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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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16,225 просмотров • 11 дней назад •via X (Twitter)

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

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LIVE NOW - Zcash Founder on Privacy, AI, and How ZEC is 'Encrypted Bitcoin' Zcash 🛡️ founder zooko🛡🦓🦓🦓 ⓩ joins Bankless to argue that privacy is back on the critical path, that AI turns chain surveillance into pattern-recognition at scale and that the real bottleneck for cypherpunk tools has always been UX and onboarding. We dig into Zcash’s “encrypted Bitcoin” meme, the dev fund’s evolving design (and why it helped Zcash survive), recent ZEC momentum, and the wallet/intents stack that makes private spending actually usable. -------------- TIMESTAMPS 0:00 Intro 0:46 What Failed 3:58 What Succeeded 5:04 Institutional Adoption 6:53 Areas Neglected 10:51 The Cypherpunk Vision 18:53 Can The Cypherpunks Win? 23:19 Crypto’s Path to Success 27:44 Crypto vs Linux 30:39 AI Cypherpunks? 33:05 Optimism 35:49 AI Surveillance 37:10 Zcash 43:02 Zcash Dev Fund 51:45 Zashi is now Zodl Governance Drama 56:32 Zcash Privacy Practices 1:15:56 AI Privacy Consequences 1:22:31 Is Zec a Store of Value 1:26:01 Encrypted Bitcoin? 1:29:01 Ethereum Privacy & Anoma 1:33:14 Closing & Disclaimers
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LIVE NOW - Zcash Founder on Privacy, AI, and How ZEC is 'Encrypted Bitcoin' Zcash 🛡️ founder zooko🛡🦓🦓🦓 ⓩ joins Bankless to argue that privacy is back on the critical path, that AI turns chain surveillance into pattern-recognition at scale and that the real bottleneck for cypherpunk tools has always been UX and onboarding. We dig into Zcash’s “encrypted Bitcoin” meme, the dev fund’s evolving design (and why it helped Zcash survive), recent ZEC momentum, and the wallet/intents stack that makes private spending actually usable. -------------- TIMESTAMPS 0:00 Intro 0:46 What Failed 3:58 What Succeeded 5:04 Institutional Adoption 6:53 Areas Neglected 10:51 The Cypherpunk Vision 18:53 Can The Cypherpunks Win? 23:19 Crypto’s Path to Success 27:44 Crypto vs Linux 30:39 AI Cypherpunks? 33:05 Optimism 35:49 AI Surveillance 37:10 Zcash 43:02 Zcash Dev Fund 51:45 Zashi is now Zodl Governance Drama 56:32 Zcash Privacy Practices 1:15:56 AI Privacy Consequences 1:22:31 Is Zec a Store of Value 1:26:01 Encrypted Bitcoin? 1:29:01 Ethereum Privacy & Anoma 1:33:14 Closing & Disclaimers

Bankless

60,682 просмотров • 8 месяцев назад

"With Monero, you're trying to hide amongst a bunch of fake transactions" Multicoin co-founder Tushar Jain on why Monero's privacy model is more at risk from AI than Zcash's "The Monero comparison is interesting, but Monero doesn't provide the same level of privacy guarantees that Zcash does." "Monero uses ring signatures, which is a type of obfuscation of privacy, whereas Zcash uses zero knowledge proofs. So the information on Zcash is just not even out there, it is not visible to the public." "I believe that as AI powered data analysis tools get more powerful, it puts that privacy model more at risk, and that is problematic."
0:43

"With Monero, you're trying to hide amongst a bunch of fake transactions" Multicoin co-founder Tushar Jain on why Monero's privacy model is more at risk from AI than Zcash's "The Monero comparison is interesting, but Monero doesn't provide the same level of privacy guarantees that Zcash does." "Monero uses ring signatures, which is a type of obfuscation of privacy, whereas Zcash uses zero knowledge proofs. So the information on Zcash is just not even out there, it is not visible to the public." "I believe that as AI powered data analysis tools get more powerful, it puts that privacy model more at risk, and that is problematic."

The Wolf Of All Streets

35,546 просмотров • 29 дней назад

Technology stole your privacy, and with it your freedom. Yannik Schrade has a plan to get it back. (0:00) Why Is Privacy So Important? (4:13) Is Schrade Prepared to Suffer for His Cause? (10:38) Why Doesn't Our Current System Protect Our Privacy? (14:16) Are We Being Surveilled? (26:19) Is the iPhone Safer Than Android? (31:25) Is It Truly Possible to Communicate Privately? (35:20) Is Signal a CIA Operation? (47:54) Is the Current State of the Art Cryptography Secure? (53:02) How Did Schrade Build This Technology? (56:42) Is It Possible to Move Money Privately? (1:03:20) The Arrest of Roman Storm (1:14:48) Are Cash Transactions Private? (1:18:37) Is Schrade Concerned for His Safety? (1:23:02) How Will This Technology Impact Our Lives? (1:28:54) Will Any Big Hardware Manufacturers Sell Truly Secure Devices? (1:35:17) Are All Our Text Messages Being Monitored? (1:39:58) Is Your Phone Listening to You? (1:44:50) Where Can People Learn More About Schrade's Work?
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Technology stole your privacy, and with it your freedom. Yannik Schrade has a plan to get it back. (0:00) Why Is Privacy So Important? (4:13) Is Schrade Prepared to Suffer for His Cause? (10:38) Why Doesn't Our Current System Protect Our Privacy? (14:16) Are We Being Surveilled? (26:19) Is the iPhone Safer Than Android? (31:25) Is It Truly Possible to Communicate Privately? (35:20) Is Signal a CIA Operation? (47:54) Is the Current State of the Art Cryptography Secure? (53:02) How Did Schrade Build This Technology? (56:42) Is It Possible to Move Money Privately? (1:03:20) The Arrest of Roman Storm (1:14:48) Are Cash Transactions Private? (1:18:37) Is Schrade Concerned for His Safety? (1:23:02) How Will This Technology Impact Our Lives? (1:28:54) Will Any Big Hardware Manufacturers Sell Truly Secure Devices? (1:35:17) Are All Our Text Messages Being Monitored? (1:39:58) Is Your Phone Listening to You? (1:44:50) Where Can People Learn More About Schrade's Work?

Tucker Carlson

2,705,212 просмотров • 7 месяцев назад

👁‍🗨Not Dead Yet | S1E2 — Eli Ben-Sasson At long last, privacy is in vogue, particularly in crypto. Running parallel to the rise in interest has been the rise of Zcash 🛡️, now even with $ZEC treasury companies forming like Cypherpunk. In episode 2 of Not Dead Yet, Robert Baggs speaks with Eli Ben-Sasson | Starknet.io. Eli is a long-time researcher of zero-knowledge proofs (the technology that underpins Zcash), the co-inventor of Zerocash protocol and ZK_STARKS, and the co-founder and CEO of StarkWare (BTCFi arc) 🥷. Timestamps: 00:00 – Intro 01:23 – Why is privacy important to you? 03:24 – Why is a lack of privacy holding blockchains back? 04:05 – Privacy is normal, so why isn’t it already normal in crypto? 06:10 – The dangers of CBDCs 09:00 – “If you have nothing to hide, you have nothing to fear” 12:53 – What are Zero Knowledge Proofs (ZKPs) and why are they important? 19:26 – What is Zcash and how does it work? 21:14 – Why is privacy suddenly trending? 23:15 – What happens if Bitcoin doesn’t address its lack of privacy? 28:03 – Can we stop a dystopian future?
30:30

👁‍🗨Not Dead Yet | S1E2 — Eli Ben-Sasson At long last, privacy is in vogue, particularly in crypto. Running parallel to the rise in interest has been the rise of Zcash 🛡️, now even with $ZEC treasury companies forming like Cypherpunk. In episode 2 of Not Dead Yet, Robert Baggs speaks with Eli Ben-Sasson | Starknet.io. Eli is a long-time researcher of zero-knowledge proofs (the technology that underpins Zcash), the co-inventor of Zerocash protocol and ZK_STARKS, and the co-founder and CEO of StarkWare (BTCFi arc) 🥷. Timestamps: 00:00 – Intro 01:23 – Why is privacy important to you? 03:24 – Why is a lack of privacy holding blockchains back? 04:05 – Privacy is normal, so why isn’t it already normal in crypto? 06:10 – The dangers of CBDCs 09:00 – “If you have nothing to hide, you have nothing to fear” 12:53 – What are Zero Knowledge Proofs (ZKPs) and why are they important? 19:26 – What is Zcash and how does it work? 21:14 – Why is privacy suddenly trending? 23:15 – What happens if Bitcoin doesn’t address its lack of privacy? 28:03 – Can we stop a dystopian future?

Cointelegraph

120,257 просмотров • 8 месяцев назад

👁‍🗨Not Dead Yet | S1E3 — Professor Daniel J. Solove As privacy dwindles and surveillance capitalism thrives, we find ourselves face-to-face with a threat poised to dwarf all else. In episode 3 of Not Dead Yet, Robert Baggs speaks with Daniel J. Solove, one of the world's most renowned thinkers on privacy, as well as one of the most cited legal scholars in history. Timestamps: 00:00 – Intro 01:15 – Why Privacy? 03:08 – How is privacy different today from 2000? 05:28 – Is the biggest threat to privacy AI? 07:05 – If we don’t act, what will the future look like? 08:55 – Why is data such a big threat? 18:44 – Why are privacy laws softening around AI? 26:42 – The myth of the privacy paradox 33:26 – The dangers of LLMs and AI chatbots 40:48 – Do you think we’re becoming easier to control than any society in history? 44:16 – “If you’ve done nothing wrong, you have nothing to fear” — why is this incorrect? 47:36 – Is the privacy problem getting worse?
50:31

👁‍🗨Not Dead Yet | S1E3 — Professor Daniel J. Solove As privacy dwindles and surveillance capitalism thrives, we find ourselves face-to-face with a threat poised to dwarf all else. In episode 3 of Not Dead Yet, Robert Baggs speaks with Daniel J. Solove, one of the world's most renowned thinkers on privacy, as well as one of the most cited legal scholars in history. Timestamps: 00:00 – Intro 01:15 – Why Privacy? 03:08 – How is privacy different today from 2000? 05:28 – Is the biggest threat to privacy AI? 07:05 – If we don’t act, what will the future look like? 08:55 – Why is data such a big threat? 18:44 – Why are privacy laws softening around AI? 26:42 – The myth of the privacy paradox 33:26 – The dangers of LLMs and AI chatbots 40:48 – Do you think we’re becoming easier to control than any society in history? 44:16 – “If you’ve done nothing wrong, you have nothing to fear” — why is this incorrect? 47:36 – Is the privacy problem getting worse?

Cointelegraph

234,490 просмотров • 8 месяцев назад

Sam Altman just told you what OpenAI is actually building. Not a chatbot. Not a search tool. Not an assistant. Altman: “Go look around my computer… read my messages… listen to my meetings… intermediate my interactions for me.” That is not a product pitch. That is the CEO of the most valuable AI company on Earth describing what he personally wants. For himself. Every day. Read his messages. Listen to his meetings. Act on his behalf. Make decisions before he knows a decision needs making. Altman: “I don’t have to think. I don’t have to ask you questions.” Every model of AI ever built runs on the prompt. You ask. It responds. You direct. It executes. The human initiates. The machine follows. Altman is describing the death of that model. The agent does not wait. It already read the email. It already heard the meeting. It already knows what you need before you form the thought. You do not operate the machine. The machine operates around you. Then came the line that makes everything else real. Altman: “You can know everything about my life. Start suggesting more things I should build.” He is not asking the AI to execute his ideas. He is asking it to generate them. From his files. His history. His patterns. His entire context. The agent does not just remove friction. It removes the blank page. You never stall. You never run dry. You never sit wondering what to build next. The machine already mapped your market, your gaps, your momentum. It tells you what comes next before you think to ask. But the individual product is not the story. Altman went further. Altman: “Automated companies… where the AI can do not just coding work, but huge amounts of what it takes to run and operate a company.” Not fully automated. He was precise about that. But accelerated to the point where one person with the right stack does what used to take departments. The billion-dollar company did not reach that valuation because the product was worth a billion. It got there because it took a thousand people to deliver it. When an agent absorbs the work of a hundred of those people, the math of every industry rewrites itself. The startup that needed fifty employees and three years of runway now needs five people and six months. The company that took a decade to scale now compounds in quarters. The person holding the line between their data and their tools is not protecting their privacy. They are protecting their ceiling. Because the cost of this leverage is total transparency. You do not get the agent that acts without being asked unless you give it everything. Your messages. Your calendar. Your files. Your patterns. Your life. Altman is not hiding that tradeoff. He is building it as the product. The people who accept it will operate at a speed the people who refuse cannot touch. Right now, two versions of the future are separating. One where you direct the machine. One where the machine already knows. Altman chose. He is building it. The question is not whether this happens. The question is which side of it finds you.
1:40

Sam Altman just told you what OpenAI is actually building. Not a chatbot. Not a search tool. Not an assistant. Altman: “Go look around my computer… read my messages… listen to my meetings… intermediate my interactions for me.” That is not a product pitch. That is the CEO of the most valuable AI company on Earth describing what he personally wants. For himself. Every day. Read his messages. Listen to his meetings. Act on his behalf. Make decisions before he knows a decision needs making. Altman: “I don’t have to think. I don’t have to ask you questions.” Every model of AI ever built runs on the prompt. You ask. It responds. You direct. It executes. The human initiates. The machine follows. Altman is describing the death of that model. The agent does not wait. It already read the email. It already heard the meeting. It already knows what you need before you form the thought. You do not operate the machine. The machine operates around you. Then came the line that makes everything else real. Altman: “You can know everything about my life. Start suggesting more things I should build.” He is not asking the AI to execute his ideas. He is asking it to generate them. From his files. His history. His patterns. His entire context. The agent does not just remove friction. It removes the blank page. You never stall. You never run dry. You never sit wondering what to build next. The machine already mapped your market, your gaps, your momentum. It tells you what comes next before you think to ask. But the individual product is not the story. Altman went further. Altman: “Automated companies… where the AI can do not just coding work, but huge amounts of what it takes to run and operate a company.” Not fully automated. He was precise about that. But accelerated to the point where one person with the right stack does what used to take departments. The billion-dollar company did not reach that valuation because the product was worth a billion. It got there because it took a thousand people to deliver it. When an agent absorbs the work of a hundred of those people, the math of every industry rewrites itself. The startup that needed fifty employees and three years of runway now needs five people and six months. The company that took a decade to scale now compounds in quarters. The person holding the line between their data and their tools is not protecting their privacy. They are protecting their ceiling. Because the cost of this leverage is total transparency. You do not get the agent that acts without being asked unless you give it everything. Your messages. Your calendar. Your files. Your patterns. Your life. Altman is not hiding that tradeoff. He is building it as the product. The people who accept it will operate at a speed the people who refuse cannot touch. Right now, two versions of the future are separating. One where you direct the machine. One where the machine already knows. Altman chose. He is building it. The question is not whether this happens. The question is which side of it finds you.

Dustin

87,680 просмотров • 5 месяцев назад

👁‍🗨Not Dead Yet | S1E6 — Yannik Schrade Privacy is finally on the main stage. This isn’t merely an incidental trend, however; it’s necessary. The rise in interest was exemplified recently when our industry's own Yannik Schrade went on Tucker Carlson's podcast to discuss the fall of privacy and the war to regain it. The episode, quite rightly, went viral. Yannik is the CEO and co-founder of Arcium and is dedicating his life and career to creating tools that strengthen the cypherpunk values we all hold dear. So, this week, Robert Baggs is joined by Yannik in the season finale of Not Dead Yet. Timestamps: 00:00 – Intro 02:03 – Tucker Carlson and the privacy narrative 06:08 – That Ring advert and what is leading people to privacy 10:28 – Why is personal data so valuable? 14:28 – Are AI becoming ethical arbiters? 19:57 – Is convenience the enemy of privacy? 22:01 – The EU and the UK’s privacy woes 31:30 – Why is decentralization important to privacy? 35:28 – With data so valuable, how can we make data privacy the norm? 37:59 – How we make privacy tools standard practice 40:12 – Tornado Cash and the regulatory threat to privacy 46:46 – Which industries would benefit the most from privacy? 49:19 – “If you’ve got nothing to hide, you have nothing to fear” 55:30 – What are some common privacy mistakes and 57:25 – Are you optimistic about the future of privacy?
57:25

👁‍🗨Not Dead Yet | S1E6 — Yannik Schrade Privacy is finally on the main stage. This isn’t merely an incidental trend, however; it’s necessary. The rise in interest was exemplified recently when our industry's own Yannik Schrade went on Tucker Carlson's podcast to discuss the fall of privacy and the war to regain it. The episode, quite rightly, went viral. Yannik is the CEO and co-founder of Arcium and is dedicating his life and career to creating tools that strengthen the cypherpunk values we all hold dear. So, this week, Robert Baggs is joined by Yannik in the season finale of Not Dead Yet. Timestamps: 00:00 – Intro 02:03 – Tucker Carlson and the privacy narrative 06:08 – That Ring advert and what is leading people to privacy 10:28 – Why is personal data so valuable? 14:28 – Are AI becoming ethical arbiters? 19:57 – Is convenience the enemy of privacy? 22:01 – The EU and the UK’s privacy woes 31:30 – Why is decentralization important to privacy? 35:28 – With data so valuable, how can we make data privacy the norm? 37:59 – How we make privacy tools standard practice 40:12 – Tornado Cash and the regulatory threat to privacy 46:46 – Which industries would benefit the most from privacy? 49:19 – “If you’ve got nothing to hide, you have nothing to fear” 55:30 – What are some common privacy mistakes and 57:25 – Are you optimistic about the future of privacy?

Cointelegraph

84,841 просмотров • 7 месяцев назад

👁‍🗨Not Dead Yet | S1E4 — Frank M. Ahearn In the age of big data, surveillance capitalism, and AI, can you truly disappear anymore? In episode 4 of Not Dead Yet, Robert Baggs speaks with Frank M. Ahearn, a man who built a career finding those who didn't want to be found. Then he switched sides. Frank is the author of the best-selling book, How to Disappear, and an expert helping everyone from whistleblowers and abuse victims to high-net worth clients, vanish. Timestamps: 00:00 – Intro 01:58 – What is a ‘Skip Tracer’? 02:41 – From finding people to helping people vanish 04:12 – Does big data make it easier or harder to hide? 05:48 – Who wants to disappear and why? 08:10 – How AI has changed the disappearing game 11:34 – How does AI threaten privacy? 12:44 – Why disinformation is still crucial to disappearing 13:53 – Balancing profile with privacy 15:10 – Why people are the biggest threat to their own privacy 16:08 – What’s the risk of social media? 18:29 – Why romance scams are so effective 19:57 – Who is the most difficult to ‘disappear’? 22:55 – What’s the most common mistake ‘disappeared’ people make? 24:20 – If I asked you to find someone right now, how would you do it? 27:29 – “If you have nothing to hide, you have nothing to fear,” why is this incorrect? 28:37 – If someone watching this wants privacy, what should they do? 31:04 – Do you think privacy is getting better or worse?
35:27

👁‍🗨Not Dead Yet | S1E4 — Frank M. Ahearn In the age of big data, surveillance capitalism, and AI, can you truly disappear anymore? In episode 4 of Not Dead Yet, Robert Baggs speaks with Frank M. Ahearn, a man who built a career finding those who didn't want to be found. Then he switched sides. Frank is the author of the best-selling book, How to Disappear, and an expert helping everyone from whistleblowers and abuse victims to high-net worth clients, vanish. Timestamps: 00:00 – Intro 01:58 – What is a ‘Skip Tracer’? 02:41 – From finding people to helping people vanish 04:12 – Does big data make it easier or harder to hide? 05:48 – Who wants to disappear and why? 08:10 – How AI has changed the disappearing game 11:34 – How does AI threaten privacy? 12:44 – Why disinformation is still crucial to disappearing 13:53 – Balancing profile with privacy 15:10 – Why people are the biggest threat to their own privacy 16:08 – What’s the risk of social media? 18:29 – Why romance scams are so effective 19:57 – Who is the most difficult to ‘disappear’? 22:55 – What’s the most common mistake ‘disappeared’ people make? 24:20 – If I asked you to find someone right now, how would you do it? 27:29 – “If you have nothing to hide, you have nothing to fear,” why is this incorrect? 28:37 – If someone watching this wants privacy, what should they do? 31:04 – Do you think privacy is getting better or worse?

Cointelegraph

147,680 просмотров • 7 месяцев назад

Nat Eliason’s (Nat Eliason) career arc is borderline absurd—but it works. He’ll spot a new tool or trend, master it, build a business around it, and move on. Nat’s pulled it off with the note-taking wave ($600k in sales from a Roam Research course), real estate (6x return flipping property in Austin), and crypto (published his insider story with Random House). Now it’s AI: he’s running a viral course on building apps with AI—$200k in pre-sales in just a week, 800 students and counting. I’ve known Nat for a long time and I think he has a great sense for where the puck is headed. He was one of the first guests I had on the podcast and I was delighted to have him on again. Here are a few takeaways from our conversation: - Coding with AI has become orders of magnitude easier for non-technical people over the last 2 years—Nat rarely has to help students fix bugs; they troubleshoot in Cursor on their own. - AI coding assistants are creating new behaviours in programming, like using a speech-to-text model to talk to an agent and having it write code for you. - The traditional learning curve of coding is flattening because AI tools let beginners build and iterate in faster feedback loops. - AI has given Nat leverage in spades—it increases his ability to be a creator while also building a robust business with as few people to manage as possible. He demos an AI book editor he coded for his sci-fi novel. - In the age of AI, software is becoming content and the barriers to create are lower than ever—but custom software for everything isn’t the answer. Nat’s model is that personalized tools make sense for that one thing you care the most about. - Nat believes that the future of writing with AI is a Cursor-style interface with a model that’s trained on your style and voice. This episode is a must-watch for writers, creators, and anyone interested in the future of product building. Watch below! Timestamps: Introduction: 00:01:45 The origins of Nat’s viral course on building apps with AI: 00:11:45 How coding with AI has evolved over the last two years: 00:18:46 Nat creates an app using Composer, Cursor’s AI assistant: 00:22:22 Tactical tips for coding with Cursor: 00:26:06 How coding with AI is creating new behaviours in programming: 00:29:06 What excites Nat the most about the future of AI: 00:32:41 A demo of Hubbard, the AI editor Nat built for his science fiction writing: 00:38:58 When does it makes sense to build custom software: 00:44:52 Nat’s take on the future of writing with AI: 00:49:18
1:00:52

Nat Eliason’s (Nat Eliason) career arc is borderline absurd—but it works. He’ll spot a new tool or trend, master it, build a business around it, and move on. Nat’s pulled it off with the note-taking wave ($600k in sales from a Roam Research course), real estate (6x return flipping property in Austin), and crypto (published his insider story with Random House). Now it’s AI: he’s running a viral course on building apps with AI—$200k in pre-sales in just a week, 800 students and counting. I’ve known Nat for a long time and I think he has a great sense for where the puck is headed. He was one of the first guests I had on the podcast and I was delighted to have him on again. Here are a few takeaways from our conversation: - Coding with AI has become orders of magnitude easier for non-technical people over the last 2 years—Nat rarely has to help students fix bugs; they troubleshoot in Cursor on their own. - AI coding assistants are creating new behaviours in programming, like using a speech-to-text model to talk to an agent and having it write code for you. - The traditional learning curve of coding is flattening because AI tools let beginners build and iterate in faster feedback loops. - AI has given Nat leverage in spades—it increases his ability to be a creator while also building a robust business with as few people to manage as possible. He demos an AI book editor he coded for his sci-fi novel. - In the age of AI, software is becoming content and the barriers to create are lower than ever—but custom software for everything isn’t the answer. Nat’s model is that personalized tools make sense for that one thing you care the most about. - Nat believes that the future of writing with AI is a Cursor-style interface with a model that’s trained on your style and voice. This episode is a must-watch for writers, creators, and anyone interested in the future of product building. Watch below! Timestamps: Introduction: 00:01:45 The origins of Nat’s viral course on building apps with AI: 00:11:45 How coding with AI has evolved over the last two years: 00:18:46 Nat creates an app using Composer, Cursor’s AI assistant: 00:22:22 Tactical tips for coding with Cursor: 00:26:06 How coding with AI is creating new behaviours in programming: 00:29:06 What excites Nat the most about the future of AI: 00:32:41 A demo of Hubbard, the AI editor Nat built for his science fiction writing: 00:38:58 When does it makes sense to build custom software: 00:44:52 Nat’s take on the future of writing with AI: 00:49:18

Dan Shipper 📧

27,207 просмотров • 1 год назад

"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

"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"

Laura Shin

12,785 просмотров • 1 месяц назад

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 просмотров • 3 месяцев назад

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 просмотров • 6 месяцев назад

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 просмотров • 2 месяцев назад

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 просмотров • 5 месяцев назад

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 просмотров • 1 месяц назад

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 просмотров • 4 месяцев назад

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 просмотров • 14 дней назад

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 просмотров • 4 месяцев назад