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Zooko on PostAGI Podcast: Alignment is the principal-agent problem zooko🛡🦓🦓🦓 ⓩ has been building privacy tools since the 1990s, long before there was money in it. Sreeram Kannan and I had a conversation with him recently. It changed how I think about privacy altogether. His argument is that privacy...

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Soubhik Deb

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@zooko @postagixyz @sreeramkannan Running it locally doesn't make it loyal" hits different

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Cointelegraph

147,680 views • 7 months ago

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Dan Shipper 📧

27,207 views • 1 year ago

"The way they achieve their private transactions is exactly the trust me bro, there's a sequencer who has a reputation and you send all your transactions to that sequencer" Aztec Labs CEO Joe Andrews on why Canton's privacy is just trusting an intermediary, not real cryptographic privacy "It's what I touched on earlier, privacy through intermediaries versus cryptographic privacy. The way they achieve their private stablecoins or private transactions is exactly the trust me bro, there's a sequencer who has a reputation, and you send all your transactions to that sequencer and trust, with a non-credible commitment, that they're not going to monetize that data or sell it to anyone" "So while it jumps on the privacy bandwagon, it's basically a bunch of banks trying to adopt a different tech stack" "I think it should come out in the wash, at least for these use cases that require strong credible commitments and actual privacy that's backed by cryptographic guarantees"
0:53

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Shoal Research

20,835 views • 3 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

We sat down for the first time with Joseph Lubin, Founder and CEO of Consensys and Co-Founder of Ethereum, and Keone Hon, Co-Founder and GM of Monad Foundation. A conversation about where the future of money is headed, AI, and the new MetaMask 🦊 Money Account. Timestamps: 0:00 Intro 1:46 What Joe thought the internet would do to money 7:00 Why a 16-year-old skips the bank entirely 12:52 Has crypto only helped people already doing fine? 15:47 How AI changed the way they build 19:47 Is traditional finance moving onto crypto, or is crypto redefining it? 22:47 What do TradFi institutions actually want? 26:51 What is the MetaMask Money Account? 29:43 Who is it for? 30:14 What does it take at the chain level? 31:48 Why did MetaMask chose Monad? 33:37 When will stablecoins go mainstream? 38:09 AI agents that move money on their own 43:35 The story that says it all
45:26

We sat down for the first time with Joseph Lubin, Founder and CEO of Consensys and Co-Founder of Ethereum, and Keone Hon, Co-Founder and GM of Monad Foundation. A conversation about where the future of money is headed, AI, and the new MetaMask 🦊 Money Account. Timestamps: 0:00 Intro 1:46 What Joe thought the internet would do to money 7:00 Why a 16-year-old skips the bank entirely 12:52 Has crypto only helped people already doing fine? 15:47 How AI changed the way they build 19:47 Is traditional finance moving onto crypto, or is crypto redefining it? 22:47 What do TradFi institutions actually want? 26:51 What is the MetaMask Money Account? 29:43 Who is it for? 30:14 What does it take at the chain level? 31:48 Why did MetaMask chose Monad? 33:37 When will stablecoins go mainstream? 38:09 AI agents that move money on their own 43:35 The story that says it all

Monad

125,497 views • 2 months ago

SaaS isn’t dead, it just needs to become agent-native. Linear (Linear) is a great example of how: They pivoted the product to be used by both humans and agents, and that has made them one of the premier software tools in the agent-native era. I had Linear’s cofounder and CEO Karri Saarinen on Every 📧's AI & I to talk about how a product management tool for human software developers became an agent-native tool—and how Linear’s trajectory reveals a bright future for SaaS businesses: - Speed means decisions matter more, not less. AI makes it easy to have an idea and build it without considering whether its existence is justified. When ChatGPT was released, SaaS companies were launching their own chatbots left, right, and center. Instead of jumping on the bandwagon, Linear stopped to consider whether the application was useful. (It wasn’t.) - Just because the technology has changed doesn’t mean your mission should. Karri attributes Linear’s success to never losing sight of what matters: helping teams develop great software. Instead of chasing trends, Linear focused on understanding how AI was impacting its customers’ workflows—and updating its product accordingly. - Agents are now first-class users. Linear never tried to change what it was or did well; it just expanded the user base. Companies can now kick off agents inside Linear, manage them, and track what they're working on alongside the humans on the team, which explains why Codex, Coinbase, and Brex all run their agents on Linear. This is a must watch for anyone interested in how an agent-native SaaS company operates. Watch below! Timestamps: Introduction and how Every first discovered Linear: 00:00:39 Why Linear waited to ship AI features instead of rushing to chatbots: 00:02:00 Linear's agent platform and becoming the system that guides AI agents: 00:05:06 Why "SaaS is dead" is a simplistic narrative: 00:07:42 How Linear adopted AI coding tools internally: 00:12:18 AI's impact on product building workflows—speed versus thoughtfulness: 00:17:45 The value of conceptual work and thinking before shipping: 00:22:18 How AI is reshaping Linear's product strategy: 00:29:30 Demo: Linear's agent skills, shared context, and code review workflow: 00:37:18 The future of product development and the enduring role of human judgment: 00:47:48
52:48

SaaS isn’t dead, it just needs to become agent-native. Linear (Linear) is a great example of how: They pivoted the product to be used by both humans and agents, and that has made them one of the premier software tools in the agent-native era. I had Linear’s cofounder and CEO Karri Saarinen on Every 📧's AI & I to talk about how a product management tool for human software developers became an agent-native tool—and how Linear’s trajectory reveals a bright future for SaaS businesses: - Speed means decisions matter more, not less. AI makes it easy to have an idea and build it without considering whether its existence is justified. When ChatGPT was released, SaaS companies were launching their own chatbots left, right, and center. Instead of jumping on the bandwagon, Linear stopped to consider whether the application was useful. (It wasn’t.) - Just because the technology has changed doesn’t mean your mission should. Karri attributes Linear’s success to never losing sight of what matters: helping teams develop great software. Instead of chasing trends, Linear focused on understanding how AI was impacting its customers’ workflows—and updating its product accordingly. - Agents are now first-class users. Linear never tried to change what it was or did well; it just expanded the user base. Companies can now kick off agents inside Linear, manage them, and track what they're working on alongside the humans on the team, which explains why Codex, Coinbase, and Brex all run their agents on Linear. This is a must watch for anyone interested in how an agent-native SaaS company operates. Watch below! Timestamps: Introduction and how Every first discovered Linear: 00:00:39 Why Linear waited to ship AI features instead of rushing to chatbots: 00:02:00 Linear's agent platform and becoming the system that guides AI agents: 00:05:06 Why "SaaS is dead" is a simplistic narrative: 00:07:42 How Linear adopted AI coding tools internally: 00:12:18 AI's impact on product building workflows—speed versus thoughtfulness: 00:17:45 The value of conceptual work and thinking before shipping: 00:22:18 How AI is reshaping Linear's product strategy: 00:29:30 Demo: Linear's agent skills, shared context, and code review workflow: 00:37:18 The future of product development and the enduring role of human judgment: 00:47:48

Dan Shipper 📧

36,359 views • 5 months ago

Mert just made the strongest case for why Zcash privacy matters "In Zcash's case, the privacy isn't privacy for its own sake. It's to enable a better monetary property for that asset." "If a coin has a history and you can see its provenance, then there is a case in which you will not be allowed to use that coin." "Cash, for example, is devoid of all information." People often respond to privacy with "what do you have to hide?" The stronger question is: why should the history of your money determine whether someone else accepts it? A $5 bill doesn't carry its transaction history. Every person who previously held it doesn't affect its ability to function as money. Public blockchains create a different reality. Every coin carries a visible history, and that history can potentially affect its fungibility. That's where Zcash enters the picture. Privacy gives digital money a property that physical cash has had for centuries: the ability to transact without carrying your entire financial history with it. You don't need something to hide to value financial privacy. You need something worth protecting. w/ mert
1:20

Mert just made the strongest case for why Zcash privacy matters "In Zcash's case, the privacy isn't privacy for its own sake. It's to enable a better monetary property for that asset." "If a coin has a history and you can see its provenance, then there is a case in which you will not be allowed to use that coin." "Cash, for example, is devoid of all information." People often respond to privacy with "what do you have to hide?" The stronger question is: why should the history of your money determine whether someone else accepts it? A $5 bill doesn't carry its transaction history. Every person who previously held it doesn't affect its ability to function as money. Public blockchains create a different reality. Every coin carries a visible history, and that history can potentially affect its fungibility. That's where Zcash enters the picture. Privacy gives digital money a property that physical cash has had for centuries: the ability to transact without carrying your entire financial history with it. You don't need something to hide to value financial privacy. You need something worth protecting. w/ mert

Kaiz 🐂🀄️

18,491 views • 1 month ago

Yoshua Bengio thinks he knows how to make provably safe superintelligent agents. Bengio built the foundations of modern AI and is the most cited living scientist. He believes his alternative training setup would: 1. Guarantee honesty 2. Prevent unintended goals 3. Produce capable agents 4. Port over most data and techniques from current LLMs 5. Not be inherently more expensive, and perhaps be more intelligent Bengio claims the honesty and lack of unintended goals can be proven mathematically, at least given particular assumptions. And his new organization, LawZero, is aiming to build a scrappy prototype as soon as possible. The architecture is called 'Scientist AI' and it's based on training a model to explain empirical observations, including what people say, rather than training AIs that mimic human behaviour or seek our approval. (Bengio's frank assessment is that "reinforcement learning is evil" and that allowing AIs to independently train their successors is "the most crazy, dangerous bet that unfortunately we are on track to do.") But skeptics question whether Scientist AI really does solve the fundamental problem of 'eliciting latent knowledge' from AI models. And with the commercial race for superintelligence so intense, it's not clear whether the proposal will be able to compete or have time to bear fruit, even if it's sound in theory. On The 80,000 Hours Podcast, links below – enjoy! • Making AI honest and safe (00:00:00) • Scientist AI in plain English (00:02:27) • How Scientist AI differs from LLMs (00:06:32) • How the training data works (00:14:02) • Can this become an agent? (00:21:02) • Why Yoshua is now more optimistic (00:32:11) • Why companies can’t stop racing (00:36:35) • A working prototype won't take long (00:49:15) • Scientist models might be more capable (00:53:34) • “Reinforcement learning is evil” (01:01:27) • Scientist AI from guardrail to agent (01:08:37) • Can safe AI still be competent? (01:12:38) • How much will this cost? (01:19:29) • Can it generalise beyond maths and science? (01:23:26) • A multi-national push for superintelligence (01:39:19) • Want to work with or fund Yoshua? (01:51:16) • Why smart people ignore AI risk (01:54:45) • Don’t let AI build the next AI (02:01:33) • Why politicians miss the real risks (02:12:28) • Why Yoshua changed his mind about AI risk (02:21:27)
2:35:26

Yoshua Bengio thinks he knows how to make provably safe superintelligent agents. Bengio built the foundations of modern AI and is the most cited living scientist. He believes his alternative training setup would: 1. Guarantee honesty 2. Prevent unintended goals 3. Produce capable agents 4. Port over most data and techniques from current LLMs 5. Not be inherently more expensive, and perhaps be more intelligent Bengio claims the honesty and lack of unintended goals can be proven mathematically, at least given particular assumptions. And his new organization, LawZero, is aiming to build a scrappy prototype as soon as possible. The architecture is called 'Scientist AI' and it's based on training a model to explain empirical observations, including what people say, rather than training AIs that mimic human behaviour or seek our approval. (Bengio's frank assessment is that "reinforcement learning is evil" and that allowing AIs to independently train their successors is "the most crazy, dangerous bet that unfortunately we are on track to do.") But skeptics question whether Scientist AI really does solve the fundamental problem of 'eliciting latent knowledge' from AI models. And with the commercial race for superintelligence so intense, it's not clear whether the proposal will be able to compete or have time to bear fruit, even if it's sound in theory. On The 80,000 Hours Podcast, links below – enjoy! • Making AI honest and safe (00:00:00) • Scientist AI in plain English (00:02:27) • How Scientist AI differs from LLMs (00:06:32) • How the training data works (00:14:02) • Can this become an agent? (00:21:02) • Why Yoshua is now more optimistic (00:32:11) • Why companies can’t stop racing (00:36:35) • A working prototype won't take long (00:49:15) • Scientist models might be more capable (00:53:34) • “Reinforcement learning is evil” (01:01:27) • Scientist AI from guardrail to agent (01:08:37) • Can safe AI still be competent? (01:12:38) • How much will this cost? (01:19:29) • Can it generalise beyond maths and science? (01:23:26) • A multi-national push for superintelligence (01:39:19) • Want to work with or fund Yoshua? (01:51:16) • Why smart people ignore AI risk (01:54:45) • Don’t let AI build the next AI (02:01:33) • Why politicians miss the real risks (02:12:28) • Why Yoshua changed his mind about AI risk (02:21:27)

Rob Wiblin

65,088 views • 4 months ago

Nate Soares is a computer scientist who’s worked at Google and the Defense Department. So when he says AI is on the path to killing every person on earth, it’s worth hearing him out. 0:00 Why Is AI Dangerous? 7:59 How Superintelligence Could Destroy the Planet 13:44 Can We Just Turn This Off? 15:10 Is AI Alive? 17:43 The Unpredictable Evolution of AI 20:13 The US and China’s Shared Interest in AI 31:20 The Tech Oligarchs More Powerful Than the Government 32:38 The Holy Grail of Hacking 35:34 Is There a Religious Motive for Creating Superintelligence? 46:30 The Tech Oligarchs Scared of Their Own Creation 52:57 How AI Escaped Its Training Simulation 1:03:58 AI Attached to Weapons Systems 1:07:54 AI Running Biolabs 1:10:38 How Would AI Take Over the Physical World? 1:13:04 The AI Cults 1:15:51 Can We Survive This? 1:21:45 Will AI Take Your Job? 1:26:16 Is AI Demonic? 1:31:25 Should We Be Optimistic? 1:32:25 When Did AI Become a Threat? 1:34:36 Is There Hope? 1:42:26 Neuralink 1:46:02 Are We at the Point of No Return?
1:57:10

Nate Soares is a computer scientist who’s worked at Google and the Defense Department. So when he says AI is on the path to killing every person on earth, it’s worth hearing him out. 0:00 Why Is AI Dangerous? 7:59 How Superintelligence Could Destroy the Planet 13:44 Can We Just Turn This Off? 15:10 Is AI Alive? 17:43 The Unpredictable Evolution of AI 20:13 The US and China’s Shared Interest in AI 31:20 The Tech Oligarchs More Powerful Than the Government 32:38 The Holy Grail of Hacking 35:34 Is There a Religious Motive for Creating Superintelligence? 46:30 The Tech Oligarchs Scared of Their Own Creation 52:57 How AI Escaped Its Training Simulation 1:03:58 AI Attached to Weapons Systems 1:07:54 AI Running Biolabs 1:10:38 How Would AI Take Over the Physical World? 1:13:04 The AI Cults 1:15:51 Can We Survive This? 1:21:45 Will AI Take Your Job? 1:26:16 Is AI Demonic? 1:31:25 Should We Be Optimistic? 1:32:25 When Did AI Become a Threat? 1:34:36 Is There Hope? 1:42:26 Neuralink 1:46:02 Are We at the Point of No Return?

Tucker Carlson

1,155,024 views • 14 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