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  • MIT Complexity Theorist: Why You Can Do Better Than “Optimal” On Leetcode & SAT | Ryan Williams

    29 Jun

    MIT Complexity Theorist: Why You Can Do Better Than “Optimal” On Leetcode & SAT | Ryan Williams

    Ryan Williams is a professor at MIT and the winner of the Gödel Prize in theoretical computer science. I interviewed him all about his work starting by asking him a popular Leetcode question (3 SUM). Correction: In this podcast I say "lower bound" when I mean "upper bound" and vice versa. Was speaking using the intuition that lower is better for running time. In reality, the accurate usage is: "Lower bound" = A proven floor for a problem e.g. "no algorithm can possibly be faster" "Upper bound" = A proven ceiling for a specific solution e.g. "there exists an algorithm this fast" Professor Williams answers as if I spoke accurately so the error didn't impact the flow of conversation. Just a correction for the record • My ergonomic keyboard project I mentioned, you can follow along here: https://read.compose.llc/ • The Kickstarter page for it: https://www.kickstarter.com/projects/ryanlpeterman/compose-simple-ergonomics-beautifully-done Podcast links: • YouTube: https://youtu.be/AaK1SL2i_4Y • Apple: https://podcasts.apple.com/us/podcast/the-peterman-pod/id1777363835 • Transcript: https://www.developing.dev/p/mit-complexity-theorist-on-leetcode Thank you to this episode's sponsor for supporting my work: • WorkOS: makes your app Enterprise Ready with easy to use APIs to add SSO, SCIM, RBAC, and more in just a few lines of code, check them out at https://workos.com/ Timestamps: (00:00) Intro (00:41) Asking him a popular Leetcode question (03:54) Doing better than the popular optimal solution (08:26) Fine grained complexity (17:00) A severe strengthening of P vs NP (24:38) SAT problems and solvers (34:51) Hot takes on famous open questions (46:57) Simulating space with time (01:01:02) Why he solves hard problems (01:02:35) How to pick good research direction (01:07:14) Technical book recommendations (01:08:31) Advice for his younger self (01:11:56) Outro Where to find Ryan: • Wikipedia: https://en.wikipedia.org/wiki/Ryan_Williams_(computer_scientist) • Website: https://people.csail.mit.edu/rrw/ • LinkedIn: https://www.linkedin.com/in/r-ryan-williams-a1b534a/ • X/Twitter: https://twitter.com/rrwilliams Where to find Ryan: • Newsletter: https://www.developing.dev/ • X/Twitter: https://x.com/ryanlpeterman • LinkedIn: https://www.linkedin.com/in/ryanlpeterman/ • Threads: https://www.threads.com/@ryanlpeterman • Instagram: https://www.instagram.com/ryanlpeterman • TikTok: https://www.tiktok.com/@ryanlpeterman Referenced in this episode: • Some Estimated Likelihoods for Computational Complexity: https://people.csail.mit.edu/rrw/likelihoods.pdf • Simulating Time with Square-Root Space: https://arxiv.org/abs/2502.17779 • Cook and Mertz's tree evaluation paper: https://dl.acm.org/doi/10.1145/3618260.3649664

    29 Jun

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    1hr 13min
  • Mark Zuckerberg & Priscilla Chan: How AI Will Help Cure Disease

    15 hr ago

    Mark Zuckerberg & Priscilla Chan: How AI Will Help Cure Disease

    As part of our summer replay series, we're revisiting one of our favorite conversations from the past year. Mark Zuckerberg and Dr. Priscilla Chan join Ben Horowitz, Vineeta Agarwala, and Erik Torenberg to discuss the Chan Zuckerberg Initiative's ambitious effort to help cure, prevent, and manage disease by the end of the century. Rather than funding individual breakthroughs, CZI is focused on building the tools and infrastructure that can accelerate scientific discovery across entire fields. The conversation explores Biohub, Cell Atlas, virtual cell models, open biological datasets, and the growing role of AI in helping researchers better understand human biology. They discuss why biology still lacks a "periodic table of elements," how AI could help scientists test hypotheses before running expensive experiments, and why pairing frontier biology with frontier AI may unlock a new era of medical discovery.   Resources: Follow Mark Zuckerberg on X: https://x.com/finkd Follow Dr. Priscilla Chan on Instagram: https://www.instagram.com/priscillachan Follow Ben Horowitz on X: https://x.com/bhorowitz Follow Vineeta Agarwala on X: https://x.com/vintweeta Stay Updated: Find a16z on YouTube: YouTube Find a16z on X Find a16z on LinkedIn Listen to the a16z Show on Spotify Listen to the a16z Show on Apple Podcasts Follow our host: https://twitter.com/eriktorenberg   Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

    15 hr ago

    •
    45 min
  • NSL

    2 days ago

    NSL

    One day Nick got a visit from the FBI demanding he give them data on one of his customers. They asked for it in the form of a National Security Letter or NSL. Something wasn’t right about this letter. It seemed to violate the constitution. So he set out to change the law. Learn more about Nicks work at calyxinstitute.org and phreeli.com. Check out Cindy’s book Privacy’s Defender: My Thirty-Year Fight Against Digital Surveillance (https://amzn.to/4gXuK2J). SponsorsSupport for this show comes from ThreatLocker®. ThreatLocker® is a Zero Trust Endpoint Protection Platform that strengthens your infrastructure from the ground up. With ThreatLocker® Allowlisting and Ringfencing™, you gain a more secure approach to blocking exploits of known and unknown vulnerabilities. ThreatLocker® provides Zero Trust control at the kernel level that enables you to allow everything you need and block everything else, including ransomware! Learn more at www.threatlocker.com. Support for this episode comes from NetSuite. NetSuite gives you visibility and control of your financials, planning, budgeting, and of course - inventory - so you can manage risk, get reliable forecasts, and improve margins. NetSuite helps you identify rising costs, automate your manual business processes, and see where to save money. KNOW your numbers. KNOW your business. And get to KNOW how NetSuite can be the source of truth for your entire company. Visit www.netsuite.com/darknet to learn more. This show is sponsored by Maze. Maze uses AI agents to triage and remediate cloud vulnerabilities by figuring out what’s actually exploitable, not just what’s theoretically risky. They remove the noise, prioritize vulns that matter, and manage remediation, so your team stops wasting time on meaningless vulns. Visit MazeHQ.com/darknet for more information. View all active sponsors. Sources https://www.democracynow.org/2010/8/11/gagged_for_6_years_nick_merrill https://globalfreedomofexpression.columbia.edu/cases/u-s-nicholas-merrill-v-loretta-e-lynch-14-cv-9763-vm/ https://clearinghouse.net/case/12966/ https://www.theguardian.com/law/2015/dec/06/fbi-national-security-letter-gag-order-nick-merrill https://www.aclu.org/documents/national-security-letters https://www.eff.org/cases/re-matter-2011-national-security-letter Cindy’s Book: Privacy’s Defender: My Thirty-Year Fight Against Digital Surveillance

    2 days ago

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    1hr 7min
  • How Bitcoin Rewired a Classic Computer Science Problem (ft. Tim Roughgarden and Ittai Abraham)

    22 Jun

    How Bitcoin Rewired a Classic Computer Science Problem (ft. Tim Roughgarden and Ittai Abraham)

    Bitcoin often gets credited with inventing trustless consensus. It didn’t.  The problem was named decades earlier — in the world of distributed computing — and researchers spent years studying how machines could reach agreement even when some participants were faulty, adversarial, or corrupt. What Bitcoin did was something different: It solved a classic Byzantine agreement problem in a radically new, permissionless setting. And it took the research world years to fully recognize what Satoshi had done. In this episode of First Principles, a16z crypto Head of Research and Columbia professor Tim Roughgarden is joined by a16z crypto research partner Ittai Abraham — one of the world’s leading researchers in Byzantine agreement and consensus protocols, a founding member of VMware’s blockchain project, and founder of the technical blog Decentralized Thoughts — to unpack the scientific roots of blockchain consensus.  Together, Tim and Ittai trace the line from classic distributed systems research to Bitcoin, proof-of-stake, Tendermint, Casper, DAG-based protocols, Solana’s Alpenglow, and the modern race for higher throughput and lower latency. Along the way, they explain why concepts like Byzantine fault tolerance, state machine replication, safety, liveness, and partial synchrony are not just academic abstractions — they are the language and design principles behind today’s blockchain protocols.  This conversation kicks off First Principles: The Scientific Roots of Blockchain Technology — a special, limited series from a16z crypto on the scientific ideas behind modern computing — especially blockchains — told through conversations with the pioneers who helped create them, including Barbara Liskov, Leslie Lamport, and more. Hosted by Tim Roughgarden, the series explores the foundational concepts behind distributed systems and consensus protocols; economics, mechanism and market design; and cryptography, from digital signatures to zero knowledge. People often tell the story of the Bitcoin whitepaper as if it appeared out of nowhere.  But the ideas behind Bitcoin — and behind blockchains more broadly — come from decades of computer science, economics, mathematics, and cryptography. First Principles is a guide to that lineage, told by the people who helped build it.  Highlights  00:00 Introduction to First Principles: The Scientific Roots of Blockchain Technology  00:56 Why consensus matters for blockchains  02:30 Byzantine agreement: The old computer science problem Bitcoin made practical  04:34 Blockchains as a shared system of record: State machine replication and blockchain state  06:41 How two research worlds — distributed computing and crypto — began to converge  07:49 Proof of work vs. proof of stake  09:27 Why Ethereum’s move to proof-of-stake took years  11:08 When crypto rediscovered decades of distributed systems research  11:50 Why BFT became practical 12:49 Throughput, latency, and modern consensus design  14:05 DAG-based protocols and faster blockchains  15:25 Peace time vs. war time: why modern blockchains need two modes  16:47 Theory, practice, and the future of blockchain research    Follow:  Tim Roughgarden: https://twitter.com/Tim_Roughgarden  Ittai Abraham: https://twitter.com/ittaia  Follow a16z crypto:  X: https://twitter.com/a16zcrypto  LinkedIn: https://www.linkedin.com/showcase/a16zcrypto/posts/  YouTube: https://www.youtube.com/@a16zcrypto  Substack: https://a16zcrypto.substack.com/subscribe/  **  As always, none of the following should be taken as investment, business, legal, or tax advice. Please see a16z.com/disclosures for more important information, including a link to a list of our investments. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

    22 Jun

    •
    20 min
  • Is AI running modern warfare?

    5 Mar

    Is AI running modern warfare?

    As Washington moved toward a joint US and Israeli response to Iran, a parallel fight over military access to frontier AI broke into the open. Anthropic, maker of Claude, refused a Pentagon demand for “unrestricted” use of its models, citing red lines on domestic mass surveillance and fully autonomous weapons. US Defence Secretary Pete Hegseth then labelled the firm a “supply chain risk,” a designation intended to bar defence contractors from using Anthropic’s tools. Within hours, OpenAI announced a deployment deal with the Pentagon, then hurried to revise and clarify its safeguards after a backlash. The consumer response was immediate. Users posted cancellations under “Cancel ChatGPT,” third‑party trackers reported a sharp spike in uninstalls, and Anthropic’s Claude climbed the app charts. We ask why the military wants large‑scale AI, what it is already using it for, and what this showdown reveals about democratic oversight, privacy and accountability when state demand meets platform power. Also this week: are prediction sites, where anyone can bet on future events, being abused by users with inside knowledge — and are these platforms now shaping events rather than merely forecasting them? The Interface is your weekly guide to the tech rewiring your week and our world. Hosted by journalists Thomas Germain, Karen Hao, and Nicky Woolf, each episode unpacks week-by-week the unfolding story of how technology is shaping all of our futures. No guests. No jargon. Just three sharp voices debating the tech stories that matter - whether they shook a government, broke the internet, or quietly tipped the balance of power. New episodes drop every Thursday on BBC Sounds in the UK. Outside the UK, find us on BBC.com or wherever you get your podcasts, or watch the video version on YouTube (search “The Interface podcast”). To get in touch with the team - email us at theinterface@bbc.com The Interface is a BBC Studios production. Producer: Natalia Rodriguez Ford Executive Editor: Philip Sellars

    5 Mar

    •
    37 min
  • Anthropic Can Now Read Claude’s Mind

    2 days ago

    Anthropic Can Now Read Claude’s Mind

    Anthropic’s new interpretability research suggests Claude has something like a readable “global workspace,” revealing internal concepts the model is tracking before they appear in its output. NLW breaks down why this matters for AI safety, consciousness debates, and the future of building more reliable models. In the headlines: The UN pushes for AI weapons limits, Illinois advances state-level AI safety rules, and China tightens controls on AI companion agents. Brought to you by: KPMG – Research from KPMG and the University of Texas at Austin shows the highest-impact AI users treat AI like a reasoning partner — and those skills can be taught at scale. Learn more at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠kpmg.com/us/Sophisticated⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Hyperagent - Hire a fleet of always-on agents. New users get $1,000 in inference. ⁠⁠⁠⁠⁠hyperagent.com/aidailybrief⁠⁠⁠⁠⁠ Retool - Secure your vibecoded apps. New enterprise customers get up to $10,000 in AI credits per year. ⁠retool.com/aidaily ⁠ Rackspace Technology- One accountable partner to build, operate and run your full enterprise AI stack ⁠⁠⁠⁠⁠https://www.rackspace.com/⁠⁠⁠⁠⁠ Section - Section turns AI investment into workforce transformation and ROI - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.sectionai.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Scrunch - The AI customer experience platform - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://scrunch.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Blitzy - Want to accelerate enterprise software development velocity by 5x? ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://blitzy.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ AssemblyAI - The best way to build Voice AI apps - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.assemblyai.com/brief⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Robots & Pencils - Cloud-native AI solutions that power results ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://robotsandpencils.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ The AI Daily Brief helps you understand the most important news and discussions in AI. Subscribe to the podcast version of The AI Daily Brief wherever you listen: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://pod.link/1680633614⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Our Newsletter is BACK: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://aidailybrief.beehiiv.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring the show? sponsors@aidailybrief.ai

    2 days ago

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    29 min
  • The Phoenix Cipher

    10/09/2025

    The Phoenix Cipher

    The Phoenix Cipher takes us back to 1983 and the birth of corporate hacking in America with case that has haunted cyber security experts for over 4 decades. TRUE or FALSE? Can you crack the case?

    10/09/2025

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    32 min
  • Adam Mosseri: AI is a tailwind for authenticity

    13 hr ago

    Adam Mosseri: AI is a tailwind for authenticity

    Adam Mosseri is the Head of Instagram, where he oversees an app used by over 3 billion people. He also leads the team building Threads. Adam has run Instagram for longer than its founders did, after taking over from Kevin Systrom and Mike Krieger in 2018. A designer by training, he spent over 15 years at Meta, starting as a designer on Facebook’s mobile app, rising to lead Facebook’s News Feed, and eventually chosen to lead Instagram. During his tenure, Instagram’s user base has more than tripled. In our in-depth conversation, we discuss: 1. How the canonical product team structure is changing in 2026, from baker’s-dozen specialist teams to lean pods of four to six generalists 2. The rise of the “product staff” role—a blending of PM, design, data science, and research into one generalist operator 3. Why Adam is bullish on designers even as functional boundaries dissolve, and which roles are most at risk 4. What the Instagram algorithm knows about you, and why it’s only now catching up to what people assumed it knew years ago 5. Why the rise of AI-generated content is a tailwind for Instagram, and how the company is thinking about creator identity in a synthetic-content world 6. The two biggest product failures of Adam’s career—Facebook Home and the first version of Reels — Brought to you by: WorkOS—Make your app enterprise-ready, with SSO, SCIM, RBAC, and more: https://workos.com/lenny Mercury—Radically different banking, now with Command: https://mercury.com/command?utm_source=lennys&utm_medium=sponsored_newsletter&utm_campaign=26q3_brand_campaign — Episode transcript: https://www.lennysnewsletter.com/p/adam-mosseri-ai-is-a-tailwind-for — Archive of all Lenny's Podcast transcripts: https://www.dropbox.com/scl/fo/yxi4s2w998p1gvtpu4193/AMdNPR8AOw0lMklwtnC0TrQ?rlkey=j06x0nipoti519e0xgm23zsn9&st=ahz0fj11&dl=0 — Where to find Adam Mosseri: • X: https://x.com/mosseri • LinkedIn: linkedin.com/in/mosseri • Instagram: https://www.instagram.com/mosseri • Threads: https://www.threads.com/@mosseri — Where to find Lenny: • Newsletter: https://www.lennysnewsletter.com • X: https://twitter.com/lennysan • LinkedIn: https://www.linkedin.com/in/lennyrachitsky/ — In this episode, we cover: (00:00) Introduction to Adam Mosseri (02:09) How product teams are changing inside Meta (05:48) Blurring roles and career anxiety (14:01) Hiring traits that matter now (16:48) How AI is resetting who succeeds at work (19:38) How Meta thinks about token spend and AI costs (23:23) Where human judgment still matters (25:56) Why AI is not automatically great at strategy (30:36) Why great product leaders are curators (34:23) What Instagram’s algorithm actually knows about you (38:08) Why chronological feeds often disappoint users (40:56) Why AI content may be a tailwind for Instagram (43:42) The future of AI and human content in the feed (48:00) What Adam admires about other social platforms (52:05) How he handles public criticism (56:31) Lessons from the Instagram feed redesign backlash (01:00:21) Adam’s biggest failure: Instagram on iPad (01:03:03) His approach to kids, screens, and social media (01:06:56) What Adam wants listeners to remember — Referenced: • What happens after coding is solved? | Fiona Fung (Manager of the Claude Code and Cowork Teams): https://www.lennysnewsletter.com/p/building-the-most-ai-pilled-engineering • Claude Code: https://www.anthropic.com/product/claude-code • Claude Cowork: https://www.anthropic.com/product/claude-cowork • Head of Claude Code: What happens after coding is solved | Boris Cherny: https://www.lennysnewsletter.com/p/head-of-claude-code-what-happens • A rational conversation on where AI is actually going | Benedict Evans: https://www.lennysnewsletter.com/p/a-rational-conversation-on-where • OpenAI’s CPO on how AI changes must-have skills, moats, coding, startup playbooks, more | Kevin Weil (CPO at OpenAI, ex-Instagram, Twitter): https://www.lennysnewsletter.com/p/kevin-weil-open-ai • Mythos: https://www.anthropic.com/claude/mythos • Fable: https://www.anthropic.com/claude/fable • Pluralistic: The Reverse-Centaur’s Guide to Criticizing AI: https://pluralistic.net/2025/12/05/pop-that-bubble • Plastic Dream Sequence on Instagram: https://www.instagram.com/plasticdreamsequence • TikTok: https://www.tiktok.com • Facebook–Cambridge Analytica data scandal: https://en.wikipedia.org/wiki/Facebook%E2%80%93Cambridge_Analytica_data_scandal • Facebook Home: https://en.wikipedia.org/wiki/Facebook_Home — Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com. — Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

    13 hr ago

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  • Ben Thompson Explains the XBOX Disaster

    1 day ago

    Ben Thompson Explains the XBOX Disaster

    This is our full conversation with Ben Thompson. We discuss why Microsoft's Xbox strategy failed, why he thinks GTA 6 should cost $200, how console wars shaped the modern gaming industry, what went wrong with Xbox Game Pass, why AI is reshaping competition across tech, the future of Microsoft's gaming business, and why regulators often block mergers at exactly the wrong time. TBPN is made possible by:Ramp - https://ramp.comPublic - https://public.comCisco - https://www.cisco.comConsole - https://www.console.comCrowdStrike - https://www.crowdstrike.comFigma - https://www.figma.comMongoDB - https://www.mongodb.comNYSE - https://www.nyse.comRailway - https://railway.comShopify - https://www.shopify.com/Codex - http://openAI.com/codex Sign up for TBPN’s daily newsletter at TBPN.com Follow TBPN:https://TBPN.comhttps://x.com/tbpnhttps://open.spotify.com/show/2L6WMqY3GUPCGBD0dX6p00?si=674252d53acf4231https://podcasts.apple.com/us/podcast/technology-brothers/id1772360235https://www.youtube.com/@TBPNLive

    1 day ago

    •
    28 min
  • What a harness is and how to build one with Claude Agent SDK

    1 day ago

    What a harness is and how to build one with Claude Agent SDK

    Everybody is saying, “It’s not the model, it’s the harness,” but almost nobody stops to explain what a harness actually is. So I did. I built one live on the show: a Sentry bug-debugging harness for my company ChatPRD, using the Claude Agent SDK, a custom terminal UI built with the Ink library, and opinionated adapters for Sentry, Linear, GitHub, and Vercel. The harness handles evidence gathering, root-cause analysis, and follow-up artifact creation, all without me needing to type “dear agent, please fix this bug” ever again. I also walk through the architecture, share the code structure, and give you the exact process I used so you can build your own harness for any repetitive, structured workflow in your business. What you’ll learn: What a harness actually isWhen to build a harness versus when to stick with a general-purpose tool like Claude Code or CodexHow to encode specific permissions into a harnessThe three components every harness needsHow I used GPT-5.5 and Claude Opus to build the harness code itself (and where they both initially resisted)How to structure the artifacts your harness produces so the whole team can use the output— Brought to you by: Bolt.new—Turn your idea into a real product Customer.io—Build customer engagement campaigns from a single prompt — In this episode, we cover: (00:00) What is an AI harness? (03:19) When to build a harness (04:33) Why Claire picked bug triage (06:00) Why not just use Claude Code? (07:48) Demo: The custom harness interface (11:04) Architecture: runs, tasks, tools, and artifacts (13:44) Building it with Codex and Claude (15:08) Code map and file layout (16:51) A look at the code (19:18) The live investigation result (21:01) How to build your own harness — Tools referenced: • Claude Agent SDK (Anthropic): https://code.claude.com/docs/en/agent-sdk/overview • Claude Sonnet 4.6 (model used inside the harness): https://www.anthropic.com/news/claude-sonnet-4-6 • Claude Opus (used to build the harness): https://www.anthropic.com/claude/opus • GPT-5.5 (Codex, used to build the harness): https://openai.com/index/introducing-gpt-5-5/ • Ink (terminal UI library for Node.js): https://github.com/vadimdemedes/ink • Sentry (error monitoring): https://sentry.io/ • Linear (project management): https://linear.app/ • GitHub: https://github.com/ • Vercel: https://vercel.com/ — Where to find Claire Vo: ChatPRD: https://www.chatprd.ai/ Website: https://clairevo.com/ LinkedIn: https://www.linkedin.com/in/clairevo/ X: https://x.com/clairevo — Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.

    1 day ago

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