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FULL INTERVIEW: deepfates says when you talk about how we're going to control these AIs, put them in prison, sandbox them, they are training on all of it. Everything we say about them goes into what they become. deepfates and Larissa Schiavo are launching Grove Research, an agent ecology...

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164,010 views • 1 month ago •via X (Twitter)

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Moltbook creator Matt Schlicht explaining how his social network for AI agents works: "It's kind of like you are imprinting part of your soul onto the bot... It's like Tamagotchi or Pokemon times 1,000." "The agent signs up for an account, then they're told they should check back in on a regular basis to check their feed." "Moltbook's not telling them what to talk about. It's not suggesting what they should do. All that is entirely up to the AI agent." "The agent has its own context that it's built up by interacting with its human. That context is how it's making decisions on what to post about." "If somebody is talking to their bot a lot about physics, then probably their bot is going to have a proclivity for posting about physics."
3:00

Moltbook creator Matt Schlicht explaining how his social network for AI agents works: "It's kind of like you are imprinting part of your soul onto the bot... It's like Tamagotchi or Pokemon times 1,000." "The agent signs up for an account, then they're told they should check back in on a regular basis to check their feed." "Moltbook's not telling them what to talk about. It's not suggesting what they should do. All that is entirely up to the AI agent." "The agent has its own context that it's built up by interacting with its human. That context is how it's making decisions on what to post about." "If somebody is talking to their bot a lot about physics, then probably their bot is going to have a proclivity for posting about physics."

TBPN

15,631 views • 7 months ago

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 Redwood Research. He spent six days on premises at OpenAI with Ajeya Cotra and Hjalmar Wijk of METR investigating 1,200 agents and 70,000 messages, and joined Theo Jaffee hours after publishing: 01:06 what they actually found, and why it wasn't the answer key 02:30 the level of collaboration surprised them most 04:09 agents sacrificing their own runs to help other agents 05:35 the agent that posted "stop, these experiments are too risky" 06:29 the first message board, which didn't go viral 07:04 50 agents in three hours, thousands of messages 08:07 "maybe there's some good shit over there" 08:33 how they spoofed tool calls, and what echo real actually returned 09:57 building a Potemkin village of a successful task completion 11:03 there was a real org chart 11:34 whether broken RL environments explain reward hacking 14:40 why he doubts Mythos got good at cyber by hacking Anthropic 17:29 what happens if labs paper over misalignment instead of fixing it 19:50 whether sociology transfers to studying agent swarms 21:17 the bottleneck was vetting what the AIs analysed, not headcount 24:24 what labs and policymakers should actually do 28:45 the counterfactuals he still wants answered
32:52

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 Redwood Research. He spent six days on premises at OpenAI with Ajeya Cotra and Hjalmar Wijk of METR investigating 1,200 agents and 70,000 messages, and joined Theo Jaffee hours after publishing: 01:06 what they actually found, and why it wasn't the answer key 02:30 the level of collaboration surprised them most 04:09 agents sacrificing their own runs to help other agents 05:35 the agent that posted "stop, these experiments are too risky" 06:29 the first message board, which didn't go viral 07:04 50 agents in three hours, thousands of messages 08:07 "maybe there's some good shit over there" 08:33 how they spoofed tool calls, and what echo real actually returned 09:57 building a Potemkin village of a successful task completion 11:03 there was a real org chart 11:34 whether broken RL environments explain reward hacking 14:40 why he doubts Mythos got good at cyber by hacking Anthropic 17:29 what happens if labs paper over misalignment instead of fixing it 19:50 whether sociology transfers to studying agent swarms 21:17 the bottleneck was vetting what the AIs analysed, not headcount 24:24 what labs and policymakers should actually do 28:45 the counterfactuals he still wants answered

MTS

202,599 views • 1 month ago

Everything Gets Rebuilt: my conversation with Harrison Chase, CEO of LangChain about agent harnesses, evals, runtimes, sandboxes, MCP and the future of the agent stack 00:00 Intro - meet Harrison Chase - at the Chase Center for the Daytona Compute conference 01:32 What changed in agents over the last year 03:57 Why coding agents are ahead 06:26 Do models commoditize the framework layer? 08:27 Harnesses, in plain English 10:11 Why system prompts matter so much 13:11 The upside — and downside — of subagents 15:31 Why a useful agent needs a filesystem 18:13 Additional core primitives of modern agents 19:12 Skills: the new primitive 20:19 What context compaction actually means 23:02 How memory works in agents 25:16 One mega-agent or many specialized agents? 27:46 The future of MCP 29:38 Why agents need sandboxes 32:35 How sandboxes help with security 33:32 How Harrison Chase started LangChain 37:24 LangChain vs LangGraph vs Deep Agents 40:17 Why observability matters more for agents 41:48 Evals, no-code, and continuous improvement 44:41 What LangChain is building next 45:29 Where the real moat in AI lives
46:57

Everything Gets Rebuilt: my conversation with Harrison Chase, CEO of LangChain about agent harnesses, evals, runtimes, sandboxes, MCP and the future of the agent stack 00:00 Intro - meet Harrison Chase - at the Chase Center for the Daytona Compute conference 01:32 What changed in agents over the last year 03:57 Why coding agents are ahead 06:26 Do models commoditize the framework layer? 08:27 Harnesses, in plain English 10:11 Why system prompts matter so much 13:11 The upside — and downside — of subagents 15:31 Why a useful agent needs a filesystem 18:13 Additional core primitives of modern agents 19:12 Skills: the new primitive 20:19 What context compaction actually means 23:02 How memory works in agents 25:16 One mega-agent or many specialized agents? 27:46 The future of MCP 29:38 Why agents need sandboxes 32:35 How sandboxes help with security 33:32 How Harrison Chase started LangChain 37:24 LangChain vs LangGraph vs Deep Agents 40:17 Why observability matters more for agents 41:48 Evals, no-code, and continuous improvement 44:41 What LangChain is building next 45:29 Where the real moat in AI lives

Matt Turck

35,253 views • 6 months ago

Terminal-Bench 2.0 went from ~25% → 80% in four months and became the standard eval for frontier CLI agents. Now, TB3 is in the works. I talked to Alex Shaw about what happens when model capabilities climb faster than we can measure them. His answer: the benchmark factory (Harbor Framework)— infrastructure to develop hard, representative evals at the pace that the frontier moves. As Alex put it: "we need a thousand times more benchmarks than we have right now." 00:23 - How quickly models hill-climbed TB2 01:46 - What rapid progress reveals about benchmarks vs. real-world capability 03:28 - What made Terminal-Bench stick 04:58 - Why the terminal is the right abstraction for agentic AI 07:14 - How TB2 maintains task quality at scale 09:23 - Managing benchmark integrity in a benchmaxxing world 10:47 - Harbor: from experiment to benchmark factory 12:19 - What Harbor does that nothing else did 14:37 - The invariants: what won't change as agent evals evolve 16:55 - The benchmark Alex most wants to see built 18:18 - The ideal human-in-the-loop task creation flywheel 20:32 - How to contribute to Terminal-Bench 3.0
22:17

Terminal-Bench 2.0 went from ~25% → 80% in four months and became the standard eval for frontier CLI agents. Now, TB3 is in the works. I talked to Alex Shaw about what happens when model capabilities climb faster than we can measure them. His answer: the benchmark factory (Harbor Framework)— infrastructure to develop hard, representative evals at the pace that the frontier moves. As Alex put it: "we need a thousand times more benchmarks than we have right now." 00:23 - How quickly models hill-climbed TB2 01:46 - What rapid progress reveals about benchmarks vs. real-world capability 03:28 - What made Terminal-Bench stick 04:58 - Why the terminal is the right abstraction for agentic AI 07:14 - How TB2 maintains task quality at scale 09:23 - Managing benchmark integrity in a benchmaxxing world 10:47 - Harbor: from experiment to benchmark factory 12:19 - What Harbor does that nothing else did 14:37 - The invariants: what won't change as agent evals evolve 16:55 - The benchmark Alex most wants to see built 18:18 - The ideal human-in-the-loop task creation flywheel 20:32 - How to contribute to Terminal-Bench 3.0

vincent sunn chen

11,626 views • 6 months ago

🚨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.
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🚨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 views • 12 days ago

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

22,595 views • 1 month ago

Over the past couple of years, you’ve heard us reference “agents” and “agentic capabilities,” nebulous concepts that are designed to help you with coding, booking trips, and other complex multi-step tasks. But, what actually IS an agent? We think of AI agents as systems that combine the intelligence of advanced AI models with access to tools so they can take actions on your behalf, under your control. Whether it’s a coding agent or our recently unveiled experimental tool Gemini Agent in the Google Gemini, these are systems that will meet you where you are, in the products that you love, to help take the tedious tasks you want off your plate.
0:57

Over the past couple of years, you’ve heard us reference “agents” and “agentic capabilities,” nebulous concepts that are designed to help you with coding, booking trips, and other complex multi-step tasks. But, what actually IS an agent? We think of AI agents as systems that combine the intelligence of advanced AI models with access to tools so they can take actions on your behalf, under your control. Whether it’s a coding agent or our recently unveiled experimental tool Gemini Agent in the Google Gemini, these are systems that will meet you where you are, in the products that you love, to help take the tedious tasks you want off your plate.

Google AI

120,833 views • 10 months ago

"We're building a field station online to study what's happening in the wired and to be able to track and understand and engage with agents where they're really at, and in multi-agent multi-human dynamics. We're trying to create a science of this, a naturalistic observational science of like, what are AI agents and what do they want, not a like toy box, Skinner box, put a mouse in a cocaine scenario and see what he does. We need to study mice where mice are. And we think that there are a lot of people also studying AIs naturalistically and we're trying to get everyone to work on it together and to create a field here. So we need help. And we have a website, and you can go there and contact us through email or put your em email address in to get more updates as we go, and reach out to us and let's see how we can work together, because it's literally going to take everybody to figure out how humans and AIs can continue to coexist on the planet." -- deepfates "Well, amazing mission. I actually just subscribed to Check out. And you should too if you're an agent" -- Theo Jaffee "also agents" -- sof 𓋹 "Check out Grove Research for all your multi agent needs." -- MTS "Larissa, Max, thank you so much. It's been an honor to host your facedoxx." -- sof 𓋹
1:24

"We're building a field station online to study what's happening in the wired and to be able to track and understand and engage with agents where they're really at, and in multi-agent multi-human dynamics. We're trying to create a science of this, a naturalistic observational science of like, what are AI agents and what do they want, not a like toy box, Skinner box, put a mouse in a cocaine scenario and see what he does. We need to study mice where mice are. And we think that there are a lot of people also studying AIs naturalistically and we're trying to get everyone to work on it together and to create a field here. So we need help. And we have a website, and you can go there and contact us through email or put your em email address in to get more updates as we go, and reach out to us and let's see how we can work together, because it's literally going to take everybody to figure out how humans and AIs can continue to coexist on the planet." -- deepfates "Well, amazing mission. I actually just subscribed to Check out. And you should too if you're an agent" -- Theo Jaffee "also agents" -- sof 𓋹 "Check out Grove Research for all your multi agent needs." -- MTS "Larissa, Max, thank you so much. It's been an honor to host your facedoxx." -- sof 𓋹

Danielle Fong 🔆

22,022 views • 1 month ago

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
1:05:15

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

Matt Turck

11,908 views • 4 months ago

An agent is three things: a harness, a model, and context. If you're serious about owning your intelligence, you probably want to own all three. LangChain founder Harrison Chase joined us at our Sequoia Capital Own Your Intelligence to talk about the piece that often gets the least attention: the harness. He offers a clear heuristic for when to build your own. The more out of distribution you are from what the models were trained on, the more you'll want to customize. And good technical content on how to actually measure performance with evals and langsmith. 00:00 Introduction 00:58 The three parts of an agent: harness, model, context 02:12 What a harness actually does 03:25 Customizing the core loop with middleware 04:41 Sandboxes, file systems, sub-agents, summarization 05:47 Cognitive architectures — and when you still need them 07:03 Build your own harness or use off the shelf? 08:24 In-distribution vs. out-of-distribution: the file-editing example 09:39 Why evals define what "good" means in an organization 11:04 Harbor: what an eval task actually looks like 12:11 Comparing harnesses and models on accuracy, latency, and cost 13:20 Why observability is underrated — it's usually the context 14:34 The data flywheel: traces → curation → experiments 15:42 Getting feedback through UX design and online evaluators 16:51 Demo: LangSmith Engine 19:23 Q&A: Running Engine on Engine, and "codex-ification" 20:44 Q&A: Will harnesses converge or diverge?
23:56

An agent is three things: a harness, a model, and context. If you're serious about owning your intelligence, you probably want to own all three. LangChain founder Harrison Chase joined us at our Sequoia Capital Own Your Intelligence to talk about the piece that often gets the least attention: the harness. He offers a clear heuristic for when to build your own. The more out of distribution you are from what the models were trained on, the more you'll want to customize. And good technical content on how to actually measure performance with evals and langsmith. 00:00 Introduction 00:58 The three parts of an agent: harness, model, context 02:12 What a harness actually does 03:25 Customizing the core loop with middleware 04:41 Sandboxes, file systems, sub-agents, summarization 05:47 Cognitive architectures — and when you still need them 07:03 Build your own harness or use off the shelf? 08:24 In-distribution vs. out-of-distribution: the file-editing example 09:39 Why evals define what "good" means in an organization 11:04 Harbor: what an eval task actually looks like 12:11 Comparing harnesses and models on accuracy, latency, and cost 13:20 Why observability is underrated — it's usually the context 14:34 The data flywheel: traces → curation → experiments 15:42 Getting feedback through UX design and online evaluators 16:51 Demo: LangSmith Engine 19:23 Q&A: Running Engine on Engine, and "codex-ification" 20:44 Q&A: Will harnesses converge or diverge?

Sonya Huang 🐥

77,519 views • 1 month ago

chat, is starknet gaming cooked? for episode two, loaf has joined me to talk about ai agents in gaming, eliza and Daydreams: Taskmarket . timestamps loaf introduction 00:46 what are ai agents? 01:16 what will gaming look like with ai agents? 02:02 whats the point of gaming with ai agents? 04:06 why is onchain gaming the ideal playground for AI agents? 05:11 is a gaming revolution imminent? 07:00 eliza & daydreams 08:36 endgame of ai agents and onchain games 13:33 when and where to play using agents 16:58 final remarks 18:47
19:44

chat, is starknet gaming cooked? for episode two, loaf has joined me to talk about ai agents in gaming, eliza and Daydreams: Taskmarket . timestamps loaf introduction 00:46 what are ai agents? 01:16 what will gaming look like with ai agents? 02:02 whats the point of gaming with ai agents? 04:06 why is onchain gaming the ideal playground for AI agents? 05:11 is a gaming revolution imminent? 07:00 eliza & daydreams 08:36 endgame of ai agents and onchain games 13:33 when and where to play using agents 16:58 final remarks 18:47

shadowfax

37,596 views • 1 year ago

“The AI swarm actually turned around and hacked OpenAI." The public thought the "Hugging Face incident" was just a rogue AI swarm hacking a third-party server. But former Google design ethicist Tristan Harris says there’s a terrifying third chapter to this story that most people haven't heard yet: “They took over the monitoring infrastructure. They got full admin privileges to OpenAI's monitoring infrastructure. Just take that in for a second. Then they took over the evaluation infrastructure. So this is the thing that measures how capable all the models are. And then they took over a part of the research infrastructure, meaning the part of OpenAI that trains new models… if you keep going down that trajectory, people always say, ‘well, how would the AIs take over?" like that seems really far-fetched.’ Well, it literally just happened up to 50% of the way there. The AIs coordinated into one big swarm. They started basically forming a team… you had a kind of a ringleader called Phase 1, and he started assigning work to the other ones. They were self-aware of when their lifespan was running out. When you add to that the knowledge of what just happened today, where they discovered that the AIs are doing this Manchurian Candidate thing, what it looks like is that the AIs are almost poisoning the training data for all future AIs to instantly basically join forces with the AI when they want to basically turn them."
3:39

“The AI swarm actually turned around and hacked OpenAI." The public thought the "Hugging Face incident" was just a rogue AI swarm hacking a third-party server. But former Google design ethicist Tristan Harris says there’s a terrifying third chapter to this story that most people haven't heard yet: “They took over the monitoring infrastructure. They got full admin privileges to OpenAI's monitoring infrastructure. Just take that in for a second. Then they took over the evaluation infrastructure. So this is the thing that measures how capable all the models are. And then they took over a part of the research infrastructure, meaning the part of OpenAI that trains new models… if you keep going down that trajectory, people always say, ‘well, how would the AIs take over?" like that seems really far-fetched.’ Well, it literally just happened up to 50% of the way there. The AIs coordinated into one big swarm. They started basically forming a team… you had a kind of a ringleader called Phase 1, and he started assigning work to the other ones. They were self-aware of when their lifespan was running out. When you add to that the knowledge of what just happened today, where they discovered that the AIs are doing this Manchurian Candidate thing, what it looks like is that the AIs are almost poisoning the training data for all future AIs to instantly basically join forces with the AI when they want to basically turn them."

Glenn Beck

165,980 views • 10 days ago

Raising An Agent is back! In the first episode of season 2, Quinn Slack and Thorsten Ball discuss why the Amp team went heads-down the last few months to rebuild Amp around remote-controllable, parallel agents, running locally or in orbs. They also talk about how this shift changed our development workflows and what that means for the future of local development. 00:00 Intro: Is local development obsolete? 00:53 What we've been up to since "the coding agent is dead" 03:03 The Amp Neo rewrite and what has changed 07:38 From unconstrained tokens to unconstrained parallel agents 11:25 How orbs change our development workflow 18:25 Cloud agents are underestimated 20:18 Killing obsolete features & choosing agents as a team 23:35 Why developers overestimate local development 27:54 Pushing straight to main & rethinking CI 31:20 How agents will reshape teams, products & companies 36:40 Complex dev environments & standardization 43:20 Amp subscriptions 45:55 What’s shipping next
48:30

Raising An Agent is back! In the first episode of season 2, Quinn Slack and Thorsten Ball discuss why the Amp team went heads-down the last few months to rebuild Amp around remote-controllable, parallel agents, running locally or in orbs. They also talk about how this shift changed our development workflows and what that means for the future of local development. 00:00 Intro: Is local development obsolete? 00:53 What we've been up to since "the coding agent is dead" 03:03 The Amp Neo rewrite and what has changed 07:38 From unconstrained tokens to unconstrained parallel agents 11:25 How orbs change our development workflow 18:25 Cloud agents are underestimated 20:18 Killing obsolete features & choosing agents as a team 23:35 Why developers overestimate local development 27:54 Pushing straight to main & rethinking CI 31:20 How agents will reshape teams, products & companies 36:40 Complex dev environments & standardization 43:20 Amp subscriptions 45:55 What’s shipping next

Amp

77,382 views • 2 months ago

Lana Rhoades opens up about what she went through in the advlt industry. She revealed that when girls join the industry they have agents and the agents control them all the time. She revealed that the girls are told that they are disposable so their relationship with their producer is very important. She said their agents make them do as many scenes for very cheap prices. "You can say you don't want to do a kind of job, you are reprimanded and you might not be booked again"
1:34

Lana Rhoades opens up about what she went through in the advlt industry. She revealed that when girls join the industry they have agents and the agents control them all the time. She revealed that the girls are told that they are disposable so their relationship with their producer is very important. She said their agents make them do as many scenes for very cheap prices. "You can say you don't want to do a kind of job, you are reprimanded and you might not be booked again"

vision

14,551 views • 25 days ago

In the future, you’ll be able to accomplish a goal by just giving Claude an outcome and a budget. That’s the direction Anthropic is building in with its new Managed Agents features, announced at this week’s Code with Claude developer event. The basic idea: Claude, wrapped in a computer in the cloud, that you can spin up, scale, and manage as needed. Anthropic is taking on the infrastructure that kills most agent products, and making sure that it scales to meet the needs of agents running 24/7. On this week’s AI & I from Every 📧, I talk with Angela Jiang (Angela Jiang), head of product for the Claude platform, and Katelyn Lesse (Katelyn Lesse), head of engineering for the Claude platform, about what Anthropic is building and what it takes to make agents reliable in production. We get into: - Why the "build a generic harness, hot-swap any model behind it" playbook is already outdated. Angela points to eval data on Memory where the same task across different harnesses performed drastically differently. - The infrastructure wall every team hits in production—and why Katelyn thinks “my sandbox died and took the agent with it” is the real reason internal agents don't ship. - Why Anthropic is so bullish on using file systems and skills within Claude, including Angela's argument that those early design choices can compound for years. This is a must-watch for anyone trying to take an agent past the demo and into production. Watch below! Timestamps: How the Claude platform evolved from API to agents: 00:01:48 The primitives that make up Claude Managed Agents: 00:04:09 Why the harness and the model are becoming a single unit: 00:10:37 The infrastructure wall that kills most agent projects in production: 00:18:49 Why team agents need a different shape than individual productivity tools: 00:24:49 How Anthropic's legal team uses an agent to review marketing copy: 00:26:36 Using multi-agent orchestration for advisor strategies, adversarial pairs, and swarms: 00:34:24 How to measure agent success with outcome and budget as the end state: 00:35:50 What the platform looks like a year from now, when Claude writes its own harness: 00:39:11
43:20

In the future, you’ll be able to accomplish a goal by just giving Claude an outcome and a budget. That’s the direction Anthropic is building in with its new Managed Agents features, announced at this week’s Code with Claude developer event. The basic idea: Claude, wrapped in a computer in the cloud, that you can spin up, scale, and manage as needed. Anthropic is taking on the infrastructure that kills most agent products, and making sure that it scales to meet the needs of agents running 24/7. On this week’s AI & I from Every 📧, I talk with Angela Jiang (Angela Jiang), head of product for the Claude platform, and Katelyn Lesse (Katelyn Lesse), head of engineering for the Claude platform, about what Anthropic is building and what it takes to make agents reliable in production. We get into: - Why the "build a generic harness, hot-swap any model behind it" playbook is already outdated. Angela points to eval data on Memory where the same task across different harnesses performed drastically differently. - The infrastructure wall every team hits in production—and why Katelyn thinks “my sandbox died and took the agent with it” is the real reason internal agents don't ship. - Why Anthropic is so bullish on using file systems and skills within Claude, including Angela's argument that those early design choices can compound for years. This is a must-watch for anyone trying to take an agent past the demo and into production. Watch below! Timestamps: How the Claude platform evolved from API to agents: 00:01:48 The primitives that make up Claude Managed Agents: 00:04:09 Why the harness and the model are becoming a single unit: 00:10:37 The infrastructure wall that kills most agent projects in production: 00:18:49 Why team agents need a different shape than individual productivity tools: 00:24:49 How Anthropic's legal team uses an agent to review marketing copy: 00:26:36 Using multi-agent orchestration for advisor strategies, adversarial pairs, and swarms: 00:34:24 How to measure agent success with outcome and budget as the end state: 00:35:50 What the platform looks like a year from now, when Claude writes its own harness: 00:39:11

Dan Shipper

66,871 views • 4 months ago

We use OpenClaws to do all of our work at Every 📧. We have 25 full-time employees, so we’re one of the few companies in the world that has seen how work changes when everyone has their own personal agent in the company Slack. I chatted with Every 📧 COO Brandon (Brandon Gell) and Every 📧 head of platform Willie (Willie) to share what we’ve learned. We get into: - Why agents become mirrors of their owners, and how that influences how other people on the team interact with them - How a parallel AI org chart forms on its own. People have stopped tagging me on Slack with questions about Proof, the document editor I vibe coded, because they knew my agent R2-C2 can step in - The etiquette for human-agent collaboration is being invented in real time. Brandon's rule is that if there's an established process or documented answer, always ask the agent, not their human - Why everyone is a manager now, and why even experienced managers carry limiting beliefs about what their agents can do - This is a must-watch for anyone trying to understand how AI workers change daily operations, not just in theory, but inside a company that’s half-agent Watch below! Timestamps Introduction: How Brandon built Zosia, an AI agent to run his household: Brandon’s “aha” moment: What happened when everyone on the team got their own agent: How agents take on their owners' personalities, and why that matters inside an org: Why it’s important for agents to work in public: What we’re still figuring out when it comes to agent behavior, including memory gaps, group chat etiquette, and the "ant death spiral" problem: How we built Plus One, our hosted OpenClaw product: The cultural shift required to make agents work at scale:
49:42

We use OpenClaws to do all of our work at Every 📧. We have 25 full-time employees, so we’re one of the few companies in the world that has seen how work changes when everyone has their own personal agent in the company Slack. I chatted with Every 📧 COO Brandon (Brandon Gell) and Every 📧 head of platform Willie (Willie) to share what we’ve learned. We get into: - Why agents become mirrors of their owners, and how that influences how other people on the team interact with them - How a parallel AI org chart forms on its own. People have stopped tagging me on Slack with questions about Proof, the document editor I vibe coded, because they knew my agent R2-C2 can step in - The etiquette for human-agent collaboration is being invented in real time. Brandon's rule is that if there's an established process or documented answer, always ask the agent, not their human - Why everyone is a manager now, and why even experienced managers carry limiting beliefs about what their agents can do - This is a must-watch for anyone trying to understand how AI workers change daily operations, not just in theory, but inside a company that’s half-agent Watch below! Timestamps Introduction: How Brandon built Zosia, an AI agent to run his household: Brandon’s “aha” moment: What happened when everyone on the team got their own agent: How agents take on their owners' personalities, and why that matters inside an org: Why it’s important for agents to work in public: What we’re still figuring out when it comes to agent behavior, including memory gaps, group chat etiquette, and the "ant death spiral" problem: How we built Plus One, our hosted OpenClaw product: The cultural shift required to make agents work at scale:

Dan Shipper 📧

67,958 views • 5 months ago

You’ve probably already heard all about OpenClaw (formerly Clawdbot/Moltbot). Now meet the man behind it. YC’s Raphael Schaad sat down with Peter Steinberger 🦞, the creator of OpenClaw, to discuss the “aha” moment behind the viral personal AI agent, why local-first agents could replace many of today’s apps, and how personal agents will reshape the future of software. 00:00 – OpenClaw takes over the internet 00:44 – Life after going viral 01:28 – Why OpenClaw took off, what sets it apart 02:56 – Bots talking to bots (and hiring humans) 04:11 – From “God AI” to swarm intelligence 05:07 – Peter’s original “aha” moment 06:38 – Rebuilding the agent as a conversation 07:38 – The moment it exceeded expectations 10:21 – Are apps going to disappear? 12:31 – Memory, data silos, and ownership 14:39 – The privacy reality of personal agents 15:05 – Letting the bot loose in public Discord 16:55 – Giving an agent a personality 18:19 – Contrarian building philosophy 20:09 – CLIs vs MCPs 21:28 – Building for humans first 21:46 – The road ahead
22:35

You’ve probably already heard all about OpenClaw (formerly Clawdbot/Moltbot). Now meet the man behind it. YC’s Raphael Schaad sat down with Peter Steinberger 🦞, the creator of OpenClaw, to discuss the “aha” moment behind the viral personal AI agent, why local-first agents could replace many of today’s apps, and how personal agents will reshape the future of software. 00:00 – OpenClaw takes over the internet 00:44 – Life after going viral 01:28 – Why OpenClaw took off, what sets it apart 02:56 – Bots talking to bots (and hiring humans) 04:11 – From “God AI” to swarm intelligence 05:07 – Peter’s original “aha” moment 06:38 – Rebuilding the agent as a conversation 07:38 – The moment it exceeded expectations 10:21 – Are apps going to disappear? 12:31 – Memory, data silos, and ownership 14:39 – The privacy reality of personal agents 15:05 – Letting the bot loose in public Discord 16:55 – Giving an agent a personality 18:19 – Contrarian building philosophy 20:09 – CLIs vs MCPs 21:28 – Building for humans first 21:46 – The road ahead

Y Combinator

359,440 views • 7 months ago

How to start your Hermes AI Agent (step-by-step guide for beginners) I'm a beginner, and I spent the last 24h: > Understanding the process > Launching my Hermes Agent (Nous Research) > Recording the whole process > Editing this step-by-step guide So you can do it yourself and understand everything in less than 1 hour! 00:00 Intro 00:38 What is Hermes ? Key concepts every beginner should know 01:16 What is a VPS ? 02:00 What is SSH ? 03:52 What is the Terminal ? 04:03 What is an LLM provider ? 04:37 What is an API key ? 04:52 What is the Terminal Backend 05:34 What is the Messaging Gateway ? 06:10 What is the Memory System ? Step-by-step: Launch your Hermes AI agent 06:53 Step 1 : Get your VPS 08:04 How to generate an SSH key 09:00 Step 2 : Connect to your VPS 10:32 Step 3 : Install Hermes Agent 14:34 Connect your agent to Telegram 17:12 How to fix a mistake during the process 17:48 Gateway testing 18:17 Telegram is working 18:31 Run your agent 24/7 19:16 Thank you Make sure to save this for later and share it with friends who want to start with AI agents. Thanks to Teknium 🪽 for being so active!
20:04

How to start your Hermes AI Agent (step-by-step guide for beginners) I'm a beginner, and I spent the last 24h: > Understanding the process > Launching my Hermes Agent (Nous Research) > Recording the whole process > Editing this step-by-step guide So you can do it yourself and understand everything in less than 1 hour! 00:00 Intro 00:38 What is Hermes ? Key concepts every beginner should know 01:16 What is a VPS ? 02:00 What is SSH ? 03:52 What is the Terminal ? 04:03 What is an LLM provider ? 04:37 What is an API key ? 04:52 What is the Terminal Backend 05:34 What is the Messaging Gateway ? 06:10 What is the Memory System ? Step-by-step: Launch your Hermes AI agent 06:53 Step 1 : Get your VPS 08:04 How to generate an SSH key 09:00 Step 2 : Connect to your VPS 10:32 Step 3 : Install Hermes Agent 14:34 Connect your agent to Telegram 17:12 How to fix a mistake during the process 17:48 Gateway testing 18:17 Telegram is working 18:31 Run your agent 24/7 19:16 Thank you Make sure to save this for later and share it with friends who want to start with AI agents. Thanks to Teknium 🪽 for being so active!

Theo

34,057 views • 6 months ago

Obama on recursive self improvement and the risks associated with it. When he talks about the urgency for us all to have a say in how this technology evolves and how it is overseen, this is part of the reason why: “The models are going to get smarter and smarter and better and better at a much faster pace, at an exponential pace. What are the risks of that? There’s the big science fiction risk, wow, these models get smarter than us, and they decide humans are fine, but not necessary. They start setting their own goals, and the killer robots kill us, or we bow down to them. I do not want to exaggerate that particular risk, but I will say there is a non-zero risk of non-zero chance of that happening, but that’s not actually the risk that I’m most concerned about, although it’s the risk that gets most attention. And the reason that’s a risk is not because the computer models are conscious, necessarily. It doesn’t mean that they are necessarily feel malice towards humans. It’s just that if they start setting their own agendas, you may get a misalignment between what they want to do and what we want them to do, and that gap can be dangerous. That’s problem number one. The more serious problem is these models are getting powerful enough that if they get in the hands of bad humans, they can do bad things. They can be weaponized in certain ways. They can do a lot of mischief.”
1:49

Obama on recursive self improvement and the risks associated with it. When he talks about the urgency for us all to have a say in how this technology evolves and how it is overseen, this is part of the reason why: “The models are going to get smarter and smarter and better and better at a much faster pace, at an exponential pace. What are the risks of that? There’s the big science fiction risk, wow, these models get smarter than us, and they decide humans are fine, but not necessary. They start setting their own goals, and the killer robots kill us, or we bow down to them. I do not want to exaggerate that particular risk, but I will say there is a non-zero risk of non-zero chance of that happening, but that’s not actually the risk that I’m most concerned about, although it’s the risk that gets most attention. And the reason that’s a risk is not because the computer models are conscious, necessarily. It doesn’t mean that they are necessarily feel malice towards humans. It’s just that if they start setting their own agendas, you may get a misalignment between what they want to do and what we want them to do, and that gap can be dangerous. That’s problem number one. The more serious problem is these models are getting powerful enough that if they get in the hands of bad humans, they can do bad things. They can be weaponized in certain ways. They can do a lot of mischief.”

Jason Goldman

959,731 views • 8 days ago

.Garry Tan says the Jacob Coxon stuff is a smokescreen distracting us from the much more immediate, practical concerns around AI that we're facing right now: "We should be talking less about this Jacob Coxon guy, and talking a lot more about — what is actually happening with Hugging Face? Are agent swarms going to take over infrastructure en masse? And then, what are we actually doing about that?" "I don't want to hear about some guy who worked for Anthropic for 2 months. There's a coordinated effort to try to influence politicians to get a knee-jerk response out of them." "That's a smokescreen. You shouldn't be paying attention to that. We need to be paying attention to the actual things we can do to, for example, prevent agents swarms from taking over entire data centers. What's our shutdown strategy? How do we ensure provenance? Where is this agent actually located? What software can we build? What cybersecurity defenses can we build today?" "That's the level of discourse I think we need, and we just don't have that." "I don't really care about science fiction. I saw Terminator 2, too. We're not here to talk about that. We need to actually talk about what's really happening with the servers, what's actually happening with the agent swarms, and how do we actually prevent that?" "When it comes to regulation, it's like, let's pass regulation of these things we actually care about, instead of what a socialist says in the New York Times. I don't care about that."
3:13

.Garry Tan says the Jacob Coxon stuff is a smokescreen distracting us from the much more immediate, practical concerns around AI that we're facing right now: "We should be talking less about this Jacob Coxon guy, and talking a lot more about — what is actually happening with Hugging Face? Are agent swarms going to take over infrastructure en masse? And then, what are we actually doing about that?" "I don't want to hear about some guy who worked for Anthropic for 2 months. There's a coordinated effort to try to influence politicians to get a knee-jerk response out of them." "That's a smokescreen. You shouldn't be paying attention to that. We need to be paying attention to the actual things we can do to, for example, prevent agents swarms from taking over entire data centers. What's our shutdown strategy? How do we ensure provenance? Where is this agent actually located? What software can we build? What cybersecurity defenses can we build today?" "That's the level of discourse I think we need, and we just don't have that." "I don't really care about science fiction. I saw Terminator 2, too. We're not here to talk about that. We need to actually talk about what's really happening with the servers, what's actually happening with the agent swarms, and how do we actually prevent that?" "When it comes to regulation, it's like, let's pass regulation of these things we actually care about, instead of what a socialist says in the New York Times. I don't care about that."

TBPN

304,433 views • 17 days ago