memory This course will give you a sophisticated understanding of memory management for LLMs, which is important for chatbots having long conversations, and for complex agentic workflows. Please sign up here!

Andrew Ng

200,788 Aufrufe • vor 1 Jahr

Google DeepMind CEO Demis Hassabis's advice to fresh graduates. Next few years is a golden era. Become 10X individual at everything during this time. From 'WIRED` YT Channel (full video link in comment)

Google DeepMind CEO Demis Hassabis's advice to fresh graduates. Next few years is a golden era. Become 10X individual at everything during this time. From 'WIRED` YT Channel (full video link in comment)

Rohan Paul

123,955 Aufrufe • vor 10 Monaten

Anthropic has rejected demands from Defense Secretary Pete Hegseth calling for full lawful use of its AI technology. CEO Dario Amodei says that AI is important to military defense, but there must be safeguards against use for mass surveillance and fully autonomous weapons. The Pentagon said it will not let “any company dictate the terms.” 60 Minutes spoke with Dario and Anthropic President Daniela Amodei in November, and this is how they described the importance of “bumpers and guardrails” to manage the risk of AI.

Anthropic has rejected demands from Defense Secretary Pete Hegseth calling for full lawful use of its AI technology. CEO Dario Amodei says that AI is important to military defense, but there must be safeguards against use for mass surveillance and fully autonomous weapons. The Pentagon said it will not let “any company dictate the terms.” 60 Minutes spoke with Dario and Anthropic President Daniela Amodei in November, and this is how they described the importance of “bumpers and guardrails” to manage the risk of AI.

60 Minutes

424,330 Aufrufe • vor 5 Monaten

In an exclusive interview with CBS News' Jo Ling Kent, Anthropic CEO Dario Amodei said that the Pentagon’s decision to designate the AI company a supply chain risk is “retaliatory and punitive.” The Pentagon made the designation, which restricts military contractors from doing business with Anthropic, after the company refused to give the military unfettered access to its AI model.

In an exclusive interview with CBS News' Jo Ling Kent, Anthropic CEO Dario Amodei said that the Pentagon’s decision to designate the AI company a supply chain risk is “retaliatory and punitive.” The Pentagon made the designation, which restricts military contractors from doing business with Anthropic, after the company refused to give the military unfettered access to its AI model.

CBS News

1,213,350 Aufrufe • vor 5 Monaten

📁 Dario Amodei, CEO of Anthropic, says AI will disrupt jobs faster and broader than anything before. Some roles, even software engineers, may shrink as models automate entire workflows. The real question is not which jobs disappear. It is whether we create new ones in time.

📁 Dario Amodei, CEO of Anthropic, says AI will disrupt jobs faster and broader than anything before. Some roles, even software engineers, may shrink as models automate entire workflows. The real question is not which jobs disappear. It is whether we create new ones in time.

Jon Hernandez

33,387 Aufrufe • vor 5 Monaten

Anthropic engineer: “80% of our engineers are using Loops. Now everyone is exploring how to build right Context for agents.” in 3-6 months, we’ll all become context engineers. Context is the most important part of agents.” in this 1-hour talk, Anthropic engineer shows how to build a self-improving agents from sratch. It’s worth more than a $500 agentic course. Watch this video, then read the article below on how to become a context architect.

Anthropic engineer: “80% of our engineers are using Loops. Now everyone is exploring how to build right Context for agents.” in 3-6 months, we’ll all become context engineers. Context is the most important part of agents.” in this 1-hour talk, Anthropic engineer shows how to build a self-improving agents from sratch. It’s worth more than a $500 agentic course. Watch this video, then read the article below on how to become a context architect.

Codez

35,350 Aufrufe • vor 6 Tagen

2 years ago, Anthropic CEO Dario Amodei warned the U.S. Congress: “The scaling of open-source models is going down a very dangerous path…” Today, Kimi K3 is already competing with frontier models. Seeing what this model can do, I think we’re getting closer to open-source models reaching Mythos-level capabilities with little to no guardrails. That’s a pretty scary thought.

2 years ago, Anthropic CEO Dario Amodei warned the U.S. Congress: “The scaling of open-source models is going down a very dangerous path…” Today, Kimi K3 is already competing with frontier models. Seeing what this model can do, I think we’re getting closer to open-source models reaching Mythos-level capabilities with little to no guardrails. That’s a pretty scary thought.

m0h

147,612 Aufrufe • vor 15 Tagen

Current robotics models really underscore how wildly efficient the human brain is. Robotics models are relatively small (low billions of parameters), are only storing a second of context, and doing 100ms inference steps. By contrast the human brain has 100 trillion synapses (roughly analogous to parameters), processes 24 frames per second, while considering hours of previous context. So how do we expect to get many order-of-magnitude improvements in robotics across all these dimensions (which are trading off against each other in terms inference compute) at the same time? And how do we explain what's going on in the brain? Is it more analogous to a software advantage or a hardware advantage? Full episode with Sergey Levine out now!

Current robotics models really underscore how wildly efficient the human brain is. Robotics models are relatively small (low billions of parameters), are only storing a second of context, and doing 100ms inference steps. By contrast the human brain has 100 trillion synapses (roughly analogous to parameters), processes 24 frames per second, while considering hours of previous context. So how do we expect to get many order-of-magnitude improvements in robotics across all these dimensions (which are trading off against each other in terms inference compute) at the same time? And how do we explain what's going on in the brain? Is it more analogous to a software advantage or a hardware advantage? Full episode with Sergey Levine out now!

Dwarkesh Patel

87,247 Aufrufe • vor 10 Monaten

$Introducing SubQ - a major breakthrough in LLM intelligence. It is the first model built on a fully sub-quadratic sparse-attention architecture (SSA), And the first frontier model with a 12 million token context window which is: - 52x faster than FlashAttention at 1MM tokens - Less than 5% the cost of Opus Transformer-based LLMs waste compute by processing every possible relationship between words (standard attention). Only a small fraction actually matter. Subquadratic finds and focuses only on the ones that do. That's nearly 1,000x less compute and a new way for LLMs to scale.$

Introducing SubQ - a major breakthrough in LLM intelligence. It is the first model built on a fully sub-quadratic sparse-attention architecture (SSA), And the first frontier model with a 12 million token context window which is: - 52x faster than FlashAttention at 1MM tokens - Less than 5% the cost of Opus Transformer-based LLMs waste compute by processing every possible relationship between words (standard attention). Only a small fraction actually matter. Subquadratic finds and focuses only on the ones that do. That's nearly 1,000x less compute and a new way for LLMs to scale.

Alexander Whedon

13,202,309 Aufrufe • vor 2 Monaten

Real-time, human-generated, diverse data from 600 million active users. This is the perfect fuel for AI models — and it's exactly what xAI needs to compete with OpenAI and Anthropic. The timing is remarkable:

Real-time, human-generated, diverse data from 600 million active users. This is the perfect fuel for AI models — and it's exactly what xAI needs to compete with OpenAI and Anthropic. The timing is remarkable:

Fernando Cao

454,991 Aufrufe • vor 1 Jahr

The CEO of Anthropic: "We might be six to twelve months away from when the model is doing most, maybe all, of what software engineers do end to end." In 30 minutes, Dario Amodei and Google DeepMind's Demis Hassabis break down the day after AGI. AGI timeline + self-improving models + the first jobs to go + who stays in control Worth more than a year of AI hot takes on your timeline

The CEO of Anthropic: "We might be six to twelve months away from when the model is doing most, maybe all, of what software engineers do end to end." In 30 minutes, Dario Amodei and Google DeepMind's Demis Hassabis break down the day after AGI. AGI timeline + self-improving models + the first jobs to go + who stays in control Worth more than a year of AI hot takes on your timeline

Phosphen

27,595 Aufrufe • vor 1 Monat

🚨 Anthropic CEO Dario Amodei on What Could Go Wrong With Their AI Model “It targets the wrong person, it shoots a civilian. It doesn't show the judgment that a human soldier would show … We don't want to sell something that we don't think is reliable.”

🚨 Anthropic CEO Dario Amodei on What Could Go Wrong With Their AI Model “It targets the wrong person, it shoots a civilian. It doesn't show the judgment that a human soldier would show … We don't want to sell something that we don't think is reliable.”

Chief Nerd

748,124 Aufrufe • vor 5 Monaten

Jensen Huang says Dario Amodei's prediction of $1T in AI revenue by 2030 is too conservative. "I believe Dario and Anthropic are going to do way better than that. Way better than that. And the reason for that is the one part that he hasn't considered: I believe every single enterprise software company will also be a value-added reseller of Anthropic's tokens. And they’re going to get this logarithmic expansion. Their go-to-market is going to expand tremendously this year." --- From The All-In Podcast YT channel (link in comment)

Jensen Huang says Dario Amodei's prediction of $1T in AI revenue by 2030 is too conservative. "I believe Dario and Anthropic are going to do way better than that. Way better than that. And the reason for that is the one part that he hasn't considered: I believe every single enterprise software company will also be a value-added reseller of Anthropic's tokens. And they’re going to get this logarithmic expansion. Their go-to-market is going to expand tremendously this year." --- From The All-In Podcast YT channel (link in comment)

Rohan Paul

98,822 Aufrufe • vor 4 Monaten

Micron is going to $4,000 and once you understand what inference actually is, the number stops sounding crazy (Save this). Dylan Patel just said that by 2030, OpenAI and Anthropic alone will need over 100 gigawatts of compute combined and by 2040, we may not even be measuring AI infrastructure in gigawatts anymore. We may be talking about terawatts. Every single one of those gigawatts needs memory to function. Without it, the compute is worthless. Most people heard that and thought about Nvidia but they should be thinking about Micron. Every AI model generating a response has two phases. The first is prefill, processing your prompt which is compute-heavy and the second is decode generating each word one token at a time and that phase is almost entirely memory-bound, not compute-bound. During decode, the GPU's processing units sit idle more than 95% of the time, waiting for data to arrive from memory. Google confirmed it in a research paper that decode-phase bottlenecks are dominated by memory bandwidth and capacity not raw compute. The GPU is not the bottleneck but the memory feeding the GPU is. This matters because inference is now where all the money lives. Training a model happens once, Inference happens billions of times a day every ChatGPT response, every Claude output, every agentic workflow running in the background and every one of those token streams is a billing event tied directly to memory performance. Adding more GPUs does not fix this because GPUs are already underutilized in inference because they are sitting idle waiting on memory. Adding more memory bandwidth and capacity is what directly reduces token cost, reduces latency, and allows the same cluster to serve dramatically more users simultaneously. Longer context windows compound the problem further, a model running a 1 million token context window requires dramatically more memory per session than a 10,000 token window, and every new model generation pushes context longer. The market treats memory as a downstream beneficiary of Nvidia orders. The correct framework is the opposite, Micron is the upstream constraint on how much value every Nvidia GPU can actually generate at inference scale. Micron guided Q4 to $50 billion in revenue, has HBM4 ramping at twice the pace of the prior generation, and CEO Sanjay Mehrotra has said supply will not catch demand before the end of 2027. At 8x forward earnings on $112 projected FY2027 EPS, Micron is the most undervalued infrastructure company in the entire AI stack. Inference is memory. Memory is Micron and the inference ramp has barely started. Milk Road Pro members are already up massively on this position and we're just getting started. If you want the full breakdown of what we're buying and why, come join us for just a dollar using the link below!

Micron is going to $4,000 and once you understand what inference actually is, the number stops sounding crazy (Save this). Dylan Patel just said that by 2030, OpenAI and Anthropic alone will need over 100 gigawatts of compute combined and by 2040, we may not even be measuring AI infrastructure in gigawatts anymore. We may be talking about terawatts. Every single one of those gigawatts needs memory to function. Without it, the compute is worthless. Most people heard that and thought about Nvidia but they should be thinking about Micron. Every AI model generating a response has two phases. The first is prefill, processing your prompt which is compute-heavy and the second is decode generating each word one token at a time and that phase is almost entirely memory-bound, not compute-bound. During decode, the GPU's processing units sit idle more than 95% of the time, waiting for data to arrive from memory. Google confirmed it in a research paper that decode-phase bottlenecks are dominated by memory bandwidth and capacity not raw compute. The GPU is not the bottleneck but the memory feeding the GPU is. This matters because inference is now where all the money lives. Training a model happens once, Inference happens billions of times a day every ChatGPT response, every Claude output, every agentic workflow running in the background and every one of those token streams is a billing event tied directly to memory performance. Adding more GPUs does not fix this because GPUs are already underutilized in inference because they are sitting idle waiting on memory. Adding more memory bandwidth and capacity is what directly reduces token cost, reduces latency, and allows the same cluster to serve dramatically more users simultaneously. Longer context windows compound the problem further, a model running a 1 million token context window requires dramatically more memory per session than a 10,000 token window, and every new model generation pushes context longer. The market treats memory as a downstream beneficiary of Nvidia orders. The correct framework is the opposite, Micron is the upstream constraint on how much value every Nvidia GPU can actually generate at inference scale. Micron guided Q4 to $50 billion in revenue, has HBM4 ramping at twice the pace of the prior generation, and CEO Sanjay Mehrotra has said supply will not catch demand before the end of 2027. At 8x forward earnings on $112 projected FY2027 EPS, Micron is the most undervalued infrastructure company in the entire AI stack. Inference is memory. Memory is Micron and the inference ramp has barely started. Milk Road Pro members are already up massively on this position and we're just getting started. If you want the full breakdown of what we're buying and why, come join us for just a dollar using the link below!

Milk Road AI

128,678 Aufrufe • vor 1 Monat