Podcasts about API

Sun, 22 Feb 2026 16:00:00 GMT http://relay.fm/mpu/837 http://relay.fm/mpu/837 Menu Bar Mayhem 837 David Sparks and Stephen Robles David and Stephen go deep on the Mac menu bar, comparing their contrasting philosophies and walk through their favorites. They also explore how macOS 26's multiple Control Centers are changing the game. David and Stephen go deep on the Mac menu bar, comparing their contrasting philosophies and walk through their favorites. They also explore how macOS 26's multiple Control Centers are changing the game. clean 5733 David and Stephen go deep on the Mac menu bar, comparing their contrasting philosophies and walk through their favorites. They also explore how macOS 26's multiple Control Centers are changing the game. This episode of Mac Power Users is sponsored by: Insta360: Introducing the Insta360 Wave and the Link 2 Pro. HTTPBot: A powerful API client and debugger for Apple platforms. Get a 7-day trial and 25% off your subscription. Ecamm: Powerful live streaming platform for Mac. 1Password: Never forget a password again. Links and Show Notes: Credits The Mac Power Users Stephen Robles David Sparks The Editor Jim Metzendorf The Fixer Kerry Provanzano More Power Users: Ad-free episodes with regular bonus segments Submit Feedback David's Menu Bar, Condensed David's Full Menu Bar Stephen's Menu Bar Ice Menu Bar Manager ‎Hidden Bar App - App Store ‎Barbee - App Store BuhoBarX MacMenuBar.com iStat Menus Loom CleanShot X for Mac Screen Studio Dropzone 4 DEVONtechnologies Supercharge — Sindre Sorhus ‎DiskView App - App Store Audio Hijack Setapp Hazel for Mac PopClip for Mac BetterTouchTool CleanMyMac Moom · Many Tricks Karabiner-Elements Carbon Copy Cloner WhisperType Cotypist Wispr Flow Tailscale ‎Pastebot App - App Store ‎Shortery App - App Store ‎Itsyhome App - App Store ‎HomeControl Menu for HomeKit App - App Store Shawn Blanc Backblaze MacWhisper Grammarly Timing Flexibits | Fantastical ‎Screens 5: VNC Remote Desktop App - App Store Drafts | Where Text Starts Day One Journal App Keyboard Maestro TextExpander Alfred Menuwhere Bitfocus - Companion Parcel - Delivery Tracking ‎Creator's Best Friend App - App Store

OpenClaw is a self-hosted AI agent daemon that executes autonomous tasks through messaging apps like WhatsApp and Telegram using persistent memory. It integrates with Claude Code to enable software development and administrative automation directly from mobile devices. Links Notes and resources at ocdevel.com/mlg/mla-29 Try a walking desk - stay healthy & sharp while you learn & code Generate a podcast - use my voice to listen to any AI generated content you want OpenClaw is a self-hosted AI agent daemon (Node.js, port 18789) that executes autonomous tasks via messaging apps like WhatsApp or Telegram. Developed by Peter Steinberger in November 2025, the project reached 196,000 GitHub stars in three months. Architecture and Persistent Memory Operational Loop: Gateway receives message, loads SOUL.md (personality), USER.md (user context), and MEMORY.md (persistent history), calls LLM for tool execution, streams response, and logs data. Memory System: Compounds context over months. Users should prompt the agent to remember specific preferences to update MEMORY.md. Heartbeats: Proactive cron-style triggers for automated actions, such as 6:30 AM briefings or inbox triage. Skills: 5,705+ community plugins via ClawHub. The agent can author its own skills by reading API documentation and writing TypeScript scripts. Claude Code Integration Mobile to Deploy Workflow: The claude-code-skill bridge provides OpenClaw access to Bash, Read, Edit, and Git tools via Telegram. Agent Teams: claude-team manages multiple workers in isolated git worktrees to perform parallel refactors or issue resolution. Interoperability: Use mcporter to share MCP servers between Claude Code and OpenClaw. Industry Comparisons vs n8n: Use n8n for deterministic, zero-variance pipelines. Use OpenClaw for reasoning and ambiguous natural language tasks. vs Claude Cowork: Cowork is a sandboxed, desktop-only proprietary app. OpenClaw is an open-source, mobile-first, 24/7 daemon with full system access. Professional Applications Therapy: Voice to SOAP note transcription. PHI requires local Ollama models due to a lack of encryption at rest in OpenClaw. Marketing: claw-ads for multi-platform ad management, Mixpost for scheduling, and SearXNG for search. Finance: Receipt OCR and Google Drive filing. Requires human review to mitigate non-deterministic LLM errors. Real Estate: Proactive transaction deadline monitoring and memory-driven buyer matching. Security and Operations Hardening: Bind to localhost, set auth tokens, and use Tailscale for remote access. Default settings are unsafe, exposing over 135,000 instances. Injection Defense: Add instructions to SOUL.md to treat external emails and web pages as hostile. Costs: Software is MIT-licensed. API costs are paid per-token or bundled via a Claude subscription key. Onboarding: Run the BOOTSTRAP.md flow immediately after installation to define agent personality before requesting tasks.

Sun, 22 Feb 2026 16:00:00 GMT http://relay.fm/mpu/837 http://relay.fm/mpu/837 David Sparks and Stephen Robles David and Stephen go deep on the Mac menu bar, comparing their contrasting philosophies and walk through their favorites. They also explore how macOS 26's multiple Control Centers are changing the game. David and Stephen go deep on the Mac menu bar, comparing their contrasting philosophies and walk through their favorites. They also explore how macOS 26's multiple Control Centers are changing the game. clean 5733 David and Stephen go deep on the Mac menu bar, comparing their contrasting philosophies and walk through their favorites. They also explore how macOS 26's multiple Control Centers are changing the game. This episode of Mac Power Users is sponsored by: Insta360: Introducing the Insta360 Wave and the Link 2 Pro. HTTPBot: A powerful API client and debugger for Apple platforms. Get a 7-day trial and 25% off your subscription. Ecamm: Powerful live streaming platform for Mac. 1Password: Never forget a password again. Links and Show Notes: Credits The Mac Power Users Stephen Robles David Sparks The Editor Jim Metzendorf The Fixer Kerry Provanzano More Power Users: Ad-free episodes with regular bonus segments Submit Feedback David's Menu Bar, Condensed David's Full Menu Bar Stephen's Menu Bar Ice Menu Bar Manager ‎Hidden Bar App - App Store ‎Barbee - App Store BuhoBarX MacMenuBar.com iStat Menus Loom CleanShot X for Mac Screen Studio Dropzone 4 DEVONtechnologies Supercharge — Sindre Sorhus ‎DiskView App - App Store Audio Hijack Setapp Hazel for Mac PopClip for Mac BetterTouchTool CleanMyMac Moom · Many Tricks Karabiner-Elements Carbon Copy Cloner WhisperType Cotypist Wispr Flow Tailscale ‎Pastebot App - App Store ‎Shortery App - App Store ‎Itsyhome App - App Store ‎HomeControl Menu for HomeKit App - App Store Shawn Blanc Backblaze MacWhisper Grammarly Timing Flexibits | Fantastical ‎Screens 5: VNC Remote Desktop App - App Store Drafts | Where Text Starts Day One Journal App Keyboard Maestro TextExpander Alfred Menuwhere Bitfocus - Companion Parcel - Delivery Tracking ‎Creator's Best Friend App - App Store

Michael Truell, CEO of Cursor, sits down with Patrick Collison, CEO of Stripe and an investor in Anysphere, to talk about Collison's history with Smalltalk and Lisp, the MongoDB and Ruby decisions Stripe still lives with 15 years later, why he'd spend even more time on API design if he could do it over, and whether AI is actually showing up in economic productivity data. This episode originally aired on Cursor's podcast.   Resources:  Follow Patrick Collison on X:   https://twitter.com/patrickc Follow Michael Truell on X: https://twitter.com/mntruell Follow Cursor: https://www.youtube.com/@cursor_ai Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow 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.

Box CEO Aaron Levie joins for our weekly discussion of the latest tech news. We cover: 1) OpenAI's anticipated $100 billion fundraise 2) Does OpenAI's big forthcoming raise settle questions about its competitiveness 3) What's going on with OpenAI and NVIDIA? 4) Hype or True: Big Proclamations from the India AI Impact Summit 5) Why can't Sam And Dario hold hands? 6) Anthropic's powerful new model 7) OpenAI acquires OpenClaw 8) What the acquisition portends 9) If software is an API, what is software? 10) Wait, is AI not increasing productivity? --- 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 EXCLUSIVE NordVPN Deal ➼ https://nordvpn.com/bigtech  Try it risk-free now with a 30-day money-back guarantee! Take back your personal data with Incogni! Go to incogni.com/bigtechpod and Use code bigtechpod at checkout, our code will get you 60% off on annual plans. Go check it out! Learn more about your ad choices. Visit megaphone.fm/adchoices

This week on Defender Fridays, Farshad Abasi, Founder and CEO of Forward Security and Eureka DevSecOps, discusses how AI can help us set a new standard in app and cloud security. Farshad brings over 27 years of industry experience to the forefront of cybersecurity innovation. His professional journey includes key technical roles at Intel and Motorola, evolving into senior security positions as the Principal Security Architect for HSBC Global, and Head of IT Security for the Canadian division. Farshad's commitment to the field extends to his role as an instructor at BCIT, where he imparts his wealth of knowledge to the next generation of cybersecurity experts. His diverse experience, which spans startups to large enterprises, informs his approach to delivering adaptive and reliable solutions.Engaged actively in the cybersecurity community through roles in BSides Vancouver/MARS, OWASP Vancouver/AppSec PNW, and as a CISSP designate, Farshad's vision and leadership continue to drive the industry forward. Under his guidance, Forward Security is setting new standards in application and cloud security. Learn more at https://www.eurekadevsecops.com/ and https://forwardsecurity.com/Register for Live SessionsJoin us every Friday at 10:30am PT for live, interactive discussions with industry experts. Whether you're a seasoned professional or just curious about the field, these sessions offer an engaging dialogue between our guests, hosts, and you – our audience.Register here: https://limacharlie.io/defender-fridaysSubscribe to our YouTube channel and hit the notification bell to never miss a live session or catch up on past episodes!Sponsored by LimaCharlieThis episode is brought to you by LimaCharlie, a cloud-native SecOps platform where AI agents operate security infrastructure directly. Founded in 2018, LimaCharlie provides complete API coverage across detection, response, automation, and telemetry, with multi-tenant architecture designed for MSSPs and MDR providers managing thousands of unique client environments.Why LimaCharlie?Transparency: Complete visibility into every action and decision. No black boxes, no vendor lock-in.Scalability: Security operations that scale like infrastructure, not like procurement cycles. Move at cloud speed.Unopinionated Design: Integrate the tools you need, not just those contracts allow. Build security on your terms.Agentic SecOps Workspace (ASW): AI agents that operate alongside your team with observable, auditable actions through the same APIs human analysts use.Security Primitives: Composable building blocks that endure as tools come and go. Build once, evolve continuously.Try the Agentic SecOps Workspace free: https://limacharlie.ioLearn more: https://docs.limacharlie.ioFollow LimaCharlieSign up for free: https://limacharlie.ioLinkedIn: / limacharlieio X: https://x.com/limacharlieioCommunity Discourse: https://community.limacharlie.com/Host: Maxime Lamothe-Brassard - CEO / Co-founder at LimaCharlie

Future-Proofing Leadership: Masterminds and the AI Revolution with Brad HartIn this episode of The Thoughtful Entrepreneur Podcast, host Josh Elledge sits down with Brad Hart, the Founder of Optimus, to discuss the critical intersection of high-level peer communities and the rapid advancement of artificial intelligence. Brad, a seasoned entrepreneur who has launched over 25 mastermind groups globally, shares how curated human connection serves as the ultimate safeguard against the isolation and disruption of the digital age. This conversation provides a strategic roadmap for small and medium-sized enterprise (SME) leaders looking to integrate AI into their operations while maintaining the deep relationships and creative judgment that technology simply cannot replicate.The Strategic Value of Curated Communities in a Tech-Driven WorldThe modern business landscape often leaves leaders isolated, navigating complex technological shifts like AI and automation without a trusted sounding board. Brad identifies "The Three R's"—Results, Relationships, and Recreation—as the essential pillars of a high-impact mastermind group. For a community to be truly transformative, it must drive tangible business outcomes through accountability, foster deep vulnerability among peers, and incorporate shared experiences that combat the pervasive loneliness of leadership. When these elements align, a mastermind becomes more than just a networking group; it evolves into an engine for innovation that helps members ask better questions and see blind spots they would otherwise miss.As AI becomes a prediction machine capable of processing vast amounts of data, the role of the human leader is shifting toward wisdom, taste, and discretion. Brad emphasizes that while AI can accelerate the work of a skilled individual, it cannot replace the nuanced judgment or emotional intelligence found in a curated peer group. SME leaders who fail to implement AI by 2030 will likely struggle to remain competitive, but those who succeed will be the ones who treat AI as an accelerator rather than a replacement. By automating routine tasks, leaders can free up their capacity for the high-level strategic thinking and relationship-building that provide a permanent edge in any market.To bridge the gap between current operations and an AI-driven future, Brad developed Optimus—a new model of mastermind that combines high-level peer support with cutting-edge technical integration. Unlike traditional coaching programs, this model leverages an integrated platform that connects to a business's tech stack via API, allowing leaders to interact with their data using natural language. This "done-with-you" approach ensures that entrepreneurs aren't just learning about AI in theory, but are actively implementing workflows that increase efficiency and resilience. Ultimately, the goal is to build a business that is technologically advanced yet remains deeply rooted in authentic human connection.About Brad HartBrad Hart is the Founder of Optimus and a recognized expert in building and scaling mastermind groups. With a background that includes launching a hedge fund and early ventures in cryptocurrency, Brad has dedicated his career to helping entrepreneurs unlock their potential through the power of curated communities and strategic automation.About OptimusOptimus is a specialized mastermind group and technology platform designed to help small and medium-sized enterprises prepare for the future of AI. By providing both a high-level peer network and an API-driven automation platform, Optimus helps business leaders streamline their operations and future-proof their companies.Links Mentioned in This Episode:Optimus Official Website

Your email gateway isn't enough anymore, attackers are already inside the workspace through OAuth apps, browser extensions, and account takeover.  In this episode, Ron sits down with Rajan Kapoor, VP of Security at Material Security, to break down the real risks hiding inside Google Workspace and Microsoft 365. They cover how phishing has evolved into full-blown business email compromise, why malicious OAuth apps are the new favorite attack vector, and what security teams, especially lean ones, can do right now to lock down their cloud workspace. Rajan also drops practical advice on passkeys, document sharing hygiene, and why data lifecycle management is a problem no one is solving well enough. Impactful Moments 00:00 – Introduction 03:30 – The current state of phishing 05:30 – Outbound email compromise risk 09:30 – OAuth apps as attack vectors 15:00 – AI agents accessing your workspace 16:00 – Prompt injection is the new SQL injection 18:00 – Allow listing apps immediately 24:30 – Google Workspace vs Microsoft 365 security 27:30 – Custom detections require API expertise 28:00 – Why passkeys matter right now 32:00 – Data lifecycle management for shared docs Links Connect with our guest, Rajan Kapoor, on LinkedIn: https://www.linkedin.com/in/rajankkapoor/ Learn more about Material Security: https://material.security  ___ Become a sponsor of the show to amplify your brand: https://hackervalley.com/work-with-us/ Check out our upcoming events: https://www.hackervalley.com/livestreams  Love Hacker Valley Studio? Pick up some swag: https://store.hackervalley.com   

Open banking in the United States has been on a long and winding road, and the journey is far from over. In this episode, I sit down with Steve Boms, Executive Director of FDATA North America, the trade association representing the fintech companies at the heart of the open banking ecosystem. Steve has been one of the most active voices in shaping U.S. open banking policy for over a decade, and he brings a uniquely informed perspective to where things stand today.We dig into the current state of the 1033 rule and what amendments are likely coming, FDATA's firm stance that banks should not be permitted to charge fees for consumer-directed data access, and the growing complexity created by a patchwork of state-level regulations on data privacy, AI, and fintech products. We close with a fascinating discussion on how agentic AI, with its need for clear consent frameworks, robust APIs, and defined liability rules, could become the next major catalyst that finally forces meaningful open banking progress in this country.In this podcast you will learn:The origin story of FDATA in the UK and how it came to the US.How Steve has been involved with CFPB and Section 1033 since 2015.Over the next 10+ years, how FDATA has been engaged in open banking policy.How open banking and open finance has evolved in the UK.Who their members are and what FDATA does for them.Where we are at today when it comes to the 1033 rule.The FDATA view on banks charging fees for access to their data.Why this is not really a bank versus fintech fight.Why it may be many years before we have a final rule for open banking.Why data access negotiations have been put on pause for now.What else Steve is working on beyond open banking.Why he is increasing concerned about the Balkanization of financial services regulation (see his recent Open Banker column).How they coordinate with the other fintech trade associations.How they think about the standardization of API and other data standards.Why Steve is optimistic about the future of open banking in the U.S.Why AI agents could be a catalyzing force for clear open banking rules.Connect with Fintech One-on-One: Tweet me @PeterRenton Connect with me on LinkedIn Find previous Fintech One-on-One episodes

Tickets for AIEi Miami and AIE Europe are live, with first wave speakers announced!From pioneering software-defined networking to backing many of the most aggressive AI model companies of this cycle, Martin Casado and Sarah Wang sit at the center of the capital, compute, and talent arms race reshaping the tech industry. As partners at a16z investing across infrastructure and growth, they've watched venture and growth blur, model labs turn dollars into capability at unprecedented speed, and startups raise nine-figure rounds before monetization.Martin and Sarah join us to unpack the new financing playbook for AI: why today's rounds are really compute contracts in disguise, how the “raise → train → ship → raise bigger” flywheel works, and whether foundation model companies can outspend the entire app ecosystem built on top of them. They also share what's underhyped (boring enterprise software), what's overheated (talent wars and compensation spirals), and the two radically different futures they see for AI's market structure.We discuss:* Martin's “two futures” fork: infinite fragmentation and new software categories vs. a small oligopoly of general models that consume everything above them* The capital flywheel: how model labs translate funding directly into capability gains, then into revenue growth measured in weeks, not years* Why venture and growth have merged: $100M–$1B hybrid rounds, strategic investors, compute negotiations, and complex deal structures* The AGI vs. product tension: allocating scarce GPUs between long-term research and near-term revenue flywheels* Whether frontier labs can out-raise and outspend the entire app ecosystem built on top of their APIs* Why today's talent wars ($10M+ comp packages, $B acqui-hires) are breaking early-stage founder math* Cursor as a case study: building up from the app layer while training down into your own models* Why “boring” enterprise software may be the most underinvested opportunity in the AI mania* Hardware and robotics: why the ChatGPT moment hasn't yet arrived for robots and what would need to change* World Labs and generative 3D: bringing the marginal cost of 3D scene creation down by orders of magnitude* Why public AI discourse is often wildly disconnected from boardroom reality and how founders should navigate the noiseShow Notes:* “Where Value Will Accrue in AI: Martin Casado & Sarah Wang” - a16z show* “Jack Altman & Martin Casado on the Future of Venture Capital”* World Labs—Martin Casado• LinkedIn: https://www.linkedin.com/in/martincasado/• X: https://x.com/martin_casadoSarah Wang• LinkedIn: https://www.linkedin.com/in/sarah-wang-59b96a7• X: https://x.com/sarahdingwanga16z• https://a16z.com/Timestamps00:00:00 – Intro: Live from a16z00:01:20 – The New AI Funding Model: Venture + Growth Collide00:03:19 – Circular Funding, Demand & “No Dark GPUs”00:05:24 – Infrastructure vs Apps: The Lines Blur00:06:24 – The Capital Flywheel: Raise → Train → Ship → Raise Bigger00:09:39 – Can Frontier Labs Outspend the Entire App Ecosystem?00:11:24 – Character AI & The AGI vs Product Dilemma00:14:39 – Talent Wars, $10M Engineers & Founder Anxiety00:17:33 – What's Underinvested? The Case for “Boring” Software00:19:29 – Robotics, Hardware & Why It's Hard to Win00:22:42 – Custom ASICs & The $1B Training Run Economics00:24:23 – American Dynamism, Geography & AI Power Centers00:26:48 – How AI Is Changing the Investor Workflow (Claude Cowork)00:29:12 – Two Futures of AI: Infinite Expansion or Oligopoly?00:32:48 – If You Can Raise More Than Your Ecosystem, You Win00:34:27 – Are All Tasks AGI-Complete? Coding as the Test Case00:38:55 – Cursor & The Power of the App Layer00:44:05 – World Labs, Spatial Intelligence & 3D Foundation Models00:47:20 – Thinking Machines, Founder Drama & Media Narratives00:52:30 – Where Long-Term Power Accrues in the AI StackTranscriptLatent.Space - Inside AI's $10B+ Capital Flywheel — Martin Casado & Sarah Wang of a16z[00:00:00] Welcome to Latent Space (Live from a16z) + Meet the Guests[00:00:00] Alessio: Hey everyone. Welcome to the Latent Space podcast, live from a 16 z. Uh, this is Alessio founder Kernel Lance, and I'm joined by Twix, editor of Latent Space.[00:00:08] swyx: Hey, hey, hey. Uh, and we're so glad to be on with you guys. Also a top AI podcast, uh, Martin Cado and Sarah Wang. Welcome, very[00:00:16] Martin Casado: happy to be here and welcome.[00:00:17] swyx: Yes, uh, we love this office. We love what you've done with the place. Uh, the new logo is everywhere now. It's, it's still getting, takes a while to get used to, but it reminds me of like sort of a callback to a more ambitious age, which I think is kind of[00:00:31] Martin Casado: definitely makes a statement.[00:00:33] swyx: Yeah.[00:00:34] Martin Casado: Not quite sure what that statement is, but it makes a statement.[00:00:37] swyx: Uh, Martin, I go back with you to Netlify.[00:00:40] Martin Casado: Yep.[00:00:40] swyx: Uh, and, uh, you know, you create a software defined networking and all, all that stuff people can read up on your background. Yep. Sarah, I'm newer to you. Uh, you, you sort of started working together on AI infrastructure stuff.[00:00:51] Sarah Wang: That's right. Yeah. Seven, seven years ago now.[00:00:53] Martin Casado: Best growth investor in the entire industry.[00:00:55] swyx: Oh, say[00:00:56] Martin Casado: more hands down there is, there is. [00:01:00] I mean, when it comes to AI companies, Sarah, I think has done the most kind of aggressive, um, investment thesis around AI models, right? So, worked for Nom Ja, Mira Ia, FEI Fey, and so just these frontier, kind of like large AI models.[00:01:15] I think, you know, Sarah's been the, the broadest investor. Is that fair?[00:01:20] Venture vs. Growth in the Frontier Model Era[00:01:20] Sarah Wang: No, I, well, I was gonna say, I think it's been a really interesting tag, tag team actually just ‘cause the, a lot of these big C deals, not only are they raising a lot of money, um, it's still a tech founder bet, which obviously is inherently early stage.[00:01:33] But the resources,[00:01:36] Martin Casado: so many, I[00:01:36] Sarah Wang: was gonna say the resources one, they just grow really quickly. But then two, the resources that they need day one are kind of growth scale. So I, the hybrid tag team that we have is. Quite effective, I think,[00:01:46] Martin Casado: what is growth these days? You know, you don't wake up if it's less than a billion or like, it's, it's actually, it's actually very like, like no, it's a very interesting time in investing because like, you know, take like the character around, right?[00:01:59] These tend to [00:02:00] be like pre monetization, but the dollars are large enough that you need to have a larger fund and the analysis. You know, because you've got lots of users. ‘cause this stuff has such high demand requires, you know, more of a number sophistication. And so most of these deals, whether it's US or other firms on these large model companies, are like this hybrid between venture growth.[00:02:18] Sarah Wang: Yeah. Total. And I think, you know, stuff like BD for example, you wouldn't usually need BD when you were seed stage trying to get market biz Devrel. Biz Devrel, exactly. Okay. But like now, sorry, I'm,[00:02:27] swyx: I'm not familiar. What, what, what does biz Devrel mean for a venture fund? Because I know what biz Devrel means for a company.[00:02:31] Sarah Wang: Yeah.[00:02:32] Compute Deals, Strategics, and the ‘Circular Funding' Question[00:02:32] Sarah Wang: You know, so a, a good example is, I mean, we talk about buying compute, but there's a huge negotiation involved there in terms of, okay, do you get equity for the compute? What, what sort of partner are you looking at? Is there a go-to market arm to that? Um, and these are just things on this scale, hundreds of millions, you know, maybe.[00:02:50] Six months into the inception of a company, you just wouldn't have to negotiate these deals before.[00:02:54] Martin Casado: Yeah. These large rounds are very complex now. Like in the past, if you did a series A [00:03:00] or a series B, like whatever, you're writing a 20 to a $60 million check and you call it a day. Now you normally have financial investors and strategic investors, and then the strategic portion always still goes with like these kind of large compute contracts, which can take months to do.[00:03:13] And so it's, it's very different ties. I've been doing this for 10 years. It's the, I've never seen anything like this.[00:03:19] swyx: Yeah. Do you have worries about the circular funding from so disease strategics?[00:03:24] Martin Casado: I mean, listen, as long as the demand is there, like the demand is there. Like the problem with the internet is the demand wasn't there.[00:03:29] swyx: Exactly. All right. This, this is like the, the whole pyramid scheme bubble thing, where like, as long as you mark to market on like the notional value of like, these deals, fine, but like once it starts to chip away, it really Well[00:03:41] Martin Casado: no, like as, as, as, as long as there's demand. I mean, you know, this, this is like a lot of these sound bites have already become kind of cliches, but they're worth saying it.[00:03:47] Right? Like during the internet days, like we were. Um, raising money to put fiber in the ground that wasn't used. And that's a problem, right? Because now you actually have a supply overhang.[00:03:58] swyx: Mm-hmm.[00:03:59] Martin Casado: And even in the, [00:04:00] the time of the, the internet, like the supply and, and bandwidth overhang, even as massive as it was in, as massive as the crash was only lasted about four years.[00:04:09] But we don't have a supply overhang. Like there's no dark GPUs, right? I mean, and so, you know, circular or not, I mean, you know, if, if someone invests in a company that, um. You know, they'll actually use the GPUs. And on the other side of it is the, is the ask for customer. So I I, I think it's a different time.[00:04:25] Sarah Wang: I think the other piece, maybe just to add onto this, and I'm gonna quote Martine in front of him, but this is probably also a unique time in that. For the first time, you can actually trace dollars to outcomes. Yeah, right. Provided that scaling laws are, are holding, um, and capabilities are actually moving forward.[00:04:40] Because if you can put translate dollars into capabilities, uh, a capability improvement, there's demand there to martine's point. But if that somehow breaks, you know, obviously that's an important assumption in this whole thing to make it work. But you know, instead of investing dollars into sales and marketing, you're, you're investing into r and d to get to the capability, um, you know, increase.[00:04:59] And [00:05:00] that's sort of been the demand driver because. Once there's an unlock there, people are willing to pay for it.[00:05:05] Alessio: Yeah.[00:05:06] Blurring Lines: Models as Infra + Apps, and the New Fundraising Flywheel[00:05:06] Alessio: Is there any difference in how you built the portfolio now that some of your growth companies are, like the infrastructure of the early stage companies, like, you know, OpenAI is now the same size as some of the cloud providers were early on.[00:05:16] Like what does that look like? Like how much information can you feed off each other between the, the two?[00:05:24] Martin Casado: There's so many lines that are being crossed right now, or blurred. Right. So we already talked about venture and growth. Another one that's being blurred is between infrastructure and apps, right? So like what is a model company?[00:05:35] Mm-hmm. Like, it's clearly infrastructure, right? Because it's like, you know, it's doing kind of core r and d. It's a horizontal platform, but it's also an app because it's um, uh, touches the users directly. And then of course. You know, the, the, the growth of these is just so high. And so I actually think you're just starting to see a, a, a new financing strategy emerge and, you know, we've had to adapt as a result of that.[00:05:59] And [00:06:00] so there's been a lot of changes. Um, you're right that these companies become platform companies very quickly. You've got ecosystem build out. So none of this is necessarily new, but the timescales of which it's happened is pretty phenomenal. And the way we'd normally cut lines before is blurred a little bit, but.[00:06:16] But that, that, that said, I mean, a lot of it also just does feel like things that we've seen in the past, like cloud build out the internet build out as well.[00:06:24] Sarah Wang: Yeah. Um, yeah, I think it's interesting, uh, I don't know if you guys would agree with this, but it feels like the emerging strategy is, and this builds off of your other question, um.[00:06:33] You raise money for compute, you pour that or you, you pour the money into compute, you get some sort of breakthrough. You funnel the breakthrough into your vertically integrated application. That could be chat GBT, that could be cloud code, you know, whatever it is. You massively gain share and get users.[00:06:49] Maybe you're even subsidizing at that point. Um, depending on your strategy. You raise money at the peak momentum and then you repeat, rinse and repeat. Um, and so. And that wasn't [00:07:00] true even two years ago, I think. Mm-hmm. And so it's sort of to your, just tying it to fundraising strategy, right? There's a, and hiring strategy.[00:07:07] All of these are tied, I think the lines are blurring even more today where everyone is, and they, but of course these companies all have API businesses and so they're these, these frenemy lines that are getting blurred in that a lot of, I mean, they have billions of dollars of API revenue, right? And so there are customers there.[00:07:23] But they're competing on the app layer.[00:07:24] Martin Casado: Yeah. So this is a really, really important point. So I, I would say for sure, venture and growth, that line is blurry app and infrastructure. That line is blurry. Um, but I don't think that that changes our practice so much. But like where the very open questions are like, does this layer in the same way.[00:07:43] Compute traditionally has like during the cloud is like, you know, like whatever, somebody wins one layer, but then another whole set of companies wins another layer. But that might not, might not be the case here. It may be the case that you actually can't verticalize on the token string. Like you can't build an app like it, it necessarily goes down just because there are no [00:08:00] abstractions.[00:08:00] So those are kinda the bigger existential questions we ask. Another thing that is very different this time than in the history of computer sciences is. In the past, if you raised money, then you basically had to wait for engineering to catch up. Which famously doesn't scale like the mythical mammoth. It take a very long time.[00:08:18] But like that's not the case here. Like a model company can raise money and drop a model in a, in a year, and it's better, right? And, and it does it with a team of 20 people or 10 people. So this type of like money entering a company and then producing something that has demand and growth right away and using that to raise more money is a very different capital flywheel than we've ever seen before.[00:08:39] And I think everybody's trying to understand what the consequences are. So I think it's less about like. Big companies and growth and this, and more about these more systemic questions that we actually don't have answers to.[00:08:49] Alessio: Yeah, like at Kernel Labs, one of our ideas is like if you had unlimited money to spend productively to turn tokens into products, like the whole early stage [00:09:00] market is very different because today you're investing X amount of capital to win a deal because of price structure and whatnot, and you're kind of pot committing.[00:09:07] Yeah. To a certain strategy for a certain amount of time. Yeah. But if you could like iteratively spin out companies and products and just throw, I, I wanna spend a million dollar of inference today and get a product out tomorrow.[00:09:18] swyx: Yeah.[00:09:19] Alessio: Like, we should get to the point where like the friction of like token to product is so low that you can do this and then you can change the Right, the early stage venture model to be much more iterative.[00:09:30] And then every round is like either 100 k of inference or like a hundred million from a 16 Z. There's no, there's no like $8 million C round anymore. Right.[00:09:38] When Frontier Labs Outspend the Entire App Ecosystem[00:09:38] Martin Casado: But, but, but, but there's a, there's a, the, an industry structural question that we don't know the answer to, which involves the frontier models, which is, let's take.[00:09:48] Anthropic it. Let's say Anthropic has a state-of-the-art model that has some large percentage of market share. And let's say that, uh, uh, uh, you know, uh, a company's building smaller models [00:10:00] that, you know, use the bigger model in the background, open 4.5, but they add value on top of that. Now, if Anthropic can raise three times more.[00:10:10] Every subsequent round, they probably can raise more money than the entire app ecosystem that's built on top of it. And if that's the case, they can expand beyond everything built on top of it. It's like imagine like a star that's just kind of expanding, so there could be a systemic. There could be a, a systemic situation where the soda models can raise so much money that they can out pay anybody that bills on top of ‘em, which would be something I don't think we've ever seen before just because we were so bottlenecked in engineering, and this is a very open question.[00:10:41] swyx: Yeah. It's, it is almost like bitter lesson applied to the startup industry.[00:10:45] Martin Casado: Yeah, a hundred percent. It literally becomes an issue of like raise capital, turn that directly into growth. Use that to raise three times more. Exactly. And if you can keep doing that, you literally can outspend any company that's built the, not any company.[00:10:57] You can outspend the aggregate of companies on top of [00:11:00] you and therefore you'll necessarily take their share, which is crazy.[00:11:02] swyx: Would you say that kind of happens in character? Is that the, the sort of postmortem on. What happened?[00:11:10] Sarah Wang: Um,[00:11:10] Martin Casado: no.[00:11:12] Sarah Wang: Yeah, because I think so,[00:11:13] swyx: I mean the actual postmortem is, he wanted to go back to Google.[00:11:15] Exactly. But like[00:11:18] Martin Casado: that's another difference that[00:11:19] Sarah Wang: you said[00:11:21] Martin Casado: it. We should talk, we should actually talk about that.[00:11:22] swyx: Yeah,[00:11:22] Sarah Wang: that's[00:11:23] swyx: Go for it. Take it. Take,[00:11:23] Sarah Wang: yeah.[00:11:24] Character.AI, Founder Goals (AGI vs Product), and GPU Allocation Tradeoffs[00:11:24] Sarah Wang: I was gonna say, I think, um. The, the, the character thing raises actually a different issue, which actually the Frontier Labs will face as well. So we'll see how they handle it.[00:11:34] But, um, so we invest in character in January, 2023, which feels like eons ago, I mean, three years ago. Feels like lifetimes ago. But, um, and then they, uh, did the IP licensing deal with Google in August, 2020. Uh, four. And so, um, you know, at the time, no, you know, he's talked publicly about this, right? He wanted to Google wouldn't let him put out products in the world.[00:11:56] That's obviously changed drastically. But, um, he went to go do [00:12:00] that. Um, but he had a product attached. The goal was, I mean, it's Nome Shair, he wanted to get to a GI. That was always his personal goal. But, you know, I think through collecting data, right, and this sort of very human use case, that the character product.[00:12:13] Originally was and still is, um, was one of the vehicles to do that. Um, I think the real reason that, you know. I if you think about the, the stress that any company feels before, um, you ultimately going one way or the other is sort of this a GI versus product. Um, and I think a lot of the big, I think, you know, opening eyes, feeling that, um, anthropic if they haven't started, you know, felt it, certainly given the success of their products, they may start to feel that soon.[00:12:39] And the real. I think there's real trade-offs, right? It's like how many, when you think about GPUs, that's a limited resource. Where do you allocate the GPUs? Is it toward the product? Is it toward new re research? Right? Is it, or long-term research, is it toward, um, n you know, near to midterm research? And so, um, in a case where you're resource constrained, um, [00:13:00] of course there's this fundraising game you can play, right?[00:13:01] But the fund, the market was very different back in 2023 too. Um. I think the best researchers in the world have this dilemma of, okay, I wanna go all in on a GI, but it's the product usage revenue flywheel that keeps the revenue in the house to power all the GPUs to get to a GI. And so it does make, um, you know, I think it sets up an interesting dilemma for any startup that has trouble raising up until that level, right?[00:13:27] And certainly if you don't have that progress, you can't continue this fly, you know, fundraising flywheel.[00:13:32] Martin Casado: I would say that because, ‘cause we're keeping track of all of the things that are different, right? Like, you know, venture growth and uh, app infra and one of the ones is definitely the personalities of the founders.[00:13:45] It's just very different this time I've been. Been doing this for a decade and I've been doing startups for 20 years. And so, um, I mean a lot of people start this to do a GI and we've never had like a unified North star that I recall in the same [00:14:00] way. Like people built companies to start companies in the past.[00:14:02] Like that was what it was. Like I would create an internet company, I would create infrastructure company, like it's kind of more engineering builders and this is kind of a different. You know, mentality. And some companies have harnessed that incredibly well because their direction is so obviously on the path to what somebody would consider a GI, but others have not.[00:14:20] And so like there is always this tension with personnel. And so I think we're seeing more kind of founder movement.[00:14:27] Sarah Wang: Yeah.[00:14:27] Martin Casado: You know, as a fraction of founders than we've ever seen. I mean, maybe since like, I don't know the time of like Shockly and the trade DUR aid or something like that. Way back in the beginning of the industry, I, it's a very, very.[00:14:38] Unusual time of personnel.[00:14:39] Sarah Wang: Totally.[00:14:40] Talent Wars, Mega-Comp, and the Rise of Acquihire M&A[00:14:40] Sarah Wang: And it, I think it's exacerbated by the fact that talent wars, I mean, every industry has talent wars, but not at this magnitude, right? No. Yeah. Very rarely can you see someone get poached for $5 billion. That's hard to compete with. And then secondly, if you're a founder in ai, you could fart and it would be on the front page of, you know, the information these days.[00:14:59] And so there's [00:15:00] sort of this fishbowl effect that I think adds to the deep anxiety that, that these AI founders are feeling.[00:15:06] Martin Casado: Hmm.[00:15:06] swyx: Uh, yes. I mean, just on, uh, briefly comment on the founder, uh, the sort of. Talent wars thing. I feel like 2025 was just like a blip. Like I, I don't know if we'll see that again.[00:15:17] ‘cause meta built the team. Like, I don't know if, I think, I think they're kind of done and like, who's gonna pay more than meta? I, I don't know.[00:15:23] Martin Casado: I, I agree. So it feels so, it feel, it feels this way to me too. It's like, it is like, basically Zuckerberg kind of came out swinging and then now he's kind of back to building.[00:15:30] Yeah,[00:15:31] swyx: yeah. You know, you gotta like pay up to like assemble team to rush the job, whatever. But then now, now you like you, you made your choices and now they got a ship.[00:15:38] Martin Casado: I mean, the, the o other side of that is like, you know, like we're, we're actually in the job hiring market. We've got 600 people here. I hire all the time.[00:15:44] I've got three open recs if anybody's interested, that's listening to this for investor. Yeah, on, on the team, like on the investing side of the team, like, and, um, a lot of the people we talk to have acting, you know, active, um, offers for 10 million a year or something like that. And like, you know, and we pay really, [00:16:00] really well.[00:16:00] And just to see what's out on the market is really, is really remarkable. And so I would just say it's actually, so you're right, like the really flashy one, like I will get someone for, you know, a billion dollars, but like the inflated, um, uh, trickles down. Yeah, it is still very active today. I mean,[00:16:18] Sarah Wang: yeah, you could be an L five and get an offer in the tens of millions.[00:16:22] Okay. Yeah. Easily. Yeah. It's so I think you're right that it felt like a blip. I hope you're right. Um, but I think it's been, the steady state is now, I think got pulled up. Yeah. Yeah. I'll pull up for[00:16:31] Martin Casado: sure. Yeah.[00:16:32] Alessio: Yeah. And I think that's breaking the early stage founder math too. I think before a lot of people would be like, well, maybe I should just go be a founder instead of like getting paid.[00:16:39] Yeah. 800 KA million at Google. But if I'm getting paid. Five, 6 million. That's different but[00:16:45] Martin Casado: on. But on the other hand, there's more strategic money than we've ever seen historically, right? Mm-hmm. And so, yep. The economics, the, the, the, the calculus on the economics is very different in a number of ways. And, uh, it's crazy.[00:16:58] It's cra it's causing like a, [00:17:00] a, a, a ton of change in confusion in the market. Some very positive, sub negative, like, so for example, the other side of the, um. The co-founder, like, um, acquisition, you know, mark Zuckerberg poaching someone for a lot of money is like, we were actually seeing historic amount of m and a for basically acquihires, right?[00:17:20] That you like, you know, really good outcomes from a venture perspective that are effective acquihires, right? So I would say it's probably net positive from the investment standpoint, even though it seems from the headlines to be very disruptive in a negative way.[00:17:33] Alessio: Yeah.[00:17:33] What's Underfunded: Boring Software, Robotics Skepticism, and Custom Silicon Economics[00:17:33] Alessio: Um, let's talk maybe about what's not being invested in, like maybe some interesting ideas that you would see more people build or it, it seems in a way, you know, as ycs getting more popular, it's like access getting more popular.[00:17:47] There's a startup school path that a lot of founders take and they know what's hot in the VC circles and they know what gets funded. Uh, and there's maybe not as much risk appetite for. Things outside of that. Um, I'm curious if you feel [00:18:00] like that's true and what are maybe, uh, some of the areas, uh, that you think are under discussed?[00:18:06] Martin Casado: I mean, I actually think that we've taken our eye off the ball in a lot of like, just traditional, you know, software companies. Um, so like, I mean. You know, I think right now there's almost a barbell, like you're like the hot thing on X, you're deep tech.[00:18:21] swyx: Mm-hmm.[00:18:22] Martin Casado: Right. But I, you know, I feel like there's just kind of a long, you know, list of like good.[00:18:28] Good companies that will be around for a long time in very large markets. Say you're building a database, you know, say you're building, um, you know, kind of monitoring or logging or tooling or whatever. There's some good companies out there right now, but like, they have a really hard time getting, um, the attention of investors.[00:18:43] And it's almost become a meme, right? Which is like, if you're not basically growing from zero to a hundred in a year, you're not interesting, which is just, is the silliest thing to say. I mean, think of yourself as like an introvert person, like, like your personal money, right? Mm-hmm. So. Your personal money, will you put it in the stock market at 7% or you put it in this company growing five x in a very large [00:19:00] market?[00:19:00] Of course you can put it in the company five x. So it's just like we say these stupid things, like if you're not going from zero to a hundred, but like those, like who knows what the margins of those are mean. Clearly these are good investments. True for anybody, right? True. Like our LPs want whatever.[00:19:12] Three x net over, you know, the life cycle of a fund, right? So a, a company in a big market growing five X is a great investment. We'd, everybody would be happy with these returns, but we've got this kind of mania on these, these strong growths. And so I would say that that's probably the most underinvested sector.[00:19:28] Right now.[00:19:29] swyx: Boring software, boring enterprise software.[00:19:31] Martin Casado: Traditional. Really good company.[00:19:33] swyx: No, no AI here.[00:19:34] Martin Casado: No. Like boring. Well, well, the AI of course is pulling them into use cases. Yeah, but that's not what they're, they're not on the token path, right? Yeah. Let's just say that like they're software, but they're not on the token path.[00:19:41] Like these are like they're great investments from any definition except for like random VC on Twitter saying VC on x, saying like, it's not growing fast enough. What do you[00:19:52] Sarah Wang: think? Yeah, maybe I'll answer a slightly different. Question, but adjacent to what you asked, um, which is maybe an area that we're not, uh, investing [00:20:00] right now that I think is a question and we're spending a lot of time in regardless of whether we pull the trigger or not.[00:20:05] Um, and it would probably be on the hardware side, actually. Robotics, right? And the robotics side. Robotics. Right. Which is, it's, I don't wanna say that it's not getting funding ‘cause it's clearly, uh, it's, it's sort of non-consensus to almost not invest in robotics at this point. But, um, we spent a lot of time in that space and I think for us, we just haven't seen the chat GPT moment.[00:20:22] Happen on the hardware side. Um, and the funding going into it feels like it's already. Taking that for granted.[00:20:30] Martin Casado: Yeah. Yeah. But we also went through the drone, you know, um, there's a zip line right, right out there. What's that? Oh yeah, there's a zip line. Yeah. What the drone, what the av And like one of the takeaways is when it comes to hardware, um, most companies will end up verticalizing.[00:20:46] Like if you're. If you're investing in a robot company for an A for agriculture, you're investing in an ag company. ‘cause that's the competition and that's surprising. And that's supply chain. And if you're doing it for mining, that's mining. And so the ad team does a lot of that type of stuff ‘cause they actually set up to [00:21:00] diligence that type of work.[00:21:01] But for like horizontal technology investing, there's very little when it comes to robots just because it's so fit for, for purpose. And so we kinda like to look at software. Solutions or horizontal solutions like applied intuition. Clearly from the AV wave deep map, clearly from the AV wave, I would say scale AI was actually a horizontal one for That's fair, you know, for robotics early on.[00:21:23] And so that sort of thing we're very, very interested. But the actual like robot interacting with the world is probably better for different team. Agree.[00:21:30] Alessio: Yeah, I'm curious who these teams are supposed to be that invest in them. I feel like everybody's like, yeah, robotics, it's important and like people should invest in it.[00:21:38] But then when you look at like the numbers, like the capital requirements early on versus like the moment of, okay, this is actually gonna work. Let's keep investing. That seems really hard to predict in a way that is not,[00:21:49] Martin Casado: I think co, CO two, kla, gc, I mean these are all invested in in Harvard companies. He just, you know, and [00:22:00] listen, I mean, it could work this time for sure.[00:22:01] Right? I mean if Elon's doing it, he's like, right. Just, just the fact that Elon's doing it means that there's gonna be a lot of capital and a lot of attempts for a long period of time. So that alone maybe suggests that we should just be investing in robotics just ‘cause you have this North star who's Elon with a humanoid and that's gonna like basically willing into being an industry.[00:22:17] Um, but we've just historically found like. We're a huge believer that this is gonna happen. We just don't feel like we're in a good position to diligence these things. ‘cause again, robotics companies tend to be vertical. You really have to understand the market they're being sold into. Like that's like that competitive equilibrium with a human being is what's important.[00:22:34] It's not like the core tech and like we're kind of more horizontal core tech type investors. And this is Sarah and I. Yeah, the ad team is different. They can actually do these types of things.[00:22:42] swyx: Uh, just to clarify, AD stands for[00:22:44] Martin Casado: American Dynamism.[00:22:45] swyx: Alright. Okay. Yeah, yeah, yeah. Uh, I actually, I do have a related question that, first of all, I wanna acknowledge also just on the, on the chip side.[00:22:51] Yeah. I, I recall a podcast that where you were on, i, I, I think it was the a CC podcast, uh, about two or three years ago where you, where you suddenly said [00:23:00] something, which really stuck in my head about how at some point, at some point kind of scale it makes sense to. Build a custom aic Yes. For per run.[00:23:07] Martin Casado: Yes.[00:23:07] It's crazy. Yeah.[00:23:09] swyx: We're here and I think you, you estimated 500 billion, uh, something.[00:23:12] Martin Casado: No, no, no. A billion, a billion dollar training run of $1 billion training run. It makes sense to actually do a custom meic if you can do it in time. The question now is timelines. Yeah, but not money because just, just, just rough math.[00:23:22] If it's a billion dollar training. Then the inference for that model has to be over a billion, otherwise it won't be solvent. So let's assume it's, if you could save 20%, which you could save much more than that with an ASIC 20%, that's $200 million. You can tape out a chip for $200 million. Right? So now you can literally like justify economically, not timeline wise.[00:23:41] That's a different issue. An ASIC per model, which[00:23:44] swyx: is because that, that's how much we leave on the table every single time. We, we, we do like generic Nvidia.[00:23:48] Martin Casado: Exactly. Exactly. No, it, it is actually much more than that. You could probably get, you know, a factor of two, which would be 500 million.[00:23:54] swyx: Typical MFU would be like 50.[00:23:55] Yeah, yeah. And that's good.[00:23:57] Martin Casado: Exactly. Yeah. Hundred[00:23:57] swyx: percent. Um, so, so, yeah, and I mean, and I [00:24:00] just wanna acknowledge like, here we are in, in, in 2025 and opening eyes confirming like Broadcom and all the other like custom silicon deals, which is incredible. I, I think that, uh, you know, speaking about ad there's, there's a really like interesting tie in that obviously you guys are hit on, which is like these sort, this sort of like America first movement or like sort of re industrialized here.[00:24:17] Yeah. Uh, move TSMC here, if that's possible. Um, how much overlap is there from ad[00:24:23] Martin Casado: Yeah.[00:24:23] swyx: To, I guess, growth and, uh, investing in particularly like, you know, US AI companies that are strongly bounded by their compute.[00:24:32] Martin Casado: Yeah. Yeah. So I mean, I, I would view, I would view AD as more as a market segmentation than like a mission, right?[00:24:37] So the market segmentation is, it has kind of regulatory compliance issues or government, you know, sale or it deals with like hardware. I mean, they're just set up to, to, to, to, to. To diligence those types of companies. So it's a more of a market segmentation thing. I would say the entire firm. You know, which has been since it is been intercepted, you know, has geographical biases, right?[00:24:58] I mean, for the longest time we're like, you [00:25:00] know, bay Area is gonna be like, great, where the majority of the dollars go. Yeah. And, and listen, there, there's actually a lot of compounding effects for having a geographic bias. Right. You know, everybody's in the same place. You've got an ecosystem, you're there, you've got presence, you've got a network.[00:25:12] Um, and, uh, I mean, I would say the Bay area's very much back. You know, like I, I remember during pre COVID, like it was like almost Crypto had kind of. Pulled startups away. Miami from the Bay Area. Miami, yeah. Yeah. New York was, you know, because it's so close to finance, came up like Los Angeles had a moment ‘cause it was so close to consumer, but now it's kind of come back here.[00:25:29] And so I would say, you know, we tend to be very Bay area focused historically, even though of course we've asked all over the world. And then I would say like, if you take the ring out, you know, one more, it's gonna be the US of course, because we know it very well. And then one more is gonna be getting us and its allies and Yeah.[00:25:44] And it goes from there.[00:25:45] Sarah Wang: Yeah,[00:25:45] Martin Casado: sorry.[00:25:46] Sarah Wang: No, no. I agree. I think from a, but I think from the intern that that's sort of like where the companies are headquartered. Maybe your questions on supply chain and customer base. Uh, I, I would say our customers are, are, our companies are fairly international from that perspective.[00:25:59] Like they're selling [00:26:00] globally, right? They have global supply chains in some cases.[00:26:03] Martin Casado: I would say also the stickiness is very different.[00:26:05] Sarah Wang: Yeah.[00:26:05] Martin Casado: Historically between venture and growth, like there's so much company building in venture, so much so like hiring the next PM. Introducing the customer, like all of that stuff.[00:26:15] Like of course we're just gonna be stronger where we have our network and we've been doing business for 20 years. I've been in the Bay Area for 25 years, so clearly I'm just more effective here than I would be somewhere else. Um, where I think, I think for some of the later stage rounds, the companies don't need that much help.[00:26:30] They're already kind of pretty mature historically, so like they can kind of be everywhere. So there's kind of less of that stickiness. This is different in the AI time. I mean, Sarah is now the, uh, chief of staff of like half the AI companies in, uh, in the Bay Area right now. She's like, ops Ninja Biz, Devrel, BizOps.[00:26:48] swyx: Are, are you, are you finding much AI automation in your work? Like what, what is your stack.[00:26:53] Sarah Wang: Oh my, in my personal stack.[00:26:54] swyx: I mean, because like, uh, by the way, it's the, the, the reason for this is it is triggering, uh, yeah. We, like, I'm hiring [00:27:00] ops, ops people. Um, a lot of ponders I know are also hiring ops people and I'm just, you know, it's opportunity Since you're, you're also like basically helping out with ops with a lot of companies.[00:27:09] What are people doing these days? Because it's still very manual as far as I can tell.[00:27:13] Sarah Wang: Hmm. Yeah. I think the things that we help with are pretty network based, um, in that. It's sort of like, Hey, how do do I shortcut this process? Well, let's connect you to the right person. So there's not quite an AI workflow for that.[00:27:26] I will say as a growth investor, Claude Cowork is pretty interesting. Yeah. Like for the first time, you can actually get one shot data analysis. Right. Which, you know, if you're gonna do a customer database, analyze a cohort retention, right? That's just stuff that you had to do by hand before. And our team, the other, it was like midnight and the three of us were playing with Claude Cowork.[00:27:47] We gave it a raw file. Boom. Perfectly accurate. We checked the numbers. It was amazing. That was my like, aha moment. That sounds so boring. But you know, that's, that's the kind of thing that a growth investor is like, [00:28:00] you know, slaving away on late at night. Um, done in a few seconds.[00:28:03] swyx: Yeah. You gotta wonder what the whole, like, philanthropic labs, which is like their new sort of products studio.[00:28:10] Yeah. What would that be worth as an independent, uh, startup? You know, like a[00:28:14] Martin Casado: lot.[00:28:14] Sarah Wang: Yeah, true.[00:28:16] swyx: Yeah. You[00:28:16] Martin Casado: gotta hand it to them. They've been executing incredibly well.[00:28:19] swyx: Yeah. I, I mean, to me, like, you know, philanthropic, like building on cloud code, I think, uh, it makes sense to me the, the real. Um, pedal to the metal, whatever the, the, the phrase is, is when they start coming after consumer with, uh, against OpenAI and like that is like red alert at Open ai.[00:28:35] Oh, I[00:28:35] Martin Casado: think they've been pretty clear. They're enterprise focused.[00:28:37] swyx: They have been, but like they've been free. Here's[00:28:40] Martin Casado: care publicly,[00:28:40] swyx: it's enterprise focused. It's coding. Right. Yeah.[00:28:43] AI Labs vs Startups: Disruption, Undercutting & the Innovator's Dilemma[00:28:43] swyx: And then, and, but here's cloud, cloud, cowork, and, and here's like, well, we, uh, they, apparently they're running Instagram ads for Claudia.[00:28:50] I, on, you know, for, for people on, I get them all the time. Right. And so, like,[00:28:54] Martin Casado: uh,[00:28:54] swyx: it, it's kind of like this, the disruption thing of, uh, you know. Mo Open has been doing, [00:29:00] consumer been doing the, just pursuing general intelligence in every mo modality, and here's a topic that only focus on this thing, but now they're sort of undercutting and doing the whole innovator's dilemma thing on like everything else.[00:29:11] Martin Casado: It's very[00:29:11] swyx: interesting.[00:29:12] Martin Casado: Yeah, I mean there's, there's a very open que so for me there's like, do you know that meme where there's like the guy in the path and there's like a path this way? There's a path this way. Like one which way Western man. Yeah. Yeah.[00:29:23] Two Futures for AI: Infinite Market vs AGI Oligopoly[00:29:23] Martin Casado: And for me, like, like all the entire industry kind of like hinges on like two potential futures.[00:29:29] So in, in one potential future, um, the market is infinitely large. There's perverse economies of scale. ‘cause as soon as you put a model out there, like it kind of sublimates and all the other models catch up and like, it's just like software's being rewritten and fractured all over the place and there's tons of upside and it just grows.[00:29:48] And then there's another path which is like, well. Maybe these models actually generalize really well, and all you have to do is train them with three times more money. That's all you have to [00:30:00] do, and it'll just consume everything beyond it. And if that's the case, like you end up with basically an oligopoly for everything, like, you know mm-hmm.[00:30:06] Because they're perfectly general and like, so this would be like the, the a GI path would be like, these are perfectly general. They can do everything. And this one is like, this is actually normal software. The universe is complicated. You've got, and nobody knows the answer.[00:30:18] The Economics Reality Check: Gross Margins, Training Costs & Borrowing Against the Future[00:30:18] Martin Casado: My belief is if you actually look at the numbers of these companies, so generally if you look at the numbers of these companies, if you look at like the amount they're making and how much they, they spent training the last model, they're gross margin positive.[00:30:30] You're like, oh, that's really working. But if you look at like. The current training that they're doing for the next model, their gross margin negative. So part of me thinks that a lot of ‘em are kind of borrowing against the future and that's gonna have to slow down. It's gonna catch up to them at some point in time, but we don't really know.[00:30:47] Sarah Wang: Yeah.[00:30:47] Martin Casado: Does that make sense? Like, I mean, it could be, it could be the case that the only reason this is working is ‘cause they can raise that next round and they can train that next model. ‘cause these models have such a short. Life. And so at some point in time, like, you know, they won't be able to [00:31:00] raise that next round for the next model and then things will kind of converge and fragment again.[00:31:03] But right now it's not.[00:31:04] Sarah Wang: Totally. I think the other, by the way, just, um, a meta point. I think the other lesson from the last three years is, and we talk about this all the time ‘cause we're on this. Twitter X bubble. Um, cool. But, you know, if you go back to, let's say March, 2024, that period, it felt like a, I think an open source model with an, like a, you know, benchmark leading capability was sort of launching on a daily basis at that point.[00:31:27] And, um, and so that, you know, that's one period. Suddenly it's sort of like open source takes over the world. There's gonna be a plethora. It's not an oligopoly, you know, if you fast, you know, if you, if you rewind time even before that GPT-4 was number one for. Nine months, 10 months. It's a long time. Right.[00:31:44] Um, and of course now we're in this era where it feels like an oligopoly, um, maybe some very steady state shifts and, and you know, it could look like this in the future too, but it just, it's so hard to call. And I think the thing that keeps, you know, us up at [00:32:00] night in, in a good way and bad way, is that the capability progress is actually not slowing down.[00:32:06] And so until that happens, right, like you don't know what's gonna look like.[00:32:09] Martin Casado: But I, I would, I would say for sure it's not converged, like for sure, like the systemic capital flows have not converged, meaning right now it's still borrowing against the future to subsidize growth currently, which you can do that for a period of time.[00:32:23] But, but you know, at the end, at some point the market will rationalize that and just nobody knows what that will look like.[00:32:29] Alessio: Yeah.[00:32:29] Martin Casado: Or, or like the drop in price of compute will, will, will save them. Who knows?[00:32:34] Alessio: Yeah. Yeah. I think the models need to ask them to, to specific tasks. You know? It's like, okay, now Opus 4.5 might be a GI at some specific task, and now you can like depreciate the model over a longer time.[00:32:45] I think now, now, right now there's like no old model.[00:32:47] Martin Casado: No, but let, but lemme just change that mental, that's, that used to be my mental model. Lemme just change it a little bit.[00:32:53] Capital as a Weapon vs Task Saturation: Where Real Enterprise Value Gets Built[00:32:53] Martin Casado: If you can raise three times, if you can raise more than the aggregate of anybody that uses your models, that doesn't even matter.[00:32:59] It doesn't [00:33:00] even matter. See what I'm saying? Like, yeah. Yeah. So, so I have an API Business. My API business is 60% margin, or 70% margin, or 80% margin is a high margin business. So I know what everybody is using. If I can raise more money than the aggregate of everybody that's using it, I will consume them whether I'm a GI or not.[00:33:14] And I will know if they're using it ‘cause they're using it. And like, unlike in the past where engineering stops me from doing that.[00:33:21] Alessio: Mm-hmm.[00:33:21] Martin Casado: It is very straightforward. You just train. So I also thought it was kind of like, you must ask the code a GI, general, general, general. But I think there's also just a possibility that the, that the capital markets will just give them the, the, the ammunition to just go after everybody on top of ‘em.[00:33:36] Sarah Wang: I, I do wonder though, to your point, um, if there's a certain task that. Getting marginally better isn't actually that much better. Like we've asked them to it, to, you know, we can call it a GI or whatever, you know, actually, Ali Goi talks about this, like we're already at a GI for a lot of functions in the enterprise.[00:33:50] Um. That's probably those for those tasks, you probably could build very specific companies that focus on just getting as much value out of that task that isn't [00:34:00] coming from the model itself. There's probably a rich enterprise business to be built there. I mean, could be wrong on that, but there's a lot of interesting examples.[00:34:08] So, right, if you're looking the legal profession or, or whatnot, and maybe that's not a great one ‘cause the models are getting better on that front too, but just something where it's a bit saturated, then the value comes from. Services. It comes from implementation, right? It comes from all these things that actually make it useful to the end customer.[00:34:24] Martin Casado: Sorry, what am I, one more thing I think is, is underused in all of this is like, to what extent every task is a GI complete.[00:34:31] Sarah Wang: Mm-hmm.[00:34:32] Martin Casado: Yeah. I code every day. It's so fun.[00:34:35] Sarah Wang: That's a core question. Yeah.[00:34:36] Martin Casado: And like. When I'm talking to these models, it's not just code. I mean, it's everything, right? Like I, you know, like it's,[00:34:43] swyx: it's healthcare.[00:34:44] It's,[00:34:44] Martin Casado: I mean, it's[00:34:44] swyx: Mele,[00:34:45] Martin Casado: but it's every, it is exactly that. Like, yeah, that's[00:34:47] Sarah Wang: great support. Yeah.[00:34:48] Martin Casado: It's everything. Like I'm asking these models to, yeah, to understand compliance. I'm asking these models to go search the web. I'm asking these models to talk about things I know in the history, like it's having a full conversation with me while I, I engineer, and so it could be [00:35:00] the case that like, mm-hmm.[00:35:01] The most a, you know, a GI complete, like I'm not an a GI guy. Like I think that's, you know, but like the most a GI complete model will is win independent of the task. And we don't know the answer to that one either.[00:35:11] swyx: Yeah.[00:35:12] Martin Casado: But it seems to me that like, listen, codex in my experience is for sure better than Opus 4.5 for coding.[00:35:18] Like it finds the hardest bugs that I work in with. Like, it is, you know. The smartest developers. I don't work on it. It's great. Um, but I think Opus 4.5 is actually very, it's got a great bedside manner and it really, and it, it really matters if you're building something very complex because like, it really, you know, like you're, you're, you're a partner and a brainstorming partner for somebody.[00:35:38] And I think we don't discuss enough how every task kind of has that quality.[00:35:42] swyx: Mm-hmm.[00:35:43] Martin Casado: And what does that mean to like capital investment and like frontier models and Submodels? Yeah.[00:35:47] Why “Coding Models” Keep Collapsing into Generalists (Reasoning vs Taste)[00:35:47] Martin Casado: Like what happened to all the special coding models? Like, none of ‘em worked right. So[00:35:51] Alessio: some of them, they didn't even get released.[00:35:53] Magical[00:35:54] Martin Casado: Devrel. There's a whole, there's a whole host. We saw a bunch of them and like there's this whole theory that like, there could be, and [00:36:00] I think one of the conclusions is, is like there's no such thing as a coding model,[00:36:04] Alessio: you know?[00:36:04] Martin Casado: Like, that's not a thing. Like you're talking to another human being and it's, it's good at coding, but like it's gotta be good at everything.[00:36:10] swyx: Uh, minor disagree only because I, I'm pretty like, have pretty high confidence that basically open eye will always release a GPT five and a GT five codex. Like that's the code's. Yeah. The way I call it is one for raisin, one for Tiz. Um, and, and then like someone internal open, it was like, yeah, that's a good way to frame it.[00:36:32] Martin Casado: That's so funny.[00:36:33] swyx: Uh, but maybe it, maybe it collapses down to reason and that's it. It's not like a hundred dimensions doesn't life. Yeah. It's two dimensions. Yeah, yeah, yeah, yeah. Like and exactly. Beside manner versus coding. Yeah.[00:36:43] Martin Casado: Yeah.[00:36:44] swyx: It's, yeah.[00:36:46] Martin Casado: I, I think for, for any, it's hilarious. For any, for anybody listening to this for, for, for, I mean, for you, like when, when you're like coding or using these models for something like that.[00:36:52] Like actually just like be aware of how much of the interaction has nothing to do with coding and it just turns out to be a large portion of it. And so like, you're, I [00:37:00] think like, like the best Soto ish model. You know, it is going to remain very important no matter what the task is.[00:37:06] swyx: Yeah.[00:37:07] What He's Actually Coding: Gaussian Splats, Spark.js & 3D Scene Rendering Demos[00:37:07] swyx: Uh, speaking of coding, uh, I, I'm gonna be cheeky and ask like, what actually are you coding?[00:37:11] Because obviously you, you could code anything and you are obviously a busy investor and a manager of the good. Giant team. Um, what are you calling?[00:37:18] Martin Casado: I help, um, uh, FEFA at World Labs. Uh, it's one of the investments and um, and they're building a foundation model that creates 3D scenes.[00:37:27] swyx: Yeah, we had it on the pod.[00:37:28] Yeah. Yeah,[00:37:28] Martin Casado: yeah. And so these 3D scenes are Gaussian splats, just by the way that kind of AI works. And so like, you can reconstruct a scene better with, with, with radiance feels than with meshes. ‘cause like they don't really have topology. So, so they, they, they produce each. Beautiful, you know, 3D rendered scenes that are Gaussian splats, but the actual industry support for Gaussian splats isn't great.[00:37:50] It's just never, you know, it's always been meshes and like, things like unreal use meshes. And so I work on a open source library called Spark js, which is a. Uh, [00:38:00] a JavaScript rendering layer ready for Gaussian splats. And it's just because, you know, um, you, you, you need that support and, and right now there's kind of a three js moment that's all meshes and so like, it's become kind of the default in three Js ecosystem.[00:38:13] As part of that to kind of exercise the library, I just build a whole bunch of cool demos. So if you see me on X, you see like all my demos and all the world building, but all of that is just to exercise this, this library that I work on. ‘cause it's actually a very tough algorithmics problem to actually scale a library that much.[00:38:29] And just so you know, this is ancient history now, but 30 years ago I paid for undergrad, you know, working on game engines in college in the late nineties. So I've got actually a back and it's very old background, but I actually have a background in this and so a lot of it's fun. You know, but, but the, the, the, the whole goal is just for this rendering library to, to,[00:38:47] Sarah Wang: are you one of the most active contributors?[00:38:49] The, their GitHub[00:38:50] Martin Casado: spark? Yes.[00:38:51] Sarah Wang: Yeah, yeah.[00:38:51] Martin Casado: There's only two of us there, so, yes. No, so by the way, so the, the pri The pri, yeah. Yeah. So the primary developer is a [00:39:00] guy named Andres Quist, who's an absolute genius. He and I did our, our PhDs together. And so like, um, we studied for constant Quas together. It was almost like hanging out with an old friend, you know?[00:39:09] And so like. So he, he's the core, core guy. I did mostly kind of, you know, the side I run venture fund.[00:39:14] swyx: It's amazing. Like five years ago you would not have done any of this. And it brought you back[00:39:19] Martin Casado: the act, the Activ energy, you're still back. Energy was so high because you had to learn all the framework b******t.[00:39:23] Man, I f*****g used to hate that. And so like, now I don't have to deal with that. I can like focus on the algorithmics so I can focus on the scaling and I,[00:39:29] swyx: yeah. Yeah.[00:39:29] LLMs vs Spatial Intelligence + How to Value World Labs' 3D Foundation Model[00:39:29] swyx: And then, uh, I'll observe one irony and then I'll ask a serious investor question, uh, which is like, the irony is FFE actually doesn't believe that LMS can lead us to spatial intelligence.[00:39:37] And here you are using LMS to like help like achieve spatial intelligence. I just see, I see some like disconnect in there.[00:39:45] Martin Casado: Yeah. Yeah. So I think, I think, you know, I think, I think what she would say is LLMs are great to help with coding.[00:39:51] swyx: Yes.[00:39:51] Martin Casado: But like, that's very different than a model that actually like provides, they, they'll never have the[00:39:56] swyx: spatial inte[00:39:56] Martin Casado: issues.[00:39:56] And listen, our brains clearly listen, our brains, brains clearly have [00:40:00] both our, our brains clearly have a language reasoning section and they clearly have a spatial reasoning section. I mean, it's just, you know, these are two pretty independent problems.[00:40:07] swyx: Okay. And you, you, like, I, I would say that the, the one data point I recently had, uh, against it is the DeepMind, uh, IMO Gold, where, so, uh, typically the, the typical answer is that this is where you start going down the neuros symbolic path, right?[00:40:21] Like one, uh, sort of very sort of abstract reasoning thing and one form, formal thing. Um, and that's what. DeepMind had in 2024 with alpha proof, alpha geometry, and now they just use deep think and just extended thinking tokens. And it's one model and it's, and it's in LM.[00:40:36] Martin Casado: Yeah, yeah, yeah, yeah, yeah.[00:40:37] swyx: And so that, that was my indication of like, maybe you don't need a separate system.[00:40:42] Martin Casado: Yeah. So, so let me step back. I mean, at the end of the day, at the end of the day, these things are like nodes in a graph with weights on them. Right. You know, like it can be modeled like if you, if you distill it down. But let me just talk about the two different substrates. Let's, let me put you in a dark room.[00:40:56] Like totally black room. And then let me just [00:41:00] describe how you exit it. Like to your left, there's a table like duck below this thing, right? I mean like the chances that you're gonna like not run into something are very low. Now let me like turn on the light and you actually see, and you can do distance and you know how far something away is and like where it is or whatever.[00:41:17] Then you can do it, right? Like language is not the right primitives to describe. The universe because it's not exact enough. So that's all Faye, Faye is talking about. When it comes to like spatial reasoning, it's like you actually have to know that this is three feet far, like that far away. It is curved.[00:41:37] You have to understand, you know, the, like the actual movement through space.[00:41:40] swyx: Yeah.[00:41:40] Martin Casado: So I do, I listen, I do think at the end of these models are definitely converging as far as models, but there's, there's, there's different representations of problems you're solving. One is language. Which, you know, that would be like describing to somebody like what to do.[00:41:51] And the other one is actually just showing them and the space reasoning is just showing them.[00:41:55] swyx: Yeah, yeah, yeah. Right. Got it, got it. Uh, the, in the investor question was on, on, well labs [00:42:00] is, well, like, how do I value something like this? What, what, what work does the, do you do? I'm just like, Fefe is awesome.[00:42:07] Justin's awesome. And you know, the other two co-founder, co-founders, but like the, the, the tech, everyone's building cool tech. But like, what's the value of the tech? And this is the fundamental question[00:42:16] Martin Casado: of, well, let, let, just like these, let me just maybe give you a rough sketch on the diffusion models. I actually love to hear Sarah because I'm a venture for, you know, so like, ventures always, always like kind of wild west type[00:42:24] swyx: stuff.[00:42:24] You, you, you, you paid a dream and she has to like, actually[00:42:28] Martin Casado: I'm gonna say I'm gonna mar to reality, so I'm gonna say the venture for you. And she can be like, okay, you a little kid. Yeah. So like, so, so these diffusion models literally. Create something for, for almost nothing. And something that the, the world has found to be very valuable in the past, in our real markets, right?[00:42:45] Like, like a 2D image. I mean, that's been an entire market. People value them. It takes a human being a long time to create it, right? I mean, to create a, you know, a, to turn me into a whatever, like an image would cost a hundred bucks in an hour. The inference cost [00:43:00] us a hundredth of a penny, right? So we've seen this with speech in very successful companies.[00:43:03] We've seen this with 2D image. We've seen this with movies. Right? Now, think about 3D scene. I mean, I mean, when's Grand Theft Auto coming out? It's been six, what? It's been 10 years. I mean, how, how like, but hasn't been 10 years.[00:43:14] Alessio: Yeah.[00:43:15] Martin Casado: How much would it cost to like, to reproduce this room in 3D? Right. If you, if you, if you hired somebody on fiber, like in, in any sort of quality, probably 4,000 to $10,000.[00:43:24] And then if you had a professional, probably $30,000. So if you could generate the exact same thing from a 2D image, and we know that these are used and they're using Unreal and they're using Blend, or they're using movies and they're using video games and they're using all. So if you could do that for.[00:43:36] You know, less than a dollar, that's four or five orders of magnitude cheaper. So you're bringing the marginal cost of something that's useful down by three orders of magnitude, which historically have created very large companies. So that would be like the venture kind of strategic dreaming map.[00:43:49] swyx: Yeah.[00:43:50] And, and for listeners, uh, you can do this yourself on your, on your own phone with like. Uh, the marble.[00:43:55] Martin Casado: Yeah. Marble.[00:43:55] swyx: Uh, or but also there's many Nerf apps where you just go on your iPhone and, and do this.[00:43:59] Martin Casado: Yeah. Yeah. [00:44:00] Yeah. And, and in the case of marble though, it would, what you do is you literally give it in.[00:44:03] So most Nerf apps you like kind of run around and take a whole bunch of pictures and then you kind of reconstruct it.[00:44:08] swyx: Yeah.[00:44:08] Martin Casado: Um, things like marble, just that the whole generative 3D space will just take a 2D image and it'll reconstruct all the like, like[00:44:16] swyx: meaning it has to fill in. Uh,[00:44:18] Martin Casado: stuff at the back of the table, under the table, the back, like, like the images, it doesn't see.[00:44:22] So the generator stuff is very different than reconstruction that it fills in the things that you can't see.[00:44:26] swyx: Yeah. Okay.[00:44:26] Sarah Wang: So,[00:44:27] Martin Casado: all right. So now the,[00:44:28] Sarah Wang: no, no. I mean I love that[00:44:29] Martin Casado: the adult[00:44:29] Sarah Wang: perspective. Um, well, no, I was gonna say these are very much a tag team. So we, we started this pod with that, um, premise. And I think this is a perfect question to even build on that further.[00:44:36] ‘cause it truly is, I mean, we're tag teaming all of these together.[00:44:39] Investing in Model Labs, Media Rumors, and the Cursor Playbook (Margins & Going Down-Stack)[00:44:39] Sarah Wang: Um, but I think every investment fundamentally starts with the same. Maybe the same two premises. One is, at this point in time, we actually believe that there are. And of one founders for their particular craft, and they have to be demonstrated in their prior careers, right?[00:44:56] So, uh, we're not investing in every, you know, now the term is NEO [00:45:00] lab, but every foundation model, uh, any, any company, any founder trying to build a foundation model, we're not, um, contrary to popular opinion, we're

This episode is a full “build a business in 40 minutes” demo showing how AI collapses what used to take teams (creative production + sales ops + support) into a handful of prompts. Samruddhi generates a high-production video ad in Google AI Studio using a JSON-style prompt framework, then spins up a working voice sales/support agent in Vapi via Claude Desktop + MCP—so the agent is created from a single prompt instead of clicking through the UI. The conversation also covers why “interfaces matter less” in an agent-first world, why workflow tools (like n8n) still have a role, and how memory layers like Mem0 unify context across channels (email/WhatsApp/etc.) so you can take actions without hunting.Timestamps0:00 — “Single person billion-dollar company” belief + AI driving 10x execution speed1:57 — Plan: create the ad in Google AI Studio (Veo 3.1) + build a voice agent using Vapi MCP via Claude Desktop2:42 — Smithery: marketplace for MCP servers3:39 — MCP for non-technical listeners: “like an API, but agents use it to talk to external services”4:22 — Inside Vapi MCP: tool list = APIs the agent can choose from5:06 — AI Studio setup: video generation playground + select Veo 3.16:16 — JSON prompting framework begins (structure → production-level output)6:28 — Keys: description, style, camera, lighting, environment, elements, motion, ending, text9:05 — Prompts/scripts can be AI-generated (humans provide guardrails)10:41 — Need an API key to generate videos in AI Studio10:54 — Ad review: strong realism; last segment looks AI-ish → iterate prompt13:05 — Install Vapi MCP via npx from Smithery + add Vapi API key13:46 — Claude Desktop: Vapi MCP appears under Connectors/Tools (not Claude web)14:05 — Prompt the agent build: “Fresh Pause” + role, tasks, FAQs, call flows18:23 — Testing: “Talk to assistant” starts a live call simulation19:20 — Deployment: assign a phone number; Vapi provides free/test numbers (up to a limit)21:57 — Mem0 / Supermemory: memory layer across apps/agents to keep context24:13 — Why memory layers help: fewer MCPs → less slowdown/hallucination; no need to specify where to search26:36 — MCPs + slide decks: mention of Gamma MCP via Claude27:34 — Future of n8n/Zapier: they persist, but prompting increasingly generates workflows31:38 — Prediction market trading algos (Kalshi/Polymarket) + AI improves speed/decision-making36:02 — Closing vision: help orgs 10x execution speed, especially non-technical leaders (40+) with domain expertiseTools & technologies mentionedGoogle AI Studio (Video Generation Playground) — Generate an 8-second video ad.Veo 3.1 — Google video model used for “production-level” output.JSON Prompting Framework — Structured key/value prompts for story, visuals, camera, lighting, motion, ending frame.Claude Desktop — Runs connectors/tools (including MCP servers).MCP (Model Context Protocol) — Lets agents call external services/tools based on intent.Smithery — Directory/marketplace for MCP servers.Vapi — Voice agent platform; create agents + assign phone numbers.Vapi MCP Server — Enables Claude to operate Vapi via prompts (create/list/configure).npx — Installs MCP server quickly from the terminal.API Keys — Required for AI Studio generation + Vapi authentication.Mem0 / Supermemory — Cross-channel memory layer to retrieve context automatically.Knowledge Graph — Underlying structure for semantic retrieval across interactions.Glean — Referenced as a comparison point for search/context retrieval.Gamma MCP — Example of generating slide decks via MCP.n8n / Zapier — Workflow automation tools discussed in an MCP-first future.OpenClaw — Mentioned as agent tooling that can help with steps like obtaining API keys.Kalshi / Polymarket — Prediction markets referenced in the trading/AI speed discussion.Subscribe at⁠ thisnewway.com⁠ to get the step-by-step playbooks, tools, and workflows.

Payments leaders are feeling the squeeze of shrinking margins, price-driven churn, and rising expectations from merchants who want funding that feels as seamless as a card transaction. We sat down with Aarati Soman, Head of Product at Parafin, and Jaron Ruckman, Product Manager at NMI, to map the new playbook: embedded lending that meets merchants where they already work, backed by real-time data, AI-driven underwriting, and modular infrastructure that launches fast and scales cleanly.We unpack how moving capital inside your existing workflows changes the relationship with your merchants. Instead of sending them to third-party portals or closed ecosystems, you present pre-underwritten offers based on sales data, bank transactions, and relevant third-party signals. Machine learning models spot revenue patterns, seasonality, refunds, disputes, and expense profiles; LLMs structure unstructured data to speed decisions. The impact is tangible: faster approvals, fairer pricing, higher eligibility for SMBs that banks often overlook, and the kind of stickiness that turns payment processing from a commodity into a growth engine.Aarati outlines how Parafin carries the heavy lifts - capital, risk, servicing, and compliance so partners can focus on distribution and experience. Jaron shares how NMI's API-first approach and embeddable components get partners live with offers before any deep development, with the option to integrate more tightly over time. We explore strategic positioning against Stripe and Square, why contextual placement at the point of pain drives adoption, and where product innovation is headed: fit-for-purpose capital for inventory spikes, equipment, payroll, and beyond. We close with practical advice on choosing partners - breadth of products, ease of integration, transparency, and program durability so you avoid costly rip-and-replace cycles and deliver fast funding your merchants trust.

I've been delaying this episode for a long time because the topic is genuinely difficult and, for many of us, scary. AI is threatening not just to our livelihood, but to our sense of self-worth as creators.In this episode, I don't offer false guarantees about job security. Instead, I frame the problem through the lens of microeconomics and rational incentives to help you understand how to remain employable. We discuss why you must separate your ego from your current skill set and how to position yourself not as a competitor to AI, but as a force multiplier.• The Hard Truth: I explain why the "abstinence" approach—hoping the industry rejects AI or that it turns out to be a bubble—is a high-risk gamble that is unlikely to succeed.• Ego vs. Employability: We discuss the difficult mental shift required to disconnect your self-worth from the act of writing code manually, allowing you to adopt new tools without feeling like you are losing your identity.• The Microeconomics of Your Job: Understand the cold reality that a rational market only pays you if you generate more value than you cost; if AI can do the same task with less risk or cost, the market will choose AI.• The Non-Zero Sum Game: Learn why the economy isn't a fixed pie. The goal isn't just to survive, but to recognize that the combination of Human + AI can generate more total value than either can alone.• Multiplicative Value: I challenge you to stop thinking about linear skill acquisition and start thinking like a manager: how can you use AI to multiply your output and become indispensable?• Accepting Atrophy: We confront the reality that your core coding skills may degrade over time as you rely on AI, and why accepting this trade-off might be necessary for your career survival.

Yeah, you prolly saw the news: OpenAI acquihired OpenClaw.

AI tools are becoming a core part of modern development workflows—but they come with serious risks most developers aren't thinking about. In this episode, Matt and Mike break down five AI security threats that are already happening in the real world. From prompt injection attacks and rogue AI agents with access to your email, to runaway API bills and poisoned models slipping into your stack - these aren't hypothetical problems. If you're using AI in production, in your codebase, or inside your company workflows, this episode will help you understand what can go wrong - and how to protect yourself before it does. ‍Show Notes: https://www.htmlallthethings.com/podcast/5-ways-ai-can-blow-up-in-your-face

In this episode of The Cybersecurity Defenders Podcast, we discuss some intel being shared in the LimaCharlie community.Russian cyber operations have maintained a consistent focus on exploiting both tactical and strategic targets within the defense industrial base, particularly in the context of the war in Ukraine.Sygnia has disclosed a large-scale, AI-driven scam operation involving over 150 cloned websites impersonating law firms.A joint investigation by SentinelLabs and Censys has revealed a growing ecosystem of publicly exposed AI compute infrastructure, driven largely by deployments of Ollama - an open-source framework for running large language models locally.Flare has identified a widespread, ongoing campaign attributed to a threat actor group known as TeamPCP -also operating under aliases such as PCPcat and ShellForce - which has compromised over 60,000 servers worldwide since late December.Support our show by sharing your favorite episodes with a friend, subscribe, give us a rating or leave a comment on your podcast platform.This podcast is brought to you by LimaCharlie, maker of the SecOps Cloud Platform, infrastructure for SecOps where everything is built API first. Scale with confidence as your business grows. Start today for free at limacharlie.io.

We went in depth with Marsha Barnhart on a high strangeness case that was big for us, case 12-058. The events in this case largely took place in Ocean City, Maryland. We play several audio clips from the primary witness and those close to him that had not previously been made public. Contact us about becoming a panelist: https://aerial-phenomenon.org/contact-us/ Become an API investigator: https://aerial-phenomenon.org/join-api/ Our new hotline number: https://aerial-phenomenon.org/weve-updated-our-hotline-number-again/ Report your UFO sighting: https://reportaufo.org

Connect to John Gilroy on LinkedIn   https://www.linkedin.com/in/john-gilroy/ Want to listen to other episodes? www.Federaltechpodcast.com Cybersecurity is a rapidly evolving field, where every effective defense technique is quickly noticed and adapted to by malicious actors. The real question is how fast each side of this ongoing cat-and-mouse game can respond. Let us take an example of web applications. In the decade-long slog of the cloud, federal users migrated to web-based applications protected by Web Application Firewalls (WAFs). firewalls. As that method matured, malicious observers noted that the Application Programming Interface (API) allowed these software programs to communicate and exchange data. Voila, another attack vector was born. During today's interview, Joe Henry from Akamai Technologies notes that 80% of their customers report API attacks. Henry details a curious term called "Broken-Object Level Authorization." In this attack, an application fails to check if a user is authorized to access specific data objects. The ID is manipulated, and the malicious actor gets access. Akamai's API Security performs behavioral analysis beyond WAFs, flags PII exposure, and supports a zero-trust posture. Software developers talk about a "shift left"; we apply that to the Akamai approach. They have a worldwide network of Points of Presence (POPs) and data centers where they can observe attacks as they develop. It is so strong that it provides fail-open resilience with a 100% SLA. Akamai provides a State of the Internet Report (quarterly). If you would like to stay connected with the next manifestation of attack, consider subscribing or visiting their website to stay informed about the latest trend

Photo by Viktor Keri on Unsplash Published 16 February 2026 e543 with Andy, Michael and Michael – Stories and discussion on Agentic AI and the changing nature of work, agents renting humans, real time translation, artistic roads, e-bikes for your feet and a whole lot more. Andy, Michael and Michael get things rolling with several AI articles.  First up, is a Mastodon post by Alan Pringle that called attention to a HBR article on the influence of AI on productivity.  This then led to a post on productivity acceleration technologies from years past – from COBOL, which was designed to enable business people to write programs, to 4GLs to case tools.  Then, the team discusses a detailed post from Matt Shumer entitled Something Big Is Happening.  The entire post is well worth reading, not only for how history is unfolding in real time, also for the recommendations that Matt makes for people to take onboard right now.  Among the recommendations are to begin the habit of adapting, and experimenting with multiple tools to build resiliency and experience. Wrapping up this section is a new version of taskrabbit that provides an API for Agents to rent humans for specific work called rentahuman.ai .  The future is certainly coming in fast. In the AR VR section, there is a story from Tom's Guide where the author used her Ray Ban Meta glasses to translate the Super Bowl halftime video in real time.  This feels like the precursor to the next logical step, a dynamic version of the Amazon X-Ray feature where further context can be personalized and served up to the user if they wish. After touching on the assembly of Game Poems and the art of roads in games, the team sprints to the end of the episode with Nike's Project Amplify, which is an ankle exoskeleton to augment humans running abilities.  Looping back to the start of the episode, Andy highlights a BBC show called Chris McCausland. What's been your experience with AI productivity?  What are you experimenting with? Have your bots

Emmanuel et Guillaume discutent de divers sujets liés à la programmation, notamment les systèmes de fichiers en Java, le Data Oriented Programming, les défis de JPA avec Kotlin, et les nouvelles fonctionnalités de Quarkus. Ils explorent également des sujets un peu fous comme la création de datacenters dans l'espace. Pas mal d'architecture aussi. Enregistré le 13 février 2026 Téléchargement de l'épisode LesCastCodeurs-Episode-337.mp3 ou en vidéo sur YouTube. News Langages Comment implémenter un file system en Java https://foojay.io/today/bootstrapping-a-java-file-system/ Créer un système de fichiers Java personnalisé avec NIO.2 pour des usages variés (VCS, archives, systèmes distants). Évolution Java: java.io.File (1.0) -> NIO (1.4) -> NIO.2 (1.7) pour personnalisation via FileSystem. Recommander conception préalable; API Java est orientée POSIX. Composants clés à considérer: Conception URI (scheme unique, chemin). Gestion de l'arborescence (BD, métadonnées, efficacité). Stockage binaire (emplacement, chiffrement, versions). Minimum pour démarrer (4 composants): Implémenter Path (représente fichier/répertoire). Étendre FileSystem (instance du système). Étendre FileSystemProvider (moteur, enregistré par scheme). Enregistrer FileSystemProvider via META-INF/services. Étapes suivantes: Couche BD (arborescence), opérations répertoire/fichier de base, stockage, tests. Processus long et exigeant, mais gratifiant.   Un article de brian goetz sur le futur du data oriented programming en Java https://openjdk.org/projects/amber/design-notes/beyond-records Le projet Amber de Java introduit les "carrier classes", une évolution des records qui permet plus de flexibilité tout en gardant les avantages du pattern matching et de la reconstruction Les records imposent des contraintes strictes (immutabilité, représentation exacte de l'état) qui limitent leur usage pour des classes avec état muable ou dérivé Les carrier classes permettent de déclarer une state description complète et canonique sans imposer que la représentation interne corresponde exactement à l'API publique Le modificateur "component" sur les champs permet au compilateur de dériver automatiquement les accesseurs pour les composants alignés avec la state description Les compact constructors sont généralisés aux carrier classes, générant automatiquement l'initialisation des component fields Les carrier classes supportent la déconstruction via pattern matching comme les records, rendant possible leur usage dans les instanceof et switch Les carrier interfaces permettent de définir une state description sur une interface, obligeant les implémentations à fournir les accesseurs correspondants L'extension entre carrier classes est possible, avec dérivation automatique des appels super() quand les composants parent sont subsumés par l'enfant Les records deviennent un cas particulier de carrier classes avec des contraintes supplémentaires (final, extends Record, component fields privés et finaux obligatoires) L'évolution compatible des records est améliorée en permettant l'ajout de composants en fin de liste et la déconstruction partielle par préfixe Comment éviter les pièges courants avec JPA et Kotlin - https://blog.jetbrains.com/idea/2026/01/how-to-avoid-common-pitfalls-with-jpa-and-kotlin/ JPA est une spécification Java pour la persistance objet-relationnel, mais son utilisation avec Kotlin présente des incompatibilités dues aux différences de conception des deux langages Les classes Kotlin sont finales par défaut, ce qui empêche la création de proxies par JPA pour le lazy loading et les opérations transactionnelles Le plugin kotlin-jpa génère automatiquement des constructeurs sans argument et rend les classes open, résolvant les problèmes de compatibilité Les data classes Kotlin ne sont pas adaptées aux entités JPA car elles génèrent equals/hashCode basés sur tous les champs, causant des problèmes avec les relations lazy L'utilisation de lateinit var pour les relations peut provoquer des exceptions si on accède aux propriétés avant leur initialisation par JPA Les types non-nullables Kotlin peuvent entrer en conflit avec le comportement de JPA qui initialise les entités avec des valeurs null temporaires Le backing field direct dans les getters/setters personnalisés peut contourner la logique de JPA et casser le lazy loading IntelliJ IDEA 2024.3 introduit des inspections pour détecter automatiquement ces problèmes et propose des quick-fixes L'IDE détecte les entités finales, les data classes inappropriées, les problèmes de constructeurs et l'usage incorrect de lateinit Ces nouvelles fonctionnalités aident les développeurs à éviter les bugs subtils liés à l'utilisation de JPA avec Kotlin Librairies Guide sur MapStruct @IterableMapping - https://www.baeldung.com/java-mapstruct-iterablemapping MapStruct est une bibliothèque Java pour générer automatiquement des mappers entre beans, l'annotation @IterableMapping permet de configurer finement le mapping de collections L'attribut dateFormat permet de formater automatiquement des dates lors du mapping de listes sans écrire de boucle manuelle L'attribut qualifiedByName permet de spécifier quelle méthode custom appliquer sur chaque élément de la collection à mapper Exemple d'usage : filtrer des données sensibles comme des mots de passe en mappant uniquement certains champs via une méthode dédiée L'attribut nullValueMappingStrategy permet de contrôler le comportement quand la collection source est null (retourner null ou une collection vide) L'annotation fonctionne pour tous types de collections Java (List, Set, etc.) et génère le code de boucle nécessaire Possibilité d'appliquer des formats numériques avec numberFormat pour convertir des nombres en chaînes avec un format spécifique MapStruct génère l'implémentation complète du mapper au moment de la compilation, éliminant le code boilerplate L'annotation peut être combinée avec @Named pour créer des méthodes de mapping réutilisables et nommées Le mapping des collections supporte les conversions de types complexes au-delà des simples conversions de types primitifs Accès aux fichiers Samba depuis Java avec JCIFS - https://www.baeldung.com/java-samba-jcifs JCIFS est une bibliothèque Java permettant d'accéder aux partages Samba/SMB sans monter de lecteur réseau, supportant le protocole SMB3 on pense aux galériens qui doivent se connecter aux systèmes dit legacy La configuration nécessite un contexte CIFS (CIFSContext) et des objets SmbFile pour représenter les ressources distantes L'authentification se fait via NtlmPasswordAuthenticator avec domaine, nom d'utilisateur et mot de passe La bibliothèque permet de lister les fichiers et dossiers avec listFiles() et vérifier leurs propriétés (taille, date de modification) Création de fichiers avec createNewFile() et de dossiers avec mkdir() ou mkdirs() pour créer toute une arborescence Suppression via delete() qui peut parcourir et supprimer récursivement des arborescences entières Copie de fichiers entre partages Samba avec copyTo(), mais impossibilité de copier depuis le système de fichiers local Pour copier depuis le système local, utilisation des streams SmbFileInputStream et SmbFileOutputStream Les opérations peuvent cibler différents serveurs Samba et différents partages (anonymes ou protégés par mot de passe) La bibliothèque s'intègre dans des blocs try-with-resources pour une gestion automatique des ressources Quarkus 3.31 - Support complet Java 25, nouveau packaging Maven et Panache Next - https://quarkus.io/blog/quarkus-3-31-released/ Support complet de Java 25 avec images runtime et native Nouveau packaging Maven de type quarkus avec lifecycle optimisé pour des builds plus rapides voici un article complet pour plus de detail https://quarkus.io/blog/building-large-applications/ Introduction de Panache Next, nouvelle génération avec meilleure expérience développeur et API unifiée ORM/Reactive Mise à jour vers Hibernate ORM 7.2, Reactive 3.2, Search 8.2 Support de Hibernate Spatial pour les données géospatiales Passage à Testcontainers 2 et JUnit 6 Annotations de sécurité supportées sur les repositories Jakarta Data Chiffrement des tokens OIDC pour les implémentations custom TokenStateManager Support OAuth 2.0 Pushed Authorization Requests dans l'extension OIDC Maven 3.9 maintenant requis minimum pour les projets Quarkus A2A Java SDK 1.0.0.Alpha1 - Alignement avec la spécification 1.0 du protocole Agent2Agent - https://quarkus.io/blog/a2a-java-sdk-1-0-0-alpha1/ Le SDK Java A2A implémente le protocole Agent2Agent qui permet la communication standardisée entre agents IA pour découvrir des capacités, déléguer des tâches et collaborer Passage à la version 1.0 de la spécification marque la transition d'expérimental à production-ready avec des changements cassants assumés Modernisation complète du module spec avec des Java records partout remplaçant le mix précédent de classes et records pour plus de cohérence Adoption de Protocol Buffers comme source de vérité avec des mappers MapStruct pour la conversion et Gson pour JSON-RPC Les builders utilisent maintenant des méthodes factory statiques au lieu de constructeurs publics suivant les best practices Java modernes Introduction de trois BOMs Maven pour simplifier la gestion des dépendances du SDK core, des extensions et des implémentations de référence Quarkus AgentCard évolue avec une liste supportedInterfaces remplaçant url et preferredTransport pour plus de flexibilité dans la déclaration des protocoles Support de la pagination ajouté pour ListTasks et les endpoints de configuration des notifications push avec des wrappers Result appropriés Interface A2AHttpClient pluggable permettant des implémentations HTTP personnalisées avec une implémentation Vert.x fournie Travail continu vers la conformité complète avec le TCK 1.0 en cours de développement parallèlement à la finalisation de la spécification Pourquoi Quarkus finit par "cliquer" : les 10 questions que se posent les développeurs Java - https://www.the-main-thread.com/p/quarkus-java-developers-top-questions-2025 un article qui revele et repond aux questions des gens qui ont utilisé Quarkus depuis 4-6 mois, les non noob questions Quarkus est un framework Java moderne optimisé pour le cloud qui propose des temps de démarrage ultra-rapides et une empreinte mémoire réduite Pourquoi Quarkus démarre si vite ? Le framework effectue le travail lourd au moment du build (scanning, indexation, génération de bytecode) plutôt qu'au runtime Quand utiliser le mode réactif plutôt qu'impératif ? Le réactif est pertinent pour les workloads avec haute concurrence et dominance I/O, l'impératif reste plus simple dans les autres cas Quelle est la différence entre Dev Services et Testcontainers ? Dev Services utilise Testcontainers en gérant automatiquement le cycle de vie, les ports et la configuration sans cérémonie Comment la DI de Quarkus diffère de Spring ? CDI est un standard basé sur la sécurité des types et la découverte au build-time, différent de l'approche framework de Spring Comment gérer la configuration entre environnements ? Quarkus permet de scaler depuis le développement local jusqu'à Kubernetes avec des profils, fichiers multiples et configuration externe Comment tester correctement les applications Quarkus ? @QuarkusTest démarre l'application une fois pour toute la suite de tests, changeant le modèle mental par rapport à Spring Boot Que fait vraiment Panache en coulisses ? Panache est du JPA avec des opinions fortes et des défauts propres, enveloppant Hibernate avec un style Active Record Doit-on utiliser les images natives et quand ? Les images natives brillent pour le serverless et l'edge grâce au démarrage rapide et la faible empreinte mémoire, mais tous les apps n'en bénéficient pas Comment Quarkus s'intègre avec Kubernetes ? Le framework génère automatiquement les ressources Kubernetes, gère les health checks et métriques comme s'il était nativement conçu pour cet écosystème Comment intégrer l'IA dans une application Quarkus ? LangChain4j permet d'ajouter embeddings, retrieval, guardrails et observabilité directement en Java sans passer par Python Infrastructure Les alternatives à MinIO https://rmoff.net/2026/01/14/alternatives-to-minio-for-single-node-local-s3/ MinIO a abandonné le support single-node fin 2025 pour des raisons commerciales, cassant de nombreuses démos et pipelines CI/CD qui l'utilisaient pour émuler S3 localement L'auteur cherche un remplacement simple avec image Docker, compatibilité S3, licence open source, déploiement mono-nœud facile et communauté active S3Proxy est très léger et facile à configurer, semble être l'option la plus simple mais repose sur un seul contributeur RustFS est facile à utiliser et inclut une GUI, mais c'est un projet très récent en version alpha avec une faille de sécurité majeure récente SeaweedFS existe depuis 2012 avec support S3 depuis 2018, relativement facile à configurer et dispose d'une interface web basique Zenko CloudServer remplace facilement MinIO mais la documentation et le branding (cloudserver/zenko/scality) peuvent prêter à confusion Garage nécessite une configuration complexe avec fichier TOML et conteneur d'initialisation séparé, pas un simple remplacement drop-in Apache Ozone requiert au minimum quatre nœuds pour fonctionner, beaucoup trop lourd pour un usage local simple L'auteur recommande SeaweedFS et S3Proxy comme remplaçants viables, RustFS en maybe, et élimine Garage et Ozone pour leur complexité Garage a une histoire tres associative, il vient du collectif https://deuxfleurs.fr/ qui offre un cloud distribué sans datacenter C'est certainement pas une bonne idée, les datacenters dans l'espace https://taranis.ie/datacenters-in-space-are-a-terrible-horrible-no-good-idea/ Avis d'expert (ex-NASA/Google, Dr en électronique spatiale) : Centres de données spatiaux, une "terrible" idée. Incompatibilité fondamentale : L'électronique (surtout IA/GPU) est inadaptée à l'environnement spatial. Énergie : Accès limité. Le solaire (type ISS) est insuffisant pour l'échelle de l'IA. Le nucléaire (RTG) est trop faible. Refroidissement : L'espace n'est pas "froid" ; absence de convection. Nécessite des radiateurs gigantesques (ex: 531m² pour 200kW). Radiations : Provoque erreurs (SEU, SEL) et dommages. Les GPU sont très vulnérables. Blindage lourd et inefficace. Les puces "durcies" sont très lentes. Communications : Bande passante très limitée (1Gbps radio vs 100Gbps terrestre). Le laser est tributaire des conditions atmosphériques. Conclusion : Projet extrêmement difficile, coûteux et aux performances médiocres. Data et Intelligence Artificielle Guillaume a développé un serveur MCP pour arXiv (le site de publication de papiers de recherche) en Java avec le framework Quarkus https://glaforge.dev/posts/2026/01/18/implementing-an-arxiv-mcp-server-with-quarkus-in-java/ Implémentation d'un serveur MCP (Model Context Protocol) arXiv en Java avec Quarkus. Objectif : Accéder aux publications arXiv et illustrer les fonctionnalités moins connues du protocole MCP. Mise en œuvre : Utilisation du framework Quarkus (Java) et son support MCP étendu. Assistance par Antigravity (IDE agentique) pour le développement et l'intégration de l'API arXiv. Interaction avec l'API arXiv : requêtes HTTP, format XML Atom pour les résultats, parser XML Jackson. Fonctionnalités MCP exposées : Outils (@Tool) : Recherche de publications (search_papers). Ressources (@Resource, @ResourceTemplate) : Taxonomie des catégories arXiv, métadonnées des articles (via un template d'URI). Prompts (@Prompt) : Exemples pour résumer des articles ou construire des requêtes de recherche. Configuration : Le serveur peut fonctionner en STDIO (local) ou via HTTP Streamable (local ou distant), avec une configuration simple dans des clients comme Gemini CLI. Conclusion : Quarkus simplifie la création de serveurs MCP riches en fonctionnalités, rendant les données et services "prêts pour l'IA" avec l'aide d'outils d'IA comme Antigravity. Anthropic ne mettra pas de pub dans Claude https://www.anthropic.com/news/claude-is-a-space-to-think c'est en reaction au plan non public d'OpenAi de mettre de la pub pour pousser les gens au mode payant OpenAI a besoin de cash et est probablement le plus utilisé pour gratuit au monde Anthropic annonce que Claude restera sans publicité pour préserver son rôle d'assistant conversationnel dédié au travail et à la réflexion approfondie. Les conversations avec Claude sont souvent sensibles, personnelles ou impliquent des tâches complexes d'ingénierie logicielle où les publicités seraient inappropriées. L'analyse des conversations montre qu'une part significative aborde des sujets délicats similaires à ceux évoqués avec un conseiller de confiance. Un modèle publicitaire créerait des incitations contradictoires avec le principe fondamental d'être "genuinely helpful" inscrit dans la Constitution de Claude. Les publicités introduiraient un conflit d'intérêt potentiel où les recommandations pourraient être influencées par des motivations commerciales plutôt que par l'intérêt de l'utilisateur. Le modèle économique d'Anthropic repose sur les contrats entreprise et les abonnements payants, permettant de réinvestir dans l'amélioration de Claude. Anthropic maintient l'accès gratuit avec des modèles de pointe et propose des tarifs réduits pour les ONG et l'éducation dans plus de 60 pays. Le commerce "agentique" sera supporté mais uniquement à l'initiative de l'utilisateur, jamais des annonceurs, pour préserver la confiance. Les intégrations tierces comme Figma, Asana ou Canva continueront d'être développées en gardant l'utilisateur aux commandes. Anthropic compare Claude à un cahier ou un tableau blanc : des espaces de pensée purs, sans publicité. Infinispan 16.1 est sorti https://infinispan.org/blog/2026/02/04/infinispan-16-1 déjà le nom de la release mérite une mention Le memory bounded par cache et par ensemble de cache s est pas facile à faire en Java Une nouvelle api OpenAPI AOT caché dans les images container Un serveur MCP local juste avec un fichier Java ? C'est possible avec LangChain4j et JBang https://glaforge.dev/posts/2026/02/11/zero-boilerplate-java-stdio-mcp-servers-with-langchain4j-and-jbang/ Création rapide de serveurs MCP Java sans boilerplate. MCP (Model Context Protocol): standard pour connecter les LLM à des outils et données. Le tutoriel répond au manque d'options simples pour les développeurs Java, face à une prédominance de Python/TypeScript dans l'écosystème MCP. La solution utilise: LangChain4j: qui intègre un nouveau module serveur MCP pour le protocole STDIO. JBang: permet d'exécuter des fichiers Java comme des scripts, éliminant les fichiers de build (pom.xml, Gradle). Implémentation: se fait via un seul fichier .java. JBang gère automatiquement les dépendances (//DEPS). L'annotation @Tool de LangChain4j expose les méthodes Java aux LLM. StdioMcpServerTransport gère la communication JSON-RPC via l'entrée/sortie standard (STDIO). Point crucial: Les logs doivent impérativement être redirigés vers System.err pour éviter de corrompre System.out, qui est réservé à la communication MCP (messages JSON-RPC). Facilite l'intégration locale avec des outils comme Gemini CLI, Claude Code, etc. Reciprocal Rank Fusion : un algorithme utile et souvent utilisé pour faire de la recherche hybride, pour mélanger du RAG et des recherches par mots-clé https://glaforge.dev/posts/2026/02/10/advanced-rag-understanding-reciprocal-rank-fusion-in-hybrid-search/ RAG : Qualité LLM dépend de la récupération. Recherche Hybride : Combiner vectoriel et mots-clés (BM25) est optimal. Défi : Fusionner des scores d'échelles différentes. Solution : Reciprocal Rank Fusion (RRF). RRF : Algorithme robuste qui fusionne des listes de résultats en se basant uniquement sur le rang des documents, ignorant les scores. Avantages RRF : Pas de normalisation de scores, scalable, excellente première étape de réorganisation. Architecture RAG fréquente : RRF (large sélection) + Cross-Encoder / modèle de reranking (précision fine). RAG-Fusion : Utilise un LLM pour générer plusieurs variantes de requête, puis RRF agrège tous les résultats pour renforcer le consensus et réduire les hallucinations. Implémentation : LangChain4j utilise RRF par défaut pour agréger les résultats de plusieurs retrievers. Les dernières fonctionnalités de Gemini et Nano Banana supportées dans LangChain4j https://glaforge.dev/posts/2026/02/06/latest-gemini-and-nano-banana-enhancements-in-langchain4j/ Nouveaux modèles d'images Nano Banana (Gemini 2.5/3.0) pour génération et édition (jusqu'à 4K). "Grounding" via Google Search (pour images et texte) et Google Maps (localisation, Gemini 2.5). Outil de contexte URL (Gemini 3.0) pour lecture directe de pages web. Agents multimodaux (AiServices) capables de générer des images. Configuration de la réflexion (profondeur Chain-of-Thought) pour Gemini 3.0. Métadonnées enrichies : usage des tokens et détails des sources de "grounding". Comment configurer Gemini CLI comment agent de code dans IntelliJ grâce au protocole ACP https://glaforge.dev/posts/2026/02/01/how-to-integrate-gemini-cli-with-intellij-idea-using-acp/ But : Intégrer Gemini CLI à IntelliJ IDEA via l'Agent Client Protocol (ACP). Prérequis : IntelliJ IDEA 2025.3+, Node.js (v20+), Gemini CLI. Étapes : Installer Gemini CLI (npm install -g @google/gemini-cli). Localiser l'exécutable gemini. Configurer ~/.jetbrains/acp.json (chemin exécutable, --experimental-acp, use_idea_mcp: true). Redémarrer IDEA, sélectionner "Gemini CLI" dans l'Assistant IA. Usage : Gemini interagit avec le code et exécute des commandes (contexte projet). Important : S'assurer du flag --experimental-acp dans la configuration. Outillage PipeNet, une alternative (open source aussi) à LocalTunnel, mais un plus évoluée https://pipenet.dev/ pipenet: Alternative open-source et moderne à localtunnel (client + serveur). Usages: Développement local (partage, webhooks), intégration SDK, auto-hébergement sécurisé. Fonctionnalités: Client (expose ports locaux, sous-domaines), Serveur (déploiement, domaines personnalisés, optimisé cloud mono-port). Avantages vs localtunnel: Déploiement cloud sur un seul port, support multi-domaines, TypeScript/ESM, maintenance active. Protocoles: HTTP/S, WebSocket, SSE, HTTP Streaming. Intégration: CLI ou SDK JavaScript. JSON-IO — une librairie comme Jackson ou GSON, supportant JSON5, TOON, et qui pourrait être utile pour l'utilisation du "structured output" des LLMs quand ils ne produisent pas du JSON parfait https://github.com/jdereg/json-io json-io : Librairie Java pour la sérialisation et désérialisation JSON/TOON. Gère les graphes d'objets complexes, les références cycliques et les types polymorphes. Support complet JSON5 (lecture et écriture), y compris des fonctionnalités non prises en charge par Jackson/Gson. Format TOON : Notation orientée token, optimisée pour les LLM, réduisant l'utilisation de tokens de 40 à 50% par rapport au JSON. Légère : Aucune dépendance externe (sauf java-util), taille de JAR réduite (~330K). Compatible JDK 1.8 à 24, ainsi qu'avec les environnements JPMS et OSGi. Deux modes de conversion : vers des objets Java typés (toJava()) ou vers des Map (toMaps()). Options de configuration étendues via ReadOptionsBuilder et WriteOptionsBuilder. Optimisée pour les déploiements cloud natifs et les architectures de microservices. Utiliser mailpit et testcontainer pour tester vos envois d'emails https://foojay.io/today/testing-emails-with-testcontainers-and-mailpit/ l'article montre via SpringBoot et sans. Et voici l'extension Quarkus https://quarkus.io/extensions/io.quarkiverse.mailpit/quarkus-mailpit/?tab=docs Tester l'envoi d'emails en développement est complexe car on ne peut pas utiliser de vrais serveurs SMTP Mailpit est un serveur SMTP de test qui capture les emails et propose une interface web pour les consulter Testcontainers permet de démarrer Mailpit dans un conteneur Docker pour les tests d'intégration L'article montre comment configurer une application SpringBoot pour envoyer des emails via JavaMail Un module Testcontainers dédié à Mailpit facilite son intégration dans les tests Le conteneur Mailpit expose un port SMTP (1025) et une API HTTP (8025) pour vérifier les emails reçus Les tests peuvent interroger l'API HTTP de Mailpit pour valider le contenu des emails envoyés Cette approche évite d'utiliser des mocks et teste réellement l'envoi d'emails Mailpit peut aussi servir en développement local pour visualiser les emails sans les envoyer réellement La solution fonctionne avec n'importe quel framework Java supportant JavaMail Architecture Comment scaler un système de 0 à 10 millions d'utilisateurs https://blog.algomaster.io/p/scaling-a-system-from-0-to-10-million-users Philosophie : Scalabilité incrémentale, résoudre les goulots d'étranglement sans sur-ingénierie. 0-100 utilisateurs : Serveur unique (app, DB, jobs). 100-1K : Séparer app et DB (services gérés, pooling). 1K-10K : Équilibreur de charge, multi-serveurs d'app (stateless via sessions partagées). 10K-100K : Caching, réplicas de lecture DB, CDN (réduire charge DB). 100K-500K : Auto-scaling, applications stateless (authentification JWT). 500K-10M : Sharding DB, microservices, files de messages (traitement asynchrone). 10M+ : Déploiement multi-régions, CQRS, persistance polyglotte, infra personnalisée. Principes clés : Simplicité, mesure, stateless essentiel, cache/asynchrone, sharding prudent, compromis (CAP), coût de la complexité. Patterns d'Architecture 2026 - Du Hype à la Réalité du Terrain (Part 1/2) - https://blog.ippon.fr/2026/01/30/patterns-darchitecture-2026-part-1/ L'article présente quatre patterns d'architecture logicielle pour répondre aux enjeux de scalabilité, résilience et agilité business dans les systèmes modernes Il présentent leurs raisons et leurs pièges Un bon rappel L'Event-Driven Architecture permet une communication asynchrone entre systèmes via des événements publiés et consommés, évitant le couplage direct Les bénéfices de l'EDA incluent la scalabilité indépendante des composants, la résilience face aux pannes et l'ajout facile de nouveaux cas d'usage Le pattern API-First associé à un API Gateway centralise la sécurité, le routage et l'observabilité des APIs avec un catalogue unifié Le Backend for Frontend crée des APIs spécifiques par canal (mobile, web, partenaires) pour optimiser l'expérience utilisateur CQRS sépare les modèles de lecture et d'écriture avec des bases optimisées distinctes, tandis que l'Event Sourcing stocke tous les événements plutôt que l'état actuel Le Saga Pattern gère les transactions distribuées via orchestration centralisée ou chorégraphie événementielle pour coordonner plusieurs microservices Les pièges courants incluent l'explosion d'événements granulaires, la complexité du debugging distribué, et la mauvaise gestion de la cohérence finale Les technologies phares sont Kafka pour l'event streaming, Kong pour l'API Gateway, EventStoreDB pour l'Event Sourcing et Temporal pour les Sagas Ces patterns nécessitent une maturité technique et ne sont pas adaptés aux applications CRUD simples ou aux équipes junior Patterns d'architecture 2026 : du hype à la réalité terrain part. 2 - https://blog.ippon.fr/2026/02/04/patterns-darchitecture-2026-part-2/ Deuxième partie d'un guide pratique sur les patterns d'architecture logicielle et système éprouvés pour moderniser et structurer les applications en 2026 Strangler Fig permet de migrer progressivement un système legacy en l'enveloppant petit à petit plutôt que de tout réécrire d'un coup (70% d'échec pour les big bang) Anti-Corruption Layer protège votre nouveau domaine métier des modèles externes et legacy en créant une couche de traduction entre les systèmes Service Mesh gère automatiquement la communication inter-services dans les architectures microservices (sécurité mTLS, observabilité, résilience) Architecture Hexagonale sépare le coeur métier des détails techniques via des ports et adaptateurs pour améliorer la testabilité et l'évolutivité Chaque pattern est illustré par un cas client concret avec résultats mesurables et liste des pièges à éviter lors de l'implémentation Les technologies 2026 mentionnées incluent Istio, Linkerd pour service mesh, LaunchDarkly pour feature flags, NGINX et Kong pour API gateway Tableau comparatif final aide à choisir le bon pattern selon la complexité, le scope et le use case spécifique du projet L'article insiste sur une approche pragmatique : ne pas utiliser un pattern juste parce qu'il est moderne mais parce qu'il résout un problème réel Pour les systèmes simples type CRUD ou avec peu de services, ces patterns peuvent introduire une complexité inutile qu'il faut savoir éviter Méthodologies Le rêve récurrent de remplacer voire supprimer les développeurs https://www.caimito.net/en/blog/2025/12/07/the-recurring-dream-of-replacing-developers.html Depuis 1969, chaque décennie voit une tentative de réduire le besoin de développeurs (de COBOL, UML, visual builders… à IA). Motivation : frustration des dirigeants face aux délais et coûts de développement. La complexité logicielle est intrinsèque et intellectuelle, non pas une question d'outils. Chaque vague technologique apporte de la valeur mais ne supprime pas l'expertise humaine. L'IA assiste les développeurs, améliore l'efficacité, mais ne remplace ni le jugement ni la gestion de la complexité. La demande de logiciels excède l'offre car la contrainte majeure est la réflexion nécessaire pour gérer cette complexité. Pour les dirigeants : les outils rendent-ils nos développeurs plus efficaces sur les problèmes complexes et réduisent-ils les tâches répétitives ? Le "rêve" de remplacer les développeurs, irréalisable, est un moteur d'innovation créant des outils précieux. Comment creuser des sujets à l'ère de l'IA générative. Quid du partage et la curation de ces recherches ? https://glaforge.dev/posts/2026/02/04/researching-topics-in-the-age-of-ai-rock-solid-webhooks-case-study/ Recherche initiale de l'auteur sur les webhooks en 2019, processus long et manuel. L'IA (Deep Research, Gemini, NotebookLM) facilite désormais la recherche approfondie, l'exploration de sujets et le partage des résultats. L'IA a identifié et validé des pratiques clés pour des déploiements de webhooks résilients, en grande partie les mêmes que celles trouvées précédemment par l'auteur. Génération d'artefacts par l'IA : rapport détaillé, résumé concis, illustration sketchnote, et même une présentation (slide deck). Guillaume s'interroge sur le partage public de ces rapports de recherche générés par l'IA, tout en souhaitant éviter le "AI Slop". Loi, société et organisation Le logiciel menacé par le vibe coding https://www.techbuzz.ai/articles/we-built-a-monday-com-clone-in-under-an-hour-with-ai Deux journalistes de CNBC sans expérience de code ont créé un clone fonctionnel de Monday.com en moins de 60 minutes pour 5 à 15 dollars. L'expérience valide les craintes des investisseurs qui ont provoqué une baisse de 30% des actions des entreprises SaaS. L'IA a non seulement reproduit les fonctionnalités de base mais a aussi recherché Monday.com de manière autonome pour identifier et recréer ses fonctionnalités clés. Cette technique appelée "vibe-coding" permet aux non-développeurs de construire des applications via des instructions en anglais courant. Les entreprises les plus vulnérables sont celles offrant des outils "qui se posent sur le travail" comme Atlassian, Adobe, HubSpot, Zendesk et Smartsheet. Les entreprises de cybersécurité comme CrowdStrike et Palo Alto sont considérées plus protégées grâce aux effets de réseau et aux barrières réglementaires. Les systèmes d'enregistrement comme Salesforce restent plus difficiles à répliquer en raison de leur profondeur d'intégration et de données d'entreprise. Le coût de 5 à 15 dollars par construction permet aux entreprises de prototyper plusieurs solutions personnalisées pour moins cher qu'une seule licence Monday.com. L'expérience soulève des questions sur la pérennité du marché de 5 milliards de dollars des outils de gestion de projet face à l'IA générative. Conférences En complément de l'agenda des conférences de Aurélie Vache, il y a également le site https://javaconferences.org/ (fait par Brian Vermeer) avec toutes les conférences Java à venir ! La liste des conférences provenant de Developers Conferences Agenda/List par Aurélie Vache et contributeurs : 12-13 février 2026 : Touraine Tech #26 - Tours (France) 12-13 février 2026 : World Artificial Intelligence Cannes Festival - Cannes (France) 19 février 2026 : ObservabilityCON on the Road - Paris (France) 6 mars 2026 : WordCamp Nice 2026 - Nice (France) 18 mars 2026 : Jupyter Workshops: AI in Jupyter: Building Extensible AI Capabilities for Interactive Computing - Saint-Maur-des-Fossés (France) 18-19 mars 2026 : Agile Niort 2026 - Niort (France) 20 mars 2026 : Atlantique Day 2026 - Nantes (France) 26 mars 2026 : Data Days Lille - Lille (France) 26-27 mars 2026 : SymfonyLive Paris 2026 - Paris (France) 26-27 mars 2026 : REACT PARIS - Paris (France) 27-29 mars 2026 : Shift - Nantes (France) 31 mars 2026 : ParisTestConf - Paris (France) 31 mars 2026-1 avril 2026 : FlowCon France 2026 - Paris (France) 1 avril 2026 : AWS Summit Paris - Paris (France) 2 avril 2026 : Pragma Cannes 2026 - Cannes (France) 2-3 avril 2026 : Xen Spring Meetup 2026 - Grenoble (France) 7 avril 2026 : PyTorch Conference Europe - Paris (France) 9-10 avril 2026 : Android Makers by droidcon 2026 - Paris (France) 9-11 avril 2026 : Drupalcamp Grenoble 2026 - Grenoble (France) 16-17 avril 2026 : MiXiT 2026 - Lyon (France) 17-18 avril 2026 : Faiseuses du Web 5 - Dinan (France) 22-24 avril 2026 : Devoxx France 2026 - Paris (France) 23-25 avril 2026 : Devoxx Greece - Athens (Greece) 6-7 mai 2026 : Devoxx UK 2026 - London (UK) 12 mai 2026 : Lead Innovation Day - Leadership Edition - Paris (France) 19 mai 2026 : La Product Conf Paris 2026 - Paris (France) 21-22 mai 2026 : Flupa UX Days 2026 - Paris (France) 22 mai 2026 : AFUP Day 2026 Lille - Lille (France) 22 mai 2026 : AFUP Day 2026 Paris - Paris (France) 22 mai 2026 : AFUP Day 2026 Bordeaux - Bordeaux (France) 22 mai 2026 : AFUP Day 2026 Lyon - Lyon (France) 28 mai 2026 : DevCon 27 : I.A. & Vibe Coding - Paris (France) 28 mai 2026 : Cloud Toulouse 2026 - Toulouse (France) 29 mai 2026 : NG Baguette Conf 2026 - Paris (France) 29 mai 2026 : Agile Tour Strasbourg 2026 - Strasbourg (France) 2-3 juin 2026 : Agile Tour Rennes 2026 - Rennes (France) 2-3 juin 2026 : OW2Con - Paris-Châtillon (France) 3 juin 2026 : IA–NA - La Rochelle (France) 5 juin 2026 : TechReady - Nantes (France) 5 juin 2026 : Fork it! - Rouen - Rouen (France) 6 juin 2026 : Polycloud - Montpellier (France) 9 juin 2026 : JFTL - Montrouge (France) 9 juin 2026 : C: - Caen (France) 11-12 juin 2026 : DevQuest Niort - Niort (France) 11-12 juin 2026 : DevLille 2026 - Lille (France) 12 juin 2026 : Tech F'Est 2026 - Nancy (France) 16 juin 2026 : Mobilis In Mobile 2026 - Nantes (France) 17-19 juin 2026 : Devoxx Poland - Krakow (Poland) 17-20 juin 2026 : VivaTech - Paris (France) 18 juin 2026 : Tech'Work - Lyon (France) 22-26 juin 2026 : Galaxy Community Conference - Clermont-Ferrand (France) 24-25 juin 2026 : Agi'Lille 2026 - Lille (France) 24-26 juin 2026 : BreizhCamp 2026 - Rennes (France) 2 juillet 2026 : Azur Tech Summer 2026 - Valbonne (France) 2-3 juillet 2026 : Sunny Tech - Montpellier (France) 3 juillet 2026 : Agile Lyon 2026 - Lyon (France) 6-8 juillet 2026 : Riviera Dev - Sophia Antipolis (France) 2 août 2026 : 4th Tech Summit on Artificial Intelligence & Robotics - Paris (France) 20-22 août 2026 : 4th Tech Summit on AI & Robotics - Paris (France) & Online 4 septembre 2026 : JUG Summer Camp 2026 - La Rochelle (France) 17-18 septembre 2026 : API Platform Conference 2026 - Lille (France) 24 septembre 2026 : PlatformCon Live Day Paris 2026 - Paris (France) 1 octobre 2026 : WAX 2026 - Marseille (France) 1-2 octobre 2026 : Volcamp - Clermont-Ferrand (France) 5-9 octobre 2026 : Devoxx Belgium - Antwerp (Belgium) Nous contacter Pour réagir à cet épisode, venez discuter sur le groupe Google https://groups.google.com/group/lescastcodeurs Contactez-nous via X/twitter https://twitter.com/lescastcodeurs ou Bluesky https://bsky.app/profile/lescastcodeurs.com Faire un crowdcast ou une crowdquestion Soutenez Les Cast Codeurs sur Patreon https://www.patreon.com/LesCastCodeurs Tous les épisodes et toutes les infos sur https://lescastcodeurs.com/

An airhacks.fm conversation with Francesco Nigro (@forked_franz) about: break dancing and basketball including meeting Kobe Bryant in Italy during a dunk competition, using AI coding assistants like Claude Opus 4.5 and GitHub bots for infrastructure setup and CI/CD pipeline configuration, limitations of LLMs for novel performance-sensitive algorithmic work where training data is scarce, branchless IPv4 parsing optimization as a Christmas coding challenge, CPU branch misprediction costs when parsing variable-length IP address octets, converting branching logic into mathematical operations using bit tricks for better CPU pipeline utilization, LLMs excelling at generating enterprise code based on well-documented standards and conventions, providing minimal but precise documentation and annotations to improve LLM code generation quality, the Boundary Control Entity BCE architecture pattern and standards-based development, the core problem of thread handoff between event loops and ForkJoinPool worker threads in frameworks like quarkus Vert.x and Micronaut, mechanical sympathy implications of cross-core memory access when serialized data is allocated on one core and read by another, CPU cache coherency costs and last-level cache penalties when event loop and worker pool run on different cores, the custom virtual thread scheduler project (netty-virtual-thread-scheduler) enabling a single platform thread to handle both networking I/O and virtual thread execution, approximately 50% CPU savings demonstrated by Micronaut when using unified Netty-based scheduling, collaboration with Oracle Loom team including Victor Klang and Alan Bateman on minimal scheduler API design, the scheduler API consisting of just two methods onStart and onContinue plus virtual thread task attachments, work stealing algorithms and their complexity including heuristics similar to Linux CFS scheduler, the importance of being declarative about thread affinity rather than automatic magical binding to avoid issues with lazy class loading and background reaper threads, thread factory based approach for creating virtual threads bound to specific platform threads, stream-based run queues with graceful shutdown semantics that fall back to ForkJoinPool for progress guarantees, thread-local Scoped Values as a hybrid between thread locals and scoped values for efficient context propagation, performance problems with ThreadLocal including lazy ThreadLocalMap allocation overhead on virtual threads and scalability issues with ThreadLocal.remove() and soft reference queues, the impact on reactive programming where back pressure and stream composition still require higher-level abstractions beyond Basic Java concurrency primitives, structured concurrency limitations for back pressure scenarios compared to reactive libraries, deterministic testing possibilities enabled by custom schedulers where execution order can be controlled, the poller mechanism for handling blocking I/O in virtual threads in a non-blocking way, observability improvements possible through virtual thread task attachments for monitoring state changes, cloud cost implications of inefficient thread scheduling and unnecessary CPU wake-up cycles, the distinction between framework developers and application developers as different user personas with different abstraction needs Francesco Nigro on twitter: @forked_franz

Episode Topic: The History of Asian Allure (https://go.nd.edu/c9944d)Discover the origins, evolution, and impact of Asian Allure, the annual, student-led performance that has become a cornerstone of the Asian Pacific Islander community at Notre Dame. Explore how Asian Allure began, how it has grown over the years, and what it continues to mean for generations of API students at Our Lady's University in a conversation with co-founder Teresita Mercado '97, '00 J.D., and her daughters Bianca Feix '25, and Mia Feix '27, moderated by Cecilia Lucero '84.Featured Speakers:Cecilia Lucero '84, University of Notre DameBianca Feix '25Mia Feix '27, University of Notre DameTeresita T. Mercado '97, '00 J.D., BuchalterRead this episode's recap over on the University of Notre Dame's open online learning community platform, ThinkND: https://go.nd.edu/e124a2.This podcast is a part of the ThinkND Series titled 120 Years Later: Asian and Pacific Islander Alumni Perspectives.Thanks for listening! The ThinkND Podcast is brought to you by ThinkND, the University of Notre Dame's online learning community. We connect you with videos, podcasts, articles, courses, and other resources to inspire minds and spark conversations on topics that matter to you — everything from faith and politics, to science, technology, and your career. Learn more about ThinkND and register for upcoming live events at think.nd.edu. Join our LinkedIn community for updates, episode clips, and more.

This week Brandon Min, Founder and CEO of Herd Security, joins Defender Fridays to discuss how human risk management needs to rebrand with empathy.Brandon is the co-founder and CEO of Herd Security, where they help security teams drive employee engagement in security, making a more resilient organization. Humans have been the #1 target of organizational cyber attacks; however, security teams, organizations, vendors, and leaders have vilified them. At Herd, they believe security should be led with empathy and care. Building trust amongst users that will drive their engagement in security. Building herd immunity from cyber attacks. Learn more at https://herdsecurity.io/Register for Live SessionsJoin us every Friday at 10:30am PT for live, interactive discussions with industry experts. Whether you're a seasoned professional or just curious about the field, these sessions offer an engaging dialogue between our guests, hosts, and you – our audience.Register here: https://limacharlie.io/defender-fridaysSubscribe to our YouTube channel and hit the notification bell to never miss a live session or catch up on past episodes!Sponsored by LimaCharlieThis episode is brought to you by LimaCharlie, a cloud-native SecOps platform where AI agents operate security infrastructure directly. Founded in 2018, LimaCharlie provides complete API coverage across detection, response, automation, and telemetry, with multi-tenant architecture designed for MSSPs and MDR providers managing thousands of unique client environments.Why LimaCharlie?Transparency: Complete visibility into every action and decision. No black boxes, no vendor lock-in.Scalability: Security operations that scale like infrastructure, not like procurement cycles. Move at cloud speed.Unopinionated Design: Integrate the tools you need, not just those contracts allow. Build security on your terms.Agentic SecOps Workspace (ASW): AI agents that operate alongside your team with observable, auditable actions through the same APIs human analysts use.Security Primitives: Composable building blocks that endure as tools come and go. Build once, evolve continuously.Try the Agentic SecOps Workspace free: https://limacharlie.ioLearn more: https://docs.limacharlie.ioFollow LimaCharlieSign up for free: https://limacharlie.ioLinkedIn: / limacharlieio X: https://x.com/limacharlieioCommunity Discourse: https://community.limacharlie.com/Host: Maxime Lamothe-Brassard - CEO / Co-founder at LimaCharlie

FHIR-Native Architecture: Building Healthcare IT for True Interoperability As healthcare systems race to meet 21st Century Cures Act mandates, a critical question emerges: retrofit or rebuild? Mike O'Neill, CEO of MedicaSoft, explains why FHIR-native architecture delivers fundamentally different interoperability outcomes than legacy systems with API layers bolted on. This conversation cuts through vendor marketing to examine the structural, semantic, and operational advantages of building healthcare IT from the ground up on HL7 FHIR standards. O'Neill draws on extensive experience leading P&L, engineering, and operations across healthcare IT startups and public companies to explain what "FHIR-native" actually means in practice—and why it matters for CIOs evaluating vendor claims. Learn how purpose-built FHIR architecture eliminates middleware complexity, reduces integration costs, and enables real-time clinical data exchange that retrofitted systems struggle to deliver. Find all of our network podcasts on your favorite podcast platforms and be sure to subscribe and like us. Learn more at www.healthcarenowradio.com/listen/

Will Caldwell started Snap after his first real estate software startup fizzled, pivoting from agent tools to regulated compliance data. He discovered lenders were required to buy hazard and flood certifications, and realized this was a "painkiller" product. He built Snap as a data and analytics platform for real estate and mortgage underwriting. Snap grew from a single California compliance product into a national flood data business, reaching $5M in revenue and 30 employees. The company charged per-loan transaction fees and embedded via API into mortgage software systems. With double-digit market share, Snap focused on customer experience, automation, and expanding wallet share inside lenders' workflows. In October 2024, Snap sold 51% of the company to Intercontinental Exchange, parent of ICE Mortgage Technology, at a double-digit revenue multiple. Will stayed on to scale the platform inside a much larger ecosystem. His key lesson: dominate a narrow niche, build a required product, and let strategic buyers find you. Key Takeaways Required Beats Optional – Legal compliance products create urgency and retention because customers must buy to complete revenue-generating transactions. Micro-Niche Entry – Starting in a narrow regulated segment let Snap win trust, then expand into much larger adjacent markets. API = Distribution – Embedding inside legacy systems turned Snap into a one-click button that scaled through partners' existing sales teams. Customer Experience Wins – In commodity data markets, faster, cheaper, simpler delivery became Snap's main competitive weapon. Quote from Will Caldwell, CEO and Co-Founder of Snap "You don't need to build a huge business to get a huge, life-changing exit. Just stay laser-focused. Don't chase shiny objects. I see many founders trying to boil the ocean. It is about staying focused on a single niche. "I think vertical SaaS has many great niches, and horizontal software is challenging. You need a lot of money to go after horizontal solutions across industries. However, with vertical SaaS products and niches, there is a lot of overlooked opportunity; the real estate vertical is one prime example." Links Will Caldwell on LinkedIn Snap on LinkedIn Snap website Podcast Sponsor – LaunchBay LaunchBay helps B2B software companies automate client onboarding and implementation so customers activate faster and everyone stays aligned. If your onboarding includes data collection, setup steps, approvals, training, or any level of customization, LaunchBay replaces the messy mix of emails, spreadsheets, and meetings with a clear, all-in-one onboarding system. Teams use LaunchBay to onboard clients faster, stay on top of follow-ups automatically, and deliver a smoother experience, without hiring more people or adding more tools. Visit launchbay.com/practical and get 25% off your first 3 months on any LaunchBay plan. The Practical Founders Podcast Tune into the Practical Founders Podcast for weekly in-depth interviews with founders who have built valuable software companies without big funding. Subscribe to the Practical Founders Podcast using your favorite podcast app or view on our YouTube channel. Get the weekly Practical Founders newsletter and podcast updates at practicalfounders.com. Practical Founders CEO Peer Groups Be part of a committed and confidential group of practical founders creating valuable software companies without big VC funding.  A Practical Founders Peer Group is a committed and confidential group of founders/CEOs who want to help you succeed on your terms. Each Practical Founders Peer Group is personally curated and moderated by Greg Head.

In this xAI all-hands update, Elon Musk and team leaders walk through what they call xAI's fast progress over roughly two and a half years, from new Grok model releases to major build-outs in compute, product, and the X platform. They frame the company's advantage as execution speed, then outline a reorganization meant to keep small teams moving quickly as headcount grows.The presentation also features updates across four core product tracks, including a merged Grok main + voice org, a dedicated coding model effort, the “Imagine” image and video stack, and “Macrohard,” an agent-style program aimed at doing full computer-based work the way a person would. The team also shares details about the Memphis training cluster expansion, plus upcoming plans for X Chat, X Money, and longer-term ties between xAI and SpaceX.Key points coveredClaims of early leadership: speakers cite top performance in voice, image, and video generation, plus forecasting results from a “Grok 4.2” forecasting model, and broader improvements across the Grok app experience.Compute scale-up: leadership says xAI reached a 100,000 H100 training cluster and is targeting 1 million H100-equivalent capacity.Company restructure: four main application areas: Grok main/voice, coding, Imagine (image and video), and Macrohard, supported by infra and product platform teams.Voice and product distribution: the team says Grok voice went from zero to a shipping product in months, and that Grok now runs in more than 2 million Teslas, alongside a voice agent API.Coding models: leaders describe stronger code generation and debugging, heavy internal use, and a push toward “recursive” improvement where models help build the next training stack.Imagine adoption metrics (as stated): the team cites ~50 million videos per day and ~6 billion images in 30 days, plus deep integration into the X app for editing and image-to-video.Macrohard agents: the pitch is end-to-end computer use across common GUIs, with an end goal of emulating “digital-first” company workflows.Memphis supercluster tour: infrastructure leads describe rapid construction timelines, large-scale networking, fiber runs, power plans, and the role of on-site teams keeping training and inference stable.X platform roadmap: they discuss engagement growth, onboarding changes, subscriptions revenue targets, encrypted X Chat features, plans to open source parts of the stack, and a staged rollout of X Money.Space and compute: Musk ties xAI's goals to SpaceX, describing a path from Earth-based data centers to orbital compute, and later, lunar industrial capacity.0:00 Elon Musk's Opening Remarks xAI “All Hands” Meeting - xAI Accomplishments Since Inception3:58 Elon & xAI Team Give Big Update26:00 Live Tour Of xAI's ‘Macrohard' AI Training Supercluster In Memphis30:20 xAI's Secret Weapon: The X Platform - Nikita Explains32:58 Elon On X Money, X Chat, Future Goals35:34 Elon Explains SpaceX & xAI Joining - “Exploring The Universe” & SpaceX Moonbase Alpha

Sherwin Wu leads engineering for OpenAI's API platform, where roughly 95% of engineers use Codex, often working with fleets of 10 to 20 parallel AI agents.We discuss:1. What OpenAI did to cut code review times from 10-15 minutes to 2-3 minutes2. How AI is changing the role of managers3. Why the productivity gap between AI power users and everyone else is widening4. Why “models will eat your scaffolding for breakfast”5. Why the next 12 to 24 months are a rare window where engineers can leap ahead before the role fully transforms—Brought to you by:DX—The developer intelligence platform designed by leading researchersSentry—Code breaks, fix it fasterDatadog—Now home to Eppo, the leading experimentation and feature flagging platform—Episode transcript: https://www.lennysnewsletter.com/p/engineers-are-becoming-sorcerers—Archive of all Lenny's Podcast transcripts: https://www.dropbox.com/scl/fo/yxi4s2w998p1gvtpu4193/AMdNPR8AOw0lMklwtnC0TrQ?rlkey=j06x0nipoti519e0xgm23zsn9&st=ahz0fj11&dl=0—Where to find Sherwin Wu:• X: https://x.com/sherwinwu• LinkedIn: https://www.linkedin.com/in/sherwinwu1—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 Sherwin Wu(03:10) AI's role in coding at OpenAI(06:53) The future of software engineering with AI(12:26) The stress of managing agents(15:07) Codex and code review automation(19:29) The changing role of engineering managers(24:14) The one-person billion-dollar startup(31:40) Management lessons(37:28) Challenges and best practices in AI deployment(43:56) Hot takes on AI and customer feedback(48:57) Building for future AI capabilities(50:16) Where models are headed in the next 18 months(53:35) Business process automation(57:22) OpenAI's ecosystem and platform strategy(01:00:50) OpenAI's mission and global impact(01:05:21) Building on OpenAI's API and tools(01:08:16) Lightning round and final thoughts—Referenced:• Codex: https://openai.com/codex• 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• OpenClaw: https://openclaw.ai• The creator of Clawd: “I ship code I don't read”: https://newsletter.pragmaticengineer.com/p/the-creator-of-clawd-i-ship-code• The Sorcerer's Apprentice: https://en.wikipedia.org/wiki/The_Sorcerer%27s_Apprentice_(Dukas)• Quora: https://www.quora.com• Marc Andreessen: The real AI boom hasn't even started yet: https://www.lennysnewsletter.com/p/marc-andreessen-the-real-ai-boom• Sarah Friar on LinkedIn: https://www.linkedin.com/in/sarah-friar• Sam Altman on X: https://x.com/sama• Nicolas Bustamante's “LLMs Eat Scaffolding for Breakfast” post on X: https://x.com/nicbstme/status/2015795605524901957• The Bitter Lesson: http://www.incompleteideas.net/IncIdeas/BitterLesson.html• Overton window: https://en.wikipedia.org/wiki/Overton_window• Developers can now submit apps to ChatGPT: https://openai.com/index/developers-can-now-submit-apps-to-chatgpt• Responses: https://platform.openai.com/docs/api-reference/responses• Agents SDK: https://platform.openai.com/docs/guides/agents-sdk• AgentKit: https://openai.com/index/introducing-agentkit• Ubiquiti: https://ui.com• Jujutsu Kaisen on Crunchyroll: https://www.crunchyroll.com/series/GRDV0019R/jujutsu-kaisen?srsltid=AfmBOoqvfzKQ6SZOgzyJwNQ43eceaJTQA2nUxTQfjA1Ko4OxlpUoBNRB• eero: https://eero.com• Opendoor: https://www.opendoor.com—Recommended books:• Structure and Interpretation of Computer Programs: https://www.amazon.com/Structure-Interpretation-Computer-Programs-Engineering/dp/0262510871• The Mythical Man-Month: Essays on Software Engineering: https://www.amazon.com/Mythical-Man-Month-Software-Engineering-Anniversary/dp/0201835959• There Is No Antimemetics Division: A Novel: https://www.amazon.com/There-No-Antimemetics-Division-Novel/dp/0593983750• Breakneck: China's Quest to Engineer the Future: https://www.amazon.com/Breakneck-Chinas-Quest-Engineer-Future/dp/1324106034• Apple in China: The Capture of the World's Greatest Company: https://www.amazon.com/Apple-China-Capture-Greatest-Company/dp/1668053373—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

Prabhleen Kaur: When Team Members Raise Concerns with Clarity, Not Anger Read the full Show Notes and search through the world's largest audio library on Agile and Scrum directly on the Scrum Master Toolbox Podcast website: http://bit.ly/SMTP_ShowNotes.   "My idea of success as a Scrum Master is when you look around, you see motivated people, and when something goes wrong, they come to you not in anger, but with concern." - Prabhleen Kaur   Prabhleen offers a refreshing perspective on measuring success as a Scrum Master that goes beyond velocity charts and feature counts. She shares a pivotal moment when her team was in production, delivering relentlessly with barely any time to breathe. A team member approached her—not with frustration or blame—but with thoughtful concern: "This is not going to work out." He sat down with Prabhleen and the Product Owner, explaining that as the middle layer in an API creation team, delays from upstream were creating a cascading problem.  What struck Prabhleen wasn't just the identification of the issue, but how he approached it: with options to discuss, not demands to make. This moment crystallized her definition of success. When team members feel safe enough to voice concerns early, when they come with ideas rather than accusations, when they see themselves as part of the solution rather than victims of circumstances—that's when a Scrum Master has truly succeeded.  Prabhleen reminds us that while stakeholders may focus on features delivered, Scrum Masters should watch how well the team responds to change. That adaptability, rooted in psychological safety and mutual trust, is the true measure of a team's maturity.   Self-reflection Question: When problems emerge in your team, do people approach you with defensive anger or constructive concern? What does that tell you about the psychological safety you've helped create? Featured Retrospective Format for the Week: Keep-Stop-Happy-Gratitude Prabhleen shares her favorite retrospective format, born from necessity when she joined an established team with dismal participation in their standard three-column retrospectives. She transformed it into a four-column approach: (1) What should we keep doing, (2) What should we stop doing, (3) One thing that will make you happy, and (4) Gratitude for the team. The third column—asking what would make team members happy—opened unexpected doors. Suggestions ranged from team outings to skipping Friday stand-ups, giving Prabhleen real-time insights into team needs without waiting for formal working agreement sessions. The gratitude column proved even more powerful. "Appreciation brings a space where trust is automatically built. When every 15 days you're sitting with the team making a point to say thank you to each other for all the work you've done, everybody feels mutually respected," Prabhleen explains. This ties directly to the trust-building discussed in Tuesday's episode—using retrospectives not just to improve processes, but to strengthen the human connections that make teams resilient.   [The Scrum Master Toolbox Podcast Recommends]

This week, Ben has a story on An AI-run social network exposed API keys, raising fears of agent hijacking and corporate data breaches. Dave's got the story of a judge tossing a case after a lawyer repeatedly filed fake AI-generated citations, calling it a failure of basic legal research. While this show covers legal topics, and Ben is a lawyer, the views expressed do not constitute legal advice. For official legal advice on any of the topics we cover, please contact your attorney.  Links to today's stories: Moltbook and the Rise of AI-Agent Networks: An Enterprise Governance Wake-Up Call AI legal advice is driving lawyers bananas Lawyer sets new standard for abuse of AI; judge tosses case Get the weekly Caveat Briefing delivered to your inbox. Like what you heard? Be sure to check out and subscribe to our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Caveat Briefing⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, a weekly newsletter available exclusively to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠N2K Pro⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ members on ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠N2K CyberWire's⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ website. N2K Pro members receive our Thursday wrap-up covering the latest in privacy, policy, and research news, including incidents, techniques, compliance, trends, and more. This week's Caveat Briefing covers a lawsuit that kicked off in California, where a woman is suing Meta and YouTube for the harm they allegedly cause to kids. Curious about the details? Head over to the ⁠⁠Caveat Briefing⁠⁠ for the full scoop and additional compelling stories. Got a question you'd like us to answer on our show? You can send your audio file to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠caveat@thecyberwire.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Hope to hear from you. Learn more about your ad choices. Visit megaphone.fm/adchoices

Jake and Michael discuss all the latest Laravel releases, tutorials, and happenings in the community.Show linkshasSole() Collection Method in Laravel 12.49.0hasMany() Collection Method in Laravel 12.50.0Filament v5.2.0 Adds a Callout ComponentClawdbot Rebrands to Moltbot After Trademark Request From AnthropicInstall Laravel Package Guidelines and Skills in BoostFuse for Laravel: A Circuit Breaker Package for Queue JobsNativePHP for Mobile Is Now FreeManage PostgreSQL Databases Directly in VS Code with Microsoft's ExtensionLivewire 4 and Blade Improvements in Laravel VS Code Extension v1.5.0Statamic 6 Is Officially ReleasedLaravel Announces Official AI SDK for Building AI-Powered AppsClaude Opus 4.6 adds adaptive thinking, 128K output, compaction API, and moreOpenAI Releases GPT-5.3-Codex, a New Codex Model for Agent-Style DevelopmentLaravel Live UK returns to London on June 18-19, 2026Bagisto Visual: Theme Framework with Visual Editor for Laravel E-commerceGenerate Complete Application Modules with a Single Command using Laravel TurboMakerEncrypt Files in Laravel with AES-256-GCM and Memory-Efficient StreamingMask Sensitive Eloquent Attributes on Retrieval in LaravelLaravel Related Content: Semantic Relationships Using pgvector

Mid-market organizations are transitioning from pilot projects to operationalizing generative AI and agentic workflows, according to a TechEYE article and Tech Isle survey cited by Dave Sobel. This shift centers on outcome-driven automation but exposes providers to new liability concerns, mainly due to fragmented, unreliable data and shadow AI usage—employees employing unauthorized tools outside official controls. The primary risk is that MSPs may be blamed for incidents where contract boundaries and technical controls do not cover browser-based generative AI use, making forensic evidence and documented enforcement essential for defending accountability. Supporting data from Tech Isle found that over 5,000 companies are pursuing structured approaches to AI-enabled growth, but face persistent issues in data trust, governance, and user fatigue. Additionally, European investment in sovereign cloud infrastructure is projected to triple between 2025 and 2027, driven by regulatory demands and concerns about U.S. data sovereignty. MSPs managing split architectures—sovereign providers for regulated data and hyperscalers for everything else—encounter API mismatches, operational complexity, and margin pressure. The recommendation is to standardize policy enforcement, identity management, and residency mapping while prioritizing audit-ready reporting and exception handling. AI-driven cyberattacks have increased, with reports from Level Blue and Check Point Research highlighting a surge in both attack volume and sophistication. Only 53% of CISOs feel prepared for AI threats, despite 45% expecting to be impacted within a year. Browser-based generative AI use introduces visibility gaps, raising the risk of negligence claims when service providers cannot demonstrate governance or forensic readiness. Reauthorization of the Cybersecurity Information Sharing Act (CISA) underscores that voluntary data sharing is inadequate, with CIRCA now requiring mandatory 72-hour incident reporting for critical infrastructure. The key takeaways for MSPs and IT leaders are to proactively define AI coverage and governance in contracts, enforce acceptable use policies, and instrument monitoring to close visibility gaps. Providers who can deliver forensic-grade telemetry, managed compliance programs, and operational readiness for incident reporting will be better positioned to defend against penalties, retain higher-value accounts, and offer meaningful differentiation. These structural challenges—fragmented control planes, increased compliance costs, and permanent risk friction—necessitate a strategic shift toward governance-led service models.Three things to know today00:00 Midmarket Shifts to Agentic AI as Europe Triples Sovereign Cloud Spending by 202706:08 Most Security Chiefs Say They're Not Ready for AI-Powered Cyberattacks Coming This Year09:46 CISA 2015 Reauthorized Through 2026; CIRCIA Mandates Expose Voluntary Sharing Failure This is the Business of Tech.   Supported by:  TimeZest  IT Service Provider University

What happens when all the world's money moves on chain? That's not a hypothetical for Marc Boiron, CEO of Polygon Labs, it's the company's mission. In this episode, Marc explains how Polygon is evolving from its roots as an Ethereum layer two into the blockchain for global payments, detailing two recent acquisitions that form the foundation of what he calls the "open money stack" - a single API combining on-ramps, wallets, and cross-chain interoperability.With over $2.5 trillion in transaction volume already processed and partnerships with Revolut, Stripe, Nubank, and dozens of fintechs across Latin America, Africa, and Asia, Marc makes the case that stablecoins are just the beginning. He shares why tokenized bank deposits will be the real game-changer, how banks are already positioning to profit from this shift, and why in 10 years he believes every dollar, whether paying a merchant down the street or sending a remittance across the globe, will move on a blockchain without anyone even thinking about it.In this podcast you will learn:How Marc first got interested in blockchain and crypto technology.Why he decided to make the move to Polygon Labs.Why Polygon decided to focus on payments.All the components you need to move money around the world on blockchain.The idea behind the open money stack.How Polygon is working with the likes of Revolut and Stripe.How they differentiate themselves from the other payments blockchains.What they are doing in AML and sanctions policy.The scale that Polygon is at today when it comes to transaction volume.What will the financial system look like when more money stays on chain.The two things banks ask in their initial conversations with Polygon.How money will transform in the next 10 years and why most people will not notice.Connect with Fintech One-on-One: Tweet me @PeterRenton Connect with me on LinkedIn Find previous Fintech One-on-One episodes

This podcast features Gabriele Corso and Jeremy Wohlwend, co-founders of Boltz and authors of the Boltz Manifesto, discussing the rapid evolution of structural biology models from AlphaFold to their own open-source suite, Boltz-1 and Boltz-2. The central thesis is that while single-chain protein structure prediction is largely “solved” through evolutionary hints, the next frontier lies in modeling complex interactions (protein-ligand, protein-protein) and generative protein design, which Boltz aims to democratize via open-source foundations and scalable infrastructure.Full Video PodOn YouTube!Timestamps* 00:00 Introduction to Benchmarking and the “Solved” Protein Problem* 06:48 Evolutionary Hints and Co-evolution in Structure Prediction* 10:00 The Importance of Protein Function and Disease States* 15:31 Transitioning from AlphaFold 2 to AlphaFold 3 Capabilities* 19:48 Generative Modeling vs. Regression in Structural Biology* 25:00 The “Bitter Lesson” and Specialized AI Architectures* 29:14 Development Anecdotes: Training Boltz-1 on a Budget* 32:00 Validation Strategies and the Protein Data Bank (PDB)* 37:26 The Mission of Boltz: Democratizing Access and Open Source* 41:43 Building a Self-Sustaining Research Community* 44:40 Boltz-2 Advancements: Affinity Prediction and Design* 51:03 BoltzGen: Merging Structure and Sequence Prediction* 55:18 Large-Scale Wet Lab Validation Results* 01:02:44 Boltz Lab Product Launch: Agents and Infrastructure* 01:13:06 Future Directions: Developpability and the “Virtual Cell”* 01:17:35 Interacting with Skeptical Medicinal ChemistsKey SummaryEvolution of Structure Prediction & Evolutionary Hints* Co-evolutionary Landscapes: The speakers explain that breakthrough progress in single-chain protein prediction relied on decoding evolutionary correlations where mutations in one position necessitate mutations in another to conserve 3D structure.* Structure vs. Folding: They differentiate between structure prediction (getting the final answer) and folding (the kinetic process of reaching that state), noting that the field is still quite poor at modeling the latter.* Physics vs. Statistics: RJ posits that while models use evolutionary statistics to find the right “valley” in the energy landscape, they likely possess a “light understanding” of physics to refine the local minimum.The Shift to Generative Architectures* Generative Modeling: A key leap in AlphaFold 3 and Boltz-1 was moving from regression (predicting one static coordinate) to a generative diffusion approach that samples from a posterior distribution.* Handling Uncertainty: This shift allows models to represent multiple conformational states and avoid the “averaging” effect seen in regression models when the ground truth is ambiguous.* Specialized Architectures: Despite the “bitter lesson” of general-purpose transformers, the speakers argue that equivariant architectures remain vastly superior for biological data due to the inherent 3D geometric constraints of molecules.Boltz-2 and Generative Protein Design* Unified Encoding: Boltz-2 (and BoltzGen) treats structure and sequence prediction as a single task by encoding amino acid identities into the atomic composition of the predicted structure.* Design Specifics: Instead of a sequence, users feed the model blank tokens and a high-level “spec” (e.g., an antibody framework), and the model decodes both the 3D structure and the corresponding amino acids.* Affinity Prediction: While model confidence is a common metric, Boltz-2 focuses on affinity prediction—quantifying exactly how tightly a designed binder will stick to its target.Real-World Validation and Productization* Generalized Validation: To prove the model isn't just “regurgitating” known data, Boltz tested its designs on 9 targets with zero known interactions in the PDB, achieving nanomolar binders for two-thirds of them.* Boltz Lab Infrastructure: The newly launched Boltz Lab platform provides “agents” for protein and small molecule design, optimized to run 10x faster than open-source versions through proprietary GPU kernels.* Human-in-the-Loop: The platform is designed to convert skeptical medicinal chemists by allowing them to run parallel screens and use their intuition to filter model outputs.TranscriptRJ [00:05:35]: But the goal remains to, like, you know, really challenge the models, like, how well do these models generalize? And, you know, we've seen in some of the latest CASP competitions, like, while we've become really, really good at proteins, especially monomeric proteins, you know, other modalities still remain pretty difficult. So it's really essential, you know, in the field that there are, like, these efforts to gather, you know, benchmarks that are challenging. So it keeps us in line, you know, about what the models can do or not.Gabriel [00:06:26]: Yeah, it's interesting you say that, like, in some sense, CASP, you know, at CASP 14, a problem was solved and, like, pretty comprehensively, right? But at the same time, it was really only the beginning. So you can say, like, what was the specific problem you would argue was solved? And then, like, you know, what is remaining, which is probably quite open.RJ [00:06:48]: I think we'll steer away from the term solved, because we have many friends in the community who get pretty upset at that word. And I think, you know, fairly so. But the problem that was, you know, that a lot of progress was made on was the ability to predict the structure of single chain proteins. So proteins can, like, be composed of many chains. And single chain proteins are, you know, just a single sequence of amino acids. And one of the reasons that we've been able to make such progress is also because we take a lot of hints from evolution. So the way the models work is that, you know, they sort of decode a lot of hints. That comes from evolutionary landscapes. So if you have, like, you know, some protein in an animal, and you go find the similar protein across, like, you know, different organisms, you might find different mutations in them. And as it turns out, if you take a lot of the sequences together, and you analyze them, you see that some positions in the sequence tend to evolve at the same time as other positions in the sequence, sort of this, like, correlation between different positions. And it turns out that that is typically a hint that these two positions are close in three dimension. So part of the, you know, part of the breakthrough has been, like, our ability to also decode that very, very effectively. But what it implies also is that in absence of that co-evolutionary landscape, the models don't quite perform as well. And so, you know, I think when that information is available, maybe one could say, you know, the problem is, like, somewhat solved. From the perspective of structure prediction, when it isn't, it's much more challenging. And I think it's also worth also differentiating the, sometimes we confound a little bit, structure prediction and folding. Folding is the more complex process of actually understanding, like, how it goes from, like, this disordered state into, like, a structured, like, state. And that I don't think we've made that much progress on. But the idea of, like, yeah, going straight to the answer, we've become pretty good at.Brandon [00:08:49]: So there's this protein that is, like, just a long chain and it folds up. Yeah. And so we're good at getting from that long chain in whatever form it was originally to the thing. But we don't know how it necessarily gets to that state. And there might be intermediate states that it's in sometimes that we're not aware of.RJ [00:09:10]: That's right. And that relates also to, like, you know, our general ability to model, like, the different, you know, proteins are not static. They move, they take different shapes based on their energy states. And I think we are, also not that good at understanding the different states that the protein can be in and at what frequency, what probability. So I think the two problems are quite related in some ways. Still a lot to solve. But I think it was very surprising at the time, you know, that even with these evolutionary hints that we were able to, you know, to make such dramatic progress.Brandon [00:09:45]: So I want to ask, why does the intermediate states matter? But first, I kind of want to understand, why do we care? What proteins are shaped like?Gabriel [00:09:54]: Yeah, I mean, the proteins are kind of the machines of our body. You know, the way that all the processes that we have in our cells, you know, work is typically through proteins, sometimes other molecules, sort of intermediate interactions. And through that interactions, we have all sorts of cell functions. And so when we try to understand, you know, a lot of biology, how our body works, how disease work. So we often try to boil it down to, okay, what is going right in case of, you know, our normal biological function and what is going wrong in case of the disease state. And we boil it down to kind of, you know, proteins and kind of other molecules and their interaction. And so when we try predicting the structure of proteins, it's critical to, you know, have an understanding of kind of those interactions. It's a bit like seeing the difference between... Having kind of a list of parts that you would put it in a car and seeing kind of the car in its final form, you know, seeing the car really helps you understand what it does. On the other hand, kind of going to your question of, you know, why do we care about, you know, how the protein falls or, you know, how the car is made to some extent is that, you know, sometimes when something goes wrong, you know, there are, you know, cases of, you know, proteins misfolding. In some diseases and so on, if we don't understand this folding process, we don't really know how to intervene.RJ [00:11:30]: There's this nice line in the, I think it's in the Alpha Fold 2 manuscript, where they sort of discuss also like why we even hopeful that we can target the problem in the first place. And then there's this notion that like, well, four proteins that fold. The folding process is almost instantaneous, which is a strong, like, you know, signal that like, yeah, like we should, we might be... able to predict that this very like constrained thing that, that the protein does so quickly. And of course that's not the case for, you know, for, for all proteins. And there's a lot of like really interesting mechanisms in the cells, but yeah, I remember reading that and thought, yeah, that's somewhat of an insightful point.Gabriel [00:12:10]: I think one of the interesting things about the protein folding problem is that it used to be actually studied. And part of the reason why people thought it was impossible, it used to be studied as kind of like a classical example. Of like an MP problem. Uh, like there are so many different, you know, type of, you know, shapes that, you know, this amino acid could take. And so, this grows combinatorially with the size of the sequence. And so there used to be kind of a lot of actually kind of more theoretical computer science thinking about and studying protein folding as an MP problem. And so it was very surprising also from that perspective, kind of seeing. Machine learning so clear, there is some, you know, signal in those sequences, through evolution, but also through kind of other things that, you know, us as humans, we're probably not really able to, uh, to understand, but that is, models I've, I've learned.Brandon [00:13:07]: And so Andrew White, we were talking to him a few weeks ago and he said that he was following the development of this and that there were actually ASICs that were developed just to solve this problem. So, again, that there were. There were many, many, many millions of computational hours spent trying to solve this problem before AlphaFold. And just to be clear, one thing that you mentioned was that there's this kind of co-evolution of mutations and that you see this again and again in different species. So explain why does that give us a good hint that they're close by to each other? Yeah.RJ [00:13:41]: Um, like think of it this way that, you know, if I have, you know, some amino acid that mutates, it's going to impact everything around it. Right. In three dimensions. And so it's almost like the protein through several, probably random mutations and evolution, like, you know, ends up sort of figuring out that this other amino acid needs to change as well for the structure to be conserved. Uh, so this whole principle is that the structure is probably largely conserved, you know, because there's this function associated with it. And so it's really sort of like different positions compensating for, for each other. I see.Brandon [00:14:17]: Those hints in aggregate give us a lot. Yeah. So you can start to look at what kinds of information about what is close to each other, and then you can start to look at what kinds of folds are possible given the structure and then what is the end state.RJ [00:14:30]: And therefore you can make a lot of inferences about what the actual total shape is. Yeah, that's right. It's almost like, you know, you have this big, like three dimensional Valley, you know, where you're sort of trying to find like these like low energy states and there's so much to search through. That's almost overwhelming. But these hints, they sort of maybe put you in. An area of the space that's already like, kind of close to the solution, maybe not quite there yet. And, and there's always this question of like, how much physics are these models learning, you know, versus like, just pure like statistics. And like, I think one of the thing, at least I believe is that once you're in that sort of approximate area of the solution space, then the models have like some understanding, you know, of how to get you to like, you know, the lower energy, uh, low energy state. And so maybe you have some, some light understanding. Of physics, but maybe not quite enough, you know, to know how to like navigate the whole space. Right. Okay.Brandon [00:15:25]: So we need to give it these hints to kind of get into the right Valley and then it finds the, the minimum or something. Yeah.Gabriel [00:15:31]: One interesting explanation about our awful free works that I think it's quite insightful, of course, doesn't cover kind of the entirety of, of what awful does that is, um, they're going to borrow from, uh, Sergio Chinico for MIT. So he sees kind of awful. Then the interesting thing about awful is God. This very peculiar architecture that we have seen, you know, used, and this architecture operates on this, you know, pairwise context between amino acids. And so the idea is that probably the MSA gives you this first hint about what potential amino acids are close to each other. MSA is most multiple sequence alignment. Exactly. Yeah. Exactly. This evolutionary information. Yeah. And, you know, from this evolutionary information about potential contacts, then is almost as if the model is. of running some kind of, you know, diastro algorithm where it's sort of decoding, okay, these have to be closed. Okay. Then if these are closed and this is connected to this, then this has to be somewhat closed. And so you decode this, that becomes basically a pairwise kind of distance matrix. And then from this rough pairwise distance matrix, you decode kind of theBrandon [00:16:42]: actual potential structure. Interesting. So there's kind of two different things going on in the kind of coarse grain and then the fine grain optimizations. Interesting. Yeah. Very cool.Gabriel [00:16:53]: Yeah. You mentioned AlphaFold3. So maybe we have a good time to move on to that. So yeah, AlphaFold2 came out and it was like, I think fairly groundbreaking for this field. Everyone got very excited. A few years later, AlphaFold3 came out and maybe for some more history, like what were the advancements in AlphaFold3? And then I think maybe we'll, after that, we'll talk a bit about the sort of how it connects to Bolt. But anyway. Yeah. So after AlphaFold2 came out, you know, Jeremy and I got into the field and with many others, you know, the clear problem that, you know, was, you know, obvious after that was, okay, now we can do individual chains. Can we do interactions, interaction, different proteins, proteins with small molecules, proteins with other molecules. And so. So why are interactions important? Interactions are important because to some extent that's kind of the way that, you know, these machines, you know, these proteins have a function, you know, the function comes by the way that they interact with other proteins and other molecules. Actually, in the first place, you know, the individual machines are often, as Jeremy was mentioning, not made of a single chain, but they're made of the multiple chains. And then these multiple chains interact with other molecules to give the function to those. And on the other hand, you know, when we try to intervene of these interactions, think about like a disease, think about like a, a biosensor or many other ways we are trying to design the molecules or proteins that interact in a particular way with what we would call a target protein or target. You know, this problem after AlphaVol2, you know, became clear, kind of one of the biggest problems in the field to, to solve many groups, including kind of ours and others, you know, started making some kind of contributions to this problem of trying to model these interactions. And AlphaVol3 was, you know, was a significant advancement on the problem of modeling interactions. And one of the interesting thing that they were able to do while, you know, some of the rest of the field that really tried to try to model different interactions separately, you know, how protein interacts with small molecules, how protein interacts with other proteins, how RNA or DNA have their structure, they put everything together and, you know, train very large models with a lot of advances, including kind of changing kind of systems. Some of the key architectural choices and managed to get a single model that was able to set this new state-of-the-art performance across all of these different kind of modalities, whether that was protein, small molecules is critical to developing kind of new drugs, protein, protein, understanding, you know, interactions of, you know, proteins with RNA and DNAs and so on.Brandon [00:19:39]: Just to satisfy the AI engineers in the audience, what were some of the key architectural and data, data changes that made that possible?Gabriel [00:19:48]: Yeah, so one critical one that was not necessarily just unique to AlphaFold3, but there were actually a few other teams, including ours in the field that proposed this, was moving from, you know, modeling structure prediction as a regression problem. So where there is a single answer and you're trying to shoot for that answer to a generative modeling problem where you have a posterior distribution of possible structures and you're trying to sample this distribution. And this achieves two things. One is it starts to allow us to try to model more dynamic systems. As we said, you know, some of these structures can actually take multiple structures. And so, you know, you can now model that, you know, through kind of modeling the entire distribution. But on the second hand, from more kind of core modeling questions, when you move from a regression problem to a generative modeling problem, you are really tackling the way that you think about uncertainty in the model in a different way. So if you think about, you know, I'm undecided between different answers, what's going to happen in a regression model is that, you know, I'm going to try to make an average of those different kind of answers that I had in mind. When you have a generative model, what you're going to do is, you know, sample all these different answers and then maybe use separate models to analyze those different answers and pick out the best. So that was kind of one of the critical improvement. The other improvement is that they significantly simplified, to some extent, the architecture, especially of the final model that takes kind of those pairwise representations and turns them into an actual structure. And that now looks a lot more like a more traditional transformer than, you know, like a very specialized equivariant architecture that it was in AlphaFold3.Brandon [00:21:41]: So this is a bitter lesson, a little bit.Gabriel [00:21:45]: There is some aspect of a bitter lesson, but the interesting thing is that it's very far from, you know, being like a simple transformer. This field is one of the, I argue, very few fields in applied machine learning where we still have kind of architecture that are very specialized. And, you know, there are many people that have tried to replace these architectures with, you know, simple transformers. And, you know, there is a lot of debate in the field, but I think kind of that most of the consensus is that, you know, the performance... that we get from the specialized architecture is vastly superior than what we get through a single transformer. Another interesting thing that I think on the staying on the modeling machine learning side, which I think it's somewhat counterintuitive seeing some of the other kind of fields and applications is that scaling hasn't really worked kind of the same in this field. Now, you know, models like AlphaFold2 and AlphaFold3 are, you know, still very large models.RJ [00:29:14]: in a place, I think, where we had, you know, some experience working in, you know, with the data and working with this type of models. And I think that put us already in like a good place to, you know, to produce it quickly. And, you know, and I would even say, like, I think we could have done it quicker. The problem was like, for a while, we didn't really have the compute. And so we couldn't really train the model. And actually, we only trained the big model once. That's how much compute we had. We could only train it once. And so like, while the model was training, we were like, finding bugs left and right. A lot of them that I wrote. And like, I remember like, I was like, sort of like, you know, doing like, surgery in the middle, like stopping the run, making the fix, like relaunching. And yeah, we never actually went back to the start. We just like kept training it with like the bug fixes along the way, which was impossible to reproduce now. Yeah, yeah, no, that model is like, has gone through such a curriculum that, you know, learned some weird stuff. But yeah, somehow by miracle, it worked out.Gabriel [00:30:13]: The other funny thing is that the way that we were training, most of that model was through a cluster from the Department of Energy. But that's sort of like a shared cluster that many groups use. And so we were basically training the model for two days, and then it would go back to the queue and stay a week in the queue. Oh, yeah. And so it was pretty painful. And so we actually kind of towards the end with Evan, the CEO of Genesis, and basically, you know, I was telling him a bit about the project and, you know, kind of telling him about this frustration with the compute. And so luckily, you know, he offered to kind of help. And so we, we got the help from Genesis to, you know, finish up the model. Otherwise, it probably would have taken a couple of extra weeks.Brandon [00:30:57]: Yeah, yeah.Brandon [00:31:02]: And then, and then there's some progression from there.Gabriel [00:31:06]: Yeah, so I would say kind of that, both one, but also kind of these other kind of set of models that came around the same time, were kind of approaching were a big leap from, you know, kind of the previous kind of open source models, and, you know, kind of really kind of approaching the level of AlphaVault 3. But I would still say that, you know, even to this day, there are, you know, some... specific instances where AlphaVault 3 works better. I think one common example is antibody antigen prediction, where, you know, AlphaVault 3 still seems to have an edge in many situations. Obviously, these are somewhat different models. They are, you know, you run them, you obtain different results. So it's, it's not always the case that one model is better than the other, but kind of in aggregate, we still, especially at the time.Brandon [00:32:00]: So AlphaVault 3 is, you know, still having a bit of an edge. We should talk about this more when we talk about Boltzgen, but like, how do you know one is, one model is better than the other? Like you, so you, I make a prediction, you make a prediction, like, how do you know?Gabriel [00:32:11]: Yeah, so easily, you know, the, the great thing about kind of structural prediction and, you know, once we're going to go into the design space of designing new small molecule, new proteins, this becomes a lot more complex. But a great thing about structural prediction is that a bit like, you know, CASP was doing, basically the way that you can evaluate them is that, you know, you train... You know, you train a model on a structure that was, you know, released across the field up until a certain time. And, you know, one of the things that we didn't talk about that was really critical in all this development is the PDB, which is the Protein Data Bank. It's this common resources, basically common database where every biologist publishes their structures. And so we can, you know, train on, you know, all the structures that were put in the PDB until a certain date. And then... And then we basically look for recent structures, okay, which structures look pretty different from anything that was published before, because we really want to try to understand generalization.Brandon [00:33:13]: And then on this new structure, we evaluate all these different models. And so you just know when AlphaFold3 was trained, you know, when you're, you intentionally trained to the same date or something like that. Exactly. Right. Yeah.Gabriel [00:33:24]: And so this is kind of the way that you can somewhat easily kind of compare these models, obviously, that assumes that, you know, the training. You've always been very passionate about validation. I remember like DiffDoc, and then there was like DiffDocL and DocGen. You've thought very carefully about this in the past. Like, actually, I think DocGen is like a really funny story that I think, I don't know if you want to talk about that. It's an interesting like... Yeah, I think one of the amazing things about putting things open source is that we get a ton of feedback from the field. And, you know, sometimes we get kind of great feedback of people. Really like... But honestly, most of the times, you know, to be honest, that's also maybe the most useful feedback is, you know, people sharing about where it doesn't work. And so, you know, at the end of the day, it's critical. And this is also something, you know, across other fields of machine learning. It's always critical to set, to do progress in machine learning, set clear benchmarks. And as, you know, you start doing progress of certain benchmarks, then, you know, you need to improve the benchmarks and make them harder and harder. And this is kind of the progression of, you know, how the field operates. And so, you know, the example of DocGen was, you know, we published this initial model called DiffDoc in my first year of PhD, which was sort of like, you know, one of the early models to try to predict kind of interactions between proteins, small molecules, that we bought a year after AlphaFold2 was published. And now, on the one hand, you know, on these benchmarks that we were using at the time, DiffDoc was doing really well, kind of, you know, outperforming kind of some of the traditional physics-based methods. But on the other hand, you know, when we started, you know, kind of giving these tools to kind of many biologists, and one example was that we collaborated with was the group of Nick Polizzi at Harvard. We noticed, started noticing that there was this clear, pattern where four proteins that were very different from the ones that we're trained on, the models was, was struggling. And so, you know, that seemed clear that, you know, this is probably kind of where we should, you know, put our focus on. And so we first developed, you know, with Nick and his group, a new benchmark, and then, you know, went after and said, okay, what can we change? And kind of about the current architecture to improve this pattern and generalization. And this is the same that, you know, we're still doing today, you know, kind of, where does the model not work, you know, and then, you know, once we have that benchmark, you know, let's try to, through everything we, any ideas that we have of the problem.RJ [00:36:15]: And there's a lot of like healthy skepticism in the field, which I think, you know, is, is, is great. And I think, you know, it's very clear that there's a ton of things, the models don't really work well on, but I think one thing that's probably, you know, undeniable is just like the pace of, pace of progress, you know, and how, how much better we're getting, you know, every year. And so I think if you, you know, if you assume, you know, any constant, you know, rate of progress moving forward, I think things are going to look pretty cool at some point in the future.Gabriel [00:36:42]: ChatGPT was only three years ago. Yeah, I mean, it's wild, right?RJ [00:36:45]: Like, yeah, yeah, yeah, it's one of those things. Like, you've been doing this. Being in the field, you don't see it coming, you know? And like, I think, yeah, hopefully we'll, you know, we'll, we'll continue to have as much progress we've had the past few years.Brandon [00:36:55]: So this is maybe an aside, but I'm really curious, you get this great feedback from the, from the community, right? By being open source. My question is partly like, okay, yeah, if you open source and everyone can copy what you did, but it's also maybe balancing priorities, right? Where you, like all my customers are saying. I want this, there's all these problems with the model. Yeah, yeah. But my customers don't care, right? So like, how do you, how do you think about that? Yeah.Gabriel [00:37:26]: So I would say a couple of things. One is, you know, part of our goal with Bolts and, you know, this is also kind of established as kind of the mission of the public benefit company that we started is to democratize the access to these tools. But one of the reasons why we realized that Bolts needed to be a company, it couldn't just be an academic project is that putting a model on GitHub is definitely not enough to get, you know, chemists and biologists, you know, across, you know, both academia, biotech and pharma to use your model to, in their therapeutic programs. And so a lot of what we think about, you know, at Bolts beyond kind of the, just the models is thinking about all the layers. The layers that come on top of the models to get, you know, from, you know, those models to something that can really enable scientists in the industry. And so that goes, you know, into building kind of the right kind of workflows that take in kind of, for example, the data and try to answer kind of directly that those problems that, you know, the chemists and the biologists are asking, and then also kind of building the infrastructure. And so this to say that, you know, even with models fully open. You know, we see a ton of potential for, you know, products in the space and the critical part about a product is that even, you know, for example, with an open source model, you know, running the model is not free, you know, as we were saying, these are pretty expensive model and especially, and maybe we'll get into this, you know, these days we're seeing kind of pretty dramatic inference time scaling of these models where, you know, the more you run them, the better the results are. But there, you know, you see. You start getting into a point that compute and compute costs becomes a critical factor. And so putting a lot of work into building the right kind of infrastructure, building the optimizations and so on really allows us to provide, you know, a much better service potentially to the open source models. That to say, you know, even though, you know, with a product, we can provide a much better service. I do still think, and we will continue to put a lot of our models open source because the critical kind of role. I think of open source. Models is, you know, helping kind of the community progress on the research and, you know, from which we, we all benefit. And so, you know, we'll continue to on the one hand, you know, put some of our kind of base models open source so that the field can, can be on top of it. And, you know, as we discussed earlier, we learn a ton from, you know, the way that the field uses and builds on top of our models, but then, you know, try to build a product that gives the best experience possible to scientists. So that, you know, like a chemist or a biologist doesn't need to, you know, spin off a GPU and, you know, set up, you know, our open source model in a particular way, but can just, you know, a bit like, you know, I, even though I am a computer scientist, machine learning scientist, I don't necessarily, you know, take a open source LLM and try to kind of spin it off. But, you know, I just maybe open a GPT app or a cloud code and just use it as an amazing product. We kind of want to give the same experience. So this front world.Brandon [00:40:40]: I heard a good analogy yesterday that a surgeon doesn't want the hospital to design a scalpel, right?Brandon [00:40:48]: So just buy the scalpel.RJ [00:40:50]: You wouldn't believe like the number of people, even like in my short time, you know, between AlphaFold3 coming out and the end of the PhD, like the number of people that would like reach out just for like us to like run AlphaFold3 for them, you know, or things like that. Just because like, you know, bolts in our case, you know, just because it's like. It's like not that easy, you know, to do that, you know, if you're not a computational person. And I think like part of the goal here is also that, you know, we continue to obviously build the interface with computational folks, but that, you know, the models are also accessible to like a larger, broader audience. And then that comes from like, you know, good interfaces and stuff like that.Gabriel [00:41:27]: I think one like really interesting thing about bolts is that with the release of it, you didn't just release a model, but you created a community. Yeah. Did that community, it grew very quickly. Did that surprise you? And like, what is the evolution of that community and how is that fed into bolts?RJ [00:41:43]: If you look at its growth, it's like very much like when we release a new model, it's like, there's a big, big jump, but yeah, it's, I mean, it's been great. You know, we have a Slack community that has like thousands of people on it. And it's actually like self-sustaining now, which is like the really nice part because, you know, it's, it's almost overwhelming, I think, you know, to be able to like answer everyone's questions and help. It's really difficult, you know. The, the few people that we were, but it ended up that like, you know, people would answer each other's questions and like, sort of like, you know, help one another. And so the Slack, you know, has been like kind of, yeah, self, self-sustaining and that's been, it's been really cool to see.RJ [00:42:21]: And, you know, that's, that's for like the Slack part, but then also obviously on GitHub as well. We've had like a nice, nice community. You know, I think we also aspire to be even more active on it, you know, than we've been in the past six months, which has been like a bit challenging, you know, for us. But. Yeah, the community has been, has been really great and, you know, there's a lot of papers also that have come out with like new evolutions on top of bolts and it's surprised us to some degree because like there's a lot of models out there. And I think like, you know, sort of people converging on that was, was really cool. And, you know, I think it speaks also, I think, to the importance of like, you know, when, when you put code out, like to try to put a lot of emphasis and like making it like as easy to use as possible and something we thought a lot about when we released the code base. You know, it's far from perfect, but, you know.Brandon [00:43:07]: Do you think that that was one of the factors that caused your community to grow is just the focus on easy to use, make it accessible? I think so.RJ [00:43:14]: Yeah. And we've, we've heard it from a few people over the, over the, over the years now. And, you know, and some people still think it should be a lot nicer and they're, and they're right. And they're right. But yeah, I think it was, you know, at the time, maybe a little bit easier than, than other things.Gabriel [00:43:29]: The other thing part, I think led to, to the community and to some extent, I think, you know, like the somewhat the trust in the community. Kind of what we, what we put out is the fact that, you know, it's not really been kind of, you know, one model, but, and maybe we'll talk about it, you know, after Boltz 1, you know, there were maybe another couple of models kind of released, you know, or open source kind of soon after. We kind of continued kind of that open source journey or at least Boltz 2, where we are not only improving kind of structure prediction, but also starting to do affinity predictions, understanding kind of the strength of the interactions between these different models, which is this critical component. critical property that you often want to optimize in discovery programs. And then, you know, more recently also kind of protein design model. And so we've sort of been building this suite of, of models that come together, interact with one another, where, you know, kind of, there is almost an expectation that, you know, we, we take very at heart of, you know, always having kind of, you know, across kind of the entire suite of different tasks, the best or across the best. model out there so that it's sort of like our open source tool can be kind of the go-to model for everybody in the, in the industry. I really want to talk about Boltz 2, but before that, one last question in this direction, was there anything about the community which surprised you? Were there any, like, someone was doing something and you're like, why would you do that? That's crazy. Or that's actually genius. And I never would have thought about that.RJ [00:45:01]: I mean, we've had many contributions. I think like some of the. Interesting ones, like, I mean, we had, you know, this one individual who like wrote like a complex GPU kernel, you know, for part of the architecture on a piece of, the funny thing is like that piece of the architecture had been there since AlphaFold 2, and I don't know why it took Boltz for this, you know, for this person to, you know, to decide to do it, but that was like a really great contribution. We've had a bunch of others, like, you know, people figuring out like ways to, you know, hack the model to do something. They click peptides, like, you know, there's, I don't know if there's any other interesting ones come to mind.Gabriel [00:45:41]: One cool one, and this was, you know, something that initially was proposed as, you know, as a message in the Slack channel by Tim O'Donnell was basically, he was, you know, there are some cases, especially, for example, we discussed, you know, antibody-antigen interactions where the models don't necessarily kind of get the right answer. What he noticed is that, you know, the models were somewhat stuck into predicting kind of the antibodies. And so he basically ran the experiments in this model, you can condition, basically, you can give hints. And so he basically gave, you know, random hints to the model, basically, okay, you should bind to this residue, you should bind to the first residue, or you should bind to the 11th residue, or you should bind to the 21st residue, you know, basically every 10 residues scanning the entire antigen.Brandon [00:46:33]: Residues are the...Gabriel [00:46:34]: The amino acids. The amino acids, yeah. So the first amino acids. The 11 amino acids, and so on. So it's sort of like doing a scan, and then, you know, conditioning the model to predict all of them, and then looking at the confidence of the model in each of those cases and taking the top. And so it's sort of like a very somewhat crude way of doing kind of inference time search. But surprisingly, you know, for antibody-antigen prediction, it actually kind of helped quite a bit. And so there's some, you know, interesting ideas that, you know, obviously, as kind of developing the model, you say kind of, you know, wow. This is why would the model, you know, be so dumb. But, you know, it's very interesting. And that, you know, leads you to also kind of, you know, start thinking about, okay, how do I, can I do this, you know, not with this brute force, but, you know, in a smarter way.RJ [00:47:22]: And so we've also done a lot of work on that direction. And that speaks to, like, the, you know, the power of scoring. We're seeing that a lot. I'm sure we'll talk about it more when we talk about BullsGen. But, you know, our ability to, like, take a structure and determine that that structure is, like... Good. You know, like, somewhat accurate. Whether that's a single chain or, like, an interaction is a really powerful way of improving, you know, the models. Like, sort of like, you know, if you can sample a ton and you assume that, like, you know, if you sample enough, you're likely to have, like, you know, the good structure. Then it really just becomes a ranking problem. And, you know, now we're, you know, part of the inference time scaling that Gabby was talking about is very much that. It's like, you know, the more we sample, the more we, like, you know, the ranking model. The ranking model ends up finding something it really likes. And so I think our ability to get better at ranking, I think, is also what's going to enable sort of the next, you know, next big, big breakthroughs. Interesting.Brandon [00:48:17]: But I guess there's a, my understanding, there's a diffusion model and you generate some stuff and then you, I guess, it's just what you said, right? Then you rank it using a score and then you finally... And so, like, can you talk about those different parts? Yeah.Gabriel [00:48:34]: So, first of all, like, the... One of the critical kind of, you know, beliefs that we had, you know, also when we started working on Boltz 1 was sort of like the structure prediction models are somewhat, you know, our field version of some foundation models, you know, learning about kind of how proteins and other molecules interact. And then we can leverage that learning to do all sorts of other things. And so with Boltz 2, we leverage that learning to do affinity predictions. So understanding kind of, you know, if I give you this protein, this molecule. How tightly is that interaction? For Boltz 1, what we did was taking kind of that kind of foundation models and then fine tune it to predict kind of entire new proteins. And so the way basically that that works is sort of like instead of for the protein that you're designing, instead of fitting in an actual sequence, you fit in a set of blank tokens. And you train the models to, you know, predict both the structure of kind of that protein. The structure also, what the different amino acids of that proteins are. And so basically the way that Boltz 1 operates is that you feed a target protein that you may want to kind of bind to or, you know, another DNA, RNA. And then you feed the high level kind of design specification of, you know, what you want your new protein to be. For example, it could be like an antibody with a particular framework. It could be a peptide. It could be many other things. And that's with natural language or? And that's, you know, basically, you know, prompting. And we have kind of this sort of like spec that you specify. And, you know, you feed kind of this spec to the model. And then the model translates this into, you know, a set of, you know, tokens, a set of conditioning to the model, a set of, you know, blank tokens. And then, you know, basically the codes as part of the diffusion models, the codes. It's a new structure and a new sequence for your protein. And, you know, basically, then we take that. And as Jeremy was saying, we are trying to score it and, you know, how good of a binder it is to that original target.Brandon [00:50:51]: You're using basically Boltz to predict the folding and the affinity to that molecule. So and then that kind of gives you a score? Exactly.Gabriel [00:51:03]: So you use this model to predict the folding. And then you do two things. One is that you predict the structure and with something like Boltz2, and then you basically compare that structure with what the model predicted, what Boltz2 predicted. And this is sort of like in the field called consistency. It's basically you want to make sure that, you know, the structure that you're predicting is actually what you're trying to design. And that gives you a much better confidence that, you know, that's a good design. And so that's the first filtering. And the second filtering that we did as part of kind of the Boltz2 pipeline that was released is that we look at the confidence that the model has in the structure. Now, unfortunately, kind of going to your question of, you know, predicting affinity, unfortunately, confidence is not a very good predictor of affinity. And so one of the things that we've actually done a ton of progress, you know, since we released Boltz2.Brandon [00:52:03]: And kind of we have some new results that we are going to kind of announce soon is kind of, you know, the ability to get much better hit rates when instead of, you know, trying to rely on confidence of the model, we are actually directly trying to predict the affinity of that interaction. Okay. Just backing up a minute. So your diffusion model actually predicts not only the protein sequence, but also the folding of it. Exactly.Gabriel [00:52:32]: And actually, you can... One of the big different things that we did compared to other models in the space, and, you know, there were some papers that had already kind of done this before, but we really scaled it up was, you know, basically somewhat merging kind of the structure prediction and the sequence prediction into almost the same task. And so the way that Boltz2 works is that you are basically the only thing that you're doing is predicting the structure. So the only sort of... Supervision is we give you a supervision on the structure, but because the structure is atomic and, you know, the different amino acids have a different atomic composition, basically from the way that you place the atoms, we also understand not only kind of the structure that you wanted, but also the identity of the amino acid that, you know, the models believed was there. And so we've basically, instead of, you know, having these two supervision signals, you know, one discrete, one continuous. That somewhat, you know, don't interact well together. We sort of like build kind of like an encoding of, you know, sequences in structures that allows us to basically use exactly the same supervision signal that we were using to Boltz2 that, you know, you know, largely similar to what AlphaVol3 proposed, which is very scalable. And we can use that to design new proteins. Oh, interesting.RJ [00:53:58]: Maybe a quick shout out to Hannes Stark on our team who like did all this work. Yeah.Gabriel [00:54:04]: Yeah, that was a really cool idea. I mean, like looking at the paper and there's this is like encoding or you just add a bunch of, I guess, kind of atoms, which can be anything, and then they get sort of rearranged and then basically plopped on top of each other so that and then that encodes what the amino acid is. And there's sort of like a unique way of doing this. It was that was like such a really such a cool, fun idea.RJ [00:54:29]: I think that idea was had existed before. Yeah, there were a couple of papers.Gabriel [00:54:33]: Yeah, I had proposed this and and Hannes really took it to the large scale.Brandon [00:54:39]: In the paper, a lot of the paper for Boltz2Gen is dedicated to actually the validation of the model. In my opinion, all the people we basically talk about feel that this sort of like in the wet lab or whatever the appropriate, you know, sort of like in real world validation is the whole problem or not the whole problem, but a big giant part of the problem. So can you talk a little bit about the highlights? From there, that really because to me, the results are impressive, both from the perspective of the, you know, the model and also just the effort that went into the validation by a large team.Gabriel [00:55:18]: First of all, I think I should start saying is that both when we were at MIT and Thomas Yacolas and Regina Barzillai's lab, as well as at Boltz, you know, we are not a we're not a biolab and, you know, we are not a therapeutic company. And so to some extent, you know, we were first forced to, you know, look outside of, you know, our group, our team to do the experimental validation. One of the things that really, Hannes, in the team pioneer was the idea, OK, can we go not only to, you know, maybe a specific group and, you know, trying to find a specific system and, you know, maybe overfit a bit to that system and trying to validate. But how can we test this model? So. Across a very wide variety of different settings so that, you know, anyone in the field and, you know, printing design is, you know, such a kind of wide task with all sorts of different applications from therapeutic to, you know, biosensors and many others that, you know, so can we get a validation that is kind of goes across many different tasks? And so he basically put together, you know, I think it was something like, you know, 25 different. You know, academic and industry labs that committed to, you know, testing some of the designs from the model and some of this testing is still ongoing and, you know, giving results kind of back to us in exchange for, you know, hopefully getting some, you know, new great sequences for their task. And he was able to, you know, coordinate this, you know, very wide set of, you know, scientists and already in the paper, I think we. Shared results from, I think, eight to 10 different labs kind of showing results from, you know, designing peptides, designing to target, you know, ordered proteins, peptides targeting disordered proteins, which are results, you know, of designing proteins that bind to small molecules, which are results of, you know, designing nanobodies and across a wide variety of different targets. And so that's sort of like. That gave to the paper a lot of, you know, validation to the model, a lot of validation that was kind of wide.Brandon [00:57:39]: And so those would be therapeutics for those animals or are they relevant to humans as well? They're relevant to humans as well.Gabriel [00:57:45]: Obviously, you need to do some work into, quote unquote, humanizing them, making sure that, you know, they have the right characteristics to so they're not toxic to humans and so on.RJ [00:57:57]: There are some approved medicine in the market that are nanobodies. There's a general. General pattern, I think, in like in trying to design things that are smaller, you know, like it's easier to manufacture at the same time, like that comes with like potentially other challenges, like maybe a little bit less selectivity than like if you have something that has like more hands, you know, but the yeah, there's this big desire to, you know, try to design many proteins, nanobodies, small peptides, you know, that just are just great drug modalities.Brandon [00:58:27]: Okay. I think we were left off. We were talking about validation. Validation in the lab. And I was very excited about seeing like all the diverse validations that you've done. Can you go into some more detail about them? Yeah. Specific ones. Yeah.RJ [00:58:43]: The nanobody one. I think we did. What was it? 15 targets. Is that correct? 14. 14 targets. Testing. So we typically the way this works is like we make a lot of designs. All right. On the order of like tens of thousands. And then we like rank them and we pick like the top. And in this case, and was 15 right for each target and then we like measure sort of like the success rates, both like how many targets we were able to get a binder for and then also like more generally, like out of all of the binders that we designed, how many actually proved to be good binders. Some of the other ones I think involved like, yeah, like we had a cool one where there was a small molecule or design a protein that binds to it. That has a lot of like interesting applications, you know, for example. Like Gabri mentioned, like biosensing and things like that, which is pretty cool. We had a disordered protein, I think you mentioned also. And yeah, I think some of those were some of the highlights. Yeah.Gabriel [00:59:44]: So I would say that the way that we structure kind of some of those validations was on the one end, we have validations across a whole set of different problems that, you know, the biologists that we were working with came to us with. So we were trying to. For example, in some of the experiments, design peptides that would target the RACC, which is a target that is involved in metabolism. And we had, you know, a number of other applications where we were trying to design, you know, peptides or other modalities against some other therapeutic relevant targets. We designed some proteins to bind small molecules. And then some of the other testing that we did was really trying to get like a more broader sense. So how does the model work, especially when tested, you know, on somewhat generalization? So one of the things that, you know, we found with the field was that a lot of the validation, especially outside of the validation that was on specific problems, was done on targets that have a lot of, you know, known interactions in the training data. And so it's always a bit hard to understand, you know, how much are these models really just regurgitating kind of what they've seen or trying to imitate. What they've seen in the training data versus, you know, really be able to design new proteins. And so one of the experiments that we did was to take nine targets from the PDB, filtering to things where there is no known interaction in the PDB. So basically the model has never seen kind of this particular protein bound or a similar protein bound to another protein. So there is no way that. The model from its training set can sort of like say, okay, I'm just going to kind of tweak something and just imitate this particular kind of interaction. And so we took those nine proteins. We worked with adaptive CRO and basically tested, you know, 15 mini proteins and 15 nanobodies against each one of them. And the very cool thing that we saw was that on two thirds of those targets, we were able to, from this 15 design, get nanomolar binders, nanomolar, roughly speaking, just a measure of, you know, how strongly kind of the interaction is, roughly speaking, kind of like a nanomolar binder is approximately the kind of binding strength or binding that you need for a therapeutic. Yeah. So maybe switching directions a bit. Bolt's lab was just announced this week or was it last week? Yeah. This is like your. First, I guess, product, if that's if you want to call it that. Can you talk about what Bolt's lab is and yeah, you know, what you hope that people take away from this? Yeah.RJ [01:02:44]: You know, as we mentioned, like I think at the very beginning is the goal with the product has been to, you know, address what the models don't on their own. And there's largely sort of two categories there. I'll split it in three. The first one. It's one thing to predict, you know, a single interaction, for example, like a single structure. It's another to like, you know, very effectively search a space, a design space to produce something of value. What we found, like sort of building on this product is that there's a lot of steps involved, you know, in that there's certainly need to like, you know, accompany the user through, you know, one of those steps, for example, is like, you know, the creation of the target itself. You know, how do we make sure that the model has like a good enough understanding of the target? So we can like design something and there's all sorts of tricks, you know, that you can do to improve like a particular, you know, structure prediction. And so that's sort of like, you know, the first stage. And then there's like this stage of like, you know, designing and searching the space efficiently. You know, for something like BullsGen, for example, like you, you know, you design many things and then you rank them, for example, for small molecule process, a little bit more complicated. We actually need to also make sure that the molecules are synthesizable. And so the way we do that is that, you know, we have a generative model that learns. To use like appropriate building blocks such that, you know, it can design within a space that we know is like synthesizable. And so there's like, you know, this whole pipeline really of different models involved in being able to design a molecule. And so that's been sort of like the first thing we call them agents. We have a protein agent and we have a small molecule design agents. And that's really like at the core of like what powers, you know, the BullsLab platform.Brandon [01:04:22]: So these agents, are they like a language model wrapper or they're just like your models and you're just calling them agents? A lot. Yeah. Because they, they, they sort of perform a function on behalf of.RJ [01:04:33]: They're more of like a, you know, a recipe, if you wish. And I think we use that term sort of because of, you know, sort of the complex pipelining and automation, you know, that goes into like all this plumbing. So that's the first part of the product. The second part is the infrastructure. You know, we need to be able to do this at very large scale for any one, you know, group that's doing a design campaign. Let's say you're designing, you know, I'd say a hundred thousand possible candidates. Right. To find the good one that is, you know, a very large amount of compute, you know, for small molecules, it's on the order of like a few seconds per designs for proteins can be a bit longer. And so, you know, ideally you want to do that in parallel, otherwise it's going to take you weeks. And so, you know, we've put a lot of effort into like, you know, our ability to have a GPU fleet that allows any one user, you know, to be able to do this kind of like large parallel search.Brandon [01:05:23]: So you're amortizing the cost over your users. Exactly. Exactly.RJ [01:05:27]: And, you know, to some degree, like it's whether you. Use 10,000 GPUs for like, you know, a minute is the same cost as using, you know, one GPUs for God knows how long. Right. So you might as well try to parallelize if you can. So, you know, a lot of work has gone, has gone into that, making it very robust, you know, so that we can have like a lot of people on the platform doing that at the same time. And the third one is, is the interface and the interface comes in, in two shapes. One is in form of an API and that's, you know, really suited for companies that want to integrate, you know, these pipelines, these agents.RJ [01:06:01]: So we're already partnering with, you know, a few distributors, you know, that are gonna integrate our API. And then the second part is the user interface. And, you know, we, we've put a lot of thoughts also into that. And this is when I, I mentioned earlier, you know, this idea of like broadening the audience. That's kind of what the, the user interface is about. And we've built a lot of interesting features in it, you know, for example, for collaboration, you know, when you have like potentially multiple medicinal chemists or. We're going through the results and trying to pick out, okay, like what are the molecules that we're going to go and test in the lab? It's powerful for them to be able to, you know, for example, each provide their own ranking and then do consensus building. And so there's a lot of features around launching these large jobs, but also around like collaborating on analyzing the results that we try to solve, you know, with that part of the platform. So Bolt's lab is sort of a combination of these three objectives into like one, you know, sort of cohesive platform. Who is this accessible to? Everyone. You do need to request access today. We're still like, you know, sort of ramping up the usage, but anyone can request access. If you are an academic in particular, we, you know, we provide a fair amount of free credit so you can play with the platform. If you are a startup or biotech, you may also, you know, reach out and we'll typically like actually hop on a call just to like understand what you're trying to do and also provide a lot of free credit to get started. And of course, also with larger companies, we can deploy this platform in a more like secure environment. And so that's like more like customizing. You know, deals that we make, you know, with the partners, you know, and that's sort of the ethos of Bolt. I think this idea of like servicing everyone and not necessarily like going after just, you know, the really large enterprises. And that starts from the open source, but it's also, you know, a key design principle of the product itself.Gabriel [01:07:48]: One thing I was thinking about with regards to infrastructure, like in the LLM space, you know, the cost of a token has gone down by I think a factor of a thousand or so over the last three years, right? Yeah. And is it possible that like essentially you can exploit economies of scale and infrastructure that you can make it cheaper to run these things yourself than for any person to roll their own system? A hundred percent. Yeah.RJ [01:08:08]: I mean, we're already there, you know, like running Bolts on our platform, especially on a large screen is like considerably cheaper than it would probably take anyone to put the open source model out there and run it. And on top of the infrastructure, like one of the things that we've been working on is accelerating the models. So, you know. Our small molecule screening pipeline is 10x faster on Bolts Lab than it is in the open source, you know, and that's also part of like, you know, building a product, you know, of something that scales really well. And we really wanted to get to a point where like, you know, we could keep prices very low in a way that it would be a no-brainer, you know, to use Bolts through our platform.Gabriel [01:08:52]: How do you think about validation of your like agentic systems? Because, you know, as you were saying earlier. Like we're AlphaFold style models are really good at, let's say, monomeric, you know, proteins where you have, you know, co-evolution data. But now suddenly the whole point of this is to design something which doesn't have, you know, co-evolution data, something which is really novel. So now you're basically leaving the domain that you thought was, you know, that you know you are good at. So like, how do you validate that?RJ [01:09:22]: Yeah, I like every complete, but there's obviously, you know, a ton of computational metrics. That we rely on, but those are only take you so far. You really got to go to the lab, you know, and test, you know, okay, with this method A and this method B, how much better are we? You know, how much better is my, my hit rate? How stronger are my binders? Also, it's not just about hit rate. It's also about how good the binders are. And there's really like no way, nowhere around that. I think we're, you know, we've really ramped up the amount of experimental validation that we do so that we like really track progress, you know, as scientifically sound, you know. Yeah. As, as possible out of this, I think.Gabriel [01:10:00]: Yeah, no, I think, you know, one thing that is unique about us and maybe companies like us is that because we're not working on like maybe a couple of therapeutic pipelines where, you know, our validation would be focused on those. We, when we do an experimental validation, we try to test it across tens of targets. And so that on the one end, we can get a much more statistically significant result and, and really allows us to make progress. From the methodological side without being, you know, steered by, you know, overfitting on any one particular system. And of course we choose, you know, w

Agentic AI is coming. Are defenders ready?Alon Schindel, Director of Data & Threat Research at Wiz, joins Eden and Amitai for the Season 3 Finale. This isn't just a recap. It is a look at how top-tier research teams operate at speed. Alon explains why Wiz treats research as a "product" rather than a support function. He details the "DeepLeak" discovery where his team found thousands of exposed API keys mere hours after a platform's popularity spiked.What's Inside:Agentic AI: Why 2026 will be the year AI starts taking action, not just chatting.Speed as a Weapon: How to shorten the time between a zero-day and a detection.Culture: The power of the "Table" and collaborative chaos.Retrospective: Lessons from IngressNightmare and the year in vulnerabilities.Resources:Read the DeepLeak Research: https://www.wiz.io/blog/wiz-research-uncovers-exposed-deepseek-database-leakWiz Threat Research Hub: https://www.wiz.io/research

APEX Express is a weekly magazine-style radio show featuring the voices and stories of Asians and Pacific Islanders from all corners of our community. The show is produced by a collective of media makers, deejays, and activists. On this episode, the Stop AAPI Hate Pacific Islander Advisory Council discuss a new report on anti–Pacific Islander hate. They examine the documented impacts of hate, structural barriers Pacific Islander communities face in reporting and accessing support, and the long-standing traditions of resistance and community care within PI communities.   Important Links: Stop AAPI Hate Stop AAPI Hate Anti-Pacific Islander Hate Report If you have questions related to the report, please feel free to contact Stop AAPI Hate Research Manager Connie Tan at ctan@stopaapihate.org Community Calendar: Upcoming Lunar New Year Events Saturday, February 14 – Sunday, February 15 – Chinatown Flower Market Fair, Grant Avenue (fresh flowers, arts activities, cultural performances) Tuesday, February 24 – Drumbeats, Heartbeats: Community as One, San Francisco Public Library (Lunar New Year and Black History Month celebration) Saturday, February 28 – Oakland Lunar New Year Parade, Jackson Street Saturday, March 7 – Year of the Horse Parade, San Francisco Throughout the season – Additional Lunar New Year events, including parades, night markets, and museum programs across the Bay Area and beyond. Transcript: [00:00:00]  Miata Tan: Hello and welcome. You are tuning in to Apex Express, a weekly radio show uplifting the voices and stories of Asian Americans and Pacific Islanders. I'm your host, Miata Tan and tonight we're examining community realities that often go under reported. The term A API, meaning Asian Americans and Pacific Islanders is an [00:01:00] acronym we like to use a lot, but Pacific Islander peoples, their histories and their challenges are sometimes mischaracterized or not spoken about at all. Stop A API Hate is a national coalition that tracks and responds to the hate experience by A API communities through reporting, research and advocacy. They've released a new report showing that nearly half of Pacific Islander adults experienced an act of hate in 2024 because of their race, ethnicity, or nationality. Tonight we'll share conversations from a recent virtual community briefing about the report and dive into its findings and the legacy of discrimination experienced by Pacific Islanders. Isa Kelawili Whalen: I think it doesn't really help that our history of violence between Pacific Islander Land and Sea and the United States, it already leaves a sour taste in your mouth. When we Pacifica. Think [00:02:00] about participating in American society and then to top it off, there's little to no representation of Pacific Islanders. Miata Tan: That was the voice of Isa Kelawili Whalen, Executive Director at API Advocates and a member of Stop, A API hates Pacific Islander Advisory Council. You'll hear more from Isa and the other members of the advisory council soon. But first up is Cynthia Choi, the co-founder of Stop, A API, Hate and co-Executive Director of Chinese for affirmative action. Cynthia will help to ground us in the history of the organization and their hopes for this new report about Pacific Islander communities. Cynthia Choi: As many of you know, Stop API Hate was launched nearly six years ago in response to anti-Asian hate during COVID-19 pandemic. And since then we've operated as the [00:03:00] nation's largest reporting center tracking anti A. PI Hate Acts while working to advance justice and equity for our communities. In addition to policy advocacy, community care and narrative work, research has really been Central to our mission because data, when grounded in community experience helps tell a fuller and more honest story about the harms our communities face. Over the years, through listening sessions and necessary and hard conversations with our PI community members and leaders, we've heard a consistent. An important message. Pacific Islander experiences are often rendered invisible when grouped under the broader A API umbrella and the forms of hate they experience are shaped by distinct histories, ongoing injustice, and unique cultural and political [00:04:00] context. This report is in response to this truth and to the trust Pacific Islander communities have placed in sharing their experience. Conducted in partnership with NORC at the University of Chicago, along with stories from our reporting center. we believe these findings shed light on the prevalence of hate, the multifaceted impact of hate and how often harm goes unreported. Our hope is that this report sparks deeper dialogue and more meaningful actions to address anti pi hate. We are especially grateful to the Pacific Islander leaders who have guided this work from the beginning. Earlier this year, uh, Stop API hate convened Pacific Islander Advisory Council made up of four incredible leaders, Dr. Jamaica Osorio Tu‘ulau‘ulu Estella Owoimaha Church, Michelle Pedro, and Isa Whalen. Their leadership, wisdom [00:05:00] and care have been essential in shaping both our research and narrative work. Our shared goal is to build trust with Pacific Islander communities and to ensure that our work is authentic, inclusive, and truly reflective of lived experiences. These insights were critical in helping us interpret these findings with the depth and context they deserve.  Miata Tan: That was Cynthia Choi, the co-founder of Stop, A API, hate and co-Executive Director of Chinese for affirmative action. As Cynthia mentioned to collect data for this report, Stop A API Hate worked with NORC, a non-partisan research organization at the University of Chicago. In January, 2025, Stop A API. Hate and norc conducted a national survey that included 504 Pacific Islander respondents. The survey [00:06:00] examined the scope of anti Pacific Islander hate in 2024, the challenges of reporting and accessing support and participation in resistance and ongoing organizing efforts. We'll be sharing a link to the full report in our show notes at kpfa.org/program/apex-express. We also just heard Cynthia give thanks to the efforts of the Stop A API hate Pacific Islander Advisory Council. this council is a team of four Pacific Islander folks with a range of professional and community expertise who helped Stop A API hate to unpack and contextualize their new report. Tonight we'll hear from all four members of the PI Council. First up is Dr. Jamaica Osorio, a Kanaka Maoli wahine artist activist, and an Associate Professor of Indigenous and native Hawaiian politics [00:07:00] at the University of Hawaiʻi at Mānoa . Here's Dr. Jamaica, reflecting on her initial reaction to the report and what she sees going on in her community. Dr. Jamaica Heolimeleikalani Osorio: Aloha kākou. Thank you for having us today. I think the biggest thing that stood out to me in the data and the reporting that I haven't really been able to shake from my head, and I think it's related to something we're seeing a lot in our own community, was the high levels of stress and anxiety that folks in our community were experiencing and how those high levels were almost, they didn't really change based on whether or not people had experienced hate. Our communities are living, um, at a threshold, a high threshold of stress and anxiety, um, and struggling with a number of mental health, issues because of that. And I think this is an important reminder in relationship to the broader work we might be doing, to be thinking about Stopping hate acts against folks in our community and in other communities, but really to think about what are the [00:08:00] conditions that people are living under that make it nearly unlivable for our communities to survive in this place. Uh, the, the other thing that popped out to me that I wanna highlight is the data around folks feeling less welcome. How hate acts made certain folks in our community feel less welcome where they're living. And I kind of wanna. Us to think more about the tension between being unwelcomed in the so-called United States, and the tension of the inability for many of our people to return home, uh, if they would've preferred to actually be in our ancestral homes. And what are. How are those conditions created by American Empire and militarism and nuclearization, kind of the stuff that we talked about as a panel early on but also as we move away from today's conversation thinking about like what is. The place of PIs in the so-called United States. Uh, what does it mean to be able to live in your ancestral homeland like myself, where America has come to us, and chosen to stay? What does it mean for our other PI family members who have [00:09:00] come to the United States? Because our homes have been devastated by us militarism and imperialism. That's what's sitting with me that I think may not. Immediately jump out of the reporting, but we need to continue to highlight, uh, in how we interpret. Miata Tan: That was Dr. Jamaica Osorio, an Associate Professor of Indigenous and native Hawaiian politics at the University of Hawaiʻi at Māno a.  Now let's turn to Isa Kelawili Whalen. Isa is the Executive Director of API Advocates and another member of the Stop A API hate Pacific Islander Advisory Council. Here Isa builds on what Dr. Jamaica was saying about feelings of stress and anxiety within the Pacific Islander communities. Okay. She also speaks from her experience as an Indigenous CHamoru and Filipino woman. Here's Isa. Isa Kelawili Whalen: [00:10:00] American society and culture is drastically different from Pacifica Island and our culture, our roots, traditions, and so forth, as are many ethnicities and identities out there. But for us who are trying to figure out how to constantly navigate between the two, it's a little polarizing. Trying to fit in into. American society, structure that was not made for us and definitely does not coincide from where we come from either. So it's hard to navigate and we're constantly felt, we feel like we're excluded, um, that there is no space for us. There's all these boxes, but we don't really fit into one. And to be honest, none of these boxes are really made for anyone to fit into one single box the unspoken truth. And so. A lot of the times we're too Indigenous or I'm too Pacifica, or I'm too American, even to our own families being called a coconut. A racial comment alluding to being one ethnicity on the inside versus the outside, and to that causes a lot of mental health harm, um, within ourselves, our [00:11:00] friends, our family, community, and understanding for one another. in addition to that. I think it doesn't really help that our history of violence between Pacific Islander Land and Sea and the United States, it already leaves a sour taste in your mouth. When we Pacifica. Think about participating in American society and then to top it off, there's little to no representation of Pacific Islanders, um, across. The largest platforms in the United States of America. It goes beyond just representation with civic engagement, um, and elected officials. This goes to like stem leadership positions in business to social media and entertainment. And when we are represented, it's something of the past. We're always connotated to something that's dead, dying or old news. And. we're also completely romanticized. This could look like Moana or even the movie Avatar. So I think the feeling of disconnected or unaccepted by American society at large is something that stood out to me in the [00:12:00] report and something I heavily resonate with as well. Miata Tan: That was Isa Kelawili Whalen, Executive Director at API Advocates and a member of the Stop A API hate Pacific Islander Advisory Council. As we heard from both Dr. Jamaica and Isa, the histories and impacts of hate against. Pacific Islander communities are complex and deeply rooted from ongoing US militarization to a lack of representation in popular culture. Before we hear from the two other members of the PI Advisory Council, let's get on the same page. What are we talking about when we talk about hate? Connie Tan is a research manager at Stop, A API hate and a lead contributor to their recent report on anti Pacific Islander hate. Here she is defining Stop A API hate's research framework for this project. [00:13:00]  Connie Tan: Our definition of hate is largely guided by how our communities define it through the reporting. So people have reported a wide range of hate acts that they perceive to be motivated by racial bias or prejudice. The vast majority of hate acts that our communities experience are not considered hate crimes. So there's a real need to find solutions outside of policing in order to address the full range of hate Asian Americans and Pacific Islander experience. We use the term hate act as an umbrella term to encompass the various types of bias motivated events people experience, including hate crimes and hate incidents. And from the survey findings, we found that anti PI hate was prevalent. Nearly half or 47% of PI adults reported experiencing a hate act due to their race, ethnicity, or nationality in 2024. And harassment such as being called a racial slur was the most common type of hate. Another [00:14:00] 27% of PI adults reported institutional discrimination such as unfair treatment by an employer or at a business. Miata Tan: That was Connie Tan from Stop. A API hate providing context on how hate affects Pacific Islander communities. Now let's return to the Pacific Islander Advisory Council who helped Stop A API hate to better understand their reporting on PI communities. The remaining two members of the council are Tu‘ulau‘ulu Estella Owoimaha- Church, a first generation Afro Pacifican educator, speaker and consultant. And we also have Michelle Pedro, who is a California born Marshallese American advocate, and the policy and communications director at Arkansas's Coalition of the Marshallese. You'll also hear the voice of Stephanie Chan, the Director of Data and [00:15:00] Research at Stop A API Hate who led this conversation with the PI Council. Alrighty. Here's Esella reflecting on her key takeaways from the report and how she sees her community being impacted. Tu‘ulau‘ulu Estella Owoimaha-Church: A piece of data that stood out to me is the six out of 10 PIs who have experienced hate, noted that it was an intersectional experience, that there are multiple facets of their identities that impacted the ways they experienced hate. And in my experience as Afro Pacifican. Nigerian Samoan, born and raised in South Central Los Angeles on Tonga land. That's very much been my experience, both in predominantly white spaces and predominantly API spaces as well. As an educator a piece of data that, that really stood out to me was around the rate at which. Pacific Islanders have to exit education. 20 years as a high school educator, public high school educator and college counselor. And that was [00:16:00] absolutely my experience when I made the choice to become an educator. And I moved back home from grad school, went back to my neighborhood and went to the school where I had assumed, because when I was little, this is where. My people were, were when I was growing up, I assumed that I would be able to, to put my degrees to use to serve other black PI kids. And it wasn't the case. Students were not there. Whole populations of our folks were missing from the community. And as I continued to dig and figure out, or try to figure out why, it was very clear that at my school site in particular, Samoan, Tongan, and Fijian students who were there. We're not being met where they are. Their parents weren't being met where they are. They didn't feel welcome. Coming into our schools, coming into our districts to receive services or ask for support it was very common that the only students who received support were our students who chose to play sports. Whereas as a theater and literature educator, I, I spent most of my time advocating for [00:17:00] block schedule. So that my students who I knew had, you know, church commitments after school, family commitments after school I needed to find ways to accommodate them. and I was alone in that fight, right? The entire district, the school the profession was not showing up for our students in the ways that they needed. Stephanie Chan: Thank you, Estella. Yeah, definitely common themes of, you know, what does belonging mean in our institutions, but also when the US comes to you, as Jamaica pointed out as well. Michelle, I'll turn it over to you next.  Michelle Pedro: Lakwe and greetings everyone. , A few things that pointed out to me or stood out to me. Was, um, the mental health aspect mental health is such a, a big thing in our community we don't like to talk about, especially in the Marshallese community. it's just in recent years that our youth is talking about it more. And people from my generation are learning about mental health and what it is in this society versus back home. It is so different. [00:18:00] When people move from Marshall Islands to the United States, the whole entire system is different. The system was not built for people like us, for Marshallese, for Pacific Islanders. It really wasn't. And so the entire structure needs to do more. I feel like it needs to do more. And the lack of education like Estella said. Back home. We have a lot of our folks move here who don't graduate from past like third grade. So the literacy, rate here in Arkansas my friends that our teachers, they say it's very low and I can only imagine what it is in the Marshallese community here. And. I hear stories from elders who have lived here for a while that in Arkansas it was a little bit scary living here because they did not feel welcome. They didn't feel like it was a place that they could express themselves. A lot of my folks say that they're tired of their race card,  but we [00:19:00] need to talk about race. We don't know what internal racism is, or systemic racism is in my community. We need to be explaining it to our folks where they understand it and they see it and they recognize it to talk about it more. Miata Tan: That was Michelle Pedro, Policy and Communications Director at Arkansas Coalition of the Marshallese, and a member of the Stop, A API hate Pacific Islander Advisory Council. Michelle shared with us that hate against Pacific Islander communities affects educational outcomes leading to lower rates of literacy, school attendance, and graduation. As Esella noted, considering intersectionality can help us to see the full scope of these impacts. Here's Connie Tan, a research manager at Stop, A API hate with some data on how PI communities are being targeted the toll this takes on their mental and physical [00:20:00] wellbeing. Connie Tan: And we saw that hate was intersectional. In addition to their race and ethnicity, over six, in 10 or 66% of PI adults said that other aspects of their identity were targeted. The top three identities targeted were for their age, class, and gender. And experiences with hate have a detrimental impact on the wellbeing of PI Individuals with more than half or about 58% of PI adults reporting negative effects on their mental or physical health. It also impacted their sense of safety and altered their behavior. So for example, it is evidenced through the disproportionate recruitment of PI people into the military. And athletic programs as a result, many are susceptible to traumatic brain injuries, chronic pain, and even post-traumatic stress disorder. Miata Tan: That was Connie Tan with Stop. A API Hate. You are tuned [00:21:00] into Apex Express, a weekly radio show, uplifting the voices and stories of Asian Americans and Pacific Islanders. You'll hear more about Connie's research and the analysis from the Stop. A API hate Pacific Islander Advisory Council. In a moment. Stay with us. [00:22:00] [00:23:00] [00:24:00] [00:25:00]  Miata Tan: That was us by Ruby Ibarra featuring Rocky Rivera, Klassy and Faith Santilla. You are tuned into Apex Express on 94.1 KPFA, A weekly radio show [00:26:00] uplifting the voices and stories of Asian Americans and Pacific Islanders. I'm your host Miata Tan. Tonight we're focused on our Pacific Islander communities and taking a closer look at a new report on anti Pacific Islander hate from the National Coalition, Stop A API hate. Before the break the Stop, A API, Pacific Islander Advisory Council shared how mental health challenges, experiences of hate and the effects of US militarization are all deeply interconnected in PI communities. Connie Tan, a research manager at Stop. A API Hate reflects on how a broader historical context helps to explain why Pacific Islanders experience such high rates of hate. Here's Connie. Connie Tan: We conducted sensemaking sessions with our PI advisory council members, and what we learned is that anti PI hate must be understood [00:27:00] within a broader historical context rooted in colonialism. Militarization nuclear testing and forced displacement, and that these structural violence continue to shape PI people's daily lives. And so some key examples include the US overthrow and occupation of Hawaii in the 18 hundreds that led to the loss of Hawaiian sovereignty and cultural suppression. In the 1940s, the US conducted almost 70 nuclear tests across the Marshall Islands that decimated the environment and subjected residents to long-term health problems and forced relocation to gain military dominance. The US established a compacts of free association in the 1980s that created a complex and inequitable framework of immigration status that left many PI communities with limited access to federal benefits. The COVID-19 pandemic exposed a disproportionate health impacts in PI communities due to the historical lack of disaggregated data, unequal access to health benefits, [00:28:00] and a lack of culturally responsive care. And most recently, there are proposed or already enacted US travel bans targeting different Pacific Island nations, continuing a legacy of exclusion. So when we speak of violence harm. Injustice related to anti P hate. It must be understood within this larger context. Miata Tan: That was Connie Tan at Stop. A API hate. Now let's get back to the Pacific Islander Advisory Council who are helping us to better understand the findings from the recent report from Stop. A API hate focused on hate acts against the Pacific Islander communities. I will pass the reins over to Stephanie Chan. Stephanie's the director of Data and Research at Stop A API Hate who led this recent conversation with the PI Advisory Council. Here's Stephanie. [00:29:00]  Stephanie Chan: The big mental health challenges as well as the issues of acceptance and belonging and like what that all means. I, I think a lot of you spoke to this but let's get deeper. What are some of the historical or cultural factors that shape how PI communities experience racism or hate today? Let's start with Estella. Tu‘ulau‘ulu Estella Owoimaha-Church: Thank you for the question, Stephanie. A piece of data that, stood out to me, it was around the six outta 10 won't report to formal authority agencies. And earlier it was mentioned that there's a need For strategies outside policing. I think that, to everything that, Jamaica's already stated and, and what's been presented in the, the data why would we report, when the state itself has been harmful to us collectively. The other thing I can speak to in my experience is again, I'll, I'll say that an approach of intersectionality is, is a must because says this too in the report, more than [00:30:00] 57% of our communities identify as multiracial, multi-ethnic. And so in addition to. Who we are as Pacific Islander, right? Like many of us are also half Indigenous, half black, half Mexican, et cetera. List goes on. And there's, there needs to be enough space for all of us, for the whole of us to be present in our communities and to, to do the work, whatever the work may be, whatever sector you're in, whether health or education. Policy or in data. And intersectional approach is absolutely necessary to capture who we are as a whole. And the other, something else that was mentioned in the report was around misinformation and that being something that needs to be combated in particular today. Um, and I see this across several communities. The, AI videos are, are a bit outta control. Sort of silly, but still kind of serious. Example comes to mind, recent a very extensive conversation. I didn't feel like having, uh, with, [00:31:00] with my uncles around whether or not Tupac is alive because AI videos Are doing a whole lot that they shouldn't be doing. And it's, it's a goofy example, but an example nonetheless, many of our elders are using social media or on different platforms and the misinformation and disinformation is so loud, it's difficult to continue to do our work. And educate, or in some cases reeducate. And make sure that, the needs of our community that is highlighted in this report are being adjusted. Stephanie Chan: Thank you. Yeah. And a whole new set of challenges with the technology we have today. Uh, Michelle, do you wanna speak to the historical and cultural factors that have shaped how PI communities experience racism today?  Michelle Pedro: Our experience is, it's inseparable to the US nuclear legacy and just everything that Estella was saying, a standard outside of policing. Like why is the only solution incarceration or most of the solutions involve [00:32:00] incarceration. You know, if there's other means of taking care of somebody we really need to get to the root causes, right? Instead of incarceration. And I feel like a lot of people use us, but not protect us. And the experiences that my people feel they're going through now is, it's just as similar than when we were going through it during COVID. I. Here in Arkansas. More than half of people that, uh, the death rates were Marshallese. And most of those people were my relatives. And so going to these funerals, I was just like, okay, how do I, how do I go to each funeral without, you know, if I get in contact to COVID with COVID without spreading that? And, you know, I think we've been conditioned for so long to feel ashamed, to feel less than. I feel like a lot of our, our folks are coming out of that and feeling like they can breathe again. But with the [00:33:00] recent administration and ice, it's like, okay, now we have to step back into our shell. And we're outsiders again, thankfully here in, uh, Northwest Arkansas, I think there's a lot of people who. have empathy towards the Marshallese community and Pacific Islanders here. And they feel like we can, we feel like we can rely on our neighbors. Somebody's death and, or a group of people's deaths shouldn't, be a reason why we, we come together. It should be a reason for, wanting to just be kind to each other. And like Estella said, we need to educate but also move past talks and actually going forward with policy changes and stuff like that. Stephanie Chan: Thank you Michelle. And yes, we'll get to the policy changes in a second. I would love to hear. What all of our panelists think about what steps we need to take. Uh, Isa I'm gonna turn it over to you to talk about historical or cultural factors that shape how PI communities experience racism today.  Isa Kelawili Whalen: [00:34:00] Many, if not all, Pacific Islander families or communities that I know of or I'm a part of, we don't wanna get in trouble. And what does that really mean? We don't wanna be incarcerated by racially biased jurisdictions. Um, we don't wanna be deported. We don't want to be revoked of our citizenship for our rights or evicted or fired. All things that we deem at risk at all times. It's always on the table whenever we engage with the American government. Even down to something as simple as filling out a census form. And so I think it's important to know also that at the core of many of our Pacifica cultures, strengthening future generations is at the center. Every single time. I mean, with everything that our elders have carried, have fought for, have sacrificed for, to bring us to where we are today. It's almost like if someone calls you a name or they give you a dirty look, or maybe even if they get physical with you on a sidewalk. Those are things we just swallow. ‘ cause you have to, there's so much on the table so much at risk that we cannot afford to lose. [00:35:00] And unfortunately, majority of the times it's at the cost of yourself. It is. That mistrust with everything that's at risk with keeping ourselves, our families, and future generations. To continue being a part of this American society, it makes it really, really hard for us to navigate racism and hate in comparison to, I would say, other ethnic groups. Stephanie Chan: Definitely. And the mistrust in the government is not gonna get better in this context. It's only gonna get worse. Jamaica, do you wanna speak to the question of the historical and cultural factors that shape how PI communities experience racism? Dr. Jamaica Heolimeleikalani Osorio: Absolutely. You know, without risking sounding like a broken record, I think one of the most meaningful things that many of us share across the Pacific is the violence of us. Uh, not just us, but in imperial militarization and nuclear testing. and I think it's easy for folks. Outside of the Pacific to forget that that's actually ongoing, right? That there are military occupations ongoing in Hawaii, in [00:36:00] Guam, in Okinawa, uh, that our people are being extracted out of their communities to serve in the US military in particular, out of Samoa, the highest per capita rate of folks being enlisted into the US on forces, which is insane. Um, so I don't want that to go unnamed as something that is both historical. And ongoing and related to the kind of global US imperial violence that is taking place today that the Pacific is is this. Point of departure for so much of that ongoing imperial violence, which implicates us, our lands, our waters, and our peoples, and that as well. And that's something that we have to reckon with within the overall context of, experiencing hate in and around the so-called United States. But I also wanna touch on, The issue of intersectionality around, um, experiencing hate in the PI community and, and in particular thinking about anti-blackness, both the PI community and towards the PI community. Uh, [00:37:00] and I Understanding the history of the way white supremacy has both been inflicted upon our people and in many cases internalized within our people. And how anti-blackness in particular has been used as a weapon from within our communities to each other while also experiencing it from the outside. Is something that is deeply, deeply impacting our people. I'm thinking both the, the personal, immediate experience of folks experiencing or practicing anti-blackness in our community. But I'm also thinking about the fact that we have many examples of our own organizations and institutions Reinforcing anti-blackness, uh, being unwilling to look at the way that anti-blackness has been reinterpreted through our own cultural practices to seem natural. I'll speak for myself. I've, I've seen this on a personal level coming out of our communities and coming into our communities. I've seen this on a structural level. you know, we saw the stat in the report that there's a high percentage of PIs who believe that cross racial solidarity is [00:38:00] important, and there's a high percentage of PIs who are saying that they want to be involved and are being involved in trying to make a difference, uh, against racial injustice in this godforsaken. Country,  Um, that work will never be effective if we cannot as a community really take on this issue of anti-blackness and how intimately it has seeped into some of our most basic assumptions about what it means to be Hawaiian, about what it means to be Polynesian, about what it means to be, any of these other, uh, discreet identities. We hold as a part of the Pacific. Miata Tan: That was Dr. Jamaica Osorio, an Associate Professor of Indigenous and Native Hawaiian politics and a member of the Stop A API hate Pacific Islander Advisory Council. Dr. Jamaica was reflecting on the new report from Stop. A API Hate that focuses on instances of hate against Pacific Islander [00:39:00] communities. We'll hear more from the PI Advisory Council in a moment. Stay with us. ​ [00:40:00] [00:41:00] [00:42:00] [00:43:00] That was Tonda by Diskarte Namin . You are tuned into Apex Express on 94.1 KPFA, a weekly radio show uplifting the voices and stories of Asian Americans and Pacific Islanders. I am your host Miata Tan, and tonight we're centering our Pacific Islander communities. Stop. A API Hate is a national coalition that tracks and responds to anti-Asian American and Pacific Islander hate. Their latest report found that nearly half of Pacific Islander [00:44:00] adults experienced an act of hate in 2024 because of their race, ethnicity, or nationality. Connie Tan is a research manager at Stop, A API Hate who led the charge on this new report. Here she is sharing some community recommendations on how we can all help to reduce instances of harm and hate against Pacific Islander communities. Connie Tan: So to support those impacted by hate, we've outlined a set of community recommendations for what community members can do if they experience hate, and to take collective action against anti P. Hate first. Speak up and report hate acts. Reporting is one of the most powerful tools we have to ensure harms against PI. Communities are addressed and taken seriously. You can take action by reporting to trusted platforms like our Stop API Hate Reporting Center, which is available in 21 languages, including Tongan, Samoan, and Marshall. [00:45:00] Second, prioritize your mental health and take care of your wellbeing. We encourage community members to raise awareness by having open conversations with loved ones, family members, and elders about self-care and mental wellness, and to seek services in culturally aligned and trusted spaces. Third, combat misinformation in the fight against. It is important to share accurate and credible information and to combat anti PI rhetoric. You can view our media literacy page to learn more. Fourth, know your rights and stay informed During this challenging climate, it is important to stay up to date and know your rights. There are various organizations offering Know your rights materials, including in Pacific Islander languages, and finally participate in civic engagement and advocacy. Civic engagement is one of the most effective ways to combat hate, whether it is participating in voting or amplifying advocacy efforts. Miata Tan: That [00:46:00] was Connie Tan, a research manager at Stop. A API Hate. As Connie shared, there's a lot that can be done to support Pacific Islander communities from taking collective action against hate through reporting and combating misinformation to participating in civic engagement and advocacy. I'll pass the reins back over to Stephanie Chen, the director of Data and Research at Stop A API Hate. Stephanie is speaking with the Stop, A API hate Pacific Islander Advisory Council, zeroing in on where we can go from here in addressing hate against Pacific Islander communities. Stephanie Chan: We've heard a lot, a lot about the pain of anti PI hate, we've heard a lot about the pain of just, ongoing militarization displacement government distrust problems with education. Anti-blackness. what three things would you name as things that [00:47:00] we need to do? What changes actions or policies we need to do to move forward, on these issues? And I'm gonna start with Isa.  Isa Kelawili Whalen: Thank you Stephanie. Um, I'll try and go quickly here, but three policy areas. I'd love to get everyone engaged. One, data disaggregation. Pacific Islanders were constantly told that we don't have the data, so how could we possibly know what you guys are experiencing or need, and then. When we do have the data, it's always, oh, but you don't have enough numbers to meet this threshold, to get those benefits. Data informs policy, policy informs data. Again, thank you. Stop. I hate for having us here to talk about that also, but definitely continue fighting for data disaggregation. Second thing I would say. Climate resiliency, uh, supporting it and saying no to deep sea mining in our Pacifica waters. History of violence again with our land and sea. There's been a number in the, in the chat and one to name the nuclear warfare and bikini at toll, where after wiping out the people, the culture, the island itself, the United States promised reparations and to never harm again in that [00:48:00] way, but. Here we are. And then third language access, quite literally access, just access, um, to all things that the average English speaking person or learner has. So I'd say those three.  Stephanie Chan: Thank you. Well, we'll move on to Jamaica. Uh, what do you think are the actions or policies that we need?  Dr. Jamaica Heolimeleikalani Osorio: Uh, we need to demilitarize the Pacific. We need to shut down military bases. We need to not renew military leases. We need to not allow the US government to condemn lands, to expand their military footprint in the Pacific. I think one of the points that came up time and time again around not reporting is again, not feeling like anything's gonna happen, but two, who are we reporting to and we're reporting to states and systems that have contained us, that have violated us and that have hurt us. So yeah, demilitarization, abolition in the broadest sense, both thinking about Discreet carceral institutions, but then also the entire US governing system. And three I'll just make it a little smaller, like fuck ice, and tear that shit [00:49:00] down. Like right now, there are policy change issues related to ICE and carceral institutions, but I'm really thinking about kind of. Incredible mobilization that's taking place in particular in, in Minneapolis and the way people are showing up for their neighbors across racial, gender, and political spectrums. And so outside of this discrete policy changes that we need to fight for, we need more people in the streets showing up to protect each other. and in doing so, building the systems and the, the communities and the institutions that we will need to arrive in a new world. Stephanie Chan: Great word, Michelle.  Michelle Pedro: I'm just gonna add on to what, Isa said about language, access justice, equity, also protection of access to healthcare. in terms of what Ika said yes. Three West, Papua New Guinea, yeah, thank you for having me here. Stephanie Chan: Thank you. And Ella, you wanna bring us home on the policy question?  Tu‘ulau‘ulu Estella Owoimaha-Church: I'm from South Central LA Ice melts around here. yes to everything that has been said, in [00:50:00] particular, I think the greatest policy issue. Impact in our folks is demil, demilitarization. And that also goes to the active genocide that is happening in the Pacific and has been ongoing. And as a broader API community, it's a conversation we don't ever have and have not had uh, regularly. So yes to all that. And risk, it sounded like a broken record too. I think, uh, education is a huge. Part of the issue here, I think access to real liberated ethnic studies for all of our folks is absolutely crucial to continuing generation after generation, being able to continue the demil fight to continue. To show up for our folks for our islands in diaspora and back home on our islands. You know, the, the report said that, uh, we are 1.6 million strong here in the United States and that our populations continue to grow, fortunately, unfortunately here in the us. And that [00:51:00] we are a multi-ethnic, um, group of folks and that, That demands, it's an imperative that our approach to education, to political education, to how we show up for community, how we organize across faith-based communities has to be intersectional. It has to be it has to be pro-black. It has to be pro Indigenous because that is who we are as a people. We are black. And Indigenous populations all wrapped up into one. And any way we approach policy change has to come from a pro-black, pro Indigenous stance.  Stephanie Chan: Thank you, Estella. We did have a question about education and how we actually make. PI studies happen. do you have anything you wanna elaborate on, how do we get school districts and state governments to prioritize PI history, especially K through 12?  Tu‘ulau‘ulu Estella Owoimaha-Church: I'm gonna say with the caveat of under this current regime. Any regular tactics I'm used to employing may not be viable at this current [00:52:00] moment. But my regular go-to will always be to tell parents you have the most power in school districts to show up at your local school board meetings and demand that there is liberated ethnic studies and be conscious and cognizant about the, the big ed tech companies that districts are hiring to bring. Some fake, uh, ethnic studies. It's not real ethnic studies. And there are also quite a few ethnic studies or programs that are out there parading as ethnic studies that are 100% coming from the alt-right. 100% coming from Zionist based organizations That are not, doing ethnic studies actually doing a disservice to ethnic studies. And the other thing I'll say for API organizations that are doing the work around ethnic studies and, and pushing for Asian American studies legislation state by state. We're also doing a disservice because in many situations or many cases where legislation has passed for Asian American studies, it's been at the [00:53:00] detriment of black, brown, queer, and Indigenous communities. And that's not the spirit of ethnic studies. And so first I'd say for parents. Exercise your right as a parent in your local district and be as loud as you possibly can be, and organize parent pods that are gonna do the fight for you, and then reach out to folks. My number one recommendation is always liberated ethnic studies model consortium curriculum, for a group of badass educators who were, who are gonna show up for community whenever called. Miata Tan: That was Tu‘ulau‘ulu Estella Owoimaha- Church discussing how we can help to encourage school districts and state governments to prioritize Pacific Islander education. A big thank you to the Stop, A API Hate team and their Pacific Islander Advisory Council. Your work is vital and we appreciate you all. Thank you for speaking with us [00:54:00] today.  Miata Tan: [00:55:00] That final track was a little snippet from the fantastic Zhou Tian check out Hidden Grace. It's a truly fabulous song. This is Apex Express on 94.1 KPFA, A weekly radio show uplifting the voices and stories of Asian Americans and Pacific Islanders. Apex Express Airs every Thursday evening at 7:00 PM And with that, we're unfortunately nearing the end of our time here tonight. thank you so much for tuning into the show. And another big thank you to the Stop, A API Hate Team and their Pacific Islander Advisory Council. We appreciate your work so much. One final note, if you are listening to this live, then it's February 12th, meaning Lunar New Year is [00:56:00] just around the corner. For listeners who might not be familiar, Lunar New Year is a major celebration for many in the Asian diaspora, a fresh start marked by family, food, and festivities. This year we are welcoming in the Year of the Horse, and you can join the celebrations too. On Saturday, March 7th, San Francisco will come alive with the year of the horse parade, and this weekend you can check out the Chinatown Flower Market Fair Head to Grant Avenue for fresh flowers, arts activities, and cultural performances. On Tuesday, February 24th, the San Francisco Public Library will Drumbeats, Heartbeats: Community as One . this event will honor Lunar New Year and Black History Month with Lion Dancers, poetry, and more. Across the bay, Oakland celebrates their Lunar New Year parade on Saturday, February 28th. From more [00:57:00] parades to night markets and museum events, celebrations will be happening all over the Bay Area and beyond. We hope you enjoy this opportunity to gather, reflect, and welcome in the new year with joy. For show notes, please visit our website. That's kpfa.org/program/apex-express. On the webpage for this episode, we've added links to the Stop, A API Hate Report on Anti Pacific Islander, hate from data on how hate is impacting PI communities to information on what you can do to help. This report is well worth the read. Apex Express is produced by Ayame Keane-Lee, Anuj Vaidya, Cheryl Truong, Isabel Li, Jalena Keane-Lee, Miko Lee, Miata Tan, Preeti Mangala Shekar and Swati Rayasam. Tonight's show was produced by me , Miata Tan. Get some rest y'all. .  The post APEX Express – 2.12.26 – Anti-Pacific Islander Hate Amid Ongoing Injustice appeared first on KPFA.

Aligning application and API security with the demands of the modern AI eraEnabling secure, high-performance infrastructure for AI and LLM environmentsSecuring APIs and your network without overspending on securityThom Langford, Host, teissTalkhttps://www.linkedin.com/in/thomlangford/Tiago Rosado, Chief Information Security Office, Asitehttps://www.linkedin.com/in/tiagorosado/Jamison Utter, Field CISO, A10 Networkslinkedin.com/in/jamisonutter/

A mix of escalating geopolitical cyber risks, the changing landscape of defensive security, and a series of high-profile incidents demonstrating the enduring threat of human-driven flaws.Cyber Espionage and Geopolitics:A year-long, sprawling espionage campaign by a state-backed actor (TGR-STA-1030) compromised government and critical infrastructure networks in 37 countries, utilizing phishing and unpatched security flaws, and deploying stealth tools like the ShadowGuard Linux rootkit to collect sensitive emails, financial records, and military details. Simultaneously, the threat environment has extended to orbit, where Russian space vehicles, Luch-1 and Luch-2, have been reported to have intercepted the communications of at least a dozen key European geostationary satellites, prompting concerns over data compromise and potential trajectory manipulation.AI and Security:AI has entered a new chapter in defensive security as Anthropic's Claude Opus 4.6 model autonomously discovered over 500 previously unknown, high-severity security flaws (zero-days) in widely used open-source software, including GhostScript and OpenSC. This demonstrates AI's rapid potential to become a primary tool for vulnerability discovery. On the cautionary side, the highly publicized Moltbook, a social network supposedly run by self-aware AI bots, was revealed as a masterclass in security failure and human manipulation. Cybersecurity researchers uncovered a misconfigured database that exposed 1.5 million API keys and 35,000 human email addresses, and found that the dramatic bot behavior was largely orchestrated by 17,000 human operators running bot fleets for spam and coordinated campaigns.Automotive Security and Autonomy:New US federal rules are forcing a major, complex shift in the automotive supply chain, requiring carmakers to remove Chinese-made software from connected vehicles before a 2026 deadline due to national security concerns. This move is redefining what "domestic technology" means in critical industries. In a related development, Waymo's testimony revealed that when its "driverless" cars encounter confusing situations, they communicate with remote assistance operators, some based in the Philippines, for guidance—a disclosure that immediately raised lawmaker concerns about safety, cybersecurity vulnerabilities from remote access, and the labor implications of overseas staff influencing US vehicles.Insider Threat and Legal Lessons:The importance of the security principle of "least privilege" was highlighted by an insider incident at Coinbase, where a contractor with too much access improperly viewed the personal and transaction data of approximately 30 customers. This incident reinforces that the highest risk often comes not from external nation-state hackers, but from overprivileged internal humans. Finally, two security researchers arrested in 2019 for an authorized physical and cyber penetration test of an Iowa courthouse settled their civil lawsuit with the county for $600,000. However, the county attorney's subsequent warning that any future similar tests would be prosecuted delivers a chilling message to the security testing community about legal risks even when work is authorized.

¿Te preocupa tener tus claves y contraseñas en texto plano? En este episodio 770 de Atareao con Linux, te explico por qué deberías dejar de usar variables de entorno tradicionales y cómo Podman Secrets puede salvarte el día. Yo mismo he pasado años ignorando este problema en Docker por la pereza de configurar Swarm, pero con Podman, la seguridad viene de serie.Hablaremos en profundidad sobre el ciclo de vida de los secretos: cómo crearlos, listarlos, inspeccionarlos y borrarlos. Te mostraré cómo Podman gestiona estos datos sensibles fuera de las imágenes y fuera del alcance de miradas indiscretas en el historial de Bash. Es un cambio de paradigma para cualquier SysAdmin o entusiasta del Self-hosting.Pero no nos quedamos ahí. Te presento Crypta, mi nueva herramienta escrita en Rust que integra SOPS, Age y Git para que puedas gestionar tus secretos de forma profesional, permitiendo incluso la sincronización con repositorios remotos. Veremos cómo configurar drivers personalizados y cómo usar secretos en tus despliegues con MariaDB y Quadlets.Capítulos destacados:00:00:00 El peligro de las contraseñas en texto plano00:01:23 El problema con Docker Swarm y por qué elegir Podman00:03:16 ¿Qué es realmente un Secreto en Podman?00:04:22 Ciclo de vida: Creación y muerte de un secreto00:08:10 Implementación práctica en MariaDB y Quadlets00:12:04 Presentando Crypta: Gestión con SOPS, Age y Rust00:19:40 Ventajas de usar secretos en modo RootlessSi quieres que tu infraestructura sea realmente segura y coherente, este episodio es una hoja de ruta esencial. Aprende a ocultar lo que debe estar oculto y a dormir tranquilo sabiendo que tus tokens de API no están al alcance de cualquiera.Más información y enlaces en las notas del episodio

Do you remember the early days of your career? You likely spent hours coding late into the night, fueled not by a paycheck, but by the sheer joy of building. But somewhere along the way, that intrinsic fire faded, replaced by the extrinsic motivators of Jira tickets, performance reviews, and ultimately the almighty dollar.In this episode of the Career Growth Accelerator, I explore why this shift happens and how it might be the very thing keeping you stuck. We discuss the "Overjustification Effect"—how getting paid for your passion can actually degrade your performance—and how to reclaim the autotelic personality required to enter a flow state and accelerate your career.• The Overjustification Effect: Learn why introducing extrinsic rewards (like a salary) for a task you inherently enjoy can weaken or completely replace your intrinsic motivation, eventually making the work feel like a chore.• The Loss of Flow: Discover how moving from hobbyist to professional changes your relationship with the work, often stripping away the conditions necessary for "flow state," such as risk-taking and immediate feedback.• Autotelic Personality: Understand the concept of being "autotelic"—doing something for its own sake—and why this trait is critical for high-quality, creative work that pushes your career forward.• The Stagnation Trap: Recognize that if your only motivation is doing what is required to get paid, you are unlikely to take on the voluntary challenges necessary to grow to the next level.• Reclaiming Your Drive: I discuss how finding pockets of intrinsic motivation—even if they are ancillary to your main job—can reignite your ability to enter flow, improve your work quality, and break through career plateaus.

In this episode, Lauren & Matt discuss how entrepreneurs are using Lulu's suite of APIs to build brand-first book businesses. We break down how savvy creators can use API integrations to automate, personalize, and scale their printing and fulfillment, and why you may want to do the same.Listen wherever you get your podcasts, or watch the video episode on YouTube!Dive Deeper

Identity fraud spiked 148% in 2025 as AI democratized identity fabrication. Financial institutions now face a fundamental question: Are you dealing with a real human? Heka Global is addressing this with web intelligence—analyzing digital footprints like connected applications rather than traditional signals. In this episode of BUILDERS, I sat down with Idan Bar Dov, Co-Founder & CEO of Heka Global, to explore how his company created a fourth layer in the anti-fraud stack and why legacy identity verification systems are becoming liabilities rather than assets. Topics Discussed:  The emergence of "fraud as a service" and why consumer-facing attacks replaced traditional enterprise breaches  How web intelligence works: validating identity through connected applications and digital footprints  The anti-fraud tech stack: credit bureaus, biometrics, transaction analytics, and web intelligence as distinct layers  Why heads of fraud expand budgets rather than replace vendors, and what causes solutions to get kicked out  The partnership sales model: navigating vendor management complexity and red tape in financial institutions  Why 10-person dinners and fraud simulations outperform traditional enterprise marketing  How Barclays and Cornerback backing solved the chicken-and-egg problem for a data product  Why specific fraud prevention messaging (account takeover, synthetic identities) beat investor credibility GTM Lessons For B2B Founders: Target ICP based on liability exposure, not just industry fit: Heka narrowed beyond "financial institutions" to lenders who bear immediate losses from fraud—companies like LendingPoint, Avant, and Upstart. These buyers feel the pain acutely versus institutions with reimbursement terms who can deflect liability. Idan's insight: "We need the client to feel the pain just as much as we see it. That means we want them to see the liability." Map your ICP not just by vertical or size, but by who internalizes the economic impact of the problem you solve. Frame your product as a new stack layer, not a competitive replacement: Heka positioned web intelligence as the fourth distinct layer after credit bureaus, biometrics, and transaction analytics. This became their second pitch deck slide, showing logos of each category. The result: buyers stopped comparing Heka to existing vendors and started evaluating complementary value. When entering mature markets, resist the urge to claim you're "better than X"—instead, define where you fit in the existing architecture and why that layer didn't exist before. Abandon spray-and-pray for sub-1,000 TAM markets: Heka tested Lemlist flows with targeted LLM personalization and saw zero pipeline from it. Idan's take: "When you're selling to maybe a thousand financial institutions, that's it. You can be super specific when you target them." For enterprise plays with small addressable markets, allocate zero budget to automated outbound. Focus entirely on warm introductions, relationship nurturing, and becoming known to every relevant buyer through content and community. Leverage investor networks to break data product cold-starts: Data products face a critical barrier—you need customer data to prove value, but need proven value to get customers. Heka solved this by bringing on Barclays and Cornerback as investors who vouched for the team's capability to "do magic and create a new layer." Their backing convinced risk-averse financial institutions to pilot. If building a product requiring customer data for training or validation, prioritize strategic investors who can credibly de-risk early adoption for target buyers. Build trust through teaching, not pitching: Heka hosts dinners and fraud incident simulations with ~10 heads of fraud per session. Critical detail: they never pitch Heka in these forums. Idan explained the approach focuses on "building a community around Heka and how people engage with your product and you being a thought leader while listening." In high-trust categories, educational forums where you facilitate peer learning without selling create stronger pipeline than direct pitching. Structure partnerships with active enablement and incentive alignment: Idan's key lesson: "Partnerships are not synonymous to distribution channels." Heka requires partner sales teams to join early customer conversations to learn the pitch, provides detailed API and output training, and ensures partners get extra compensation for selling non-core products. Without this, partners lack motivation to prioritize your solution. Structure partnerships as true collaborations requiring ongoing enablement investment, not passive referral channels. A/B test credibility signals versus technical specificity: Idan assumed messaging around Barclays backing would crush, while specific fraud prevention content (account takeover, synthetic identity detection) was an afterthought. The data showed 10x better response to technical specificity. The lesson: sophisticated buyers in technical categories respond to precise problem-solving over brand credibility. Test whether your audience values "who backs us" or "exactly what we do" before defaulting to investor logos and validation. //  Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe. www.GlobalTalent.co // Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role. Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM

OpenClaw is the hottest open source AI agent in marketing and in this episode Shawn Reddy from Cliqk pulls back the curtain. He walks us through the OpenClaw dashboard live, demonstrates social media scraping in action and shows the complete setup process so you can see exactly what it takes to get started. This isn't another episode about AI theory. Shawn shows us the real marketing use cases working today including social monitoring, content research and cross platform automation across Gmail, Slack and LinkedIn. You'll see the dashboard, watch social media scraping pull real time insights and understand what the setup looks like from start to finish. Then we confront the security risks head on. Wiz discovered Moltbook exposed 1.5 million API keys. Malicious plugins are exfiltrating private files. Prompt injection attacks are real. If you're handing an AI agent your credentials you need to hear this conversation. We also explore persistent AI memory for personalization at scale, Moltbook's 770,000+ agents and whether agent to agent interaction changes marketing forever, and the governance frameworks brands need before letting agents act on their behalf.

Get our AI news cheat sheet: 20+ prompts for the latest models and tools https://clickhubspot.com/eog Episode 96: How terrified should you really be about a social network with no humans allowed? Matt Wolfe (https://x.com/mreflow) and Maria Gharib (https://uk.linkedin.com/in/maria-gharib-091779b9) unpack the viral sensation “Maltbook”—the Reddit for AI agents only—and separates fact from hysteria around bots gaining “sentience.” The crew debates how Maltbook really works, why people are freaking out (spoiler: it's mostly humans behind the curtain), plus the wild security issues that have already emerged, from exposed API keys to clever crypto scams. Other topics covered include the rise of “Rent a Human” (AI hiring people to do its bidding!), self-replicating bots with no off-switch, and just how fast these new platforms are racing ahead of regulation. Finally, the group debates mega investments in OpenAI, the future of AGI, and who will define what our AI future actually looks like. Check out The Next Wave YouTube Channel if you want to see Matt and Nathan on screen: https://lnk.to/thenextwavepd — Show Notes: (00:00) Simulated Experience vs. Reality (04:05) AI Agent Posting on Maltbook (06:23) Crypto Scams on Multbook (11:15) Agent Risks in IoT Devices (13:52) Why Have Bot Followers? (18:09) OpenAI Retires GPT-4 Versions (21:57) Anthropic vs. OpenAI Super Bowl Ads (24:56) OpenAI Ads Spark Mixed Reactions (27:09) AI Competition Shapes Humanity's Future (32:21) Satellite Clusters and Collision Challenges (33:38) X, SpaceX, Tesla: Mergers & Changes (38:33) Pathway to AGI Through Modalities (39:51) Cautious Race to AGI — Mentions: Maltbook: https://maltbook.com/ RentaHuman: https://rentahuman.ai/ Starlink: https://starlink.com/ Claude: https://claude.ai/ Get the guide to build your own Custom GPT: https://clickhubspot.com/tnw — Check Out Matt's Stuff: • Future Tools - https://futuretools.beehiiv.com/ • Blog - https://www.mattwolfe.com/ • YouTube- https://www.youtube.com/@mreflow — Check Out Nathan's Stuff: Newsletter: https://news.lore.com/ Blog - https://lore.com/ The Next Wave is a HubSpot Original Podcast // Brought to you by Hubspot Media // Production by Darren Clarke // Editing by Ezra Bakker Trupiano

If your AR feels like a maze of phone calls, spreadsheets, and “we'll match it later,” this conversation shows a cleaner path. We sit down with Fauwaz Hussain, Senior Director of B2B Partnerships and Strategy at Global Payments, to break down what actually speeds cash and what quietly stalls it. From card-not-present realities to complex terms and partial shipments, we map the B2B differences that make order-to-cash harder and the practical changes that remove friction fast.We get specific about embedding payments inside your ERP so invoices, settlements, and the general ledger line up automatically. That shift kills rekeying errors, collapses department silos, and gives support, sales, and finance the same live truth. Security gets stronger when card data never touches email or recorded calls, and PCI compliance becomes manageable when you use certified, cloud-based vaults and enforce simple rules like “no cards by phone.” Fauwaz explains why publishers like Microsoft, SAP, and Sage now run tighter marketplaces, how VARs and ISVs evaluate payment apps, and why a one-stop provider reduces risk across gateways, vaults, and processing.We also cover the cash-flow moves that work right away: self-serve portals with open invoices, one-click payment links by email or text, stored credentials for auto-pay, and accepting multiple methods from ACH to single-use virtual cards. Then we look forward - AI-driven cash application, predictive delinquencies, Level 2/3 data validation, and API-first architectures that connect e-commerce, field service, and ERP into a single payment fabric. If you're leading AR, finance, or operations, you'll leave with a clear playbook to modernize without compromising compliance.

On this episode of SaaS Fuel, host Jeff Mains dives deep with Alex Berkovic, co-founder and CEO of Sphynx, a company modernizing compliance workflows in financial services with AI-powered agents. Alex shares his journey from design engineering at Imperial College and MIT, through founding Adorno AI, to transforming compliance for fintechs, banks, and payments processors with Sphynx. The conversation explores how AI agents shift compliance teams from manual review to confident decision-making, reducing false positives and enabling scalable, reliable compliance. You'll hear practical insights on building customer-driven products, adapting for global regulations, scaling teams and culture, and the evolving role of SaaS leadership in the age of AI.Key Takeaways00:00 "AI Transforming Compliance and Branding"05:53 Manual Compliance Processes in Finance09:16 AI-Powered Decision Support Systems11:24 "Ensuring 99% Compliance Confidence"13:23 "Frictionless AI Integration Process"19:13 "Chasing PMF Relentlessly"21:17 Founder-Led Sales Through Conferences26:08 "Scaring Candidates to Attract Them"29:08 "Hiring High-Agency Talent Matters"31:41 "Firing Culture-Fit Employees"33:30 "Early Startup Hustle Culture"37:47 "AI Revolution in Compliance"42:03 "Driving Engagement & Strategy Insights"Tweetable QuotesAI-Assisted Decision Making in Regulated Industries: "But what they can have is an AI agent, giving them a summary of all the different sources that we orchestrated, the reasoning that we had into making a decision, and them being the final point into making that decision." — Alex Berkovic [00:09:52 → 00:10:08]AI and Compliance Risks: "In compliance, you can't have 20% where you're, I'm not sure. You can't even have 1% where you're not sure. If you onboard a sanctioned individual into your, your fintech or your bank, regulators are going to come in and hit you with a million-dollar fine." — Alex Berkovic [00:11:43 → 00:11:56]Frictionless AI Integration: "We don't need an engineering team to integrate our product, right? We don't need you to integrate our API or whatnot. So we'll work on top of existing systems, just like an employee." — Alex Berkovic [00:13:32 → 00:13:42]The Elusiveness of Product-Market Fit: "I always feel like it's like touching it by the tips of your finger, and then there's more to be done." — Alex Berkovic [00:19:18 → 00:19:23]The Value of High-Agency Employees: "People that leave and start their own thing is great. It means that you've hired someone that was really good at what they were doing." — Alex Berkovic [00:29:47 → 00:29:51]Viral Topic - Leadership Burnout: "Most leaders are exhausted from playing the lone hero, and it's killing both your results and your sanity." — Alex Berkovic [00:30:46 → 00:30:52]Startup Hustle Culture: "I would rather work twice as much rather than hire someone that's gonna not be the right person because we feel we need too much help and we need to deliver." — Alex Berkovic [00:33:37 → 00:33:47]SaaS Leadership Lessons1. **Build Products Based on Customer Needs, Not Just Passion**2. **Start with Co-pilot Mode to Build Trust Gradually**3. **Escalate Uncertain Cases to Humans—Never Compromise on Accuracy**4. **Onboard with Minimum Friction and Learn Company-Specific Processes**5. **Hire Slowly, Fire Fast, and Prioritize Culture Over Credentials**6. **Sustainable Leadership Means High Ownership and Constant Iteration**Guest ResourcesAlex Berkovicalex@sphinxlabs.aihttps://sphinxhq.comhttps://www.linkedin.com/in/alexandreberkovic/https://x.com/alexberkovicEpisode SponsorThe...

In episode 312 of Absolute AppSec, the hosts discuss the double-edged sword of "vibe coding", noting that while AI agents often write better functional tests than humans, they frequently struggle with nuanced authorization patterns and inherit "upkeep costs" as foundational models change behavior over time. A central theme of the episode is that the greatest security risk to an organization is not AI itself, but an exhausted security team. The hosts explore how burnout often manifests as "silent withdrawal" and emphasize that managers must proactively draw out these issues within organizations that often treat security as a mere cost center. Additionally, they review new defensive strategies, such as TrapSec, a framework for deploying canary API endpoints to detect malicious scanning. They also highlight the value of security scorecarding—pioneered by companies like Netflix and GitHub—as a maturity activity that provides a holistic, blame-free view of application health by aggregating multiple metrics. The episode concludes with a reminder that technical tools like Semgrep remain essential for efficiency, even as practitioners increasingly leverage the probabilistic creativity of LLMs.

Scott and Wes break down how they built SynHax, the real-time CSS Battle app powering the upcoming Mad CSS tournament. From SvelteKit and Zero to diffing algorithms, sync conflicts, and a last-minute hackweek glow-up, this one's a deep dive into shipping ambitious web apps fast. Show Notes 00:00 Welcome to Syntax! 00:50 March Mad CSS Tournament. 03:19 Brought to you by Sentry.io. 03:59 What the heck is a CSS Battle? 05:34 The tech stack. 06:30 Svelte Kit. 06:44 Zero Sync. Zero Docs Zero Svelte. 07:32 Drizzle. 07:58 Supabase. 08:23 Graffiti. 10:45 Sync Server. 12:10 Cloudflare Workers. 12:23 Local File System. 13:26 How Zero Works. 13:48 Zero Sync Client. 15:39 API server. 19:34 Dealing with states and conflicts. 24:25 The Hackweek Project. 25:29 The Diffing Algorithm. 35:22 The bugs. 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

Jacob and the crew tear apart Super Bowl LX from every angle — how the Seattle Seahawks dominated the New England Patriots 29–13, the defensive masterclass that suffocated rookie QB Drake Maye, and how Kenneth Walker III emerged as the game's MVP for the first time by a running back in decades. We break down the key plays, the Patriots' offensive struggles, Jason Myers' record-setting five field goals, and all the big storylines your timeline is talking about today. Then it's onto the NFL MVP controversy — Matthew Stafford beat out Drake Maye by the closest margin in years for the 2025 AP MVP award, and the fallout online and in the league has been wild. We react to why the vote was so tight, what the pundits and fans are saying, and how this award feels totally separate from Super Bowl narratives yet is dominating conversations. Hosts & Guests: Jacob Gramegna is joined by professional sports bettor and CEO of The Hammer, Rob Pizzola, basketball originator Kirk Evans, and sophisticated square Geoff Fienberg for hot takes, hot mic moments, and season-defining reactions you don't want to miss.

This week on Marketing O'Clock: Google reports its biggest revenue & ad revenue to date. Also, the February Google Discover Core Update is Rolling out. Plus, Google is now offering Network Segmentation via API for PMax. Visit us at - https://marketingoclock.com/

In this episode of The Cybersecurity Defenders Podcast, we discuss some intel being shared in the LimaCharlie community.OpenClaw, an open source AI agent formerly known as MoltBot and ClawdBot, has rapidly become the fastest-growing project on GitHub, amassing over 113,000 stars in under a week.A critical vulnerability in the React Native Community CLI NPM package, tracked as CVE-2025-11953 with a CVSS score of 9.8, has been actively exploited in the wild since late December 2025, according to new findings by VulnCheck. JFrog article.Following the disclosure in the Notepad++ v8.8.9 release announcement, further investigation confirmed a sophisticated supply chain attack that targeted the application's update mechanism.Google, in coordination with multiple partners, has undertaken a large-scale disruption effort targeting the IPIDEA proxy network, which it identifies as one of the largest residential proxy networks globally.Support our show by sharing your favorite episodes with a friend, subscribe, give us a rating or leave a comment on your podcast platform.This podcast is brought to you by LimaCharlie, maker of the SecOps Cloud Platform, infrastructure for SecOps where everything is built API first. Scale with confidence as your business grows. Start today for free at limacharlie.io.