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Cyber defense is entering the superintelligence era. ⚡ Introducing the CrowdStrike Cyber Superintelligence Lab, the first frontier AI research organization built for cyberdefense and AI safety. The Lab delivers CrowdStrike SafeMind, a family of purpose-built security models and harnesses. SafeMind runs as one system designed to deliver AI safety:...

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56,769 views • 3 days ago •via X (Twitter)

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NVIDIA and CrowdStrike are advancing agentic cyber defense with SafeMind, a new family of security models and harnesses built on NVIDIA Nemotron. Introduced at Fal.Con, SafeMind is customized with CrowdStrike threat data to support triage and detection generation for cyber defense.
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NVIDIA and CrowdStrike are advancing agentic cyber defense with SafeMind, a new family of security models and harnesses built on NVIDIA Nemotron. Introduced at Fal.Con, SafeMind is customized with CrowdStrike threat data to support triage and detection generation for cyber defense.

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$CRWD and $NVDA just launched SafeMind to bring frontier AI to cyber defense with George Kurtz saying “the attackers had frontier AI and the defenders didn’t. That changes now.” SafeMind is CrowdStrike’s answer using autonomous red and blue agents to continuously attack, learn and improve defenses at machine speed.
1:04

$CRWD and $NVDA just launched SafeMind to bring frontier AI to cyber defense with George Kurtz saying “the attackers had frontier AI and the defenders didn’t. That changes now.” SafeMind is CrowdStrike’s answer using autonomous red and blue agents to continuously attack, learn and improve defenses at machine speed.

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Introducing Ricursive Intelligence, a frontier AI lab enabling a recursive self-improvement loop between AI and the chips that fuel it. Learn more at
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Introducing Ricursive Intelligence, a frontier AI lab enabling a recursive self-improvement loop between AI and the chips that fuel it. Learn more at

Ricursive Intelligence

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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."

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Most enterprise AI projects stall because no one has prepared the content, built the workflow, and kept it improving as models change. That role is essential. Box has launched Forward Deployed Engineers to fill it. Model-agnostic, content-first, and built for the way enterprise AI actually works.
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Most enterprise AI projects stall because no one has prepared the content, built the workflow, and kept it improving as models change. That role is essential. Box has launched Forward Deployed Engineers to fill it. Model-agnostic, content-first, and built for the way enterprise AI actually works.

Box

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We gave our AI system a spec, and in under 2 weeks, it designed, verified, and deployed a chip that beats NVIDIA. It’s built for low-power physical AI workloads. We’re running live inference on >B+ parameter models like Llama, Qwen, and Kimi, serving at 3.4x better perf/watt than NVIDIA Jetson. From just a specification, our AI system autonomously generated all of the RTL Design, UVM verification, formal proofs, firmware, drivers, and kernels, co-designing the model, software and silicon as one optimization loop. Better AI can now design better chips to run AI, leading to a loop of recursive-self improvement towards our path to abundant intelligence.
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We gave our AI system a spec, and in under 2 weeks, it designed, verified, and deployed a chip that beats NVIDIA. It’s built for low-power physical AI workloads. We’re running live inference on >B+ parameter models like Llama, Qwen, and Kimi, serving at 3.4x better perf/watt than NVIDIA Jetson. From just a specification, our AI system autonomously generated all of the RTL Design, UVM verification, formal proofs, firmware, drivers, and kernels, co-designing the model, software and silicon as one optimization loop. Better AI can now design better chips to run AI, leading to a loop of recursive-self improvement towards our path to abundant intelligence.

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662,310 views • 8 days ago

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1:05

Today we're announcing Base Labs, a dedicated research organization focused on advancing open-source AI. We believe in a healthy, open frontier model ecosystem. To enable this, we are working on: - Blue-sky research on continual learning, the science of RL, and how models learn across their full lifecycle, with every experiment and recipe shared openly. - The BaseHub Data Foundry: the highest-quality open RL environments, training data, and real-world benchmarks, built for anyone to train and benchmark on. - Post-post training: taking open-source models and making them better, safer, and more aligned through continual post-training, built on our research, and deployed with our frontier safety stack so organizations can use open-source models with confidence. - Making models cheaper and more performant through our model performance research. This is a mission-driven research effort, not a commercial product. We believe the health of the open-source AI ecosystem matters and that the best way to advance it is to do science in the open. We’re hiring engineers, researchers, and research fellows to advance this mission.

Base Labs

196,820 views • 1 day ago

TARS Research is now live, the first AI research lab built entirely for Solana. Research is where real innovation begins. If TARS is to lead AI infrastructure onchain, it must define the frameworks others will follow. TARS Research is built for that: > A dedicated lab advancing agent architecture, tokenization, and onchain intelligence > Powering everything from the AI Marketplace to Sona > Focused on setting standards, not following them AI on Solana starts here.
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TARS Research is now live, the first AI research lab built entirely for Solana. Research is where real innovation begins. If TARS is to lead AI infrastructure onchain, it must define the frameworks others will follow. TARS Research is built for that: > A dedicated lab advancing agent architecture, tokenization, and onchain intelligence > Powering everything from the AI Marketplace to Sona > Focused on setting standards, not following them AI on Solana starts here.

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🤝 Alphabet and NVIDIA are expanding their decade-long partnership to advance agentic AI, robotics, drug discovery, and more. This involves deep co-engineering with integrated platforms, open-source frameworks, and managed services. ✅ Google Cloud is one of the first to bring the NVIDIA Blackwell platform to the cloud. ✅ Google Distributed Cloud using NVIDIA Confidential Computing on #NVIDIABlackwell for enterprises to run Google Gemini on-premises. ✅ Integration of the NVIDIA AI platform across Vertex AI, Cluster Director and Google Kubernetes Engine. ✅ Bringing the NVIDIA Nemotron family of open models to Vertex AI Model Garden. Watch the full video to learn more ➡️
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🤝 Alphabet and NVIDIA are expanding their decade-long partnership to advance agentic AI, robotics, drug discovery, and more. This involves deep co-engineering with integrated platforms, open-source frameworks, and managed services. ✅ Google Cloud is one of the first to bring the NVIDIA Blackwell platform to the cloud. ✅ Google Distributed Cloud using NVIDIA Confidential Computing on #NVIDIABlackwell for enterprises to run Google Gemini on-premises. ✅ Integration of the NVIDIA AI platform across Vertex AI, Cluster Director and Google Kubernetes Engine. ✅ Bringing the NVIDIA Nemotron family of open models to Vertex AI Model Garden. Watch the full video to learn more ➡️

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ScaleDown just dropped task-specific small models that are 15x cheaper, 63x faster, and more accurate than GPT-5.4 Mini. Built for the 70-80% of AI workloads that never needed a frontier model in the first place.
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ScaleDown just dropped task-specific small models that are 15x cheaper, 63x faster, and more accurate than GPT-5.4 Mini. Built for the 70-80% of AI workloads that never needed a frontier model in the first place.

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Today we are announcing Corma, and our $60M seed round led by Sequoia Capital. Corma is building foundation models toward superintelligence for defensive cybersecurity. We do that because general AI is becoming exponentially more capable in offensive security, while it hasn't been able to improve at the same rate in cyber defense - creating an inherent imbalance between attackers and defenders that otherwise cannot be addressed. Recent events have shown that building frontier AI models for defensive cybersecurity is one of the crucial unsolved problems of the AI age - and solving it is the generational mission Corma was founded on. Our foundation model has already far surpassed every general-purpose frontier model on defensive cybersecurity tasks, and we are committed to remaining the frontier of defensive cybersecurity intelligence. Corma is already protecting Fortune 100 companies and some of the world's most important organizations, across healthcare, finance, critical infrastructure, retail, and more. This is just the beginning, as we see it as our calling to protect every organization in the world that needs frontier defense. We are grateful to our partners Shaun Maguire and Sonya Huang 🐥 at Sequoia Capital , Vinod Khosla at Khosla Ventures , Dan Rose at COATUE , and Lia Weiner-Tsur at Netz Capital, who believed in our ability to lead the intelligence race in cybersecurity and make sure the defenders always win.

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299,602 views • 25 days ago

🚀 Our "technical" marketer might not be looped in, but today is our biggest launch day yet. We're introducing two new products to serve the inference lifecycle: Model APIs and Training. Model APIs are frontier models running on the Baseten Inference Stack, purpose-built for production. Baseten Training (Beta) provides infra and tooling without limitations for AI models destined for production. Huge shoutout to the many partners and customers we've worked with as we built these two new products—more details below.
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🚀 Our "technical" marketer might not be looped in, but today is our biggest launch day yet. We're introducing two new products to serve the inference lifecycle: Model APIs and Training. Model APIs are frontier models running on the Baseten Inference Stack, purpose-built for production. Baseten Training (Beta) provides infra and tooling without limitations for AI models destined for production. Huge shoutout to the many partners and customers we've worked with as we built these two new products—more details below.

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35,350 views • 1 year ago

Chips are the foundation of every AI experience. That's why understanding the hardware behind the software has never mattered more. When you ask a chatbot a question or generate an image, there's a physical chip somewhere doing the heavy lifting. For most of computing history, that was a CPU—the "brain" of a computer—great for general tasks needed to run software and operating systems. AI is more complex: for workloads called training and inference, AI needs to perform trillions of calculations in parallel. That's where AI accelerators come in. Purpose-built accelerators can deliver significantly better performance and efficiency than general-purpose chips. Amazon Web Services Trainium chips are an example—purpose-built for AI training and inference. New chips for a new era. ⬇️
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Chips are the foundation of every AI experience. That's why understanding the hardware behind the software has never mattered more. When you ask a chatbot a question or generate an image, there's a physical chip somewhere doing the heavy lifting. For most of computing history, that was a CPU—the "brain" of a computer—great for general tasks needed to run software and operating systems. AI is more complex: for workloads called training and inference, AI needs to perform trillions of calculations in parallel. That's where AI accelerators come in. Purpose-built accelerators can deliver significantly better performance and efficiency than general-purpose chips. Amazon Web Services Trainium chips are an example—purpose-built for AI training and inference. New chips for a new era. ⬇️

Amazon

27,151 views • 3 months ago

In the AI era, the traditional biopharma industry is the underdog. Big tech and AI labs are building wet labs. China has overtaken Europe in molecules produced. But the tools available to the industry discuss science, not do it. The hard problem in AI for science is at the interface between the physical and digital worlds. We built an AI Scientist at that seam. It wires together the digital and physical worlds of R&D. Predictive models, data infrastructure, wet lab execution feed into a single loop that reasons, acts, and improves with every experiment. Our ambition: get molecules to the clinic twice as fast. Last fall I wrote about why biotech needs to be rebuilt for the AI era. Today I'm sharing the next chapter: what the AI Scientist is, a blueprint for how it works, and why even Richard Feynman couldn't hack it in a wet lab.
1:44

In the AI era, the traditional biopharma industry is the underdog. Big tech and AI labs are building wet labs. China has overtaken Europe in molecules produced. But the tools available to the industry discuss science, not do it. The hard problem in AI for science is at the interface between the physical and digital worlds. We built an AI Scientist at that seam. It wires together the digital and physical worlds of R&D. Predictive models, data infrastructure, wet lab execution feed into a single loop that reasons, acts, and improves with every experiment. Our ambition: get molecules to the clinic twice as fast. Last fall I wrote about why biotech needs to be rebuilt for the AI era. Today I'm sharing the next chapter: what the AI Scientist is, a blueprint for how it works, and why even Richard Feynman couldn't hack it in a wet lab.

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30,767 views • 4 months ago

Introducing Qwen 3 + Deep Research! We built a deep research agent with Qwen 3 and it performed better than most open source models that we've tried. the code is completely open source and free! built with 's Langgraph Together AI and uses Perplexity Tavily for search here's the code link:
0:57

Introducing Qwen 3 + Deep Research! We built a deep research agent with Qwen 3 and it performed better than most open source models that we've tried. the code is completely open source and free! built with 's Langgraph Together AI and uses Perplexity Tavily for search here's the code link:

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34,566 views • 1 year ago

Inside Nemotron and NVIDIA's AI lab: my conversation with Bryan Catanzaro (Bryan Catanzaro). NVIDIA is a chip company. So why does it put hundreds of researchers on building AI models - and then give them away for free? We go deep into the Nemotron models, what it takes to build a top AI lab, and the future of frontier AI. 01:33 - Is open source AI catching the frontier? 05:29 - Do closed labs blocking distillation slow open source down? 07:42 - Is the US falling behind China? 10:30 - Why companies actually choose open models 12:39 - A "crazy" 2008 bet: machine learning on GPUs 15:33 - Working with Andrew Ng and Dario Amodei at Baidu 17:41 - Coming back to NVIDIA: DLSS and the birth of Megatron 21:55 - The real reason NVIDIA builds its own models 24:28 - Is Moore's Law really dead? 33:37 - The Nemotron family: Nano, Super, Ultra 35:09 - Built for agents: why NVIDIA bets on speed 36:02 - How you train a 550B model in 4 bits 39:25 - Hybrid Mamba-Transformer, explained simply 42:31 - Mixture of experts, and why NVIDIA built NVL72 around it 47:26 - Why a 1-million-token context window matters 49:26 - Multi-token prediction: how the model predicts 5 tokens at once 52:47 - Multi-teacher distillation: teaching one model from many 58:01 - Where reinforcement learning goes next 01:00:16 - Inside NVIDIA's research org: "the mission is the boss" 01:04:03 - How NVIDIA decides who gets the GPUs 01:10:53 - Why NVIDIA still feels entrepreneurial after 33 years 01:12:58 - Why Bryan doesn't believe in the singularity 01:17:50 - The AI backlash 01:19:18 - The controversial case: open AI is safer than closed
1:22:57

Inside Nemotron and NVIDIA's AI lab: my conversation with Bryan Catanzaro (Bryan Catanzaro). NVIDIA is a chip company. So why does it put hundreds of researchers on building AI models - and then give them away for free? We go deep into the Nemotron models, what it takes to build a top AI lab, and the future of frontier AI. 01:33 - Is open source AI catching the frontier? 05:29 - Do closed labs blocking distillation slow open source down? 07:42 - Is the US falling behind China? 10:30 - Why companies actually choose open models 12:39 - A "crazy" 2008 bet: machine learning on GPUs 15:33 - Working with Andrew Ng and Dario Amodei at Baidu 17:41 - Coming back to NVIDIA: DLSS and the birth of Megatron 21:55 - The real reason NVIDIA builds its own models 24:28 - Is Moore's Law really dead? 33:37 - The Nemotron family: Nano, Super, Ultra 35:09 - Built for agents: why NVIDIA bets on speed 36:02 - How you train a 550B model in 4 bits 39:25 - Hybrid Mamba-Transformer, explained simply 42:31 - Mixture of experts, and why NVIDIA built NVL72 around it 47:26 - Why a 1-million-token context window matters 49:26 - Multi-token prediction: how the model predicts 5 tokens at once 52:47 - Multi-teacher distillation: teaching one model from many 58:01 - Where reinforcement learning goes next 01:00:16 - Inside NVIDIA's research org: "the mission is the boss" 01:04:03 - How NVIDIA decides who gets the GPUs 01:10:53 - Why NVIDIA still feels entrepreneurial after 33 years 01:12:58 - Why Bryan doesn't believe in the singularity 01:17:50 - The AI backlash 01:19:18 - The controversial case: open AI is safer than closed

Matt Turck

56,954 views • 2 months ago

Recent AI breakthroughs challenge the status quo narrative that only closed, mega labs have the ability to push the frontier of superintelligence. Today we announce Nous Psyche built on Solana - a cooperative training network for generative AI. Psyche coordinates heterogeneous hardware to join a run and train open-source models. We retell the myth of Psyche — a mortal’s quest for retribution against divine odds:
3:55

Recent AI breakthroughs challenge the status quo narrative that only closed, mega labs have the ability to push the frontier of superintelligence. Today we announce Nous Psyche built on Solana - a cooperative training network for generative AI. Psyche coordinates heterogeneous hardware to join a run and train open-source models. We retell the myth of Psyche — a mortal’s quest for retribution against divine odds:

Nous Research

754,949 views • 1 year ago

Introducing Paygent.01 The quickest way to spin up an AI Agent that moves real money. Literally just one API key is need and you have your own personal AI Agent that can. - Pay your bills - Send money to that one relative - Go out and ask for money Fully open-source. Built using the AI SDK and Payman.
0:58

Introducing Paygent.01 The quickest way to spin up an AI Agent that moves real money. Literally just one API key is need and you have your own personal AI Agent that can. - Pay your bills - Send money to that one relative - Go out and ask for money Fully open-source. Built using the AI SDK and Payman.

tyllen

18,772 views • 1 year ago

NVIDIA Vera Rubin is in full production and arrives just in time for the next frontier of AI. The #NVIDIARubin platform uses extreme co-design across six new chips to accelerate agentic AI, advanced reasoning and massive-scale MoE model inference. NVIDIA Vera Rubin NVL72: Six new chips, one AI supercomputer — see how we built it.
2:50

NVIDIA Vera Rubin is in full production and arrives just in time for the next frontier of AI. The #NVIDIARubin platform uses extreme co-design across six new chips to accelerate agentic AI, advanced reasoning and massive-scale MoE model inference. NVIDIA Vera Rubin NVL72: Six new chips, one AI supercomputer — see how we built it.

NVIDIA Data Center

27,643 views • 8 months ago