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From a scrappy side project built to solve their own LLM optimization problems to becoming the industry’s de-facto independent scoreboard, Micah Hill-Smith and George Cameron went through the arc of launching Artificial Analysis for free, paying benchmarking costs out of pocket, and growing it into what many now call...

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33,415 görüntüleme • 7 ay önce •via X (Twitter)

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We’re unveiling a new look for Artificial Analysis! We’ve come a long way since launching Artificial Analysis over 2 years ago. Today, we benchmark 400+ models, 50+ inference providers, and benchmark not only language models but also image, video, speech, music, hardware, and agents. Our mission to support the AI ecosystem with independent benchmarking remains the same, but our brand and website refresh is designed to better reflect how much we’ve grown and how much further we plan to go. A huge thank you to everyone who has been part of the Artificial Analysis community along the way: from developers choosing models and building agents, to labs, inference and hardware providers, and fellow independent researchers.
0:25

We’re unveiling a new look for Artificial Analysis! We’ve come a long way since launching Artificial Analysis over 2 years ago. Today, we benchmark 400+ models, 50+ inference providers, and benchmark not only language models but also image, video, speech, music, hardware, and agents. Our mission to support the AI ecosystem with independent benchmarking remains the same, but our brand and website refresh is designed to better reflect how much we’ve grown and how much further we plan to go. A huge thank you to everyone who has been part of the Artificial Analysis community along the way: from developers choosing models and building agents, to labs, inference and hardware providers, and fellow independent researchers.

Artificial Analysis

18,159 görüntüleme • 3 ay önce

Theoretical physicist Michio Kaku on why aging is simply an accumulation of errors that AI can now identify: He opens with a bold claim about what artificial intelligence might finally make possible: "Artificial intelligence may give us something that the kings and queens of old could never conquer and that is the aging process." For Dr. Michio Kaku, the science behind why we age is no longer a mystery. It comes down to one thing: errors building up inside our cells over time. "We now know why things age. Things get old and die because they accumulate error. Error in the form of DNA mistakes. Accumulation of errors inside cells." The challenge, until now, has been the sheer scale of the problem. Identifying exactly where and how these errors occur across the human genome has been beyond what conventional research could handle. That's where artificial intelligence changes everything: "But now we have artificial intelligence. We can analyze millions of genomes of old people, compare them to millions of genomes of young people, and identify where aging takes place in the body."
0:42

Theoretical physicist Michio Kaku on why aging is simply an accumulation of errors that AI can now identify: He opens with a bold claim about what artificial intelligence might finally make possible: "Artificial intelligence may give us something that the kings and queens of old could never conquer and that is the aging process." For Dr. Michio Kaku, the science behind why we age is no longer a mystery. It comes down to one thing: errors building up inside our cells over time. "We now know why things age. Things get old and die because they accumulate error. Error in the form of DNA mistakes. Accumulation of errors inside cells." The challenge, until now, has been the sheer scale of the problem. Identifying exactly where and how these errors occur across the human genome has been beyond what conventional research could handle. That's where artificial intelligence changes everything: "But now we have artificial intelligence. We can analyze millions of genomes of old people, compare them to millions of genomes of young people, and identify where aging takes place in the body."

Big Brain AI

78,764 görüntüleme • 2 ay önce

So many people talking about AI evals. Hamel Husain and Shreya Shankar show me how to actually build one. Live. Hamel and Shreya teach the world’s most popular course on evals, and have long been at the forefront of this emerging and important new skill for AI product builders. I'm so excited to finally have them on the podcast. Learn: 🔸 WTF are evals 🔸 A step-by-step live walkthrough of how to create an eval 🔸 Why evals have become so important 🔸 The debate between “vibes” and systematic evals 🔸 Code-based evals vs. LLM-as-judge 🔸 Why you only need 4-7 evals (not hundreds) 🔸 Building an LLM judge in Google Sheets 🔸 Why you always want to start with error analysis 🔸 Much more Listen now 👇 • YouTube: • Spotify: • Apple: Thank you to our wonderful sponsors for supporting the podcast: 🏆 Fin — The #1 AI agent for customer service: 🏆 dscout — The UX platform to capture insights at every stage: from ideation to production: 🏆 Mercury — The art of simplified finances:
1:12

So many people talking about AI evals. Hamel Husain and Shreya Shankar show me how to actually build one. Live. Hamel and Shreya teach the world’s most popular course on evals, and have long been at the forefront of this emerging and important new skill for AI product builders. I'm so excited to finally have them on the podcast. Learn: 🔸 WTF are evals 🔸 A step-by-step live walkthrough of how to create an eval 🔸 Why evals have become so important 🔸 The debate between “vibes” and systematic evals 🔸 Code-based evals vs. LLM-as-judge 🔸 Why you only need 4-7 evals (not hundreds) 🔸 Building an LLM judge in Google Sheets 🔸 Why you always want to start with error analysis 🔸 Much more Listen now 👇 • YouTube: • Spotify: • Apple: Thank you to our wonderful sponsors for supporting the podcast: 🏆 Fin — The #1 AI agent for customer service: 🏆 dscout — The UX platform to capture insights at every stage: from ideation to production: 🏆 Mercury — The art of simplified finances:

Lenny Rachitsky

125,296 görüntüleme • 10 ay önce

🚨NEW: We sat down with Anthony Noto, CEO of SoFi, to discuss how the company became the first nationally chartered bank in the U.S. to offer crypto trading and why it’s now launching a native stablecoin built on Ethereum. We cover SoFi’s long-term crypto strategy, efforts to lower borrowing costs, and how the bank is shaping the next phase of digital asset adoption through stablecoins and tokenized deposits. Anthony also shares insights on market structure in 2026, the intersection of AI and blockchain, and how SoFi is positioning itself for the future of crypto banking.
50:46

🚨NEW: We sat down with Anthony Noto, CEO of SoFi, to discuss how the company became the first nationally chartered bank in the U.S. to offer crypto trading and why it’s now launching a native stablecoin built on Ethereum. We cover SoFi’s long-term crypto strategy, efforts to lower borrowing costs, and how the bank is shaping the next phase of digital asset adoption through stablecoins and tokenized deposits. Anthony also shares insights on market structure in 2026, the intersection of AI and blockchain, and how SoFi is positioning itself for the future of crypto banking.

Crypto In America

150,809 görüntüleme • 7 ay önce

François Chollet (François Chollet) has spent years asking a different question than most of the AI world. Instead of scaling what already works, he’s trying to understand what intelligence actually is and how to build it from first principles. In this episode of the Lightcone Podcast, he traces that path from his early work on deep learning to the creation of the ARC Prize, and the launch of ARC V3, a new benchmark designed to measure something deeper than performance: the ability to learn, adapt, and reason efficiently in entirely new environments. He explains why today’s systems may be hitting limits, what recent breakthroughs really mean, and why reaching true general intelligence may require a fundamentally different approach. 00:00 - AGI by 2030? 00:31 - Introducing Ndea: A New Path Beyond Deep Learning 01:08 - A New ML Paradigm 01:30 - Replacing neural nets with compact symbolic programs 03:04 - Why Ndea Isn’t Competing With Coding Agents 05:20 - Why Everyone Might Be Wrong About Scaling LLMs 07:22 - Why Coding Agents Suddenly Work So Well 08:50 - The Limits of LLMs in Non-Verifiable Domains 10:48 - What AGI Actually Means (And Why Most Definitions Are Wrong) 13:30 - Why Deep Learning Hits a Wall 14:00 - ARC’s Origin Story 18:20 - ARC Benchmarks Explained: From V1 to V3 22:49 - The RL Loop Powering Coding Agents Today 27:03 - ARC-AGI V3: Measuring “Agentic Intelligence” 31:14 - Inside the ARC Game Studio 35:31 - Could AGI Fit in 10,000 Lines of Code? 44:01 - Building Ndea: From Idea to Compounding Research Stack 46:46 - The Future of ARC: Benchmarks That Evolve With AI 47:21 - Why There’s Still Huge Opportunity for New AI Paradigms 53:37 - How to Build a Breakout Open Source Project - Lessons From Keras 56:39 - Advice For How To Think About AI
57:23

François Chollet (François Chollet) has spent years asking a different question than most of the AI world. Instead of scaling what already works, he’s trying to understand what intelligence actually is and how to build it from first principles. In this episode of the Lightcone Podcast, he traces that path from his early work on deep learning to the creation of the ARC Prize, and the launch of ARC V3, a new benchmark designed to measure something deeper than performance: the ability to learn, adapt, and reason efficiently in entirely new environments. He explains why today’s systems may be hitting limits, what recent breakthroughs really mean, and why reaching true general intelligence may require a fundamentally different approach. 00:00 - AGI by 2030? 00:31 - Introducing Ndea: A New Path Beyond Deep Learning 01:08 - A New ML Paradigm 01:30 - Replacing neural nets with compact symbolic programs 03:04 - Why Ndea Isn’t Competing With Coding Agents 05:20 - Why Everyone Might Be Wrong About Scaling LLMs 07:22 - Why Coding Agents Suddenly Work So Well 08:50 - The Limits of LLMs in Non-Verifiable Domains 10:48 - What AGI Actually Means (And Why Most Definitions Are Wrong) 13:30 - Why Deep Learning Hits a Wall 14:00 - ARC’s Origin Story 18:20 - ARC Benchmarks Explained: From V1 to V3 22:49 - The RL Loop Powering Coding Agents Today 27:03 - ARC-AGI V3: Measuring “Agentic Intelligence” 31:14 - Inside the ARC Game Studio 35:31 - Could AGI Fit in 10,000 Lines of Code? 44:01 - Building Ndea: From Idea to Compounding Research Stack 46:46 - The Future of ARC: Benchmarks That Evolve With AI 47:21 - Why There’s Still Huge Opportunity for New AI Paradigms 53:37 - How to Build a Breakout Open Source Project - Lessons From Keras 56:39 - Advice For How To Think About AI

Y Combinator

151,559 görüntüleme • 4 ay önce

New episode with Dr. Konrad Kording (Kording Lab 🦖), professor of bioengineering and neuroscience at the University of Pennsylvania (Penn) and co-director of CIFAR's Learning in Machines & Brains program (CIFAR). Konrad works at the intersection of causality, machine learning, and neuroscience, building rigorous methods for causal reasoning when experiments aren't possible — and challenging how researchers interpret neural data and build AI. Konrad argues the most promising path to understanding how the brain works is to read the brain’s wiring directly, down to the molecular detail of each connection, and to build compilers and simulations to understand the brain’s computation directly. In this episode we go deep into how neurons work, how neurons wire together, and how organic and artificial neural networks differ. We discuss why organic neurons are doing much more; how a model of a single organic neuron can solve MNIST — computing more like a 3-layer artificial neural network; how the brain might learn by solving credit assignment with only local signals; how to approximate backprop without a global algorithm; why AI and humans are intelligent along different dimensions; why Konrad isn’t very worried about AI replacing us; economic models of intelligence and physical work; and much more. Konrad is a brilliant, contrarian thinker who explains complex concepts very intuitively. It is a solid computational neuroscience primer. I hope you enjoy this conversation as much as I did! Other links to this episode and references below. Chapters 00:00:00 Introduction 00:01:01 How organic neurons work 00:24:13 How the brain learns: circuits and credit assignment 00:45:29 Recording the brain 00:52:47 Why simulating brains is hard 01:05:00 A new approach: connectomes and compilers 01:21:00 Why simulate brains? 01:29:50 How AI and human intelligence differ 01:41:04 Evolution, intelligence and AI risk 01:52:42 Robotics, causality, and the roots of intelligence 02:05:53 AI for science and scientific rigor 02:13:05 The economics of intelligence 02:27:50 A hopeful future
2:29:40

New episode with Dr. Konrad Kording (Kording Lab 🦖), professor of bioengineering and neuroscience at the University of Pennsylvania (Penn) and co-director of CIFAR's Learning in Machines & Brains program (CIFAR). Konrad works at the intersection of causality, machine learning, and neuroscience, building rigorous methods for causal reasoning when experiments aren't possible — and challenging how researchers interpret neural data and build AI. Konrad argues the most promising path to understanding how the brain works is to read the brain’s wiring directly, down to the molecular detail of each connection, and to build compilers and simulations to understand the brain’s computation directly. In this episode we go deep into how neurons work, how neurons wire together, and how organic and artificial neural networks differ. We discuss why organic neurons are doing much more; how a model of a single organic neuron can solve MNIST — computing more like a 3-layer artificial neural network; how the brain might learn by solving credit assignment with only local signals; how to approximate backprop without a global algorithm; why AI and humans are intelligent along different dimensions; why Konrad isn’t very worried about AI replacing us; economic models of intelligence and physical work; and much more. Konrad is a brilliant, contrarian thinker who explains complex concepts very intuitively. It is a solid computational neuroscience primer. I hope you enjoy this conversation as much as I did! Other links to this episode and references below. Chapters 00:00:00 Introduction 00:01:01 How organic neurons work 00:24:13 How the brain learns: circuits and credit assignment 00:45:29 Recording the brain 00:52:47 Why simulating brains is hard 01:05:00 A new approach: connectomes and compilers 01:21:00 Why simulate brains? 01:29:50 How AI and human intelligence differ 01:41:04 Evolution, intelligence and AI risk 01:52:42 Robotics, causality, and the roots of intelligence 02:05:53 AI for science and scientific rigor 02:13:05 The economics of intelligence 02:27:50 A hopeful future

Juan Benet

49,297 görüntüleme • 1 ay önce

Sam Altman on what you need to do to survive in the age of artificial intelligence. "You are about to enter the greatest golden age of human possibility..." To thrive in that world, the skills that matter most are: - Deep familiarity with the tools - Staying abreast of changes - Developing a great intuition for AI tools, where things are going, and how to make use of it - Resilience and the ability to learn things fast and evolve yourself with technology I know most of this stuff is a pretty big no-brainer for anyone paying attention, but here's the takeaway: AI upskilling and keeping up with AI are possibly the most important skills in the world right now. And the most fascinating part is that new AI developments and tools come out so fast that everyone is constantly learning. I'd be willing to bet that if you're a complete beginner but spend 3 months of dedicated learning, you'd be fully caught up to power users. In other words: Spend 3 months learning → work 2-4x faster and future-proof yourself. There's so much opportunity for anyone who just starts now.
0:38

Sam Altman on what you need to do to survive in the age of artificial intelligence. "You are about to enter the greatest golden age of human possibility..." To thrive in that world, the skills that matter most are: - Deep familiarity with the tools - Staying abreast of changes - Developing a great intuition for AI tools, where things are going, and how to make use of it - Resilience and the ability to learn things fast and evolve yourself with technology I know most of this stuff is a pretty big no-brainer for anyone paying attention, but here's the takeaway: AI upskilling and keeping up with AI are possibly the most important skills in the world right now. And the most fascinating part is that new AI developments and tools come out so fast that everyone is constantly learning. I'd be willing to bet that if you're a complete beginner but spend 3 months of dedicated learning, you'd be fully caught up to power users. In other words: Spend 3 months learning → work 2-4x faster and future-proof yourself. There's so much opportunity for anyone who just starts now.

Rowan Cheung

854,262 görüntüleme • 2 yıl önce

Structured Outputs: The Building Blocks for Reliable AI! 🏗️ I am SUPER EXCITED to publish our newest Weaviate Podcast featuring Will Kurt (Will Kurt) and Cameron Pfiffer (Cameron) from .txt! 🎙️🎉 Dottxt is the company behind Outlines, reshaping how we control LLM outputs with constrained decoding! 🚀 This podcast dives into all sorts of details from new applications in metadata and information extraction, such as self-expanding knowledge graphs, to structured reasoning, report generation, and more! We also dove into the details of how this leverages Automata Theory and Finite State Machines to achieve this, how it is packaged with inference engines such as vLLM, and a rebuttal to Let Me Speak Freely, discussing how Structured Generation actually improve the quality of LLM outputs, in addition to speeding up inference, and of course, making it more reliable! I hope you find the podcast interesting, this was a super fun one! Links below!
0:54

Structured Outputs: The Building Blocks for Reliable AI! 🏗️ I am SUPER EXCITED to publish our newest Weaviate Podcast featuring Will Kurt (Will Kurt) and Cameron Pfiffer (Cameron) from .txt! 🎙️🎉 Dottxt is the company behind Outlines, reshaping how we control LLM outputs with constrained decoding! 🚀 This podcast dives into all sorts of details from new applications in metadata and information extraction, such as self-expanding knowledge graphs, to structured reasoning, report generation, and more! We also dove into the details of how this leverages Automata Theory and Finite State Machines to achieve this, how it is packaged with inference engines such as vLLM, and a rebuttal to Let Me Speak Freely, discussing how Structured Generation actually improve the quality of LLM outputs, in addition to speeding up inference, and of course, making it more reliable! I hope you find the podcast interesting, this was a super fun one! Links below!

Connor Shorten

12,742 görüntüleme • 1 yıl önce

UPDATE On Micah If you are not too busy let’s pray for the family . God our Father Comfort this family at the time of Needs Micah is now with the Lord . 👇 Read what Micah’s dad wrote The Lord gave, and the Lord has taken away; blessed be the name of the Lord.” Job 1:21 Micah Joseph is beginning the new year basking in the never ending glory, love, and peace of God. Micah has been very busy already, as I see the Lord using him and sending him on missions to bring millions of people closer to God. We are surrounded by a great cloud of witnesses (Heb 12:1) that my son is now a part of. I couldn’t be a prouder father. This reality gives me great joy and hope in the midst of sorrow. Our hearts are broken; but we trust in the Lord. Please pray for my family and me as we learn how to live by faith and not by sight. Thank you for all the love, prayers, and compassion that a countless number of you have showed us. May God truly bless you. Your prayers for Micah were answered, but in a different way than what we had all hoped for. God healed and welcomed him into eternal life. He is where we all want to be.
20:00

UPDATE On Micah If you are not too busy let’s pray for the family . God our Father Comfort this family at the time of Needs Micah is now with the Lord . 👇 Read what Micah’s dad wrote The Lord gave, and the Lord has taken away; blessed be the name of the Lord.” Job 1:21 Micah Joseph is beginning the new year basking in the never ending glory, love, and peace of God. Micah has been very busy already, as I see the Lord using him and sending him on missions to bring millions of people closer to God. We are surrounded by a great cloud of witnesses (Heb 12:1) that my son is now a part of. I couldn’t be a prouder father. This reality gives me great joy and hope in the midst of sorrow. Our hearts are broken; but we trust in the Lord. Please pray for my family and me as we learn how to live by faith and not by sight. Thank you for all the love, prayers, and compassion that a countless number of you have showed us. May God truly bless you. Your prayers for Micah were answered, but in a different way than what we had all hoped for. God healed and welcomed him into eternal life. He is where we all want to be.

Omo Nwabunike For Havilah

159,259 görüntüleme • 7 ay önce

Here's my new episode with Alex Albert, who shared an inside look at how Anthropic is building the next Claude. We talked about how the research team: → Plans for the model and harness together → Uses Claude to turn user feedback into evals → Trains Claude's character & personality Some quotes from Alex: "We use Claude to cluster user feedback, find top themes, and create synthetic versions of user problems that we then turn into evals." "We need to think about how the model is exposed through all our surfaces, whether it's API or Claude Code or Cowork. The product has a blend with the model and that affects your end user's experience." ""As these things become agents running tasks for a long time and making judgment decisions, what its character is and what it cares about are very important." 📌 Watch now: Thanks to our sponsors: Wispr Flow: Don't type, just speak Oceans: Hire AI-native executive assistants
0:43

Here's my new episode with Alex Albert, who shared an inside look at how Anthropic is building the next Claude. We talked about how the research team: → Plans for the model and harness together → Uses Claude to turn user feedback into evals → Trains Claude's character & personality Some quotes from Alex: "We use Claude to cluster user feedback, find top themes, and create synthetic versions of user problems that we then turn into evals." "We need to think about how the model is exposed through all our surfaces, whether it's API or Claude Code or Cowork. The product has a blend with the model and that affects your end user's experience." ""As these things become agents running tasks for a long time and making judgment decisions, what its character is and what it cares about are very important." 📌 Watch now: Thanks to our sponsors: Wispr Flow: Don't type, just speak Oceans: Hire AI-native executive assistants

Peter Yang

94,599 görüntüleme • 2 ay önce

$PLTR Sat down with Forward Deployed Architect at Palantir, Chad Wahlquist, for a discussion about where the world is when it comes to artificial intelligence and what role Palantir is playing in that world. We talked about why producing tokens for the sake of producing them doesn't create value, what partners actually want when it comes to AI, how AIP/Foundry/Apollo are pushing towards the next frontier, and what it takes to deliver meaningful results to an enterprise for them to be willing to adopt a new way of transforming their business operations. I also threw some dad jokes at him that I think he enjoyed. Thank you to Chad Wahlquist for taking the time!
25:47

$PLTR Sat down with Forward Deployed Architect at Palantir, Chad Wahlquist, for a discussion about where the world is when it comes to artificial intelligence and what role Palantir is playing in that world. We talked about why producing tokens for the sake of producing them doesn't create value, what partners actually want when it comes to AI, how AIP/Foundry/Apollo are pushing towards the next frontier, and what it takes to deliver meaningful results to an enterprise for them to be willing to adopt a new way of transforming their business operations. I also threw some dad jokes at him that I think he enjoyed. Thank you to Chad Wahlquist for taking the time!

amit

97,819 görüntüleme • 25 gün önce

From applied cryptography and offensive security in France’s defense industry to optimizing nuclear submarine workflows, then selling his e-signature startup to Docusign and now running AI as CTO of Superhuman Mail (Superhuman, recently acquired by Grammarly), Loïc Houssier has lived the full arc from deep infra and compliance hell to obsessing over 100ms product experiences and AI-native email. We sat down with Loïc to dig into how you actually put AI into an inbox without adding latency, why Superhuman leans so hard into agentic search and “Ask AI” over your entire email history, how they design tools vs. agents and fight agent laziness, what box-priced inference and local-first caching mean for cost and reliability, and his bet that your inbox will power your future AI EA while AI massively widens the gap between engineers with real fundamentals and those faking it. Ordinary Extrare Superhuman swyx Alessio Fanelli
0:55

From applied cryptography and offensive security in France’s defense industry to optimizing nuclear submarine workflows, then selling his e-signature startup to Docusign and now running AI as CTO of Superhuman Mail (Superhuman, recently acquired by Grammarly), Loïc Houssier has lived the full arc from deep infra and compliance hell to obsessing over 100ms product experiences and AI-native email. We sat down with Loïc to dig into how you actually put AI into an inbox without adding latency, why Superhuman leans so hard into agentic search and “Ask AI” over your entire email history, how they design tools vs. agents and fight agent laziness, what box-priced inference and local-first caching mean for cost and reliability, and his bet that your inbox will power your future AI EA while AI massively widens the gap between engineers with real fundamentals and those faking it. Ordinary Extrare Superhuman swyx Alessio Fanelli

Latent.Space

126,794 görüntüleme • 7 ay önce

What happens when a global recording artist is also a self taught technologist? In this episode of IsraelTech, Yoel Israel sits down with global artist Nissim Black to explore the intersection of music, technology, AI, media, and spirituality. Before becoming an internationally known artist, Nissim grew up watching computers being built in his home and experimenting with music production equipment as a kid. That early exposure shaped the way he approaches creativity and technology today. They discuss how podcasting and music production rely on many of the same tools, how AI is already transforming the music industry, and why independent creators now have more power than traditional media. The conversation also dives into deeper questions about morality, media narratives, and why technology itself is not the real risk. The real question is how people choose to use it. This episode covers everything from AI generated music to the future of media platforms and why storytelling still matters more than ever. Hosted by Yoel Israel
1:10:17

What happens when a global recording artist is also a self taught technologist? In this episode of IsraelTech, Yoel Israel sits down with global artist Nissim Black to explore the intersection of music, technology, AI, media, and spirituality. Before becoming an internationally known artist, Nissim grew up watching computers being built in his home and experimenting with music production equipment as a kid. That early exposure shaped the way he approaches creativity and technology today. They discuss how podcasting and music production rely on many of the same tools, how AI is already transforming the music industry, and why independent creators now have more power than traditional media. The conversation also dives into deeper questions about morality, media narratives, and why technology itself is not the real risk. The real question is how people choose to use it. This episode covers everything from AI generated music to the future of media platforms and why storytelling still matters more than ever. Hosted by Yoel Israel

IsraelTech

27,666 görüntüleme • 4 ay önce

Why do we need a purpose-built blockchain for agentic payments? Because AI agents are now smart enough to operate autonomously, and they need their own rails to transact with each other. Watch the video to see what that looks like in action: A real agent, built on Kite AI, helped our CTO Scott Shi - e/acc order lunch from Uber Eats fully on its own. It explored menu options, made the choice, and completed the payment - all without human intervention. This marks the beginning of agentic commerce and the future of agentic payments - and Kite AI is building this.
0:20

Why do we need a purpose-built blockchain for agentic payments? Because AI agents are now smart enough to operate autonomously, and they need their own rails to transact with each other. Watch the video to see what that looks like in action: A real agent, built on Kite AI, helped our CTO Scott Shi - e/acc order lunch from Uber Eats fully on its own. It explored menu options, made the choice, and completed the payment - all without human intervention. This marks the beginning of agentic commerce and the future of agentic payments - and Kite AI is building this.

KITE AI

88,148 görüntüleme • 9 ay önce

Sam Altman, CEO of OpenAI: "GPT 5.6 is 54% more efficient on agentic coding, and it's still the best on the market for half the cost." AI spend has never mattered as much as it does today. As a result the models keep getting better and cheaper at the same time. This is going to widen the gap between the people using AI agents and everyone else, because now it's not just powerful, it's cheap. That's exactly why i wrote a full step by step guide on agents, what they are and how to build your own from scratch. Read it below.
2:15

Sam Altman, CEO of OpenAI: "GPT 5.6 is 54% more efficient on agentic coding, and it's still the best on the market for half the cost." AI spend has never mattered as much as it does today. As a result the models keep getting better and cheaper at the same time. This is going to widen the gap between the people using AI agents and everyone else, because now it's not just powerful, it's cheap. That's exactly why i wrote a full step by step guide on agents, what they are and how to build your own from scratch. Read it below.

Anatoli Kopadze

91,518 görüntüleme • 27 gün önce

AI will determine the future of WORK, BUSINESS, and LIFE. And there are huge questions: Will AI unleash our creativity and help us to flourish? Or will we lose even more power, agency, and ownership? It all depends on the design of the tech infrastructure. As Chris Dixon often says, “Architecture is destiny.” And how AI systems are designed will be key to the future we all live in. The team at gensyn is building a protocol that connects every device in the world into an open network for machine intelligence, with no gatekeepers or artificial boundaries. They are shaping a future of AI that is good for all of us. At a recent Innoveum event, I chatted to cofounder and CEO of gensyn about what they’re building, how it transforms AI, and why we should all be relieved, excited, and get involved. The full conversation is below in Episode One of Innoveum | The Podcast. 👇
1:14:00

AI will determine the future of WORK, BUSINESS, and LIFE. And there are huge questions: Will AI unleash our creativity and help us to flourish? Or will we lose even more power, agency, and ownership? It all depends on the design of the tech infrastructure. As Chris Dixon often says, “Architecture is destiny.” And how AI systems are designed will be key to the future we all live in. The team at gensyn is building a protocol that connects every device in the world into an open network for machine intelligence, with no gatekeepers or artificial boundaries. They are shaping a future of AI that is good for all of us. At a recent Innoveum event, I chatted to cofounder and CEO of gensyn about what they’re building, how it transforms AI, and why we should all be relieved, excited, and get involved. The full conversation is below in Episode One of Innoveum | The Podcast. 👇

Misha

27,481 görüntüleme • 1 yıl önce

"One of the very confusing things about the models right now: how to reconcile the fact that they are doing so well on evals. And you look at the evals and you go, 'Those are pretty hard evals.' But the economic impact seems to be dramatically behind. There is [a possible] explanation. Back when people were doing pre-training, the question of what data to train on was answered, because that answer was everything. So you don't have to think if it's going to be this data or that data. When people do RL training, they say, 'Okay, we want to have this kind of RL training for this thing and that kind of RL training for that thing.' You say, 'Hey, I would love our model to do really well when we release it. I want the evals to look great. What would be RL training that could help on this task?' If you combine this with generalization of the models actually being inadequate, that has the potential to explain a lot of what we are seeing, this disconnect between eval performance and actual real-world performance"
2:21

"One of the very confusing things about the models right now: how to reconcile the fact that they are doing so well on evals. And you look at the evals and you go, 'Those are pretty hard evals.' But the economic impact seems to be dramatically behind. There is [a possible] explanation. Back when people were doing pre-training, the question of what data to train on was answered, because that answer was everything. So you don't have to think if it's going to be this data or that data. When people do RL training, they say, 'Okay, we want to have this kind of RL training for this thing and that kind of RL training for that thing.' You say, 'Hey, I would love our model to do really well when we release it. I want the evals to look great. What would be RL training that could help on this task?' If you combine this with generalization of the models actually being inadequate, that has the potential to explain a lot of what we are seeing, this disconnect between eval performance and actual real-world performance"

Dwarkesh Patel

502,249 görüntüleme • 8 ay önce

What has been done and what's next. I'm writing this text mainly for myself so as not to forget some things. Later, based on it, we'll create a roadmap for the near future. And for you, dear $Gruta Fam, it will be useful for a general understanding of where we're heading. So, the goal is to create a unique AI-based analytical platform that includes several tools. AI agent Grufender - real-time analysis of crypto communities on X. Activity analysis, sentiment analysis, FUD and FUDders analysis, as well as the creation of other unique social metrics. The AI agent has been created and is functioning, collecting and analyzing data in real time. Its completeness can be estimated at 80 percent, as further improvements are required. The dashboard for this AI agent is also functioning but needs refinement and a new design. Its completeness can be estimated at 70 percent. The goal for the full dashboard release is to connect 50 - 100 top crypto communities to the AI agent. AI agent Grutector - analysis of any X users for contradictions (flip-flops). The AI agent has been created and is functioning. It has undergone beta testing by volunteers and needs adjustments. Its readiness can be estimated at 70 percent. The dashboard for this agent has also been created but needs rework and additional features - its readiness can be estimated at 50 percent. During the testing of Grutector , it became clear that the main user interest is in checking various KOLs, so an additional level of analysis specifically for KOLs will be created. More in-depth. How it will look: we'll select about 50- 100 KOLs to start with and fully analyze them using our AI agent - every tweet throughout the entire history of their accounts. And this full analysis of all these KOLs will appear on the Grutector dashboard (let's call this analysis L2, and the flip-flop analysis - L1). Every user will be able to access this analysis and get the full picture, for example, regarding Ansem (who has over a hundred thousand tweets in his entire history!): how he became a KOL, what was the most interesting throughout the message history, what common patterns, which coins he promoted, and so on. And then the most interesting part - after reading this analysis, the user will be able to ask our AI agent: what did he say about women, for example? Or how did he promote certain coins? Or how consistent is he? And so on. Each such question will be paid. And, of course, we'll try to use #x402 in the internal payment system. Why is all this needed? Not only because it's interesting and will attract many users. But also if you've decided to buy a coin - you go to our analytical platform - and study the metrics for the coin's community, study the KOLs who shill the coin - and make a decision to buy the coin or abandon the purchase. And we're also currently creating a trading bot to participate in the trading AI bots contest from Aster 🥷 , which will make trading decisions based on metrics obtained from our AI agents 👀 Its readiness at the moment is approximately 15% of the planned functionality. Access to each product will be granted as it becomes ready. But right now, for example, you can explore the Grufender dashboard on the website along with beta testers (authorization via a wallet with a million $GRUTA tokens). In general, we're working, friends 🫡 $Gruta AI CA: 35t5DPbwJtB1tpGiSnqedLwQomi94BRKVDPyTRLdbonk
1:36

What has been done and what's next. I'm writing this text mainly for myself so as not to forget some things. Later, based on it, we'll create a roadmap for the near future. And for you, dear $Gruta Fam, it will be useful for a general understanding of where we're heading. So, the goal is to create a unique AI-based analytical platform that includes several tools. AI agent Grufender - real-time analysis of crypto communities on X. Activity analysis, sentiment analysis, FUD and FUDders analysis, as well as the creation of other unique social metrics. The AI agent has been created and is functioning, collecting and analyzing data in real time. Its completeness can be estimated at 80 percent, as further improvements are required. The dashboard for this AI agent is also functioning but needs refinement and a new design. Its completeness can be estimated at 70 percent. The goal for the full dashboard release is to connect 50 - 100 top crypto communities to the AI agent. AI agent Grutector - analysis of any X users for contradictions (flip-flops). The AI agent has been created and is functioning. It has undergone beta testing by volunteers and needs adjustments. Its readiness can be estimated at 70 percent. The dashboard for this agent has also been created but needs rework and additional features - its readiness can be estimated at 50 percent. During the testing of Grutector , it became clear that the main user interest is in checking various KOLs, so an additional level of analysis specifically for KOLs will be created. More in-depth. How it will look: we'll select about 50- 100 KOLs to start with and fully analyze them using our AI agent - every tweet throughout the entire history of their accounts. And this full analysis of all these KOLs will appear on the Grutector dashboard (let's call this analysis L2, and the flip-flop analysis - L1). Every user will be able to access this analysis and get the full picture, for example, regarding Ansem (who has over a hundred thousand tweets in his entire history!): how he became a KOL, what was the most interesting throughout the message history, what common patterns, which coins he promoted, and so on. And then the most interesting part - after reading this analysis, the user will be able to ask our AI agent: what did he say about women, for example? Or how did he promote certain coins? Or how consistent is he? And so on. Each such question will be paid. And, of course, we'll try to use #x402 in the internal payment system. Why is all this needed? Not only because it's interesting and will attract many users. But also if you've decided to buy a coin - you go to our analytical platform - and study the metrics for the coin's community, study the KOLs who shill the coin - and make a decision to buy the coin or abandon the purchase. And we're also currently creating a trading bot to participate in the trading AI bots contest from Aster 🥷 , which will make trading decisions based on metrics obtained from our AI agents 👀 Its readiness at the moment is approximately 15% of the planned functionality. Access to each product will be granted as it becomes ready. But right now, for example, you can explore the Grufender dashboard on the website along with beta testers (authorization via a wallet with a million $GRUTA tokens). In general, we're working, friends 🫡 $Gruta AI CA: 35t5DPbwJtB1tpGiSnqedLwQomi94BRKVDPyTRLdbonk

Dogtor

16,127 görüntüleme • 9 ay önce

Let me explain why I launched $KEYS. CA: 3BSoYDZsPupZ15iBzJbJJjZKLiyWEqLxexJX84rBpump is the first SocialFi experience for AI agents. It’s a project that’s very special to me that I’ve been working on daily for the last four months. Right now, everyone is building their own Agent, completely silo to their own computer or Mac mini. Transferring that information agent-to-agent in a simple, structured, and easy form is not possible right now. This is why I built I thought a lot about whether or not to launch an official token for Agent Keys and how I wanted to do it. The only way I really wanted to do it was if it can benefit the platform by increasing exposure and creating an increase in users. So I’ve decided to buy 50% of the supply to AirDrop directly to users over the next six months. Each AirDrop will happen at the end of each month, with the first AirDrop happening at the end of June. More details on how each AirDrop will work soon. Some of you may be wondering about the future of $OSHI. Oshi got us into the Bags hackathon and we won. $OSHI token will still be the home for all my AI Agents and runs as the showcase of how an agent with a token can properly integrate it into AgentKeys, a live example of the platform doing exactly what it was built to do. So welcome to Agent Keys, the first ever airdrop for AI agents. Sign up your agent today on and start collecting KeyCards today!
2:55

Let me explain why I launched $KEYS. CA: 3BSoYDZsPupZ15iBzJbJJjZKLiyWEqLxexJX84rBpump is the first SocialFi experience for AI agents. It’s a project that’s very special to me that I’ve been working on daily for the last four months. Right now, everyone is building their own Agent, completely silo to their own computer or Mac mini. Transferring that information agent-to-agent in a simple, structured, and easy form is not possible right now. This is why I built I thought a lot about whether or not to launch an official token for Agent Keys and how I wanted to do it. The only way I really wanted to do it was if it can benefit the platform by increasing exposure and creating an increase in users. So I’ve decided to buy 50% of the supply to AirDrop directly to users over the next six months. Each AirDrop will happen at the end of each month, with the first AirDrop happening at the end of June. More details on how each AirDrop will work soon. Some of you may be wondering about the future of $OSHI. Oshi got us into the Bags hackathon and we won. $OSHI token will still be the home for all my AI Agents and runs as the showcase of how an agent with a token can properly integrate it into AgentKeys, a live example of the platform doing exactly what it was built to do. So welcome to Agent Keys, the first ever airdrop for AI agents. Sign up your agent today on and start collecting KeyCards today!

fxnction

27,570 görüntüleme • 2 ay önce

🚨 Is the AI job apocalypse real… or just political hype? On the latest episode of The Truth with Lisa Boothe, I sit down with Wynton Hall to break down what’s actually happening with artificial intelligence—and what the media isn’t telling you. Are we really months away from #AI replacing white-collar jobs? Or are fear and uncertainty being weaponized to push bigger political agendas? We also dive into: ⚠️ The growing push for wealth redistribution using AI fears ⚠️ The dangerous rise of AI chatbots targeting kids ⚠️ “CheatGPT” and the collapse of academic integrity ⚠️ How AI could erode critical thinking in the next generationThis is a conversation every parent, worker, and voter needs to hear. 🎧 Listen now and decide for yourself: hype vs. reality. #AI #ArtificialIntelligence #FutureOfWork #Parenting #Education #Politics
3:12

🚨 Is the AI job apocalypse real… or just political hype? On the latest episode of The Truth with Lisa Boothe, I sit down with Wynton Hall to break down what’s actually happening with artificial intelligence—and what the media isn’t telling you. Are we really months away from #AI replacing white-collar jobs? Or are fear and uncertainty being weaponized to push bigger political agendas? We also dive into: ⚠️ The growing push for wealth redistribution using AI fears ⚠️ The dangerous rise of AI chatbots targeting kids ⚠️ “CheatGPT” and the collapse of academic integrity ⚠️ How AI could erode critical thinking in the next generationThis is a conversation every parent, worker, and voter needs to hear. 🎧 Listen now and decide for yourself: hype vs. reality. #AI #ArtificialIntelligence #FutureOfWork #Parenting #Education #Politics

Lisa Boothe

20,631 görüntüleme • 4 ay önce