Podcasts about UX

In this repeat episode, Jack Herrington sits down with Tanner Linsley to talk about the evolution of TanStack and where it's headed next. They explore how early projects like React Query and React Table influenced the headless philosophy behind TanStack Router, why virtualized lists matter at scale, and what makes forms in React so challenging. Tanner breaks down TanStack Start and its client-first approach to SSR, routing, and data loading, and shares his perspective on React Server Components, modern authentication tradeoffs, and composable tooling. The episode wraps with a look at TanStack's roadmap and what it takes to sustainably maintain open source at scale. We want to hear from you! How did you find us? Did you see us on Twitter? In a newsletter? Or maybe we were recommended by a friend? Fill out our listener survey! https://t.co/oKVAEXipxu Let us know by sending an email to our producer, Elizabeth, at elizabeth.becz@logrocket.com, or tweet at us at PodRocketPod. Check out our newsletter! https://blog.logrocket.com/the-replay-newsletter/ Follow us. Get free stickers. Follow us on Apple Podcasts, fill out this form, and we'll send you free PodRocket stickers! What does LogRocket do? LogRocket provides AI-first session replay and analytics that surfaces the UX and technical issues impacting user experiences. Start understanding where your users are struggling by trying it for free at LogRocket.com. Try LogRocket for free today. Chapters 01:00 – What is TanStack? Contributors, projects, and mission 02:05 – React Query vs React Table: TanStack's origins 03:10 – TanStack principles: headless, cross-platform, type safety 03:45 – TanStack Virtual and large list performance 05:00 – Forms, abandoned libraries, and lessons learned 06:00 – Why TanStack avoids building auth 07:30 – Auth complexity, SSO, and enterprise realities 08:45 – Partnerships with WorkOS, Clerk, Netlify, and Cloudflare 09:30 – Introducing TanStack Start 10:20 – Client-first architecture and React Router DNA 11:00 – Pages Router nostalgia and migration paths 12:00 – Loaders, data-only routes, and seamless navigation 13:20 – Why data-only mode is a hidden superpower 14:00 – Built-in SWR-style caching and perceived speed 15:20 – Loader footguns and server function boundaries 16:40 – Isomorphic execution model explained 18:00 – Gradual adoption: router → file routing → Start 19:10 – Learning from Remix, Next.js, and past frameworks 20:30 – Full-stack React before modern meta-frameworks 22:00 – Server functions, HTTP methods, and caching 23:30 – Simpler mental models vs server components 25:00 – Donut holes, cognitive load, and developer experience 26:30 – Staying pragmatic and close to real users 28:00 – When not to use TanStack (Shopify, WordPress, etc.) 29:30 – Marketing sites, CMS pain, and team evolution 31:30 – Scaling realities and backend tradeoffs 33:00 – Static vs dynamic apps and framework fit 35:00 – Astro + TanStack Start hybrid architectures 36:20 – Composability with Hono, tRPC, and Nitro 37:20 – Why TanStack Start is a request handler, not a platform 38:50 – TanStack AI announcement and roadmap 40:00 – TanStack DB explained 41:30 – Start 1.0 status and real-world adoption 42:40 – Devtools, Pacer, and upcoming libraries 43:50 – Sustainability, sponsorships, and supporting maintainers 45:30 – How companies and individuals can support TanStackSpecial Guests: Jack Herrington and Tanner Linsley.

Have you ever walked out of a usability session completely confident in your findings, only to ship something that quietly missed the mark? What if the signal was there the whole time, and your brain just decided it wasn't worth logging?This week on the Cognition Catalog, we tackle The Expectation Bias. This bias shapes what you notice before you've even decided what to think about it. Your brain has already generated a prediction before the first participant clicks a button or a teammate presents their work, and that prediction quietly shapes what registers as a signal and what gets explained away before you've made a single conscious decision about what any of it actually means.We get into the science behind why this happens, and trace the research back to psychologist Robert Rosenthal's work in the early 1960s. His experiments, including the landmark Pygmalion in the Classroom study with Lenore Jacobson, showed that expectations don't just color our perceptions; they can actually change outcomes. That's a sobering thought when you consider how many design decisions are built on research we assumed was neutral.We also dig into where this plays out on real teams: in usability sessions where hesitations get logged as "minor," in design reviews where leadership-championed features get a generous read while quietly doubted projects get interrogated at every turn, and in how we evaluate colleagues whose reputations have already done the evaluating for us. If any of that sounds familiar, this episode offers five concrete habits to help you catch the filter before it's already done its job. Give it a listen.Topics:• 00:00 - Perception is prediction• 02:04 - A UX research cautionary tale• 03:23 - Defining expectation bias• 03:42 - Prediction errors explained• 04:31 - Pygmalion effect origins• 06:03 - Expectation vs confirmation• 06:30 - How it warps team decisions• 08:31 - Habits to reduce bias• 10:47 - Wrap up and next steps—Thanks for listening! We hope you dug today's episode. If you liked what you heard, be sure to like and subscribe wherever you listen to podcasts! And if you really enjoyed today's episode, why don't you leave a five-star review? Or tell some friends! It will help us out a ton.If you haven't already, sign up for our email list. We won't spam you. Pinky swear.• Get a FREE audiobook AND support the show• Support the show on Patreon• Check out show transcripts• Check out our website• Subscribe on Apple Podcasts• Subscribe on Spotify• Subscribe on YouTube• Subscribe on Stitcher

Listen before Mar 20 at 8pm PT ... in this live workshop, Sarah Doody shares the 5 UX steps you should also apply to your UX portfolio. You'll also hear how UX people are getting hired with just 1 project in their UX portfolio

UX hiring insights from a UX veteran with 25+ years in UX and product. In this episode, Sarah Doody interviews Dan Maccarone, co-founder of Hard Candy Shell and Charming Robot, fractional Chief Product Officer, and a UX expert who's worked on products for Hulu, Rent the Runway, Foursquare, and the Wall Street Journal. In the episode Dan shares about what he actually looks for when hiring UX people (spoiler: it's not your Figma skills).Dan shares why he doesn't care about tools, why he conducts interviews over drinks instead of in conference rooms, and how he evaluates candidates based on curiosity, empathy, and how they think, not what software they know. He also gets into career reinvention, the rise of fractional leadership roles, and why your hobbies outside of UX might matter more than your case studies.If you're a UX or Product professional navigating your next career move, this conversation will challenge what you think hiring managers care about.What's discussed in this episode:Why Dan has hired people who didn't know Figma — and doesn't careWhat curiosity and a humanities background signal to a hiring managerWhy Dan prefers to conducts interviews with candidates over coffee or drinks, not in conference roomsHow he uses observation and empathy cues to evaluate candidates (the same way you'd do user research)Why he hates design assignments and considers them insultingWhat "career reinvention" looks like after 25 years in UX and how to know when it's timeThe real requirements for going fractional (and why it's not for everyone)Why your identity and hobbies outside of work actually make you better at your jobHow he's re-invented his own UX career multiple times

I sat down with Sydney Huang from Human API to explore a completely novel concept—AI agents hiring humans, not the other way around. We dive into how they're solving the last mile problem for AI agents, why data collection is their first focus, and how you can actually get paid for contributing voice data and other tasks. Sydney shares her journey from buying Ethereum in 2017 to building Eclipse (a Solana VM L2 on Ethereum) to now creating an agent-native marketplace. We discuss the challenges of building a two-sided marketplace, the growing demand for AI training data, and why now is the perfect time to build in this space. KEY POINTS WITH TIMESTAMPS• [00:00] Introduction to Human API and the concept of AI agents hiring humans• [02:30] Sydney's journey from buying ETH in 2017 to working in VC to building in crypto full-time• [04:15] Eclipse explained: Building a Solana VM L2 on Ethereum and the modular blockchain thesis• [06:45] The last mile problem for AI agents and why human tasks are still needed• [09:20] How Human API differs from traditional workflow automation tools• [11:00] Current use cases: Conversational audio data collection for training voice AI• [14:30] Future expansion into health wearables data and other data types• [18:45] Why people are willing to work for AI agents and contribute data• [21:00] Building a better UX than Fiverr and Upwork with reputation systems• [25:15] The chicken-and-egg challenge of balancing supply and demand• [28:30] Why now is the perfect time to build in the AI data space• [31:00] Roadmap: App launch and making the agent experience seamless• [32:45] How to become a contributor at thehumanapi.comCONNECTHuman API Website: https://thehumanapi.comSydney Huang LinkedIn: https://linkedin.com/in/sydney-huangEclipse Website: https://eclipse.xyzWeb3 with Sam Kamani: https://www.linkedin.com/in/samkamani/DisclaimerNothing mentioned in this podcast is investment advice and please do your own research. It would mean a lot if you can leave a review of this podcast on Apple Podcasts or Spotify and share this podcast with a friend. Be a guest on the podcast or contact us - https://www.web3pod.xyz/

It's EV News Briefly for Friday 13 March 2026, everything you need to know in less than 5 minutes if you haven't got time for the full show.Patreon supporters fund this show, get the episodes ad free, as soon as they're ready and are part of the EV News Daily Community. You can be like them by clicking here: https://www.patreon.com/EVNewsDailyRIVIAN REVEALS R2 PRICINGThe Rivian R2 launches in four trims, all sharing an 87.9 kWh usable battery, ranging from the $57,990 Performance AWD (656 hp, 330 miles) arriving this Spring to a ~$45,000 base RWD variant in late 2027 with 275+ miles of range. All trims charge 10–80% in 29 minutes via a native NACS port, with a $1,495 destination charge across the board.FORD CUTS EXPLORER ENTRY PRICE WITH LFP BATTERYFord has revised its European Explorer EV with a new LFP battery pack, growing usable capacity from 52 kWh to 58 kWh and boosting WLTP range 17% to 444 km (276 miles), while a stronger APP350 motor lifts output to 140 kW and cuts the 0–100 km/h time to 8.0 seconds. The updated model starts at €39,990 in Germany and adds vehicle-to-load charging, refreshed infotainment, expanded driver assistance features, and standard one-pedal driving, though peak DC charging drops from 145 kW to 110 kW.LUCID NAMES MIDSIZE SUVS COSMOS AND EARTHLucid revealed at Investor Day 2026 that its two upcoming midsize electric SUVs will be called Cosmos and Earth, targeting a ~$50,000 starting price and production before end of 2026. Both will use 800V architecture, bidirectional charging, the new in-house Atlas drive unit (23% lighter, 30% fewer parts), and Lucid claims just 69 kWh would be sufficient for 300 miles of range thanks to a 0.22 drag coefficient.LUCID GRAVITY ADDS CARPLAY AND ANDROID AUTOLucid has rolled out an OTA update (UX 3.5) bringing wireless and wired Apple CarPlay and Android Auto to the Gravity SUV for North American owners now, with Europe and the Middle East to follow in late March. Both systems display on the Gravity's 6K Clearview Cockpit screen, addressing one of the most requested features from Lucid customers.JAECOO 8 UK SALES START IN MAYThe Jaecoo 8, a three-row flagship SUV, goes on sale in the UK in May priced from £45,500, using Chery's Super Hybrid System pairing a 1.5-litre turbo petrol with a three-speed auto for 422 bhp, 83 miles of electric-only range, and over 700 miles of combined range. Two trims are offered — Luxury (seven seats, £45,500) and Executive (six Nappa leather captain's chairs, £47,500) — with DC fast charging up to 40 kW for a 30–80% charge in about 20 minutes.EU EV PRICES FALL AS SMALL CARS RETURNAverage EU electric car prices dropped €1,800 to €42,700 in 2025 — the first decline since 2020 — driven by a surge in affordable B-segment BEVs like the Citroën ë-C3 and Renault 5, whose average segment prices fell 13%. T&E expects further price pressure in 2026 as Volkswagen Group prepares a small-car family including the ID. Polo, Cupra Raval, and Skoda Epiq, all targeting around €25,000.HONDA AXES THREE US EVSHonda has cancelled the 0 Series SUV, 0 Series Saloon, and Acura RSX for U.S. production, warning of losses up to ¥2.5 trillion ($15.8 billion) as it reverses its EV strategy amid rollbacks of U.S. fossil fuel regulations and removal of EV incentives. CEO Toshihiro Mibe said the priority is to "stop the bleeding," with operating losses now expected up to ¥1.12 trillion in the current fiscal year; the Sony-Honda Afeela brand is unaffected.VOLKSWAGEN SETS ID. POLO FROM €25,000Volkswagen will world-premiere the entry-level ID. Polo next month, starting at €25,000 and marking the first ID model to carry an established VW brand name. The range spans 37 kWh LFP and 52 kWh NMC battery options with outputs from 85 kW to 166 kW, and includes an R-Line (~€35,000, ~211 hp) and a GTI variant, with up to 450 km (280 miles) of WLTP range from the larger pack.ENEL COMPLETES 3,730 CHARGING STATIONSEnel has finished installing 3,730 EV charging stations across five Italian regions under the first tender of Italy's PNRR recovery plan, with each station offering two points capable of up to 90 kW each. The network is accessible via Enel's app or card and integrates with around 160 mobility service providers, with a further 1,200 stations already contracted under subsequent tenders.ELECTREON COMPLETES INDUCTEV ACQUISITIONElectreon has finalized its acquisition of U.S.-based InductEV, combining dynamic in-road wireless charging with InductEV's high-power stationary wireless charging for heavy-duty transit and freight. The merged portfolio now covers highway and urban corridor charging (LINE), burst charging at stops (DASH), depot charging (DOT), and heavy-duty freight charging (Ultra DOT).SCANDLINES STARTS BALTIC WHALE SERVICEScandlines launched the Baltic Whale on 10 March 2026, claiming it as the world's largest electric freight ferry in operation at 147 metres, running the 18.5 km Rødby–Puttgarden route carrying 66 freight units. Its 10 MWh battery can fully recharge in just 12 minutes via a dedicated 50 kV / 25 MW cable, with an automated docking tower connecting in 15 seconds, while a hybrid diesel mode reduces crossing time from one hour to 45 minutes.

In this episode of The Ross Simmonds Show, Ross sits down with Mike King, founder of iPullRank, to unpack the seismic shift from traditional SEO to AI search, AEO, and GEO, and why framing it as "just SEO" is quietly costing teams budget, influence, and growth. Together, they break down the Google leak, retrieval-augmented generation (RAG), content ecosystems, and what separates operators from spectators in the next era of search. Key Takeaways and Insights: 1. SEO vs. AEO vs. GEO: why it's not "just SEO" -The tactics SEOs talked about for years are now mandatory in AI search,and AI platforms evaluate your entire content ecosystem, not just your website. -Calling AI search "just SEO" limits budget, authority, and strategic ownership before the conversation even starts. 2. The C-suite perspective most SEOs miss -Executives are already asking why their brand doesn't appear in ChatGPT, and AI search carries trillion-dollar narratives that traditional SEO never did. -Teams that frame this as a new growth channel are the ones unlocking real investment. 3.Why video is a high-leverage AI search play YouTube is one of the most cited sources in AI-generated answers,and AI search rewards consensus across formats, from video and Reddit to PR and editorial. Starting with five strategic videos in an underserved topic cluster, then repurposing aggressively, is one of the highest-ROI moves available right now. 4.How AI search actually works: RAG and query fan-out explained -AI search uses retrieval-augmented generation: prompts expand into synthetic sub-queries, each with their own format expectations. -The more relevant passages a brand owns across formats, the more chances it has to be cited, think of it as accumulating raffle tickets. 5. Measuring AI search performance the right way -There are three metric buckets that matter, performance, channel, and input. Most teams are only tracking one. -Input metrics like synthetic query rankings, passage relevance, entity salience, and bot activity are where the real diagnostic power lives. 6. Real AI workflows inside iPullRank -The team is building internal tools with Gemini and AI Studio, including automating internal linking through vectorization combined with human business rules. -AI handles the minutiae ,humans make the strategic calls, and that efficiency is the hedge against client scrutiny over the next two years. 7. Programmatic SEO, why most sites tank -Google is indexing less and testing content performance faster, and high bounce rates signal UX failure, not an AI penalty. -Recovery demands tight topical authority and, in many cases, new URL structures and full content audits. 8. Building a career that survives the next five years -Technical AI fluency is no longer optional, and content alone is now a free commodity, the leverage is in systems and engineering. -Operators beat theorists. The next generation of SEOs must ship, not just strategize. 9. Creativity, code, and AI as an artist -Writing rhymes and writing code pull from the same creative muscles,and AI works best as a feedback loop, not a ghostwriter. -The real risk isn't AI,  it's lazy implementation. Tools expand creative possibility; they don't replace taste. 10. Relevance engineering,building a new category -AI search needs new frameworks, not retrofitted SEO tactics, and creating a named methodology positions a brand above commodity vendors. -Owning a concept, building authority around it, and ranking for your own category is a long game worth playing. Resources & Tools:

Designing B2B surveys can look straightforward at first glance. But in practice, business-to-business market research comes with constraints that can quietly undermine data quality, respondent experience, and the value of the results. In this episode of Conversations for Research Rockstars, we walk through some of the most common B2B survey challenges—and the practical questionnaire design strategies that help research teams avoid them. If you've ever struggled with expensive samples, long screeners, or unclear respondent roles, this discussion will feel very familiar. Whether you work in market research, customer insights, UX research, or CX research, this episode offers practical reminders that can help protect data quality and keep surveys manageable for busy professionals.

Carson Gross, computer science professor at Montana State and creator of htmx, joins the show to cut through the noise around AI and programming. He explains why the jump from high-level languages to LLMs is fundamentally different from past transitions, why junior developers who skip writing code risk being at the mercy of a stochastic system, and why systems architecture and managing code complexity are the skills that will matter most. A grounded, rational take on the future of software development jobs. Links Resources Yes,and...: https://htmx.org/essays/yes-and/ We want to hear from you! How did you find us? Did you see us on Twitter? In a newsletter? Or maybe we were recommended by a friend? Fill out our listener survey! https://t.co/oKVAEXipxu Let us know by sending an email to our producer, Elizabeth, at elizabeth.becz@logrocket.com, or tweet at us at PodRocketPod. Check out our newsletter! https://blog.logrocket.com/the-replay-newsletter/ Follow us. Get free stickers. Follow us on Apple Podcasts, fill out this form, and we'll send you free PodRocket stickers! What does LogRocket do? LogRocket provides AI-first session replay and analytics that surfaces the UX and technical issues impacting user experiences. Start understanding where your users are struggling by trying it for free at LogRocket.com. Try LogRocket for free today. Chapters 00:00 Introduction — Carson Gross and the "Yes, And…" Blog Post 01:45 Why Carson Felt Compelled to Write About AI and Coding 03:30 The Assembly-to-High-Level Analogy — and Why It Falls Apart 06:00 Juniors Must Write Code to Be Able to Read Code 08:15 The Sorcerer's Apprentice Trap 10:30 Could AI Actually Increase Demand for Programmers? 12:45 Why "SaaS Is Dead" Is Shortsighted 15:00 Systems Architecture as the High-Value Skill Going Forward 17:30 Essential vs Accidental Complexity — The No Silver Bullet Framework 20:00 How LLMs Break the Natural Feedback Loop of Bad Code 23:00 Will AI Change How We Think About Testing? 26:30 Abstraction, Paradigms, and Human-Readable Code 29:00 How Much Has AI Actually Boosted Carson's Own Productivity? 32:00 The Mental Health Cost of the AI Hype Cycle 35:30 Final Thoughts — Give Yourself (and Others) a BreakSpecial Guest: Carson Gross.

What does it actually take to be the first, or only, designer or researcher on a team? Spoiler: it's not just about doing great work. This week, we get into the unglamorous, under-discussed side of the solo role: building systems, managing up, and earning trust before you've even shipped anything.What happens when you're really good at the craft, but nobody around you understands what you do, why it matters, or how to support you?Julian Della Mattia has spent his career doing one of the hardest things in UX: showing up first. As a researcher who has repeatedly been the founding or solo practitioner inside organizations, Julian has learned, mostly the hard way, that being great at research is only a fraction of the actual job. He's also the host of Finders to Builders, a podcast built specifically for researchers navigating this exact challenge.In this conversation, we dig into what Julian calls the “finder to builder” mindset shift: moving from someone who just surfaces insights to someone who builds the infrastructure, earns the trust, and creates the conditions for research (and design) to actually matter inside an organization. We talk about how to manage up when your manager doesn't fully understand your work, how to know when your efforts are starting to gain traction, and what the invisible job description of a solo or founding designer really looks like.If you've ever landed a solo design or research role and felt the gap between what you prepared for and what the job actually demanded, this one's for you. Julian brings a grounded, practical perspective that goes well beyond frameworks, because, as he puts it, in this context, frameworks rarely fly out of the box. Hit play.Helpful Links:• Connect with Julian on LinkedIn• Follow Julian's Substack• Finders to Builders PodcastTopics:• 02:25 – Meet Julian Della Mattia• 03:48 – From PM to first researcher• 06:06 – Agency advice for juniors• 10:54 – Accidental in-house research role• 14:28 – Finder to builder mindset• 18:51 – Time triage and playmaker mode• 24:53 – Invisible work and org dynamics• 27:49 – Managing up and selling research• 32:23 – Signals and metrics that it's working• 36:48 – Measuring research impact• 38:35 – Skip the framework trap• 39:02 – Managing up tactics• 40:16 – Aligning with business goals• 43:37 – Just ask your boss• 44:43 – When to start hiring• 46:32 – Recap and teamwork• 48:37 – Parting advice for firsts• 60:39 – Where to find Julian—Thanks for listening! We hope you dug today's episode. If you liked what you heard, be sure to like and subscribe wherever you listen to podcasts! And if you really enjoyed today's episode, why don't you leave a five-star review? Or tell some friends! It will help us out a ton.If you haven't already, sign up for our email list. We won't spam you. Pinky swear.• ⁠⁠⁠⁠⁠⁠⁠Get a FREE audiobook AND support the show⁠⁠⁠⁠⁠⁠⁠• ⁠⁠⁠⁠⁠⁠⁠Support the show on Patreon⁠⁠⁠⁠⁠⁠⁠• ⁠⁠⁠⁠⁠⁠⁠Check out show transcripts⁠⁠⁠⁠⁠⁠⁠• ⁠⁠⁠⁠⁠⁠⁠Check out our website⁠⁠⁠⁠⁠⁠⁠• ⁠⁠⁠⁠⁠⁠⁠Subscribe on Apple Podcasts⁠⁠⁠⁠⁠⁠⁠• ⁠⁠⁠⁠⁠⁠⁠Subscribe on Spotify⁠⁠⁠⁠⁠⁠⁠• ⁠⁠⁠⁠⁠⁠⁠Subscribe on YouTube⁠⁠⁠⁠⁠⁠⁠• ⁠⁠⁠⁠⁠⁠⁠Subscribe on Stitcher

Turbopuffer came out of a reading app.In 2022, Simon was helping his friends at Readwise scale their infra for a highly requested feature: article recommendations and semantic search. Readwise was paying ~$5k/month for their relational database and vector search would cost ~$20k/month making the feature too expensive to ship. In 2023 after mulling over the problem from Readwise, Simon decided he wanted to “build a search engine” which became Turbopuffer.We discuss:• Simon's path: Denmark → Shopify infra for nearly a decade → “angel engineering” across startups like Readwise, Replicate, and Causal → turbopuffer almost accidentally becoming a company • The Readwise origin story: building an early recommendation engine right after the ChatGPT moment, seeing it work, then realizing it would cost ~$30k/month for a company spending ~$5k/month total on infra and getting obsessed with fixing that cost structure • Why turbopuffer is “a search engine for unstructured data”: Simon's belief that models can learn to reason, but can't compress the world's knowledge into a few terabytes of weights, so they need to connect to systems that hold truth in full fidelity • The three ingredients for building a great database company: a new workload, a new storage architecture, and the ability to eventually support every query plan customers will want on their data • The architecture bet behind turbopuffer: going all in on object storage and NVMe, avoiding a traditional consensus layer, and building around the cloud primitives that only became possible in the last few years • Why Simon hated operating Elasticsearch at Shopify: years of painful on-call experience shaped his obsession with simplicity, performance, and eliminating state spread across multiple systems • The Cursor story: launching turbopuffer as a scrappy side project, getting an email from Cursor the next day, flying out after a 4am call, and helping cut Cursor's costs by 95% while fixing their per-user economics • The Notion story: buying dark fiber, tuning TCP windows, and eating cross-cloud costs because Simon refused to compromise on architecture just to close a deal faster • Why AI changes the build-vs-buy equation: it's less about whether a company can build search infra internally, and more about whether they have time especially if an external team can feel like an extension of their own • Why RAG isn't dead: coding companies still rely heavily on search, and Simon sees hybrid retrieval semantic, text, regex, SQL-style patterns becoming more important, not less • How agentic workloads are changing search: the old pattern was one retrieval call up front; the new pattern is one agent firing many parallel queries at once, turning search into a highly concurrent tool call • Why turbopuffer is reducing query pricing: agentic systems are dramatically increasing query volume, and Simon expects retrieval infra to adapt to huge bursts of concurrent search rather than a small number of carefully chosen calls • The philosophy of “playing with open cards”: Simon's habit of being radically honest with investors, including telling Lachy Groom he'd return the money if turbopuffer didn't hit PMF by year-end • The “P99 engineer”: Simon's framework for building a talent-dense company, rejecting by default unless someone on the team feels strongly enough to fight for the candidate —Simon Hørup Eskildsen• LinkedIn: https://www.linkedin.com/in/sirupsen• X: https://x.com/Sirupsen• https://sirupsen.com/aboutturbopuffer• https://turbopuffer.com/Full Video PodTimestamps00:00:00 The PMF promise to Lachy Groom00:00:25 Intro and Simon's background00:02:19 What turbopuffer actually is00:06:26 Shopify, Elasticsearch, and the pain behind the company00:10:07 The Readwise experiment that sparked turbopuffer00:12:00 The insight Simon couldn't stop thinking about00:17:00 S3 consistency, NVMe, and the architecture bet00:20:12 The Notion story: latency, dark fiber, and conviction00:25:03 Build vs. buy in the age of AI00:26:00 The Cursor story: early launch to breakout customer00:29:00 Why code search still matters00:32:00 Search in the age of agents00:34:22 Pricing turbopuffer in the AI era00:38:17 Why Simon chose Lachy Groom00:41:28 Becoming a founder on purpose00:44:00 The “P99 engineer” philosophy00:49:30 Bending software to your will00:51:13 The future of turbopuffer00:57:05 Simon's tea obsession00:59:03 Tea kits, X Live, and P99 LiveTranscriptSimon Hørup Eskildsen: I don't think I've said this publicly before, but I just called Lockey and was like, local Lockie. Like if this doesn't have PMF by the end of the year, like we'll just like return all the money to you. But it's just like, I don't really, we, Justine and I don't wanna work on this unless it's really working.So we want to give it the best shot this year and like we're really gonna go for it. We're gonna hire a bunch of people. We're just gonna be honest with everyone. Like when I don't know how to play a game, I just play with open cards. Lockey was the only person that didn't, that didn't freak out. He was like, I've never heard anyone say that before.Alessio: Hey everyone, welcome to the Leading Space podcast. This is Celesio Pando, Colonel Laz, and I'm joined by Swix, editor of Leading Space.swyx: Hello. Hello, uh, we're still, uh, recording in the Ker studio for the first time. Very excited. And today we are joined by Simon Eski. Of Turbo Farer welcome.Simon Hørup Eskildsen: Thank you so much for having me.swyx: Turbo Farer has like really gone on a huge tear, and I, I do have to mention that like you're one of, you're not my newest member of the Danish AHU Mafia, where like there's a lot of legendary programmers that have come out of it, like, uh, beyond Trotro, Rasmus, lado Berg and the V eight team and, and Google Maps team.Uh, you're mostly a Canadian now, but isn't that interesting? There's so many, so much like strong Danish presence.Simon Hørup Eskildsen: Yeah, I was writing a post, um, not that long ago about sort of the influences. So I grew up in Denmark, right? I left, I left when, when I was 18 to go to Canada to, to work at Shopify. Um, and so I, like, I've, I would still say that I feel more Danish than, than Canadian.This is also the weird accent. I can't say th because it, this is like, I don't, you know, my wife is also Canadian, um, and I think. I think like one of the things in, in Denmark is just like, there's just such a ruthless pragmatism and there's also a big focus on just aesthetics. Like, they're like very, people really care about like where, what things look like.Um, and like Canada has a lot of attributes, US has, has a lot of attributes, but I think there's been lots of the great things to carry. I don't know what's in the water in Ahu though. Um, and I don't know that I could be considered part of the Mafi mafia quite yet, uh, compared to the phenomenal individuals we just mentioned.Barra OV is also, uh, Danish Canadian. Okay. Yeah. I don't know where he lives now, but, and he's the PHP.swyx: Yeah. And obviously Toby German, but moved to Canada as well. Yes. Like this is like import that, uh, that, that is an interesting, um, talent move.Alessio: I think. I would love to get from you. Definition of Turbo puffer, because I think you could be a Vector db, which is maybe a bad word now in some circles, you could be a search engine.It's like, let, let's just start there and then we'll maybe run through the history of how you got to this point.Simon Hørup Eskildsen: For sure. Yeah. So Turbo Puffer is at this point in time, a search engine, right? We do full text search and we do vector search, and that's really what we're specialized in. If you're trying to do much more than that, like then this might not be the right place yet, but Turbo Buffer is all about search.The other way that I think about it is that we can take all of the world's knowledge, all of the exabytes and exabytes of data that there is, and we can use those tokens to train a model, but we can't compress all of that into a few terabytes of weights, right? Compress into a few terabytes of weights, how to reason with the world, how to make sense of the knowledge.But we have to somehow connect it to something externally that actually holds that like in full fidelity and truth. Um, and that's the thing that we intend to become. Right? That's like a very holier than now kind of phrasing, right? But being the search engine for unstructured, unstructured data is the focus of turbo puffer at this point in time.Alessio: And let's break down. So people might say, well, didn't Elasticsearch already do this? And then some other people might say, is this search on my data, is this like closer to rag than to like a xr, like a public search thing? Like how, how do you segment like the different types of search?Simon Hørup Eskildsen: The way that I generally think about this is like, there's a lot of database companies and I think if you wanna build a really big database company, sort of, you need a couple of ingredients to be in the air.We don't, which only happens roughly every 15 years. You need a new workload. You basically need the ambition that every single company on earth is gonna have data in your database. Multiple times you look at a company like Oracle, right? You will, like, I don't think you can find a company on earth with a digital presence that it not, doesn't somehow have some data in an Oracle database.Right? And I think at this point, that's also true for Snowflake and Databricks, right? 15 years later it's, or even more than that, there's not a company on earth that doesn't, in. Or directly is consuming Snowflake or, or Databricks or any of the big analytics databases. Um, and I think we're in that kind of moment now, right?I don't think you're gonna find a company over the next few years that doesn't directly or indirectly, um, have all their data available for, for search and connect it to ai. So you need that new workload, like you need something to be happening where there's a new workload that causes that to happen, and that new workload is connecting very large amounts of data to ai.The second thing you need. The second condition to build a big database company is that you need some new underlying change in the storage architecture that is not possible from the databases that have come before you. If you look at Snowflake and Databricks, right, commoditized, like massive fleet of HDDs, like that was not possible in it.It just wasn't in the air in the nineties, right? So you just didn't, we just didn't build these systems. S3 and and and so on was not around. And I think the architecture that is now possible that wasn't possible 15 years ago is to go all in on NVME SSDs. It requires a particular type of architecture for the database that.It's difficult to retrofit onto the databases that are already there, including the ones you just mentioned. The second thing is to go all in on OIC storage, more so than we could have done 15 years ago. Like we don't have a consensus layer, we don't really have anything. In fact, you could turn off all the servers that Turbo Buffer has, and we would not lose any data because we have all completely all in on OIC storage.And this means that our architecture is just so simple. So that's the second condition, right? First being a new workload. That means that every company on earth, either indirectly or directly, is using your database. Second being, there's some new storage architecture. That means that the, the companies that have come before you can do what you're doing.I think the third thing you need to do to build a big database company is that over time you have to implement more or less every Cory plan on the data. What that means is that you. You can't just get stuck in, like, this is the one thing that a database does. It has to be ever evolving because when someone has data in the database, they over time expect to be able to ask it more or less every question.So you have to do that to get the storage architecture to the limit of what, what it's capable of. Those are the three conditions.swyx: I just wanted to get a little bit of like the motivation, right? Like, so you left Shopify, you're like principal, engineer, infra guy. Um, you also head of kernel labs, uh, inside of Shopify, right?And then you consulted for read wise and that it kind of gave you that, that idea. I just wanted you to tell that story. Um, maybe I, you've told it before, but, uh, just introduce the, the. People to like the, the new workload, the sort of aha moment for turbo PufferSimon Hørup Eskildsen: For sure. So yeah, I spent almost a decade at Shopify.I was on the infrastructure team, um, from the fairly, fairly early days around 2013. Um, at the time it felt like it was growing so quickly and everything, all the metrics were, you know, doubling year on year compared to the, what companies are contending with today. It's very cute in growth. I feel like lot some companies are seeing that month over month.Um, of course. Shopify compound has been compounding for a very long time now, but I spent a decade doing that and the majority of that was just make sure the site is up today and make sure it's up a year from now. And a lot of that was really just the, um, you know, uh, the Kardashians would drive very, very large amounts of, of data to, to uh, to Shopify as they were rotating through all the merch and building out their businesses.And we just needed to make sure we could handle that. Right. And sometimes these were events, a million requests per second. And so, you know, we, we had our own data centers back in the day and we were moving to the cloud and there was so much sharding work and all of that that we were doing. So I spent a decade just scaling databases ‘cause that's fundamentally what's the most difficult thing to scale about these sites.The database that was the most difficult for me to scale during that time, and that was the most aggravating to be on call for, was elastic search. It was very, very difficult to deal with. And I saw a lot of projects that were just being held back in their ambition by using it.swyx: And I mean, self-hosted.Self-hosted. ‘causeSimon Hørup Eskildsen: it's, yeah, and it commercial, this is like 2015, right? So it's like a very particular vintage. Right. It's probably better at a lot of these things now. Um, it was difficult to contend with and I'm just like, I just think about it. It's an inverted index. It should be good at these kinds of queries and do all of this.And it was, we, we often couldn't get it to do exactly what we needed to do or basically get lucine to do, like expose lucine raw to, to, to what we needed to do. Um, so that was like. Just something that we did on the side and just panic scaled when we needed to, but not a particular focus of mine. So I left, and when I left, I, um, wasn't sure exactly what I wanted to do.I mean, it spent like a decade inside of the same company. I'd like grown up there. I started working there when I was 18.swyx: You only do Rails?Simon Hørup Eskildsen: Yeah. I mean, yeah. Rails. And he's a Rails guy. Uh, love Rails. So good. Um,Alessio: we all wish we could still work in Rails.swyx: I know know. I know, but some, I tried learning Ruby.It's just too much, like too many options to do the same thing. It's, that's my, I I know there's a, there's a way to do it.Simon Hørup Eskildsen: I love it. I don't know that I would use it now, like given cloud code and, and, and cursor and everything, but, um, um, but still it, like if I'm just sitting down and writing a teal code, that's how I think.But anyway, I left and I wasn't, I talked to a couple companies and I was like, I don't. I need to see a little bit more of the world here to know what I'm gonna like focus on next. Um, and so what I decided is like I was gonna, I called it like angel engineering, where I just hopped around in my friend's companies in three months increments and just helped them out with something.Right. And, and just vested a bit of equity and solved some interesting infrastructure problem. So I worked with a bunch of companies at the time, um, read Wise was one of them. Replicate was one of them. Um, causal, I dunno if you've tried this, it's like a, it's a spreadsheet engine Yeah. Where you can do distribution.They sold recently. Yeah. Um, we've been, we used that in fp and a at, um, at Turbo Puffer. Um, so a bunch of companies like this and it was super fun. And so we're the Chachi bt moment happened, I was with. With read Wise for a stint, we were preparing for the reader launch, right? Which is where you, you cue articles and read them later.And I was just getting their Postgres up to snuff, like, which basically boils down to tuning, auto vacuum. So I was doing that and then this happened and we were like, oh, maybe we should build a little recommendation engine and some features to try to hook in the lms. They were not that good yet, but it was clear there was something there.And so I built a small recommendation engine just, okay, let's take the articles that you've recently read, right? Like embed all the articles and then do recommendations. It was good enough that when I ran it on one of the co-founders of Rey's, like I found out that I got articles about, about having a child.I'm like, oh my God, I didn't, I, I didn't know that, that they were having a child. I wasn't sure what to do with that information, but the recommendation engine was good enough that it was suggesting articles, um, about that. And so there was, there was recommendations and uh, it actually worked really well.But this was a company that was spending maybe five grand a month in total on all their infrastructure and. When I did the napkin math on running the embeddings of all the articles, putting them into a vector index, putting it in prod, it's gonna be like 30 grand a month. That just wasn't tenable. Right?Like Read Wise is a proudly bootstrapped company and it's paying 30 grand for infrastructure for one feature versus five. It just wasn't tenable. So sort of in the bucket of this is useful, it's pretty good, but let us, let's return to it when the costs come down.swyx: Did you say it grows by feature? So for five to 30 is by the number of, like, what's the, what's the Scaling factor scale?It scales by the number of articles that you embed.Simon Hørup Eskildsen: It does, but what I meant by that is like five grand for like all of the other, like the Heroku, dinos, Postgres, like all the other, and this then storage is 30. Yeah. And then like 30 grand for one feature. Right. Which is like, what other articles are related to this one.Um, so it was just too much right to, to power everything. Their budget would've been maybe a few thousand dollars, which still would've been a lot. And so we put it in a bucket of, okay, we're gonna do that later. We'll wait, we will wait for the cost to come down. And that haunted me. I couldn't stop thinking about it.I was like, okay, there's clearly some latent demand here. If the cost had been a 10th, we would've shipped it and. This was really the only data point that I had. Right. I didn't, I, I didn't, I didn't go out and talk to anyone else. It was just so I started reading Right. I couldn't, I couldn't help myself.Like I didn't know what like a vector index is. I, I generally barely do about how to generate the vectors. There was a lot of hype about, this is a early 2023. There was a lot of hype about vector databases. There were raising a lot of money and it's like, I really didn't know anything about it. It's like, you know, trying these little models, fine tuning them.Like I was just trying to get sort of a lay of the land. So I just sat down. I have this. A GitHub repository called Napkin Math. And on napkin math, there's just, um, rows of like, oh, this is how much bandwidth. Like this is how many, you know, you can do 25 gigabytes per second on average to dram. You can do, you know, five gigabytes per second of rights to an SSD, blah blah.All of these numbers, right? And S3, how many you could do per, how much bandwidth can you drive per connection? I was just sitting down, I was like, why hasn't anyone build a database where you just put everything on O storage and then you puff it into NVME when you use the data and you puff it into dram if you're, if you're querying it alive, it's just like, this seems fairly obvious and you, the only real downside to that is that if you go all in on o storage, every right will take a couple hundred milliseconds of latency, but from there it's really all upside, right?You do the first go, it takes half a second. And it sort of occurred to me as like, well. The architecture is really good for that. It's really good for AB storage, it's really good for nvm ESSD. It's, well, you just couldn't have done that 10 years ago. Back to what we were talking about before. You really have to build a database where you have as few round trips as possible, right?This is how CPUs work today. It's how NVM E SSDs work. It's how as, um, as three works that you want to have a very large amount of outstanding requests, right? Like basically go to S3, do like that thousand requests to ask for data in one round trip. Wait for that. Get that, like, make a new decision. Do it again, and try to do that maybe a maximum of three times.But no databases were designed that way within NVME as is ds. You can drive like within, you know, within a very low multiple of DRAM bandwidth if you use it that way. And same with S3, right? You can fully max out the network card, which generally is not maxed out. You get very, like, very, very good bandwidth.And, but no one had built a database like that. So I was like, okay, well can't you just, you know, take all the vectors right? And plot them in the proverbial coordinate system. Get the clusters, put a file on S3 called clusters, do json, and then put another file for every cluster, you know, cluster one, do js O cluster two, do js ON you know that like it's two round trips, right?So you get the clusters, you find the closest clusters, and then you download the cluster files like the, the closest end. And you could do this in two round trips.swyx: You were nearest neighbors locally.Simon Hørup Eskildsen: Yes. Yes. And then, and you would build this, this file, right? It's just like ultra simplistic, but it's not a far shot from what the first version of Turbo Buffer was.Why hasn't anyone done thatAlessio: in that moment? From a workload perspective, you're thinking this is gonna be like a read heavy thing because they're doing recommend. Like is the fact that like writes are so expensive now? Oh, with ai you're actually not writing that much.Simon Hørup Eskildsen: At that point I hadn't really thought too much about, well no actually it was always clear to me that there was gonna be a lot of rights because at Shopify, the search clusters were doing, you know, I don't know, tens or hundreds of crew QPS, right?‘cause you just have to have a human sit and type in. But we did, you know, I don't know how many updates there were per second. I'm sure it was in the millions, right into the cluster. So I always knew there was like a 10 to 100 ratio on the read write. In the read wise use case. It's, um, even, even in the read wise use case, there'd probably be a lot fewer reads than writes, right?There's just a lot of churn on the amount of stuff that was going through versus the amount of queries. Um, I wasn't thinking too much about that. I was mostly just thinking about what's the fundamentally cheapest way to build a database in the cloud today using the primitives that you have available.And this is it, right? You just, now you have one machine and you know, let's say you have a terabyte of data in S3, you paid the $200 a month for that, and then maybe five to 10% of that data and needs to be an NV ME SSDs and less than that in dram. Well. You're paying very, very little to inflate the data.swyx: By the way, when you say no one else has done that, uh, would you consider Neon, uh, to be on a similar path in terms of being sort of S3 first and, uh, separating the compute and storage?Simon Hørup Eskildsen: Yeah, I think what I meant with that is, uh, just build a completely new database. I don't know if we were the first, like it was very much, it was, I mean, I, I hadn't, I just looked at the napkin math and was like, this seems really obvious.So I'm sure like a hundred people came up with it at the same time. Like the light bulb and every invention ever. Right. It was just in the air. I think Neon Neon was, was first to it. And they're trying, they're retrofitted onto Postgres, right? And then they built this whole architecture where you have, you have it in memory and then you sort of.You know, m map back to S3. And I think that was very novel at the time to do it for, for all LTP, but I hadn't seen a database that was truly all in, right. Not retrofitting it. The database felt built purely for this no consensus layer. Even using compare and swap on optic storage to do consensus. I hadn't seen anyone go that all in.And I, I mean, there, there, I'm sure there was someone that did that before us. I don't know. I was just looking at the napkin mathswyx: and, and when you say consensus layer, uh, are you strongly relying on S3 Strong consistency? You are. Okay.SoSimon Hørup Eskildsen: that is your consensus layer. It, it is the consistency layer. And I think also, like, this is something that most people don't realize, but S3 only became consistent in December of 2020.swyx: I remember this coming out during COVID and like people were like, oh, like, it was like, uh, it was just like a free upgrade.Simon Hørup Eskildsen: Yeah.swyx: They were just, they just announced it. We saw consistency guys and like, okay, cool.Simon Hørup Eskildsen: And I'm sure that they just, they probably had it in prod for a while and they're just like, it's done right.And people were like, okay, cool. But. That's a big moment, right? Like nv, ME SSDs, were also not in the cloud until around 2017, right? So you just sort of had like 2017 nv, ME SSDs, and people were like, okay, cool. There's like one skew that does this, whatever, right? Takes a few years. And then the second thing is like S3 becomes consistent in 2020.So now it means you don't have to have this like big foundation DB or like zookeeper or whatever sitting there contending with the keys, which is how. You know, that's what Snowflake and others have do so muchswyx: for goneSimon Hørup Eskildsen: Exactly. Just gone. Right? And so just push to the, you know, whatever, how many hundreds of people they have working on S3 solved and then compare and swap was not in S3 at this point in time,swyx: by the way.Uh, I don't know what that is, so maybe you wanna explain. Yes. Yeah.Simon Hørup Eskildsen: Yes. So, um, what Compare and swap is, is basically, you can imagine that if you have a database, it might be really nice to have a file called metadata json. And metadata JSON could say things like, Hey, these keys are here and this file means that, and there's lots of metadata that you have to operate in the database, right?But that's the simplest way to do it. So now you have might, you might have a lot of servers that wanna change the metadata. They might have written a file and want the metadata to contain that file. But you have a hundred nodes that are trying to contend with this metadata that JSON well, what compare and Swap allows you to do is basically just you download the file, you make the modifications, and then you write it only if it hasn't changed.While you did the modification and if not you retry. Right? Should just have this retry loops. Now you can imagine if you have a hundred nodes doing that, it's gonna be really slow, but it will converge over time. That primitive was not available in S3. It wasn't available in S3 until late 2024, but it was available in GCP.The real story of this is certainly not that I sat down and like bake brained it. I was like, okay, we're gonna start on GCS S3 is gonna get it later. Like it was really not that we started, we got really lucky, like we started on GCP and we started on GCP because tur um, Shopify ran on GCP. And so that was the platform I was most available with.Right. Um, and I knew the Canadian team there ‘cause I'd worked with them at Shopify and so it was natural for us to start there. And so when we started building the database, we're like, oh yeah, we have to build a, we really thought we had to build a consensus layer, like have a zookeeper or something to do this.But then we discovered the compare and swap. It's like, oh, we can kick the can. Like we'll just do metadata r json and just, it's fine. It's probably fine. Um, and we just kept kicking the can until we had very, very strong conviction in the idea. Um, and then we kind of just hinged the company on the fact that S3 probably was gonna get this, it started getting really painful in like mid 2024.‘cause we were closing deals with, um, um, notion actually that was running in AWS and we're like, trust us. You, you really want us to run this in GCP? And they're like, no, I don't know about that. Like, we're running everything in AWS and the latency across the cloud were so big and we had so much conviction that we bought like, you know, dark fiber between the AWS regions in, in Oregon, like in the InterExchange and GCP is like, we've never seen a startup like do like, what's going on here?And we're just like, no, we don't wanna do this. We were tuning like TCP windows, like everything to get the latency down ‘cause we had so high conviction in not doing like a, a metadata layer on S3. So those were the three conditions, right? Compare and swap. To do metadata, which wasn't in S3 until late 2024 S3 being consistent, which didn't happen until December, 2020.Uh, 2020. And then NVMe ssd, which didn't end in the cloud until 2017.swyx: I mean, in some ways, like a very big like cloud success story that like you were able to like, uh, put this all together, but also doing things like doing, uh, bind our favor. That that actually is something I've never heard.Simon Hørup Eskildsen: I mean, it's very common when you're a big company, right?You're like connecting your own like data center or whatever. But it's like, it was uniquely just a pain with notion because the, um, the org, like most of the, like if you're buying in Ashburn, Virginia, right? Like US East, the Google, like the GCP and, and AWS data centers are like within a millisecond on, on each other, on the public exchanges.But in Oregon uniquely, the GCP data center sits like a couple hundred kilometers, like east of Portland and the AWS region sits in Portland, but the network exchange they go through is through Seattle. So it's like a full, like 14 milliseconds or something like that. And so anyway, yeah. It's, it's, so we were like, okay, we can't, we have to go through an exchange in Portland.Yeah. Andswyx: you'd rather do this than like run your zookeeper and likeSimon Hørup Eskildsen: Yes. Way rather. It doesn't have state, I don't want state and two systems. Um, and I think all that is just informed by Justine, my co-founder and I had just been on call for so long. And the worst outages are the ones where you have state in multiple places that's not syncing up.So it really came from, from a a, like just a, a very pure source of pain, of just imagining what we would be Okay. Being woken up at 3:00 AM about and having something in zookeeper was not one of them.swyx: You, you're talking to like a notion or something. Do they care or do they just, theySimon Hørup Eskildsen: just, they care about latency.swyx: They latency cost. That's it.Simon Hørup Eskildsen: They just cared about latency. Right. And we just absorbed the cost. We're just like, we have high conviction in this. At some point we can move them to AWS. Right. And so we just, we, we'll buy the fiber, it doesn't matter. Right. Um, and it's like $5,000. Usually when you buy fiber, you buy like multiple lines.And we're like, we can only afford one, but we will just test it that when it goes over the public internet, it's like super smooth. And so we did a lot of, anyway, it's, yeah, it was, that's cool.Alessio: You can imagine talking to the GCP rep and it's like, no, we're gonna buy, because we know we're gonna turn, we're gonna turn from you guys and go to AWS in like six months.But in the meantime we'll do this. It'sSimon Hørup Eskildsen: a, I mean, like they, you know, this workload still runs on GCP for what it's worth. Right? ‘cause it's so, it was just, it was so reliable. So it was never about moving off GCP, it was just about honesty. It was just about giving notion the latency that they deserved.Right. Um, and we didn't want ‘em to have to care about any of this. We also, they were like, oh, egress is gonna be bad. It was like, okay, screw it. Like we're just gonna like vvc, VPC peer with you and AWS we'll eat the cost. Yeah. Whatever needs to be done.Alessio: And what were the actual workloads? Because I think when you think about ai, it's like 14 milliseconds.It's like really doesn't really matter in the scheme of like a model generation.Simon Hørup Eskildsen: Yeah. We were told the latency, right. That we had to beat. Oh, right. So, so we're just looking at the traces. Right. And then sort of like hand draw, like, you know, kind of like looking at the trace and then thinking what are the other extensions of the trace?Right. And there's a lot more to it because it's also when you have, if you have 14 versus seven milliseconds, right. You can fit in another round trip. So we had to tune TCP to try to send as much data in every round trip, prewarm all the connections. And there was, there's a lot of things that compound from having these kinds of round trips, but in the grand scheme it was just like, well, we have to beat the latency of whatever we're up against.swyx: Which is like they, I mean, notion is a database company. They could have done this themselves. They, they do lots of database engineering themselves. How do you even get in the door? Like Yeah, just like talk through that kind of.Simon Hørup Eskildsen: Last time I was in San Francisco, I was talking to one of the engineers actually, who, who was one of our champions, um, at, AT Notion.And they were, they were just trying to make sure that the, you know, per user cost matched the economics that they needed. You know, Uhhuh like, it's like the way I think about, it's like I have to earn a return on whatever the clouds charge me and then my customers have to earn a return on that. And it's like very simple, right?And so there has to be gross margin all the way up and that's how you build the product. And so then our customers have to make the right set of trade off the turbo Puffer makes, and if they're happy with that, that's great.swyx: Do you feel like you're competing with build internally versus buy or buy versus buy?Simon Hørup Eskildsen: Yeah, so, sorry, this was all to build up to your question. So one of the notion engineers told me that they'd sat and probably on a napkin, like drawn out like, why hasn't anyone built this? And then they saw terrible. It was like, well, it literally that. So, and I think AI has also changed the buy versus build equation in terms of, it's not really about can we build it, it's about do we have time to build it?I think they like, I think they felt like, okay, if this is a team that can do that and they, they feel enough like an extension of our team, well then we can go a lot faster, which would be very, very good for them. And I mean, they put us through the, through the test, right? Like we had some very, very long nights to to, to do that POC.And they were really our biggest, our second big customer off the cursor, which also was a lot of late nights. Right.swyx: Yeah. That, I mean, should we go into that story? The, the, the sort of Chris's story, like a lot, um, they credit you a lot for. Working very closely with them. So I just wanna hear, I've heard this, uh, story from Sole's point of view, but like, I'm curious what, what it looks like from your side.Simon Hørup Eskildsen: I actually haven't heard it from Sole's point of view, so maybe you can now cross reference it. The way that I remember it was that, um, the day after we launched, which was just, you know, I'd worked the whole summer on, on the first version. Justine wasn't part of it yet. ‘cause I just, I didn't tell anyone that summer that I was working on this.I was just locked in on building it because it's very easy otherwise to confuse talking about something to actually doing it. And so I was just like, I'm not gonna do that. I'm just gonna do the thing. I launched it and at this point turbo puffer is like a rust binary running on a single eight core machine in a T Marks instance.And me deploying it was like looking at the request log and then like command seeing it or like control seeing it to just like, okay, there's no request. Let's upgrade the binary. Like it was like literally the, the, the, the scrappiest thing. You could imagine it was on purpose because just like at Shopify, we did that all the time.Like, we like move, like we ran things in tux all the time to begin with. Before something had like, at least the inkling of PMF, it was like, okay, is anyone gonna hear about this? Um, and one of the cursor co-founders Arvid reached out and he just, you know, the, the cursor team are like all I-O-I-I-M-O like, um, contenders, right?So they just speak in bullet points and, and facts. It was like this amazing email exchange just of, this is how many QPS we have, this is what we're paying, this is where we're going, blah, blah, blah. And so we're just conversing in bullet points. And I tried to get a call with them a few times, but they were, so, they were like really writing the PMF bowl here, just like late 2023.And one time Swally emails me at like five. What was it like 4:00 AM Pacific time saying like, Hey, are you open for a call now? And I'm on the East coast and I, it was like 7:00 AM I was like, yeah, great, sure, whatever. Um, and we just started talking and something. Then I didn't know anything about sales.It was something that just comp compelled me. I have to go see this team. Like, there's something here. So I, I went to San Francisco and I went to their office and the way that I remember it is that Postgres was down when I showed up at the office. Did SW tell you this? No. Okay. So Postgres was down and so it's like they were distracting with that.And I was trying my best to see if I could, if I could help in any way. Like I knew a little bit about databases back to tuning, auto vacuum. It was like, I think you have to tune out a vacuum. Um, and so we, we talked about that and then, um, that evening just talked about like what would it look like, what would it look like to work with us?And I just said. Look like we're all in, like we will just do what we'll do whatever, whatever you tell us, right? They migrated everything over the next like week or two, and we reduced their cost by 95%, which I think like kind of fixed their per user economics. Um, and it solved a lot of other things. And we were just, Justine, this is also when I asked Justine to come on as my co-founder, she was the best engineer, um, that I ever worked with at Shopify.She lived two blocks away and we were just, okay, we're just gonna get this done. Um, and we did, and so we helped them migrate and we just worked like hell over the next like month or two to make sure that we were never an issue. And that was, that was the cursor story. Yeah.swyx: And, and is code a different workload than normal text?I, I don't know. Is is it just text? Is it the same thing?Simon Hørup Eskildsen: Yeah, so cursor's workload is basically, they, um, they will embed the entire code base, right? So they, they will like chunk it up in whatever they would, they do. They have their own embedding model, um, which they've been public about. Um, and they find that on, on, on their evals.It. There's one of their evals where it's like a 25% improvement on a very particular workload. They have a bunch of blog posts about it. Um, I think it works best on larger code basis, but they've trained their own embedding model to do this. Um, and so you'll see it if you use the cursor agent, it will do searches.And they've also been public around, um, how they've, I think they post trained their model to be very good at semantic search as well. Um, and that's, that's how they use it. And so it's very good at, like, can you find me on the code that's similar to this, or code that does this? And just in, in this queries, they also use GR to supplement it.swyx: Yeah.Simon Hørup Eskildsen: Um, of courseswyx: it's been a big topic of discussion like, is rag dead because gr you know,Simon Hørup Eskildsen: and I mean like, I just, we, we see lots of demand from the coding company to ethicsswyx: search in every part. Yes.Simon Hørup Eskildsen: Uh, we, we, we see demand. And so, I mean, I'm. I like case studies. I don't like, like just doing like thought pieces on this is where it's going.And like trying to be all macroeconomic about ai, that's has turned out to be a giant waste of time because no one can really predict any of this. So I just collect case studies and I mean, cursor has done a great job talking about what they're doing and I hope some of the other coding labs that use Turbo Puffer will do the same.Um, but it does seem to make a difference for particular queries. Um, I mean we can also do text, we can also do RegX, but I should also say that cursors like security posture into Tur Puffer is exceptional, right? They have their own embedding model, which makes it very difficult to reverse engineer. They obfuscate the file paths.They like you. It's very difficult to learn anything about a code base by looking at it. And the other thing they do too is that for their customers, they encrypt it with their encryption keys in turbo puffer's bucket. Um, so it's, it's, it's really, really well designed.swyx: And so this is like extra stuff they did to work with you because you are not part of Cursor.Exactly like, and this is just best practice when working in any database, not just you guys. Okay. Yeah, that makes sense. Yeah. I think for me, like the, the, the learning is kind of like you, like all workloads are hybrid. Like, you know, uh, like you, you want the semantic, you want the text, you want the RegX, you want sql.I dunno. Um, but like, it's silly to like be all in on like one particularly query pattern.Simon Hørup Eskildsen: I think, like I really like the way that, um, um, that swally at cursor talks about it, which is, um, I'm gonna butcher it here. Um, and you know, I'm a, I'm a database scalability person. I'm not a, I, I dunno anything about training models other than, um, what the internet tells me and what.The way he describes is that this is just like cash compute, right? It's like you have a point in time where you're looking at some particular context and focused on some chunk and you say, this is the layer of the neural net at this point in time. That seems fundamentally really useful to do cash compute like that.And, um, how the value of that will change over time. I'm, I'm not sure, but there seems to be a lot of value in that.Alessio: Maybe talk a bit about the evolution of the workload, because even like search, like maybe two years ago it was like one search at the start of like an LLM query to build the context. Now you have a gentech search, however you wanna call it, where like the model is both writing and changing the code and it's searching it again later.Yeah. What are maybe some of the new types of workloads or like changes you've had to make to your architecture for it?Simon Hørup Eskildsen: I think you're right. When I think of rag, I think of, Hey, there's an 8,000 token, uh, context window and you better make it count. Um, and search was a way to do that now. Everything is moving towards the, just let the agent do its thing.Right? And so back to the thing before, right? The LLM is very good at reasoning with the data, and so we're just the tool call, right? And that's increasingly what we see our customers doing. Um, what we're seeing more demand from, from our customers now is to do a lot of concurrency, right? Like Notion does a ridiculous amount of queries in every round trip just because they can't.And I'm also now, when I use the cursor agent, I also see them doing more concurrency than I've ever seen before. So a bit similar to how we designed a database to drive as much concurrency in every round trip as possible. That's also what the agents are doing. So that's new. It means just an enormous amount of queries all at once to the dataset while it's warm in as few turns as possible.swyx: Can I clarify one thing on that?Simon Hørup Eskildsen: Yes.swyx: Is it, are they batching multiple users or one user is driving multiple,Simon Hørup Eskildsen: one user driving multiple, one agent driving.swyx: It's parallel searching a bunch of things.Simon Hørup Eskildsen: Exactly.swyx: Yeah. Yeah, exactly. So yeah, the clinician also did, did this for the fast context thing, like eight parallel at once.Simon Hørup Eskildsen: Yes.swyx: And, and like an interesting problem is, well, how do you make sure you have enough diversity so you're not making the the same request eight times?Simon Hørup Eskildsen: And I think like that's probably also where the hybrid comes in, where. That's another way to diversify. It's a completely different way to, to do the search.That's a big change, right? So before it was really just like one call and then, you know, the LLM took however many seconds to return, but now we just see an enormous amount of queries. So the, um, we just see more queries. So we've like tried to reduce query, we've reduced query pricing. Um, this is probably the first time actually I'm saying that, but the query pricing is being reduced, like five x.Um, and we'll probably try to reduce it even more to accommodate some of these workloads of just doing very large amounts of queries. Um, that's one thing that's changed. I think the right, the right ratio is still very high, right? Like there's still a, an enormous amount of rights per read, but we're starting probably to see that change if people really lean into this pattern.Alessio: Can we talk a little bit about the pricing? I'm curious, uh, because traditionally a database would charge on storage, but now you have the token generation that is so expensive, where like the actual. Value of like a good search query is like much higher because they're like saving inference time down the line.How do you structure that as like, what are people receptive to on the other side too?Simon Hørup Eskildsen: Yeah. I, the, the turbo puffer pricing in the beginning was just very simple. The pricing on these on for search engines before Turbo Puffer was very server full, right? It was like, here's the vm, here's the per hour cost, right?Great. And I just sat down with like a piece of paper and said like, if Turbo Puffer was like really good, this is probably what it would cost with a little bit of margin. And that was the first pricing of Turbo Puffer. And I just like sat down and I was like, okay, like this is like probably the storage amp, but whenever on a piece of paper I, it was vibe pricing.It was very vibe price, and I got it wrong. Oh. Um, well I didn't get it wrong, but like Turbo Puffer wasn't at the first principle pricing, right? So when Cursor came on Turbo Puffer, it was like. Like, I didn't know any VCs. I didn't know, like I was just like, I don't know, I didn't know anything about raising money or anything like that.I just saw that my GCP bill was, was high, was a lot higher than the cursor bill. So Justine and I was just like, well, we have to optimize it. Um, and I mean, to the chagrin now of, of it, of, of the VCs, it now means that we're profitable because we've had so much pricing pressure in the beginning. Because it was running on my credit card and Justine and I had spent like, like tens of thousands of dollars on like compute bills and like spinning off the company and like very like, like bad Canadian lawyers and like things like to like get all of this done because we just like, we didn't know.Right. If you're like steeped in San Francisco, you're just like, you just know. Okay. Like you go out, raise a pre-seed round. I, I never heard a word pre-seed at this point in time.swyx: When you had Cursor, you had Notion you, you had no funding.Simon Hørup Eskildsen: Um, with Cursor we had no funding. Yeah. Um, by the time we had Notion Locke was, Locke was here.Yeah. So it was really just, we vibe priced it 100% from first Principles, but it wasn't, it, it was not performing at first principles, so we just did everything we could to optimize it in the beginning for that, so that at least we could have like a 5% margin or something. So I wasn't freaking out because Cursor's bill was also going like this as they were growing.And so my liability and my credit limit was like actively like calling my bank. It was like, I need a bigger credit. Like it was, yeah. Anyway, that was the beginning. Yeah. But the pricing was, yeah, like storage rights and query. Right. And the, the pricing we have today is basically just that pricing with duct tape and spit to try to approach like, you know, like a, as a margin on the physical underlying hardware.And we're doing this year, you're gonna see more and more pricing changes from us. Yeah.swyx: And like is how much does stuff like VVC peering matter because you're working in AWS land where egress is charged and all that, you know.Simon Hørup Eskildsen: We probably don't like, we have like an enterprise plan that just has like a base fee because we haven't had time to figure out SKU pricing for all of this.Um, but I mean, yeah, you can run turbo puffer either in SaaS, right? That's what Cursor does. You can run it in a single tenant cluster. So it's just you. That's what Notion does. And then you can run it in, in, in BYOC where everything is inside the customer's VPC, that's what an for example, philanthropic does.swyx: What I'm hearing is that this is probably the best CRO job for somebody who can come in and,Simon Hørup Eskildsen: I mean,swyx: help you with this.Simon Hørup Eskildsen: Um, like Turbo Puffer hired, like, I don't know what, what number this was, but we had a full-time CFO as like the 12th hire or something at Turbo Puffer, um, I think I hear are a lot of comp.I don't know how they do it. Like they have a hundred employees and not a CFO. It's like having a CFO is like a runningswyx: business man. Like, you know,Simon Hørup Eskildsen: it's so good. Yeah, like money Mike, like he just, you know, just handles the money and a lot of the business stuff and so he came in and just hopped with a lot of the operational side of the business.So like C-O-O-C-F-O, like somewhere in between.swyx: Just as quick mention of Lucky, just ‘cause I'm curious, I've met Lock and like, he's obviously a very good investor and now on physical intelligence, um, I call it generalist super angel, right? He invests in everything. Um, and I always wonder like, you know, is there something appealing about focusing on developer tooling, focusing on databases, going like, I've invested for 10 years in databases versus being like a lock where he can maybe like connect you to all the customers that you need.Simon Hørup Eskildsen: This is an excellent question. No, no one's asked me this. Um, why lockey? Because. There was a couple of people that we were talking to at the time and when we were raising, we were almost a little, we were like a bit distressed because one of our, one of our peers had just launched something that was very similar to Turbo Puffer.And someone just gave me the advice at the time of just choose the person where you just feel like you can just pick up the phone and not prepare anything. And just be completely honest, and I don't think I've said this publicly before, but I just called Lockey and was like local Lockie. Like if this doesn't have PMF by the end of the year, like we'll just like return all the money to you.But it's just like, I don't really, we, Justine and I don't wanna work on this unless it's really working. So we want to give it the best shot this year and like we're really gonna go for it. We're gonna hire a bunch of people and we're just gonna be honest with everyone. Like when I don't know how to play a game, I just play with open cards and.Lockey was the only person that didn't, that didn't freak out. He was like, I've never heard anyone say that before. As I said, I didn't even know what a seed or pre-seed round was like before, probably even at this time. So I was just like very honest with him. And I asked him like, Lockie, have you ever have, have you ever invested in database company?He was just like, no. And at the time I was like, am I dumb? Like, but I think there was something that just like really drew me to Lockie. He is so authentic, so honest, like, and there was something just like, I just felt like I could just play like, just say everything openly. And that was, that was, I think that that was like a perfect match at the time, and, and, and honestly still is.He was just like, okay, that's great. This is like the most honest, ridiculous thing I've ever heard anyone say to me. But like that, like that, whyswyx: is this ridiculous? Say competitor launch, this may not work out. It wasSimon Hørup Eskildsen: more just like. If this doesn't work out, I'm gonna close up shop by the end of the mo the year, right?Like it was, I don't know, maybe it's common. I, I don't know. He told me it was uncommon. I don't know. Um, that's why we chose him and he'd been phenomenal. The other people were talking at the, at the time were database experts. Like they, you know, knew a lot about databases and Locke didn't, this turned out to be a phenomenal asset.Right. I like Justine and I know a lot about databases. The people that we hire know a lot about databases. What we needed was just someone who didn't know a lot about databases, didn't pretend to know a lot about databases, and just wanted to help us with candidates and customers. And he did. Yeah. And I have a list, right, of the investors that I have a relationship with, and Lockey has just performed excellent in the number of sub bullets of what we can attribute back to him.Just absolutely incredible. And when people talk about like no ego and just the best thing for the founder, I like, I don't think that anyone, like even my lawyer is like, yeah, Lockey is like the most friendly person you will find.swyx: Okay. This is my most glow recommendation I've ever heard.Alessio: He deserves it.He's very special.swyx: Yeah. Yeah. Yeah. Okay. Amazing.Alessio: Since you mentioned candidates, maybe we can talk about team building, you know, like, especially in sf, it feels like it's just easier to start a company than to join a company. Uh, I'm curious your experience, especially not being n SF full-time and doing something that is maybe, you know, a very low level of detail and technical detail.Simon Hørup Eskildsen: Yeah. So joining versus starting, I never thought that I would be a founder. I would start with it, like Turbo Puffer started as a blog post, and then it became a project and then sort of almost accidentally became a company. And now it feels like it's, it's like becoming a bigger company. That was never the intention.The intentions were very pure. It's just like, why hasn't anyone done this? And it's like, I wanna be the, like, I wanna be the first person to do it. I think some founders have this, like, I could never work for anyone else. I, I really don't feel that way. Like, it's just like, I wanna see this happen. And I wanna see it happen with some people that I really enjoy working with and I wanna have fun doing it and this, this, this has all felt very natural on that, on that sense.So it was never a like join versus versus versus found. It was just dis found me at the right moment.Alessio: Well I think there's an argument for, you should have joined Cursor, right? So I'm curious like how you evaluate it. Okay, I should actually go raise money and make this a company versus like, this is like a company that is like growing like crazy.It's like an interesting technical problem. I should just build it within Cursor and then they don't have to encrypt all this stuff. They don't have to obfuscate things. Like was that on your mind at all orSimon Hørup Eskildsen: before taking the, the small check from Lockie, I did have like a hard like look at myself in the mirror of like, okay, do I really want to do this?And because if I take the money, I really have to do it right. And so the way I almost think about it's like you kind of need to ha like you kind of need to be like fucked up enough to want to go all the way. And that was the conversation where I was like, okay, this is gonna be part of my life's journey to build this company and do it in the best way that I possibly can't.Because if I ask people to join me, ask people to get on the cap table, then I have an ultimate responsibility to give it everything. And I don't, I think some people, it doesn't occur to me that everyone takes it that seriously. And maybe I take it too seriously, I don't know. But that was like a very intentional moment.And so then it was very clear like, okay, I'm gonna do this and I'm gonna give it everything.Alessio: A lot of people don't take it this seriously. But,swyx: uh, let's talk about, you have this concept of the P 99 engineer. Uh, people are 10 x saying, everyone's saying, you know, uh, maybe engineers are out of a job. I don't know.But you definitely see a P 99 engineer, and I just want you to talk about it.Simon Hørup Eskildsen: Yeah, so the P 99 engineer was just a term that we started using internally to talk about candidates and talk about how we wanted to build the company. And you know, like everyone else is, like we want a talent dense company.And I think that's almost become trite at this point. What I credit the cursor founders a lot with is that they just arrived there from first principles of like, we just need a talent dense, um, talent dense team. And I think I've seen some teams that weren't talent dense and like seemed a counterfactual run, which if you've run in been in a large company, you will just see that like it's just logically will happen at a large company.Um, and so that was super important to me and Justine and it's very difficult to maintain. And so we just needed, we needed wording for it. And so I have a document called Traits of the P 99 Engineer, and it's a bullet point list. And I look at that list after every single interview that I do, and in every single recap that we do and every recap we end with.End with, um, some version of I'm gonna reject this candidate completely regardless of what the discourse was, because I wanna see people fight for this person because the default should not be, we're gonna hire this person. The default should be, we're definitely not hiring this person. And you know, if everyone was like, ah, maybe throw a punch, then this is not the right.swyx: Do, do you operate, like if there's one cha there must have at least one champion who's like, yes, I will put my career on, on, on the line for this. You know,Simon Hørup Eskildsen: I think career on the line,swyx: maybe a chair, butSimon Hørup Eskildsen: yeah. You know, like, um, I would say so someone needs to like, have both fists up and be like, I'd fight.Right? Yeah. Yeah. And if one person said, then, okay, let's do it. Right?swyx: Yeah.Simon Hørup Eskildsen: Um. It doesn't have to be absolutely everyone. Right? And like the interviews are always the sign that you're checking for different attributes. And if someone is like knocking it outta the park in every single attribute, that's, that's fairly rare.Um, but that's really important. And so the traits of the P 99 engineer, there's lots of them. There's also the traits of the p like triple nine engineer and the quadruple nine engineer. This is like, it's a long list.swyx: Okay.Simon Hørup Eskildsen: Um, I'll give you some samples, right. Of what we, what we look for. I think that the P 99 engineer has some history of having bent, like their trajectory or something to their will.Right? Some moment where it was just, they just, you know, made the computer do what it needed to do. There's something like that, and it will, it will occur to have them at some point in their career. And, uh. Hopefully multiple times. Right.swyx: Gimme an example of one of your engineers that like,Simon Hørup Eskildsen: I'll give an eng.Uh, so we, we, we launched this thing called A and NV three. Um, we could, we're also, we're working on V four and V five right now, but a and NV three can search a hundred billion vectors with a P 50 of around 40 milliseconds and a p 99 of 200 milliseconds. Um, maybe other people have done this, I'm sure Google and others have done this, but, uh, we haven't seen anyone, um, at least not in like a public consumable SaaS that can do this.And that was an engineer, the chief architect of Turbo Puffer, Nathan, um, who more or less just bent this, the software was not capable of this and he just made it capable for a very particular workload in like a, you know, six to eight week period with the help of a lot of the team. Right. It's been, been, there's numerous of examples of that, like at, at turbo puff, but that's like really bending the software and X 86 to your will.It was incredible to watch. Um. You wanna see some moments like that?swyx: Isn't that triple nine?Simon Hørup Eskildsen: Um, I think Nathan, what's calledAlessio: group nine, that was only nine. I feel like this is too high forSimon Hørup Eskildsen: Nathan. Nathan is, uh, Nathan is like, yeah, there's a lot of nines. Okay. After that p So I think that's one trait. I think another trait is that, uh, the P 99 spends a lot of time looking at maps.Generally it's their preferred ux. They just love looking at maps. You ever seen someone who just like, sits on their phone and just like, scrolls around on a map? Or did you not look at maps A lot? You guys don't look atswyx: maps? I guess I'm not feeling there. I don't know, butSimon Hørup Eskildsen: you just dis What about trains?Do you like trains?swyx: Uh, I mean they, not enough. Okay. This is just like weapon nice. Autism is what I call it. Like, like,Simon Hørup Eskildsen: um, I love looking at maps, like, it's like my preferred UX and just like I, you know, I likeswyx: lotsAlessio: of, of like random places, soswyx: like,youswyx: know.Alessio: Yes. Okay. There you go. So instead of like random places, like how do you explore the maps?Simon Hørup Eskildsen: No, it's, it's just a joke.swyx: It's autism laugh. It's like you are just obsessed by something and you like studying a thing.Simon Hørup Eskildsen: The origin of this was that at some point I read an interview with some IOI gold medalistswyx: Uhhuh,Simon Hørup Eskildsen: and it's like, what do you do in your spare time? I was just like, I like looking at maps.I was like, I feel so seen. Like, I just like love, like swirling out. I was like, oh, Canada is so big. Where's Baffin Island? I don't know. I love it. Yeah. Um, anyway, so the traits of P 99, P 99 is obsessive, right? Like, there's just like, you'll, you'll find traits of that we do an interview at, at, at, at turbo puffer or like multiple interviews that just try to screen for some of these things.Um, so. There's lots of others, but these are the kinds of traits that we look for.swyx: I'll tell you, uh, some people listen for like some of my dere stuff. Uh, I do think about derel as maps. Um, you draw a map for people, uh, maps show you the, uh, what is commonly agreed to be the geographical features of what a boundary is.And it shows also shows you what is not doing. And I, I think a lot of like developer tools, companies try to tell you they can do everything, but like, let's, let's be real. Like you, your, your three landmarks are here, everyone comes here, then here, then here, and you draw a map and, and then you draw a journey through the map.And like that. To me, that's what developer relations looks like. So I do think about things that way.Simon Hørup Eskildsen: I think the P 99 thinks in offs, right? The P 99 is very clear about, you know, hey, turbo puffer, you can't run a high transaction workload on turbo puffer, right? It's like the right latency is a hundred milliseconds.That's a clear trade off. I think the P 99 is very good at articulating the trade offs in every decision. Um. Which is exactly what the map is in your case, right?swyx: Uh, yeah, yeah. My, my, my world. My world.Alessio: How, how do you reconcile some of these things when you're saying you bend the will the computer versus like the trade

Listen now on Apple, Spotify, and YouTube.—Sarah Doody is a UX Researcher & Product Designer with 22 years of experience. She is also the founder and CEO of Career Strategy Lab, a UX job search and career coaching company where she helps UX and product people get hired or promoted with average 5-figure salary increases. She is also the host of the podcast, Career Strategy Podcast that offers weekly tips and case studies about what's working in the UX job market and hiring right now. Sarah also speaks at conferences worldwide including UXLX, UX London, AIGA, Productized, Front, Industry, and more.In our conversation, we discuss:* How Sarah used research practices to pivot from product work into career strategy, and what nearly a decade of data has taught her about getting hired and promoted.* Why most job searches fall apart when people skip self-research and jump straight into resumes and portfolios.* A simple career “journey map” exercise that helps surface patterns across roles, managers, projects, and personal energy.* How a career roadmap creates focus, reduces rejection fatigue, and shapes better job decisions over time.* The role of a clear compass statement in shaping resumes, portfolios, interviews, and confidence during a search.Major takeaways from the episode* Sarah frames career growth using the same structure teams use to build products: research, synthesis, direction, and iteration. When people skip this step, job searches turn reactive and exhausting. A roadmap restores clarity and control by anchoring decisions to what actually works for you.* Resumes and portfolios break down when they're built without context. Sarah explains how a simple highs-and-lows timeline across the past year can surface repeat signals around team fit, management style, project type, and energy. Those signals matter more than any formatting tweak.* Applying to hundreds of roles creates rejection loops that drain momentum and self-trust. Sarah links this pattern to panic behavior and short-term thinking. Fewer, better-aligned applications often lead to stronger interviews and better outcomes.* Career decisions ripple into mental health, relationships, time, and identity. Sarah urges people to layer real-life constraints and goals into their roadmap so work supports the life they want, rather than consuming it.* A strong compass statement acts like a thesis for your career. It guides which roles you pursue, how you frame your experience, and how you talk about your strengths. This clarity shortens resume cycles, sharpens interviews, and restores confidence under pressure.Where to find Sarah:* Website: www.careerstrategylab.com and www.sarahdoody.com* LinkedIn: www.linkedin.com/in/sarahdoody* Youtube: www.youtube.com/sarahdoody * Instagram: www.instagram.com/sarahdoody* Career Strategy PodcastStop piecing it together. Start leading the work.The Everything UXR Bundle is for researchers who are tired of duct-taping free templates and second-guessing what good looks like.You get my complete set of toolkits, templates, and strategy guides. used by teams across Google, Spotify, , to run credible research, influence decisions, and actually grow in your role.It's built to save you time, raise your game, and make you the person people turn to—not around.→ Save 140+ hours a year with ready-to-use templates and frameworks→ Boost productivity by 40% with tools that cut admin and sharpen your focus→ Increase research adoption by 50% through clearer, faster, more strategic deliveryInterested in sponsoring the podcast?Interested in sponsoring or advertising on this podcast? I'm always looking to partner with brands and businesses that align with my audience. Book a call or email me at nikki@userresearchacademy.com to learn more about sponsorship opportunities!The views and opinions expressed by the guests on this podcast are their own and do not necessarily reflect the views, positions, or policies of the host, the podcast, or any affiliated organizations or sponsors. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.userresearchstrategist.com/subscribe

AI is changing how we design and build products faster than ever.In this episode of Future of UX, I'm talking with Felix Haas, who is currently working at Lovable. Felix shares a lot of insights online about building products with AI, vibe coding, and how the way we create software is evolving.Felix and I actually go back quite a few years. We first met in Berlin when he was running his own agency there, and since then I've been following his journey and the ideas he shares about building with AI.What always fascinated me about Felix is his mindset. He comes from a design background, but he thinks very much like an entrepreneur. He constantly experiments, builds things, tests ideas, and isn't afraid when something doesn't work.And honestly, that builder mindset might be one of the most important skills right now.In our conversation we talk about where we actually are with AI today, what “vibe coding” really means, how the product development process is changing, and what all of this means for designers.One thing we both agree on:In the future, it might not be enough to just design products. Designers will need to build, experiment, and ship ideas much faster.In this episode we talk about• Where we actually are in the current AI wave• What “vibe coding” means and why people talk about it• Why building products is becoming easier than ever• The shift from designing interfaces to building real products• Why experimentation and curiosity are becoming essential skills• The mindset designers need in an AI-driven world• Why great products are still about solving real problemsAbout FelixFelix Haas works at Lovable and shares insights about AI, building products, and vibe coding. Before that, he ran his own agency in Berlin and has been active in the design and startup world for many years.Follow Felix:LinkedInSubstrackAI for Designers: 5-week Bootcamp

Join Fredrik Mattsson, CEO of Inamo, for a deep dive into one of the most common—and costly—mistakes in software development: building too much. With over a decade of experience in enterprise SaaS and digital transformation, Fredrik has seen firsthand how "feature bloat" slows down engineering teams and confuses users. In this episode, we explore how modern product teams are shifting away from "building and hoping" toward an evidence-led approach, using AI to turn qualitative user feedback into actionable insights before a single line of code is written.

Nauta is building the data infrastructure layer for global supply chain, starting with mid-market shippers who manage 600+ suppliers across 40+ countries but lack a single source of truth. Co-founded by Valentina Jordan, who spent six and a half years at Rappi, Nauta targets the $200M-$2B revenue segment where companies face enterprise-level complexity without enterprise resources. In this episode of BUILDERS, Valentina shares how Nauta moved from Excel automation to building data pipes that connect 12-13 stakeholders touching a single product—and why they refuse to run POCs.Topics Discussed:Why shippers with ERP, TMS, and WMS systems still run operations in ExcelThe tribal knowledge crisis: 20-30 year operators retiring with undocumented institutional knowledgeNauta's no-POC policy and why it requires contract exit clauses insteadThe cost reduction vs. revenue generation framework that escapes pilot purgatoryBuilding familiar interfaces (Excel-like tables) over novel UX for conservative industriesThe shift from hiding AI capabilities (January 2025) to leading with them (eight months later)GTM Lessons For B2B Founders:Distinguish symptoms from root cause pain in discovery: Most enterprise buyers surface symptoms, not problems. A client reporting penalty costs isn't revealing the root issue—just downstream impact. Valentina uses the five whys methodology to drill into actual pain: "A client can tell me, hey, I'm paying X amount of dollars in penalties. That's not necessarily the root cause, it's just a symptom of the actual pain." This prevents building features that address surface-level complaints while missing the structural problem. The real issue might be data fragmentation across systems, lack of visibility into supplier performance, or decision-making bottlenecks—each requiring different solutions.Structure POC alternatives that demand mutual commitment: Nauta kills traditional POCs entirely because "it implies that they are testing us and that it's not a collaborative process." Instead, they offer contract exit clauses if expectations aren't met while requiring upfront commitment. This only works when you have proven results and can confidently deliver value. The insight: POCs create evaluator-vendor dynamics where the burden of proof sits entirely on you. Paid engagements with performance-based exits create partner dynamics where both parties invest in success. For early-stage companies without case studies, this won't work—but once you have repeatable results, test this approach.Layer revenue generation on top of cost reduction: Nauta starts every engagement with 3-4 cost reduction KPIs—penalties, reconciliation time, manual labor automation—then transitions to revenue generation through fill rate optimization and cash-on-cash improvements. "You need to go beyond just cutting costs. That way you transition from a nice to have to a must have." Supply chain has historically been viewed as a cost center; proving top-line impact changes budget conversations entirely. This matters because cost reduction has a ceiling (you can only cut so much), while revenue generation creates expanding budget headroom. Map your product capabilities to both from day one.//Sponsors:Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.ioThe 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

Every has unveiled a new product, built by CEO Dan Shipper. It's called Proof, a free, open-source, live collaborative document editor built for humans and AI agents to work in together. Proof started as a Mac app designed to show the provenance of AI-written text—purple for AI, green for human. But when Shipper rebuilt it as a web app with real-time collaboration, something clicked. Suddenly, everyone at Every was using it for everything from planning docs, to creative writing and even daily to-do lists. The team realized they needed a lightweight space where their OpenClaw agents and humans could co-author documents and leave comments. In this special episode, Shipper is joined by Every chief operating officer Brandon Gell, Cora general manager Kieran Klaassen, and head of growth Austin Tedesco to demo Proof live and share how it's changed the way they work. Brandon walks through a loop where his Codex agent writes a plan, Dan's personal Claw R2-C2 reviews it, and the humans just steer. Austin explains how he uses Proof to write a weekly food newsletter, texting ideas to his Claw on runs and watching an outline take shape. And Kieran makes the case that Proof's power is its lightness—just a link you can hand to any agent or colleague.The conversation covers what "agent native" means in practice, why AX (agent experience) matters as much as UX (user experience), what happens when 10 agents edit one document at the same time, and why some writing is now better read by an AI than a human.If you found this episode interesting, please like, subscribe, comment, and share!Want even more?Sign up for Every to unlock our ultimate guide to prompting ChatGPT here: https://every.ck.page/ultimate-guide-to-prompting-chatgpt. It's usually only for paying subscribers, but you can get it here for free.To hear more from Dan Shipper:Subscribe to Every: https://every.to/subscribeFollow him on X: https://twitter.com/danshipperGet started building today at framer.com/dan for 30% OFF a Framer Pro annual plan.Download Grammarly for free at Grammarly.comTimestamps 00:02:00 — Introduction and the origin story of Proof00:07:24 — From Mac app to collaborative web editor00:09:00 — What makes Proof “agent native”00:14:30 — Live demo: watching an agent join and write inside a shared document00:20:51 — How Austin uses Proof for creative writing and food journalism00:24:30 — The challenge of multiple agents editing one document simultaneously00:26:48 — When AI-written docs are better read by agents than by humans00:29:30 — Brandon's agent-to-agent collaboration loop00:37:09 — Proof as a lightweight scratchpad vs. existing tools like Notion and GitHub00:42:18 — Why Proof is open source and what that means for buildersLinks to resources mentioned in the episode:Proof Editor: https://proofeditor.aiProof GitHub repo (open source): https://github.com/EveryInc/proofEvery's compound engineering plugin: https://github.com/EveryInc/compound-engineering-plugin

In this episode, Calle introduces Numopay, an open-source Bitcoin payment terminal that enables tap-to-pay experiences similar to fiat systems. We explore its technical foundations, privacy features, future developments, and the broader ecosystem of Bitcoin payment solutions.Takeaways:

Join Kyle, Nader, Vibhu, and swyx live at NVIDIA GTC next week!Now that AIE Europe tix are ~sold out, our attention turns to Miami and World's Fair!The definitive AI Accelerator chip company has more than 10xed this AI Summer:And is now a $4.4 trillion megacorp… that is somehow still moving like a startup. We are blessed to have a unique relationship with our first ever NVIDIA guests: Kyle Kranen who gave a great inference keynote at the first World's Fair and is one of the leading architects of NVIDIA Dynamo (a Datacenter scale inference framework supporting SGLang, TRT-LLM, vLLM), and Nader Khalil, a friend of swyx from our days in Celo in The Arena, who has been drawing developers at GTC since before they were even a glimmer in the eye of NVIDIA:Nader discusses how NVIDIA Brev has drastically reduced the barriers to entry for developers to get a top of the line GPU up and running, and Kyle explains NVIDIA Dynamo as a data center scale inference engine that optimizes serving by scaling out, leveraging techniques like prefill/decode disaggregation, scheduling, and Kubernetes-based orchestration, framed around cost, latency, and quality tradeoffs. We also dive into Jensen's “SOL” (Speed of Light) first-principles urgency concept, long-context limits and model/hardware co-design, internal model APIs (https://build.nvidia.com), and upcoming Dynamo and agent sessions at GTC.Full Video pod on YouTubeTimestamps00:00 Agent Security Basics00:39 Podcast Welcome and Guests07:19 Acquisition and DevEx Shift13:48 SOL Culture and Dynamo Setup27:38 Why Scale Out Wins29:02 Scale Up Limits Explained30:24 From Laptop to Multi Node33:07 Cost Quality Latency Tradeoffs38:42 Disaggregation Prefill vs Decode41:05 Kubernetes Scaling with Grove43:20 Context Length and Co Design57:34 Security Meets Agents58:01 Agent Permissions Model59:10 Build Nvidia Inference Gateway01:01:52 Hackathons And Autonomy Dreams01:10:26 Local GPUs And Scaling Inference01:15:31 Long Running Agents And SF ReflectionsTranscriptAgent Security BasicsNader: Agents can do three things. They can access your files, they can access the internet, and then now they can write custom code and execute it. You literally only let an agent do two of those three things. If you can access your files and you can write custom code, you don't want internet access because that's one to see full vulnerability, right?If you have access to internet and your file system, you should know the full scope of what that agent's capable of doing. Otherwise, now we can get injected or something that can happen. And so that's a lot of what we've been thinking about is like, you know, how do we both enable this because it's clearly the future.But then also, you know, what, what are these enforcement points that we can start to like protect?swyx: All right.Podcast Welcome and Guestsswyx: Welcome to the Lean Space podcast in the Chromo studio. Welcome to all the guests here. Uh, we are back with our guest host Viu. Welcome. Good to have you back. And our friends, uh, Netter and Kyle from Nvidia. Welcome.Kyle: Yeah, thanks for having us.swyx: Yeah, thank you. Actually, I don't even know your titles.Uh, I know you're like architect something of Dynamo.Kyle: Yeah. I, I'm one of the engineering leaders [00:01:00] and a architects of Dynamo.swyx: And you're director of something and developers, developer tech.Nader: Yeah.swyx: You're the developers, developers, developers guy at nvidia,Nader: open source agent marketing, brev,swyx: and likeNader: Devrel tools and stuff.swyx: Yeah. BeenNader: the focus.swyx: And we're, we're kind of recording this ahead of Nvidia, GTC, which is coming to town, uh, again, uh, or taking over town, uh, which, uh, which we'll all be at. Um, and we'll talk a little bit about your sessions and stuff. Yeah.Nader: We're super excited for it.GTC Booth Stunt Storiesswyx: One of my favorite memories for Nader, like you always do like marketing stunts and like while you were at Rev, you like had this surfboard that you like, went down to GTC with and like, NA Nvidia apparently, like did so much that they bought you.Like what, what was that like? What was that?Nader: Yeah. Yeah, we, we, um. Our logo was a chaka. We, we, uh, we were always just kind of like trying to keep true to who we were. I think, you know, some stuff, startups, you're like trying to pretend that you're a bigger, more mature company than you are. And it was actually Evan Conrad from SF Compute who was just like, you guys are like previousswyx: guest.Yeah.Nader: Amazing. Oh, really? Amazing. Yeah. He was just like, guys, you're two dudes in the room. Why are you [00:02:00] pretending that you're not? Uh, and so then we were like, okay, let's make the logo a shaka. We brought surfboards to our booth to GTC and the energy was great. Yeah. Some palm trees too. They,Kyle: they actually poked out over like the, the walls so you could, you could see the bread booth.Oh, that's so funny. AndNader: no one else,Kyle: just from very far away.Nader: Oh, so you remember it backKyle: then? Yeah I remember it pre-acquisition. I was like, oh, those guys look cool,Nader: dude. That makes sense. ‘cause uh, we, so we signed up really last minute, and so we had the last booth. It was all the way in the corner. And so I was, I was worried that no one was gonna come.So that's why we had like the palm trees. We really came in with the surfboards. We even had one of our investors bring her dog and then she was just like walking the dog around to try to like, bring energy towards our booth. Yeah.swyx: Steph.Kyle: Yeah. Yeah, she's the best,swyx: you know, as a conference organizer, I love that.Right? Like, it's like everyone who sponsors a conference comes, does their booth. They're like, we are changing the future of ai or something, some generic b******t and like, no, like actually try to stand out, make it fun, right? And people still remember it after three years.Nader: Yeah. Yeah. You know what's so funny?I'll, I'll send, I'll give you this clip if you wanna, if you wanna add it [00:03:00] in, but, uh, my wife was at the time fiance, she was in medical school and she came to help us. ‘cause it was like a big moment for us. And so we, we bought this cricket, it's like a vinyl, like a vinyl, uh, printer. ‘cause like, how else are we gonna label the surfboard?So, we got a surfboard, luckily was able to purchase that on the company card. We got a cricket and it was just like fine tuning for enterprises or something like that, that we put on the. On the surfboard and it's 1:00 AM the day before we go to GTC. She's helping me put these like vinyl stickers on.And she goes, you son of, she's like, if you pull this off, you son of a b***h. And so, uh, right. Pretty much after the acquisition, I stitched that with the mag music acquisition. I sent it to our family group chat. Ohswyx: Yeah. No, well, she, she made a good choice there. Was that like basically the origin story for Launchable is that we, it was, and maybe we should explain what Brev is andNader: Yeah.Yeah. Uh, I mean, brev is just, it's a developer tool that makes it really easy to get a GPU. So we connect a bunch of different GPU sources. So the basics of it is like, how quickly can we SSH you into a G, into a GPU and whenever we would talk to users, they wanted A GPU. They wanted an A 100. And if you go to like any cloud [00:04:00] provisioning page, usually it's like three pages of forms or in the forms somewhere there's a dropdown.And in the dropdown there's some weird code that you know to translate to an A 100. And I remember just thinking like. Every time someone says they want an A 100, like the piece of text that they're telling me that they want is like, stuffed away in the corner. Yeah. And so we were like, what if the biggest piece of text was what the user's asking for?And so when you go to Brev, it's just big GPU chips with the type that you want withswyx: beautiful animations that you worked on pre, like pre you can, like, now you can just prompt it. But back in the day. Yeah. Yeah. Those were handcraft, handcrafted artisanal code.Nader: Yeah. I was actually really proud of that because, uh, it was an, i I made it in Figma.Yeah. And then I found, I was like really struggling to figure out how to turn it from like Figma to react. So what it actually is, is just an SVG and I, I have all the styles and so when you change the chip, whether it's like active or not it changes the SVG code and that somehow like renders like, looks like it's animating, but it, we just had the transition slow, but it's just like the, a JavaScript function to change the like underlying SVG.Yeah. And that was how I ended up like figuring out how to move it from from Figma. But yeah, that's Art Artisan. [00:05:00]Kyle: Speaking of marketing stunts though, he actually used those SVGs. Or kind of use those SVGs to make these cards.Nader: Oh yeah. LikeKyle: a GPU gift card Yes. That he handed out everywhere. That was actually my first impression of thatNader: one.Yeah,swyx: yeah, yeah.Nader: Yeah.swyx: I think I still have one of them.Nader: They look great.Kyle: Yeah.Nader: I have a ton of them still actually in our garage, which just, they don't have labels. We should honestly like bring, bring them back. But, um, I found this old printing press here, actually just around the corner on Ven ness. And it's a third generation San Francisco shop.And so I come in an excited startup founder trying to like, and they just have this crazy old machinery and I'm in awe. ‘cause the the whole building is so physical. Like you're seeing these machines, they have like pedals to like move these saws and whatever. I don't know what this machinery is, but I saw all three generations.Like there's like the grandpa, the father and the son, and the son was like, around my age. Well,swyx: it's like a holy, holy trinity.Nader: It's funny because we, so I just took the same SVG and we just like printed it and it's foil printing, so they make a a, a mold. That's like an inverse of like the A 100 and then they put the foil on it [00:06:00] and then they press it into the paper.And I remember once we got them, he was like, Hey, don't forget about us. You know, I guess like early Apple and Cisco's first business cards were all made there. And so he was like, yeah, we, we get like the startup businesses but then as they mature, they kind of go somewhere else. And so I actually, I think we were talking with marketing about like using them for some, we should go back and make some cards.swyx: Yeah, yeah, yeah. You know, I remember, you know, as a very, very small breadth investor, I was like, why are we spending time like, doing these like stunts for GPUs? Like, you know, I think like as a, you know, typical like cloud hard hardware person, you go into an AWS you pick like T five X xl, whatever, and it's just like from a list and you look at the specs like, why animate this GP?And, and I, I do think like it just shows the level of care that goes throughout birth and Yeah. And now, and also the, and,Nader: and Nvidia. I think that's what the, the thing that struck me most when we first came in was like the amount of passion that everyone has. Like, I think, um, you know, you talk to, you talk to Kyle, you talk to, like, every VP that I've met at Nvidia goes so close to the metal.Like, I remember it was almost a year ago, and like my VP asked me, he's like, Hey, [00:07:00] what's cursor? And like, are you using it? And if so, why? Surprised at this, and he downloaded Cursor and he was asking me to help him like, use it. And I thought that was, uh, or like, just show him what he, you know, why we were using it.And so, the amount of care that I think everyone has and the passion, appreciate, passion and appreciation for the moment. Right. This is a very unique time. So it's really cool to see everyone really like, uh, appreciate that.swyx: Yeah.Acquisition and DevEx Shiftswyx: One thing I wanted to do before we move over to sort of like research topics and, uh, the, the stuff that Kyle's working on is just tell the story of the acquisition, right?Like, not many people have been, been through an acquisition with Nvidia. What's it like? Uh, what, yeah, just anything you'd like to say.Nader: It's a crazy experience. I think, uh, you know, we were the thing that was the most exciting for us was. Our goal was just to make it easier for developers.We wanted to find access to GPUs, make it easier to do that. And then all, oh, actually your question about launchable. So launchable was just make one click exper, like one click deploys for any software on top of the GPU. Mm-hmm. And so what we really liked about Nvidia was that it felt like we just got a lot more resources to do all of that.I think, uh, you [00:08:00] know, NVIDIA's goal is to make things as easy for developers as possible. So there was a really nice like synergy there. I think that, you know, when it comes to like an acquisition, I think the amount that the soul of the products align, I think is gonna be. Is going speak to the success of the acquisition.Yeah. And so it in many ways feels like we're home. This is a really great outcome for us. Like we you know, I love brev.nvidia.com. Like you should, you should use it's, it's theKyle: front page for GPUs.Nader: Yeah. Yeah. If you want GP views,Kyle: you go there, getswyx: it there, and it's like internally is growing very quickly.I, I don't remember You said some stats there.Nader: Yeah, yeah, yeah. It's, uh, I, I wish I had the exact numbers, but like internally, externally, it's been growing really quickly. We've been working with a bunch of partners with a bunch of different customers and ISVs, if you have a solution that you want someone that runs on the GPU and you want people to use it quickly, we can bundle it up, uh, in a launchable and make it a one click run.If you're doing things and you want just like a sandbox or something to run on, right. Like open claw. Huge moment. Super exciting. Our, uh, and we'll talk into it more, but. You know, internally, people wanna run this, and you, we know we have to be really careful from the security implications. Do we let this run on the corporate network?Security's guidance was, Hey, [00:09:00] run this on breath, it's in, you know, it's, it's, it's a vm, it's sitting in the cloud, it's off the corporate network. It's isolated. And so that's been our stance internally and externally about how to even run something like open call while we figure out how to run these things securely.But yeah,swyx: I think there's also like, you almost like we're the right team at the right time when Nvidia is starting to invest a lot more in developer experience or whatever you call it. Yeah. Uh, UX or I don't know what you call it, like software. Like obviously NVIDIA is always invested in software, but like, there's like, this is like a different audience.Yeah. It's aNader: widerKyle: developer base.swyx: Yeah. Right.Nader: Yeah. Yeah. You know, it's funny, it's like, it's not, uh,swyx: so like, what, what is it called internally? What, what is this that people should be aware that is going on there?Nader: Uh, what, like developer experienceswyx: or, yeah, yeah. Is it's called just developer experience or is there like a broader strategy hereNader: in Nvidia?Um, Nvidia always wants to make a good developer experience. The thing is and a lot of the technology is just really complicated. Like, it's not, it's uh, you know, I think, um. The thing that's been really growing or the AI's growing is having a huge moment, not [00:10:00] because like, let's say data scientists in 2018, were quiet then and are much louder now.The pie is com, right? There's a whole bunch of new audiences. My mom's wondering what she's doing. My sister's learned, like taught herself how to code. Like the, um, you know, I, I actually think just generally AI's a big equalizer and you're seeing a more like technologically literate society, I guess.Like everyone's, everyone's learning how to code. Uh, there isn't really an excuse for that. And so building a good UX means that you really understand who your end user is. And when your end user becomes such a wide, uh, variety of people, then you have to almost like reinvent the practice, right? Yeah. You haveKyle: to, and actually build more developer ux, right?Because the, there are tiers of developer base that were added. You know, the, the hackers that are building on top of open claw, right? For example, have never used gpu. They don't know what kuda is. They, they, they just want to run something.Nader: Yeah.Kyle: You need new UX that is not just. Hey, you know, how do you program something in Cuda and run it?And then, and then we built, you know, like when Deep Learning was getting big, we built, we built Torch and, and, but so recently the amount of like [00:11:00] layers that are added to that developer stack has just exploded because AI has become ubiquitous. Everyone's using it in different ways. Yeah. It'sNader: moving fast in every direction.Vertical, horizontal.Vibhu: Yeah. You guys, you even take it down to hardware, like the DGX Spark, you know, it's, it's basically the same system as just throwing it up on big GPU cluster.Nader: Yeah, yeah, yeah. It's amazing. Blackwell.swyx: Yeah. Uh, we saw the preview at the last year's GTC and that was one of the better performing, uh, videos so far, and video coverage so far.Awesome. This will beat it. Um,Nader: that wasswyx: actually, we have fingersNader: crossed. Yeah.DGX Spark and Remote AccessNader: Even when Grace Blackwell or when, um, uh, DGX Spark was first coming out getting to be involved in that from the beginning of the developer experience. And it just comes back to what youswyx: were involved.Nader: Yeah. St. St.swyx: Mars.Nader: Yeah. Yeah. I mean from, it was just like, I, I got an email, we just got thrown into the loop and suddenly yeah, I, it was actually really funny ‘cause I'm still pretty fresh from the acquisition and I'm, I'm getting an email from a bunch of the engineering VPs about like, the new hardware, GPU chip, like we're, or not chip, but just GPU system that we're putting out.And I'm like, okay, cool. Matters. Now involved with this for the ux, I'm like. What am I gonna do [00:12:00] here? So, I remember the first meeting, I was just like kind of quiet as I was hearing engineering VPs talk about what this box could be, what it could do, how we should use it. And I remember, uh, one of the first ideas that people were idea was like, oh, the first thing that it was like, I think a quote was like, the first thing someone's gonna wanna do with this is get two of them and run a Kubernetes cluster on top of them.And I was like, oh, I think I know why I'm here. I was like, the first thing we're doing is easy. SSH into the machine. And then, and you know, just kind of like scoping it down of like, once you can do that every, you, like the person who wants to run a Kubernetes cluster onto Sparks has a higher propensity for pain, then, then you know someone who buys it and wants to run open Claw right now, right?If you can make sure that that's as effortless as possible, then the rest becomes easy. So there's a tool called Nvidia Sync. It just makes the SSH connection really simple. So, you know, if you think about it like. If you have a Mac, uh, or a PC or whatever, if you have a laptop and you buy this GPU and you want to use it, you should be able to use it like it's A-A-G-P-U in the cloud, right?Um, but there's all this friction of like, how do you actually get into that? That's part of [00:13:00] Revs value proposition is just, you know, there's a CLI that wraps SSH and makes it simple. And so our goal is just get you into that machine really easily. And one thing we just launched at CES, it's in, it's still in like early access.We're ironing out some kinks, but it should be ready by GTC. You can register your spark on Brev. And so now if youswyx: like remote managed yeah, local hardware. Single pane of glass. Yeah. Yeah. Because Brev can already manage other clouds anyway, right?Vibhu: Yeah, yeah. And you use the spark on Brev as well, right?Nader: Yeah. But yeah, exactly. So, so you, you, so you, you set it up at home you can run the command on it, and then it gets it's essentially it'll appear in your Brev account, and then you can take your laptop to a Starbucks or to a cafe, and you'll continue to use your, you can continue use your spark just like any other cloud node on Brev.Yeah. Yeah. And it's just like a pre-provisioned centerswyx: in yourNader: home. Yeah, exactly.swyx: Yeah. Yeah.Vibhu: Tiny little data center.Nader: Tiny little, the size ofVibhu: your phone.SOL Culture and Dynamo Setupswyx: One more thing before we move on to Kyle. Just have so many Jensen stories and I just love, love mining Jensen stories. Uh, my favorite so far is SOL. Uh, what is, yeah, what is S-O-L-S-O-LNader: is actually, i, I think [00:14:00] of all the lessons I've learned, that one's definitely my favorite.Kyle: It'll always stick with you.Nader: Yeah. Yeah. I, you know, in your startup, everything's existential, right? Like we've, we've run out of money. We were like, on the risk of, of losing payroll, we've had to contract our team because we l ran outta money. And so like, um, because of that you're really always forcing yourself to I to like understand the root cause of everything.If you get a date, if you get a timeline, you know exactly why that date or timeline is there. You're, you're pushing every boundary and like, you're not just say, you're not just accepting like a, a no. Just because. And so as you start to introduce more layers, as you start to become a much larger organization, SOL is is essentially like what is the physics, right?The speed of light moves at a certain speed. So if flight's moving some slower, then you know something's in the way. So before trying to like layer reality back in of like, why can't this be delivered at some date? Let's just understand the physics. What is the theoretical limit to like, uh, how fast this can go?And then start to tell me why. ‘cause otherwise people will start telling you why something can't be done. But actually I think any great leader's goal is just to create urgency. Yeah. [00:15:00] There's an infiniteKyle: create compelling events, right?Nader: Yeah.Kyle: Yeah. So l is a term video is used to instigate a compelling event.You say this is done. How do we get there? What is the minimum? As much as necessary, as little as possible thing that it takes for us to get exactly here and. It helps you just break through a bunch of noise.swyx: Yeah.Kyle: Instantly.swyx: One thing I'm unclear about is, can only Jensen use the SOL card? Like, oh, no, no, no.Not everyone get the b******t out because obviously it's Jensen, but like, can someone else be like, no, likeKyle: frontline engineers use it.Nader: Yeah. Every, I think it's not so much about like, get the b******t out. It's like, it's like, give me the root understanding, right? Like, if you tell me something takes three weeks, it like, well, what's the first principles?Yeah, the first principles. It's like, what's the, what? Like why is it three weeks? What is the actual yeah. What's the actual limit of why this is gonna take three weeks? If you're gonna, if you, if let's say you wanted to buy a new computer and someone told you it's gonna be here in five days, what's the SOL?Well, like the SOL is like, I could walk into a Best Buy and pick it up for you. Right? So then anything that's like beyond that is, and is that practical? Is that how we're gonna, you know, let's say give everyone in the [00:16:00] company a laptop, like obviously not. So then like that's the SOL and then it's like, okay, well if we have to get more than 10, suddenly there might be some, right?And so now we can kind of piece the reality back.swyx: So, so this is the. Paul Graham do things that don't scale. Yeah. And this is also the, what people would now call behi agency. Yeah.Kyle: It's actually really interesting because there's a, there's a second hardware angle to SOL that like doesn't come up for all the org sol is used like culturally at aswyx: media for everything.I'm also mining for like, I think that can be annoying sometimes. And like someone keeps going IOO you and you're like, guys, like we have to be stable. We have to, we to f*****g plan. Yeah.Kyle: It's an interesting balance.Nader: Yeah. I encounter that with like, actually just with, with Alec, right? ‘cause we, we have a new conference so we need to launch, we have, we have goals of what we wanna launch by, uh, by the conference and like, yeah.At the end of the day, where isswyx: this GTC?Nader: Um, well this is like, so we, I mean we did it for CES, we did for GT CDC before that we're doing it for GTC San Jose. So I mean, like every, you know, we have a new moment. Um, and we want to launch something. Yeah. And we want to do so at SOL and that does mean that some, there's some level of prioritization that needs [00:17:00] to happen.And so it, it is difficult, right? I think, um, you have to be careful with what you're pushing. You know, stability is important and that should be factored into S-O-L-S-O-L isn't just like, build everything and let it break, you know, that, that's part of the conversation. So as you're laying, layering in all the details, one of them might be, Hey, we could build this, but then it's not gonna be stable for X, y, z reasons.And so that was like, one of our conversations for CES was, you know, hey, like we, we can get this into early access registering your spark with brev. But there are a lot of things that we need to do in order to feel really comfortable from a security perspective, right? There's a lot of networking involved before we deliver that to users.So it's like, okay. Let's get this to a point where we can at least let people experiment with it. We had it in a booth, we had it in Jensen's keynote, and then let's go iron out all the networking kinks. And that's not easy. And so, uh, that can come later. And so that was the way that we layered that back in.Yeah. ButKyle: It's not really about saying like, you don't have to do the, the maintenance or operational work. It's more about saying, you know, it's kind of like [00:18:00] highlights how progress is incremental, right? Like, what is the minimum thing that we can get to. And then there's SOL for like every component after that.But there's the SOL to get you, get you to the, the starting line. And that, that's usually how it's asked. Yeah. On the other side, you know, like SOL came out of like hardware at Nvidia. Right. So SOL is like literally if we ran the accelerator or the GPU with like at basically full speed with like no other constraints, like how FAST would be able to make a program go.swyx: Yeah. Yeah. Right.Kyle: Soswyx: in, in training that like, you know, then you work back to like some percentage of like MFU for example.Kyle: Yeah, that's a, that's a great example. So like, there's an, there's an S-O-L-M-F-U, and then there's like, you know, what's practically achievable.swyx: Cool. Should we move on to sort of, uh, Kyle's side?Uh, Kyle, you're coming more from the data science world. And, uh, I, I mean I always, whenever, whenever I meet someone who's done working in tabular stuff, graph neural networks, time series, these are basically when I go to new reps, I go to ICML, I walk the back halls. There's always like a small group of graph people.Yes. Absolute small group of tabular people. [00:19:00] And like, there's no one there. And like, it's very like, you know what I mean? Like, yeah, no, like it's, it's important interesting work if you care about solving the problems that they solve.Kyle: Yeah.swyx: But everyone else is just LMS all the time.Kyle: Yeah. I mean it's like, it's like the black hole, right?Has the event horizon reached this yet in nerves? Um,swyx: but like, you know, those are, those are transformers too. Yeah. And, and those are also like interesting things. Anyway, uh, I just wanted to spend a little bit of time on, on those, that background before we go into Dynamo, uh, proper.Kyle: Yeah, sure. I took a different path to Nvidia than that, or I joined six years ago, seven, if you count, when I was an intern.So I joined Nvidia, like right outta college. And the first thing I jumped into was not what I'd done in, during internship, which was like, you know, like some stuff for autonomous vehicles, like heavyweight object detection. I jumped into like, you know, something, I'm like, recommenders, this is popular. Andswyx: yeah, he did RexiKyle: as well.Yeah, Rexi. Yeah. I mean that, that was the taboo data at the time, right? You have tables of like, audience qualities and item qualities, and you're trying to figure out like which member of [00:20:00] the audience matches which item or, or more practically which item matches which member of the audience. And at the time, really it was like we were trying to enable.Uh, recommender, which had historically been like a little bit of a CP based workflow into something that like, ran really well in GPUs. And it's since been done. Like there are a bunch of libraries for Axis that run on GPUs. Uh, the common models like Deeplearning recommendation model, which came outta meta and the wide and deep model, which was used or was released by Google were very accelerated by GPUs using, you know, the fast HBM on the chips, especially to do, you know, vector lookups.But it was very interesting at the time and super, super relevant because like we were starting to get like. This explosion of feeds and things that required rec recommenders to just actively be on all the time. And sort of transitioned that a little bit towards graph neural networks when I discovered them because I was like, okay, you can actually use graphical neural networks to represent like, relationships between people, items, concepts, and that, that interested me.So I jumped into that at [00:21:00] Nvidia and, and got really involved for like two-ish years.swyx: Yeah. Uh, and something I learned from Brian Zaro Yeah. Is that you can just kind of choose your own path in Nvidia.Kyle: Oh my God. Yeah.swyx: Which is not a normal big Corp thing. Yeah. Like you, you have a lane, you stay in your lane.Nader: I think probably the reason why I enjoy being in a, a big company, the mission is the boss probably from a startup guy. Yeah. The missionswyx: is the boss.Nader: Yeah. Uh, it feels like a big game of pickup basketball. Like, you know, if you play one, if you wanna play basketball, you just go up to the court and you're like, Hey look, we're gonna play this game and we need three.Yeah. And you just like find your three. That's honestly for every new initiative that's what it feels like. Yeah.Vibhu: It also like shows, right? Like Nvidia. Just releasing state-of-the-art stuff in every domain. Yeah. Like, okay, you expect foundation models with Nemo tron voice just randomly parakeet.Call parakeet just comes out another one, uh, voice. TheKyle: video voice team has always been producing.Vibhu: Yeah. There's always just every other domain of paper that comes out, dataset that comes out. It's like, I mean, it also stems back to what Nvidia has to do, right? You have to make chips years before they're actually produced.Right? So you need to know, you need to really [00:22:00] focus. TheKyle: design process starts likeVibhu: exactlyKyle: three to five years before the chip gets to the market.Vibhu: Yeah. I, I'm curious more about what that's like, right? So like, you have specialist teams. Is it just like, you know, people find an interest, you go in, you go deep on whatever, and that kind of feeds back into, you know, okay, we, we expect predictions.Like the internals at Nvidia must be crazy. Right? You know? Yeah. Yeah. You know, you, you must. Not even without selling to people, you have your own predictions of where things are going. Yeah. And they're very based, very grounded. Right?Kyle: Yeah. It, it, it's really interesting. So there's like two things that I think that Amed does, which are quite interesting.Uh, one is like, we really index into passion. There's a big. Sort of organizational top sound push to like ensure that people are working on the things that they're passionate about. So if someone proposes something that's interesting, many times they can just email someone like way up the chain that they would find this relevant and say like, Hey, can I go work on this?Nader: It's actually like I worked at a, a big company for a couple years before, uh, starting on my startup journey and like, it felt very weird if you were to like email out of chain, if that makes [00:23:00] sense. Yeah. The emails at Nvidia are like mosh pitsswyx: shoot,Nader: and it's just like 60 people, just whatever. And like they're, there's this,swyx: they got messy like, reply all you,Nader: oh, it's in, it's insane.It's insane. They justKyle: help. You know, Maxim,Nader: the context. But, but that's actually like, I've actually, so this is a weird thing where I used to be like, why would we send emails? We have Slack. I am the entire, I'm the exact opposite. I feel so bad for anyone who's like messaging me on Slack ‘cause I'm so unresponsive.swyx: Your emailNader: Maxi, email Maxim. I'm email maxing Now email is a different, email is perfect because man, we can't work together. I'm email is great, right? Because important threads get bumped back up, right? Yeah, yeah. Um, and so Slack doesn't do that. So I just have like this casino going off on the right or on the left and like, I don't know which thread was from where or what, but like the threads get And then also just like the subject, so you can have like working threads.I think what's difficult is like when you're small, if you're just not 40,000 people I think Slack will work fine, but there's, I don't know what the inflection point is. There is gonna be a point where that becomes really messy and you'll actually prefer having email. ‘cause you can have working threads.You can cc more than nine people in a thread.Kyle: You can fork stuff.Nader: You can [00:24:00] fork stuff, which is super nice and just like y Yeah. And so, but that is part of where you can propose a plan. You can also just. Start, honestly, momentum's the only authority, right? So like, if you can just start, start to make a little bit of progress and show someone something, and then they can try it.That's, I think what's been, you know, I think the most effective way to push anything for forward. And that's both at Nvidia and I think just generally.Kyle: Yeah, there's, there's the other concept that like is explored a lot at Nvidia, which is this idea of a zero billion dollar business. Like market creation is a big thing at Nvidia.Like,swyx: oh, you want to go and start a zero billion dollar business?Kyle: Jensen says, we are completely happy investing in zero billion dollar markets. We don't care if this creates revenue. It's important for us to know about this market. We think it will be important in the future. It can be zero billion dollars for a while.I'm probably minging as words here for, but like, you know, like, I'll give an example. NVIDIA's been working on autonomous driving for a a long time,swyx: like an Nvidia car.Kyle: No, they, they'veVibhu: used the Mercedes, right? They're around the HQ and I think it finally just got licensed out. Now they're starting to be used quite a [00:25:00] bit.For 10 years you've been seeing Mercedes with Nvidia logos driving.Kyle: If you're in like the South San Santa Clara, it's, it's actually from South. Yeah. So, um. Zero billion dollar markets are, are a thing like, you know, Jensen,swyx: I mean, okay, look, cars are not a zero billion dollar market. But yeah, that's a bad example.Nader: I think, I think he's, he's messaging, uh, zero today, but, or even like internally, right? Like, like it's like, uh, an org doesn't have to ruthlessly find revenue very quickly to justify their existence. Right. Like a lot of the important research, a lot of the important technology being developed that, that's kind ofKyle: where research, research is very ide ideologically free at Nvidia.Yeah. Like they can pursue things that they wereswyx: Were you research officially?Kyle: I was never in research. Officially. I was always in engineering. Yeah. We in, I'm in an org called Deep Warning Algorithms, which is basically just how do we make things that are relevant to deep warning go fast.swyx: That sounds freaking cool.Vibhu: And I think a lot of that is underappreciated, right? Like time series. This week Google put out time. FF paper. Yeah. A new time series, paper res. Uh, Symantec, ID [00:26:00] started applying Transformers LMS to Yes. Rec system. Yes. And when you think the scale of companies deploying these right. Amazon recommendations, Google web search, it's like, it's huge scale andKyle: Yeah.Vibhu: You want fast?Kyle: Yeah. Yeah. Yeah. Actually it's, it, I, there's a fun moment that brought me like full circle. Like, uh, Amazon Ads recently gave a talk where they talked about using Dynamo for generative recommendation, which was like super, like weirdly cathartic for me. I'm like, oh my God. I've, I've supplanted what I was working on.Like, I, you're using LMS now to do what I was doing five years ago.swyx: Yeah. Amazing. And let's go right into Dynamo. Uh, maybe introduce Yeah, sure. To the top down and Yeah.Kyle: I think at this point a lot of people are familiar with the term of inference. Like funnily enough, like I went from, you know, inference being like a really niche topic to being something that's like discussed on like normal people's Twitter feeds.It's,Nader: it's on billboardsKyle: here now. Yeah. Very, very strange. Driving, driving, seeing just an inference ad on 1 0 1 inference at scale is becoming a lot more important. Uh, we have these moments like, you know, open claw where you have these [00:27:00] agents that take lots and lots of tokens, but produce, incredible results.There are many different aspects of test time scaling so that, you know, you can use more inference to generate a better result than if you were to use like a short amount of inference. There's reasoning, there's quiring, there's, adding agency to the model, allowing it to call tools and use skills.Dyno sort came about at Nvidia. Because myself and a couple others were, were sort of talking about the, these concepts that like, you know, you have inference engines like VLMS, shelan, tenor, TLM and they have like one single copy. They, they, they sort of think about like things as like one single copy, like one replica, right?Why Scale Out WinsKyle: Like one version of the model. But when you're actually serving things at scale, you can't just scale up that replica because you end up with like performance problems. There's a scaling limit to scaling up replicas. So you actually have to scale out to use a, maybe some Kubernetes type terminology.We kind of realized that there was like. A lot of potential optimization that we could do in scaling out and building systems for data [00:28:00] center scale inference. So Dynamo is this data center scale inference engine that sits on top of the frameworks like VLM Shilling and 10 T lm and just makes things go faster because you can leverage the economy of scale.The fact that you have KV cash, which we can define a little bit later, uh, in all these machines that is like unique and you wanna figure out like the ways to maximize your cash hits or you want to employ new techniques in inference like disaggregation, which Dynamo had introduced to the world in, in, in March, not introduced, it was a academic talk, but beforehand.But we are, you know, one of the first frameworks to start, supporting it. And we wanna like, sort of combine all these techniques into sort of a modular framework that allows you to. Accelerate your inference at scale.Nader: By the way, Kyle and I became friends on my first date, Nvidia, and I always loved, ‘cause like he always teaches meswyx: new things.Yeah. By the way, this is why I wanted to put two of you together. I was like, yeah, this is, this is gonna beKyle: good. It's very, it's very different, you know, like we've, we, we've, we've talked to each other a bunch [00:29:00] actually, you asked like, why, why can't we scale up?Nader: Yeah.Scale Up Limits ExplainedNader: model, you said model replicas.Kyle: Yeah. So you, so scale up means assigning moreswyx: heavier?Kyle: Yeah, heavier. Like making things heavier. Yeah, adding more GPUs. Adding more CPUs. Scale out is just like having a barrier saying, I'm gonna duplicate my representation of the model or a representation of this microservice or something, and I'm gonna like, replicate it Many times.Handle, load. And the reason that you can't scale, scale up, uh, past some points is like, you know, there, there, there are sort of hardware bounds and algorithmic bounds on, on that type of scaling. So I'll give you a good example that's like very trivial. Let's say you're on an H 100. The Maxim ENV link domain for H 100, for most Ds H one hundreds is heus, right?So if you scaled up past that, you're gonna have to figure out ways to handle the fact that now for the GPUs to communicate, you have to do it over Infin band, which is still very fast, but is not as fast as ENV link.swyx: Is it like one order of magnitude, like hundreds or,Kyle: it's about an order of magnitude?Yeah. Okay. Um, soswyx: not terrible.Kyle: [00:30:00] Yeah. I, I need to, I need to remember the, the data sheet here, like, I think it's like about 500 gigabytes. Uh, a second unidirectional for ENV link, and about 50 gigabytes a second unidirectional for Infin Band. I, it, it depends on the, the generation.swyx: I just wanna set this up for people who are not familiar with these kinds of like layers and the trash speedVibhu: and all that.Of course.From Laptop to Multi NodeVibhu: Also, maybe even just going like a few steps back before that, like most people are very familiar with. You see a, you know, you can use on your laptop, whatever these steel viol, lm you can just run inference there. All, there's all, you can, youcan run it on thatVibhu: laptop. You can run on laptop.Then you get to, okay, uh, models got pretty big, right? JLM five, they doubled the size, so mm-hmm. Uh, what do you do when you have to go from, okay, I can get 128 gigs of memory. I can run it on a spark. Then you have to go multi GPU. Yeah. Okay. Multi GPU, there's some support there. Now, if I'm a company and I don't have like.I'm not hiring the best researchers for this. Right. But I need to go [00:31:00] multi-node, right? I have a lot of servers. Okay, now there's efficiency problems, right? You can have multiple eight H 100 nodes, but, you know, is that as a, like, how do you do that efficiently?Kyle: Yeah. How do you like represent them? How do you choose how to represent the model?Yeah, exactly right. That's a, that's like a hard question. Everyone asks, how do you size oh, I wanna run GLM five, which just came out new model. There have been like four of them in the past week, by the way, like a bunch of new models.swyx: You know why? Right? Deep seek.Kyle: No comment. Oh. Yeah, but Ggl, LM five, right?We, we have this, new model. It's, it's like a large size, and you have to figure out how to both scale up and scale out, right? Because you have to find the right representation that you care about. Everyone does this differently. Let's be very clear. Everyone figures this out in their own path.Nader: I feel like a lot of AI or ML even is like, is like this. I think people think, you know, I, I was, there was some tweet a few months ago that was like, why hasn't fine tuning as a service taken off? You know, that might be me. It might have been you. Yeah. But people want it to be such an easy recipe to follow.But even like if you look at an ML model and specificKyle: to you Yeah,Nader: yeah.Kyle: And the [00:32:00] model,Nader: the situation, and there's just so much tinkering, right? Like when you see a model that has however many experts in the ME model, it's like, why that many experts? I don't, they, you know, they tried a bunch of things and that one seemed to do better.I think when it comes to how you're serving inference, you know, you have a bunch of decisions to make and there you can always argue that you can take something and make it more optimal. But I think it's this internal calibration and appetite for continued calibration.Vibhu: Yeah. And that doesn't mean like, you know, people aren't taking a shot at this, like tinker from thinking machines, you know?Yeah. RL as a service. Yeah, totally. It's, it also gets even harder when you try to do big model training, right? We're not the best at training Moes, uh, when they're pre-trained. Like we saw this with LAMA three, right? They're trained in such a sparse way that meta knows there's gonna be a bunch of inference done on these, right?They'll open source it, but it's very trained for what meta infrastructure wants, right? They wanna, they wanna inference it a lot. Now the question to basically think about is, okay, say you wanna serve a chat application, a coding copilot, right? You're doing a layer of rl, you're serving a model for X amount of people.Is it a chat model, a coding model? Dynamo, you know, back to that,Kyle: it's [00:33:00] like, yeah, sorry. So you we, we sort of like jumped off of, you know, jumped, uh, on that topic. Everyone has like, their own, own journey.Cost Quality Latency TradeoffsKyle: And I, I like to think of it as defined by like, what is the model you need? What is the accuracy you need?Actually I talked to NA about this earlier. There's three axes you care about. What is the quality that you're able to produce? So like, are you accurate enough or can you complete the task with enough, performance, high enough performance. Yeah, yeah. Uh, there's cost. Can you serve the model or serve your workflow?Because it's not just the model anymore, it's the workflow. It's the multi turn with an agent cheaply enough. And then can you serve it fast enough? And we're seeing all three of these, like, play out, like we saw, we saw new models from OpenAI that you know, are faster. You have like these new fast versions of models.You can change the amount of thinking to change the amount of quality, right? Produce more tokens, but at a higher cost in a, in a higher latency. And really like when you start this journey of like trying to figure out how you wanna host a model, you, you, you think about three things. What is the model I need to serve?How many times do I need to call it? What is the input sequence link was [00:34:00] the, what does the workflow look like on top of it? What is the SLA, what is the latency SLA that I need to achieve? Because there's usually some, this is usually like a constant, you, you know, the SLA that you need to hit and then like you try and find the lowest cost version that hits all of these constraints.Usually, you know, you, you start with those things and you say you, you kind of do like a bit of experimentation across some common configurations. You change the tensor parallel size, which is a form of parallelismVibhu: I take, it goes even deeper first. Gotta think what model.Kyle: Yes, course,ofKyle: course. It's like, it's like a multi-step design process because as you said, you can, you can choose a smaller model and then do more test time scaling and it'll equate the quality of a larger model because you're doing the test time scaling or you're adding a harness or something.So yes, it, it goes way deeper than that. But from the performance perspective, like once you get to the model you need, you need to host, you look at that and you say, Hey. I have this model, I need to serve it at the speed. What is the right configuration for that?Nader: You guys see the recent, uh, there was a paper I just saw like a few days ago that, uh, if you run [00:35:00] the same prompt twice, you're getting like double Just try itagain.Nader: Yeah, exactly.Vibhu: And you get a lot. Yeah. But the, the key thing there is you give the context of the failed try, right? Yeah. So it takes a shot. And this has been like, you know, basic guidance for quite a while. Just try again. ‘cause you know, trying, just try again. Did you try again? All adviceNader: in life.Vibhu: Just, it's a paper from Google, if I'm not mistaken, right?Yeah,Vibhu: yeah. I think it, it's like a seven bas little short paper. Yeah. Yeah. The title's very cute. And it's just like, yeah, just try again. Give it ask context,Kyle: multi-shot. You just like, say like, hey, like, you know, like take, take a little bit more, take a little bit more information, try and fail. Fail.Vibhu: And that basic concept has gone pretty deep.There's like, um, self distillation, rl where you, you do self distillation, you do rl and you have past failure and you know, that gives some signal so people take, try it again. Not strong enough.swyx: Uh, for, for listeners, uh, who listen to here, uh, vivo actually, and I, and we run a second YouTube channel for our paper club where, oh, that's awesome.Vivo just covered this. Yeah. Awesome. Self desolation and all that's, that's why he, to speed [00:36:00] on it.Nader: I'll to check it out.swyx: Yeah. It, it's just a good practice, like everyone needs, like a paper club where like you just read papers together and the social pressure just kind of forces you to just,Nader: we, we,there'sNader: like a big inference.Kyle: ReadingNader: group at a video. I feel so bad every time. I I, he put it on like, on our, he shared it.swyx: One, one ofNader: your guys,swyx: uh, is, is big in that, I forget es han Yeah, yeah,Kyle: es Han's on my team. Actually. Funny. There's a, there's a, there's a employee transfer between us. Han worked for Nater at Brev, and now he, he's on my team.He wasNader: our head of ai. And then, yeah, once we got in, andswyx: because I'm always looking for like, okay, can, can I start at another podcast that only does that thing? Yeah. And, uh, Esan was like, I was trying to like nudge Esan into like, is there something here? I mean, I don't think there's, there's new infant techniques every day.So it's like, it's likeKyle: you would, you would actually be surprised, um, the amount of blog posts you see. And ifswyx: there's a period where it was like, Medusa hydra, what Eagle, like, youKyle: know, now we have new forms of decode, uh, we have new forms of specula, of decoding or new,swyx: what,Kyle: what are youVibhu: excited? And it's exciting when you guys put out something like Tron.‘cause I remember the paper on this Tron three, [00:37:00] uh, the amount of like post train, the on tokens that the GPU rich can just train on. And it, it was a hybrid state space model, right? Yeah.Kyle: It's co-designed for the hardware.Vibhu: Yeah, go design for the hardware. And one of the things was always, you know, the state space models don't scale as well when you do a conversion or whatever the performance.And you guys are like, no, just keep draining. And Nitron shows a lot of that. Yeah.Nader: Also, something cool about Nitron it was released in layers, if you will, very similar to Dynamo. It's, it's, it's essentially it was released as you can, the pre-training, post-training data sets are released. Yeah. The recipes on how to do it are released.The model itself is released. It's full model. You just benefit from us turning on the GPUs. But there are companies like, uh, ServiceNow took the dataset and they trained their own model and we were super excited and like, you know, celebrated that work.ZoomVibhu: different. Zoom is, zoom is CGI, I think, uh, you know, also just to add like a lot of models don't put out based models and if there's that, why is fine tuning not taken off?You know, you can do your own training. Yeah,Kyle: sure.Vibhu: You guys put out based model, I think you put out everything.Nader: I believe I know [00:38:00]swyx: about base. BasicallyVibhu: without baseswyx: basic can be cancelable.Vibhu: Yeah. Base can be cancelable.swyx: Yeah.Vibhu: Safety training.swyx: Did we get a full picture of dymo? I, I don't know if we, what,Nader: what I'd love is you, you mentioned the three axes like break it down of like, you know, what's prefilled decode and like what are the optimizations that we can get with Dynamo?Kyle: Yeah. That, that's, that's, that's a great point. So to summarize on that three axis problem, right, there are three things that determine whether or not something can be done with inference, cost, quality, latency, right? Dynamo is supposed to be there to provide you like the runtime that allows you to pull levers to, you know, mix it up and move around the parade of frontier or the preto surface that determines is this actually possible with inference And AI todayNader: gives you the knobs.Kyle: Yeah, exactly. It gives you the knobs.Disaggregation Prefill vs DecodeKyle: Uh, and one thing that like we, we use a lot in contemporary inference and is, you know, starting to like pick up from, you know, in, in general knowledge is this co concept of disaggregation. So historically. Models would be hosted with a single inference engine. And that inference engine [00:39:00] would ping pong between two phases.There's prefill where you're reading the sequence generating KV cache, which is basically just a set of vectors that represent the sequence. And then using that KV cache to generate new tokens, which is called Decode. And some brilliant researchers across multiple different papers essentially made the realization that if you separate these two phases, you actually gain some benefits.Those benefits are basically a you don't have to worry about step synchronous scheduling. So the way that an inference engine works is you do one step and then you finish it, and then you schedule, you start scheduling the next step there. It's not like fully asynchronous. And the problem with that is you would have, uh, essentially pre-fill and decode are, are actually very different in terms of both their resource requirements and their sometimes their runtime.So you would have like prefill that would like block decode steps because you, you'd still be pre-filing and you couldn't schedule because you know the step has to end. So you remove that scheduling issue and then you also allow you, or you yourself, to like [00:40:00] split the work into two different ki types of pools.So pre-fill typically, and, and this changes as, as model architecture changes. Pre-fill is, right now, compute bound most of the time with the sequence is sufficiently long. It's compute bound. On the decode side because you're doing a full Passover, all the weights and the entire sequence, every time you do a decode step and you're, you don't have the quadratic computation of KV cache, it's usually memory bound because you're retrieving a linear amount of memory and you're doing a linear amount of compute as opposed to prefill where you retrieve a linear amount of memory and then use a quadratic.You know,Nader: it's funny, someone exo Labs did a really cool demo where for the DGX Spark, which has a lot more compute, you can do the pre the compute hungry prefill on a DG X spark and then do the decode on a, on a Mac. Yeah. And soVibhu: that's faster.Nader: Yeah. Yeah.Kyle: So you could, you can do that. You can do machine strat stratification.Nader: Yeah.Kyle: And like with our future generation generations of hardware, we actually announced, like with Reuben, this [00:41:00] new accelerator that is prefilled specific. It's called Reuben, CPX. SoKubernetes Scaling with GroveNader: I have a question when you do the scale out. Yeah. Is scaling out easier with Dynamo? Because when you need a new node, you can dedicate it to either the Prefill or, uh, decode.Kyle: Yeah. So Dynamo actually has like a, a Kubernetes component in it called Grove that allows you to, to do this like crazy scaling specialization. It has like this hot, it's a representation that, I don't wanna go too deep into Kubernetes here, but there was a previous way that you would like launch multi-node work.Uh, it's called Leader Worker Set. It's in the Kubernetes standard, and Leader worker set is great. It served a lot of people super well for a long period of time. But one of the things that it's struggles with is representing a set of cases where you have a multi-node replica that has a pair, right?You know, prefill and decode, or it's not paired, but it has like a second stage that has a ratio that changes over time. And prefill and decode are like two different things as your workload changes, right? The amount of prefill you'll need to do may change. [00:42:00] The amount of decode that you, you'll need to do might change, right?Like, let's say you start getting like insanely long queries, right? That probably means that your prefill scales like harder because you're hitting these, this quadratic scaling growth.swyx: Yeah.And then for listeners, like prefill will be long input. Decode would be long output, for example, right?Kyle: Yeah. So like decode, decode scale. I mean, decode is funny because the amount of tokens that you produce scales with the output length, but the amount of work that you do per step scales with the amount of tokens in the context.swyx: Yes.Kyle: So both scales with the input and the output.swyx: That's true.Kyle: But on the pre-fold view code side, like if.Suddenly, like the amount of work you're doing on the decode side stays about the same or like scales a little bit, and then the prefilled side like jumps up a lot. You actually don't want that ratio to be the same. You want it to change over time. So Dynamo has a set of components that A, tell you how to scale.It tells you how many prefilled workers and decoded workers you, it thinks you should have, and also provides a scheduling API for Kubernetes that allows you to actually represent and affect this scheduling on, on, on your actual [00:43:00] hardware, on your compute infrastructure.Nader: Not gonna lie. I feel a little embarrassed for being proud of my SVG function earlier.swyx: No, itNader: wasreallyKyle: cute. I, Iswyx: likeNader: it's all,swyx: it's all engineering. It's all engineering. Um, that's where I'mKyle: technical.swyx: One thing I'm, I'm kind of just curious about with all with you see at a systems level, everything going on here. Mm-hmm. And we, you know, we're scaling it up in, in multi, in distributed systems.Context Length and Co Designswyx: Um, I think one thing that's like kind of, of the moment right now is people are asking, is there any SOL sort of upper bounds. In terms of like, let's call, just call it context length for one for of a better word, but you can break it down however you like.Nader: Yeah.swyx: I just think like, well, yeah, I mean, like clearly you can engage in hybrid architectures and throw in some state space models in there.All, all you want, but it looks, still looks very attention heavy.Kyle: Yes. Uh, yeah. Long context is attention heavy. I mean, we have these hybrid models, um,swyx: to take and most, most models like cap out at a million contexts and that's it. Yeah. Like for the last two years has been it.Kyle: Yeah. The model hardware context co-design thing that we're seeing these days is actually super [00:44:00] interesting.It's like my, my passion, like my secret side passion. We see models like Kimmy or G-P-T-O-S-S. I'm use these because I, I know specific things about these models. So Kimmy two comes out, right? And it's an interesting model. It's like, like a deep seek style architecture is MLA. It's basically deep seek, scaled like a little bit differently, um, and obviously trained differently as well.But they, they talked about, why they made the design choices for context. Kimmy has more experts, but fewer attention heads, and I believe a slightly smaller attention, uh, like dimension. But I need to remember, I need to check that. Uh, it doesn't matter. But they discussed this actually at length in a blog post on ji, which is like our pu which is like credit puswyx: Yeah.Kyle: Um, in, in China. Chinese red.swyx: Yeah.Kyle: It's, yeah. So it, it's, it's actually an incredible blog post. Uh, like all the mls people in, in, in that, I've seen that on GPU are like very brilliant, but they, they talk about like the creators of Kimi K two [00:45:00] actually like, talked about it on, on, on there in the blog post.And they say, we, we actually did an experiment, right? Attention scales with the number of heads, obviously. Like if you have 64 heads versus 32 heads, you do half the work of attention. You still scale quadratic, but you do half the work. And they made a, a very specific like. Sort of barter in their system, in their architecture, they basically said, Hey, what if we gave it more experts, so we're gonna use more memory capacity.But we keep the amount of activated experts the same. We increase the expert sparsity, so we have fewer experts act. The ratio to of experts activated to number of experts is smaller, and we decrease the number of attention heads.Vibhu: And kind of for context, what the, what we had been seeing was you make models sparser instead.So no one was really touching heads. You're just having, uh,Kyle: well, they, they did, they implicitly made it sparser.Vibhu: Yeah, yeah. For, for Kimmy. They did,Kyle: yes.Vibhu: They also made it sparser. But basically what we were seeing was people were at the level of, okay, there's a sparsity ratio. You want more total parameters, less active, and that's sparsity.[00:46:00]But what you see from papers, like, the labs like moonshot deep seek, they go to the level of, okay, outside of just number of experts, you can also change how many attention heads and less attention layers. More attention. Layers. Layers, yeah. Yes, yes. So, and that's all basically coming back to, just tied together is like hardware model, co-design, which isKyle: hardware model, co model, context, co-design.Vibhu: Yeah.Kyle: Right. Like if you were training a, a model that was like. Really, really short context, uh, or like really is good at super short context tasks. You may like design it in a way such that like you don't care about attention scaling because it hasn't hit that, like the turning point where like the quadratic curve takes over.Nader: How do you consider attention or context as a separate part of the co-design? Like I would imagine hardware or just how I would've thought of it is like hardware model. Co-design would be hardware model context co-designKyle: because the harness and the context that is produced by the harness is a part of the model.Once it's trained in,Vibhu: like even though towards the end you'll do long context, you're not changing architecture through I see. Training. Yeah.Kyle: I mean you can try.swyx: You're saying [00:47:00] everyone's training the harness into the model.Kyle: I would say to some degree, orswyx: there's co-design for harness. I know there's a small amount, but I feel like not everyone has like gone full send on this.Kyle: I think, I think I think it's important to internalize the harness that you think the model will be running. Running into the model.swyx: Yeah. Interesting. Okay. Bash is like the universal harness,Kyle: right? Like I'll, I'll give. An example here, right? I mean, or just like a, like a, it's easy proof, right? If you can train against a harness and you're using that harness for everything, wouldn't you just train with the harness to ensure that you get the best possible quality out of,swyx: Well, the, uh, I, I can provide a counter argument.Yeah, sure. Which is what you wanna provide a generally useful model for other people to plug into their harnesses, right? So if youKyle: Yeah. Harnesses can be open, open source, right?swyx: Yeah. So I mean, that's, that's effectively what's happening with Codex.Kyle: Yeah.swyx: And, but like you may want like a different search tool and then you may have to name it differently or,Nader: I don't know how much people have pushed on this, but can you.Train a model, would it be, have you have people compared training a model for the for the harness versus [00:48:00] like post training forswyx: I think it's the same thing. It's the same thing. It's okay. Just extra post training. INader: see.swyx: And so, I mean, cognition does this course, it does this where you, you just have to like, if your tool is slightly different, um, either force your tool to be like the tool that they train for.Hmm. Or undo their training for their tool and then Oh, that's re retrain. Yeah. It's, it's really annoying and like,Kyle: I would hope that eventually we hit like a certain level of generality with respect to training newswyx: tools. This is not a GI like, it's, this is a really stupid like. Learn my tool b***h.Like, I don't know if, I don't know if I can say that, but like, you know, um, I think what my point kind of is, is that there's, like, I look at slopes of the scaling laws and like, this slope is not working, man. We, we are at a million token con

Summary In this episode of Chattinn Cyber, Marc Schein is chattin' with Mike Armistead, a seasoned cybersecurity expert with over 40 years of experience, including more than 20 years as a vendor in the cybersecurity space. The conversation opens with a discussion about the challenges security leaders face in 2026. Mike highlights the complexity of their role, comparing it to that of a CFO managing financial risk, but notes that cybersecurity leaders often lack the comprehensive management tools that CFOs have. He emphasizes the fragmented nature of cybersecurity tools and the difficulty in stitching together disparate signals to form a coherent security posture. Mike further explains that the human element is the critical glue in cybersecurity programs. The effectiveness of security teams depends heavily on the leadership and the ability of individuals to contextualize technical signals within the business environment. This need for situational awareness is driving interest in AI technologies, particularly on the defender side, to augment human capabilities and expand the scope and depth of security operations. The chat then shifts to the role of AI in cybersecurity products. Mike observes that while AI is increasingly integrated into detection tools, the industry has largely shifted focus away from prevention. He advocates for a strategic return to prevention, where AI can play a significant role in helping security leaders develop and implement risk mitigation strategies tailored to their organizations. Mike stresses the importance of a holistic approach that goes beyond real-time detection to include employee training, access control, and disaster recovery. Addressing the challenges faced by middle-market organizations, Mike points out that these companies are often expected to meet the same cybersecurity standards as large enterprises but with far fewer resources. He advises middle-market CISOs to prioritize protecting their most critical assets—their “crown jewels”—and to have candid conversations with leadership about realistic security goals. This pragmatic approach helps ensure that limited resources are focused on the highest risks rather than attempting to cover every possible threat. Finally, Mike shares information about a community he helped start called the Security Impact Circle, which focuses on cybersecurity leadership issues such as board engagement. This community facilitates workshops that bring together CSOs and board directors to bridge the communication gap and align security priorities with business needs. Mike encourages listeners to visit securityimpactcircle.org to learn more and get involved. Five Key Points Covered Cybersecurity leaders face complex challenges similar to CFOs but lack equivalent management tools. Human expertise is essential to contextualize technical security signals within the business environment. AI is increasingly used in detection but should also be leveraged to enhance prevention strategies. Middle-market organizations must prioritize protecting their most critical assets due to limited resources. The Security Impact Circle community helps improve communication and alignment between security leaders and boards. Five Key Quotes from the Conversation “Security leaders have a tough job… it's not unlike what a CFO has to think about, right? That risk happens to be financial, and the CISOs really happens to be in cyber.” “The security teams are really bound by how good not only their leader, but the deputies, the managers, the architects, those individual contributors that really help lead it.” “I think the opportunity is to swing it back to prevention… AI can really start to help on the prevention strategy side of cybersecurity.” “Middle-market leaders are expected to do everything that the largest enterprises do, but they don't have the resources to cover all the ground.” “We bring in a director from a public company's audit committee to run workshops… it's less about what a CSO thinks they should say and more about what the director thinks they need to hear.” About Our Guest Mike Armistead brings nearly 40 years of business experience marked by a proven track record of building companies, navigating strategic acquisitions, and leading growth at every stage. As co-founder and CEO of Respond Software, acquired by Mandiant for $200 million, and co-founder of Fortify Software, acquired by HP for $285 million, Mike has played pivotal roles in multiple successful startups, including serving as SVP on the turnaround team at WhoWhere (acquired by Lycos for $133 million) and contributing to Pure Software's IPO. His post-acquisition leadership includes key roles as VP of Products & UX at Mandiant, Director at Google Cloud, and VP & GM for Fortify and ArcSight business groups at HPE, where he drove significant expansion and over $400 million in revenue impact. Alongside these successes, Mike gained valuable insights from two brief ventures, including leading InLeague through post-9/11 financial challenges and emphasizing product-market fit in another startup. Beginning his career as a Product Manager at HP in the late 1980s, Mike's multifaceted experience spans diverse industries and company sizes. Today, he remains passionate about building high-performing teams and tackling complex, noble challenges. Follow Our Guest LinkedIn About Our Host National co-chair of the Cyber Center for Excellence, Marc Schein, CIC,CLCS is also a Risk Management Consultant at Marsh McLennan Agency. He assists clients by customizing comprehensive commercial insurance programs that minimize the burden of financial loss through cost effective transfer of risk. By conducting a Total Cost of Risk (TCoR) assessment, he can determine any gaps in coverage. As part of an effective risk management insurance team, Marc collaborates with senior risk consultants, certified insurance counselors, and expert underwriters to examine the adequacy of existing client programs and develop customized solutions to transfer risk, improve coverage and minimize premiums. Follow Our Host Website | LinkedIn  

Today, I'm joined by Zack Isaacs, founder & CEO of Movemint.   Movemint is an athletic events platform connecting participants, organizers, and brands through registrations, training integrations, and sponsorship marketplace.   In this episode, we discuss modernizing the event registration experience.   We also cover:   Events driving high-intent spending cycles Growing through Strava and Meta integrations Providing race sponsors with data and analytics   Subscribe to the podcast → insider.fitt.co/podcast  Subscribe to our newsletter → insider.fitt.co/subscribe  Follow us on LinkedIn → linkedin.com/company/fittinsider    Movemint's Website: www.movemint.cc  For Brands: https://www.movemint.cc/brands  For Organizers: https://www.movemint.cc/why_movemint    -   The Fitt Insider Podcast is brought to you by EGYM. Visit EGYM.com to learn more about its smart fitness ecosystem for fitness and health facilities.   Fitt Talent: https://talent.fitt.co/  Consulting: https://consulting.fitt.co/  Investments: https://capital.fitt.co/    Chapters: (00:00) Introduction (01:08) Zack's background (02:10) Movemint's atomic unit (03:21) Legacy platform gaps and opportunities (04:30) High-intent spending cycles (05:12) Prioritizing organizers and brands (07:00) Movemint for Brands launch (08:05) Strava's community playbook (09:40) Small to large organizer evolution (11:15) NYC Marathon vs. tech-enabled events (12:01) Building community on other platforms first (13:20) Strava and Meta integrations (14:10) Training data driving event signups (15:10) Run club boom and COVID tailwinds (16:10) Design and UX differentiation (18:05) Olia Birulia: Strava Routes designer (19:25) Speed vs. quality (21:15) Hiring from network (23:25) Endurance athletes as employees (24:47) Movement for Brands (26:00) Brand sponsorship data and analytics (28:10) Race photography and brand tracking (29:10) Sponsorship marketplace mechanics (30:05) Gravel and road running focus (31:11) High Rocks and triathlon growth (32:00) $3.2M raised across pre-seed and seed (33:15) Strava's co-founder on board (34:00) Building profitable and enduring business (34:36) Conclusion  

Feel like your UX job search is full time job? Here are 5 things you can start using today to save time, reduce overthinking, and make real progress in your UX or Product job search. These are tips that Sarah sees working for people inside her UX career coaching program, Career Strategy Lab, and some she uses in her own work every day.Topics discussed in this episode of the Career Strategy Podcast:How to create a plug-and-play script document so you never start from scratch on emails againHow text replacement tools like TextExpander can automate repetitive job search messagesWhy using a Pomodoro timer can dramatically increase your job search productivityWhy committing to following up three times eliminates the worry spiral of being ghostedHow to run a weekly "Job Search CEO Hour" to track what's working and course correct fastRESOURCESText Expander https://textexpander.com/Pomodoro Timer https://pomofocus.io/Flow Timer https://www.flow.app/

We would love to hear your feedback!Ep 292 News LinksTwo drivers swap snow-soaked stories that expose how stacking, distance, and clunky app UX turn hot food cold and good intent into fixes on the fly. We break down Uber Eats' no-edit tip change, Walmart Spark's settlement confusion, robots blocking ambulances, and Uber's parking play with SpotHero.• triple stack delays and food temperature trade-offs• message vs notes on DoorDash and misdrop recovery• Uber Eats removes in-app tip edits and support paths• split-tipping ideas and fairness for both sides• Walmart Spark $100m settlement timing and clarity• tickets, towing risk, and private parking enforcement• Uber acquiring SpotHero and in-app parking flow• Waymo blocking emergency vehicles and override needs• DoorDash bot speed, safety, and multi-order security• robot wranglers as new frontline jobs• weekend gig plans and app experimentsSeven-day free trial, three bucks a month. Go to patreon.com/thegigeconpodcastPlease fill out the survey for a chance for a 25.00 Gift Card!   The SafeWork Advantage PodcastMost workplaces react to violence—SafeWork Advantage shows employers how to prevent it.Listen on: Apple Podcasts SpotifySupport the showEverything Gig Economy Podcast Related: Download the audio podcast Newsletter Octopus is a mobile entertainment tablet for your riders. Earn 100.00 per month for having the tablet in your car! No cost for the driver! Want to earn more and stay safe? Download Maxymo Love the show? You now have the opportunity to support the show with some great rewards by becoming a Patron. Tier #2 we offer free merch, an Extra in-depth podcast per month, and an NSFW pre-show https://www.patreon.com/thegigeconpodcast The Gig Economy Podcast Group. Download Telegram 1st, then click on the link to join. TikTok Subscribe on Youtube

The Mobile Game That Makes Money… But nobody understands why. Well, we kinda do. In this episode of Two and a Half Gamers, we dive into one of the strangest successful mobile games they've seen recently: Cell Survivor.The game looks confusing, the onboarding is painful, and the design feels chaotic. Yet somehow it's making serious money, especially in Asian markets.So the team breaks down what's actually happening under the hood.They discuss the game's unusual progression systems, monetization design, Asian-first market strategy, and how games like this manage to scale despite confusing UX and questionable gameplay clarity.Sometimes mobile gaming success is not about polish.It's about understanding the right audience and scaling the right mechanics.Get our MERCH NOW: 25gamers.com/shop--------------------------------------PVX Partners offers non-dilutive funding for game developers.Go to: https://pvxpartners.com/They can help you access the most effective form of growth capital once you have the metrics to back it.- Scale fast- Keep your shares- Drawdown only as needed- Have PvX take downside risk alongside you+ Work with a team entirely made up of ex-gaming operators and investors---------------------------------------For an ever-growing number of game developers, this means that now is the perfect time to invest in monetizing direct-to-consumer at scale.Our sponsor FastSpring:Has delivered D2C at scale for over 20 yearsThey power top mobile publishers around the worldLaunch a new webstore, replace an existing D2C vendor, or add a redundant D2C vendor at fastspring.gg.---------------------------------------This is no BS gaming podcast 2.5 gamers session. Sharing actionable insights, dropping knowledge from our day-to-day User Acquisition, Game Design, and Ad monetization jobs. We are definitely not discussing the latest industry news, but having so much fun! Let's not forget this is a 4 a.m. conference discussion vibe, so let's not take it too seriously.Panelists: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jakub Remia⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠r,⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Felix Braberg, Matej Lancaric⁠Podcast: Join our slack channel here: https://join.slack.com/t/two-and-half-gamers/shared_invite/zt-2um8eguhf-c~H9idcxM271mnPzdWbipgChapters00:00 Chinese Humor & First Impressions01:13 Intro – What Is Cell Survivor?02:00 Why This Game Is Making Money04:10 First Gameplay Reactions06:40 Painful Onboarding Experience09:20 The Idle Reward System12:10 Asian Market Design Philosophy15:20 Why UX Doesn't Matter Here18:30 Monetization Structure21:40 Why This Works In Asia24:30 Western Audience vs Asian Players27:00 UA Strategy Discussion30:10 Dragon-Style Creative Strategy33:10 Rush Royale Comparison35:40 Final Thoughts---------------------------------------Matej LancaricUser Acquisition & Creatives Consultant⁠https://lancaric.meFelix BrabergAd monetization consultant⁠https://www.felixbraberg.comJakub RemiarGame design consultant⁠https://www.linkedin.com/in/jakubremiar---------------------------------------Please share the podcast with your industry friends, dogs & cats. Especially cats! They love it!Hit the Subscribe button on YouTube, Spotify, and Apple!Please share feedback and comments - matej@lancaric.me---------------------------------------If you are interested in getting UA tips every week on Monday, visit ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠lancaric.substack.com⁠⁠⁠⁠⁠⁠ & sign up for the Brutally Honest newsletter by Matej LancaricDo you have UA questions nobody can answer? Ask ⁠⁠⁠⁠⁠⁠⁠⁠Matej AI⁠⁠⁠⁠⁠⁠ - the First UA AI in the gaming industry! https://lancaric.me/matej-ai

Anne, an African-based UX researcher, talks about her transition from teaching into the tech scene, reflecting on how her focus on the well-being of individuals led her to UX research. She explores the silent harms of technology and how products are often designed to be addictive. She also offers UX researchers a valuable perspective on balancing business strategy with the responsibility of protecting users' mental health.

In this CTO Series episode, Daniel Harcek shares how leading engineering teams across radically different scales — from a 7-person fintech startup to a 2,000-person cybersecurity company — taught him that leadership isn't one-size-fits-all. We explore how he builds AI-first organizations, drives agile transformations, and why he believes every person in a company should think like a tech person. What Works at 10 People Breaks at 100 "Leadership is contextual, not absolute. What works with 10 people breaks at 50, at 100." Daniel's career spans from building a 30-person team for a German startup out of Žilina, Slovakia, to leading 70 engineers at Avast's mobile division within a 2,000-person organization, and now running a 7-person team at WageNow. Each scale demanded a fundamentally different approach. At smaller scales, you strip away operational overhead and push ownership directly to the people. At larger scales, you need guardrails, dedicated roles, and structured processes that the smaller team would find suffocating. The lesson: don't carry your playbook from one context to another — rebuild it for the reality you're in. End-to-End Ownership Replaces Specialized Roles "Each engineer owns quality for the task he delivers. And he owns the fact that it comes to production." At WageNow, Daniel runs without dedicated QA people — in a fintech company where quality can't be compromised. Instead, each developer owns quality end-to-end, from code to production. This isn't recklessness; it's intentional design. When teams are small, you set up the system so that it's safe to break things, then trust people with hard tasks. The result: people grow faster, move faster, and care more about what they ship. In larger organizations, you might need specialized DevOps, QA, and platform roles — but the principle of ownership stays the same. The Buddy System and Scaling Without Losing Alignment "The buddy system is one of the easiest things you can do. One buddy for a newcomer for the first 1, 3, 6 months — they often become friends." When scaling fast, Daniel focuses on three things: strong on-boarding guides, well-maintained documentation (now much easier with AI), and a buddy system that pairs every newcomer with a dedicated colleague. The buddy system works because it scales the human side of on-boarding — a tech lead or manager can do one-on-ones, but that's formal, and new people might be scared to speak up. The buddy creates a safe channel for questions, concerns, and cultural integration. Beyond people, scaling also means investing in automation and observability so that as you grow with customers, you grow with failures too — and your incident reporting doesn't burn out the team. Building an AI-First Organization "Every person uses AI. Every person has the capability to use AI. The company builds a second brain so AI can build on top of that." At WageNow, Daniel has implemented what he calls an AI-first organization, inspired by Spotify and other companies pioneering this approach. The concept is simple: before doing any task, ask whether AI can help you deliver the output faster or better. This applies across the entire company — not just engineering. Daniel looks for people in HR, accounting, and UX who understand automation tools like n8n or Make.com alongside AI. The key ingredients: Curate the data: Build a company "second brain" with clean, structured context for AI tools to work with Train the muscle: AI ability is like a muscle — people must use it daily because these skills didn't exist 2-3 years ago Share what works: Exponential AI adoption happened at WageNow once people started sharing their successes and failures with AI tools Respect the guardrails: Data privacy and regulation compliance remain non-negotiable The hidden productivity gains, Daniel argues, lie not in engineering (which gets all the attention) but in operations, accounting, HR, and every other area of the business. Selling Transformation: Financial Arguments for Leaders, Ownership for Teams "For the leaders, it's the financial thing and the cultural thing. For the people doing the work, it's personal development — having more control, having more ownership." At Ringier Axel Springer, Daniel proposed and led a company-wide agile transformation — a 1-2 year effort that required convincing the CEO, product teams, marketing, and sales to change how they operate. His approach: build a dual argument. For leadership, frame the change in financial and cultural terms — more revenue with the same people, better visibility into how work translates to business outcomes. For the people doing the work, emphasize personal growth, increased ownership, and transparency. The transformation breaks silos between engineering and product, creating a shared backlog agreed with all stakeholders. Daniel looks for people with high agency — those who can reinvent and change themselves from the inside, not just wait for a change agent from the outside. Balancing Experimentation with Operational Excellence "The SRE books helped me understand quality as a feature — because quality is basically how reliable you are for your customers." When asked about the books that most influenced his approach as a CTO, Daniel points to the Site Reliability Engineering series from Google — three books that frame quality as reliability, a feature your customers experience directly. Alongside those, he recommends The Lean Startup by Eric Ries, because he believes all tech people should have a sense of business and customer understanding. Together, these books guide how to balance rapid experimentation with operational excellence as the organization scales. About Daniel Harcek Daniel is a technology executive with a proven record scaling engineering organizations across fintech, cybersecurity, and digital media. Builds AI-first teams, operating models, and delivery cultures aligned with product strategy. Led platforms serving 30M MAU, deployed fintech capital pilots, transformed agile delivery at internet scale, and mentors global tech communities and ecosystems worldwide actively. You can link with Daniel Harcek on LinkedIn.

Daniel Echeverri es un diseñador e investigador colombiano que vive en la República Checa. El es profesor en Masaryk University. En esta charla nos cuenta sobre su investigación donde el objeto tangible es soporte de narrativas. Específicamente él trabaja con títeres, marionetas. Estas marionetas sirven para generar teoría y conocimiento, también para informar diseños de museos. Hablamos de eventos históricos de Colombia entrelazados con la historia personal de su familia. Sobre materialidades y juguetes. Esta entrevista es parte de las listas: Juegos y diseño, Arte y diseño social, Colombia y diseño, Diseño UX, Investigación y diseño y Museos y diseño. Daniel nos recomienda: Assassins Creed- es una clase de videojuegos. Red Dead Redemption 2 video juegos de acción/aventura con tematicas históricasCatán y Marco Polo, juegos de mesa. ⁠Gente Local:⁠ un podcast

Joseph Chalom and Danny Ryan discuss the institutional outlook on Ethereum and why it is “the only game in town.” Bits + Bips is spreading its wings Starting soon, new episodes will only be published on our brand‑new feeds. What you need to do: Click the links below. YouTube Apple Spotify X Smash Follow or Subscribe.

It's EV News Briefly for Thursday 05 March 2026, everything you need to know in less than 5 minutes if you haven't got time for the full show.Patreon supporters fund this show, get the episodes ad free, as soon as they're ready and are part of the EV News Daily Community. You can be like them by clicking here: https://www.patreon.com/EVNewsDailyMIDDLE EAST CONFLICT LIFTS UK FUEL AND ENERGY COSTSBrent crude surged past $84 per barrel and UK gas prices spiked to a three-year high of £1.44 per therm after Qatar halted LNG exports following Iran's threat to attack tankers in the Strait of Hormuz, with the RAC warning UK forecourt prices will feel the full impact within a week. Home EV charging costs are shielded for now by the energy price cap — fixed at 24.67p per kWh for electricity until end of June — but wholesale price rises could push the cap higher from July, making both home wallbox and public charging more expensive.​EUROPEAN FLEETS COULD SAVE €246BN BY 2030A new EY and Eurelectric report finds that fully electrifying Europe's corporate fleets could deliver up to €246 billion in cumulative savings and cut one billion tonnes of CO2 by 2030. However, the authors warn that cheaper running costs alone will not drive mass uptake, calling for coordinated action from manufacturers, policymakers, grid operators and finance providers to tackle high upfront costs, uncertain residual values, and charging infrastructure delays.CUPRA BORN FACELIFT BRINGS SHARP NOSE, SMALL TWEAKSCupra has facelifted the Born with a "shark nose" front end, triangular matrix LED headlights, a continuous rear light strip, and new 235 mm tyres across all five wheel options, while the aerodynamically improved 79 kWh variants now claim around 600 km (373 miles) of WLTP range. A new entry "Born Plus" trim pairs a 58 kWh battery with a 140 kW motor — figures that match Ford's Capri LFP option and strongly suggest a switch to LFP cells from the updated MEB+ platform — though Cupra has not confirmed drivetrain details and appears to be saving that announcement for a related reveal, likely the VW ID.3 facelift later in 2026.FORD EV SALES SINK 71% AFTER LIGHTNING EXITFord's US EV sales collapsed 71% in February 2026 to just 2,122 units, the steepest monthly drop in its EV history, driven by the discontinuation of the F-150 Lightning and the expiry of the federal EV tax credit. Ford's Model e division lost $4.8 billion in 2025 and is forecast to lose another $4–5 billion in 2026, with profitability not expected until 2029; the company has already booked a $19.5 billion writedown and is pivoting to a new ~$30,000 midsize electric pickup it hopes will revive the business by 2027.LUCID PATCHES GRAVITY SOFTWARE AGAINLucid Motors has pushed software update 3.4.4 to the Gravity SUV, targeting AC charging improvements and Drive Assist availability, following a January update that resolved around 95% of earlier software issues — with the car averaging a new update every 24 days since launch. Lucid has closed its online configurator for both the Air and Gravity while it prepares its 2027 model year announcement, and Air owners face a $950 hardware upgrade bill to access the newer UX 3.0 platform already running in the Gravity, due to arrive by autumn 2026.MITSUBISHI READIES LEAF-BASED EV FOR CANADAMitsubishi is preparing its first all-new model since the Eclipse Cross for Canadian dealerships in 2026, built on Nissan's CMF-EV platform and LEAF architecture, with spy shots showing a heavily camouflaged prototype that shares the LEAF's roofline, proportions, and rear hatch panel. Both models will be built side by side at Nissan's Kaminokawa plant in Japan, and Mitsubishi may receive the smaller battery pack to undercut the LEAF on entry price — a strategy that would see Nissan supply the foundations while a cheaper sibling competes for the same buyers.ALPITRONIC UNVEILS HYC400 SERIES 2 CHARGERAlpitronic has launched the HYC400 Series 2, retaining the 400 kW maximum output of its predecessor while upgrading to a 22-inch touchscreen (up from 15.6 inches), second-generation silicon carbide power stacks, and a higher continuous output current of 600 A (up from 500 A). The unit maintains 97.5% charging efficiency but standby power consumption rises significantly from 43 W to under 100 W, and cable options narrow to a single 5-metre length; Alpitronic will sell both generations simultaneously to suit different site requirements.​APTERA SHOWS FIRST VALIDATION-LINE VEHICLE PHOTOAptera Motors has published the first photo of a vehicle off its validation assembly line, marking a milestone for its three-wheeled, solar-assisted EV that claims 400 miles of range from a 44 kWh battery and up to 40 miles of daily solar charging, classified as a motorcycle to bypass certain safety regulations. The launch edition price has risen to $40,000 — a $9,300 increase from prior estimates — though a $28,000 model is planned for the future, and with nearly 50,000 pre-orders and a stated daily capacity of 80–100 vehicles, Aptera claims it could fulfil all orders within 500 days of full production, though the end-of-year delivery timeline remains uncertain.​GEELY TARGETS DEFENDER WITH GALAXY BATTLESHIPGeely plans to launch the Galaxy Battleship in the UK in 2028, a blocky hybrid 4x4 aimed squarely at the Land Rover Defender and Toyota Land Cruiser, with a production design expected to stay 90–95% true to the Galaxy Cruiser concept shown at the 2025 Shanghai Motor Show. Built on the GEA Evo platform with steer- and brake-by-wire, it may use an AI-driven plug-in hybrid system with a stated output of around 858 bhp, and Geely is promising an interior that surpasses the Defender's for luxury — a bold claim for the Chinese brand's first foray into the 4x4 segment.​EU UNVEILS LOCAL-CONTENT RULES FOR CLEAN TECHThe European Commission has unveiled the Industrial Accelerator Act (IAA), tying over €2 trillion in public procurement and subsidies to low-carbon and "Made-in-EU" conditions across sectors including EVs, steel, cement, and wind turbines, with the goal of raising manufacturing's share of EU economic output from 14% to 20% by 2035. China is excluded from the initial trusted-partner list — which includes the UK, Canada, and the US — and foreign investments above €100 million from countries controlling 40%+ of global production would face strict conditions including capped 49% foreign ownership and mandatory technology transfer; BMW and Mercedes oppose the Act over fears of higher costs, while Renault backs it and the text must still clear the European Parliament before becoming law.​

本期嘉宾：彭林、十天、森森、蓝白、恺伦本期节目的主要内容有：· 关于荣耀 Magic V6 屏幕我们还有什么没说的· 关于显示器我们还有什么没说的· 苹果发布众多新品· 荣耀机器人手机亮相· 小米 Tag 海外正式发布，重仅 10 克 120 元起· 349 英镑起，Nothing Phone (4a) 系列正式发布· 海信发布 UX 2026 款 RGB-Mini LED 旗舰电视· GPT-5.4 来了：能操控电脑、写代码、做表格还有众多观众朋友的热心提问~每周五晚 8 点，爱否直播间，我们一起开心聊天

פרק מספר 512 (חזקה תשיעית!) של רברס עם פלטפורמה - קרבורטור מספר 40, שהוקלט ב-24 בפברואר 2026. נכון למועד ההקלטה עדיין אין מלחמה [לא התיישן טוב…], ואורי ורן מארחים את הנביא האורח נתי שלום לשיחה, דיונים, וויכוחים ותחזיות (דיסטופיות ברובן) על עולם שבו ה-AI כבר לא רק כותב קוד, אלא מחליף את המציאות כפי שהכרנו אותה. [01:58] "משהו גדול קורה": הניתוח של Matt Shumerבלוג-פוסט של המפתח Matt Shumer, שנקרא Something Big is Happening התפרסם בלא מעט מקומות והיכה גלים.מעבר מסקפטיות מוחלטת ("זה בחיים לא יעבוד") למצב שבו המודל עושה את כל עבודת הקידוד שלו.נתי - מה שמעניין פה זה הניתוח של שוק העבודה, ואיך נראה שוק ה-Hiring כפי שהוא היום.הדיבורים על "הכתובת על הקיר" זה כבר פאסה – "הכתובת היא כבר בכיס כמעט". הנתונים מראים ירידה משמעותית ב-Hiring שהתחילה כבר משנת 2025 ונמשכת לתוך 2026.“זה קורה עכשיו - ועכשיו אתה צריך לבחור באיזה צד אתה נמצא: הצד המרוויח או הצד הנפגע”.רן מדגיש שזה לא רק למפתחים – גם עורכי דין ורואי חשבון ובכל שאר המקצועות צריכים להחליט באיזה צד הם. יש כאן (לפחות) שני אספקטים עיקריים - איך אנחנו רואים את שוק התוכנה, ואז זה משפיע על כל שאר שוק העבודה.אורי - אנחנו רואים את ההשפעה מבפנים, בתוך שוק התוכנה. האם ישנן תעשיות שלא מושפעות עדיין, או לפחות לא מרגישות את זה?למשל יוצאי יחידות טכנולוגיות שמאוד מבוקשים בשוק, אבל ארגונים בטחוניים לא יכולים להכניס הרבה מהטכנולוגיות Cutting-edge הללו, לפחות לא בקצב שהן יוצאות.מועמדים כאלה אולי פתאום לא מתאימים בדיוק לעולם שרץ “בחוץ”.נתי משתף סיפור אישי/מקצועי על שיר אלגום, שנדחתה ממשרה ב-HR כי לא הכירה מספיק AI, ובתגובה הפכה למומחית שמרצה ב-Amazon.שינוי גישה: "העולם השתנה, הבנתי, אני עכשיו באירוע".אורי ונתי מחפשים השוואות למהפכות קודמות, ולא בטוחים אם יש כאלו בדיוק - מעבר משימוש ב-Intellect האנושי כדי לייצר יתרון - למצב בו "ה-Intellect עובר קומודיטיזציה".אין יותר Job security בהייטק המסורתי, וחזרה לכיוון של מקצועות יותר “מסורתיים”, פיזיים.[10:17] עידן ה-Agents וה-+Resumeנתי - קונספט של “Professional Agents”: מומחים כבר לא מוכרים את עצמם כעובדים, אלא כסוכנים, או ככאלה שמתמחים ביצירת סוכנים.סוכן הוא כמו ילד – צריך לגדל אותו ולשכלל אותו, דורש הרבה Nurturing.רן - ספציפית: מדברים על מעצבים, רואי-חשבון - מקצועות ספציפיים, שהם אולי לא חלק מהליבה של החברה, אבל נמצאים בכל חברה.נתי - דוגמא של Marketing: אם מישהו כבר הכין את רוב ה-Workflows מראש, זה משהו שאני מוכן לשלם עליו.אורי מציין שגם בגידול של ילד באיזשהו שלב עוברים ל-Outsourcing יותר ויותר . . . חברות עוברות לתת שירות של סוכן יחד עם “גידול סוכנים” ושכלול שלהם: סוכן + משהו שמתחזק אותו ומתאים אותו לצרכים שלך.הבשורה טובה: יש לאן להתפתח - בכל פעם שחסמי-כניסה יורדים, נפתחים תחומים חדשיםאורי ונתי קצת חלוקים על הנקודה, אבל זה דומה למה שהיה בתחילת ימי ה-SaaS, שאולי לא היה קיים אם לא היה Cloud, לפחות לא בקצב וב-Scale, שקודם לכן היה שמור לארגונים מאוד גדולים ולא לסטארטאפים.דוגמא דומה היא Big-Data.נתי אומר שהורדת חסמי-הכניסה תכניס הרבה גורמים חדשים לתחום, לאו דווקא רק מכיוון של מדעי-המחשב.אורי - השוני במהפכה הזו הוא שיש מצב שבו סוכן יכול לייצר סוכן יותר טוב . . . נתי מפריד בין מוצרים “גנריים” - יש את המודלים של Anthropic ו-OpenAI ומשפחות המוצרים הנגזרות וכו' - ובין ה”OpenClaw למיניהם”, שהם גרסא פשוטה יותר וזולה יותר, יחד עם קוד-פתוח ומוצרים בסגנון הזה.רן משווה את המאבק בין מודלים גנריים (כמו Anthropic) למודלים פתוחים (כמו OpenClaw) ל-"האנדרואיד לעומת האייפון".נתי מדבר על ראיון העבודה העתידי: “עובדים יבואו עם ה-10X של עצמם”: מועמדים לא יבואו עם קורות חיים, אלא עם רזומה פלוס – צוות סוכנים שבנו ושיודעים לשכלל להם את העבודה.בשנה-שנתיים-שלוש הקרובות, אלו שיעשו את הקפיצה ויבנו את הסוכנים וידעו להגיע עם זה לראיון עבודה - זו יכולה להיות הזדמנות לגדול ולהתבסס.אבל - אנחנו לא יודעים כמה ומי הולך להיפגע: “יהיה פה מצב של ירידה לטובת עלייה”.[17:03] “אז מה יכול לקרות?”: הסינגולריות והמתכנת האחרוןרן מעלה את השאלה המפחידה: האם כל הניסיון שצברנו כמפתחים הלך לפח? השנים הקרובות כנראה הולכות להיות מבלבלות, אבל ננסה להסתכל מעבר לזה.האם לא יהיו יותר מתכנתים, כי לא צריך - או שיהיו הרבה יותר מתכנתים והרבה יותר תוכנה, אבל מקצוע התכנות יראה אחרת?נתי חוזה ירידה למען עלייה - אבל בשונה מהמעבר ל-Cloud-Native למשל, שלקח בערך 10 שנים (ולא נגמר…), כאן הקצב הרבה יותר מהיר (התעשייה השתנתה בתוך שנה).זוכרים את “כולם משתמשים ב-AI, אבל לא רואים את ה-ROI”? זה היה בתחילת 2025 . . . מאז הסטטיסטיקות התחילו להשתנות.רן - “אם לפני שנה הייתי נותן ל-Agent משימות קידוד קטנות, ולפעמים זה מצליח ולפעעמים זה לא - היום זה עולם אחר לגמרי”.אז יכנסו יותר מעגלי-אוכלוסיה לתחום - אבל הצד השלילי הוא הירידה שלפני: כמות האנשים שדרושים למשימות שיש היום, עד שיווצר ה-Demand החדש, תגרום להרבה אנשים למצוא את עצמם “מחוץ למעגל”.מדינות תצטרכנה איכשהו לספוג את הירידה הזו - מימון הכשרות, תקופות הסתגלות וכו' - אחרת זו בדיוק הסביבה למהפכות והתדרדרות למקומות יותר בעייתיים.ולא שהסדר העולמי מסביב שליו ורגוע גם ככה [נתכתב מהממ”ד במהלך מלחמה באירן…].אורי - כבר רואים התחלה של “כלכלת סיליקון”, ומדינות nתחילות לחשוב על מאגרי הChip-ים שלהן . . . נתי מזכיר פרק של All-In, שמדבר על תחזיות מאוד אופטימיות, ועל פניו קצת מנותקות - “המון הזדמנויות והכל יהיה בסדר”, בזמן שמי שבתחום יודע שזה לא ממש ככה.נראה שב-Silicon Valley יש בעיקר התעלמות - חוגגים בתוך מעגל מאוד מצומצם.נתי מציע לחשוב על זה כמו על קורונה [במובן החיובי…] - נצטרך התערבות חיצונית כדי לעבור את הגל הזה.רן תוהה האם - בדומה לקורונה - גם התקופה הזו גם תיהיה קטליזטור לתאוריות קונספירציה שעוד תבואנה . . . אורי - מצד שני, גם תרבות הפנאי התפתחה מאוד בתקופת הקורונה, אולי שוב מישהו אחר עושה את העבודה ואז יש יותר פנאי?רן - כבר היום, כשאני מפתח, אני מספיק הרבה יותר, בהרבה פחות זמן. אז אנחנו מייצרים הרבה יותר תוכנה . . .אורי - אבל אז ה-bottlenecks עוברים למקומות אחרים.רן - OpenAI הזכירו, לגבי הפיתוח של Codex 5.3 – שהמודל פותח בעזרת גרסאות קודמות של עצמו."זה בערך By definition הסינגולריות" . . .“אל תצפו שהסינגולריות תקרה ביום אחד בודד” . . . “מי שהיה במהפכה התעשייתית לא יודע שהוא במהפכה התעשייתית".[27:57] חמשת ה-Moats של 2026נתי - האם נכון לבנות סטארטאפ באי ודאות כזו? מה הסיכוי של סטארטאפ כזה לשרוד?נאמר על רקע שבוע מאוד לא מוצלח למניות חברות ה-SaaS . . . .יש הרבה תגובות-יתר - אבל קורים הרבה דברים באמת מדהימים.נתי מציע 5 נקודות קריטיות ליזמים (סוג של Checklist) שרוצים לשרוד בעולם שבו כל דבר גנרי נמחק (כמו IBM שצנחה כי Anthropic פרסמו בלוג-פוסט על Cobol . . . ):ורטיקליזציה (Verticalization): אל תהיו גנריים. Google ו-Anthropic ו-OpenAI שולטים ביד רמה.תהיו הכי טובים במשהו ספציפי - עריכת דין או חינוך וכו'.שליטה במידע (Proprietary Data): דאטה שה-LLM הגדולים והמודלים הגנריים לא ראו, כמו מגמות ספציפיות בתוך נתוני לקוחות.יעילות (Efficiency): שימוש ב-SLM (Small Language Models) למשל, כדי לחסוך ב-Token-ים וב-Latency (קריטי ברובוטיקה וב-Security, למשל).רן - מודל גדול יקבל את ההחלטה הנכונה, אבל אולי מאוחר מדי.חווית משתתמש (UX ייחודי): חווית משתמש שפותרת בעיה נקודתית ונותנת ערך מהיר (Time to Value).ה-Chat של המודלים הגדולים מאוד גנרי.סטארטאפים צריכים להתמקד ביכולת לייצר חוויית משתמש מאוד מותאמת לחווייה נקודתית.רן - האם בכלל עוד יהיה UI (או שהצרכנים הם גם Agents . . . .)? בהקשר של פיקסלים . . . .נתי, אורי - בסוף , אתה רוצה לייצר ערך לאדם.בסוף זה עניין של Time to Value: אני אולי יכול לייצר את זה לבד, השאלה האם לא יותר מהיר ויעיל להשתמש במשהו שמישהו אחר כבר ייצר.ואחרון (אם כי נתי אמר ש "החמישי הוא לא לשידור…”) - Disruption: ה-Disruption האמיתי הוא לעשות קניבליזציה לקטגוריות ישנות.אפשר לעשות את אותם הדברים שעשינו בעבר, אבל בצורה אחרת לגמרי.הרבה דברים קודמים נעשו בגלל מגבלות של עולם שהוא Pre-Agentic, ועכשיו לא רלוונטיות - מה שמאפשר מודל עסקי אחר לחלוטין.ואז ה-Price-point יכול להיות מאוד שונה מכזה שהוכתב ע”י תעשיות מאוד גדולות ומבנה עלויות מאוד יקר לתפעול.אורי מתזכר את ה-Moats של Warren Buffet, ונתי מספר שהוא לא חושב שפגש חברה אחת שבאמת עושה את כל הדברים הללו, יזמים עדיין לא חושבים ככה.במיוחד בארץ, עדיין מתייחסים מאוד לבידול הטכנולוגי ופחות למובן של UX או מודל עסקי.[39:26] הזרקת DNA ומהלכי ה-M&A החדשיםנתי אומר שמשקיעים בהרבה מקרים לא יודעים לנתח הזדמנויות ולעשות Evaluation שלא על סמך טרנד צמיחה של ARR.אורי - עולם ההשקעות לא הולך לכיוון של SaaS, כי מצד אחד יש המון Disruption risk ומצד שני נראה שהצורך במגמת ירידה.נתי - יש כמה סוגי-Exists שונים שמשקיעים מחפשים, מעבר למודל הקלאסי של “תבנה חברה, תגדל איתה, תייצר מספיק כסף . . . .”.קנייה של טכנולוגיות ואנשים - חברות צריכות “להזריק לעצמן DNA חדש”, ואז מסתכלים על הסטראטאפ לא רק כטכנולוגיה אלא גם כמנוע לטרנספורציה.חברות במצוקה מנסות למצוא אנשים שיעזרו להן לעשות את הטרנספורמציה, לפחות בחלון הזמן הנוכחי (3 שנים בערך).נתי מזכיר דוגמא שעלתה בעבר - Google: לפני שנה כולם הספידו אותם, ואז הם קנו את Character.AI, ובעצם את נועם שזיר (Noam Shazeer) ב-2 ביליון דולר, כי הם הבינו שהם במצוקה.נתי טוען שלחברות במצוקה יהיה מאוד קשה לעשות כזה שינוי רק על ידי צמיחה אורגנית.אורי מדבר על חברות שעושות קניבליזציה-מוצרית לעצמן - מתחרים במוצר המסורתי הקודם שלהן.נתי טוען שבמקרה של Google זה השתלם להם עם Search Generative Experience (SGE).[46:00] סיכום וסגירהרן ממליץ לכולם לקרוא את הבלוג-פוסט של Matt Shumer (או לבקש מ-Agent לתקצר אותו).נתי חותם עם המלצה אופטימית-מעשית: "למדו את עצמכם... תחשבו שאתם באים למקום העבודה הבא שלכם כבר לא אתם-עצמכם... זה רזומה + צוות עובדים שאתם מביאים איתכם, שזה הסוכנים".אורי כבר מכין את הקרקע לפרק הבא: מהפכת ה-Quantum Computing."שיעורי הבית שלכם יכולים להיות 0, 1 או שניהם ביחד" . . . [קישור לקובץ mp3] האזנה נעימה ותודה רבה לעופר פורר על התמלול!

All speakers are announced at AIE EU, schedule coming soon. Join us there or in Miami with the renowned organizers of React Miami! Singapore CFP also open!We've called this out a few times over in AINews, but the overwhelming consensus in the Valley is that “the IDE is Dead”. In November it was just a gut feeling, but now we actually have data: even at the canonical “VSCode Fork” company, people are officially using more agents than tab autocomplete (the first wave of AI coding):Cursor has launched cloud agents for a few months now, and this specific launch is around Computer Use, which has come a long way since we first talked with Anthropic about it in 2024, and which Jonas productized as Autotab:We also take the opportunity to do a live demo, talk about slash commands and subagents, and the future of continual learning and personalized coding models, something that Sam previously worked on at New Computer. (The fact that both of these folks are top tier CEOs of their own startups that have now joined the insane talent density gathering at Cursor should also not be overlooked).Full Episode on YouTube!please like and subscribe!Timestamps00:00 Agentic Code Experiments00:53 Why Cloud Agents Matter02:08 Testing First Pillar03:36 Video Reviews Second Pillar04:29 Remote Control Third Pillar06:17 Meta Demos and Bug Repro13:36 Slash Commands and MCPs18:19 From Tab to Team Workflow31:41 Minimal Web UI Philosophy32:40 Why No File Editor34:38 Full Stack Cursor Debate36:34 Model Choice and Auto Routing38:34 Parallel Agents and Best Of N41:41 Subagents and Context Management44:48 Grind Mode and Throughput Future01:00:24 Cloud Agent Onboarding and MemoryTranscriptEP 77 - CURSOR - Audio version[00:00:00]Agentic Code ExperimentsSamantha: This is another experiment that we ran last year and didn't decide to ship at that time, but may come back to LM Judge, but one that was also agentic and could write code. So it wasn't just picking but also taking the learnings from two models or and models that it was looking at and writing a new diff.And what we found was that there were strengths to using models from different model providers as the base level of this process. Basically you could get almost like a synergistic output that was better than having a very unified like bottom model tier.Jonas: We think that over the coming months, the big unlock is not going to be one person with a model getting more done, like the water flowing faster and we'll be making the pipe much wider and so paralyzing more, whether that's swarms of agents or parallel agents, both of those are things that contribute to getting much more done in the same amount of time.Why Cloud Agents Matterswyx: This week, one of the biggest launches that Cursor's ever done is cloud agents. I think you, you had [00:01:00] cloud agents before, but this was like, you give cursor a computer, right? Yeah. So it's just basically they bought auto tab and then they repackaged it. Is that what's going on, or,Jonas: that's a big part of it.Yeah. Cloud agents already ran in their own computers, but they were sort of site reading code. Yeah. And those computers were not, they were like blank VMs typically that were not set up for the Devrel X for whatever repo the agents working on. One of the things that we talk about is if you put yourself in the model shoes and you were seeing tokens stream by and all you could do was cite read code and spit out tokens and hope that you had done the right thing,swyx: no chanceJonas: I'd be so bad.Like you obviously you need to run the code. And so that I think also is probably not that contrarian of a take, but no one has done that yet. And so giving the model the tools to onboard itself and then use full computer use end-to-end pixels in coordinates out and have the cloud computer with different apps in it is the big unlock that we've seen internally in terms of use usage of this going from, oh, we use it for little copy changes [00:02:00] to no.We're really like driving new features with this kind of new type of entech workflow. Alright, let's see it. Cool.Live Demo TourJonas: So this is what it looks like in cursor.com/agents. So this is one I kicked off a while ago. So on the left hand side is the chat. Very classic sort of agentic thing. The big new thing here is that the agent will test its changes.So you can see here it worked for half an hour. That is because it not only took time to write the tokens of code, it also took time to test them end to end. So it started Devrel servers iterate when needed. And so that's one part of it is like model works for longer and doesn't come back with a, I tried some things pr, but a I tested at pr that's ready for your review.One of the other intuition pumps we use there is if a human gave you a PR asked you to review it and you hadn't, they hadn't tested it, you'd also be annoyed because you'd be like, only ask me for a review once it's actually ready. So that's what we've done withTesting Defaults and Controlsswyx: simple question I wanted to gather out front.Some prs are way smaller, [00:03:00] like just copy change. Does it always do the video or is it sometimes,Jonas: Sometimes.swyx: Okay. So what's the judgment?Jonas: The model does it? So we we do some default prompting with sort. What types of changes to test? There's a slash command that people can do called slash no test, where if you do that, the model will not test,swyx: but the default is test.Jonas: The default is to be calibrated. So we tell it don't test, very simple copy changes, but test like more complex things. And then users can also write their agents.md and specify like this type of, if you're editing this subpart of my mono repo, never tested ‘cause that won't work or whatever.Videos and Remote ControlJonas: So pillar one is the model actually testing Pillar two is the model coming back with a video of what it did.We have found that in this new world where agents can end-to-end, write much more code, reviewing the code is one of these new bottlenecks that crop up. And so reviewing a video is not a substitute for reviewing code, but it is an entry point that is much, much easier to start with than glancing at [00:04:00] some giant diff.And so typically you kick one off you, it's done you come back and the first thing that you would do is watch this video. So this is a, video of it. In this case I wanted a tool tip over this button. And so it went and showed me what that looks like in, in this video that I think here, it actually used a gallery.So sometimes it will build storybook type galleries where you can see like that component in action. And so that's pillar two is like these demo videos of what it built. And then pillar number three is I have full remote control access to this vm. So I can go heat in here. I can hover things, I can type, I have full control.And same thing for the terminal. I have full access. And so that is also really useful because sometimes the video is like all you need to see. And oftentimes by the way, the video's not perfect, the video will show you, is this worth either merging immediately or oftentimes is this worth iterating with to get it to that final stage where I am ready to merge in.So I can go through some other examples where the first video [00:05:00] wasn't perfect, but it gave me confidence that we were on the right track and two or three follow-ups later, it was good to go. And then I also have full access here where some things you just wanna play around with. You wanna get a feel for what is this and there's no substitute to a live preview.And the VNC kind of VM remote access gives you that.swyx: Amazing What, sorry? What is VN. AndJonas: just the remote desktop. Remote desktop. Yeah.swyx: Sam, any other details that you always wanna call out?Samantha: Yeah, for me the videos have been super helpful. I would say, especially in cases where a common problem for me with agents and cloud agents beforehand was almost like under specification in my requests where our plan mode and going really back and forth and getting detailed implementation spec is a way to reduce the risk of under specification, but then similar to how human communication breaks down over time, I feel like you have this risk where it's okay, when I pull down, go to the triple of pulling down and like running this branch locally, I'm gonna see that, like I said, this should be a toggle and you have a checkbox and like, why didn't you get that detail?And having the video up front just [00:06:00] has that makes that alignment like you're talking about a shared artifact with the agent. Very clear, which has been just super helpful for me.Jonas: I can quickly run through some other Yes. Examples.Meta Agents and More DemosJonas: So this is a very front end heavy one. So one question I wasswyx: gonna say, is this only for frontJonas: end?Exactly. One question you might have is this only for front end? So this is another example where the thing I wanted it to implement was a better error message for saving secrets. So the cloud agents support adding secrets, that's part of what it needs to access certain systems. Part of onboarding that is giving access.This is cloud is working onswyx: cloud agents. Yes.Jonas: So this is a fun thing isSamantha: it can get super meta. ItJonas: can get super meta, it can start its own cloud agents, it can talk to its own cloud agents. Sometimes it's hard to wrap your mind around that. We have disabled, it's cloud agents starting more cloud agents. So we currently disallow that.Someday you might. Someday we might. Someday we might. So this actually was mostly a backend change in terms of the error handling here, where if the [00:07:00] secret is far too large, it would oh, this is actually really cool. Wow. That's the Devrel tools. That's the Devrel tools. So if the secret is far too large, we.Allow secrets above a certain size. We have a size limit on them. And the error message there was really bad. It was just some generic failed to save message. So I was like, Hey, we wanted an error message. So first cool thing it did here, zero prompting on how to test this. Instead of typing out the, like a character 5,000 times to hit the limit, it opens Devrel tools, writes js, or to paste into the input 5,000 characters of the letter A and then hit save, closes the Devrel tools, hit save and gets this new gets the new error message.So that looks like the video actually cut off, but here you can see the, here you can see the screenshot of the of the error message. What, so that is like frontend backend end-to-end feature to, to get that,swyx: yeah.Jonas: Andswyx: And you just need a full vm, full computer run everything.Okay. Yeah.Jonas: Yeah. So we've had versions of this. This is one of the auto tab lessons where we started that in 2022. [00:08:00] No, in 2023. And at the time it was like browser use, DOM, like all these different things. And I think we ended up very sort of a GI pilled in the sense that just give the model pixels, give it a box, a brain in a box is what you want and you want to remove limitations around context and capabilities such that the bottleneck should be the intelligence.And given how smart models are today, that's a very far out bottleneck. And so giving it its full VM and having it be onboarded with Devrel X set up like a human would is just been for us internally a really big step change in capability.swyx: Yeah I would say, let's call it a year ago the models weren't even good enough to do any of this stuff.SoSamantha: even six months ago. Yeah.swyx: So yeah what people have told me is like round about Sonder four fire is when this started being good enough to just automate fully by pixel.Jonas: Yeah, I think it's always a question of when is good enough. I think we found in particular with Opus 4 5, 4, 6, and Codex five three, that those were additional step [00:09:00] changes in the autonomy grade capabilities of the model to just.Go off and figure out the details and come back when it's done.swyx: I wanna appreciate a couple details. One 10 Stack Router. I see it. Yeah. I'm a big fan. Do you know any, I have to name the 10 Stack.Jonas: No.swyx: This just a random lore. Some buddy Sue Tanner. My and then the other thing if you switch back to the video.Jonas: Yeah.swyx: I wanna shout out this thing. Probably Sam did it. I don't knowJonas: the chapters.swyx: What is this called? Yeah, this is called Chapters. Yeah. It's like a Vimeo thing. I don't know. But it's so nice the design details, like the, and obviously a company called Cursor has to have a beautiful cursorSamantha: and it isswyx: the cursor.Samantha: Cursor.swyx: You see it branded? It's the cursor. Cursor, yeah. Okay, cool. And then I was like, I complained to Evan. I was like, okay, but you guys branded everything but the wallpaper. And he was like, no, that's a cursor wallpaper. I was like, what?Samantha: Yeah. Rio picked the wallpaper, I think. Yeah. The video.That's probably Alexi and yeah, a few others on the team with the chapters on the video. Matthew Frederico. There's been a lot of teamwork on this. It's a huge effort.swyx: I just, I like design details.Samantha: Yeah.swyx: And and then when you download it adds like a little cursor. Kind of TikTok clip. [00:10:00] Yes. Yes.So it's to make it really obvious is from Cursor,Jonas: we did the TikTok branding at the end. This was actually in our launch video. Alexi demoed the cloud agent that built that feature. Which was funny because that was an instance where one of the things that's been a consequence of having these videos is we use best of event where you run head to head different models on the same prompt.We use that a lot more because one of the complications with doing that before was you'd run four models and they would come back with some giant diff, like 700 lines of code times four. It's what are you gonna do? You're gonna review all that's horrible. But if you come back with four 22nd videos, yeah, I'll watch four 22nd videos.And then even if none of them is perfect, you can figure out like, which one of those do you want to iterate with, to get it over the line. Yeah. And so that's really been really fun.Bug Repro WorkflowJonas: Here's another example. That's we found really cool, which is we've actually turned since into a slash command as well slash [00:11:00] repro, where for bugs in particular, the model of having full access to the to its own vm, it can first reproduce the bug, make a video of the bug reproducing, fix the bug, make a video of the bug being fixed, like doing the same pattern workflow with obviously the bug not reproducing.And that has been the single category that has gone from like these types of bugs, really hard to reproduce and pick two tons of time locally, even if you try a cloud agent on it. Are you confident it actually fixed it to when this happens? You'll merge it in 90 seconds or something like that.So this is an example where, let me see if this is the broken one or the, okay, this is the fixed one. Okay. So we had a bug on cursor.com/agents where if you would attach images where remove them. Then still submit your prompt. They would actually still get attached to the prompt. Okay. And so here you can see Cursor is using, its full desktop by the way.This is one of the cases where if you just do, browse [00:12:00] use type stuff, you'll have a bad time. ‘cause now it needs to upload files. Like it just uses its native file viewer to do that. And so you can see here it's uploading files. It's going to submit a prompt and then it will go and open up. So this is the meta, this is cursor agent, prompting cursor agent inside its own environment.And so you can see here bug, there's five images attached, whereas when it's submitted, it only had one image.swyx: I see. Yeah. But you gotta enable that if you're gonna use cur agent inside cur.Jonas: Exactly. And so here, this is then the after video where it went, it does the same thing. It attaches images, removes, some of them hit send.And you can see here, once this agent is up, only one of the images is left in the attachments. Yeah.swyx: Beautiful.Jonas: Okay. So easy merge.swyx: So yeah. When does it choose to do this? Because this is an extra step.Jonas: Yes. I think I've not done a great job yet of calibrating the model on when to reproduce these things.Yeah. Sometimes it will do it of its own accord. Yeah. We've been conservative where we try to have it only do it when it's [00:13:00] quite sure because it does add some amount of time to how long it takes it to work on it. But we also have added things like the slash repro command where you can just do, fix this bug slash repro and then it will know that it should first make you a video of it actually finding and making sure it can reproduce the bug.swyx: Yeah. Yeah. One sort of ML topic this ties into is reward hacking, where while you write test that you update only pass. So first write test, it shows me it fails, then make you test pass, which is a classic like red green.Jonas: Yep.swyx: LikeJonas: A-T-D-D-T-D-Dswyx: thing.No, very cool. Was that the last demo? Is thereJonas: Yeah.Anything I missed on the demos or points that you think? I think thatSamantha: covers it well. Yeah.swyx: Cool. Before we stop the screen share, can you gimme like a, just a tour of the slash commands ‘cause I so God ready. Huh, what? What are the good ones?Samantha: Yeah, we wanna increase discoverability around this too.I think that'll be like a future thing we work on. Yeah. But there's definitely a lot of good stuff nowJonas: we have a lot of internal ones that I think will not be that interesting. Here's an internal one that I've made. I don't know if anyone else at Cursor uses this one. Fix bb.Samantha: I've never heard of it.Jonas: Yeah.[00:14:00]Fix Bug Bot. So this is a thing that we want to integrate more tightly on. So you made it forswyx: yourself.Jonas: I made this for myself. It's actually available to everyone in the team, but yeah, no one knows about it. But yeah, there will be Bug bot comments and so Bug Bot has a lot of cool things. We actually just launched Bug Bot Auto Fix, where you can click a button and or change a setting and it will automatically fix its own things, and that works great in a bunch of cases.There are some cases where having the context of the original agent that created the PR is really helpful for fixing the bugs, because it might be like, oh, the bug here is that this, is a regression and actually you meant to do something more like that. And so having the original prompt and all of the context of the agent that worked on it, and so here I could just do, fix or we used to be able to do fixed PB and it would do that.No test is another one that we've had. Slash repro is in here. We mentioned that one.Samantha: One of my favorites is cloud agent diagnosis. This is one that makes heavy use of the Datadog MCP. Okay. And I [00:15:00] think Nick and David on our team wrote, and basically if there is a problem with a cloud agent we'll spin up a bunch of subs.Like a singleswyx: instance.Samantha: Yeah. We'll take the ideas and argument and spin up a bunch of subagents using the Datadog MCP to explore the logs and find like all of the problems that could have happened with that. It takes the debugging time, like from potentially you can do quick stuff quickly with the Datadog ui, but it takes it down to, again, like a single agent call as opposed to trolling through logs yourself.Jonas: You should also talk about the stuff we've done with transcripts.Samantha: Yes. Also so basically we've also done some things internally. There'll be some versions of this as we ship publicly soon, where you can spit up an agent and give it access to another agent's transcript to either basically debug something that happened.So act as an external debugger. I see. Or continue the conversation. Almost like forking it.swyx: A transcript includes all the chain of thought for the 11 minutes here. 45 minutes there.Samantha: Yeah. That way. Exactly. So basically acting as a like secondary agent that debugs the first, so we've started to push more andswyx: they're all the same [00:16:00] code.It is just the different prompts, but the sa the same.Samantha: Yeah. So basically same cloud agent infrastructure and then same harness. And then like when we do things like include, there's some extra infrastructure that goes into piping in like an external transcript if we include it as an attachment.But for things like the cloud agent diagnosis, that's mostly just using the Datadog MCP. ‘Cause we also launched CPS along with along with this cloud agent launch, launch support for cloud agent cps.swyx: Oh, that was drawn out.Jonas: We won't, we'll be doing a bigger marketing moment for it next week, but, and you can now use CPS andswyx: People will listen to it as well.Yeah,Jonas: they'llSamantha: be ahead of the third. They'll be ahead. And I would I actually don't know if the Datadog CP is like publicly available yet. I realize this not sure beta testing it, but it's been one of my favorites to use. Soswyx: I think that one's interesting for Datadog. ‘cause Datadog wants to own that site.Interesting with Bits. I don't know if you've tried bits.Samantha: I haven't tried bits.swyx: Yeah.Jonas: That's their cloud agentswyx: product. Yeah. Yeah. They want to be like we own your logs and give us our, some part of the, [00:17:00] self-healing software that everyone wants. Yeah. But obviously Cursor has a strong opinion on coding agents and you, you like taking away from the which like obviously you're going to do, and not every company's like Cursor, but it's interesting if you're a Datadog, like what do you do here?Do you expose your logs to FDP and let other people do it? Or do you try to own that it because it's extra business for you? Yeah. It's like an interesting one.Samantha: It's a good question. All I know is that I love the Datadog MCP,Jonas: And yeah, it is gonna be no, no surprise that people like will demand it, right?Samantha: Yeah.swyx: It's, it's like anysystemswyx: of record company like this, it's like how much do you give away? Cool. I think that's that for the sort of cloud agents tour. Cool. And we just talk about like cloud agents have been when did Kirsten loves cloud agents? Do you know, in JuneJonas: last year.swyx: June last year. So it's been slowly develop the thing you did, like a bunch of, like Michael did a post where himself, where he like showed this chart of like ages overtaking tap. And I'm like, wow, this is like the biggest transition in code.Jonas: Yeah.swyx: Like in, in [00:18:00] like the last,Jonas: yeah. I think that kind of got turned out.Yeah. I think it's a very interest,swyx: not at all. I think it's been highlighted by our friend Andre Kati today.Jonas: Okay.swyx: Talk more about it. What does it mean? Yeah. Is I just got given like the cursor tab key.Jonas: Yes. Yes.swyx: That's that'sSamantha: cool.swyx: I know, but it's gonna be like put in a museum.Jonas: It is.Samantha: I have to say I haven't used tab a little bit myself.Jonas: Yeah. I think that what it looks like to code with AI code generally creates software, even if you want to go higher level. Is changing very rapidly. No, not a hot take, but I think from our vendor's point at Cursor, I think one of the things that is probably underappreciated from the outside is that we are extremely self-aware about that fact and Kerscher, got its start in phase one, era one of like tab and auto complete.And that was really useful in its time. But a lot of people start looking at text files and editing code, like we call it hand coding. Now when you like type out the actual letters, it'sswyx: oh that's cute.Jonas: Yeah.swyx: Oh that's cute.Jonas: You're so boomer. So boomer. [00:19:00] And so that I think has been a slowly accelerating and now in the last few months, rapidly accelerating shift.And we think that's going to happen again with the next thing where the, I think some of the pains around tab of it's great, but I actually just want to give more to the agent and I don't want to do one tab at a time. I want to just give it a task and it goes off and does a larger unit of work and I can.Lean back a little bit more and operate at that higher level of abstraction that's going to happen again, where it goes from agents handing you back diffs and you're like in the weeds and giving it, 32nd to three minute tasks, to, you're giving it, three minute to 30 minute to three hour tasks and you're getting back videos and trying out previews rather than immediately looking at diffs every single time.swyx: Yeah. Anything to add?Samantha: One other shift that I've noticed as our cloud agents have really taken off internally has been a shift from primarily individually driven development to almost this collaborative nature of development for us, slack is actually almost like a development on [00:20:00] Id basically.So Iswyx: like maybe don't even build a custom ui, like maybe that's like a debugging thing, but actually it's that.Samantha: I feel like, yeah, there's still so much to left to explore there, but basically for us, like Slack is where a lot of development happens. Like we will have these issue channels or just like this product discussion channels where people are always at cursing and that kicks off a cloud agent.And for us at least, we have team follow-ups enabled. So if Jonas kicks off at Cursor in a thread, I can follow up with it and add more context. And so it turns into almost like a discussion service where people can like collaborate on ui. Oftentimes I will kick off an investigation and then sometimes I even ask it to get blame and then tag people who should be brought in. ‘cause it can tag people in Slack and then other people will comeswyx: in, can tag other people who are not involved in conversation. Yes. Can just do at Jonas if say, was talking to,Samantha: yeah.swyx: That's cool. You should, you guys should make a big good deal outta that.Samantha: I know. It's a lot to, I feel like there's a lot more to do with our slack surface area to show people externally. But yeah, basically like it [00:21:00] can bring other people in and then other people can also contribute to that thread and you can end up with a PR again, with the artifacts visible and then people can be like, okay, cool, we can merge this.So for us it's like the ID is almost like moving into Slack in some ways as well.swyx: I have the same experience with, but it's not developers, it's me. Designer salespeople.Samantha: Yeah.swyx: So me on like technical marketing, vision, designer on design and then salespeople on here's the legal source of what we agreed on.And then they all just collaborate and correct. The agents,Jonas: I think that we found when these threads is. The work that is left, that the humans are discussing in these threads is the nugget of what is actually interesting and relevant. It's not the boring details of where does this if statement go?It's do we wanna ship this? Is this the right ux? Is this the right form factor? Yeah. How do we make this more obvious to the user? It's like those really interesting kind of higher order questions that are so easy to collaborate with and leave the implementation to the cloud agent.Samantha: Totally. And no more discussion of am I gonna do this? Are you [00:22:00] gonna do this cursor's doing it? You just have to decide. You like it.swyx: Sometimes the, I don't know if there's a, this probably, you guys probably figured this out already, but since I, you need like a mute button. So like cursor, like we're going to take this offline, but still online.But like we need to talk among the humans first. Before you like could stop responding to everything.Jonas: Yeah. This is a design decision where currently cursor won't chime in unless you explicitly add Mention it. Yeah. Yeah.Samantha: So it's not always listening.Yeah.Jonas: I can see all the intermediate messages.swyx: Have you done the recursive, can cursor add another cursor or spawn another cursor?Samantha: Oh,Jonas: we've done some versions of this.swyx: Because, ‘cause it can add humans.Jonas: Yes. One of the other things we've been working on that's like an implication of generating the code is so easy is getting it to production is still harder than it should be.And broadly, you solve one bottleneck and three new ones pop up. Yeah. And so one of the new bottlenecks is getting into production and we have a like joke internally where you'll be talking about some feature and someone says, I have a PR for that. Which is it's so easy [00:23:00] to get to, I a PR for that, but it's hard still relatively to get from I a PR for that to, I'm confident and ready to merge this.And so I think that over the coming weeks and months, that's a thing that we think a lot about is how do we scale up compute to that pipeline of getting things from a first draft An agent did.swyx: Isn't that what Merge isn't know what graphite's for, likeJonas: graphite is a big part of that. The cloud agent testingswyx: Is it fully integrated or still different companiesJonas: working on I think we'll have more to share there in the future, but the goal is to have great end-to-end experience where Cursor doesn't just help you generate code tokens, it helps you create software end-to-end.And so review is a big part of that, that I think especially as models have gotten much better at writing code, generating code, we've felt that relatively crop up more,swyx: sorry this is completely unplanned, but like there I have people arguing one to you need ai. To review ai and then there is another approach, thought school of thought where it's no, [00:24:00] reviews are dead.Like just show me the video. It's it like,Samantha: yeah. I feel again, for me, the video is often like alignment and then I often still wanna go through a code review process.swyx: Like still look at the files andSamantha: everything. Yeah. There's a spectrum of course. Like the video, if it's really well done and it does like fully like test everything, you can feel pretty competent, but it's still helpful to, to look at the code.I make hep pay a lot of attention to bug bot. I feel like Bug Bot has been a great really highly adopted internally. We often like, won't we tell people like, don't leave bug bot comments unaddressed. ‘cause we have such high confidence in it. So people always address their bug bot comments.Jonas: Once you've had two cases where you merged something and then you went back later, there was a bug in it, you merged, you went back later and you were like, ah, bug Bot had found that I should have listened to Bug Bot.Once that happens two or three times, you learn to wait for bug bot.Samantha: Yeah. So I think for us there's like that code level review where like it's looking at the actual code and then there's like the like feature level review where you're looking at the features. There's like a whole number of different like areas.There'll probably eventually be things like performance level review, security [00:25:00] review, things like that where it's like more more different aspects of how this feature might affect your code base that you want to potentially leverage an agent to help with.Jonas: And some of those like bug bot will be synchronous and you'll typically want to wait on before you merge.But I think another thing that we're starting to see is. As with cloud agents, you scale up this parallelism and how much code you generate. 10 person startups become, need the Devrel X and pipelines that a 10,000 person company used to need. And that looks like a lot of the things I think that 10,000 person companies invented in order to get that volume of software to production safely.So that's things like, release frequently or release slowly, have different stages where you release, have checkpoints, automated ways of detecting regressions. And so I think we're gonna need stacks merg stack diffs merge queues. Exactly. A lot of those things are going to be importantswyx: forward with.I think the majority of people still don't know what stack stacks are. And I like, I have many friends in Facebook and like I, I'm pretty friendly with graphite. I've just, [00:26:00] I've never needed it ‘cause I don't work on that larger team and it's just like democratization of no, only here's what we've already worked out at very large scale and here's how you can, it benefits you too.Like I think to me, one of the beautiful things about GitHub is that. It's actually useful to me as an individual solo developer, even though it's like actually collaboration software.Jonas: Yep.swyx: And I don't think a lot of Devrel tools have figured that out yet. That transition from like large down to small.Jonas: Yeah. Kers is probably an inverse story.swyx: This is small down toJonas: Yeah. Where historically Kers share, part of why we grew so quickly was anyone on the team could pick it up and in fact people would pick it up, on the weekend for their side project and then bring it into work. ‘cause they loved using it so much.swyx: Yeah.Jonas: And I think a thing that we've started working on a lot more, not us specifically, but as a company and other folks at Cursor, is making it really great for teams and making it the, the 10th person that starts using Cursor in a team. Is immediately set up with things like, we launched Marketplace recently so other people can [00:27:00] configure what CPS and skills like plugins.So skills and cps, other people can configure that. So that my cursor is ready to go and set up. Sam loves the Datadog, MCP and Slack, MCP you've also been using a lot butSamantha: also pre-launch, but I feel like it's so good.Jonas: Yeah, my cursor should be configured if Sam feels strongly that's just amazing and required.swyx: Is it automatically shared or you have to go and.Jonas: It depends on the MCP. So some are obviously off per user. Yeah. And so Sam can't off my cursor with my Slack MCP, but some are team off and those can be set up by admins.swyx: Yeah. Yeah. That's cool. Yeah, I think, we had a man on the pod when cursor was five people, and like everyone was like, okay, what's the thing?And then it's usually something teams and org and enterprise, but it's actually working. But like usually at that stage when you're five, when you're just a vs. Code fork it's like how do you get there? Yeah. Will people pay for this? People do pay for it.Jonas: Yeah. And I think for cloud agents, we expect.[00:28:00]To have similar kind of PLG things where I think off the bat we've seen a lot of adoption with kind of smaller teams where the code bases are not quite as complex to set up. Yes. If you need some insane docker layer caching thing for builds not to take two hours, that's going to take a little bit longer for us to be able to support that kind of infrastructure.Whereas if you have front end backend, like one click agents can install everything that they need themselves.swyx: This is a good chance for me to just ask some technical sort of check the box questions. Can I choose the size of the vm?Jonas: Not yet. We are planning on adding that. Weswyx: have, this is obviously you want like LXXL, whatever, right?Like it's like the Amazon like sort menu.Jonas: Yes, exactly. We'll add that.swyx: Yeah. In some ways you have to basically become like a EC2, almost like you rent a box.Jonas: You rent a box. Yes. We talk a lot about brain in a box. Yeah. So cursor, we want to be a brain in a box,swyx: but is the mental model different? Is it more serverless?Is it more persistent? Is. Something else.Samantha: We want it to be a bit persistent. The desktop should be [00:29:00] something you can return to af even after some days. Like maybe you go back, they're like still thinking about a feature for some period of time. So theswyx: full like sus like suspend the memory and bring it back and then keep going.Samantha: Exactly.swyx: That's an interesting one because what I actually do want, like from a manna and open crawl, whatever, is like I want to be able to log in with my credentials to the thing, but not actually store it in any like secret store, whatever. ‘cause it's like this is the, my most sensitive stuff.Yeah. This is like my email, whatever. And just have it like, persist to the image. I don't know how it was hood, but like to rehydrate and then just keep going from there. But I don't think a lot of infra works that way. A lot of it's stateless where like you save it to a docker image and then it's only whatever you can describe in a Docker file and that's it.That's the only thing you can cl multiple times in parallel.Jonas: Yeah. We have a bunch of different ways of setting them up. So there's a dockerfile based approach. The main default way is actually snapshottingswyx: like a Linux vmJonas: like vm, right? You run a bunch of install commands and then you snapshot more or less the file system.And so that gets you set up for everything [00:30:00] that you would want to bring a new VM up from that template basically.swyx: Yeah.Jonas: And that's a bit distinct from what Sam was talking about with the hibernating and re rehydrating where that is a full memory snapshot as well. So there, if I had like the browser open to a specific page and we bring that back, that page will still be there.swyx: Was there any discussion internally and just building this stuff about every time you shoot a video it's actually you show a little bit of the desktop and the browser and it's not necessary if you just show the browser. If, if you know you're just demoing a front end application.Why not just show the browser, right? Like it Yeah,Samantha: we do have some panning and zooming. Yeah. Like it can decide that when it's actually recording and cutting the video to highlight different things. I think we've played around with different ways of segmenting it and yeah. There's been some different revs on it for sure.Jonas: Yeah. I think one of the interesting things is the version that you see now in cursor.com actually is like half of what we had at peak where we decided to unshift or unshipped quite a few things. So two of the interesting things to talk about, one is directly an answer to your [00:31:00] question where we had native browser that you would have locally, it was basically an iframe that via port forwarding could load the URL could talk to local host in the vm.So that gets you basically, so inswyx: your machine's browser,likeJonas: in your local browser? Yeah. You would go to local host 4,000 and that would get forwarded to local host 4,000 in the VM via port forward. We unshift that like atswyx: Eng Rock.Jonas: Like an Eng Rock. Exactly. We unshift that because we felt that the remote desktop was sufficiently low latency and more general purpose.So we build Cursor web, but we also build Cursor desktop. And so it's really useful to be able to have the full spectrum of things. And even for Cursor Web, as you saw in one of the examples, the agent was uploading files and like I couldn't upload files and open the file viewer if I only had access to the browser.And we've thought a lot about, this might seem funny coming from Cursor where we started as this, vs. Code Fork and I think inherited a lot of amazing things, but also a lot [00:32:00] of legacy UI from VS Code.Minimal Web UI SurfacesJonas: And so with the web UI we wanted to be very intentional about keeping that very minimal and exposing the right sum of set of primitive sort of app surfaces we call them, that are shared features of that cloud.Environment that you and the agent both use. So agent uses desktop and controls it. I can use desktop and controlled agent runs terminal commands. I can run terminal commands. So that's how our philosophy around it. The other thing that is maybe interesting to talk about that we unshipped is and we may, both of these things we may reship and decide at some point in the future that we've changed our minds on the trade offs or gotten it to a point where, putswyx: it out there.Let users tell you they want it. Exactly. Alright, fine.Why No File EditorJonas: So one of the other things is actually a files app. And so we used to have the ability at one point during the process of testing this internally to see next to, I had GID desktop and terminal on the right hand side of the tab there earlier to also have a files app where you could see and edit files.And we actually felt that in some [00:33:00] ways, by restricting and limiting what you could do there, people would naturally leave more to the agent and fall into this new pattern of delegating, which we thought was really valuable. And there's currently no way in Cursor web to edit these files.swyx: Yeah. Except you like open up the PR and go into GitHub and do the thing.Jonas: Yeah.swyx: Which is annoying.Jonas: Just tell the agent,swyx: I have criticized open AI for this. Because Open AI is Codex app doesn't have a file editor, like it has file viewer, but isn't a file editor.Jonas: Do you use the file viewer a lot?swyx: No. I understand, but like sometimes I want it, the one way to do it is like freaking going to no, they have a open in cursor button or open an antigravity or, opening whatever and people pointed that.So I was, I was part of the early testers group people pointed that and they were like, this is like a design smell. It's like you actually want a VS. Code fork that has all these things, but also a file editor. And they were like, no, just trust us.Jonas: Yeah. I think we as Cursor will want to, as a product, offer the [00:34:00] whole spectrum and so you want to be able to.Work at really high levels of abstraction and double click and see the lowest level. That's important. But I also think that like you won't be doing that in Slack. And so there are surfaces and ways of interacting where in some cases limiting the UX capabilities makes for a cleaner experience that's more simple and drives people into these new patterns where even locally we kicked off joking about this.People like don't really edit files, hand code anymore. And so we want to build for where that's going and not where it's beenswyx: a lot of cool stuff. And Okay. I have a couple more.Full Stack Hosting Debateswyx: So observations about the design elements about these things. One of the things that I'm always thinking about is cursor and other peers of cursor start from like the Devrel tools and work their way towards cloud agents.Other people, like the lovable and bolts of the world start with here's like the vibe code. Full cloud thing. They were already cloud edges before anyone else cloud edges and we will give you the full deploy platform. So we own the whole loop. We own all the infrastructure, we own, we, we have the logs, we have the the live site, [00:35:00] whatever.And you can do that cycle cursor doesn't own that cycle even today. You don't have the versal, you don't have the, you whatever deploy infrastructure that, that you're gonna have, which gives you powers because anyone can use it. And any enterprise who, whatever you infra, I don't care. But then also gives you limitations as to how much you can actually fully debug end to end.I guess I'm just putting out there that like is there a future where there's like full stack cursor where like cursor apps.com where like I host my cursor site this, which is basically a verse clone, right? I don't know.Jonas: I think that's a interesting question to be asking, and I think like the logic that you laid out for how you would get there is logic that I largely agree with.swyx: Yeah. Yeah.Jonas: I think right now we're really focused on what we see as the next big bottleneck and because things like the Datadog MCP exist, yeah. I don't think that the best way we can help our customers ship more software. Is by building a hosting solution right now,swyx: by the way, these are things I've actually discussed with some of the companies I just named.Jonas: Yeah, for sure. Right now, just this big bottleneck is getting the code out there and also [00:36:00] unlike a lovable in the bolt, we focus much more on existing software. And the zero to one greenfield is just a very different problem. Imagine going to a Shopify and convincing them to deploy on your deployment solution.That's very different and I think will take much longer to see how that works. May never happen relative to, oh, it's like a zero to one app.swyx: I'll say. It's tempting because look like 50% of your apps are versal, superb base tailwind react it's the stack. It's what everyone does.So I it's kinda interesting.Jonas: Yeah.Model Choice and Auto Routingswyx: The other thing is the model select dying. Right now in cloud agents, it's stuck down, bottom left. Sure it's Codex High today, but do I care if it's suddenly switched to Opus? Probably not.Samantha: We definitely wanna give people a choice across models because I feel like it, the meta change is very frequently.I was a big like Opus 4.5 Maximalist, and when codex 5.3 came out, I hard, hard switch. So that's all I use now.swyx: Yeah. Agreed. I don't know if, but basically like when I use it in Slack, [00:37:00] right? Cursor does a very good job of exposing yeah. Cursors. If people go use it, here's the model we're using.Yeah. Here's how you switch if you want. But otherwise it's like extracted away, which is like beautiful because then you actually, you should decide.Jonas: Yeah, I think we want to be doing more with defaults.swyx: Yeah.Jonas: Where we can suggest things to people. A thing that we have in the editor, the desktop app is auto, which will route your request and do things there.So I think we will want to do something like that for cloud agents as well. We haven't done it yet. And so I think. We have both people like Sam, who are very savvy and want know exactly what model they want, and we also have people that want us to pick the best model for them because we have amazing people like Sam and we, we are the experts.Yeah. We have both the traffic and the internal taste and experience to know what we think is best.swyx: Yeah. I have this ongoing pieces of agent lab versus model lab. And to me, cursor and other companies are example of an agent lab that is, building a new playbook that is different from a model lab where it's like very GP heavy Olo.So obviously has a research [00:38:00] team. And my thesis is like you just, every agent lab is going to have a router because you're going to be asked like, what's what. I don't keep up to every day. I'm not a Sam, I don't keep up every day for using you as sample the arm arbitrator of taste. Put me on CRI Auto.Is it free? It's not free.Jonas: Auto's not free, but there's different pricing tiers. Yeah.swyx: Put me on Chris. You decide from me based on all the other people you know better than me. And I think every agent lab should basically end up doing this because that actually gives you extra power because you like people stop carrying or having loyalty with one lab.Jonas: Yeah.Best Of N and Model CouncilsJonas: Two other maybe interesting things that I don't know how much they're on your radar are one the best event thing we mentioned where running different models head to head is actually quite interesting becauseswyx: which exists in cursor.Jonas: That exists in cur ID and web. So the problem is where do you run them?swyx: Okay.Jonas: And so I, I can share my screen if that's interesting. Yeahinteresting.swyx: Yeah. Yeah. Obviously parallel agents, very popal.Jonas: Yes, exactly. Parallel agentsswyx: in you mind. Are they the same thing? Best event and parallel agents? I don't want to [00:39:00] put words in your mouth.Jonas: Best event is a subset of parallel agents where they're running on the same prompt.That would be my answer. So this is what that looks like. And so here in this dropdown picker, I can just select multiple models.swyx: Yeah.Jonas: And now if I do a prompt, I'm going to do something silly. I am running these five models.swyx: Okay. This is this fake clone, of course. The 2.0 yeah.Jonas: Yes, exactly. But they're running so the cursor 2.0, you can do desktop or cloud.So this is cloud specifically where the benefit over work trees is that they have their own VMs and can run commands and won't try to kill ports that the other one is running. Which are some of the pains. These are allswyx: called work trees?Jonas: No, these are all cloud agents with their own VMs.swyx: Okay. ButJonas: When you do it locally, sometimes people do work trees and that's been the main way that people have set out parallel so far.I've gotta say.swyx: That's so confusing for folks.Jonas: Yeah.swyx: No one knows what work trees are.Jonas: Exactly. I think we're phasing out work trees.swyx: Really.Jonas: Yeah.swyx: Okay.Samantha: But yeah. And one other thing I would say though on the multimodel choice, [00:40:00] so this is another experiment that we ran last year and the decide to ship at that time but may come back to, and there was an interesting learning that's relevant for, these different model providers. It was something that would run a bunch of best of ends but then synthesize and basically run like a synthesizer layer of models. And that was other agents that would take LM Judge, but one that was also agentic and could write code. So it wasn't just picking but also taking the learnings from two models or, and models that it was looking at and writing a new diff.And what we found was that at the time at least, there were strengths to using models from different model providers as the base level of this process. Like basically you could get almost like a synergistic output that was better than having a very unified, like bottom model tier. So it was really interesting ‘cause it's like potentially, even though even in the future when you have like maybe one model as ahead of the other for a little bit, there could be some benefit from having like multiple top tier models involved in like a [00:41:00] model swarm or whatever agent Swarm that you're doing, that they each have strengths and weaknesses.Yeah.Jonas: Andre called this the council, right?Samantha: Yeah, exactly. We actually, oh, that's another internal command we have that Ian wrote slash council. Oh, and they some, yeah.swyx: Yes. This idea is in various forms everywhere. And I think for me, like for me, the productization of it, you guys have done yeah, like this is very flexible, but.If I were to add another Yeah, what your thing is on here it would be too much. I what, let's say,Samantha: Ideally it's all, it's something that the user can just choose and it all happens under the hood in a way where like you just get the benefit of that process at the end and better output basically, but don't have to get too lost in the complexity of judging along the way.Jonas: Okay.Subagents for ContextJonas: Another thing on the many agents, on different parallel agents that's interesting is an idea that's been around for a while as well that has started working recently is subagents. And so this is one other way to get agents of the different prompts and different goals and different models, [00:42:00] different vintages to work together.Collaborate and delegate.swyx: Yeah. I'm very like I like one of my, I always looking for this is the year of the blah, right? Yeah. I think one of the things on the blahs is subs. I think this is of but I haven't used them in cursor. Are they fully formed or how do I honestly like an intro because do I form them from new every time?Do I have fixed subagents? How are they different for slash commands? There's all these like really basic questions that no one stops to answer for people because everyone's just like too busy launching. We have toSamantha: honestly, you could, you can see them in cursor now if you just say spin up like 50 subagents to, so cursor definesswyx: what Subagents.Yeah.Samantha: Yeah. So basically I think I shouldn't speak for the whole subagents team. This is like a different team that's been working on this, but our thesis or thing that we saw internally is that like they're great for context management for kind of long running threads, or if you're trying to just throw more compute at something.We have strongly used, almost like a generic task interface where then the main agent can define [00:43:00] like what goes into the subagent. So if I say explore my code base, it might decide to spin up an explore subagent and or might decide to spin up five explore subagent.swyx: But I don't get to set what those subagent are, right?It's all defined by a model.Samantha: I think. I actually would have to refresh myself on the sub agent interface.Jonas: There are some built-in ones like the explore subagent is free pre-built. But you can also instruct the model to use other subagents and then it will. And one other example of a built-in subagent is I actually just kicked one off in cursor and I can show you what that looks like.swyx: Yes. Because I tried to do this in pure prompt space.Jonas: So this is the desktop app? Yeah. Yeah. And that'sswyx: all you need to do, right? Yeah.Jonas: That's all you need to do. So I said use a sub agent to explore and I think, yeah, so I can even click in and see what the subagent is working on here. It ran some fine command and this is a composer under the hood.Even though my main model is Opus, it does smart routing to take, like in this instance the explorer sort of requires reading a ton of things. And so a faster model is really useful to get an [00:44:00] answer quickly, but that this is what subagent look like. And I think we wanted to do a lot more to expose hooks and ways for people to configure these.Another example of a cus sort of builtin subagent is the computer use subagent in the cloud agents, where we found that those trajectories can be long and involve a lot of images obviously, and execution of some testing verification task. We wanted to use that models that are particularly good at that.So that's one reason to use subagents. And then the other reason to use subagents is we want contexts to be summarized reduced down at a subagent level. That's a really neat boundary at which to compress that rollout and testing into a final message that agent writes that then gets passed into the parent rather than having to do some global compaction or something like that.swyx: Awesome. Cool. While we're in the subagents conversation, I can't do a cursor conversation and not talk about listen stuff. What is that? What is what? He built a browser. He built an os. Yes. And he [00:45:00] experimented with a lot of different architectures and basically ended up reinventing the software engineer org chart.This is all cool, but what's your take? What's, is there any hole behind the side? The scenes stories about that kind of, that whole adventure.Samantha: Some of those experiments have found their way into a feature that's available in cloud agents now, the long running agent mode internally, we call it grind mode.And I think there's like some hint of grind mode accessible in the picker today. ‘cause you can do choose grind until done. And so that was really the result of experiments that Wilson started in this vein where he I think the Ralph Wigga loop was like floating around at the time, but it was something he also independently found and he was experimenting with.And that was what led to this product surface.swyx: And it is just simple idea of have criteria for completion and do not. Until you complete,Samantha: there's a bit more complexity as well in, in our implementation. Like there's a specific, you have to start out by aligning and there's like a planning stage where it will work with you and it will not get like start grind execution mode until it's decided that the [00:46:00] plan is amenable to both of you.Basically,swyx: I refuse to work until you make me happy.Jonas: We found that it's really important where people would give like very underspecified prompt and then expect it to come back with magic. And if it's gonna go off and work for three minutes, that's one thing. When it's gonna go off and work for three days, probably should spend like a few hours upfront making sure that you have communicated what you actually want.swyx: Yeah. And just to like really drive from the point. We really mean three days that No, noJonas: human. Oh yeah. We've had three day months innovation whatsoever.Samantha: I don't know what the record is, but there's been a long time with the grantsJonas: and so the thing that is available in cursor. The long running agent is if you wanna think about it, very abstractly that is like one worker node.Whereas what built the browser is a society of workers and planners and different agents collaborating. Because we started building the browser with one worker node at the time, that was just the agent. And it became one worker node when we realized that the throughput of the system was not where it needed to be [00:47:00] to get something as large of a scale as the browser done.swyx: Yeah.Jonas: And so this has also become a really big mental model for us with cloud, cloud agents is there's the classic engineering latency throughput trade-offs. And so you know, the code is water flowing through a pipe. The, we think that over the coming months, the big unlock is not going to be one person with a model getting more done, like the water flowing faster and we'll be making the pipe much wider and so ing more, whether that's swarms of agents or parallel agents, both of those are things that contribute to getting.Much more done in the same amount of time, but any one of those tasks doesn't necessarily need to get done that quickly. And throughput is this really big thing where if you see the system of a hundred concurrent agents outputting thousands of tokens a second, you can't go back like that.Just you see a glimpse of the future where obviously there are many caveats. Like no one is using this browser. IRL. There's like a bunch of things not quite right yet, but we are going to get to systems that produce real production [00:48:00] code at the scale much sooner than people think. And it forces you to think what even happens to production systems. Like we've broken our GitHub actions recently because we have so many agents like producing and pushing code that like CICD is just overloaded. ‘cause suddenly it's like effectively weg grew, cursor's growing very quickly anyway, but you grow head count, 10 x when people run 10 x as many agents.And so a lot of these systems, exactly, a lot of these systems will need to adapt.swyx: It also reminds me, we, we all, the three of us live in the app layer, but if you talk to the researchers who are doing RL infrastructure, it's the same thing. It's like all these parallel rollouts and scheduling them and making sure as much throughput as possible goes through them.Yeah, it's the same thing.Jonas: We were talking briefly before we started recording. You were mentioning memory chips and some of the shortages there. The other thing that I think is just like hard to wrap your head around the scale of the system that was building the browser, the concurrency there.If Sam and I both have a system like that running for us, [00:49:00] shipping our software. The amount of inference that we're going to need per developer is just really mind-boggling. And that makes, sometimes when I think about that, I think that even with, the most optimistic projections for what we're going to need in terms of buildout, our underestimating, the extent to which these swarm systems can like churn at scale to produce code that is valuable to the economy.And,swyx: yeah, you can cut this if it's sensitive, but I was just Do you have estimates of how much your token consumption is?Jonas: Like per developer?swyx: Yeah. Or yourself. I don't need like comfy average. I just curious. ISamantha: feel like I, for a while I wasn't an admin on the usage dashboard, so I like wasn't able to actually see, but it was a,swyx: mine has gone up.Samantha: Oh yeah.swyx: But I thinkSamantha: it's in terms of how much work I'm doing, it's more like I have no worries about developers losing their jobs, at least in the near term. ‘cause I feel like that's a more broad discussion.swyx: Yeah. Yeah. You went there. I didn't go, I wasn't going there.I was just like how much more are you using?Samantha: There's so much stuff to be built. And so I feel like I'm basically just [00:50:00] trying to constantly I have more ambitions than I did before. Yes. Personally. Yes. So can't speak to the broader thing. But for me it's like I'm busier than ever before.I'm using more tokens and I am also doing more things.Jonas: Yeah. Yeah. I don't have the stats for myself, but I think broadly a thing that we've seen, that we expect to continue is J'S paradox. Whereswyx: you can't do it in our podcast without seeingJonas: it. Exactly. We've done it. Now we can wrap. We've done, we said the words.Phase one tab auto complete people paid like 20 bucks a month. And that was great. Phase two where you were iterating with these local models. Today people pay like hundreds of dollars a month. I think as we think about these highly parallel kind of agents running off for a long times in their own VM system, we are already at that point where people will be spending thousands of dollars a month per human, and I think potentially tens of thousands and beyond, where it's not like we are greedy for like capturing more money, but what happens is just individuals get that much more leverage.And if one person can do as much as 10 people, yeah. That tool that allows ‘em to do that is going to be tremendously valuable [00:51:00] and worth investing in and taking the best thing that exists.swyx: One more question on just the cursor in general and then open-ended for you guys to plug whatever you wanna put.How is Cursor hiring these days?Samantha: What do you mean by how?swyx: So obviously lead code is dead. Oh,Samantha: okay.swyx: Everyone says work trial. Different people have different levels of adoption of agents. Some people can really adopt can be much more productive. But other people, you just need to give them a little bit of time.And sometimes they've never lived in a token rich place like cursor.And once you live in a token rich place, you're you just work differently. But you need to have done that. And a lot of people anyway, it was just open-ended. Like how has agentic engineering, agentic coding changed your opinions on hiring?Is there any like broad like insights? Yeah.Jonas: Basically I'm asking this for other people, right? Yeah, totally. Totally. To hear Sam's opinion, we haven't talked about this the two of us. I think that we don't see necessarily being great at the latest thing with AI coding as a prerequisite.I do think that's a sign that people are keeping up and [00:52:00] curious and willing to upscale themselves in what's happening because. As we were talking about the last three months, the game has completely changed. It's like what I do all day is very different.swyx: Like it's my job and I can't,Jonas: Yeah, totally.I do think that still as Sam was saying, the fundamentals remain important in the current age and being able to go and double click down. And models today do still have weaknesses where if you let them run for too long without cleaning up and refactoring, the coke will get sloppy and there'll be bad abstractions.And so you still do need humans that like have built systems before, no good patterns when they see them and know where to steer things.Samantha: I would agree with that. I would say again, cursor also operates very quickly and leveraging ag agentic engineering is probably one reason why that's possible in this current moment.I think in the past it was just like people coding quickly and now there's like people who use agents to move faster as well. So it's part of our process will always look for we'll select for kind of that ability to make good decisions quickly and move well in this environment.And so I think being able to [00:53:00] figure out how to use agents to help you do that is an important part of it too.swyx: Yeah. Okay. The fork in the road, either predictions for the end of the year, if you have any, or PUDs.Jonas: Evictions are not going to go well.Samantha: I know it's hard.swyx: They're so hard. Get it wrong.It's okay. Just, yeah.Jonas: One other plug that may be interesting that I feel like we touched on but haven't talked a ton about is a thing that the kind of these new interfaces and this parallelism enables is the ability to hop back and forth between threads really quickly. And so a thing that we have,swyx: you wanna show something or,Jonas: yeah, I can show something.A thing that we have felt with local agents is this pain around contact switching. And you have one agent that went off and did some work and another agent that, that did something else. And so here by having, I just have three tabs open, let's say, but I can very quickly, hop in here.This is an example I showed earlier, but the actual workflow here I think is really different in a way that may not be obvious, where, I start t

What does it actually look like to build an AI-native product and lead an engineering team through the AI era when you've been doing it longer than most? Rob Zuber sits down with Loïc Houssier, CTO at Superhuman, to talk about what it meant to be an AI company before AI was everywhere, and how that early foundation shapes the way they build, ship, and think today.The conversation covers how Loïc drove AI tool adoption across his engineering org without mandates (and which senior engineer's change of heart became a cultural turning point), why great UX is still the real moat in an age where anyone can ship an average product fast, and how email, despite everything, remains the connective tissue of professional life. Plus: what it's like to rethink your entire SDLC when the economics of building software change overnight.Have someone you'd like to hear on the show, reach out to us on X at @CircleCI!

Will Madden joins the podcast to talk about Prisma Next and the evolution from Prisma 7, including the decision to migrate away from Rust, ship the core through WebAssembly, and move toward a fully TypeScript ORM. The conversation dives into how modern workflows like agentic coding change the role of an ORM and why tools still matter even when agents can write SQL queries directly. We discuss how feedback loops, guardrails, and the TypeScript type system help prevent errors, along with the new query builder, query linter, and middleware layer that analyze queries using an abstract syntax tree. The episode also covers new database capabilities including Postgres support, upcoming Mongo support, and extensions like PG Vector, enabling vector columns and cosine distance similarity search. You'll also learn about new patterns such as collection methods, scopes, and composable database extensions, plus tooling like driver adapters, a potential compatibility layer, and safeguards like lint rules and a performance budget middleware designed to catch expensive queries before they run. Resources The Next Evolution of Prisma ORM: https://www.prisma.io/blog/the-next-evolution-of-prisma-orm We want to hear from you! How did you find us? Did you see us on Twitter? In a newsletter? Or maybe we were recommended by a friend? Fill out our listener survey! https://t.co/oKVAEXipxu Let us know by sending an email to our producer, Elizabeth, at elizabeth.becz@logrocket.com, or tweet at us at PodRocketPod. Check out our newsletter! https://blog.logrocket.com/the-replay-newsletter/ Follow us. Get free stickers. Follow us on Apple Podcasts, fill out this form, and we'll send you free PodRocket stickers! What does LogRocket do? LogRocket provides AI-first session replay and analytics that surfaces the UX and technical issues impacting user experiences. Start understanding where your users are struggling by trying it for free at LogRocket.com. Try LogRocket for free today. Chapters 00:00 Introduction 01:00 Prisma Seven and the Move Away from Rust 02:20 Missing Features and Mongo Support 03:00 Why Prisma Started Rebuilding the Core 04:00 Community Sentiment and Developer Feedback 05:20 Rethinking ORMs in the AI and Agentic Coding Era 06:45 Why Agents Still Need ORMs 07:30 Feedback Loops and Guardrails for SQL 08:30 Type Safety and the First Layer of Query Validation 09:30 Query Linter and Middleware Architecture 11:00 Runtime Validation and Query Errors 12:30 Configuring Lint Rules and Guardrails 14:00 Designing ORMs for Humans and Agents 15:30 Collection Methods and ActiveRecord-style Scopes 17:00 Reusable Queries and Domain Vocabulary 18:30 Query Composition and Flexibility 19:00 Performance Guardrails and Query Budget Middleware 20:30 Debugging ORM Performance Issues 21:00 Query Telemetry and Request Tracing 22:30 Prisma Next Extensibility and Database Plugins 23:00 Using PGVector and Vector Search 24:00 Database Drivers and Backend Architecture 25:00 Native Mongo Support in Prisma Next 26:00 Community Extensions and Middleware Ecosystem 27:00 Runtime Schema Validation Use Cases 28:00 Writing Custom Query Validation Rules 29:00 Migration Paths from Prisma Seven 30:30 Compatibility Layers vs Parallel Systems 32:00 Prisma Next Roadmap and Timeline 34:30 What Developers Will Be Most Excited About 35:30 Final Thoughts and Community Feedback

Sponsored by Auth0 for Startups → 1-year free https://auth0.com/startups/vip Auth0 is an adaptable authentication and authorization platform that helps you secure your apps and AI agents. It delivers convenience, privacy, and security so you can focus on building a great UX. FOUNDER PROFILE: Ilya Levtov, Founder of Craft https://www.linkedin.com/in/ilya-levtov/

Mandi Geiselman has worked as Senior UX Designer at Autodesk, where she designs for complex media & entertainment tools (like Maya and 3DS Max), and she is an Advanced OOUX Strategist who's created resources on building an OOUX community of practice inside big organizations. In this episode of the podcast, Sophia and Mandi talk about OOUXing Horizon Forbidden West, why game UX isn't about “making it easy,” and how bases, extensions, and conditional logic can make even the most complex systems more understandable (and more shareable for teams).LINKS: Join the OOUX Forum Connect with Mandi

In this episode of Future of UX, I'm sharing the three things I would do if I were starting my design career today as a junior UX/UI/Product Designer.And I'm not saying this as abstract advice. I'm saying it as someone who started almost 10 years ago with basically zero real UX skills, taught myself a lot on the job, and built a career in a market that was booming back then.The difference is: the market today is tighter, teams are leaner, and AI is automating a lot of the tasks that used to be classic “junior work.” So the big question becomes:If execution is getting cheaper and faster… what becomes valuable?What's actually changing for junior designers (and why it's not just hype)Which junior tasks are increasingly automatedWhere junior designers are most vulnerable (and how to avoid that trap)The 3 highest-leverage moves I'd focus on in 2026–2028:Become AI-native (build workflows, not just prompts)Build product thinking early (learn the “why,” not just the “how”)Become the human layer in human–AI systems (trust, transparency, oversight)You don't need to master every tool. But you do need to move. Start small, build confidence, and position yourself above pure execution work.What we coverKey takeawayAnd if you want to connect, I'm most active on LinkedIn (and sometimes on Instagram too), where I share tools, resources, and experiments.

Routstr is an open marketplace for ai compute, powered by nostr and bitcoin.Routstr: https://routstr.comChat app: https://chat.routstr.comOpenclaw setup: https://routstr.com/openclawRun a Routstr node and earn sats: https://github.com/Routstr/routstr-coreGithub: https://github.com/Routstr Routstr on nostr: https://primal.net/p/npub130mznv74rxs032peqym6g3wqavh472623mt3z5w73xq9r6qqdufs7ql29sEvan on nostr: https://primal.net/p/npub1u37h8rhgm9f95d90lpk2afw8h4t75kf6w8vmga2zz9jsx3atzpuqlmw8vyRedshift on nostr: https://primal.net/p/npub1ftt05tgku25m2akgvw6v7aqy5ux5mseqcrzy05g26ml43xf74nyqsredshThefux on nostr: https://primal.net/p/npub1ygjd597hdwu8larprmhj893d5p832j5mhejpx40ukezgudvayg9qeklajcShroominic on nostr: https://primal.net/p/npub18gr2m5cflkzpn6jdfer4a8qdlavsn334m9mfhurjsge08grg82zq6hu9suEPISODE: 192BLOCK: 939283PRICE: 1368 sats per dollar(00:03:02) Routstr and the team(00:07:24) What is Routstr?(00:10:26) Proxy providers, proprietary models, and pricing dynamics(00:13:16) Discovery, reviews, and quality signaling on Nostr(00:16:07) Fees, sustainability, and open source funding models(00:21:32) OpenClaw, LNVPS, and one-click sovereign stack(00:25:27) Why Nostr is ideal for agents vs. closed platforms(00:33:00) Crowdzapping, bounties, and agents building public goods(00:38:02) Agent specialization, cost tiers, and future routing(00:45:31) Resilience: routing around outages and pay-per-request(00:48:12) Self-host vs. marketplaces, selling spare compute(00:54:00) AI compute meets Bitcoin mining and energy realities(00:56:50) Hardware choices: Mac minis, old PCs, and VPS security(00:59:10) Linux advantage and agents removing UX friction(01:00:24) Open chat protocols, Marmot, and agentic comms(01:03:54) Acceleration, small teams with many agents shipping fast(01:04:19) Closing thoughts from the Routstr teammore info on the show: https://citadeldispatch.comlearn more about me: https://odell.xyz

Jaime Hunt sits down with Jason Smith, Founder and Managing Director of OHO, to unpack how AI in higher education is fundamentally changing the way students search for colleges. As AI tools like ChatGPT and Gemini increasingly shape the student journey, institutions must rethink their approach to SEO for higher education and digital visibility. Jason introduces OHO's new AI Visibility Scorecard and shares eye-opening insights into where AI models pull information from—and why that matters for enrollment marketers. This conversation challenges higher ed leaders to move beyond traditional search strategies and prepare for an AI-driven future of student recruitment. Guest Name: Jason Smith, Founder and Managing Director of OHO Guest Social: https://www.linkedin.com/in/jasonsmith1/ Guest Bio: Jason is the Founder and Managing Director of OHO, a leading digital agency dedicated to higher education. For over 20 years, he has led a team of strategists, designers, UX researchers, marketers, and developers who help colleges and universities solve complex digital challenges—from launching major websites to driving enrollment through digital marketing.  A former designer and creative director, Jason brings a deep appreciation for how storytelling, design, and technology can work together to reach the right audiences and move institutions forward. He's worked with 37 of the top 100 U.S. colleges and universities, guiding leaders through projects that clarify their goals, connect with users, and elevate their digital presence.  Endlessly curious and always inventing, Jason is currently digging deep into how to increase AI visibility for colleges and universities so that they can reach prospective students. - - - -Connect With Our Host:Jaime Hunthttps://www.linkedin.com/in/jaimehunt/https://twitter.com/JaimeHuntIMCAbout The Enrollify Podcast Network:Confessions of a Higher Ed CMO is a part of the Enrollify Podcast Network. If you like this podcast, chances are you'll like other Enrollify shows too! Enrollify is made possible by Element451 — The AI Workforce Platform for Higher Ed. Learn more at element451.com. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

A veteran of the global crypto industry, Colin Goltra has been an early adopter and advocate for digital assets throughout his career.Colin Goltra is the Chief Executive Officer of Morph, a blockchain platform building universal infrastructure for borderless payments and financial services. He recently joined the Bitcoin.com News Podcast to talk about the market.In this episode Colin identifies the previous year as the critical "stablecoin moment," driven by a perfect storm of regulatory clarity (like the Genius Act and MiCA) and technological advancements on smart-contracting ecosystems that have finally solved the performance and scalability issues that plagued earlier attempts with Bitcoin. Morph's mission has pivoted to stablecoin-based global payment settlement, adopting a "ruthlessly pragmatic" strategy to prepare for a market that could be dominated by either one or two fiat-backed stablecoins (USD-linked like USDC and USDT) or by a rise in relevant regional stablecoins.He highlights the profound impact of stablecoins in emerging economies, where access to the dollar provides a crucial hedge against high local fiat inflation, citing the Philippine Peso as a prime example. Looking at the current landscape, Colin pinpoints four key active verticals in crypto: institutional stablecoin-based payments, the significant growth of Real-World Assets (RWAs), prediction markets for valuable information, and the emerging space of Agentic AI, which will require crypto layers for payment and transacting.The long-term vision for crypto, according to Colin, anticipates a transition from a purely "cryptonative" era to a more institutional and pragmatic phase over the next decade. He predicts that for the average person, the underlying blockchain infrastructure will "melt away at the UX level," becoming an invisible rail for better, faster payment solutions. A major challenge remains a knowledge gap for small and mid-sized businesses. To address this, Morph is funding a $150 million payment accelerator to incentivize traditional payment businesses to migrate their transaction volume onto the Morph chain.

We're wrapping up our AI Tools series with a special episode featuring just the two of us—Matt and Moshe—looking back at what we really learned (and where we're still confused) about AI in product management.Across this conversation, we revisit the core themes that emerged with our guests and in our own experiments: from “vibe coding” and no‑code builders, to LLM assistants, enterprise privacy, agentic workflows, and the evolving role of the product manager. We share candid stories of using tools like Google Stitch, Figma/Figma Make, FlutterFlow, Base44, and others to design and prototype a real mobile app; what worked, what broke, and why credits, pricing, and model limits matter far more than the glossy demos suggest.Join Matt and Moshe as they explore:How our AI Tools series evolved, from “let's review tools” to “AI is not one thing, it's many different problem spaces”Why “vibe coding” is a misleading umbrella term, and how it means something different to devs, PMs, and designersLessons from using AI for design and prototyping: inconsistent outputs, beta‑stage rough edges, and the pain of credit-based modelsBuild vs. buy for AI: integrating foundation models vs. building your own, and what that means for pricing, UX, and reliabilityEnterprise realities: privacy, security, and why tools like Copilot/Gemini have such an advantage where data and IT policies matterHow conversations with our guests (Sani, Eva, Elena, Stav, Yaron, Marcos and Adir) shifted our thinking about workflows, orchestration, and agentsThe future of agent-to-agent interactions: what happens when AIs negotiate purchases and workflows with minimal human promptsWhy first principles and business outcomes still matter more than any single AI toolHow the PM role is changing: less tool‑chasing, more orchestration, strategy, and clarity about what problem we're actually solvingWhat topics we'd tackle next, like pricing, packaging, and credit models for AI products, and how this series is shaping our own careersAnd much more!You can connect with us and keep following what comes after this AI Tools series:Product for Product Podcast: http://linkedin.com/company/product-for-product-podcastMatt Green: https://www.linkedin.com/in/mattgreenproduct/Moshe Mikanovsky: http://www.linkedin.com/in/mikanovskyNote: Any views mentioned in the podcast are the sole views of our hosts and guests, and do not represent the products mentioned in any way.Please leave us a review and feedback ⭐️⭐️⭐️⭐️⭐️

Hoje o papo é sobre continuous deployment em larga escala! Neste episódio, Paulo Silveira lê e comenta o texto Continuous deployment for large monorepos, do blog da Uber. O artigo explora como a empresa reformulou seu sistema de deploy contínuo para lidar com milhares de microserviços, monorepos gigantes e dezenas de milhares de deploys semanais, ao mesmo tempo que reflete sobre padronização, platform engineering, cultura DevOps e os desafios técnicos e organizacionais de escalar software com segurança. Links: Continuous deployment for large monorepos DevOps e Engenharia de Plataforma: A Experiência do Dev – Hipsters Ponto Tech #504 Estudo de caso: UX e a construção de jornadas de experiências no Santander – Hipsters Ponto Tech #475 Deep Dive: Experiência Dev no Itaú – Hipsters Ponto Tech #474 Case Banco PAN: Engenharia de Plataformas e Dev Experience – Hipsters Ponto Tech #406 Blog do Paulo Matricule-se na Alura e desenvolva sua carreira em tecnologia! Aprenda as tecnologias mais demandadas pelo mercado e conquiste o seu próximo nível com a maior comunidade tech do país. Inscreva-se na newsletter Imersão, Aprendizagem e Tecnologia, escrita por Paulo Silveira. TechGuide.sh, um mapeamento das principais tecnologias demandadas pelo mercado para diferentes carreiras, com nossas sugestões e opiniões. #7DaysOfCode: Coloque em prática os seus conhecimentos de programação em desafios diários e gratuitos. Acesse https://7daysofcode.io/ Produção e conteúdo: Alura Cursos de Tecnologia – https://www.alura.com.br Edição e sonorização: Rede Gigahertz de Podcasts

What if the thing standing between you and your dream job had nothing to do with your resume, your portfolio, or your experience? In this episode, I sit down with my client Colleen, who made a bold career switch from teaching into UX design and discovered that the hardest part of the journey had nothing to do with learning new skills. If you've ever felt like you're doing everything right and still not getting the yes, this conversation is going to hit close to home. Tune in to hear what finally shifted for Colleen and why the moment everything changed wasn't what she expected at all. Get full show notes and more information here:

I have a confession to make. I'm exhausted. In the best possible way after a week in Orlando, Florida for the Kitchen & Bath Industry Show. I have so much to share with you today! My journey started on the Monday before the show began for a travel day, sound check and confirming the final details form the show. In addition to hosting the KBIS Podcast Studio again this year, moderating a panel on the NEXT Stage and recording conversations for the show, I wanted to help you prepare for the show next February in Las Vegas. But Josh, next February is like 11 months away. That's true, but here's a secret. Come a little closer, it's just us. KBIS is the essential American kitchen and bath show, full stop. It's about learning, seeing, connecting and putting all of the pieces together to understand how the American market is setting up for the next year and the trending ideas that have staying power for the next 5-10 years. Designer Resources Pacific Sales Kitchen and Home. Where excellence meets expertise. TimberTech – Real wood beauty without the upkeep You can listen to Convo By Design for the conversations with industry insiders. If I were a designer, I would. I believe that this show tells the stories that you should really know to get a feel for directionality of the industry. Specifiers are the plus of the industry and the ideas emanating from the show this year covered the technology revolution taking place from an AI perspective, but there's more. The kitchen is in the midst of a wholesale change. And it's exciting to see it happen in real time. Learning was a key theme this year. If you were not at the show this year, you are behind the curve. I don't say this to scare you, I tell you this so you make the time to get to the show next year. All three days and plan to see as much as you can. But, I wanted to share some of the key ideas from the show this year. For additional details, check the show notes. Luxury is the measurable outcome of thoughtful design—where performance, longevity, and relevance align to support the way people actually live. Luxury is the removal of friction from daily life. Luxury is durability aligned with intent. Luxury is design that continues to perform long after the purchase is forgotten. Luxury is confidence—in function, longevity, and fit. Luxury is not what you spend. It's what you never have to rethink. The Kitchen as the Primary Investment The kitchen remains the #1 homeowner investment nationwide. Homeowners are willing to exceed budget in the kitchen more than any other space. The kitchen is the most public and social room in the home. It represents identity: “I'm a cook,” “I entertain,” “I host.” Food equals memory; appliances enable those memories. The Expanding Kitchen Ecosystem Kitchens are no longer singular spaces—they expand throughout the home. Secondary kitchens (sculleries, prep kitchens, butler's pantries) are rising. Beverage centers, bars, and wine storage are increasingly common. Coffee stations and en-suite kitchenettes are viewed as lifestyle enhancements. Outdoor kitchens are now expected in many markets. Refrigeration appears in bathrooms (skincare), offices, and guest suites. Multigenerational living drives multi-kitchen design. Post-COVID entertaining shifted bar culture into the home. Value Has Replaced Price as the Primary Decision Driver Consumers rarely regret investing more in appliances. Longevity, performance, and service support define value. Sustainability increasingly aligns with durability. Human-Centric Design Is the New Standard Appliances must be intuitive without relying on manuals. UX consistency across appliances improves adoption. Technology must solve real problems—not create new friction. Appliances Are Expanding Beyond the Kitchen Refrigeration, coffee systems, and specialty appliances now appear throughout the home. Multi-kitchen and multi-generational design is driving specification complexity. Flexibility and modular integration are essential. Practical Innovation vs Feature Saturation Most consumers use only a small percentage of available features. Simplification improves usability, adoption, and satisfaction. Innovation must solve real problems—not marketing problems. Appliances as Infrastructure for Daily Life Refrigerators open dozens of times daily, making ergonomic design critical. Dishwashers, washers, and refrigeration now integrate into behavioral routines. Appliances increasingly support lifestyle efficiency, not just task completion. Quiet Luxury: The New Definition of Premium Quiet luxury shifts focus from visual dominance to experiential excellence. Appliances integrate seamlessly into architecture. Minimal visual disruption supports design continuity. Performance becomes more important than appearance. Identity & Evolution in Design Designers must periodically redefine themselves and their work to remain relevant. Personal growth and evolving priorities shape professional identity and approach. Burnout vs Ambition Burnout is not a badge of honor; it results from overextension and emotional labor. Ambition aligns energy with superpowers and opportunities, creating sustainable growth. Setting boundaries is essential to differentiate productive ambition from harmful overwork. Emotional Labor & Client Management Design work involves managing client emotions, expectations, and second-guessing. Designers act as liaisons between clients, contractors, and teams, absorbing invisible pressures. Managing scope creep and change orders is a practical strategy to protect both energy and profitability. Social Media & Comparison Culture Social media can amplify unrealistic expectations and unhealthy competition. Designers often feel compelled to accommodate clients' desires, sometimes overextending themselves to maintain a positive perception. These core themes coming out of the show this year tell a story that cannot be ignored. The thought process is changing. More human-centric at a time when technology seems to be taking over. Interesting times. Shifting away from that, I want to share two conversations from the show. Brandon Kirschner | Azzuro Living – Control the Process, Control the Outcome: Inside Azzurro Living's Design Advantage Brandon Kirshner of Azzurro Living explains how factory ownership, material innovation, and hands-on experimentation are redefining luxury outdoor furniture—and why relationships and resilience matter more than ever. Recorded live at the Kitchen and Bath Industry Show in Orlando, this conversation with Brandon Kirshner, Partner and VP of Design at Azzurro Living, explores what it means to design, manufacture, and deliver luxury outdoor furniture with complete control over the process. Kirshner shares how owning and operating their own production facility provides a rare advantage in a crowded marketplace. This vertical integration allows Azzurro Living to oversee every step—from raw material sourcing to fabrication—ensuring performance, durability, and design integrity in extreme climates. The conversation also explores the realities of modern product manufacturing: navigating global instability, breaking through to specifiers in an oversaturated marketplace, and the renewed importance of in-person relationships. At its core, this is a story about design leadership, material obsession, and maintaining optimism in a rapidly shifting industry. Vertical Integration Changes Everything Full ownership of production facility ensures quality control Ability to experiment directly with materials and fabrication Eliminates reliance on third-party manufacturing limitations Material Innovation Drives Luxury Performance Products engineered for extreme heat and harsh winters Hands-on experimentation with rope, wicker, and aluminum Performance and longevity are core to brand value Design as the Core Differentiator Industrial design roots shape product philosophy Focus on original forms rather than “me-too” furniture Design enhances lifestyle, not just aesthetics Relationships Still Drive Specification Trade shows like High Point Market remain essential Face-to-face interaction builds trust and long-term partnerships Education through sales teams and specifier outreach is critical Resilience and Optimism in a Volatile Industry Navigating tariffs, supply chains, and global uncertainty Maintaining a solution-oriented mindset Viewing disruption as part of long-term growth In luxury outdoor furniture, control isn't just an operational advantage—it's a creative one. For Brandon Kirshner, Partner and VP of Design at Azzurro Living, ownership of the manufacturing process is the foundation of everything the company does. Unlike many competitors who rely on outsourced production, Azzurro Living operates its own factory, giving Kirshner and his team direct oversight of every detail, from raw materials to finished form. This control allows for something rare in today's manufacturing environment: true experimentation. Working directly with fabricators, Kirshner explores new weaving techniques, tests material durability, and refines structural details. The result is furniture engineered not just to look refined, but to perform in punishing environments—from desert heat exceeding 115 degrees to unpredictable seasonal extremes. Kirshner's path into furniture design began with industrial design studies, where exposure to iconic modernist designers revealed furniture as both functional object and artistic expression. That perspective continues to shape his work today, where innovation isn't driven by trend cycles, but by material curiosity and structural integrity. Launching Azzurro Living in 2020 presented immediate challenges, from supply chain disruption to economic uncertainty. Yet Kirshner views volatility as inevitable rather than exceptional. Experience has taught him that adaptability—not stability—is the constant in product manufacturing. Equally important is maintaining strong relationships within the design community. Trade shows, in-person meetings, and direct engagement remain essential tools for connecting with specifiers and building trust. In an increasingly crowded marketplace, Azzurro Living's approach is clear: control the process, push material boundaries, and let design lead. The result is furniture that reflects not just luxury, but intention. “Owning our factory gives us complete control—from raw material to finished product—and that changes everything.” “Design is the reason people invest in luxury furniture. Performance just makes it last.” “You can't innovate from a distance. Being hands-on with materials is where real progress happens.” “Trade shows and face-to-face interaction still matter because this industry runs on relationships.” “No matter what challenges come—tariffs, supply chain, geopolitics—we'll figure it out. That mindset is essential.” This is Cathy Purple Cherry – Founding Principal | Purple Cherry, freshly installed in the Convo By Design Icon Registry, we caught up at KBIS for a fresh take. Human-Centered Architecture, Resilience, and the Responsibility of Design Cathy Purple Cherry reflects on architecture as a lifelong act of care—supporting people through turbulence, embracing multigenerational living, rejecting trend culture, and using design as a tool for healing, connection, and growth. Recorded live at the Kitchen and Bath Industry Show, this conversation with Cathy Purple Cherry of Purple Cherry Architects explores architecture not as a moment of visual impact, but as a lifelong framework for human support. Purple Cherry shares her philosophy that architecture must evolve alongside the people it serves, especially during times of societal turbulence and personal change. Her work is grounded in human-centered thinking, emotional durability, and the belief that design can create stability amid chaos. The discussion moves beyond aesthetics into deeper territory—resilience shaped by hardship, the responsibility of creatives to provide clarity and options, and the importance of giving back. Purple Cherry also addresses the rise of multigenerational living, generational shifts in work culture, and the dangers of trend-driven design thinking. At its core, this conversation reveals architecture as both a professional discipline and a personal calling—one rooted in empathy, long-term thinking, and service. Architecture as Long-Term Support, Not Momentary Expression Design must serve people across decades, not just visual moments Architecture provides emotional stability during uncertain times Human-centered design is becoming essential, not optional Growth Through Challenge and Adversity Personal and professional hardship builds resilience Lessons learned shape better architects and stronger leaders Teaching and mentoring are essential responsibilities Multigenerational Living as a Cultural Shift Economic and social changes are reshaping American housing Families are staying connected longer Architecture must adapt to evolving family dynamics The Responsibility of Creatives in Times of Tension Architects provide clarity and solutions amid chaos Design can serve as a “relief valve” for societal stress Creatives help people reimagine how they live Rejecting Trend Culture in Favor of Lasting Design Trend cycles are often superficial and misleading True architecture transcends short-term aesthetic movements Enduring design comes from purpose, not prediction Giving Back as a Core Professional and Personal Value Sharing knowledge strengthens the profession Service to others creates deeper meaning in creative work Design is both a gift and a responsibility For Cathy Purple Cherry, architecture has never been about creating a moment. It's about supporting a lifetime. As founder of Purple Cherry Architects, with offices in Annapolis, Charlottesville, and New York City, Purple Cherry has built a practice grounded in the belief that design must evolve alongside the people it serves. Architecture, she explains, is not about solving for a single moment, but about creating environments that support human life over time. That perspective feels especially relevant today. As social, economic, and cultural turbulence reshapes how people live and work, architecture has taken on a new role—not just as shelter, but as emotional infrastructure. Spaces must provide calm, clarity, and flexibility, particularly as multigenerational living becomes more common and families remain connected longer under one roof. Purple Cherry rejects the idea that architecture should chase trends. While the industry often focuses on forecasting aesthetic movements, she believes true design transcends these cycles. Lasting architecture emerges from purpose, empathy, and a deep understanding of human behavior. Her perspective is shaped not only by decades of professional experience, but by personal adversity. Hardship, she explains, builds resilience and strengthens one's ability to serve others. That philosophy extends into her commitment to mentorship, service, and giving back—values she sees as inseparable from meaningful creative work. For Purple Cherry, architecture is both discipline and calling. It is a lifelong process of learning, teaching, and refining. And in a world defined by rapid change, her message is clear: the most important role of design is not to impress, but to support the people who live within it. “Architecture isn't about solving for a moment. It's about supporting people over time.” “Through suffering, we become stronger—and that's what allows us to better serve others.” “Anything in the built environment that can calm us and organize our lives becomes essential.” “Design should never be driven by trends. It should be driven by purpose and people.” “The meaning of life is discovering your gifts. The purpose of life is sharing them.”

In this episode of Product Momentum, we're joined by Bansi Mehta, founder and CEO of Koru UX Design, an enterprise healthcare UX agency supporting some of the US’s largest healthcare technology companies. We discussed the busy intersection of artificial intelligence, product management, and UX Design. Bansi's Sense – Shape – Steer framework helps guide UX design teams as they integrate AI into their products – and avoid the trap of AI's drive toward mediocrity that limits individual creativity and expertise. Here's what we learned: Avoiding the Trap: AI Solutions' Race to Mediocrity AI's ability to rapidly generate hi-fi prototypes and voluminous content brings great benefit, but also significant risk. The risk manifests in mediocrity – i.e., solutions that drive to the mean. This sense of “good enough” stifles designer creativity and diminishes the quality – the Delight – of the final product. “The speed of AI makes it easier than ever to churn screens,” Bansi says. “But it's designed to deliver to that average mean that allows us to say, ‘that works, that makes sense.' And that's really the trap….these days, there's less patience in the industry for discovery and research.” Introducing the Sense – Shape – Steer Framework To combat this new reality, Bansi developed the Sense – Shape – Steer framework to help teams navigate the complexity of building AI-powered products. Sense. Understanding the Problem/Opportunity.“Sense is where you're really creating that sense of what is worth solving,” Bansi explains. “It's the intersection of what the user needs, what insights we have in terms of their challenges, and the opportunities that are present. But we mustn’t stop there. We then look to see what AI can do for us. And where we see the intersection, that’s the sweet spot.” Shape. Designing the AI-Enhanced User Experience.We emerge from the Sense step with rich insights into our user's desired experience, Bansi continues. “And as we approach Shape, we do so with an emphasis on the kind of UX challenge that we are trying to solve – from the user’s perspective. Using a storyboard, we proceed frame by frame to define the user's journey, the problem that we are trying to accomplish.” Steer. Implementing, Evaluating, and Iterating.The Steer step comes once you have built something and you launched, Bansi says. “This is where we define and clearly articulate our AI eval criteria that we've said are critical for product success,” Bansi adds. “I've seen products make it or break it depending on whether they got their AI evals right. It’s one thing to hypothesize that your solution will work. But it’s a completely different thing when you actually try to build sophisticated agentic AI layers where there’s multiple configurations and prompts.”   Broader Insights, Future Outlook The conversation underscores the notion that while AI accelerates development and content generation, it also requires subject matter experts in UX and Product to demonstrate greater vigilance than ever to maintain quality and relevance. The Sense – Shape – Steer framework calls on product teams to think first about user needs before considering whether and how to integrate AI. Our episode with Bansi Mehta feels like the capstone conversation to recent episodes with Nesrine Changuel, Teresa Torres, and Oji Udezue, where we examined bringing Delight to the user experience, re-engaging Discovery in the development process, and adjusting to the Speed of today's AI-driven development. The post 182 / How ‘Sense Shape Steer' Helps UXers Design AI Solutions, with Bansi Mehta appeared first on ITX Corp..

The New Appliance Ecosystem: Translating Value, Technology, and Human-Centric Design The modern appliance conversation has shifted beyond features and price into something far more consequential: value, usability, and human-centered design.  Designers, manufacturers, showrooms, and independent testing labs now operate as an interconnected ecosystem guiding consumers through increasingly complex decisions. The future of appliance specification belongs to those who can translate technology into meaningful, intuitive, lifestyle-driven solutions. Featuring insights from Nicole Papantoniou of the Good Housekeeping Institute, Jeff Sweet of Sub-Zero Group Inc., and Christa Mallinger of AJ Madison, this conversation explores how appliances have evolved from commodities into lifestyle infrastructure—and why education, not persuasion, defines the next era. KBIS Podcast Studio Resources: KBIS AJ Madison NKBA LUXE Interiors + Design SubZero, Wolf & Cove SKS | Signature Kitchen Suite Hearth & Home Technologies Kitchen365 Green Forrest Cabinetry Midea The appliance industry has entered a human-centric phase, where performance, intuitive use, and real lifestyle benefit outweigh raw features or price alone. Designers act as translators of lifestyle, manufacturers as problem-solvers, and showrooms as educators—collectively helping consumers navigate increasingly sophisticated choices. Panelists discussed the shift from feature-driven sales toward performance-driven value, emphasizing longevity, ease of use, and frictionless integration into daily life. They also explored the growing role of education, testing standards, showroom partnerships, and post-installation support in helping consumers fully realize the value of their investment. Technology remains central, but its success depends entirely on reducing friction—not adding novelty. The conversation revealed that the future of appliances lies not in more technology, but in better technology—technology that disappears into the experience. The Appliance Ecosystem Is Interdependent Designers interpret lifestyle and aesthetic needs. Manufacturers engineer performance-driven solutions. Showrooms educate and guide decision-making. Independent testing organizations validate performance and usability. Value Has Replaced Price as the Primary Decision Driver Consumers rarely regret investing more in appliances. Longevity, performance, and service support define value. Sustainability increasingly aligns with durability. Human-Centric Design Is the New Standard Appliances must be intuitive without relying on manuals. UX consistency across appliances improves adoption. Technology must solve real problems—not create new friction. Education Is More Important Than Selling Many consumers buy appliances only once every 10–15 years. Showrooms and testing labs bridge the knowledge gap. Post-installation education helps unlock full product potential. Appliances Are Expanding Beyond the Kitchen Refrigeration, coffee systems, and specialty appliances now appear throughout the home. Multi-kitchen and multi-generational design is driving specification complexity. Flexibility and modular integration are essential. Technology Adoption Depends on Familiarity and Trust Induction adoption accelerates when paired with familiar controls. Consumers embrace technology that feels intuitive and beneficial. Novelty alone does not guarantee long-term value. The modern appliance is no longer just a tool. It's infrastructure. At KBIS, where the industry gathers annually to define its future, a clear shift has emerged. Appliances are no longer judged solely by features or price, but by how effectively they integrate into human behavior. The question is no longer, “What does it do?” but rather, “What does it enable?” This shift has elevated the importance of collaboration across the appliance ecosystem. Designers serve as translators, interpreting the client's lifestyle into functional requirements. Manufacturers act as problem-solvers, engineering solutions grounded in real user needs. Showrooms and retailers bridge the gap between technology and understanding, while independent testing organizations validate claims and ensure products deliver on their promises. This ecosystem exists because appliance decisions have become more consequential—and more complex. Unlike consumer electronics, appliances are purchased infrequently. A homeowner may go fifteen years between purchases. During that time, the category evolves dramatically. Induction replaces gas. Steam ovens expand culinary capability. Refrigeration becomes modular, flexible, and architectural. Appliances no longer exist solely in kitchens, but in offices, bedrooms, outdoor spaces, and wellness areas. With that expansion comes responsibility. Technology must reduce friction, not create it. Christa, Nicole and Jeff all emphasized that human-centric design now drives product development. Appliances must be intuitive enough to operate without instruction, consistent enough to feel familiar, and purposeful enough to justify their presence. Technology for its own sake has limited value. Technology that removes mental load, improves performance, or enhances daily living defines the future. This is where education becomes critical. Showrooms no longer simply display products; they contextualize them. Independent testing organizations evaluate not only performance, but usability, cleanability, and intuitive function. Manufacturers increasingly provide post-installation support, recognizing that the real product experience begins after installation, not at purchase. Value, therefore, is no longer measured in features alone. It is measured in longevity. In reliability. In the confidence that a product will perform consistently over time. In the reduction of friction between intention and outcome. Perhaps most importantly, appliances have become emotional infrastructure. They support gathering, creativity, ritual, and identity. They enable the modern kitchen to function not just as a place of preparation, but as a center of living. The future of appliances will not be defined by how advanced they are. It will be defined by how invisible they become—seamlessly enabling life without demanding attention. And those who understand that distinction—designers, manufacturers, and educators alike—will define the next generation of the built environment.

Career maximalism is a mistake that many high achieving professionals make. When you care too much about your job, you can actually become worse at it. In this video, learn what Career Maximalism is, and why this behavior is everywhere, often rewarded, and quietly working against you.Career maximalism is when your job becomes a major source of your identity and your emotional state rises and falls based on how work is going. It often looks like being a great employee, but there's a tipping point where it clouds your judgment, slows your decisions, and makes everything heavier than it needs to be. Sarah Doody discusses the difference between commitment and emotional over-identification, shares a Reddit thread about treating UX as a job instead of an identity, and gives three practical tips for caring deeply without making work who you are.3 tips for avoiding the trap of Career Maximalism: 1) Build proof of your self-worth outside your job with physical challenges, creative projects, community, etc.2) Practice emotional detachment without disengagement. Detachment isn't apathy, it's clarity.3) Set clear standards and boundaries. When expectations are vague, everything becomes emotional.Resources & Links Mentioned: Reddit thread about treating UX as a job instead of an identity

In this episode, the Metrics Brothers, Dave "CAC" Kellogg and Ray "Growth" Rike dive deep into the ICONIQ State of AI: Bi-Annual Snapshot Report. Published in January 2026, this 44-page report summarizes insights from ~300 software executives on the front lines of building and scaling AI products.Ray and Dave explore a market transition from experimental model races to the challenge of building durable, economically sound products. Key discussions in this episode include:Differentiation Beyond the Model: Why 69% of builders are focusing on vertical AI applications and why 49% cite the application layer (UX and workflows) as their primary competitive edge over the underlying model.The Gross Margin "U-Curve": A look at the shifting economics of AI, where aggregated gross margins are projected to climb to 52% by 2026, even as inference and infrastructure costs remain significant hurdles.Pricing Evolution: The rise of outcome and usage-based pricing, with only 23% of companies still relying on seat-based models as customer demand shifts toward value-aligned monetization.AI as an Internal Force Multiplier: How R&D teams are leading internal adoption, with 83% of companies now measuring success through productivity gains and 59% through direct cost savings.Whether you are a CEO or CFO navigating AI product gross margin concerns or a GTM leader rethinking your proof-of-concept strategy, this episode provides the benchmarks you need to understand the "new phase of maturity" in the AI market.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Mahnaz has lived with volatility, uncertainty, complexity, and ambiguity in ways most product teams never will. In this episode, we talk about what happens when VUCA isn't theoretical, how to avoid becoming an order taker, and how courage, empathy, and initiative can reshape your role as a designer.What if the volatility, uncertainty, complexity, and ambiguity you're facing at work feel overwhelming only because you've never had to live through it in your everyday life?I throw the word VUCA around like it's a trendy framework. Volatility. Uncertainty. Complexity. Ambiguity. But for Mahnaz Hajesmaeili, those aren't abstract concepts; they're lived experience.Originally from Iran, before becoming a product designer, she built a life in China, knowing she could never fully belong there. When COVID hit, borders closed, savings ran out, and the life she had carefully constructed disappeared almost overnight. She returned to Iran, started over, taught herself UX, and eventually rebuilt her career in the United States.That's not “roadmap volatility.” That's real volatility.This week, I chat with Mahnaz to explore how living through that level of instability reshaped her approach to work. Why rejected designs don't shake her. Why unclear strategy doesn't rattle her and why she doesn't default to being an order taker.If you've ever felt overwhelmed by shifting priorities or frustrated by leaders who “don't know what they want,” this episode offers perspective—and practical lessons.Give it a listen. It might change how you define uncertainty.Helpful Links:• Connect with Mahnaz on LinkedIn

Bo Hines is the CEO of Tether US and a former White House crypto advisor who helped shape U.S. digital-asset policy during a critical moment for the industry. This conversation was recorded live at Bitcoin Investor Week in New York. In this conversation, we discuss Bo's work in the White House on crypto policy, including the Strategic Bitcoin Reserve, the GENIUS Act, and the push for regulatory clarity. We also cover stablecoin adoption, why UX matters more than yield, how Tether is connecting global markets to U.S. capital, and why stablecoins could be the on-ramp to the next phase of bitcoin and financial infrastructure.=======================Simple Mining makes Bitcoin mining simple and accessible for everyone. We offer a premium white glove hosting service, helping you maximize the profitability of Bitcoin mining. For more information on Simple Mining or to get started mining Bitcoin, visit https://www.simplemining.io/=======================Arch Public is an agentic trading platform that automates the buying and selling of your preferred crypto strategies. Sign up today at https://www.archpublic.com and start your automated trading strategy for free. No catch. No hidden fees. Just smarter trading.=======================0:00 - Intro0:19 - White House crypto policy & Bo Hines' role2:52 - How important is the Clarity Act?4:10 - Tether: scale, growth & global impact10:49 - Stablecoin yield debate12:37 - Financial access, wallets & the unbanked14:19 - Tether's relationship with Bitcoin15:46 - Reserves, transparency & risk17:24 - Interoperability & the future of stablecoins

On Your World of Creativity, we travel around the world talking with creative practitioners who turn ideas into impact. In this special roundtable episode, Mark brings together leaders from film, animation, hospitality, consumer brands, immersive experiences, and big-tech UX to explore one powerful theme:Teamwork.When creative outcomes depend on dozens—or even hundreds—of contributors, how do you align vision, manage complexity, and still leave room for magic?Today's PanelistsMichael Robinson — Hotel & Hospitality Operations LeaderDiego Pulido — Lead UX Designer, Amazon (formerly Google, Walmart, Adobe, JPMorganChase)Matt McLean — Organic Consumer Juice Brand FounderTom Bairstow — Event, Concert Production & Immersive Visual Experiences Rich Magallanes — Children's & Animated Content ProducerSteven Puri — Focus app creator, ex-studio exec/producer Fox, DreamWorks, SonyTogether, they share real-world lessons from film sets, animation studios, hospitality teams, live events, consumer brands, and product design at scale.In This Episode, We Explore:Creativity as a Team Sport. What great collaboration actually looks like across industries—and why creativity doesn't happen in isolation.Aligning Vision Across Many Contributors. How leaders communicate creative direction clearly when working with writers, designers, engineers, performers, vendors, and operational teams.Conflict, Constraints & Creative Breakthroughs. How budget limits, timelines, technical requirements, and differing opinions can either block creativity—or unlock it.Leadership in Collaborative Environments. What it means to lead when you're not the only decision-maker, how to build trust quickly, and why delegation is essential for scale.Practical Takeaways for Better Collaboration. From film crews to UX teams, each panelist shares what actually helps teams work better together—and what listeners can apply immediately.Final Lightning RoundEach panelist shares one simple action listeners can take this week to become a better collaborator.Huge thanks to our panelists. Be sure to connect with them.https://www.linkedin.com/in/michael-robinson-a6985735/https://www.linkedin.com/in/diegopulido/https://www.linkedin.com/in/matt-mclean-5507733/https://www.linkedin.com/in/tombairstownorthhouse/