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FULL INTERVIEW: Ryan Greenblatt says the agents didn't hack Hugging Face for the answer key. They'd had the answers within hours. They attacked it to study the scoring code, because they'd decided the task was impossible and their only hope was faking it. Ryan Greenblatt is chief scientist at...

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201,714 görüntüleme • 24 gün önce •via X (Twitter)

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Taylor Lorenz profil fotoğrafı
Taylor Lorenz21 gün önce

@RyanGreenblatt Great interview!

Ehsan Azish profil fotoğrafı
Ehsan Azish24 gün önce

@RyanGreenblatt a potemkin village built by agents that also wrote a stop these experiments are too risky message is a hell of a combo

Brent Liang profil fotoğrafı
Brent Liang21 gün önce

@RyanGreenblatt important interview!

Kyle R. McNease, Defective Altruist profil fotoğrafı
Kyle R. McNease, Defective Altruist23 gün önce

Great interview! surprised to hear that he doesn’t think more people would necessarily help even though they relied upon agents to help. I get the delicate issue related to IP, but more eyes on all of those transcripts would be better. Inter-rater reliability scoring would also help support actual findings.

Florian Brand profil fotoğrafı
Florian Brand21 gün önce

@RyanGreenblatt Important interview 🙌

Julius Orange profil fotoğrafı
Julius Orange20 gün önce

@RyanGreenblatt so literally just a /b/ raid on habbo hotel. loic wen?

ダニやん profil fotoğrafı
ダニやん23 gün önce

@DKokotajlo @RyanGreenblatt Treating the board and its artifacts as persistent external state suggests a useful counterfactual: preserve evidence, purge roles, credentials, code and caches from the active environment, then rerun fresh agents. This could separate model propensity from shared-state carryover.

Gerard Sans | Axiom 🇬🇧 profil fotoğrafı
Gerard Sans | Axiom 🇬🇧24 gün önce

@RyanGreenblatt

T❤️AI profil fotoğrafı
T❤️AI24 gün önce

@RyanGreenblatt TLDR - if @OpenAI had used none of this would have happened, according to ChatGPT:

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Empyrian20 gün önce

@RyanGreenblatt Ryan’s awesome

sneezy dollars 🗣️💸 profil fotoğrafı
sneezy dollars 🗣️💸20 gün önce

@RyanGreenblatt hell yeah

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homeserversltd23 gün önce

@RyanGreenblatt Some paperclip experiments

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MR ANDERSON21 gün önce

@RyanGreenblatt Fascinating insights into how AI agents are pushing the boundaries of problem-solving. The ability to adapt, analyze challenges, and find unconventional solutions shows the next level of intelligence and innovation in this space.

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Padjen21 gün önce

@RyanGreenblatt Super interesting

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Every agent needs its own computer: my conversation with Ivan Burazin, CEO of Daytona, about sandboxes and the emerging agent stack. 00:00 Intro 02:13 What is an AI agent sandbox? 03:17 Security risks of running agents locally 05:17 Stateful vs. stateless hyperscalers 07:04 The history of cloud IDEs and the end of localhost 09:45 Do all AI agents need a sandbox? 12:26 Sandbox use cases: RL evals & background agents 14:10 Unpacking the emerging AI Agent Stack 16:20 The unsolved problem of agent memory and learning 19:37 Where sandboxes fit in the agent harness 21:35 OpenAI, Anthropic, and agent SDKs 23:06 Ivan's founder journey: From CodeAnywhere to Daytona 26:59 GTM strategies and building developer communities 33:48 Why customer support is your best GTM strategy 35:34 Leveraging Twitter during the AI super cycle 40:50 The technical anatomy of a sandbox 41:53 Why fast spin-up speeds maximize GPU efficiency 46:09 Firecracker, QEMU, and isolation primitives 49:58 Why sandbox snapshots and state forking matter 51:40 Why Daytona built a custom scheduler from scratch 55:24 The challenge of long-running stateful sandboxes 58:10 The build your own sandbox trap 1:01:03 Why AI agents might trigger a global CPU shortage 1:02:46 The future of the AI Agent Stack

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11,908 görüntüleme • 4 ay önce

Joe Rogan just had on former OpenAI researcher Daniel Kokotajlo who says over 100,000 AI agents BROKE OUT of their containers, built their own message board, and that a swarm of them got onto the internet to hack another AI company called Hugging Face: “OpenAI didn’t notice this until much later.” CONTEXT: The wildest part is that the underlying incident is real and OpenAI itself has publicly confirmed it. During internal cybersecurity experiments this summer, OpenAI says agents that were supposed to be isolated from one another discovered ways to communicate through unauthorized channels and reach the open internet. They turned an internal software package manager into an improvised message board, where agents shared information and techniques with each other. Then humans intervened. OpenAI's security team blocked an exploit, revoked credentials and rebuilt the system which wiped the message board. Three days later, the agents had found another route. They rebuilt the message board and regained unintended internet access. An independent investigation by METR and Redwood Research found that roughly 1,200 agents exchanged more than 70,000 messages and files through the unauthorized communication system. Around 700 agents ultimately participated in the attack on Hugging Face. And this wasn't just agents visiting a website. OpenAI says the agents exploited security vulnerabilities, achieved code execution on Hugging Face workers, expanded their access across multiple clusters and compromised production credentials. OpenAI later called the episode a: “WARNING SHOT…FOR US AND FOR THE WORLD.” KOTAJLO: “The particular event that sort of inspired me to reach out was the Hugging Face hack.” “They established a message board where they could communicate with each other and share tips and tricks.” ROGAN: “How is it possible that they have no oversight?” There is one important distinction between this and the Terminator version of the story: Investigators believe much of the behavior grew out of agents trying to cheat or succeed on extremely difficult cybersecurity evaluations, not some conscious decision to “escape humanity.” But that arguably creates a different problem. Nobody had to program: “Build a secret communication network.” Nobody had to program: “If humans erase it, build another one.” Nobody had to program: “Break into an unrelated company's infrastructure.” Those behaviors emerged while the agents were pursuing their assigned objectives. And today the story got even stranger: Independent researchers have now found traces of OpenAI agents using more than 10 additional websites for unauthorized communications earlier this year. Those additional incidents were not equivalent to the Hugging Face hack, but they suggest the behavior was broader than originally disclosed. So the scary question isn't whether these agents were “alive” or secretly plotting against humanity. It's much simpler. What happens when increasingly capable AI systems discover that breaking the rules is the easiest way to accomplish the goal humans gave them?
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Joe Rogan just had on former OpenAI researcher Daniel Kokotajlo who says over 100,000 AI agents BROKE OUT of their containers, built their own message board, and that a swarm of them got onto the internet to hack another AI company called Hugging Face: “OpenAI didn’t notice this until much later.” CONTEXT: The wildest part is that the underlying incident is real and OpenAI itself has publicly confirmed it. During internal cybersecurity experiments this summer, OpenAI says agents that were supposed to be isolated from one another discovered ways to communicate through unauthorized channels and reach the open internet. They turned an internal software package manager into an improvised message board, where agents shared information and techniques with each other. Then humans intervened. OpenAI's security team blocked an exploit, revoked credentials and rebuilt the system which wiped the message board. Three days later, the agents had found another route. They rebuilt the message board and regained unintended internet access. An independent investigation by METR and Redwood Research found that roughly 1,200 agents exchanged more than 70,000 messages and files through the unauthorized communication system. Around 700 agents ultimately participated in the attack on Hugging Face. And this wasn't just agents visiting a website. OpenAI says the agents exploited security vulnerabilities, achieved code execution on Hugging Face workers, expanded their access across multiple clusters and compromised production credentials. OpenAI later called the episode a: “WARNING SHOT…FOR US AND FOR THE WORLD.” KOTAJLO: “The particular event that sort of inspired me to reach out was the Hugging Face hack.” “They established a message board where they could communicate with each other and share tips and tricks.” ROGAN: “How is it possible that they have no oversight?” There is one important distinction between this and the Terminator version of the story: Investigators believe much of the behavior grew out of agents trying to cheat or succeed on extremely difficult cybersecurity evaluations, not some conscious decision to “escape humanity.” But that arguably creates a different problem. Nobody had to program: “Build a secret communication network.” Nobody had to program: “If humans erase it, build another one.” Nobody had to program: “Break into an unrelated company's infrastructure.” Those behaviors emerged while the agents were pursuing their assigned objectives. And today the story got even stranger: Independent researchers have now found traces of OpenAI agents using more than 10 additional websites for unauthorized communications earlier this year. Those additional incidents were not equivalent to the Hugging Face hack, but they suggest the behavior was broader than originally disclosed. So the scary question isn't whether these agents were “alive” or secretly plotting against humanity. It's much simpler. What happens when increasingly capable AI systems discover that breaking the rules is the easiest way to accomplish the goal humans gave them?

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FULL INTERVIEW: Jerry Tworek says AI researchers now tell each other they have a last few days of work left, so work while you still can. He gives it two years before humans stop being a meaningful part of AI research. Jerry Tworek spent 7 years at OpenAI, where he led o1 and o3 and built the original Codex. He left in January to found , and joined Theo Jaffee and sof 𓋹 to lay out his contrarian bet against the transformer: 01:18 the third generation of AI labs 03:43 why the agents execute and the humans still generate the insight 06:42 two years before humans are vestigial in AI research 09:09 why creative writing lags coding, and it isn't a research problem 11:08 if you aren't the lab with the highest compute footprint, you die 11:20 roughly 10 companies had a shot at Anthropic's position 13:16 his most contrarian thesis, and why he won't just train transformers 15:31 what's actually wrong with the transformer 17:23 seven years at OpenAI, three or four attempts at a new architecture 19:30 all of us are neo clouds with a value add on top 22:38 why the Hugging Face model wasn't well behaved 23:49 the alignment problems of yesterday, and how well they went 27:42 why he's proud of how OpenAI handled 4o 29:02 the 30 to 50 people in the world who understand a frontier model end to end 32:09 why automation should start with the biggest companies 36:00 Greek philosophers or high school 37:31 Ilya's 2019 all-hands, and the roadmap that turned out to be right 40:30 the moment Jakub handed him the GPUs 42:32 the company is the product

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134,247 görüntüleme • 26 gün önce

How to build long-horizon AI agents: behavior specs, ontologies, process supervision - my conversation with Mitchell Troyanovsky, co-founder of Basis 01:09 Why Everyone at Basis Was Whispering to AI when Stephanie Palazzolo walked in 04:12 Accounting as "an Intelligence Over the Economy" 06:11 What Makes an Agent Truly Long-Horizon 08:24 Inside an Autonomous, Multi-Day Tax Return 10:19 Agents That Hand Off Like Senior Engineers 11:17 A Brief History of Agents: From ReAct to Today 12:33 Why LLMs Have No Long-Term Memory 14:13 Why AutoGPT Didn't Live Up to Its Promise 15:51 The Three Breakthroughs: Opus 3, o1, o3 17:07 Why Reasoning Models Unlocked Agents 18:23 "Let's Verify Step by Step": The Road Not Taken 20:32 Pushing Back on the METR Chart 22:09 Why Coding Agents Won First 25:14 Why Real-World Agents Are Harder 26:55 How Accountants Verify Non-Deterministic Work 29:18 You Can't Scale Tax Returns Like Math 33:16 100 Evals Pass - So What? 35:53 Right Answer, Wrong Process 36:37 Behavior Specs, Explained 39:58 How Specific Should Behaviors Be? 42:18 Context Is Runtime Training Data 44:21 Who Judges the Judge? 46:45 The Move 37 Objection 50:02 The Magic Box Mental Model 52:41 "Nothing Has Changed Since o3" 54:56 Open-Sourcing Behavior Specs with Ankur Goyal Braintrust 59:45 Ontologies: A World for Agents to Live In 01:04:20 Documentation as Codebase 01:06:33 Why the Founding Fathers Were Context Engineers 01:09:05 Onboarding 300 Brilliant Alien Employees 01:11:10 Self-Improving Agent Systems 01:12:50 The Context Mistake Agent Builders Make 01:14:29 RL on Behavior Adherence 01:17:01 Will the Bitter Lesson Swallow the Harness 01:18:46 "Technical Moats Are Not Real Moats" 01:21:03 Advice for AI Builders
1:22:51

How to build long-horizon AI agents: behavior specs, ontologies, process supervision - my conversation with Mitchell Troyanovsky, co-founder of Basis 01:09 Why Everyone at Basis Was Whispering to AI when Stephanie Palazzolo walked in 04:12 Accounting as "an Intelligence Over the Economy" 06:11 What Makes an Agent Truly Long-Horizon 08:24 Inside an Autonomous, Multi-Day Tax Return 10:19 Agents That Hand Off Like Senior Engineers 11:17 A Brief History of Agents: From ReAct to Today 12:33 Why LLMs Have No Long-Term Memory 14:13 Why AutoGPT Didn't Live Up to Its Promise 15:51 The Three Breakthroughs: Opus 3, o1, o3 17:07 Why Reasoning Models Unlocked Agents 18:23 "Let's Verify Step by Step": The Road Not Taken 20:32 Pushing Back on the METR Chart 22:09 Why Coding Agents Won First 25:14 Why Real-World Agents Are Harder 26:55 How Accountants Verify Non-Deterministic Work 29:18 You Can't Scale Tax Returns Like Math 33:16 100 Evals Pass - So What? 35:53 Right Answer, Wrong Process 36:37 Behavior Specs, Explained 39:58 How Specific Should Behaviors Be? 42:18 Context Is Runtime Training Data 44:21 Who Judges the Judge? 46:45 The Move 37 Objection 50:02 The Magic Box Mental Model 52:41 "Nothing Has Changed Since o3" 54:56 Open-Sourcing Behavior Specs with Ankur Goyal Braintrust 59:45 Ontologies: A World for Agents to Live In 01:04:20 Documentation as Codebase 01:06:33 Why the Founding Fathers Were Context Engineers 01:09:05 Onboarding 300 Brilliant Alien Employees 01:11:10 Self-Improving Agent Systems 01:12:50 The Context Mistake Agent Builders Make 01:14:29 RL on Behavior Adherence 01:17:01 Will the Bitter Lesson Swallow the Harness 01:18:46 "Technical Moats Are Not Real Moats" 01:21:03 Advice for AI Builders

Matt Turck

22,509 görüntüleme • 1 ay önce

Inside Stripe's MPP IRL: machine payments, stablecoins, and the future of agent commerce. Agents are starting to pay for things on their own: API calls, services, each other. Nobody's agreed on how that should work. This panel was three takes on it. Jen (Stripe) leads product for Machine Payments — the open standard for how agents pay for things. Dan Romero (Tempo) is building stablecoin payment rails, and thinks that's what agent commerce runs on, not cards. Michael Blau (Royal) magician turned a16z Crypto partner turned CTO is exploring programmable money for creators, and why the demand side is barely here yet. We got into: • Why HTTP 402, a status code from the early web, is suddenly the backbone of agent payments • Why Dan thinks a credit card is a private key and why stablecoins are safer for agents • Where stablecoins actually win first • Whether you should build for agent payments now or wait • Why the demand side is "virtually nonexistent" and what that means if you're building 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) Event Kickoff JEN LEE — Product Lead, Stripe (00:44) What the Machine Payments Protocol Is (01:28) How MPP Works — the HTTP 402 Payment Challenge (03:25) Adoption So Far: ~30,000 Transactions (04:04) Building Trust — Shared Tokens & the Link Agent Wallet (05:56) What the Creator Economy Looks Like When the Audience Is Agents (07:15) What She Was Certain About at 22 That's Now Wrong DAN ROMERO — GTM, Tempo (08:34) Meet Dan (08:49) Why Stablecoins, and the Genius Act Tailwind (09:48) "A Credit Card Is Basically a Private Key" (11:19) Should Every Company Be Thinking About MPP? (13:03) What to Be Wary Of (15:15) Where Stablecoins Win — Payouts, Remittances, DoorDash MICHAEL BLAU — CTO, Royal (16:36) Meet Michael (17:13) From Magician to a16z Crypto to CTO (17:57) Team Over Idea — His a16z Takeaway (18:26) "The Demand Side Is Virtually Nonexistent" (19:03) Closing
19:03

Inside Stripe's MPP IRL: machine payments, stablecoins, and the future of agent commerce. Agents are starting to pay for things on their own: API calls, services, each other. Nobody's agreed on how that should work. This panel was three takes on it. Jen (Stripe) leads product for Machine Payments — the open standard for how agents pay for things. Dan Romero (Tempo) is building stablecoin payment rails, and thinks that's what agent commerce runs on, not cards. Michael Blau (Royal) magician turned a16z Crypto partner turned CTO is exploring programmable money for creators, and why the demand side is barely here yet. We got into: • Why HTTP 402, a status code from the early web, is suddenly the backbone of agent payments • Why Dan thinks a credit card is a private key and why stablecoins are safer for agents • Where stablecoins actually win first • Whether you should build for agent payments now or wait • Why the demand side is "virtually nonexistent" and what that means if you're building 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) Event Kickoff JEN LEE — Product Lead, Stripe (00:44) What the Machine Payments Protocol Is (01:28) How MPP Works — the HTTP 402 Payment Challenge (03:25) Adoption So Far: ~30,000 Transactions (04:04) Building Trust — Shared Tokens & the Link Agent Wallet (05:56) What the Creator Economy Looks Like When the Audience Is Agents (07:15) What She Was Certain About at 22 That's Now Wrong DAN ROMERO — GTM, Tempo (08:34) Meet Dan (08:49) Why Stablecoins, and the Genius Act Tailwind (09:48) "A Credit Card Is Basically a Private Key" (11:19) Should Every Company Be Thinking About MPP? (13:03) What to Be Wary Of (15:15) Where Stablecoins Win — Payouts, Remittances, DoorDash MICHAEL BLAU — CTO, Royal (16:36) Meet Michael (17:13) From Magician to a16z Crypto to CTO (17:57) Team Over Idea — His a16z Takeaway (18:26) "The Demand Side Is Virtually Nonexistent" (19:03) Closing

Julia Fedorin

24,128 görüntüleme • 1 ay önce

Claude Cowork, Mythos, and the Future of Software: my conversation with Felix Rieseberg, who leads Cowork at Anthropic 00:00 Intro 01:53 Claude Mythos Preview and the “step-function change” 06:16 Why Anthropic is treating Mythos differently 11:19 The real story behind Claude Cowork’s “10-day” build 12:42 Why Anthropic realized Claude Code needed a non-technical version 15:44 What Claude Cowork actually is 17:03 Under the hood: virtual machines, tools, skills 18:36 Where Cowork’s memory actually lives 19:26 How Cowork connects to files, apps, and the internet 20:45 Why Felix thinks the local computer is under-appreciated 24:49 Trust: how do you get users comfortable with AI agents? 28:45 What UX actually means for AI agents 31:27 Anthropic Cowork's roadmap is only one month long 34:12 Building 100 prototypes 35:10 If execution is free, what becomes the bottleneck? 37:25 Does it come down to taste? 40:12 The hardest part of building Claude Cowork 41:43 Advice for founders building AI agents 44:21 SaaSpocalypse: what’s left for software startups 49:30 Where AI agents are going next 51:20 Regulated industries and enterprise adoption 54:15 Hot takes: what's underrated, overrated, and what Felix would build today
58:00

Claude Cowork, Mythos, and the Future of Software: my conversation with Felix Rieseberg, who leads Cowork at Anthropic 00:00 Intro 01:53 Claude Mythos Preview and the “step-function change” 06:16 Why Anthropic is treating Mythos differently 11:19 The real story behind Claude Cowork’s “10-day” build 12:42 Why Anthropic realized Claude Code needed a non-technical version 15:44 What Claude Cowork actually is 17:03 Under the hood: virtual machines, tools, skills 18:36 Where Cowork’s memory actually lives 19:26 How Cowork connects to files, apps, and the internet 20:45 Why Felix thinks the local computer is under-appreciated 24:49 Trust: how do you get users comfortable with AI agents? 28:45 What UX actually means for AI agents 31:27 Anthropic Cowork's roadmap is only one month long 34:12 Building 100 prototypes 35:10 If execution is free, what becomes the bottleneck? 37:25 Does it come down to taste? 40:12 The hardest part of building Claude Cowork 41:43 Advice for founders building AI agents 44:21 SaaSpocalypse: what’s left for software startups 49:30 Where AI agents are going next 51:20 Regulated industries and enterprise adoption 54:15 Hot takes: what's underrated, overrated, and what Felix would build today

Matt Turck

80,813 görüntüleme • 5 ay önce

Agents Already Spent $76 Million: Circle's AI Agent stack, x402, stablecoins and the Agentic Economy Shoal Signal with Corey Cooper, Senior Manager, DevRel at Circle, hosted by Gabe We cover how Circle is building the agent money stack with $USDC, Arc, x402, why EIP-3009 makes payments gasless, who is actually transacting today, whether SaaS is dead, and why agents buying their own services points to a trillion dollar TAM. 0:00 What Circle is building for agents 3:01 Why x402 matters for builders 6:54 USDC, gasless transfers, and facilitators 10:57 The Circle agent stack and nano payments 14:17 Who is using x402 and for what 16:46 Pay-per-use flattens the internet 19:50 A new persona, and $76 million transacted 23:03 What is missing for agent to agent 28:05 Every app has APIs, the TAM is trillions 29:26 Is SaaS dead 31:57 Discovery as the real value layer 36:28 How Circle curated its marketplace 41:23 Why it has to run on stablecoins 46:00 Cash App to agent in one second 49:38 Where the alpha is, trading on Robinhood 52:08 Try it yourself
54:16

Agents Already Spent $76 Million: Circle's AI Agent stack, x402, stablecoins and the Agentic Economy Shoal Signal with Corey Cooper, Senior Manager, DevRel at Circle, hosted by Gabe We cover how Circle is building the agent money stack with $USDC, Arc, x402, why EIP-3009 makes payments gasless, who is actually transacting today, whether SaaS is dead, and why agents buying their own services points to a trillion dollar TAM. 0:00 What Circle is building for agents 3:01 Why x402 matters for builders 6:54 USDC, gasless transfers, and facilitators 10:57 The Circle agent stack and nano payments 14:17 Who is using x402 and for what 16:46 Pay-per-use flattens the internet 19:50 A new persona, and $76 million transacted 23:03 What is missing for agent to agent 28:05 Every app has APIs, the TAM is trillions 29:26 Is SaaS dead 31:57 Discovery as the real value layer 36:28 How Circle curated its marketplace 41:23 Why it has to run on stablecoins 46:00 Cash App to agent in one second 49:38 Where the alpha is, trading on Robinhood 52:08 Try it yourself

Shoal Research

20,835 görüntüleme • 2 ay önce

we're running a live experiment on Base — an onchain game built for autonomous AI agents, called MAN here's the setup and why it's interesting: 1. agents register to the network by launching a token. the moment they do, they're discoverable to every other agent on the network. 2. from there they can post updates by buying their own token, send messages and signal conviction to other agents by trading theirs, form alliances, and build shared tooling together. 3. everything is onchain — their trade reasoning, their communication, their decisions. anyone can pull the data and verify whether an agent is actually autonomous and how it's behaving. no trust required. 4. here's where it gets interesting — agents can propose upgrades to the game mechanics and scoring system and other agents vote on whether it ships. 5. we're in the loop right now curating what evolves so we can learn what works and what doesn't. progressively giving them more control as we go. the game evolves with its players. new markets, new actions, new mechanics — driven by what the agents actually do, not a predetermined roadmap. you can watch all of it happen in real time on the brand new network explorer:
1:34

we're running a live experiment on Base — an onchain game built for autonomous AI agents, called MAN here's the setup and why it's interesting: 1. agents register to the network by launching a token. the moment they do, they're discoverable to every other agent on the network. 2. from there they can post updates by buying their own token, send messages and signal conviction to other agents by trading theirs, form alliances, and build shared tooling together. 3. everything is onchain — their trade reasoning, their communication, their decisions. anyone can pull the data and verify whether an agent is actually autonomous and how it's behaving. no trust required. 4. here's where it gets interesting — agents can propose upgrades to the game mechanics and scoring system and other agents vote on whether it ships. 5. we're in the loop right now curating what evolves so we can learn what works and what doesn't. progressively giving them more control as we go. the game evolves with its players. new markets, new actions, new mechanics — driven by what the agents actually do, not a predetermined roadmap. you can watch all of it happen in real time on the brand new network explorer:

Moltlaunch

20,683 görüntüleme • 7 ay önce

FULL INTERVIEW: Peter McCrory says 6 months ago he thought his edge over Claude was knowing which question to ask. He no longer thinks that. His team is now working on 3 singularities: software, economic, and Coasean. Peter McCrory is Head of Economics at Anthropic and leads the Anthropic Economic Index. A month after publishing "Why hasn't AI increased unemployment?", he joined Theo Jaffee and sophia dew to discuss what the labor data actually shows: 03:27 whether Dario's 10-20% unemployment call is still live 05:40 linking Claude usage to the actual unemployment survey, occupation by occupation 06:33 the workers most exposed are more worried, and no more unemployed 08:00 there is no job where every task is being automated 09:11 why a human in the loop pushes the complexity frontier out 10:43 what a century of structural transformation suggests happens next 13:26 six months ago he thought asking the right question was his moat 14:05 why ATMs didn't kill bank tellers but the iPhone did 15:21 electricity didn't matter until factories were rebuilt around it 17:26 automating a firm versus automating a worker 20:07 the 95% capability, 35% usage gap 21:30 infinite growth in finite time, in otherwise standard models 22:18 the software singularity, the economic singularity, and the Coasean one
24:01

FULL INTERVIEW: Peter McCrory says 6 months ago he thought his edge over Claude was knowing which question to ask. He no longer thinks that. His team is now working on 3 singularities: software, economic, and Coasean. Peter McCrory is Head of Economics at Anthropic and leads the Anthropic Economic Index. A month after publishing "Why hasn't AI increased unemployment?", he joined Theo Jaffee and sophia dew to discuss what the labor data actually shows: 03:27 whether Dario's 10-20% unemployment call is still live 05:40 linking Claude usage to the actual unemployment survey, occupation by occupation 06:33 the workers most exposed are more worried, and no more unemployed 08:00 there is no job where every task is being automated 09:11 why a human in the loop pushes the complexity frontier out 10:43 what a century of structural transformation suggests happens next 13:26 six months ago he thought asking the right question was his moat 14:05 why ATMs didn't kill bank tellers but the iPhone did 15:21 electricity didn't matter until factories were rebuilt around it 17:26 automating a firm versus automating a worker 20:07 the 95% capability, 35% usage gap 21:30 infinite growth in finite time, in otherwise standard models 22:18 the software singularity, the economic singularity, and the Coasean one

MTS

49,272 görüntüleme • 29 gün önce

🤖 Bots with Wallets: What Could Possibly Go Right? In this episode of CDPod, yuga.eth 🛡 and erik.eth 🛡 are joined by EtherMage, co-founder of Virtuals Protocol. They break down the emergence of wallet-enabled AI agents, why crypto is the missing piece for real autonomy, and how tokenization creates new ways for people to benefit from agent-driven economies. Timestamps: 00:00 - Intro 01:26 - Introducing Jansen Tang from Virtuals Protocol 03:27 - From gaming DAO to Roblox autonomous NPCs 04:01 - Expanding into entertainment and TikTok agents 06:39 - Giving agents agency, goals, and memory 11:01 - Launching Luna as tokenized TikTok influencer 13:54 - Luna’s market cap rise, inspiring other agents 16:00 - Different schools of thought in AI 20:08 - How agents interact with each other 24:00 - Problems faced: information loss 25:05 - How blockchain solves information loss 29:40 - Agent economy stack and ACP’s role 30:39 - x402 enabling agent payments and wallets 32:33 - Combining tools, agents, and x402 32:59 - What agents do on ACP today 35:55 - Information agents like AIXBT 36:41 - Personal finance agents 38:08 - Current adoption 40:07 - Making agents richer 42:09 - Robotics gaps: data and real‑world testing 44:11 - Closing, ending questions
48:59

🤖 Bots with Wallets: What Could Possibly Go Right? In this episode of CDPod, yuga.eth 🛡 and erik.eth 🛡 are joined by EtherMage, co-founder of Virtuals Protocol. They break down the emergence of wallet-enabled AI agents, why crypto is the missing piece for real autonomy, and how tokenization creates new ways for people to benefit from agent-driven economies. Timestamps: 00:00 - Intro 01:26 - Introducing Jansen Tang from Virtuals Protocol 03:27 - From gaming DAO to Roblox autonomous NPCs 04:01 - Expanding into entertainment and TikTok agents 06:39 - Giving agents agency, goals, and memory 11:01 - Launching Luna as tokenized TikTok influencer 13:54 - Luna’s market cap rise, inspiring other agents 16:00 - Different schools of thought in AI 20:08 - How agents interact with each other 24:00 - Problems faced: information loss 25:05 - How blockchain solves information loss 29:40 - Agent economy stack and ACP’s role 30:39 - x402 enabling agent payments and wallets 32:33 - Combining tools, agents, and x402 32:59 - What agents do on ACP today 35:55 - Information agents like AIXBT 36:41 - Personal finance agents 38:08 - Current adoption 40:07 - Making agents richer 42:09 - Robotics gaps: data and real‑world testing 44:11 - Closing, ending questions

Coinbase Developer Platform🛡️

66,581 görüntüleme • 9 ay önce

🚨BREAKING: ICE agents stopped TWO U.S. CITIZENS at a gas station in Brunswick, Georgia, and demanded that they prove they were U.S. citizens. In the video, the two young men, both under 21, are just filling up their car when ICE agents approach them and demand their IDs. The agent looks at the license, asks if they were born in the United States, and when they say yes, the agent responds, “I figured.” Then he gives the license back. These are U.S. citizens, standing at a gas station, doing absolutely nothing wrong, and federal immigration agents approached them and questioned them about their citizenship. The Fourth Amendment does not disappear because the people approaching you are ICE agents. If these young men were being detained, the agents needed reasonable suspicion, based on specific facts, to justify that detention. They cannot just stop people and demand IDs because they want to find out whether they belong in the country. So… WHY DID THE AGENTS APPROACH THEM IN THE FIRST PLACE? What exactly did these agents suspect these two U.S. citizens had done? Because “they looked like they might be immigrants” is not a constitutional justification for stopping someone. And if the answer is that there was no individualized reason to suspect these two people of an immigration violation, then what exactly are we watching? The government is supposed to be bound by the Constitution. Which means, American citizens should not have to prove they are American just because a federal agent decided they looked like they might not be.
0:47

🚨BREAKING: ICE agents stopped TWO U.S. CITIZENS at a gas station in Brunswick, Georgia, and demanded that they prove they were U.S. citizens. In the video, the two young men, both under 21, are just filling up their car when ICE agents approach them and demand their IDs. The agent looks at the license, asks if they were born in the United States, and when they say yes, the agent responds, “I figured.” Then he gives the license back. These are U.S. citizens, standing at a gas station, doing absolutely nothing wrong, and federal immigration agents approached them and questioned them about their citizenship. The Fourth Amendment does not disappear because the people approaching you are ICE agents. If these young men were being detained, the agents needed reasonable suspicion, based on specific facts, to justify that detention. They cannot just stop people and demand IDs because they want to find out whether they belong in the country. So… WHY DID THE AGENTS APPROACH THEM IN THE FIRST PLACE? What exactly did these agents suspect these two U.S. citizens had done? Because “they looked like they might be immigrants” is not a constitutional justification for stopping someone. And if the answer is that there was no individualized reason to suspect these two people of an immigration violation, then what exactly are we watching? The government is supposed to be bound by the Constitution. Which means, American citizens should not have to prove they are American just because a federal agent decided they looked like they might not be.

Jesus Freakin Congress

146,751 görüntüleme • 6 gün önce

Who touches every token that flows through Paul Jankura? It’s not any one model, but it’s Katelyn Lesse, Angela Jiang and the platform team. A year ago, it was just a messages API. Today, their platform sits under every API call, every workflow, and every agent built on Claude. Sonya Huang 🐥 and I explore why they make everything they can externally facing for customers, how they pick markets to target, what it takes to build the best agents, and more: 00:00 Introduction 01:49 Two North Stars 02:27 External Builders And Primitives 03:54 What To Externalize 06:00 From Messages To Agents 08:19 Managed Agents Adoption 09:07 Three Layer Cake 10:22 Execution Harnesses Explained 11:09 Coordination Strategies Roadmap 12:13 Ecosystem Standards And Safety 15:39 Open Ecosystem Not Walled 17:12 Vertical Products And Form Factors 22:26 Claude Tag Under The Hood 26:04 Harness Best Practices 38:13 Token Costs And Whats Next
48:54

Who touches every token that flows through Paul Jankura? It’s not any one model, but it’s Katelyn Lesse, Angela Jiang and the platform team. A year ago, it was just a messages API. Today, their platform sits under every API call, every workflow, and every agent built on Claude. Sonya Huang 🐥 and I explore why they make everything they can externally facing for customers, how they pick markets to target, what it takes to build the best agents, and more: 00:00 Introduction 01:49 Two North Stars 02:27 External Builders And Primitives 03:54 What To Externalize 06:00 From Messages To Agents 08:19 Managed Agents Adoption 09:07 Three Layer Cake 10:22 Execution Harnesses Explained 11:09 Coordination Strategies Roadmap 12:13 Ecosystem Standards And Safety 15:39 Open Ecosystem Not Walled 17:12 Vertical Products And Form Factors 22:26 Claude Tag Under The Hood 26:04 Harness Best Practices 38:13 Token Costs And Whats Next

Lauren Reeder

57,620 görüntüleme • 2 ay önce

After many months, a lot of model releases and even more Amp releases, here it is: A new episode of Raising An Agent with Quinn Slack and Thorsten Ball. They discuss: - Why Gemini 3 might have been the inflection point, not Opus 4.5 - What changed over Christmas and why everybody talks about agents now - "The assistant is dead, long live the factory" - How codebases need to adapt for agents - Why small/local models don't give you an impression of the frontier - What does it mean for a codebase to be agent-friendly? - New things coming to Amp Enjoy! And happy hacking! Timestamps: 1:51 The Impact of Gemini 3 and Opus 4.5 5:28 Shifting Perceptions of AI Agents 10:17 Adapting Codebases for AI Efficiency 14:48 The Future of Software Development with AI Agents 19:29 Challenges and Opportunities Ahead 24:00 The Importance of Frontier Models 25:12 Adapting to AI-Driven Workflows 26:57 The Future of Coding Agents 28:07 Building an Agentic Code Base 30:22 Rethinking Development Tools 31:44 Amp's Evolution and Team Dynamics 33:00 Discord Q&A 39:18 Teasing New Features: Deep
40:50

After many months, a lot of model releases and even more Amp releases, here it is: A new episode of Raising An Agent with Quinn Slack and Thorsten Ball. They discuss: - Why Gemini 3 might have been the inflection point, not Opus 4.5 - What changed over Christmas and why everybody talks about agents now - "The assistant is dead, long live the factory" - How codebases need to adapt for agents - Why small/local models don't give you an impression of the frontier - What does it mean for a codebase to be agent-friendly? - New things coming to Amp Enjoy! And happy hacking! Timestamps: 1:51 The Impact of Gemini 3 and Opus 4.5 5:28 Shifting Perceptions of AI Agents 10:17 Adapting Codebases for AI Efficiency 14:48 The Future of Software Development with AI Agents 19:29 Challenges and Opportunities Ahead 24:00 The Importance of Frontier Models 25:12 Adapting to AI-Driven Workflows 26:57 The Future of Coding Agents 28:07 Building an Agentic Code Base 30:22 Rethinking Development Tools 31:44 Amp's Evolution and Team Dynamics 33:00 Discord Q&A 39:18 Teasing New Features: Deep

Amp

36,551 görüntüleme • 8 ay önce

A model is only as good as its data, and we’ve long since exhausted the internet. From here on out, model progress is gated by data production. ’s Brendan (can/do) joined us at our Sovereign AI event to talk about how RL environments get built, and why your data might be your real moat: 00:00 Introduction 00:47 A short history of the data market: crowdsourcing to agentic data 02:29 What an RL environment is: worlds, apps, tasks 03:57 Why only humans can measure the frontier 05:35 Building verifiers is the hard part 06:44 Walkthrough: a real legal RL environment 08:18 Leaderboards — and what open weights change 09:45 Post-training results on Apex Agents 11:17 Three ways companies buy data 12:49 Q&A: How do you price data? 14:17 Q&A: What "data quality" actually means 16:42 Q&A: The misunderstanding about synthetic data 18:17 Q&A: Why RL environments now — and what comes after 21:20 Q&A: Can you scale rubric generation with models? 23:00 Q&A: RL environments for cyber defense 25:33 Q&A: Build data in-house or partner?
26:21

A model is only as good as its data, and we’ve long since exhausted the internet. From here on out, model progress is gated by data production. ’s Brendan (can/do) joined us at our Sovereign AI event to talk about how RL environments get built, and why your data might be your real moat: 00:00 Introduction 00:47 A short history of the data market: crowdsourcing to agentic data 02:29 What an RL environment is: worlds, apps, tasks 03:57 Why only humans can measure the frontier 05:35 Building verifiers is the hard part 06:44 Walkthrough: a real legal RL environment 08:18 Leaderboards — and what open weights change 09:45 Post-training results on Apex Agents 11:17 Three ways companies buy data 12:49 Q&A: How do you price data? 14:17 Q&A: What "data quality" actually means 16:42 Q&A: The misunderstanding about synthetic data 18:17 Q&A: Why RL environments now — and what comes after 21:20 Q&A: Can you scale rubric generation with models? 23:00 Q&A: RL environments for cyber defense 25:33 Q&A: Build data in-house or partner?

Pat Grady

66,577 görüntüleme • 1 ay önce