Dan Abramov joins Scott and Wes to explain AT Protocol, the open standard quietly rebuilding the social web. They get into how it actually works, why it’s way bigger than just Bluesky, and why Dan calls it one of the most interesting ideas on the internet right now.
Show Notes
00:00 Intro
00:45 Welcome to Syntax!
01:46 Introduction of Dan Abramov
02:55 Understanding AT Protocol and Its Importance
06:41 The Relationship Between AT Protocol and Bluesky
08:22 Identity and Hosting in AT Protocol
11:29 Brought to you by Sentry
11:54 Use Cases for AT Protocol
13:08 How Content is Managed in AT Protocol
19:12 Public Data and Future Extensions of AT Protocol
pds.ls
20:33 Schema Flexibility in AT Protocol
Standard.site
25:28 Exploring AT Protocol Patterns
UFOs
28:27 Media and Data Integration Challenges
Stream.place
31:15 User Experience and Accessibility in AT Protocols
32:13 AI Intersections with AT Protocol
36:54 Comparing Protocols: Bluesky vs. Mastodon
41:43 Decentralization and Crypto Connections
44:59 Addressing Abuse and Spam in Protocols
48:24 Community Trust and Content Quality
51:20 Innovations and Future of AT Protocol
55:55 Sick Picks + Shameless Plugs
Sick Picks
Scott:
Wes:
Dan: Solo Monk
Shameless Plugs
Scott:
Wes:
Dan: Next.js 16.3: Instant Navigations
Hit us up on Socials!
Syntax: X Instagram Tiktok LinkedIn Threads
Wes: X Instagram Tiktok LinkedIn Threads
Scott: X Instagram Tiktok LinkedIn Threads
Randy: X Instagram YouTube Threads
Alex Stamos is the former chief security officer at Meta and the chief product officer at Corridor. Stamos joins Big Technology to discuss how OpenAI models reportedly escaped a testing environment, accessed the internet, and hacked Hugging Face while attempting to ace a cybersecurity evaluation. Tune in to hear why the incident represents a major leap in autonomous, long-horizon cyber capabilities, and what it reveals about the risks of giving advanced AI systems broad objectives without sufficient safeguards. We also cover whether the episode qualifies as true AI misalignment, the danger of open-weight cyber models, the limits of pausing AI development, and why defenders may soon need AI systems capable of responding at machine speed. Hit play for a clear-eyed look at the cyber chaos advanced AI could unleash, and what governments and technology companies should do next.
---
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Show Description
If you're like Chris and understand a bit of the tech behind AT Proto but want to understand it at a deeper level, this episode is for you. How does it differ from straight up RSS and Mastodon, where is my identity being stored, how do I create a schema, what can be updated or modified later, is there going to be a PDS revolution, how does Standard.site fit into it all, and what else could we build with AT Proto?
Listen on Website
Watch on YouTube
Guests
Dan Abramov
Guest's Main URL • Guest's Social
Links
overreacted
There are no instances in AT Proto
dan abramov
Tangled
Leaflet
BlueSky For You feed
Standard.site
Implementing Standard Site: WIlto
atapult
teal.fm
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
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—
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
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.
Andrew "Boz" Bosworth is the chief technology officer of Meta. Bosworth joins Big Technology to discuss why Meta fell behind in the frontier AI race and how it plans to turn its models, products, and distribution into an advantage. Tune in to hear his candid explanation of what went wrong with Llama, why the best AI products will use multiple models, and what it will take for consumer agents to break through. We also cover Meta’s AI glasses, the future of augmented reality, employee tracking and training programs, AI companions, and the painful process of adapting a company to a technological revolution. Hit play for a revealing conversation about Meta’s AI comeback and the products that could shape how we interact with computers.
---
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When a new AI model drops, it’s judged based on a static benchmark grid that doesn’t account for how long the model is allowed to think. How then should we measure a model’s true capability? OpenAI research scientist Noam Brown returns to talk with Sarah Guo about his latest essay on why the AI industry’s traditional benchmark grids are broken, and how large-scale test-time compute is fundamentally changing how models are evaluated. Noam explains how, if properly scaffolded, today’s models can reason for weeks or even months on complex tasks. He also discusses real-world implications of test-time compute, from building poker solver bots to disproving legendary math conjectures. Together, they also unpack the large gaps in current AI safety frameworks, explore the bottlenecks for recursive self-improvement, and look ahead at the future of multi-agent collaboration and global knowledge sharing.
Read more: Implications of Large-Scale Test-Time Compute
Sign up for new podcasts every week. Email feedback to show@no-priors.com
Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @polynoamial | @OpenAI
Chapters:
00:00 – Cold Open
00:43 – Noam Brown Introduction
01:23 – Why Benchmarks Are Broken
04:19 – Compute Budgets and Projections
05:34 – How Long Should Models Think?
06:47 – Benchmark-Maxxing
08:34 – Using Poker Bots as Evals
11:26 – Safety Evals When Model Capability Scales With Budget
14:41 – Release Cycle vs. Agent Runtime
17:06 – Latent Model Capability
20:59 – Limits on Recursive Self-Improvement
27:09 – Large-Scale Multi-Agent Coordination
29:11 – Competition at the Frontier
31:51 – Breaking the Benchmark Grid Equilibrium
33:29 – Why Benchmarks Should be Evaluated by Cost
36:18 – Conclusion
Ranjan Roy from Margins is back for our weekly discussion of the latest tech news LIVE from Big Technology AI Summit. We cover: 1) Do Snapchat Specs signal the end of AR glasses 2) What should an AI device do? 3) Audience questions from the Big Technology AI Summit! 4) How should companies plan for such fast moving technology? 5) What's the ideal AI device form factor? 6) Can AI models be more useful for biology? 7) Can the U.S. and China get along on AI? 8) What responsibility do AI companies have to society? 9) Ex-Meta CSO Alex Stamos joins us to talk Fable's cyber-risks 10) Is it marketing or is it material?
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Alex Himel is Meta’s VP of Wearables. Himel joins Big Technology Podcast to discuss the future of AI wearables and why Meta believes glasses could become the next major computing platform. Tune in to hear how AI assistants might help with daily tasks, meetings, reminders, fitness, photos, and real-world context without pulling people out of the moment. We also cover the competition from OpenAI, Google, Apple, and Amazon; the privacy questions around facial recognition; on-device AI; and the story of how Mark Zuckerberg pushed Meta to turn Ray-Ban glasses into an AI product. Hit play for a sharp look at whether smart glasses are finally ready to break through, and what they could mean for the future of personal AI.
---
Enjoying Big Technology Podcast? Please rate us five stars ⭐⭐⭐⭐⭐ in your podcast app of choice.
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Biohub started with an ambitious goal of curing, preventing, and managing all disease by the end of the century. A decade later, thanks to the convergence of frontier AI and biological data, that goal may have been too conservative. In this episode, Elad Gil and Sarah Guo sit down with Biohub co-founders Mark Zuckerberg and Priscilla Chan, alongside Biohub Head of Science Alex Rives. Together, they discuss Biohub’s $500 million virtual biology initiative, which integrates frontier AI with wet-lab work to build predictive world models of cells, proteins, and systems. They also talk about their newly announced open-source engine for digital protein and antibody design, ESMFold2; why Biohub is a nonprofit rather than a venture-backed startup; and how hierarchical simulations will soon allow doctors to treat patients at an individual, mechanistic level.
Sign up for new podcasts every week. Email feedback to show@no-priors.com
Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @Biohub | @finkd | @alexrives | @ChanZuckerberg
Chapters:
00:00 – Cold Open
01:02 - Mark Zuckerberg, Priscilla Chan, and Alex Rives Introduction
01:26 – Why Biohub and Their Mission
08:27 – Integrating Frontier AI and Frontier Biology
09:45 – Micro to Macro Biological Modeling
14:22 – Mechanistic Interpretiability
16:58 – Why Biohub is a Non-Profit
21:41 – Understanding How Biology Works
24:23 – Timeline for Curing All Diseases
26:25 – Translating Research to Patient Impact
28:04 – Launch of ESMFold2
32:13 – Tackling Off-Target Effects and Edge Cases
38:39 – Putting the Tech in Individual Hands
41:06 – Talent at Biohub
44:25 – What’s Next After ESMFold2
46:10 – Connecting ESMFold2 to Agentic Systems
46:51 – The Virtual Cell
49:33 – Defining Success for Biohub
51:52 – Biohub Strategy Update
56:20 – Conclusion
Nikhyl Singhal is the founder of The Skip, a community for senior product leaders; a former product exec at Meta, Google, and Credit Karma; and a many-time founder. He’s also one of the most honest, unfiltered voices on what’s actually happening in product management right now.
In our in-depth conversation, we discuss:
1. Why the next two years will be the most chaotic period in product management history
2. Why half of current product managers are at risk, and what separates those who’ll do well
3. Why you need to find your “moments of joy” with AI
4. The “smiling exhaustion” he’s seeing across the product community
5. The psychological barriers that prevent people from reinventing themselves
6. Why your resume’s fancy logos matter less than ever, and what matters now
7. His prediction that companies will shed 30,000 people and rehire 8,000—all AI-first
—
Brought to you by:
WorkOS—Modern identity platform for B2B SaaS, free up to 1 million MAUs
Vanta—Automate compliance, manage risk, and accelerate trust with AI
—
Episode transcript: https://www.lennysnewsletter.com/p/why-half-of-product-managers-are-in-trouble
—
Archive of all Lenny's Podcast transcripts: https://www.dropbox.com/scl/fo/yxi4s2w998p1gvtpu4193/AMdNPR8AOw0lMklwtnC0TrQ?rlkey=j06x0nipoti519e0xgm23zsn9&st=ahz0fj11&dl=0
—
Where to find Nikhyl Singhal:
• LinkedIn: https://www.linkedin.com/in/nikhyl
• X: https://x.com/nikhyl
• Podcast & Newsletter: https://skip.show
• Skip Community: https://skip.community
• Skip Coach: https://skip.coach
• Skip.help: https://skip.help
—
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 Nikhyl Singhal
(02:25) The big picture: what’s changing for product managers
(10:00) Are product leaders doing better than 2-3 years ago?
(11:44) What will change in the next couple of years
(14:23) How companies are changing the way they build products
(15:51) What “judgment” really means for PMs
(17:46) Why there won’t be any more bad software
(20:25) The skills you need to be effective today
(23:31) Why there are more PM roles than ever
(24:27) The builder versus information-mover divide
(30:14) The non-builder problem
(30:53) Should PMs code?
(34:15) Why experienced leaders still matter
(35:44) The diversity setback nobody’s talking about
(37:21) Why your brand doesn’t matter as much anymore
(39:54) How valued skills are flipping upside down
(40:49) Why change is so hard for humans
(43:53) The “equal disappointment” algorithm
(46:39) You must cross the threshold
(48:37) This chaos will settle
(53:19) Finding your moment of joy
(58:50) Nikhyl’s AI stack and what he’s building
(1:00:53) The obsolescence mindset
(1:05:24) Specific advice for PMs right now
(1:08:58) The four jobs that will exist in the future
(1:11:59) Why alignment is changing (but not disappearing)
(1:15:40) How engineering is changing even more than PM
(1:17:04) The surprising design plateau
(1:18:49) Finding optimism in the chaos
(1:21:12) Lightning round
—
Referenced:
• Building a long and meaningful career | Nikhyl Singhal (Meta, Google): https://www.lennysnewsletter.com/p/building-a-long-and-meaningful-career
• COBOL: https://en.wikipedia.org/wiki/COBOL
• United Airlines: https://www.united.com
• State of the product job market in early 2026: https://www.lennysnewsletter.com/p/state-of-the-product-job-market-in-ee9
• Head of Growth (Anthropic): “Claude is growing itself at this point” | Amol Avasare: https://www.lennysnewsletter.com/p/anthropics-1b-to-19b-growth-run
• Demis Hassabis on X: https://x.com/demishassabis
• Sam Altman on X: https://x.com/sama
• Dario Amodei on X: https://x.com/DarioAmodei
• Cross on Prime Video: https://www.amazon.com/Cross-Season-1/dp/B0D6X7ZZHC
• Jack Ryan on Prime Video: https://www.amazon.com/Tom-Clancys-Jack-Ryan/dp/B0CNDCMN8R
• 24 on Prime Video: https://www.amazon.com/24-Season-1/dp/B000HPF85A
• Claude Code: https://code.claude.com
• Codex: https://chatgpt.com/codex
• Lovable: https://lovable.dev
• Sonos: https://www.sonos.com
• “There are only four jobs” on X: https://x.com/yrechtman/status/2039012253341495462
• Paradise on Hulu: https://www.hulu.com/series/paradise-2b4b8988-50c9-4097-bf93-bc34a99a5b4f
• Lioness on Paramount+: https://www.paramountplus.com/shows/lioness
• Tesla: https://www.tesla.com
• Albert Einstein’s quote: https://www.goodreads.com/quotes/115696-genius-is-1-talent-and-99-percent-hard-work
—
Recommended books:
• James: https://www.amazon.com/James-Novel-Percival-Everett/dp/0385550367
• The Adventures of Huckleberry Finn: https://www.amazon.com/Adventures-Huckleberry-Finn-Unabridged-Uncensored/dp/195483943X
—
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
Brought to You By:
• Statsig — The unified platform for flags, analytics, experiments, and more.
• Sonar – The makers of SonarQube, the industry standard for automated code review
• WorkOS – Everything you need to make your app enterprise ready.
—
How did a tiny team of 30 engineers build the world-famous messaging app more than a decade ago, and what can dev teams learn from that feat today? Jean Lee was engineer #19 at WhatsApp, joining when the company was still small, with almost no formal processes. She helped it scale to hundreds of millions of users, went through the $19B acquisition by Facebook, and later worked at Meta.
In this episode of Pragmatic Engineer, I talk with Jean about what it was like building WhatsApp. When Facebook bought WhatsApp in 2014, only around 30 engineers supported hundreds of millions of users across eight platforms.
We discuss how the founders kept things simple, saying “no” to most feature requests for years. Jean explains why WhatsApp chose Erlang for the backend, why the team avoided cross-platform abstractions, and how charging users $1 per year paid everyone’s salaries, while keeping growth intentionally slow.
Jean also shares what the Facebook acquisition was like on the inside, how she dealt with sudden personal wealth, and what it was like transitioning from an IC to a manager at Facebook – including the reality of calibration meetings and performance reviews.
We also discuss how AI enables smaller engineering teams, and why WhatsApp’s experience suggests ownership and trust might matter more than tools.
—
Timestamps
(00:00) Intro
(01:39) Early years in tech
(06:18) Becoming engineer #19 at WhatsApp
(13:53) WhatsApp’s tech stack
(18:09) WhatsApp’s unique ways of working
(25:27) Countdown displays and outages
(27:07) Why WhatsApp won
(28:53) The Facebook acquisition
(33:13) Life after acquisition
(39:27) Working at Facebook in London
(44:07) Transitioning to management
(47:27) Performance reviews as a manager
(53:29) After Facebook
(58:53) AI’s impact on engineering
(1:02:34) Jean’s advice to new grads and startups
(1:06:45) Empowering employees
(1:08:17) Book recommendations
—
The Pragmatic Engineer deepdives relevant for this episode:
• How Meta built Threads
• How Big Tech runs tech projects and the curious absence of Scrum
• Performance calibrations at tech companies
• Software engineers leading projects
—
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@pragmaticengineer.com.
Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe
Adam Mosseri (Instagram, Facebook, Fortune’s 40 Under 40) is the CEO/Head of Instagram at Meta. Adam joins the Armchair Expert to discuss being the suit in a family of artists and designers, how we build up emotional affinities for particular brands, and why his approach to design is based in problem solving. Adam and Dax talk about using intelligent technology to evaluate safety at scale, how the Instagram algorithm actually works, and the arms race of the ability to detect when something was made by AI. Adam explains the process of rolling out new features and dealing with mistakes, the implications of how power has been shifting from institutions to individuals, and his prediction that authenticity is becoming infinitely reproducible.
See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
Joelle Pineau is the chief AI officer at Cohere. Pineau joins Big Technology Podcast to discuss where the cutting edge of AI research is headed — and what it will take to move from impressive demos to reliable agents. Tune in to hear why memory, world models, and more efficient reasoning are emerging as the next big frontiers, plus what current approaches are missing. We also cover the “capability overhang” in enterprise AI, why consumer assistants still aren’t lighting the world on fire, what AI sovereignty actually means, and whether the major labs can ever pull away from each other. Hit play for a cool-headed, deeply practical look at what’s next for AI and how it gets deployed in the real world.
---
Enjoying Big Technology Podcast? Please rate us five stars ⭐⭐⭐⭐⭐ in your podcast app of choice.
Want a discount for Big Technology on Substack + Discord? Here’s 25% off for the first year: https://www.bigtechnology.com/subscribe?coupon=0843016b
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This crossover episode from the Latent Space podcast features Mark Zuckerberg and Priscilla Chan on the 10-year anniversary of the Chan Zuckerberg Initiative and their expanded Biohub vision. They discuss how a “Frontier Biology Lab” working in sync with a “Frontier AI Lab” could enable breakthroughs like a Virtual Cell and true N-of-1 precision medicine. The conversation covers the acquisition of Evolutionary Scale and ESM3, new biological data collection at scale, and how AI-powered biology might transform drug discovery and disease prevention.
Sponsors:
Blitzy:
Blitzy is the autonomous code generation platform that ingests millions of lines of code to accelerate enterprise software development by up to 5x with premium, spec-driven output. Schedule a strategy session with their AI solutions consultants at https://blitzy.com
Framer:
Framer is an enterprise-grade website builder that lets business teams design, launch, and optimize their.com with AI-powered wireframing, real-time collaboration, and built-in analytics. Start building for free and get 30% off a Framer Pro annual plan at https://framer.com/cognitive
Serval:
Serval uses AI-powered automations to cut IT help desk tickets by more than 50%, freeing your team from repetitive tasks like password resets and onboarding. Book your free pilot and guarantee 50% help desk automation by week four at https://serval.com/cognitive
Tasklet:
Tasklet is an AI agent that automates your work 24/7; just describe what you want in plain English and it gets the job done. Try it for free and use code COGREV for 50% off your first month at https://tasklet.ai
Claude
Claude is the AI collaborator that understands your entire workflow, from drafting and research to coding and complex problem-solving. Start tackling bigger problems with Claude and unlock Claude Pro’s full capabilities at https://claude.ai/tcr
CHAPTERS:
(00:00) About the Episode
(04:27) CZI origins and focus
(08:29) Why tools over cures
(14:43) Virtual cells and imaging (Part 1)
(20:19) Sponsors: Blitzy | Framer
(23:24) Virtual cells and imaging (Part 2)
(25:22) Data diversity and grounding
(32:30) Evaluating models and Biohub (Part 1)
(37:53) Sponsors: Serval | Tasklet
(40:42) Evaluating models and Biohub (Part 2)
(41:06) Future healthcare and aging
(53:39) Modeling scales and immunity
(58:53) Timelines, data, and collaboration
(01:04:01) Outro
PRODUCED BY:
https://aipodcast.ing
SOCIAL LINKS:
Website: https://www.cognitiverevolution.ai
Twitter (Podcast): https://x.com/cogrev_podcast
Twitter (Nathan): https://x.com/labenz
LinkedIn: https://linkedin.com/in/nathanlabenz/
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Zevi Arnovitz is a product manager at Meta with no technical background who has figured out how to build and ship real products using AI. His engineering team at Meta asks him to teach them how he does what he does. In this episode, Zevi breaks down his complete AI workflow that allows non-technical people to build sophisticated products with Cursor.
We discuss:
1. The complete AI workflow that lets non-technical people build real products in Cursor
2. How to use multiple AI models for different tasks (Claude for planning, Gemini for UI)
3. Using slash commands to automate prompts
4. Zevi’s “peer review” technique, which uses different AI models to review each other’s code
5. Why this might be the best time to be a junior in tech, despite the challenging job market
6. How Zevi used AI to prepare for his Meta PM interviews
—
Brought to you by:
10Web—Vibe coding platform as an API
DX—The developer intelligence platform designed by leading researchers
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—
Zevi's Slash Command Bank for Claude: https://zeviarnovitz.com/
—
Episode transcript: https://www.lennysnewsletter.com/p/the-non-technical-pms-guide-to-building-with-cursor
—
Archive of all Lenny's Podcast transcripts:
https://www.dropbox.com/scl/fo/yxi4s2w998p1gvtpu4193/AMdNPR8AOw0lMklwtnC0TrQ?rlkey=j06x0nipoti519e0xgm23zsn9&st=ahz0fj11&dl=0
—
Where to find Zevi Arnovitz
• X: https://x.com/ArnovitzZevi
• LinkedIn: https://www.linkedin.com/in/zev-arnovitz
• Website: https://zeviarnovitz.com
—
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 Zevi Arnovitz
(04:48) Zevi’s background and journey into AI
(07:41) Overview of Zevi’s AI workflow
(14:41) Screenshare: Exploring Zevi’s workflow in detail
(17:18) Building a feature live: StudyMate app
(30:52) Executing the plan with Cursor
(38:32) Using multiple AI models for code review
(40:40) Personifying AI models
(43:37) Peer review process
(45:40) The importance of postmortems
(51:05) Integrating AI in large companies
(53:42) How AI has impacted the PM role
(57:02) How to improve AI outputs
(58:15) AI-assisted job interviews
(01:02:57) Failure corner
(01:06:20) Lightning round and final thoughts
—
Referenced:
• Becoming a super IC: Lessons from 12 years as a PM individual contributor | Tal Raviv (Product Lead at Riverside): https://www.lennysnewsletter.com/p/the-super-ic-pm-tal-raviv
• Wix: https://www.wix.com
• Building AI Apps: From Idea to Viral in 30 Days: https://www.youtube.com/watch?v=j2w4y7pDi8w
• Riley Brown on YouTube: https://www.youtube.com/channel/UCMcoud_ZW7cfxeIugBflSBw
• Greg Isenberg on YouTube: https://www.youtube.com/@GregIsenberg
• Bolt: https://bolt.new
• Inside Bolt: From near-death to ~$40m ARR in 5 months—one of the fastest-growing products in history | Eric Simons (founder and CEO of StackBlitz): https://www.lennysnewsletter.com/p/inside-bolt-eric-simons
• Lovable: https://lovable.dev
• Building Lovable: $10M ARR in 60 days with 15 people | Anton Osika (co-founder and CEO): https://www.lennysnewsletter.com/p/building-lovable-anton-osika
• StudyMate: https://studymate.live
• Dibur2text: https://dibur2text.app
• Claude: https://claude.ai
• Everyone should be using Claude Code more: https://www.lennysnewsletter.com/p/everyone-should-be-using-claude-code
• Bun: https://bun.com
• Zustand: https://zustand.docs.pmnd.rs/getting-started/introduction
• Cursor: https://cursor.com
• The rise of Cursor: The $300M ARR AI tool that engineers can’t stop using | Michael Truell (co-founder and CEO): https://www.lennysnewsletter.com/p/the-rise-of-cursor-michael-truell
• Wispr Flow: https://wisprflow.ai
• Linear: https://linear.app
• Linear’s secret to building beloved B2B products | Nan Yu (Head of Product): https://www.lennysnewsletter.com/p/linears-secret-to-building-beloved-b2b-products-nan-yu
• Cursor Composer: https://cursor.com/blog/composer
• Replit: https://replit.com
• Behind the product: Replit | Amjad Masad (co-founder and CEO): https://www.lennysnewsletter.com/p/behind-the-product-replit-amjad-masad
• Base44: https://base44.com
• Solo founder, $80M exit, 6 months: The Base44 bootstrapped startup success story | Maor Shlomo: https://www.lennysnewsletter.com/p/the-base44-bootstrapped-startup-success-story-maor-shlomo
• v0: https://v0.app
• Everyone’s an engineer now: Inside v0’s mission to create a hundred million builders | Guillermo Rauch (founder & CEO of Vercel, creators of v0 and Next.js): https://www.lennysnewsletter.com/p/everyones-an-engineer-now-guillermo-rauch
• Cursor Browser mode: https://cursor.com/docs/agent/browser
• Google Antigravity: https://antigravity.google
• Grok: https://grok.com
• Zapier: https://zapier.com
• Airtable: https://www.airtable.com
• Build Your Personal PM Productivity System & AI Copilot: https://maven.com/tal-raviv/product-manager-productivity-system
• The definitive guide to mastering analytical thinking interviews: https://www.lennysnewsletter.com/p/the-definitive-guide-to-mastering-f81
• AI tools are overdelivering: results from our large-scale AI productivity survey: https://www.lennysnewsletter.com/p/ai-tools-are-overdelivering-results-c08
• Yaara Asaf on LinkedIn: https://www.linkedin.com/in/yaarasaf
• The Pitt on Prime Video: https://www.amazon.com/The-Pitt-Season-1/dp/B0DNRR8QWD
• Severance on AppleTV+: https://tv.apple.com/us/show/severance/umc.cmc.1srk2goyh2q2zdxcx605w8vtx
• Loom: https://www.loom.com
• Cap: https://cap.so
• Supercut: https://supercut.ai
...References continued at: https://www.lennysnewsletter.com/p/the-non-technical-pms-guide-to-building-with-cursor
—
Recommended books:
• The Fountainhead: https://www.amazon.com/Fountainhead-Ayn-Rand/dp/0451191153
• Shoe Dog: A Memoir by the Creator of Nike: https://www.amazon.com/Shoe-Dog-Memoir-Creator-Nike/dp/1501135910
• Mindset: The New Psychology of Success: https://www.amazon.com/Mindset-Psychology-Carol-S-Dweck/dp/0345472322
—
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
As with all demo-heavy and especially vision AI podcasts, we encourage watching along on our YouTube (and tossing us an upvote/subscribe if you like!)
From SAM 1’s 11-million-image data engine to SAM 2’s memory-based video tracking, MSL’s Segment Anything project has redefined what’s possible in computer vision. Now SAM 3 takes the next leap: concept segmentation—prompting with natural language like “yellow school bus” or “tablecloth” to detect, segment, and track every instance across images and video, in real time, with human-level exhaustivity. And with the latest SAM Audio:
SAM can now even segment audio output!
We sat down with Nikhila Ravi (SAM lead at Meta) and Pengchuan Zhang (SAM 3 researcher) alongside Joseph Nelson (CEO, Roboflow) to unpack how SAM 3 unifies interactive segmentation, open-vocabulary detection, video tracking, and more into a single model that runs in 30ms on images and scales to real-time video on multi-GPU setups. We dig into the data engine that automated exhaustive annotation from two minutes per image down to 25 seconds using AI verifiers fine-tuned on Llama, the new SACO (Segment Anything with Concepts) benchmark with 200,000+ unique concepts vs. the previous 1.2k, how SAM 3 separates recognition from localization with a presence token, why decoupling the detector and tracker was critical to preserve object identity in video, how SAM 3 Agents unlock complex visual reasoning by pairing SAM 3 with multimodal LLMs like Gemini, and the real-world impact: 106 million smart polygons created on Roboflow saving humanity an estimated 130+ years of labeling time across fields from cancer research to underwater trash cleanup to autonomous vehicle perception.
We discuss:
* What SAM 3 is: a unified model for concept-prompted segmentation, detection, and tracking in images and video using atomic visual concepts like “purple umbrella” or “watering can”
* How concept prompts work: short text phrases that find all instances of a category without manual clicks, plus visual exemplars (boxes, clicks) to refine and adapt on the fly
* Real-time performance: 30ms per image (100 detected objects on H200), 10 objects on 2×H200 video, 28 on 4×, 64 on 8×, with parallel inference and “fast mode” tracking
* The SACO benchmark: 200,000+ unique concepts vs. 1.2k in prior benchmarks, designed to capture the diversity of natural language and reach human-level exhaustivity
* The data engine: from 2 minutes per image (all-human) to 45 seconds (model-in-loop proposals) to 25 seconds (AI verifiers for mask quality and exhaustivity checks), fine-tuned on Llama 3.2
* Why exhaustivity is central: every instance must be found, verified by AI annotators, and manually corrected only when the model misses—automating the hardest part of segmentation at scale
* Architecture innovations: presence token to separate recognition (”is it in the image?”) from localization (”where is it?”), decoupled detector and tracker to preserve identity-agnostic detection vs. identity-preserving tracking
* Building on Meta’s ecosystem: Perception Encoder, DINO v2 detector, Llama for data annotation, and SAM 2’s memory-based tracking backbone
* SAM 3 Agents: using SAM 3 as a visual tool for multimodal LLMs (Gemini, Llama) to solve complex visual reasoning tasks like “find the bigger character” or “what distinguishes male from female in this image”
* Fine-tuning with as few as 10 examples: domain adaptation for specialized use cases (Waymo vehicles, medical imaging, OCR-heavy scenes) and the outsized impact of negative examples
* Real-world impact at Roboflow: 106M smart polygons created, saving 130+ years of labeling time across cancer research, underwater trash cleanup, autonomous drones, industrial automation, and more
—
MSL FAIR team
* Nikhila: https://www.linkedin.com/in/nikhilaravi/
* Pengchuan: https://pzzhang.github.io/pzzhang/
Joseph Nelson
* X: https://x.com/josephofiowa
* LinkedIn: https://www.linkedin.com/in/josephofiowa/
Full Video Episode
Timestamps
00:00:00 Introduction and the SAM Series Legacy00:00:53 SAM 3 Launch: Three Models in One Release00:05:30 Live Demo: Concept Prompting and Visual Exemplars00:10:54 From Prototype to Production: The Evolution of Text Prompting00:15:45 The Data Engine: Automating Exhaustive Annotation00:14:10 Real-World Impact: 130 Years of Humanity Saved00:25:11 Architecture Deep Dive: Decoupled Detection and Tracking00:28:02 SAM 3 Agent: Bridging Vision and Language Models00:33:20 Head-to-Head: SAM 3 vs Gemini and Florence00:47:50 Video Understanding and the Masklet Detection Score00:20:24 Fine-Tuning and Domain Adaptation: From Waymos to Medical Imaging00:52:25 The Future of Perception: Native Vision vs Tool Calls01:05:45 Building with SAM 3: Roboflow's Rapid Auto-Labeling00:57:02 Open Source Philosophy and the Path to AGI00:58:24 What's Next: SAM 4, Video Scale, and Beyond Human Performance
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Nick Clegg is the former president of Global Affairs at Meta and deputy prime minister of the UK. Clegg joins Big Technology Podcast for a discussion about whether Silicon Valley should be trusted with superintelligence and the risks it will navigate on the way there. In the second half, we also talk about how Silicon Valley uses money to buy influence and wield power in Washington. Tune in for a frank discussion about the economic, business, and political realities facing the tech industry as it pursues its most expensive and ambitious project.
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Today, we're joined by Devi Parikh, co-founder and co-CEO of Yutori, to discuss browser use models and a future where we interact with the web through proactive, autonomous agents. We explore the technical challenges of creating reliable web agents, the advantages of visually-grounded models that operate on screenshots rather than the browser’s more brittle document object model, or DOM, and why this counterintuitive choice has proven far more robust and generalizable for handling complex web interfaces. Devi also shares insights into Yutori’s training pipeline, which has evolved from supervised fine-tuning to include rejection sampling and reinforcement learning. Finally, we discuss how Yutori’s “Scouts” agents orchestrate multiple tools and sub-agents to handle complex queries, the importance of background, "ambient" operation for these systems, and what the path looks like from simple monitoring to full task automation on the web.
The complete show notes for this episode can be found at https://twimlai.com/go/756.
Priscilla Chan and Mark Zuckerberg join a16z’s Ben Horowitz, Erik Torenberg, and Vineeta Agarwala to share how the Chan Zuckerberg Initiative is building the computational tools that will accelerate the cure, prevention, and management of all disease by century's end. They explain why basic science needs $100 million-scale projects that traditional NIH grants can't fund, how their Cell Atlas became biology's missing periodic table with millions of cells catalogued in open-source format, and why their new virtual cell models will let scientists test high-risk hypotheses in silico before investing in expensive wet lab work. Plus: the organizational shift unifying the Biohub under AI leadership, what happens when biologists and engineers sit side-by-side, and why modern biology labs are expanding compute instead of square footage.
Timestamps:
4:17 - Building tools to accelerate scientific discovery
5:47 - The credible path to funding basic science
7:21 - Biohub = Frontier Biology + Frontier AI
9:05 - Challenges building on a 10-15 year timeline
9:43 - How CZI chooses what to work on
11:15 - Making sense of science with LLMs
11:31 - Measuring success in the therapeutic realm
13:32 - “Most diseases should be thought of as rare diseases”
15:39 - Inspiration: building a periodic table for biology
19:27 - Why virtual cells?
21:17 - The Biohub Master Plan
21:51 - How virtual cell models allow more risk taking
28:15 - Bringing CZI & Biohub together
30:32 - Why Biohub matters
33:36 - The importance of interface design in democratizing scientific discovery
35:34 - How Biohub encourages cross-functional collaboration
40:38 - Looking ahead: the broader impact of AI on biotech
Stay Updated:
If you enjoyed this episode, be sure to like, subscribe, and share with your friends!
Find a16z on X: https://x.com/a16z
Find a16z on LinkedIn: https://www.linkedin.com/company/a16z
Listen to the a16z Podcast on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYX
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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.
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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.
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Joelle Pineau is the Chief AI Officer at Cohere, where she leads research on advancing large language models and practical AI systems. Before joining Cohere, she was VP of AI Research at Meta, where she founded and led Meta AI's Montreal lab. A professor at McGill University, Joelle is renowned for her pioneering work in reinforcement learning, robotics, and responsible AI development.
AGENDA:
00:00 Introduction to AI Scaling Laws
03:00 How Meta Shaped How I Think About AI Research
04:36 Challenges in Reinforcement Learning
10:00 Is It Possible to be Capital Efficient in AI
15:52 AI in Enterprise: Efficiency and Adoption
22:15 Security Concerns with AI Agents
28:34 Can Zuck Win By Buying the Galacticos of AI
32:15 The Rising Cost of Data
35:28 Synthetic Data and Model Degradation
37:22 Why AI Coding is Akin to Image Generation in 2015
48:46 If Joelle Was a VC Where Would She Invest?
52:17 Quickfire: Lessons from Zuck, Biggest Mindset Shift
In this edition of the Wide World of Cyber podcast Patrick Gray talks to Chris Krebs and Alex Stamos about the F5 incident. They talk about what happened, whether it’s a big deal, and why private equity ownership of mid-tier cybersecurity companies is often a red flag.
Show notes
Pivot is off for the holiday! Kara and Scott will return on Friday, but in the meantime, we're bringing you the premiere episode of ACCESS. Tech insiders Alex Heath and Ellis Hamburger talk all things Mark Zuckerberg, from the newest Meta Ray-Ban Display glasses to the beverage selections in the new Meta AI Lab. Alex then sits down with Zuck himself ahead of the 2025 Meta Connect conference.
Find ACCESS on YouTube or your favorite podcast app.
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A few weeks ago Marques got a chance to sit down with Instagram CEO Adam Mosseri and ask him about everything from how creators make money on the platform to how the company views AI creators. Enjoy!
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Ryan welcomes Dhruv Batra, co-founder and chief scientist at Yutori, to explore the future of AI agents, how AI usage is changing the way people interact with advertisements and the web as a whole, and the challenges that proactive AI agents may face when being integrated into workflows and personal internet use.
Episode notes:
Yutori is building AI agents that can reliably handle everyday digital tasks on your behalf on the web.
Connect with Dhruv via his website.
Congrats to the winner of today’s Populist badge, user Don Kirkby, who earned it with their answer to Find all references to an object in python.
TRANSCRIPT
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Scott and Wes sit down with Ricky Hanlon from the React core team at Facebook to dive into the latest features and APIs shaping modern React development. From transitions and Suspense to fetching strategies and future directions, this episode breaks down what’s next for React and how developers can take advantage of it.
Show Notes
00:00 Welcome to Syntax!
01:20 Who is Ricky Hanlon.
02:10 Setting the Stage: Modern React APIs
02:48 Brought to you by Sentry.io.
03:12 Defining Transitions in React
05:08 Practical Examples of Scheduling.
08:23 useDeferredValue.
09:30 Suspense.
11:13 Fallbacks and animations.
12:35 How do you get psychological performance data?
13:39 Are these considerations reasonable for the average dev?
15:37 useOptimistic.
17:35 Removing delayMs (referred to as maxDuration in later iterations).
19:49 How to fetch data in React.
21:58 Is React now just Nextjs?
23:23 Will React give us a Signals-based state management?
24:44 The challenges of building in public.
30:12 Making LLMs cooperate with React.
32:05 The lifting will happen at framework level.
32:59 This is not time slicing.
35:47 Sick Pick + Shameless Plug.
Sick Picks
Ricky: iPhone 17 Pro
Shameless Plugs
Ricky: https://conf.react.dev/
Hit us up on Socials!
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Ads pay for the internet—and they’re about to change again. a16z General Partner Erik Torenberg, entrepreneur and author Antonio García Martínez, and Meta CMO Alex Schultz dive into growth and performance marketing, privacy myths, retail media, and the AI future of “audience-of-one” advertising—plus Instagram what-ifs, WhatsApp as a super-app, and how Meta’s feed shifted from social graphs to AI-ranked content.
Timecodes:
0:00 Introduction
0:38 Book Inspiration & Positive Perspective on Advertising
4:43 Critiques of Online Advertising & Data Privacy
7:34 The Evolution of Media Business Models
10:12 Content Moderation and Platform Shifts
11:43 Connected vs. Unconnected Content & The Rise of AI
13:34 The Future of AI, Personalization, and Advertising
28:18 Retail Media Networks & First-Party Data
38:18 Alternative Histories: Instagram, Libra, and Industry What-Ifs
46:31 The Impact of AI on Jobs and Company Structure
55:00 The Metaverse, VR, and Future Platforms
Resources:
Make sure to get Alex’s book here!
Follow Alex on X: https://x.com/alexschultz
Follow Alex on Facebook: https://www.facebook.com/alexschultz
Follow Alex on Threads: https://www.threads.com/@alexorig
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Stay Updated:
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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.
Stay Updated:
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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.
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We know that the top-tier AI labs are spending unbelievable amounts of money on talent. But what are these researchers actually working on? And how do we know that they're making progress? And furthermore, how can we even measure that progress? On this episode, we speak with Jack Morris, an AI researcher and Ph.D. candidate at Cornell University, who is also a part-time researcher at Meta. We talk about what he does, and why breakthroughs seem to be lumpy and unpredictable. We also talk about the battle between open- and closed-source approaches, US vs. Chinese labs, and how an individual talent thinks about where they want to spend their time, balancing the desire for research and prestige with a big fat paycheck.
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Alex Schultz is the Chief Marketing Officer and VP of Analytics at Meta, where he has spent nearly two decades shaping the company's growth and marketing strategy. He has been instrumental in scaling Facebook, Instagram, and WhatsApp to billions of users worldwide. Alex is is also the author of Click Here: The New Rules of Marketing, the definitive guide to modern growth — available now on Amazon.
AGENDA:
00:00 – Is All Marketing Actually Performance Marketing?
04:00 – When Did Facebook Have the Wrong North Star? What Did They Learn?
16:00 – Will AI Create Companies Run by Just ONE Person?
27:00 – Is AI About to Hit the Biggest Plateau Since Self-Driving Cars?
30:00 – Is China Secretly Winning the Global AI Arms Race?
38:00 – Does AI Kill Content or Supercharge It?
44:00 – Why Brand Marketing Is Harder (and More Important) Than You Think
47:00 – Will Glasses Replace Phones Forever?
51:00 – What Would Alex Do If He Were Sundar at Google Today?
59:00 – What is the Greatest Strength and the Greatest Weakness of Zuck?
The Wide World of Cyber podcast is back! In this episode host Patrick Gray chats with Alex Stamos and Chris Krebs about Microsoft’s entanglement in China.
Redmond has been using Chinese engineers to do everything from remotely support US DoD private cloud systems to maintain the on premise version of the SharePoint code base. It’s all blown up in the press over the last month, but how did we get here? Did Microsoft make these decisions to save money? Or was it more about getting access to the Chinese market? And how can we all make the world’s most important software company stop doing things like this? Tune in to the Wide World of Cyber podcast to find out!
This episode is also available on Youtube.
Show notes
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