Podcasts about UI

The SaaS world is in the middle of a brutal reckoning. Products that looked innovative 18 months ago are quietly becoming redundant — not because markets disappeared, but because the floor rose. In this episode, Jeff Mains sits down with Brian Herr, a 30-year technology and SaaS veteran, to dissect what it actually takes to build a software business that survives — and wins — in the age of AI.Brian brings sharp investor-grade thinking to the conversation, drawing on his work with startups, venture studios, and PE-backed companies. They cover the death of thin-wrapper SaaS, why blocking AI agents is a catastrophic mistake, how security and compliance have become unexpected competitive moats, and the critical distinction between a product that helps and one that solves. If you build software or provide services, this episode is non-negotiable.Key Takeaways4:08 — The value expectation from SaaS platforms is shifting fast. Thin wrappers around someone else's AI model have no future — customers will ask why they're paying when they can do it themselves.4:53 — Companies that survive will be the ones that solve real problems, curate the right data, and give meaningful feedback — not just deliver a slick interface.6:46 — Investor rubrics have changed. A key new question before committing capital: "Can this be replicated as a Claude skill or agent in six months?" If yes, it's not fundable.7:39 — Where physical world meets digital data is a major investment magnet. These companies have stronger moats, are more AI-resistant, and occupy underserved territory.13:53 — Natural language interfaces are no longer a differentiator — they're an expectation. And Brian's crystal ball: local on-device AI will push this even further into everyday life.14:29 — Natural language is democratizing technology for older users. If you don't have a conversational interface, the market will pass you by.20:23 — Agents are no longer just for technologists. CFOs and revenue officers are using them. Blocking agents is a strategic blunder — competitors are advertising agent compatibility while you're building walls.21:00 — The smart play: figure out what people are doing with agents hitting your platform and monetize it. Blocking just pushes them to your API — or to a competitor.21:20 — Every SaaS company needs a quarterly gut-check: What is my value? What do I do well? How do I evolve? A business plan from one year ago doesn't fit today's market.33:04 — Security, compliance, and certifiability are the new defensible moat. You literally cannot vibe-code your way into SOC 2, HIPAA, or AI trust scores. That's the value story.33:59 — The AIUC-1 framework is making AI applications insurable for the first time. MITRE has joined the consortium. If your SaaS uses AI, this becomes part of your trust story.39:50 — The single most important product question: Does it help, or does it solve? Helpful gets cut from budgets. Essential doesn't.43:09 — Going niche gives you orders-of-magnitude higher odds of success. Trying to do what everyone else is doing? Your chance of success drops to 13% or less.47:37 — Brand trust and human relationships are more important than ever. People do business with people. When you become indifferent to your customers, you become a vendor. Vendors don't survive.Tweetable Quotes"If someone opened a fresh ChatGPT window right now and got roughly the same result your product delivers — would your customers notice the difference, or would they even care?" — Jeff Mains"The thin wrappers aren't going to make it very long. What's going to survive is companies that still solve real problems, curate the right data, and give the right feedback." — Brian Herr"One of our investment rubrics now: Can this be turned into a Claude skill or agent in six months? If so, it doesn't make sense for us to invest." — Brian Herr"Natural language interfaces are now an expectation, not a differentiator. If you think you'll eventually get around to it, the market will pass you." — Brian Herr"Helpful solutions get cut from the budget first. Solutions that solve don't. Stop asking whether you can bolt on AI and start asking whether customers actually need YOUR data and process to make it work at all." — Jeff Mains"Agents are becoming for everyone — especially as the interface evolves. Blocking them is evolve or die." — Brian Herr"Figure out what people are doing with agents and monetize it. People will pay for it. By being a blocker, you're just pushing them to find another way." — Brian Herr"People do business with people. When you become indifferent to your customers, you stop being a partner and become a vendor. Vendors have a hard time surviving." — Brian Herr"Success is a journey, not an endpoint. The founders who make it understand you're going to be a little wrong — as long as you course correct in the right direction." — Brian HerrSaaS Leadership Lessons1. Moat = Data + IP + Experience, Not Interface A beautiful UI sitting on top of a commodity model is not a business — it's a countdown clock. Your defensible moat is proprietary data, domain expertise, and institutional knowledge that competitors cannot prompt their way into.2. Run a Quarterly Value Audit Especially in the $5M–$15M revenue range, ask yourself every quarter: Does my business plan still match the market? What do I do well, and how am I evolving? Founders who don't course-correct veer further off target every quarter until they no longer recognize where the target moved.3. Embrace Agents as a Revenue Channel, Not a Threat Your API traffic spikes are signals, not attacks. When agents are hitting your platform, that's demand you haven't monetized yet. Build for agent access, charge for it, and let your competitors play defense while you build offense.4. Compliance and Trust Are Your Unfair Advantage In a world where anyone can vibe-code a competitor over a weekend, the thing they cannot replicate is your certifications, your compliance posture, your years of regulated-market experience, and your insurance-grade AI trust scores (AIUC-1). Make this part of your sales story.5. Help vs. Solve Is the Only Product Question That Matters Helpful products live in discretionary budgets — they're the first cut when times get hard. Products that solve real, urgent problems command non-negotiable budget lines. Every feature you build, every market you target: ask which one it is.6. Stay a Partner, Never Become a Vendor When customers feel like a transaction to you, you become a commodity to them. In the $5M–$15M range, clients know your team personally — that trust is a competitive advantage. Build systems to maintain it as you scale, or risk waking up one day to find out you've been quietly moved to the vendor pile.Guest ResourcesWebsite: https://www.startingblocks.io/LinkedIn (6k): https://www.linkedin.com/in/brian-herr/https://drive.google.com/drive/folders/1kS1WsPODEaqtMnB_g1_Dut6oTv1FYYvXEpisode SponsorThe Futureproof Series - https://www.youtube.com/playlist?list=PLfkXKUPZ5xuOqMPR7_gzGybncTtavyR1NThe Captain's KeysSmall Fish, Big Pond – https://smallfishbigpond.com/ Use the promo code ‘SaaSFuel'Champion Leadership Group – https://championleadership.com/https://jeffmains.com/books/SaaS Fuel ResourcesWebsite - https://championleadership.com/Jeff Mains on LinkedIn - https://www.linkedin.com/in/jeffkmains/Twitter - https://twitter.com/jeffkmainsFacebook - https://www.facebook.com/thesaasguy/Instagram - https://instagram.com/jeffkmains

Notes Fa unes setmanes vam rebre la visita del papa, que va venir per beneir la torre de Jesús de la Sagrada Família, amb la qual el temple s'ha convertit en l'església més alta del món. La presència del pontífex, però, no va arribar pas lliure de polèmiques. En aquest episodi, el Joan i l'Andreu en comenten algunes i aprofiten l'ocasió per parlar de la seva relació amb la religió. Espectacle de llums, focs artificials i drons a la Sagrada Família Bonus El Joan i l'Andreu comenten la controvèrsia que va generar el fet d'impedir a un grup de cantaires de poder cantar "Els segadors" a l'acte de celebració que es va fer al temple. Transcripció interactiva i vocabulari Fes-te membre d'Easy Catalan i tindràs accés a l'ajuda de vocabulari, la transcripció interactiva i el bonus de cada episodi: easycatalan.org/membership Transcripció Andreu: [0:16] Bon dia, Joan! Joan: [0:17] Bon dia, bon dia! Andreu: [0:18] Estàs nerviós? Joan: [0:21] No. Andreu: [0:22] No? Joan: [0:22] Estic impacient. Andreu: [0:23] Impacient. Impacient, per què? Joan: [0:25] Per la setmana que ve, que serà una passada! Andreu: [0:28] Clar, la setmana que ve ja és el Campus. Potser molts dels oients que estan escoltant ara aquest episodi, doncs… ja estem en el Campus, perquè clar, cadascú escolta els episodis quan pot i quan vol, no? Joan: [0:40] Sí, sí, sí. Andreu: [0:41] Però sí, estem aquí a pocs dies de començar la quarta edició d'aquest Campus d'Estiu i farem moltes coses, moltes activitats, ja ho hem explicat. Mentrestant, aquesta setmana és l'última dels cursos de conversa, els cursos intensius, que també han estat tres setmanes, doncs… intenses, de parlar molt. Aleshores, si algú no ha tingut oportunitat, ocasió, no li anaven bé els horaris i no ha pogut apuntar-se a aquests cursos, com ho pot fer ara per practicar, aquests mesos que venen? Joan: [1:14] Sí, doncs nosaltres tenim una supersupersupereina, que és la nostra… en diem "membership platform", però al final és una web on pots accedir a tots els continguts que creem, no només al pòdcast, sinó també als vídeos, i hi han exercicis en el cas dels vídeos, en el cas del pòdcast hi ha la transcripció interactiva, hi ha un munt d'avantatges, no?, als quals poden accedir. I el meu preferit és Discord. Andreu: [1:42] Discord, sí, que ho hem explicat molts cops però, vaja, és on fem les xerrades, les hores de conversa, que… bé, a l'agost potser n'hi haurà menys, perquè farem vacances, però cada setmana hi ha hores de conversa disponibles per a tots els membres de la comunitat. Això també és un missatge que vull donar, perquè segur que hi ha membres de la comunitat que ens donen suport, i ens sembla superbé, o sigui, estem superagraïts, però que potser no han fet el pas encara de participar en aquestes xerrades. I jo vull reivindicar la utilitat d'aquestes hores de conversa, perquè és com si quedéssim per fer un cafè, però ho fem per videotrucada. Aleshores, és una hora de conversa espontània, on hi haurà sempre una persona (nadiua), un membre de l'equip, que dinamitzarà la conversa. I això, no ho sé, crec que és una estona molt relaxada en què parlem de tot i de res, i això, i podeu practicar el català. Joan: [2:43] S'hi pot venir d'oient, també, no cal… si no t'atreveixes, pots estar escoltant allà i poder a la tercera t'atreveixes a dir alguna cosa o… Andreu: [2:49] Sí, és veritat, a vegades hi ha persones, per exemple el Jan, que sempre s'hi connecta mentre està conduint, llavors, només escolta, saps? Es posa allà el mans lliures i escolta. Joan: [3:01] Doncs això, és molt útil i ens ajudeu a fer viable el projecte, o sigui que no hi ha excusa. Andreu: [3:09] I també, per a aquells que viviu a Barcelona, o a Catalunya i podeu acostar-vos a Barcelona, si sou membres de la comunitat podreu participar en les activitats que fem presencials. Joan: [3:19] Ah, vau anar al Museu del Disseny, oi? Andreu: [3:21] Sí. Això, clar… Joan: [3:22] Ah, i com va anar? Andreu: [3:22] Va ser el mes passat, però vam fer una activitat que va ser anar al Museu del Disseny de Barcelona, que jo no hi havia estat mai, a veure una exposició que es diu "Seny i rauxa. Notícia de l'arquitectura catalana", i és bàsicament una exposició sobre com… o sigui, com és l'arquitectura catalana a partir d'aquests dos conceptes del seny i la rauxa, que els hem explicat alguna vegada, però serien com l'ordre i el caos o… Joan: [3:51] I el desordre. Andreu: [3:51] La raó i l'emoció, no? Joan: [3:55] La disbauxa. Andreu: [3:57] La disbauxa, la rauxa. Doncs com aquests dos elements es combinen en el que ha sigut l'arquitectura catalana dels últims 100 anys, no? I llavors hi havia com plànols, molts plànols d'edificis, de cases, però també mobles… I hi havia coses que eren com molt racionals, no? Tot línies rectes, tot molt quadrat, molt funcional, etc., que això seria el seny, però també hi havia coses que eren totalment rauxa. Si tu et mires el plànol, per exemple, de la casa Milà, la Pedrera, allà no hi ha ni una línia recta, o sigui… Joan: [4:30] El Gaudí estava una mica volat. Andreu: [4:32] Sí. O de mobles, també hi ha mobles així noucentistes, que són tot línies corbes. Hi havia una taula que es deia "taula inestable", que es podia plegar de mil maneres i era com… no ho sé, una cosa molt rara. Sí, sí. I… Sí, va ser interessant. Joan: [4:50] I éreu gaires? Quants mecenes hi van anar? Andreu: [4:52] Doncs vam ser, en total… Havíem de ser deu i vam ser nou. Joan: [4:56] Nou! Andreu: [4:56] Sí. Joan: [4:57] Incloent-te a tu? Andreu: [4:57] Incloent-me a mi. Que això, potser algú pensarà: "Ui, que pocs". A veure, la comunitat és bastant més gran, però... Joan: [5:05] Nou és moltíssim! Andreu: [5:06] Sí, està bé. Joan: [5:07] A tu et sembla poc? Andreu: [5:08] Per mi és un grup, un nombre perfecte de persones, perquè amb un grup de 9-10 persones pots trobar lloc en un bar, no? Després nosaltres vam anar a fer un… vam anar prendre alguna cosa i vam anar a sopar. Llavors, sí, és un grup perfecte, allò… pocs i ben avinguts, doncs genial. Doncs això, si voleu participar en les converses en línia o les activitats presencials, feu-vos membres amb l'enllaç… Joan: [5:35] Easycatalan.org/membership. Andreu: [5:37] Perfecte.

Marketing strategy, brand distinctiveness, and the courage to stop blending in: Louis Grenier shares fresh research on why 80% of B2B companies come across as invisible or generic. Digital beige is everywhere, and it's costing brands the very thing they're trying to build, memory.Louis audited 100 B2B tech companies against eight recognised brand asset types, colour, logo, visual device, typography, taglines, human identity, product UI, and sound, and found that only one made it to iconic. He breaks down what's overused (colour and logo), what's sitting completely untouched (sound and human identity, despite being scientifically proven to trigger stronger memory recall), and exactly what to do about it.Louis is a positioning and go to market expert for B2B and founder of STFO. He was previously the host of Everyone Hates Marketers, where he interviewed some of the biggest names in the industry, including a famous conversation with Seth Godin.And if you haven't met Louis before, expect a bit of swearing and some very honest answers!

Since January, 138 striking workers have collected 642 weeks of unemployment benefits totaling nearly $506,000 under Washington's new law — and Washington Policy Center's Elizabeth New (Hovde) argues the employer-funded UI system was never designed to subsidize labor disputes. https://www.clarkcountytoday.com/opinion/opinion-ui-fund-has-a-government-made-leak-harming-workers-and-their-employers/ #UnemploymentInsurance #WashingtonState #LaborPolicy #SB5041 #WorkersRights #EmployerTaxes #WashingtonPolicyCenter #ClarkCounty

Topics covered in this episode: The trusted-publishing debate: how to do it right vs. why you shouldn't trust it JupyterLab 4.6 and Notebook 7.6 are out! Tau – new small, readable terminal coding agent Django Tasks and Django 6.1 Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Consulting from Six Feet Up Connect with the hosts Michael: Mastodon / BlueSky / X / LinkedIn Calvin: Mastodon / BlueSky / X / LinkedIn Show: Mastodon / BlueSky / X Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesday at 7am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Calvin #1: The trusted-publishing debate: how to do it right vs. why you shouldn't trust it https://snarky.ca/how-to-publish-to-pypi-using-github-actions-securely/ (Brett Cannon) and https://blog.yossarian.net/2026/07/07/You-shouldnt-trust-trusted-publishing (William Woodruff) Trusted Publishing (PyPI's OIDC-based auth scheme, also now used by npm, RubyGems, crates.io, NuGet) replaces long-lived API tokens with short-lived, auto-scoped credentials tied to CI/CD machine identity. Yossarian's post: it's purely an authentication mechanism between a machine identity and a package — it says nothing about package safety or quality. PyPI deliberately avoids any "verified/trusted" badge for it, unlike its verified-URL checkmarks. Same logic applies to PyPI attestations: anyone can sign with any machine identity they control, so an attestation's presence isn't itself a trust signal. Bottom line from that post: don't confuse "trusted" (machine-to-machine) with "trustworthy" (human judgment about the package). Snarky.ca's companion piece is more practical: given GitHub Actions compromises in the news, the real fix is 3 concrete steps — run zizmor to lock down workflow permissions/checkout credentials and pin actions to commit hashes, adopt Trusted Publishing to eliminate stored PyPI tokens, and require manual approval via a GitHub environment before any publish job runs. Takeaway for listeners: Trusted Publishing is good hygiene for how you authenticate to PyPI, but it's not a substitute for securing your CI pipeline itself — or for actually vetting the packages you install. Michael #2: JupyterLab 4.6 and Notebook 7.6 are out! Michał Krassowski's rundown - a chunky minor release: 68 features, 97 bug fixes, 95 contributors, one of the biggest ever. Scratchpad console (Notebook 7.6 headliner) - a console next to your notebook sharing its kernel, for throwaway experiments. Ctrl+B. Jump to last-edited cell - new commands hop through recently edited cells. File browser glow-up - Date Created column, editable breadcrumbs with Tab-completion, and Open in Terminal. Debugger - sources open in the main area, floating step/continue overlay, live kernel-sources filter. Custom layouts (Lab) - activity bar top/bottom, draggable panels, four-way tab splits, per-panel Ctrl+scroll zoom. ~5x faster extension builds - webpack → Rspack, and jupyter-builder means no full Lab install needed to build extensions. Keyboard/a11y - add shortcuts from the UI (no JSON), Find & Replace in Edit menu (Ctrl+H). Calvin #3: Tau – new small, readable terminal coding agent Tau – new small, readable terminal coding agent (Python 3.12+), built as both a working tool and a teaching project for how coding agents work under the hood Install via uv tool install tau-ai, pipx, or pip; ships a tau CLI Three-layer architecture: tau_ai (provider-neutral model layer) → tau_agent (reusable "brain": messages, tools, events, loop) → tau_coding (CLI/TUI, file & shell tools, sessions) Supports OpenAI, Anthropic, OpenAI Codex, OpenRouter, Hugging Face, and custom/local OpenAI-compatible endpoints Built-in tools (read/write/edit/bash), durable JSONL sessions with resume/branching, project instructions via AGENTS.md, and context compaction Core harness is UI-agnostic — same brain can power the TUI, print mode, or a custom frontend — usable as a standalone library too Michael #4: Django Tasks and Django 6.1 Django 6.0 finally ships first-party background tasks (django.tasks) - out of Jake Howard's DEP 14, accepted May 2024, after two decades of everyone bolting on Celery/RQ/Huey. It's an API, not a worker. Django handles task definition, validation, queuing, and result storage - it does not execute them. You bring the backend. The default backend traps people. ImmediateBackend runs tasks inline on the request thread and blocks until done - so out of the box .enqueue() backgrounds nothing (a 5-second task means a 5-second response). The other built-in, DummyBackend, runs nothing at all. Both are dev/test only. Nice API otherwise: slap @task on a function, call .enqueue(), get back a TaskResult you look up later by id - with async twins like aenqueue(). Gotcha: args and return values must survive a JSON round-trip, so a tuple sneakily comes back as a list. The community local backend to know: django-tasks-local by Chris Beaven (SmileyChris). A ThreadPoolExecutor backend that gives real background threads with zero infrastructure - no Redis, no Celery, no database - plus a ProcessPoolBackend for CPU-bound work → github.com/lincolnloop/django-tasks-local Its catch: results live in memory, so pending tasks vanish on restart or deploy. Great for dev and low-traffic production; for persistence, drop to Jake Howard's django-tasks (DatabaseBackend + worker command). Extras Calvin: Fixing the dictionary with Python 3.14 — Hugo van Kemenade stumbled on - and got fixed - a markup bug in the OED's own citation of a 1706 use of the pi symbol. Michael: Bunny DNS is now free Jokes: What's the object-oriented way to become wealthy? Inheritance To understand what recursion is... You must first understand what recursion is 3 SQL statements walk into a NoSQL bar. Soon, they walk out They couldn't find a table.

Jeremy catches up with friend of the show, Chris Nguyen, who's rebuilt his business twice since his last appearance. They talk about why designers can't count on job security the way they used to, what it actually takes to build something of your own, and why the perfect plan is a myth worth abandoning early.If you can't count on your job to protect you, what are you doing to protect yourself?Chris Nguyen has already rebuilt his business once since he last joined the show, and by the time this episode airs, he'll be in the middle of doing it again. He built UX Playbook into a recognized education brand, tried a community project called Backlog that fizzled out, took several months off to recover, and landed on Rectangles: a live-stream and newsletter brand built around the design conversations he wishes he'd had years ago. None of it happened on a straight line, and that's kind of the point.Jeremy and Chris talk candidly about why designers can't afford to treat a single employer as their whole safety net anymore. Jeremy's own team recently made a drastic tooling change that upended how designers on his team work day to day, and he connects that disruption directly to a bigger argument: if the ground can shift under you that fast, having something outside your job, whether it's a side project, a creative outlet, or a small business, isn't optional anymore. It's how you stay steady.The conversation keeps circling back to a simple, unglamorous truth: nobody has the plan figured out in advance. Chris talks about sitting on the Rectangles idea for the better part of a year before finally committing to a two-week sprint to get it out the door, and admits he still doesn't fully know what it'll become. Jeremy pushes on that idea with his own reflections on burnout, reinvention, and why doing something just for yourself, outside of work entirely, might be the most stabilizing thing a designer can do right now. Give this one a listen if you've been sitting on an idea and waiting for the right moment.Topics:• 03:16 – Catching up on 100 episodes and the never-ending edit grind• 05:33 – What Chris has been building since his last appearance• 06:20 – The funk, the time off, and the shift from product to media• 09:37 – Why running a media company means the content is the product• 11:36 – Chris breaks down what Rectangles actually is• 14:04 – Going beyond UX and UI into big D design• 15:26 – The early 90s aesthetic behind the Rectangles brand• 20:10 – Why Jeremy's team just walked away from their Figma license• 22:42 – Designing straight into Cursor with a component library• 29:20 – The case for building income outside a single job• 32:36 – How UX education content has changed as the industry shifts• 34:22 – The unglamorous side of building something online• 52:56 – Why a latte art post outperformed a bias breakdown on LinkedIn• 57:03 – Chris's closing advice on ideas versus execution• 58:23 – Consumption versus creation and why the balance matters• 58:43 – Where to find Chris and the Rectangles launch detailsHelpful Links:• Connect with Chris on LinkedIn• Subscribe to Rectangles• Get your UX Playbook—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

Wann baust du einen KI-Agenten — und wann ist eine maßgeschneiderte Applikation die bessere Wahl? TJ erklärt den Unterschied anhand eines konkreten Bewerbungs-Tools und zeigt, warum autonome Agenten oft enttäuschen, während kleine, zweckgebundene Apps überraschend viel leisten. Wer KI vom Anwendungsfall her denkt, gewinnt — und das hörst du in dieser Folge. Vom Anwendungsfall denken — nicht von der Technologie TJ eröffnet mit einer Forderung, die in vielen KI-Kursen fehlt: Fang nicht bei der Technologie an, fang beim Problem an. Was kostet dich gerade Zeit? Wo liegt der echte Schmerzpunkt? Für Holger aus dem Kurs war die Antwort klar: Bewerbungen. Statt lange über das richtige Tool zu diskutieren, stellt sich die konkrete Frage — wie oft durchläufst du diesen Prozess, und lohnt es sich, ihn zu automatisieren? Genau diese Haltung, konsequent vom Intent her zu denken, ist der Ausgangspunkt für alles, was TJ in dieser Episode auseinandernimmt. Warum der Bewerbungsagent enttäuscht hat Im Kurs haben die Teilnehmer die klassische Lernkurve durchlaufen: Prompts, dann Workflows, dann Custom GPTs mit Mentions-Funktion, dann eigene Skills — und schließlich Agenten. Der Bewerbungsagent hat fünf Stellen rausgesucht und eine Entwurfs-Mail geschrieben. Klingt solide, war aber „nicht wirklich ergiebig". Der Höhepunkt kam, als ein Agententeam bei Perplexity — Chef-Agent, Researcher, Autor, Analyst — eine Stunde lang im Hintergrund gearbeitet hat, ohne einen wirklichen Durchbruch zu liefern. Das ist der Moment, an dem TJ den Schalter umlegt: Nicht mehr Agent, sondern Applikation. Drei konkrete Unterschiede: Plattform, Zugang, Fähigkeiten TJ zieht eine klare Linie zwischen beiden Welten. Erstens die Plattform: Agenten laufen in einem „Harness" des jeweiligen Providers — OpenAI, Claude, Perplexity. Sie sind nicht in der freien Wildbahn. Eine App dagegen braucht eine eigene Basis: lokal auf dem Rechner, auf Vercel oder einem eigenen Virtual Private Server. Zweitens der Zugang zur Außenwelt: Agenten nutzen vorgefertigte Konnektoren, die direkt im Provider-Interface konfigurierbar sind — Gmail, HubSpot, fertig. Apps sprechen über APIs und MCPs mit der Außenwelt, was mehr Flexibilität bringt, aber eigene API-Keys und ein bisschen Setup erfordert. Drittens die Fähigkeiten: Im Agenten entwickeln sie sich dynamisch, in der App sind sie definiert und codiert — kontrollierbarer, aber auch bewusster gestaltet. Wegwerf-Software ist kein Makel — sie ist das Ziel Holgers Bewerbungs-App wird in ein paar Wochen in der Schublade verschwinden. Nicht weil sie schlecht ist, sondern weil er dann einen Job hat. TJ nennt das Wegwerf-Applikation — und meint es als Kompliment. Eine App, die einen Menschen präzise durch eine Lebensphase begleitet und danach irrelevant wird, hat ihren Zweck erfüllt. Das ist effizienter als ein generisches Agenten-System, das für alle funktionieren soll und deshalb für niemanden wirklich passt. Die Pointe: Der Prompt, den du für den Agenten geschrieben hättest, funktioniert als Anforderungsbeschreibung für die App genauso gut — der Aufwand ist ähnlich, der Output kontrollierbarer. Fazit: Apps schaffen ein Universum für einen Anwendungsfall TJ fasst es pointiert zusammen: Mit einer eigenen Applikation baust du dir ein Universum für genau einen Zweck — wiederholbar, steuerbar, erweiterbar. Das ist etwas, das ein Agent in dieser Form nicht leisten kann. Der Einstieg ist einfacher als gedacht: einmal klären, wo die App lebt, einmal die nötigen Schnittstellen andocken — und dann läuft ein Werkzeug, das exakt auf deine Bedürfnisse zugeschnitten ist. Wer KI wirklich nutzen will, denkt nicht in Tools, sondern in Problemen. Und baut dann das Kleinstmögliche, das dieses Problem löst. Das nimmst du mit: • Wenn dein Agent lange läuft und trotzdem keine brauchbaren Ergebnisse liefert, ist eine kleine App mit klarem UI die bessere Wahl. • Denk KI immer vom Anwendungsfall her: Welches Problem taucht häufig auf, und lohnt es sich, es zu automatisieren? • Der gleiche Prompt, der einen Agenten beschreibt, taugt direkt als Anforderung für eine App — der Aufwand ist ähnlich, der Output kontrollierbarer. • Agenten leben im Harness des Providers (OpenAI, Claude, Perplexity). Apps brauchen eine eigene Plattform — lokal, auf Vercel oder einem eigenen Server. • Apps verbinden sich über APIs und MCPs mit der Außenwelt, nicht über vorgefertigte Konnektoren — mehr Flexibilität, aber mit eigenem API-Key-Setup. • Wegwerf-Applikationen sind kein Verschwendung: Eine App, die Holger durch die Jobsuche bringt und danach wegkommt, ist ein Erfolg. Kapitel: 00:00 Hook: Die Welt der Wegwerf-Applikationen 00:26 KI vom Anwendungsfall her denken 01:36 Rückblick: Von Prompts über Workflows zu Agenten 02:46 Warum der Bewerbungsagent enttäuscht hat 03:41 Der Shift: Agent vs. Applikation 05:27 Unterschied 1: Plattform und Hosting 06:53 Unterschied 2: Konnektoren vs. API / MCP 08:06 Unterschied 3: Fähigkeiten — Skills vs. Code 08:43 Was das konkret für dich bedeutet Noch mehr von den Koertings ... Das KI-Café ... jede Woche Mittwoch (>350 Teilnehmer) von 08:30 bis 10:00 Uhr ... online via Zoom .. kostenlos und nicht umsonst Jede Woche Mittwoch um 08:30 Uhr öffnet das KI-Café seine Online-Pforten ... wir lösen KI-Anwendungsfälle live auf der Bühne ... moderieren Expertenpanel zu speziellen Themen (bspw. KI im Recruiting ... KI in der Qualitätssicherung ... KI im Projektmanagement ... und vieles mehr) ... ordnen die neuen Entwicklungen in der KI-Welt ein und geben einen Ausblick ... und laden Experten ein für spezielle Themen ... und gehen auch mal in die Tiefe und durchdringen bestimmte Bereiche ganz konkret ... alles für dein Weiterkommen. Melde dich kostenfrei an ... www.koerting-institute.com/ki-cafe/ Mit jedem Prompt ein WOW! ... für Selbstständige und Unternehmer Ein klarer Leitfaden für Unternehmer, Selbstständige und Entscheider, die Künstliche Intelligenz nicht nur verstehen, sondern wirksam einsetzen wollen. Dieses Buch zeigt dir, wie du relevante KI-Anwendungsfälle erkennst und die KI als echten Sparringspartner nutzt, um diese Realität werden zu lassen. Praxisnah, mit echten Beispielen und vollständig umsetzungsorientiert. Das Buch ist ein Geschenk, nur Versandkosten von 9,95 € fallen an. Perfekt für Anfänger und Fortgeschrittene, die mit KI ihr Potenzial ausschöpfen möchten. Das Buch in deinen Briefkasten ... https://koerting-institute.com/shop/buch-mit-jedem-prompt-ein-wow/ Die KI-Lounge ... unsere Community für den Einstieg in die KI (>2800 Mitglieder) Die KI-Lounge ist eine Community für alle, die mehr über generative KI erfahren und anwenden möchten. Mitglieder erhalten exklusive monatliche KI-Updates, Experten-Interviews, Vorträge des KI-Speaker-Slams, KI-Café-Aufzeichnungen und einen 3-stündigen ChatGPT-Kurs. Tausche dich mit über 4.000 KI-Enthusiasten aus, stelle Fragen und starte durch. Initiiert von Torsten & Birgit Koerting, bietet die KI-Lounge Orientierung und Inspiration für den Einstieg in die KI-Revolution. Hier findet der Austausch statt ... www.koerting-institute.com/ki-lounge/ Starte mit uns in die 1:1 Zusammenarbeit Wenn du direkt mit uns arbeiten und KI in deinem Business integrieren möchtest, buche dir einen Termin für ein persönliches Gespräch. Gemeinsam finden wir Antworten auf deine Fragen und finden heraus, wie wir dich unterstützen können. Klicke hier, um einen Termin zu buchen und deine Fragen zu klären. Buche dir jetzt deinen Termin mit uns ... www.koerting-institute.com/termin/ Weitere Impulse im Netflix Stil ... Wenn du auf der Suche nach weiteren spannenden Impulsen für deine Selbstständigkeit bist, dann gehe jetzt auf unsere Impulseseite und lass die zahlreichen spannenden Impulse auf dich wirken. Inspiration pur ... www.koerting-institute.com/impulse/ Koerting Institute auf die Ohren ... Wenn dir diese Podcastfolge gefallen hat, dann höre dir jetzt noch weitere informative und spannende Folgen an ... über 500 Folgen findest du hier ... www.koerting-institute.com/podcast/ Wir freuen uns darauf, dich auf deinem Weg zu begleiten!

« ARRÊTEZ DE RECRUTER DES HUMAINS » — la pub qui s'affiche partout à San Francisco

Wes' brother's PC is toast, making this the perfect moment to switch him to Linux. If our ambitious plan doesn't scare him away first.Sponsored By:Jupiter Party Annual Membership: Put your support on automatic with our annual plan, and get one month of membership for free!Managed Nebula: Meet Managed Nebula from Defined Networking. A decentralized VPN built on the open-source Nebula platform that we love.Support LINUX UnpluggedLinks:Web Boost — Send us a boost via sats or USD

Frank releases UI.md, a practical markdown guide distilled from 25 years of software development that captures essential UI/UX principles to help AI (and developers) build better interfaces. The hosts dive deep into why users expect consistency, how small details create polish, and why understanding your users' mental models beats flashy design every time—with real-world examples from their own apps. Follow Us Frank: Twitter, Blog, GitHub James: Twitter, Blog, GitHub Merge Conflict: Twitter, Facebook, Website, Chat on Discord Music : Amethyst Seer - Citrine by Adventureface ⭐⭐ Review Us ⭐⭐ Machine transcription available on http://mergeconflict.fm

Today we are talking about Content, syndication, and Synchronization between Drupal Sites with guest Thiemo Müller. We'll also cover Drupal core 11.4 as our module of the week. For show notes visit: https://www.talkingDrupal.com/560 Topics Origins and Use Cases Hub Model and Flexibility Media Sync and Governance Composable Pages Challenge Governance With Blocks Canvas And Recipes Real Time Syndication Scaling To Thousands GEO And AEO Explained GEO Audits And Loops ContentSync Recommendations Permissions And Drupal 11 AIM Assess Improve Monitor Boosting Drupal AI Presence Ecosystem Alignment Signals Recency And Messaging Tips Resources Content Sync Content Sync A-I-M Content Sync Drupal Module Deprecated extensions meta issue GEO Generative engine optimization Semrush Peec ai Otterly ai Profound Guests Thiemo Müller - content-sync.io thiemo Hosts Nic Laflin - nLighteneddevelopment.com nicxvan John Picozzi - epam.com johnpicozzi Ashraf Abed - drupito.com ashrafabed MOTW Correspondent Martin Anderson-Clutz - mandclu.com mandclu Brief description: Are you excited for a feature release of Drupal core that delivers even more performance acceleration, a modernized developer experience, and a slew of administrator and editor improvements? Drupal core 11.4 delivers all that and more Module name/project name: Drupal core 11.4 Brief history How old: created on July 1 2026 by catch of Tag1 Changes Performance improvements When Drupal 11.3 was released, we talked about what a massive performance jump it represented, the biggest improvement in a decade. 11.4 has done it again! Database queries are reduced by half, across a range of requests due to optimizations in how entity fields are loaded. Overall, that represents a nearly ⅔ improvement for database and cache lookups on a cold cache compared to Drupal 11.0 or 10.6 Entity listing queries have also been refactored to use fewer table joins, reducing slow queries. Additionally, the link field introduces a resolvable_uri property and token, which returns a ready-to-use front-end link (like /#main-content) right out of the API instead of raw internal URIs, which will be a huge benefit for anyone using Drupal for decoupled and JSON:API-based use cases Applying recipes in Drupal 11.4 is significantly faster, reportedly twice as fast, and that includes installing Drupal CMS Drupal now supports Brotli compression, which should yield 15-25% better compression of CSS and JS assets Security Drupal 11.4 offers a new password hashing algorithm, argon2id, that will become the default in Drupal 12 later this year Also, the drupal/core-recommended package no longer strictly locks minor versions for critical dependencies like Guzzle, Twig, or Symfony Polyfills, making it easier to immediately receive important security updates Drupal's default robots.txt now blocks well-behaved search crawlers from indexing search queries, helping to solve a potential source of traffic overload on sites using faceted search Developer experience There's been a significant shift towards the adoption of PHP Attributes in recent Drupal releases, and 11.4 is no exception You can now define application routes directly within your PHP controller and form classes using the Symfony #[Route] attribute. This drastically reduces the need to jump back and forth into *.routing.yml files The new #[Bundle] attribute allows developers to define bundle classes directly, eliminating the need to write old-school entity_type_info or entity_type_info_alter hook implementations. All core .theme and .theme-settings.php files have been moved entirely to PHP classes. Support for legacy .theme files will be dropped in Drupal 13. Furthermore, dozens of core .module files have been fully converted into clean PHP classes Front controllers now leverage the symfony/runtime component to isolate bootstrapping logic from request handling, preparing the Drupal core architecture for advanced environments like FrankenPHP, known for its blazing-fast performance, among other features Drupal 11.4 introduces a native, extensible command-line tool (./vendor/bin/dr) built in partnership with Drush maintainers. This kicks off a transitional period where Drush commands will gradually be migrated to the core native binary Also, the new HttpKernelUiHelperTrait for kernel tests lets developers make mock HTTP requests and assertions without running the full Drupal site installer. This allows many traditional browser tests to be rewritten as much faster kernel tests Editor experience Drupal 11.4 includes the new Default Admin theme, a version of the popular Gin admin theme, now in core The Navigation module is now enabled by default, replacing the legacy toolbar CKEditor once again has a fullscreen button available without a contrib add-on, allowing editors to fully immerse themselves in a WYSIWYG element's content, great for working on long-format pieces Deprecations The initial 11.4.0 release actually removed a number of core recipes. They were since restored in an 11.4.1 release, but they are deprecated and will be removed from Drupal 12 Also on their way out are a number of modules, including Ban, Contact, Field Layout, History, Migrate Drupal and its UI, Search, Settings Tray, Shortcut, Telephone, Toolbar, and a flag module called layout_builder_expose_all_field_blocks. For themes, Claro, Stable 9, and Olivero are all deprecated, and will be moved from core. We'll include the meta issue about these deprecation in the show notes, and if any of these are important to you, it's worth tracking where they are on the path of moving to contrib

https://youtu.be/1Fjm6P43i8k Anastasia Golovko, President and CEO of Tino Digital Agency, is helping businesses build user experiences that work by creating intuitive digital experiences that simplify complex systems and improve the way people interact with technology. By combining strategy, design, and engineering, she helps startups, enterprises, and organizations in highly regulated industries deliver products that people actually enjoy using.  In this conversation, Anastasia introduces the Agency Growth Framework: Build Relationships, Find What’s Broken, Help Your Team Flourish, Nurture Creativity, and Offer Your Customers Relief. She explains why trust is the foundation of lasting client relationships, how identifying user friction creates immediate value, and why empowering creative teams leads to better products and stronger business outcomes. Anastasia also shares how eliminating user friction through live UX audits accelerates business growth, improves customer satisfaction and conversion rates, and demonstrates the importance of user experience in government digital services. — Build User Experiences That Work with Anastasia Golovko  Good day. Steve Preda here with the Management Blueprint Podcast, and my guest today is Anastasia Golovko, President and CEO of Tino Digital Agency, a team of experts specializing in helping companies and startups achieve their goals by providing comprehensive solutions in strategy, design, and engineering. Anastasia, welcome to the show.  Thank you so much. Thank you for having me.  Well, it’s very interesting that you combine strategy, design, and engineering. I have not seen this combination before. So where does this come from?  The foundation for all of this is user experience and the connection to the user that we’re trying to build for many companies. As soon as you start thinking about the user and their experience, they get to enjoy the product more and more, and they get to find the buttons that the clients want them to find in their product.  Okay. So basically, you have to have a strategy first, then you come up with a design, and then you engineer it. Yeah.  The design supports the strategy, and then you have a good product.  Exactly. We also work with startups, and we work with established companies. With startups, we encourage them to reach the market as fast as possible so that they get to prove their hypothesis. Because up until it reaches the customer and starts getting feedback from the customer, it’s all in numbers. It’s all in the… But it’s still a hypothesis of how it will perform. The sooner we’re there—with as few features as possible, while still being valuable—the better. Some clients call it an MVP, and some clients call it a Minimum Lovable Product. Then they can start getting feedback, and those relationships start building between the final user and the product. Yeah, that makes sense. And Lovable is actually also a vibe coding platform, isn’t it? Yeah.  Yeah. That’s right. Maybe that’s where it comes from—that you can get a lovable MVP very fast with vibe coding. So let me start by asking you, what is your personal “why,” and how are you manifesting it in Tino Digital Agency? I recalled such a wonderful story that I want to share with everyone from my childhood. I was born when it was still the USSR. I’m originally from Ukraine, and when the USSR fell apart, everyone was poor. When I was 13 years old, I remember talking to my friends and saying, “When I grow up, I’m going to drive a Jeep Grand Cherokee.” They completely mocked me. They said, “That’s impossible.” I remember that question to this day: “Do you even know how much money that costs? Have you ever seen that much money?” My brain just flipped a switch. I thought to myself, “That’s just money.  It can be made.” Money is there to be made. That mindset, I think, separated me from my childhood friends by giving myself permission to dream big without limitations. Just like somebody dreamed about sending a car to space, they gave themselves permission to think that up. So, ironically, I ended up owning a Jeep Grand Cherokee. We had it for 10 years. A while ago, there were floods in Houston, and the Jeep Grand Cherokee has very good ground clearance, so we were able to get through them.  After I got that car, I remembered this conversation from back in the day. So we started our agency with zero capital and zero connections. Without growing into connections here, that created a set of blocks. So we were attracted to subcontracting for top agencies in LA, Silicon Valley, and Switzerland. Eventually, we landed a massive contract with one of the top Swiss banks, modernizing their digital products as well as rethinking their strategy toward the younger generation. That was my wake-up call. I realized, “Wow, we’re incredibly good at this.” The Swiss bank had such high standards that we had to fit within their branding. They couldn’t rebrand just because we thought something up.  We had to fit into their tight branding and produce a good product within those guidelines. It made me look around the States and realize that—I had already lived here for more than half of my life—we have so much anxiety around finance and healthcare. After that moment, I decided to move our company in those two directions. Maybe our team can actually reduce that anxiety and make a real dent in the well-being of the nation—the well-being of the people who interact with products that are convenient, don't add to the problem, but instead resolve it and make it intuitive.Share on X  Yeah. That’s great. I never heard the combination of finance and healthcare phrased this way—that both of them are a big source of anxiety for people. And if you can fix their problems in these areas, then you can actually reduce their anxiety and increase their well-being. That makes perfect sense. I also love the example of, you know, “what the mind can conceive and believe, it can achieve” kind of thing. Visualization is very powerful. It worked in my life as well.  So this is a podcast about frameworks. It’s called Management Blueprint, and what I’m looking for is some kind of, it could be a mental model. It could be some kind of process. Something that simplifies the world and allows you to see things more clearly, make better decisions, or create a bigger impact. So what comes to mind?  I love this question. I love this question, and I love viewing the answers from your other guests to this question, and how everybody approaches it differently. For different people, it is their own world that they built. So, in our world, first, we build relationships. No cold calls. We build these relationships with our clients and with our team.Share on X The digital agency market is oversaturated with low-quality providers who sign the client, then disappear for four weeks, come back, and drop a product that doesn’t relate at all.  Then they come to us. Some of our clients come to us and say that the agency disappears. It’s really important for us to stay in contact. So our first step is building these in-person relationships. That also means visiting conferences, visiting events, showing up wherever it’s needed—before, during, and after the process as well. So I think what we do by building relationships is that we sell trust, not just the code or the design.  Of course. Yeah, that makes sense. In this AI age, when there’s so much noise. When you’re online, you don’t know what’s real and what’s not. But when you meet someone in person at a conference, then you know they’re a flesh-and-blood human being. Yes. And also, describing to a person that user experience is important, that I can do it, and that I do it really well doesn’t really give them value. Rather than just telling them, I can look through the site face to face and find certain things that are broken right there on the spot. It’s not a simple presentation. It’s already proof of my value. So that helps. So that’s first. The next one is the human element. The human element of the team. Our designers and developers are not machines. Each of those teams needs its own approach. They need their own conditions to flourish.  They need their own conditions to be successful. If we keep a creative person locked into FinTech, compliance-driven products for too long, they’ll burn out. It’s two-faced. We try to avoid that as much as possible, but we consider what they’re telling us. We listen. If a designer tells us something isn’t working out, or they’ve hit a creative block, we try to rotate them, give them different exercises, give them a break, and so on. So, listening and rotating. Rotating between different types of projects. But whenever the FinTech project comes back, we need the FinTech team to work on it.  So at this point… Sorry, I have a clarifying question. The previous point was helping your team flourish, basically. By rotating them, is it about helping them gain more experience, not burn out, or create more ideas? So what’s behind this idea of rotating?  It’s the creativity that for it to exist, it needs different challenges. Let’s imagine a FinTech dashboard. It’s tables. It’s tabular. It’s a similar task that they’re solving. They’re solving how to convert tabular formats into digestible formats. Converting tabular formats into something that the user can comprehend quickly. With a food delivery app or a calorie-tracking app, they get to add more colors, use different styles, and solve a different problem. So I think that when they get to solve different problems, they keep their full creative potential.Share on X  Yeah. You’re not allowing them to fall into a rut and just go on autopilot, basically.  Not everyone. Not everyone. We mainly listen. We listen, and whenever we hear the signs that a person is asking for something more creative, we do that. Happy humans build great products.  Yeah, that’s true.  And we’re a service company. Our team is our main advantage.  All right. So you build relationships, you find what’s broken, you help your team flourish, you stimulate or nurture their creativity. So what’s next? What’s the last piece of the puzzle? Is there another piece of the puzzle, or is that it?  Yes. The next step is to provide relief. We could be building everything for everyone, but over time—my company is 10 years old now—and over those 10 years, we became really good at turning something that is complex or heavily regulated. For some reason, clients know that it has to be intuitive. To be competitive, it has to be convenient. Sometimes it’s in industries where they have a lot of competition. Whoever is more convenient for the client will move forward. Not for the client—I mean, for the final user.  Apple philosophy. Yeah. So, to be a relief, to be their right hand, we need to be looking for companies that are undergoing some changes. Maybe it’s a digital transformation. Once, we completed a merger for companies that were providing hosting and security. They merged into one. They needed all of that infrastructure to be brought together. Their internal teams were too busy doing their internal tasks. So for that overhaul, that digital transformation, we come in as a relief squad with the skill to make it intuitive. So we can call it becoming a reliefShare on X Offering relief.  Yeah.  Yeah. It’s a mindset, really. It’s thinking about, “How can I make these people’s lives easier? can I help them do their work better, with less mental strain and less cognitive load?”  And for them, in the end, it’s for their users as well. I often say that we build experiences that work. Every business owner comes up with an idea of what they want their end users to do, but they don’t always find it. They don’t always find it because, somewhere along that chain, there was a UX designer, a UI designer, and a developer who all worked together on that user experience, and something didn’t go right. Typically, with a little bit of data, or just complaints, or just suggestions, the owners and the product owners know what’s not working.  They can see that some registrations aren’t happening, some sign-ups aren’t happening, some applications aren’t being filled out completely, and so on. Some tickets aren’t getting booked. Marketing worked fine, but somehow the carts are being abandoned. Why is my cart being abandoned at the moment when we spend so much money on marketing? We don’t do marketing. We know what marketing is trying to do, but what happened then? So you’re treating the client’s effort as a complex system, and you want to optimize that system so that everything serves the same purpose, it’s simple, and you have a team that keeps improving it.  Yeah. If they have some kind of Google Analytics or Hotjar analytics, it’s always easier to put a number to the words. We can say that this user experience is broken because the consumer is overloaded at the cart. Maybe you’ve seen those shopping carts where there’s a timer, additional upsells, and also some additional decisions they have to make, whether they want insurance or not, and so on. It becomes very overloaded. Sometimes people just close it. It’s too much. So we look at the cart and see how it can be improved to be more pleasant.  Yeah. That’s great. That’s fantastic. So, switching gears here a little bit, what drives growth in your particular business?  That’s what I actually started to talk about. I now realize that this friction audit, UX review, UX evaluation—that gives immediate value. It is the foundation of our business growth.Share on X The thing is how it’s delivered. If I present it in a room and talk about user experience, how it’s important, and how to lay things out, the perceived value is hard to communicate. It can sound abstract unless we’re talking about something specific. Our real growth driver is a live, maybe casual, friction audit.  For example, at a recent conference, a business owner asked me to look at her website. Right there on my phone, I pointed out a few specific UX blocks that were delaying the purchase. I was like, “Look here. Every second user is delayed. They’re drifting away. They have to make two extra clicks to get where they need to be. This click is not necessary. You can remove it. You can place it here instead.” Control dropped. She said, “That’s exactly what I needed.” When business owners see exactly where they’re losing money in real time, that value is undeniable. So you go to these conferences, then you talk to people, they show you their website, and you point out a couple of friction points. Immediately they see, “Wow, you guys could fix that.” That already is value. Then, probably, if you found two friction points in two minutes, if you dive deeper, then you’ll find a lot more, right?  Yeah. That is the ultimate path, which we don’t always take. We can only go to so many conferences and have so many of these conversations. But the opportunity to have that conversation is where the most growth comes from. That’s the optimal way to grow—by showing, not just telling. We definitely have a good presence on the platform for designers called Dribbble. If you search on Dribbble for finance or FinTech products, you'll find me. That already speaks to our experience.Share on X  But this kind of conversion of experience into value for our clients, for those business owners, that’s an add-on. That doesn’t come from a pretty picture. I have many pretty layouts, and those who understand user experience can see it, but that’s not always the case. To understand user experience, you have to have experience. You have to be in that area. Typically, that’s for repeat entrepreneurs. A part of our clients are serial entrepreneurs who keep opening businesses. They can’t stop opening businesses. I think when you’re in that mindset, it’s hard to stop.  Yeah. Some people are good at starting companies, and other people are good at building companies and growing them.  Yeah.  So what’s one thing, Anastasia, that you’re actively trying to figure out in your business right now? I’m actively trying to figure out how to get into the government space—government contracting. That’s a whole different animal. To get a government contract, one of the rating criteria is previous government experience. Today, it’s 2026. Every agency has a website. It means they have a vendor. It means they’ve had a vendor, or somebody has a preferred group of vendors, and so on. Large companies are taking a big part of that market. The market is oversaturated.  Somebody comes in and does a website for dirt cheap, and it’s not good. Not always, but we’ve seen it. We’ve been trying to break into our local market in the city near where I used to live. They had an RFP for a website. I thought, “Oh, I wish I could do that. I know them so well.” But another company came in with a lower offer. I think the website became worse than it was before.  You know, there’s a big gap between commercial company websites and government websites. You try to pay a bill to the government, like taxes, and there are a million clicks. It’s so complicated, and it doesn’t have to be. I can totally relate to this. I say, “Why don’t these people fix this up?”  And I think it partially comes from one of the points that I was making before. It’s that understanding user experience and understanding the value of it isn’t a given. It’s not something that existed a while ago.  That was not one part of the rubric when they evaluated the offers. Exactly. It has to match the existing system. It has to transmit data. It has to be ADA compliant. But nowhere does it say that it has to be convenient for the users.  Yeah.  And we can quantify it. We can gather a user group, gather their feedback, and so on. So right now, on Monday, I’m doing a demo for our first nearly completed government contract. That client actually prioritizes user experience, and she asked, “Could we possibly do user testing with my actual users?” I cannot tell you how much I love that question. Just the fact that she prioritizes that is exactly what we do.  Yeah. Well, you bring a lot of passion to solving problems, and I love that. So if you had a magic wand to fix one thing inside your company in the next 12 months, what would that be?  Being the person who’s always trying to fix something—and in my family, it’s great that I have my business, because otherwise I’d be going around fixing them. Yeah. Yes. My husband would be just enough. So I think the first thing would be to focus on our team and make sure they're always heard. That they get the time they need. That they get their problems resolved on time by senior leadership, by HR, or by whoever is needed.Share on X I’ve noticed several situations where that’s been postponed, or the person doesn’t speak up, or they see that I’m busy, someone else is busy, and they just think, “We didn’t want to bother you.”  Then there comes a point where they’re not happy. That’s too late. We don’t want them to become unhappy. So if I had a magic wand, I would make sure that my team is heard. That’s the most important thing. Once they’re heard, then we can see if it’s solvable, if it’s not solvable, and so on.  Yeah. That’s very important because, ultimately, you said that you want a happy team. Because a happy team does great work. If people feel heard, they’re going to be happier and deliver.  And if somebody’s selling widgets, it’s the equivalent of saying, “I want my widget to be perfect.” For my widget to be perfect… Because we test for skill before we hire, so they already have the skill. They already have good teamwork. I think the environment that we’ve built is already there. We have those foundations for them to be able to apply themselves properly. But then, what if something happens along the way?  Yeah. Love it. I love this human-centric approach that you take—not just for your team, but also how you look at the human user experience, the people, to improve the lives of your customers by designing good products.  Yeah.  So if someone would like to take advantage of your services, or they have some friction that they want to alleviate, or they’re a government agency and they figure, “Well, maybe these guys down in Houston can help us,” where can they reach you, and how can they connect with you personally? It’s very easy. Tino.design is our website. There, you can schedule a call with me. Just choose any time on my calendar and speak with me or my right-hand person. Alexander is also available. Whichever time is more convenient, we can just have a conversation and see whether we can help or not. Okay. So you heard Anastasia Golovko, President and CEO of Tino Digital Agency, down in Houston. She really cares about your company, your user experience, and making sure your people aren’t frustrated using your technology. If you liked what you heard, reach out to them. Check out Tino.design. Book a call. If you enjoyed this episode, make sure you follow us on YouTube, give us a review on Apple Podcasts, and stay tuned because every week we have an exciting entrepreneur come and share their best-kept secret framework with us. So thanks, Anastasia, for coming, and thank you for listening. Thank you, Steve. Important Links: Anastasia's  LinkedIn Anastasia's  website

The infinite scroll in social media as we know it today is despised by all. But it always start as a bad idea. It originated as a result of UI change to relief the backend database from inefficient queries. I explore this in this episode of the backend engineering. 0:00 Intro1:00 Classic Paged results 6:23 Limitations of paging9:00 The scroll interface 12:00 How TikTok to infinite scroll 14:00 The current state

"Не покупай новую видеокарту пока не посмотришь это видео!", "Пользователи Mac обезумели когда увидели...", "Для эффективной работы с локальными LLM нужен простой советский..." и прочие кликбейты, только сегодня, только сейчас!!!Спасибо всем, кто нас слушает. Ждем Ваши комментарии.Музыка из выпуска: - https://artists.landr.com/056870627229- https://t.me/angry_programmer_screamsВесь плейлист курса "Kubernetes для DotNet разработчиков": https://www.youtube.com/playlist?list=PLbxr_aGL4q3SrrmOzzdBBsdeQ0YVR3Fc7Бесплатный открытый курс "Rust для DotNet разработчиков": https://www.youtube.com/playlist?list=PLbxr_aGL4q3S2iE00WFPNTzKAARURZW1ZСсылки:- https://www.jan.ai : Удобный UI для моделей- https://huggingface.co/LiquidAI/LFM2.5-8B-A1B-MLX-8bit : LFM- https://huggingface.co/mlx-community/Qwen2.5-Coder-14B-Instruct-4bit: Qwen- https://huggingface.co/mlx-community/DeepSeek-Coder-V2-Lite-Instruct-4bit : DeepSeek- https://huggingface.co/mlx-community/Codestral-22B-v0.1-4bit : Codestral- https://huggingface.co/mlx-community/Meta-Llama-3.1-8B-Instruct-4bit : LlamaВидео: https://youtube.com/live/Ff89196FAgI Слушайте все выпуски: https://dotnetmore.mave.digitalYouTube: https://www.youtube.com/playlist?list=PLbxr_aGL4q3R6kfpa7Q8biS11T56cNMf5Twitch: https://www.twitch.tv/dotnetmoreОбсуждайте:- Telegram: https://t.me/dotnetmore_chatСледите за новостями:– Twitter: https://twitter.com/dotnetmore– Telegram channel: https://t.me/dotnetmoreCopyright: https://creativecommons.org/licenses/by-sa/4.0/

"Не покупай новую видеокарту пока не посмотришь это видео!", "Пользователи Mac обезумели когда увидели...", "Для эффективной работы с локальными LLM нужен простой советский..." и прочие кликбейты, только сегодня, только сейчас!!!Спасибо всем, кто нас слушает. Ждем Ваши комментарии.Музыка из выпуска: - https://artists.landr.com/056870627229- https://t.me/angry_programmer_screamsВесь плейлист курса "Kubernetes для DotNet разработчиков": https://www.youtube.com/playlist?list=PLbxr_aGL4q3SrrmOzzdBBsdeQ0YVR3Fc7Бесплатный открытый курс "Rust для DotNet разработчиков": https://www.youtube.com/playlist?list=PLbxr_aGL4q3S2iE00WFPNTzKAARURZW1ZShownotes: 00:00:00 Вступление00:12:40 Как настроить локальные llm00:24:05 Стоит ли брать подержанный M1 Max00:30:00 Codestral на 5060 RTX00:38:00 Стоит ли брать Air M500:47:00 Стоит ли M5 Max своих денег?00:50:00 Deepseek это круто?00:53:00 Deepseek на M1 Max отстой00:59:00 Deepseek на 506001:01:40 Deepseek на M5 Air01:12:40 На 5060 все заработало :)01:34:00 Deepseek: M5 Max vs 506001:36:30 Наш любимый Lfm01:53:50 Llama стоит ли того?02:01:40 Финальный босс: Qwen2.502:09:00 Qwen3.6 на M5 MaxСсылки:- https://www.jan.ai : Удобный UI для моделей- https://huggingface.co/LiquidAI/LFM2.5-8B-A1B-MLX-8bit :LFM- https://huggingface.co/mlx-community/Qwen2.5-Coder-14B-Instruct-4bit : Qwen- https://huggingface.co/mlx-community/DeepSeek-Coder-V2-Lite-Instruct-4bit : DeepSeek- https://huggingface.co/mlx-community/Codestral-22B-v0.1-4bit: Codestral- https://huggingface.co/mlx-community/Meta-Llama-3.1-8B-Instruct-4bit : LlamaВидео: https://youtube.com/live/7qCtWU-LK58 Слушайте все выпуски: https://dotnetmore.mave.digitalYouTube: https://www.youtube.com/playlist?list=PLbxr_aGL4q3R6kfpa7Q8biS11T56cNMf5Twitch: https://www.twitch.tv/dotnetmoreОбсуждайте:- Telegram: https://t.me/dotnetmore_chatСледите за новостями:– Twitter: https://twitter.com/dotnetmore– Telegram channel: https://t.me/dotnetmoreCopyright: https://creativecommons.org/licenses/by-sa/4.0/

If you're new here, welcome girl!I'm not sure if this show found you or if you found it, but either way, you're here — and I'm so glad you are.Faith In Between has been a long-running show, but this season feels like something new. Around here, we talk about the journey of growing, learning, healing, trusting God, and walking through real life with faith — the good, the hard, and everything else that happens in between.I'm Ui, a local girl from the island of Oʻahu who loves God, loves people, and believes there is purpose in the relationships, seasons, and stories God allows us to walk through.I believe you landed here for a reason, and whatever that reason may be, I pray this space blesses you, encourages you, and reminds you that God is with you in the in-between.Thanks for stopping by. I'll see you on the show.Mahalo,
Ui Kumuhone

An airhacks.fm conversation with Tomasz Ptak about: discussion about the guest's path from an Atari and a 486 to professional Java development, loading games from cassette tapes, building a clock with the Logo programming language, making websites with PHP for a community, studying data management and computer science, learning Perl, Bash, Pascal, Python, C, C++, Ruby and Java, Java 1.4 and Java 5 with generics and annotations, an island optimization algorithm switching from Python to Java for memory control, preference for strictly typed languages, first job at motorola Solutions building a server-side Java configuration system with SNMP and SNMP4J, moving from Tomcat to Netty, using Ant and Maven, managing a Jenkins server, rebuilding a buggy no-code Spring CRUD generator, rewriting an application with Apache Wicket for stateful web development, comparing Wicket structure coupling with Jakarta Faces, event sourcing with the Axon Framework and domain objects, bitemporal awareness and Hibernate Envers versioning, the Naked Objects pattern and object-oriented UI generation, third job at Open Market stabilizing a legacy Java SMS gateway, weekly outages and same-day retrospectives, containerizing bare-metal systems with Testcontainers and docker Compose, near zero-downtime deployment with Ansible, migrating from Maven to Gradle and removing the Buck build tool, upgrading legacy systems from Java 1.4 to Java 8, minimalistic Maven usage, a zero-dependency Java builder zb and zero-dependency unit runner zunit using only built-in compiler and jar tools, Java 25 as an automation tool replacing Python scripts, executable JARs without external dependencies, shebang instance-method scripting, reactive or infinite streams and stream gatherers, Git-tag-based versioning for monorepos, the AWS DeepRacer and AWS AI community, the mediocris blog Tomasz Ptak on linkedin: https://www.linkedin.com/in/tomasz-ptak

We've been running a bit of an Agent Cloud series surveying all the top inference/compute/cloud providers, from Databricks to Daytona to Railway and, even further back, E2B, but we're excited to conclude this series returning to Modal, which has just raised a monster $355M Series C.The cloud was built for developers. But agents are now changing that.The old infra stack was designed for a human who could read docs, reason through YAML, and understand dashboards to figure out what they need when something broke. While this was painful for developers, it worked since they could fill in missing context in their heads.However, agents don't have that luxury. Now in this new era of agents, everything has to be tighter.They need a place to write code, run it, inspect the output, change the environment, debug failures, and try again. Fast iteration and feedback loops with all the necessary context are crucial for agents to operate properly. Furthermore, sandboxes are a clear representation of this shift as agents can easily spin up isolated environments. This programmatic infra even extends to research:Two years ago, we were one of the first to cover Modal with CEO Erik Bernhardsson and Alessio designed our favorite LS thumbnail of all time:At the time, Modal was just a teeny little company with a $17M Series A.Today, fresh off their $355M Series C, Modal is one of the clearest examples of the agent cloud future being built in real time: a cloud platform moving past traditional web app assumptions toward the workloads AI actually creates such as elastic inference, sandboxes, GPU burst, post-training, background agents, and infrastructure that agents themselves can operate.In this episode, Modal CTO Akshat Bubna joins swyx and Vibhu to unpack why AI applications don't fit traditional cloud assumptions, why Kubernetes was never designed for bursty compute-heavy workloads, and why Modal is now shifting from developer experience to agent experience.We go deep on Modal's AI infra stack: serverless functions, decorator-based infrastructure, elastic inference for custom models, GPU snapshotting, DeFlash, speculative decoding, Auto Endpoints, sandboxes, persistent storage, networked containers, private IPv6, RDMA, multi-node training, and Modal's capacity pool across 17 cloud providers. Akshat also explains why RL rollouts can require 100,000 sandboxes, why production agents need hard guardrails, why observability may matter more than reading code, and why AI has made infrastructure exciting again.We discuss:* Why Kubernetes wasn't built for bursty AI workloads* How Modal started as a better runtime before becoming an AI cloud* Why Modal added GPUs before ChatGPT* The shift from developer experience to agent experience* Why observability matters when agents are writing the code* Elastic inference for custom models across audio, video, robotics, and comp bio* GPU snapshotting, cold starts, and why inference workloads are so bursty* Why RL rollouts can require 100,000 sandboxes* DeFlash, speculative decoding, and frontier-level inference performance* Auto Endpoints and making optimized inference easier to deploy* What Modal adds beyond vLLM, SGLang, and raw GPU rental* Modal's 17-cloud capacity pool and supercloud strategy* Networked sandboxes, sidecars, private IPv6, and RDMA* Serverless multi-node training for post-training and research workloads* Auto-research, model-guided sweeps, and agents launching GPU experiments* Compute strategy, capacity planning, and batch tiers* Why production agents need specialized sandboxes and hard guardrails* Modal's take on managed agents, CI, Gitpod/Ona, Python, TypeScript, and Modal BenchAkshat Bubna* LinkedIn: https://www.linkedin.com/in/akshat-bubna-188885103* X: https://x.com/akshat_bModal* Website: https://modal.comTimestamps00:00:00 Introduction00:00:39 Modal's origin and why Kubernetes wasn't enough00:04:32 Developer Experience → Agent Experience00:06:21 Modal's AI cloud primitives00:09:14 Sandboxes, agent loops, and proto-Cognition00:12:12 Elastic inference, GPU snapshotting, and 100,000 sandboxes00:15:24 DeFlash, speculative decoding, and Auto Endpoints00:19:59 Production-grade inference beyond raw GPUs00:22:00 Background agents, Ramp Inspect, and the agent lifecycle00:24:08 Modal's 17-cloud supercloud strategy00:26:40 Networked sandboxes, private IPv6, and RDMA00:32:48 Multi-node training, post-training, and auto research00:37:36 Compute strategy, capacity planning, and batch tiers00:40:55 Open models, real-time AI, and production agent infra00:43:06 Hard guardrails, managed agents, and specialized sandboxes00:46:06 Why AI made infrastructure exciting again00:48:30 Model APIs, differentiated products, and agentic video00:51:50 CI, coding-agent infra, SDKs, and Modal Bench00:57:28 Closing ThoughtsTranscriptIntroduction: Modal, Series C, and the Art PartySwyx [00:00:00]: We're here with Akshat, CTO of Modal, together with Vibhu. Congrats on your Series C.Akshat [00:00:10]: Thank you.Swyx [00:00:11]: Your party yesterday was amazing.Akshat [00:00:15]: Yeah.Swyx [00:00:15]: From all the photos and all the swag.Akshat [00:00:17]: We had a bunch of art installations, which was fun, seeing, like, our products on pedestals next to, like, Rodin.Swyx [00:00:25]: Very nice. Very nice. When you started, it was not the GPU inference company. Maybe it was in your mind. Take us back to the origin story.Modal's Origin: A New Runtime Beyond KubernetesAkshat [00:00:39]: I first met Eric, who's the CEO, through an investor. Back then Eric was already thinking about building, a new runtime, and he got there thinking through why are workflow orchestration products so hard to use. It's because you have to run them on Kubernetes. Kubernetes is hard to manage. It's not built for burstiness and, custom images,Swyx [00:01:03]: YeahAkshat [00:01:03]: It has a terrible developer experience.Swyx [00:01:05]: And I'll, I'll interjectAkshat [00:01:06]: YeahSwyx [00:01:07]: For listeners, who are new, we interviewed Eric two years ago, and there's a bit more of the story there from Spotify and all those things.Swyx [00:01:14]: And I came across Eric through Data Council because he did that talk on the serverless container stack that you guys did, which was like, that was my first like, “Okay, I need to take Modal very seriously” moment.Akshat [00:01:26]: Yeah.Swyx [00:01:26]: But it was still very unclear, like, do I need all this for just my data pipelines?Akshat [00:01:33]: Yeah. initially what we were thinking about was if we build a better runtime, it's a very useful primitive in itself. It's There's a lot of things that, get solved by serverless functions, like you can do, ETL stuff, you can do job queues, you can do all this, like, bursty processing, which it turns out every company had needs for. but then we also were thinking about this as like, this is a primitive that we can build a whole collection of products on, which are very verticalized. So perhaps data engineering would've been the first one, but we were thinking about inference. Back then it was more classical inference, like computer vision stuff and running XGBoosts and whatnot. But we added GPUs to the product a year before ChatGPT came out.From Serverless Containers to GPU WorkloadsSwyx [00:02:19]: Nice.Akshat [00:02:19]: We just didn't think it would be that big of a deal.Swyx [00:02:22]: Yeah, just like add A100.Vibhu [00:02:23]: Was there any, like, early key problem that really sparked off why you built it?Akshat [00:02:28]: Yeah. Primarily it's just, none of the tooling that was out there was built for, one, a really great developer experience, and also there's a general trend of, a lot of the workloads that we were seeing were very. I wish there was a better word for it, but compute-heavy. Like, they need, one, like, need a lot more resources, so you need to burst up and down a lot, versus like Kubernetes designed for, like, slow scaling and, more for, like, web server use cases. And also there's just a lot more specialization in, like, what kinds of environments these workloads run in. Like, we had sometimes they need accelerators, sometimes they need different kinds of images, and this is just like a consistent thing that we saw across a lot of companies. That would be the next step.Software-Defined Infrastructure and Decorator-Based DXSwyx [00:03:13]: Yeah. Yeah. Be nice. I don't know how much this factored into the early story, but I wrote a post when I was at Temporal about infrastructure, software-defined infrastructure or something like that.Akshat [00:03:22]: Yeah, the self-provisioningSwyx [00:03:23]: Self-provisioning.Akshat [00:03:24]: Yeah.Swyx [00:03:24]: Yeah. I can't even remember my own post.Swyx [00:03:26]: And then you put me on the landing page.Akshat [00:03:28]: Yeah. We really like, the term and so we stole it.Swyx [00:03:32]: Because you had the insight that everything can just be in decorators co-located with the code, right?Akshat [00:03:37]: Yeah.Swyx [00:03:37]: Was that a big part of the originalAkshat [00:03:39]: YesSwyx [00:03:39]: Story or it was just like a DX layer?Akshat [00:03:41]: That was, really important because we really didn't want people to spend, so much time, writing YAML, and it seemed like you could really condense the surface area of what you're doing, put it in code so you can operate on it just like you operate on other code, and like build stuff that's more expressive and dynamic. and so yeah, that was always a very important part.Swyx [00:04:04]: Then the pushback is this is a DSL.Akshat [00:04:07]: Yeah.Swyx [00:04:07]: It's you're closed source. I am locked into Modal.Akshat [00:04:11]: Yeah. We never really got pushback for that because the nice thing about Modal is you can bring whatever code you have, and sure, the DSL is at the configuration layer for, what hardware you're using, how you're scaling things up, but you still own the code.Akshat [00:04:27]: And that's, that's been an important, part of our story, even as we do inference now.Swyx [00:04:32]: Yeah.Vibhu [00:04:32]: How much of do you think still stays the same today? Like if you were to build something today, DevX very important, but I feel like, a lot of this has been changed with just hook it up to an agent, have Claude Code, have Codex implement a tool. there's very agent native primitives that are different than if I'm doing this myself, right?Developer Experience → Agent ExperienceAkshat [00:04:54]: We've changed our SDK team to think about agent experience instead of, developer experience and we think that the same benefits that apply for DX also apply for AX, which is why would you have an agent read through hundreds of Kubernetes files and like write YAML that's not even typed when it can make a couple of changes in a decorator and it gets this self-provisioning runtime of, being able to see its changes live in action? yeah, it just seems from the customers we talk to, they find Modal is much faster for agents to use versus operating on a different substrate.Swyx [00:05:34]: Yeah, because like you, again, you co-locate the infrastructure requirements to the code that runs it.Akshat [00:05:38]: Yeah.Swyx [00:05:38]: Well, the negative thesis now is that nobody's looking at their code anymore, so there's no point.Akshat [00:05:44]: Yeah, people aren't looking at code. one thing we still see is really important is observability.Swyx [00:05:51]: Yeah.Akshat [00:05:51]: Like how good is your dashboard? And of course, like we have, we push a lot of it to the CLI so the agents can do their own investigation, but you still need humans to go interpret what's going on and, make judgment calls and whatnot. and that's I feel like, Maybe more important now than looking at the code itself.Swyx [00:06:11]: Yes, because like, you can try to treat the code as a black box and then use, see the observable action that comes out of it, and then just prompt a change.What Modal Is For: AI Cloud PrimitivesAkshat [00:06:21]: Yeah.Swyx [00:06:22]: So I think it takes a bit of restraint to not specialize, to say, “I want to ship a new primitive,” and then just be general purpose.Swyx [00:06:31]: People ask you, “What are you for?” You're like, “ I don't know. We can do this, we can do that.”Vibhu [00:06:36]: Well, I'd be curious to see, like, okay, if we were to ask you, like, what is Modal for even at a high level? There's a lot you guys do, sandboxes, GPUs, everything. How do you answer?Akshat [00:06:46]: Modal is a cloud platform that's built for, where we've built the primitives from scratch for AI applications. and right now it covers, inference, training, batch processing, and sandbox workloads.Akshat [00:07:00]: But we're building a lot moreSwyx [00:07:02]: I noticed you didn't say web server, so there is still a role for, like, the always-on large-scale Kubernetes type things.Akshat [00:07:09]: Yeah, absolutely. We're, we're not trying to compete with the renders of the world, because yeah, we think the differentiator for us is the, are the workloads that need specialized compute, need to scale up and down a lot. yeah, they're, they're, they're just shaped differently.Working Alongside Frontier StartupsVibhu [00:07:26]: I think you're building a lot of it alongside the startups, right? They're innovating quite a bit, even in your, like, latest blog post. Like, even in the series C, the customers that you mention here, the cognitions, technical ones, ramps and whatnot, they're, they're innovating with you, right? And that's not something AWS is doing directly with.Akshat [00:07:45]: Yeah, absolutely. I think, this is again classic. We're a small team. We can move really fast. our engineers are working with our customers and figuring it out. Yeah.Swyx [00:07:54]: So my first week at Cognition, I walked in, there was someone wearing a Modal shirt. I was like, “What are you doing here?” They're like, “Yeah, I just. I am embedded inside of Cog.”Akshat [00:08:05]: Yeah, I think that was Peyton. We sent him overSwyx [00:08:07]: Yeah.Akshat [00:08:07]: Because, the latency of communication was too high otherwise.Swyx [00:08:12]: Yeah, distributed node, you have to - you have to place one and collocate.Vibhu [00:08:16]: Yeah.Swyx [00:08:16]: So I had a, I had direct personal experience, right? So I worked on smol developer three years ago. it was inspired by Claude 1. I think you onboarded me at some point, like, just before, and I was like, “Oh, like, I need some bursty compute. Like, I was just gonna try using Modal.” And it was a, it was a pretty pleasant experience. apparently, I showed up in the board meeting, like the analytics.smol developer, Sandboxes, and Proto-CognitionAkshat [00:08:39]: Yeah, you blew up on Hacker News and,Swyx [00:08:41]: YeahAkshat [00:08:41]: We got a big traffic spike. I. I think the way you used smol developer was Modal functions for running stuff, which was. Like, the, that was a good use case. but then, yeah.Swyx [00:08:53]: Yeah. That - So to me, that was proto-cognition.Akshat [00:08:55]: Right.Swyx [00:08:56]: If only I had, like, stuck to it.Swyx [00:08:58]: Like, that was like, if - did you say draw the tech treeAkshat [00:09:00]: AbsolutelySwyx [00:09:00]: You're just like, “Yeah, like, probably this will happen.”Akshat [00:09:02]: Yeah. Like, he was so close. You were just rebuilding upon usSwyx [00:09:04]: I just didn't realize.Akshat [00:09:05]: But the funny story there is at the same time, we were talking to a bunch of customers who needed something like sandboxing.Swyx [00:09:14]: Yeah.Akshat [00:09:14]: This is like twenty-three.Swyx [00:09:15]: Yeah.Akshat [00:09:16]: So we builtSwyx [00:09:17]: You introduced a new API right after that.Akshat [00:09:18]: Yeah.Swyx [00:09:19]: Yes.Akshat [00:09:19]: Like, we built sandboxes in May of twenty-three before anyone was even knew this was gonna be a thing. And the first example we published was, we took smol developerSwyx [00:09:28]: Smol developerAkshat [00:09:28]: And put it in a loop, so the agent can iterate on itself.Swyx [00:09:33]: Loops are hot these days.Vibhu [00:09:34]: It's the looper.Akshat [00:09:34]: Yeah.Vibhu [00:09:35]: Loops in. When was this, twenty-three?Akshat [00:09:38]: Yeah.Vibhu [00:09:39]: A small check.Akshat [00:09:39]: Yeah.Swyx [00:09:39]: It's like twenty-three. so the. the, those for listeners, like, the problem was the models are not built for any of this, right?Swyx [00:09:46]: Like, you're just trying to like. They're not post-training to understand, like, looping and, like, self-correction and tool calling was there, but, like, also not that great.Akshat [00:09:55]: Yeah.Akshat [00:09:55]: I don't remember if you used tool calling in this one, but yeah, the models would just diverge after like ten iterations and not produce anything meaningful.Swyx [00:10:03]: Yeah. But like, then. So okay, like now talking to myself three years ago, the answerVibhu [00:10:08]: Of course they will get betterSwyx [00:10:09]: Collect all the failures, build benchmark, and then collect all the, examples, build the RL environmentAkshat [00:10:15]: RightSwyx [00:10:15]: Sell it for like ten billion dollars to Meta.Swyx [00:10:17]: And then also train a model and then sell that for sixty billion dollars to Elon. And this isAkshat [00:10:23]: Yeah, of courseSwyx [00:10:23]: The funny machine. Like, it's like, it's about the hardware.Akshat [00:10:28]: It's hard to have that inherent conviction that the stuff will get that much better.Swyx [00:10:33]: In retrospect, it's so f*****g obvious.Akshat [00:10:36]: Fair enough.Swyx [00:10:37]: Like, what else were we doing back then? I don't know. anyway. Yeah. So this. That was the start of your sandboxing journey, right? I feel like it didn't blow up until, like, last year.Akshat [00:10:49]: Yeah.Swyx [00:10:50]: So there was like a couple years of quietness.Akshat [00:10:52]: Exactly, yeah. We wereVibhu [00:10:53]: I think very underrated product value. Like, my experience with Modal, Charles, before he had joined Modal, met this guy at a hackathon, and he really insisted we wanted to run some small model, not hosted anywhere, and he's like, “ there's this cool company, Modal. They'll like spin up a GPU sandbox, we can throw it on there. They'll take a Hugging Face link.” And like there's so much value just right there, right? Like instant hosting, spin it up, spin it down. It'll stay cold, but we run the demo a few days later, it'll come back up and like all this stuff in retrospect, like it's still what we needed like today.Akshat [00:11:27]: Yeah, it's still needed today. workload shapes have changed a lot as, we run stuff for people with really massive production scale and, there it's it's not about scaling from zero to one, but it's how do we scale really elastically, from like thousand to fifteen hundred GPUs very quickly in a given region. It's the same shape problem.Elastic Inference, GPU Autoscaling, and Custom ModelsVibhu [00:11:50]: Okay. So you look at, say, Cursor Composer, right?Akshat [00:11:53]: Yeah.Vibhu [00:11:53]: They had a. “We'll do RL on a model every couple hours.” you guys have a whole version of RL inference gym and whatnot.Vibhu [00:12:01]: When you look at workloads like that, you're doing train runs where you need to scale up, scale down every hour thousands of GPUs, right? That's the example for we do need it, right?Akshat [00:12:12]: Yeah. Well, so I'll, I'll take a step back and, maybe talk about like how people use Modal today. because our biggest use case is, elastic inference. And the thing we first found product market fit, with was inference for custom models. So we stayed away from the LLM space, and we were serving companies like Suno for audio, Runway for video, robotics, comp bio companies that train their own model elsewhere. But Modal is the best black box that for deployment, scaling to however many GPUs you need as your traffic pattern changes. And we saw all of them like have a very unpredict- predict- predictable, traffic pattern. it's like diurnal. It's Some days, like the company will do a launch and, they'll need like, way more. And it's not just one model that they deploy. They-- all these companies deploy, lots of different models in different regions, and so the autoscaling problem becomes even harder because then you have to scale within a certain region, and those cycles are offset. So different times you scale up in different regions.Akshat [00:13:20]: So that's like our sortVibhu [00:13:22]: And thatAkshat [00:13:22]: YeahVibhu [00:13:22]: That in and of itself is a huge category. There's a bunch of inference providers which, provide this fireworks, does this as a service together, whatnot, Base10. that's carved into its own niche for language models, at least right now.Akshat [00:13:36]: Yeah. the thing that we have specialized in is the autoscaling aspect.Vibhu [00:13:41]: Yeah.Akshat [00:13:41]: Because we found that it's not universally true that everyone else can autoscale, and we've gone deeper into it on the tech side by, we've incorporated GPU snapshotting into the product so we can take the GPU state, like your torch.compile model, snapshot it, and the next cold start is way faster. And so going back to your question, it's That's why you need a lot of burstiness for inference. But then people also do a lot of demand training, like for RL stuff, your rollouts are bursty, as you said. People also do a lot of batch jobs. So we'll see, a lot of companies, before they have a training run, they'll need thousands of GPUs to run encoding or something like that. And I think those things are much more bursty than. I agree that agents are not that bursty. sandboxes are, except when you're doing RL. RL is justRL, Batch Jobs, and 100,000 SandboxesVibhu [00:14:28]: Or commerceAkshat [00:14:28]: Insanely bursty.Vibhu [00:14:29]: Yeah.Akshat [00:14:30]: Yeah. Like when you're doing, rollouts, you sometimes need a hundred thousand sandboxes in your sandboxes.Vibhu [00:14:37]: Yeah. I'm curious if you've seen early sparks of continual learning. There are some people, like our friends, ngram, recently announced thisAkshat [00:14:45]: YeahVibhu [00:14:45]: They're, they're trying to do training. That also seems like a different workload, right? If you're doing training twenty-four/seven per se, there's a very weird dynamic of how you're using GPUs between people and whatnot, but seems like something you guys would work for.Akshat [00:15:00]: As you said, we're, we're fortunate to work with a number of, customers at the frontier and grab some of our customers. and they are taking the primitives we have, and trying to use them in very interesting ways, like continual learning. It's possible as the stuff gets better, some of that will be part of, our offering as well if, more people need it. but we're, we're just waiting to seeVibhu [00:15:23]: YeahAkshat [00:15:23]: How it shakes out.Vibhu [00:15:24]: Is there a primitive that you added after sandboxing that was the next step in the story?LLM Inference, DeFlash, and Speculative DecodingAkshat [00:15:32]: I guess we've been going much deeper into LLM inferenceVibhu [00:15:35]: YeahAkshat [00:15:35]: Because we realized that some of the advantages we have with like autoscaling, again, especially in different regions and whatnot, are, not present elsewhere. and the place where we had a gap was we weren't, working on the model layer itself. Like we were a black box. And, we realized that, we can get to frontier-level model performance, with, by having great people who work on this. And, we've been open sourcing a lot of our work, in terms of, Recently, we, shared our work on DeFlash, which is a block-based, speculator, and we've open sourced, all of it. So, you can - By using open source DeFlash, you can get the same performance as you would with one of the proprietary providers. And the next thing we're thinking about hereVibhu [00:16:23]: I thought this wasAkshat [00:16:24]: YeahVibhu [00:16:24]: An interesting blog post as well, right? Like, I think in here you make a claim that. Not a claim, just that how effective speculative deco-decoding really just get to.Akshat [00:16:33]: Yeah.Vibhu [00:16:33]: Anything you wanna point out from this around, what people should know?Akshat [00:16:39]: Yeah, absolutely. the high-level summary is, it would help to describe what speculative decoding is.Vibhu [00:16:44]: Yes.Akshat [00:16:44]: I will, yes.Vibhu [00:16:45]: I think, likeAkshat [00:16:46]: YeahVibhu [00:16:46]: So we've covered like Eagle and all thisAkshat [00:16:47]: YeahVibhu [00:16:47]: Like Hydra and all those things, but it was like two years ago.Akshat [00:16:51]: Yeah.Vibhu [00:16:51]: I think it doesn't hurt, right?Akshat [00:16:52]: Yeah. Speculative decoding is you have a smaller model, called a draft model, predict tokens ahead of the bigger model, and then you have the bigger model, verify all of this, all the tokens are predicted. And the reason it's faster is if you're predicting, one token at once, you're bound by memory bandwidth. But if you can batch the verification of, the draft model, then you're much more efficient using compute, and it's faster, and as long as your draft model is producing a lot of tokens that can get accepted, which is called the accept length, you can get a speed up that's, multiple times of, the original model speed. and well, that's what we highlight here. It's Like people talk a lot about we made these kernels faster and whatnot, but improving kernel will only give you like few percentage points of improvement, and, increasing accept length, literally is a multiplicative decreaseVibhu [00:17:47]: Like two to four X.Akshat [00:17:48]: Yeah, exactly.Vibhu [00:17:48]: Without much head-on performance.Akshat [00:17:50]: Yeah. I think it may - you are running a second model, right? So it may be something more expensive in the compute,Vibhu [00:17:57]: I meant quality performanceAkshat [00:17:58]: Probably not by muchVibhu [00:17:58]: But yeah. I thinkAkshat [00:17:59]: So there's no drop in quality performanceVibhu [00:18:01]: YeahAkshat [00:18:01]: Because you're always. You're never accepting a token that the big modelVibhu [00:18:04]: It's strictly betterAkshat [00:18:05]: YeahVibhu [00:18:05]: Or it's same.Akshat [00:18:06]: Exactly.Vibhu [00:18:07]: Right. Yeah.Akshat [00:18:08]: And so we've been working a bunch on DeFlash, which is a block-based speculator. so it's instead of predicting, one token at a time, it's predicting a block. And we've been open sourcing our work with it. The next thing for us here is for helping people train speculators and custom models. it's it's something that traditionally is very forward-deployed engineering driven, support deployed, engineer driven, like you work with customers and help them do that. And our vision for. This is why we launched Auto Endpoints, is we want to make frontier-level performance available to everyone. And so, we mentioned this in the announcement, we teased it. The next thing we're, we're launching is, as you run an auto endpoint, we shadow trafficAuto Endpoints and Frontier-Level PerformanceVibhu [00:18:54]: Do you want to explain what auto endpoints are?Akshat [00:18:57]: Yeah.Vibhu [00:18:57]: I lovely, yeah.Akshat [00:18:58]: Yeah. So, this is, I guess, going back to your Modal is you touch the code, but, sometimes people don't wanna touch the code, and they wanna get started with an endpoint that works and has all the great performance and, scalability that Modal has. So we've made that easier with, a way to create an endpoint from our UI, from the CLI, that has all of our optimizations that we talked about, like the DeFlash stuff already baked in, and there's full transparency. So we give you the code, you can go run it yourself, and if you want, you can eject out into the full Modal experience, which we see as people get sophisticated, they do wanna tweak the models, they wanna, fine-tune stuff. You can still do all of that. It's it's not a black box. And yeah, the next thing, as we teased later in the post, is how do we give you value even beyond this in terms of having your draft models evolve as your data distribution evolves, again, without having to talk to a person and, yeah.Vibhu [00:19:59]: I guess just to understand it directly, you have the GPUs, you have an endpoint that's compatible, you serve open model. If someone was to do this themselves, what's the delta that you guys provide? So you do a lot of open source great work on effective inference. how does it compare to, say, I take the same model, 5.2 FP8, take shelf inference engine, vLLM, SGLang, get compute of similar capacity, similar cost. What's the delta that plugging into something this, like this offers outside of the benefit of, scaling?Production Inference Beyond Raw GPUsAkshat [00:20:34]: It's interesting because we've taken the approach of open sourcing our contributions and upstreaming them. we work closely with the SGLang team. We want the improvements that our team, comes up with to be, there in open source for others to use, even outside of Modal. The benefit to us is we have a team that has significant expertise in terms of if you do have something that is not there, our team can help you get that performance, first. the other thing is with these endpoints, we are way more elastic, as you said, than, anyone else, and you have true scaling to zero. you have true, burstiness, and in practice, that matters a lot more to people than just finding, the GPU and, running Modal code on something.Vibhu [00:21:20]: Yeah. And I will say it's not that straightforward to just. like what I said is easier said than done, right?Akshat [00:21:26]: Yeah.Vibhu [00:21:27]: It's I think still for the average person, still hard to just gut check using different. There's, there's quite a bit of combinations you can make there. the trade-offs aren't really known at face value.Akshat [00:21:40]: Yeah. it's it's not just that. I think it's it's that running production-grade inference is a hard infer problem.Vibhu [00:21:49]: YeahAkshat [00:21:49]: Even if you subtract out the autoscalingVibhu [00:21:50]: YeahAkshat [00:21:51]: Is controlling things like tail latency and, making sure every, request is delivered at least once and whatnot.The Model and Agent LifecycleVibhu [00:22:00]: There's a lot of innovation that you can do here. I think, it's very interesting that you're starting to encroach on, like as you become a full cloud, you're starting to encroach on other people's turf.Vibhu [00:22:09]: What will you not do?Akshat [00:22:13]: Well, we wanna follow our users and, make sure they get like a platform that has everything that works well together. so right now we're focused on the model lifecycle and the agent, lifecycle. so both like going from data prep to training to inference, and then also if I want to deploy a background agent, let's say, sandbox, do persistent storage, a whole bunch of other stuff.Vibhu [00:22:38]: We talked to Cole, who did, OpenInspect. Yeah.Akshat [00:22:42]: Yeah.Vibhu [00:22:42]: And RealInspect also is on Modal.Akshat [00:22:44]: Yeah. So Ramp Inspect was a great example of a background agent that was really successful because they, were able to use some of the primitives like snapshotting and fast scaling to just have something that feels really reactive and works well.Ramp Inspect and Background AgentsVibhu [00:23:02]: Yeah. That's the new CTO of, Ramp right there.Akshat [00:23:05]: Yeah, Rahul.Vibhu [00:23:08]: It was really fun. yeah, okay, I think, all very bullish. Like, one of my reflections was also I did not originally. So when I met you guysThe Inference Inflection: CPU, GPU, and Co-LocationVibhu [00:23:19]: You weren't that much in the GPU game, and now you're all about, inference. And one of the points that I hinged on for Jensen's keynote at GTC this year was, what we're calling like the inference inflection, right? That let's say in AI workloads or machine learning workloads, it used to be like, let's call it eight to one GPU to CPU, and now it's more like one to one, which is like a interesting. Like, - because of how much agents are blocked or call out to this, to CPU heavy stuff the actual, like, limiting factor, like, swings back and forth from GPU to CPU a lot more than it used to be all GPU and then occasional CPU.Akshat [00:24:01]: Yeah.Vibhu [00:24:02]: GPU, CPU. And now it's like just constantly, and you just have to locate everything.Seventeen Clouds and the Supercloud StrategyAkshat [00:24:08]: Yeah. And that's one of the things that, again, we see as, something appealing about Modal, which is we've built this capacity pool that spans, 17 cloud providers, so we're, we're very good at Running on various kinds of cloud capacity across the worldSwyx [00:24:24]: You don't have your own data centers?Akshat [00:24:25]: We don't have our own data centers. We just run across a lot of neo cloudsSwyx [00:24:29]: Yeah. AreAkshat [00:24:30]: Metal providers.Swyx [00:24:30]: Yeah. Question mark.Swyx [00:24:31]: Yeah. You're, you're running the math, and you're like, “What's the cutover point where you're like.”Akshat [00:24:36]: Yeah, it's a good question. part of it is we see our differentiator in the software layer, and, being capital light and focusing on the software helps us move really fast. so far it's worked out well because there are so many other people building data centers that we're able to work effectively with them, and again, focus on what makes us, special.Swyx [00:24:55]: Yeah.Swyx [00:24:56]: 17 gets you into, like, the local providers sometimes. LikeAkshat [00:25:00]: The,Swyx [00:25:01]: Which was the most interesting one?Akshat [00:25:02]: There are a lot more neo clouds than you expect, and they all have various degrees of, various levels of reliability. And, that's why it's something we've invested a lot of time in, is building our own reliability layer on top. so if the GPU falls off the bus or something happens, we user workloads are not affected, and that lets us use a lot more capacity than,Swyx [00:25:30]: YeahAkshat [00:25:30]: You as a user would be able to.Swyx [00:25:32]: It's a useful thing to have because like now everyone knows, like, what layer you are and, like, you optimize for being the super cloud of all clouds.Akshat [00:25:41]: Yeah. That's, that's, that's the idea. and so I guess when you mentioned colocation, that's, that's another interesting thing where, one thing we've seen is people come to us when they want, very specifically located, CPUs or GPUs, like they wantSwyx [00:25:57]: Oh, they pin it in likeAkshat [00:25:58]: YeahSwyx [00:25:58]: EU?Akshat [00:25:59]: Exactly. Or EU, US.Swyx [00:26:01]: Right. Data resiliencyAkshat [00:26:02]: AustraliaSwyx [00:26:02]: Locality thing or performance or what?Akshat [00:26:04]: It's either data locality or latency, yeah.Swyx [00:26:07]: Yeah.Akshat [00:26:07]: Like, you want your. They're running sandboxes and model. They want them to be right next to aSwyx [00:26:10]: Yeah, it's easy thenAkshat [00:26:11]: YeahSwyx [00:26:12]: To. That is important in all those things. and so, like, you've accidentally, I don't know if it's accident, but, like, you've built the perfect primitive for agents to express themselves. And then, like, it's almost very funny how every extra development just involves more file system, just involves more CPU.Akshat [00:26:30]: Yeah.Swyx [00:26:31]: Just like the things that you already have. I don't know much about, if there's any, like, networking usages that are interesting, but you've also done some good work on networking.Networking, Sidecars, Private IPv6, and SandboxesAkshat [00:26:40]: Yeah, that's exactly right. Like, we're just taking compute storage and networking and building stuff on that layer, for, again, the stuff people need.Swyx [00:26:49]: YeahAkshat [00:26:50]: We see a few interesting networking things coming up. one is people want networked sandboxes. so we haveSwyx [00:26:57]: For like a Docker cluster type thing.Akshat [00:26:59]: Yeah.Swyx [00:26:59]: Sorry, Docker Swarm. Oh, f**k. What is it called?Akshat [00:27:02]: Compose.Swyx [00:27:03]: Compose type thing.Akshat [00:27:04]: Yeah. So if you want Docker Compose, our sandboxes now support, this thing called sidecars. So you can. A sandbox is a pod of containers, and you can run multiple containers in, a sandbox. also useful because, going back to networking, people want a lot of control over, outbound networking from a sandbox.Swyx [00:27:23]: Yeah.Akshat [00:27:23]: Like, they might wanna run a middle proxy for, like, maybe logging stuff for RL or, controlling how egress can happen to a domain, injecting credentials. and yeah. So we've, we've had to build a lot of that stuff ourselves.Swyx [00:27:38]: Yeah.Akshat [00:27:39]: But then also sometimes people want, sandboxes spanning multiple nodes to talk to each other, which is an emerging thing we're seeing. We have support for that for a different reason, and yeah, we'll see if that becomes stable.Swyx [00:27:52]: Like, just an open socket. It's a. This is directly like mTLS.Akshat [00:27:56]: We do support that, which is you can, expose a tunnel inside a sandbox.Swyx [00:28:01]: Yeah.Akshat [00:28:01]: And then you can either expose it to public internet or it can be, you can add like a HTTP, auth layer above it. But we have this thing called I6PN, which we haven't talked about, which is this, like, overlay network using IPv6 addresses. so if Modal containers, within the same workspace, when this is enabled, can address each other using this private IPv6 address, and no one else can.Akshat [00:28:28]: So it's like private networking, for containers. We built it because we needed it as a primitive for our distributed training product. so we have this other feature, which is you can add a decorator to a function, and you get a cluster of GPUs. and they have RDMA networking. so you can run a distributed training job, that's truly serverless. and we did the overlay network for that. But then we've seen that people are using it for other reasons, and, I'm intrigued to yeah, what would people do with it.Swyx [00:28:59]: Build primitives and let people figure it out, right?Akshat [00:29:01]: Yeah, exactly.Swyx [00:29:02]: You put out a pretty interestingAkshat [00:29:03]: They're like, they read the docs webpage. Let me use thatSwyx [00:29:06]: YeahAkshat [00:29:06]: Something they never intended to work. This is literally not even in our docs page. People somehow found it, and they're using it.RDMA, Memory Movement, and Distributed TrainingSwyx [00:29:12]: Huh.Swyx [00:29:14]: The way you portrayed it with, like, RDMA versus TCP, like, very well laid out, but just the transfer speed change at scale for RL, like yeah, you have it, you have it built in. I'm sure someone found it. It's found it to be a lot more efficient before you made a thing out of it, right?Akshat [00:29:32]: Yeah. And not to split hairs, I guess the overlay network is the TCP overlay network.Akshat [00:29:39]: The reason we have that is you need that to do the key exchange for RDMA before you set up the RDMA network on top of that. but then people found the TCP part.Swyx [00:29:48]: Can I tell you, this is like a big aha moment for me becauseAkshat [00:29:51]: YeahSwyx [00:29:51]: So I review 2,200 submissions for the World's Fair.Akshat [00:29:56]: Yeah.Swyx [00:29:57]: And then I got this from John OsterhoutAkshat [00:29:58]: HuhSwyx [00:29:59]: Who I don't know if. Do John Osterhout by name?Akshat [00:30:01]: The name sounds familiar.Swyx [00:30:02]: He published a. He's a well-known professor, published a lot of interesting software design books, and this is the talk he chose to submit, is on RDMA at Inference. And I'm like, you wouldn't think that this guy, who is like operating systems guy, would care about RDMA.Akshat [00:30:20]: I, it makes sense to me because I,Swyx [00:30:24]: This is the cloud, right? YeahAkshat [00:30:25]: Like, the way you move around your KV cache and how efficiently you can do it, how efficiently you move, your weights from your training GPUs to your inference GPUs in RL is there's a lot of degrees of freedom, and it is a systems problemSwyx [00:30:41]: YeahAkshat [00:30:41]: Moving memory aroundSwyx [00:30:42]: YeahAkshat [00:30:43]: Scheduling.Swyx [00:30:44]: This shows you how primitive my understanding of networking stuff is.Swyx [00:30:46]: Is this like the domain of WireGuard as well?Akshat [00:30:50]: Not quite.Swyx [00:30:51]: It's adjacent?Swyx [00:30:53]: Explain everything.Akshat [00:30:54]: Sure.Swyx [00:30:56]: How do we move memory around GPUs?Akshat [00:30:58]: Well, so sorry. Yeah, that is memory. Sorry, I was talking more, and maybe I was talking like five minutes back, about the private IPv6, addressing that you've set up.Swyx [00:31:09]: Yeah.Akshat [00:31:09]: Is it like it's a VPN?Swyx [00:31:10]: Yeah, it is like a VPN, and yeah, WireGuard is, yeah, you're right. It is,Akshat [00:31:16]: Right. Yeah, you already moved on to new topicsSwyx [00:31:17]: A similarAkshat [00:31:18]: OkaySwyx [00:31:19]: In the same space, WireGuard is, encrypted and this is,Akshat [00:31:23]: And you don't need encryption.Swyx [00:31:23]: Yeah.Akshat [00:31:24]: Yeah.Swyx [00:31:24]: This is not encrypted. that's the main difference. This is TCP and we have eBPF programs that will reject or allow the TCP connection based on whether you're allowed to do it.Akshat [00:31:35]: Used to involve a full sidecar, but now you have eBPF in the Linux kernel.Swyx [00:31:39]: Yeah.Akshat [00:31:40]: Yeah. I don't know if this is a natural follow-on to the topic of like my skepticism on distributed training is that while, like, people spend a lot of money on, like, cables to hook up GPUs, and even that is not, like, fast enough, and that's the bottleneck, is your networking fast enough?Swyx [00:31:59]: Yeah. So I guess you're talking about fully distributed training like, Dialog or something which is like cross data centerAkshat [00:32:06]: That would be, yes.Swyx [00:32:07]: That's the extreme.Akshat [00:32:08]: Yeah.Swyx [00:32:08]: You're in the middle, and then other people would have like the Mellanox cables up in, like, their actual data center.Akshat [00:32:14]: When you run multi-node training on Modal, RDMA, I think Mellanox, is, or InfiniBand is like a, is all seen as RDMA. but it's a way to bypass the TCP networking stack and, transfer, stuff much faster, between one node, to the other. And we have I think like 3 terabit per second, internal networkingSwyx [00:32:40]: OkayAkshat [00:32:40]: Which is the standard that's needed.Swyx [00:32:42]: Okay. So I misunderstood whatAkshat [00:32:43]: 50Swyx [00:32:43]: What part of the stack you wereAkshat [00:32:44]: 50 gigs overSwyx [00:32:45]: YeahAkshat [00:32:45]: If you wentSwyx [00:32:45]: YeahAkshat [00:32:46]: RDMA.Swyx [00:32:46]: Okay.Swyx [00:32:48]: Yeah. I, very impressive work.Multi-Node Training, Post-Training, and Auto ResearchSwyx [00:32:52]: So effectively you're extending like the model philosophy to the training cluster, like, yeah.Akshat [00:32:59]: Yeah. And we're, we're not going for like large scale training runs. the thing that we've built multi-node training for is, we see a lot of, smaller scale post-training. like, people are post-training like medium sized fund models, so they can, get higher quality on inference. this is a perfect fit, for something like that.Swyx [00:33:21]: Yeah. That is my impression of how a lot of these labs explore branches in post-training and then eventually merge whatever they find in.Akshat [00:33:31]: Yeah. The other use case we've seen for multi-node training is even if you have a big cluster, your researchers are still doing small runsSwyx [00:33:38]: YesAkshat [00:33:39]: Having elasticity thereSwyx [00:33:40]: Right, sureAkshat [00:33:40]: Matters a lot more.Swyx [00:33:41]: Yeah. the, like, this is like the current limiting factor for auto research, which is like you need to give your model some GPUs in order for it to completely run.Akshat [00:33:51]: We have a blog post on auto resource and model is,Swyx [00:33:55]: YeahAkshat [00:33:56]: Yeah, like, turns out to be pretty good substrate for that.Swyx [00:33:59]: So my impression is auto research means many things, likeAkshat [00:34:01]: YeahSwyx [00:34:01]: Anything that Andrej coins. Right now it's still science fair, right? Like not like, I don't know how many people are doing this.Akshat [00:34:08]: We're having a golf.Swyx [00:34:08]: Yeah.Akshat [00:34:09]: I thought the same thing.Swyx [00:34:11]: Yeah, you would know.Akshat [00:34:12]: We, like, our internal both training and inference teams use this the general shape of this quite a bit. like we have this one internal repo called auto inference, which essentially we've automated our own forward-deployed engineering efforts using, this harness, which is, the agent will just spin up a sweep of different things. It'll even run like, NVIDIA inside profiler and it'll like tweak configs and it'll arrive the right thing. it'll change your GPUs both from H200 to B200, and works really well.Swyx [00:34:47]: Nice.Akshat [00:34:47]: So yeah.Swyx [00:34:48]: By the way, I enjoy that your forward-deployed engineering is so technical that you have to do these things.Swyx [00:34:52]: It's very different from forward-deployed engineering from other people.Akshat [00:34:54]: Yeah. For our forward-deployed engineering team is, essentially they're like applied inference researchers or applied training researchers.Swyx [00:35:02]: Someone told me like they have to be able to build, but they also have to be able to sell. do they have to sell or are they like they're good, they're just like post-sale type of thing?Akshat [00:35:09]: It does, being able to talk to a customer and engage effectively with themSwyx [00:35:13]: YeahAkshat [00:35:13]: Matters a lot.Swyx [00:35:14]: They want the same thing.Akshat [00:35:15]: Yeah.Swyx [00:35:15]: ?Akshat [00:35:15]: But it's it's not really a sales, thing. We pair them with-- We have solution architects as well that are more on the sales side.Swyx [00:35:23]: Okay. Let's spend a bit more time on auto research. This is a big focus for for this year. Where does this go? like, have people explored enough? Like, there's all these beautiful charts of like improve and then level off a bit and then you find the next thing. Is this one abstraction up from normal training? Is that how we think about it, or do you think about it differently? Like model level training versus high, like driven hyperparameter search.Auto Inference and Modal BenchAkshat [00:35:51]: Yeah, like,Swyx [00:35:51]: Someone, some people call it like neural architecture search or whatever, right? Like.Akshat [00:35:54]: Yeah, - So the stuff I've seen people do with it is nowhere on the architecture level. It's pretty much tweaking parameters, but it's it's a hyperparameter sweep that's guided by some model intuition, so it's like much more efficient than, whatever other, sweep you would have.Swyx [00:36:12]: Yeah, it's just, it's just a question of where you want to spend your compute?Akshat [00:36:16]: Right.Swyx [00:36:16]: ‘Cause yeah, you can just throw infinite amounts of money on this and somehow you'll bang out Shakespeare?Akshat [00:36:22]: Yeah, infinite monkey.Swyx [00:36:24]: Yeah, so like the very good for model. and I think it's also very important that agents can spin up other agents, can spin up their infrastructure. Like very good for you. how good is our LLMs at generating model code? Like the benefit of existing LLMs is that you are in the data.Akshat [00:36:42]: Yeah. They're, they're surprisingly good. I think like pre Cloud 4 they were not, and then now they're able to shot, stuff out of the box. But we're playing around with releasing like a Modal Bench for like the harderSwyx [00:36:55]: YeahAkshat [00:36:55]: Things, that the LLMs cannot do yet and maybeSwyx [00:36:59]: What's an example of that?Akshat [00:37:01]: I think the things that- Sometimes agents struggle with, without right guidance and a skill is, how to, use the rest of our observability. Like how to. Something is failing, like how do you look at the logs and then update the right thing? It's reasoning about that. But they're able to shot, likeSwyx [00:37:23]: Yeah. You can just add a skill to it?Compute Strategy and Capacity PlanningAkshat [00:37:26]: Yeah. So we have a Modal skill now that. Which is why we built this Modal Bench. It's to find things like that, so we can address them in our tool.Swyx [00:37:35]: Tune a skill. Yeah.Akshat [00:37:36]: Yeah.Swyx [00:37:36]: No. it's it's good. are you facing any shortages? like we talk a lot about GPU shortages, but also CPU, also memory.Swyx [00:37:44]: Yeah.Akshat [00:37:45]: We have had a lot of growth, which means that, there's - we've had to be much better aboutSwyx [00:37:53]: PlanningAkshat [00:37:54]: Proactive capacity planning.Swyx [00:37:55]: Yeah.Akshat [00:37:55]: So we have,Swyx [00:37:57]: Which by the way, like it's like a MBA's like dreamAkshat [00:38:00]: YesSwyx [00:38:00]: Is like just planning this stuff. I think last time you and I talked about something maybe about this.Akshat [00:38:03]: Yeah. we have a really competent team of people that we call, The role is called compute strategy. so yeah, if anyone listening here or wants to work on thatSwyx [00:38:13]: Compute strategy?Akshat [00:38:13]: Yeah.Swyx [00:38:14]: I think,Akshat [00:38:14]: I feel like,Swyx [00:38:15]: I think the normies call it FP&A or something.Akshat [00:38:18]: Well, it's more It's it's not FP&A. It's it's There's a lot of interesting financial questions of like what is the blend between one year and three-year reservations? how do we forecast our own capacity? how do we. especially since our capacity is very fungible across different GPU types and different regions, like you have to model a lot of it. and you also have to have an opinion on how the supply chain is gonna evolve, and then you have to like, take bets,Swyx [00:38:49]: YeahAkshat [00:38:49]: Based on that.Swyx [00:38:50]: Tokenomics.Akshat [00:38:50]: Yeah.Swyx [00:38:51]: This is like probably a not a real point, but, I was trying to think about like what other industries. I was trying to think about like, we cannot be first to like these kinds of problems.Akshat [00:38:59]: Yeah.Swyx [00:39:00]: And what other industries have had this? And I was like, airlines with fuel and like they have to hedge their fuel and like, I think for a long time Southwest because they made like a hero fuel bet, they like were like super low cost becauseAkshat [00:39:12]: OhSwyx [00:39:12]: Compared to everyone else.Akshat [00:39:14]: Yeah. I hadn't thought about that.Vibhu [00:39:16]: We're at a fun time too?Akshat [00:39:18]: Yeah. It's. A lot of the compute business in general, for us is also about being very good about capacity management. That is how you have great unit, economics. but also over time it's how you can unlock more value for customers. Like, one of the things we're building now is like a way for customers to get, If they don't care about latency, like get much cheaper pricing and they'll get results back in like next 24 hours or something, like a batch tier essentially.Batch Tiers and Latency-Insensitive WorkloadsSwyx [00:39:47]: Yeah.Akshat [00:39:47]: And those are levers we have because we control the whole stack and scheduling and whatnot to give people a sufficientSwyx [00:39:53]: Yeah. I feel like they're not as popular. Like those, like the Frontier Labs have all those APIs. They're not as popular as they should be.Akshat [00:40:00]: The demand that we see for something like that is not for LLMs. although sometimes people wanna run evals andSwyx [00:40:08]: OkayAkshat [00:40:08]: Synthetic data prep and there it makes sense.Swyx [00:40:10]: Okay.Akshat [00:40:11]: But it's from a lot of LLM companies, like people who are doing computational bio, like they have to run really big batch jobs and they don't care about when they get it back.Swyx [00:40:22]: Yeah. And like they have a reasonable. It's it's also like a cousin to the stopping problem of like, will this finish in time?Akshat [00:40:30]: Yeah. You can bound it.Swyx [00:40:33]: Yeah.Akshat [00:40:33]: Like you can give peopleSwyx [00:40:34]: YeahAkshat [00:40:34]: SLAs on it.Swyx [00:40:35]: Yeah. I think what's, what's interesting is like the next phase of model.Swyx [00:40:38]: Like what, do people expect from you, now that you're established and you're like well-known compute player among all these leading companies. You had an inference launch week, and we talked a little bit about the launches. like what else? Like what else should people know?What Modal Builds NextAkshat [00:40:55]: We are building primitives that make our users' lives much easier. So, I think for example, with LLM inference, thousands more companies are gonna post-train their own models and, deploy open source models for inference. so we're thinking a lot about what is the best product shape for that. And, that involves everything from our training gym to, then, endpoints that get frontier-level performance. again, but I haven't talked to anyone. It looks somewhat different on other verticals. Like, we're also seeing a lot of real-time, audio-video stuff in there, which is why like, we're working on things like regional routing, with fallbacks. So you can get GPUs that are as close to users as possible. so you get like low latency for video streaming and whatnot. And then on the agent side, it's,Akshat [00:41:52]: We're still working very closely with our customers because stuff is changing so fast in terms of what they need. And, I think beyond sandboxes and persistent file systems, there's a lot of other things people will need from this agent stack as they build production agents. So yeah, we're thinking about those other things that fit in there.Swyx [00:42:13]: I want to ask what the other things are.Akshat [00:42:15]: Yeah. I probably should share right now.Swyx [00:42:17]: I think-- I think, okay, so, I do think a lot about the principal components of cloud, and you do talk about compute storage networking.Akshat [00:42:25]: Yeah.Swyx [00:42:25]: Because so far for me, it's fine. so far for the. the first couple generations of cloud, it's fine. What's different, qualitatively different about agents that you need some new permission level? Like a lot of people, okay, and I'll just kinda spew tokens at you until it like hopefully sparks something.Akshat [00:42:43]: Yeah.Swyx [00:42:44]: Like the new level now is whatever Claude Code does, which is dangerously scope permissions or like allow list by command or like whatever, right? And sometimes they're like, “Well, okay, we have like this adaptive thinking mode where like, just trust me, bro. I will make the calls for you.” Is that it? like mediated permissions.Hard Guardrails vs. LLM-Mediated PermissionsVibhu [00:43:03]: Now you're looping it with a goal and letting it roll.Akshat [00:43:06]: Yeah, I'm, I'm skeptical of LLM media permission for stuff that is at the sandbox level because you do want hard boundaries.Swyx [00:43:16]: Yeah.Akshat [00:43:16]: Otherwise, someone can exfiltrate stuff.Swyx [00:43:20]: But likeAkshat [00:43:20]: YeahSwyx [00:43:20]: Maybe that's old school thinking. Maybe we're the dinosaurs.Swyx [00:43:23]: Maybe the AI OS or the LLM OS is really the kernel is a goddamn LLM.Swyx [00:43:30]: Like it makes you feel uncomfortable.Akshat [00:43:31]: Yeah, I'm, I'm toldSwyx [00:43:32]: But that's what trusting the LLM is. Like imagine a spherical cow perfect LLM.Akshat [00:43:36]: Right.Swyx [00:43:37]: That it.Akshat [00:43:39]: Maybe.Swyx [00:43:41]: I wanna test the boundaries, right?Akshat [00:43:42]: Yeah.Swyx [00:43:42]: Like, and I don't believe that, but I wanna see where I'm wrong ‘cause that's, that's the consensus.Akshat [00:43:49]: Yeah. I think you always need hard guardrails when you want, And you can pair those with softer guardrails, right? And that's gonna be a lot of mediated.Managed Agents and Specialized SandboxesSwyx [00:44:00]: There. I'll also get you a end with a couple of your commentary on like the ecosystem outside of Modal. Manage agents. Everyone has one. Gemini, OpenAI, Claude, very useful for you, but also like it is their way of starting to edge into your space.Akshat [00:44:17]: Yeah.Swyx [00:44:17]: What's going on?Akshat [00:44:19]: Yeah, we're, very excited to partner with Anthropic and some of the other foundation labs, will not name who we're also working with. the way we see it is the manage agent thing is a great place to start if you're starting out building an agent and, But then when you get to, building something more production grade, like you're a company that's like Ramp that's building their own, Ramp also runs their accounting agent on us, so their external-facing agent. You need a lot more control over, your compute primitive on things like, what sort - how do you persist different files that the agent has access to, and how do you snapshot and restore? How do you control the networking? maybe you want GPUs. When you get to that point, you kinda want, a specialized sandbox provider, that gives you those things, and that's the role that we are trying to play.Swyx [00:45:15]: YeahAkshat [00:45:16]: We don't really have an opinion on the harness, whether it runs - it's a cloud-managed agent, and you hook it up to Model Sandbox, or you run the harness in Model Sandbox. We'll see where people converge with that.Swyx [00:45:26]: Yeah. Do you any opinions on like the meta harnesses, or just another layer on top of these things?Akshat [00:45:31]: You mean like the OpenPipeSwyx [00:45:33]: OpenPipe is one. I think Vercel had one, which I can't remember the name of right now. Fredshot had one. and then, to me, most recently was Data Databricks that had Omnigen. All these are meta harness. Like it's kinda pseudo agent cloud type things.Akshat [00:45:50]: I personally have not played around with them.Swyx [00:45:53]: Yeah.Akshat [00:45:53]: Build agents with them.Swyx [00:45:54]: Everything's bullish Modal, as long as it consumes more infra.Akshat [00:45:57]: That's why we're focusing on the infra layer. It's somewhere where our, relative competence is and, also it's a hard problem to solve.Swyx [00:46:06]: Yeah. I will say like just generally reflecting on that, I don't know if - if there's other topics on Modal, but like just generally reflecting as an infra person, not as intense as you, but in that field, this has like been the most exciting time in infra. Like it was boring for a while, and you couldn't really get people excited about data infrastructure. Like Eric would get on Data Console, everyone just watched the video and like say, “Look at how many sandboxes I can spin up,” and no one gave a crap.Why Infrastructure Became Exciting AgainAkshat [00:46:39]: Yeah.Swyx [00:46:40]: And like now everyone gives a crap.Akshat [00:46:42]: That's true. It is a very exciting time, and I think a lot of that's driven by just the amount of scale all of this stuff needs.Swyx [00:46:50]: I think the, like a lot of your initiatives or a lot of your like product directions make sense in retrospect, which is like the best kind, but I wouldn't necessarily have thought about it myself, which.Akshat [00:47:00]: We need the predictions.Swyx [00:47:02]: I think there's a lot that you just don't even see, right? Like you have the batch, you have the voice, you have the multimodal, but what else?Akshat [00:47:10]: What else is coming up for usSwyx [00:47:11]: Yeah. Where do you see things going?Akshat [00:47:13]: Yeah. I, in generalBiotech, Robotics, and Non-LLM AI WorkloadsAkshat [00:47:15]: It's it's clear that there's there's a huge shift happening. I think one thing that's not as obvious to people because LLM inference gets talked about so much and is also we work a lot of companies that are, doing things like drug discovery and computational bio, like the Chai Discoveries of the world. Big things are probably gonna happen there. we work a lot of robotics companies that are putting robots in like active deployments and getting good results out of them.Swyx [00:47:45]: Is there Air Gap Modal? Is there a version that is like prem air gapped whatever?Akshat [00:47:50]: No. We,Swyx [00:47:51]: You should cloud only.Akshat [00:47:51]: Yeah.Swyx [00:47:52]: Yeah. Okay. But yeah, so what you're saying is like because you're focused on primitives and they're good primitives, you find use cases in all these kinds of things.Akshat [00:48:01]: Yeah.Swyx [00:48:01]: Probably diversifies you a little bit away from LMS all the time.Akshat [00:48:05]: Yeah, absolutely. We're, we'- our goal isn't to only serve the LLM inference market.Swyx [00:48:10]: There are a lot just on the website, the audio,Akshat [00:48:12]: Yeah. We said both onSwyx [00:48:14]: Computational bio images. Yeah, there's a lot here. There's QTA TTS, customizing. Oh, Chatterbox. there was customizing Whisper.Akshat [00:48:24]: Okay. Yeah.Swyx [00:48:25]: This screen reminds me of a fallen competitor, which Replicate.Model APIs vs. Differentiated AI ProductsSwyx [00:48:31]: What's your postmortem on what happened?Akshat [00:48:34]: This is one thing we've stayed away from is providing an API for models because I think providing model APIs is some of it ends up serving like a really hobbyist market, which is much less sticky.Swyx [00:48:50]: Yeah.Akshat [00:48:50]: And we've always wanted to build for companies that are building products and need more flexibility that's not just an API.Swyx [00:48:57]: Which you can build an API for a model and this is clearly what it is. But you - but what you're saying, you can wrap it into a more fully functioning back end that you run.Akshat [00:49:06]: Yeah. So all of our examples, it's not that spin up this model, here's an API token, use it. They're all code.Swyx [00:49:13]: Okay.Akshat [00:49:13]: And so the point is that this is just an example.Swyx [00:49:16]: Starter code.Akshat [00:49:17]: Yeah. But you can tweak it however you want.Swyx [00:49:20]: Yeah.Akshat [00:49:21]: And if you're like a company building a product, like, computational bio whatnot, yeah.Swyx [00:49:26]: I guess I'm trying to tease out for listenersAkshat [00:49:28]: YeahSwyx [00:49:28]: When does it stop becoming, oh, you're just an API call and you're just a wrapper on API to becoming what you call a product, right?Swyx [00:49:36]: Like, what is that layer? Like what-- Like, more lines of code, but like beyond that, what is the substance that people add that qualifies it to be something more?Akshat [00:49:46]: I think there's a little bit of like a selection effect of like a lot of the companies who do wanna get deeper into that level are probably building something that's more differentiated. And, I think, an example is like - with LLM inference, originally we, worked with companies that were building their own post-training frameworks or they were, - Ramp early in the day was training their own tokenizer and like swapping out the tokenizer in Llama and whatnot. I'm not saying that's, that successful, in that case. But a better example is like, let's say Suno. because Suno, does not use Modal for training.Swyx [00:50:26]: Mikey on the pod. Yeah.Akshat [00:50:27]: But they use Modal for all their inference and that's because they have like a custom-- They have completely custom model architecture and that means that they have to be at the code level and tweak things that are not, just an API.Swyx [00:50:41]: It's interesting as well, like we had, Ethan, most recently on the xAI Groq team make a prediction that like the next tier in video gen is not a better video model, it's a better model or agent that orchestrates video models.Video Agents and Production WorkflowsAkshat [00:50:56]: Oh, interesting.Vibhu [00:50:56]: Language model backbone that can use toolsAkshat [00:50:58]: RightVibhu [00:50:59]: And write code.Akshat [00:51:00]: Like, yes, I can make my second video or my second video from Groq, but I want my minute video.Akshat [00:51:06]: And I'm not going there through normal video gen.Swyx [00:51:10]: Yeah, that's interesting. I - So we have GPU sandboxes and recently have seen a few companies doing agents that do video manipulation or,Akshat [00:51:22]: Yeah. Give it FFmpeg and just do it.Swyx [00:51:23]: Run FFmpeg. But likeAkshat [00:51:25]: That's not enough.Swyx [00:51:25]: Yeah.Akshat [00:51:26]: You need to give it Adobe.Swyx [00:51:27]: Yeah, I hadn't put it together with like it would be a video production thing. in my mind these things were going more towards editingAkshat [00:51:36]: Yeah.Vibhu [00:51:36]: Well, shout out Mantis.Akshat [00:51:37]: I think about this a lot.Swyx [00:51:38]: .Akshat [00:51:41]: Yeah. Sorry.Vibhu [00:51:41]: Luma. Luma Agent is a version of this for video production, but it's a off.Swyx [00:51:46]: I was gonna get your quick takes, on some other stuff that happensGitpod/Ona, CI, and Runtime SandboxesSwyx [00:51:50]: In recent news and just-just see if you have anything interesting. Gitpod, very li

This week on Dev Interrupted, Slack's Chief Product Officer, Jaime DeLanghe, joins the show to explain why enterprise AI value depends on embedding custom bots directly into your existing team communication loops rather than deploying them inside isolated, single-player chat silos. She breaks down the platform's shift toward open ecosystem standards like the Model Context Protocol (MCP) and how dynamic UI frameworks are transforming standard channels into active execution environments. Jaime details the operational realities of managing autonomous software fleets, including a striking look at how leading companies are placing hundreds of custom agents directly onto their corporate org charts.Life Beyond Tokenmaxxing Workshop: Watch the full replay on demand at linearb.io Follow the show:Subscribe to our Substack Follow us on LinkedInSubscribe to our YouTube ChannelLeave us a ReviewFollow the hosts:Follow AndrewFollow BenFollow DanFollow today's guest:Slackbot MCP Client: Learn more about connecting your tools to Slackbot via the Model Context Protocol at the Slack BlogSlack Developer Hub: Start building your own agentic workflows and explore the latest tools at slack.devConnect with Jaime: LinkedInOFFERSStart Free Trial: Get started with LinearB's AI productivity platform for free.Book a Demo: Learn how you can ship faster, improve DevEx, and lead with confidence in the AI era.LEARN ABOUT LINEARBAI Code Reviews: Automate reviews to catch bugs, security risks, and performance issues before they hit production.AI & Productivity Insights: Go beyond DORA with AI-powered recommendations and dashboards to measure and improve performance.AI-Powered Workflow Automations: Use AI-generated PR descriptions, smart routing, and other automations to reduce developer toil.MCP Server: Interact with your engineering data using natural language to build custom reports and get answers on the fly.

Today, I'm talking to Rana Gujral, CEO of Behavioral Signals, which provides AI that interprets human behavioral cues in speech to help route call center conversations more effectively, improve customer service performance, and detect voice-based fraud. Their moat is a decade of voice data tied to real business outcomes, not the model itself, as Rana explains.   During our conversation, Rana shares his practical framework for making the value of their AI obvious to the various humans in the loop that the product needs to “touch,” and he argues that a one size [UI] doesn't fit all. In Rana's product, they discovered that customer service reps need ambient assistance, supervisors need aggregate patterns, compliance teams need audit trails, and executives need outcome metrics tied to business results.   He also explains why having measurable ROI isn't enough. Early renewals for Behavioral Signals suffered because the people signing the checks couldn't actually see the product's impact. Rana's solution? “Ship the meter” alongside the intelligence. If your AI works quietly in the background, you still need reporting UIs that clearly communicate the product's value.   For founders struggling with stalled POCs, Rana breaks down the three-stage evaluation journey his team developed after repeatedly seeing deals fail at predictable moments. By designing the customer experience around those milestones, his team transformed how buyers gained confidence throughout the evaluation process.   Finally, we explored why great B2B AI products don't succeed by becoming another dashboard. Rather, they succeed by closing the loop between decisions, outcomes, and learning. Rana also fills me in on his upcoming book, The AI Instinct, which focuses on how AI changes human judgment rather than simply advancing model capabilities. And his parting advice? Listen to find out!   Highlights / Skip to: Making “invisible AI” value clear (3:57) The four surfaces of visibility the product team dials into to ensure Behavioral Signals is indispensable to customers(6:26) Behavioral Signals' intentionality behind their three-phase model to address deals not closing (15:35) How Rana's team deals with AI moving downstream problems further upstream (19:56) Determining their product's boundaries: when do you stop building? (22:55) Why proprietary data makes for such a good moat (24:57) What Rana would do the same and differently if he were starting over (28:45) Rana's book: The AI Instinct: The Future of AI and Human Decision-Making (39:29) Rana Gujral's closing advice (44:35) Links Behavioral Signals  The AI Instinct: The Future of AI and Human Decision-Making  Rana Gujral's website   Rana Gujral's LinkedIn

The kernel taketh away, so we bringeth back. We build an AppleTalk LAN, ditch TCP/IP, and give a legendary retro network protocol the send-off it deserves.Sponsored By:Jupiter Party Annual Membership: Put your support on automatic with our annual plan, and get one month of membership for free!Managed Nebula: Meet Managed Nebula from Defined Networking. A decentralized VPN built on the open-source Nebula platform that we love.Support LINUX UnpluggedLinks:AppleTalk 1985-2026 Memorial StickerJupiter Garage SWAGSorry, I only open regular files StickerWeb Boost — Send us a boost via sats or USD

https://www.patreon.com/windahTranskrip: https://www.patreon.com/windah/posts/222-dunia-kerja-162930711?utm_medium=clipboard_copy&utm_source=copyLink&utm_campaign=postshare_creator&utm_content=join_linkTerjemahan: https://www.patreon.com/windah/posts/eng-222-dunia-vs-162930718?utm_medium=clipboard_copy&utm_source=copyLink&utm_campaign=postshare_creator&utm_content=join_linkDi episode ini, aku membahas dunia kerja Indonesia, terutama soal pilihan “gaji atau gengsi?”, mulai dari cerita lulusan UI, sampai pengalaman pribadiku. Dengarkan episode ini!Tautan video: https://vt.tiktok.com/ZSCg9cVSy/Terima kasih banyak atas dukungannya untuk:SAHABAT WINDAHAkiramJayNyong Jago Bob GenericJohn nyMartin JankovskýWilliam ChenDawid GerstelDnsSebastianAlexander ScholtesJrobabuja11 RoboNicholai LidowAliteJack William HusbandsAndre ChampouxDemiKatherine WalkerLino ArboledaLeon KwekCameron Edinger-ReeveIsmail OtchiChrisRussell BarlowMary Pope帥志 Shuai Chih LinBjornrappangeHossein KhoshtaghazaParis LuckowskiMatthew O'ConnorRussell OgdenYaszalixBart van de KampArthur NazaryanDaniel KaposiEmily HuangBenjamin SayHa Nguyen Jena StringerFrédéric UhrweillerQuran and sunnahEdward HearnJennifer Foley태용 심Cameron ClarkOxana SaimoAudrey DeliviaJohn RichardsonMarkRuby den BoerTata HelenkiRowenaRolandJoey ProwCarmen ChuiAngelia OngCatherine Collier AntjeChanatip TatiyakaroonwongHariyanto H.kaydee donkersDavid McLeanTomo振 袁Bali SamRuRu_pagino GroenevelJeshua MorrisseDaniel GrahamKartikaTed ChanTomiGiacomo GrisonGeorgeJulia LDerynAndrewAndrew Joesoef宇豪 蔡JoeSLMatt潤菁 李JohnTEMAN WINDAHJohn McBrideP. Clayton D. Causey, CT  Vanessa HackJohn ShumLuis PaezCraig RedriffMariusCharlotteJonny 5Jose LorenzoJeremyLulunMadeleine MillerAngelo CaonRossi von der BorchSicily FiennesMeredith R NormanTim DoolingDevin NailAlissa Sjuryadi-TrowbridgeBillEric EmerTarquam James McKennaAmanda BlossStephen MBen HarrisonNaota YanagiharaHans WagnerJustin WilsonZane RubaiiBenjaminAlexH HMatt WintersAlec MitchellVinceBertiAtsuko MaenoMosaStephen GrahamColleen Thornton-WardAilise Sweeney-LoweJimmyYng KenjicnxuFlorian HopfKurt VerschuerenJoakimRyosuke SudaBerberJeroen VellekoopMatthewTakeshi YamafujiNatePatrickMiquelFeeJingle YanMathias朗 桑田Ben PlayfordLauraKenji YanaguRicky ZhangVacanza Tropicale惠羽 蔡Sophie Hoestereyこ ぱるDouglas HerrickTim SomervilleFSF BEddoMarc EberJin Kimivy babyDevlin KuyekDawn TanNeoKimchiSpiritPaulie MoraPaula BradleyRoman PicardMartin AwalYohiEnrico WelderYoichiroKatoRoanna MTacoButter동원 이MojaNabi KunisadaTDaniel Tanlego meister昭儒 吳Thanh-Nhi VoJ YonkmanMarjaAndrea Deckeroc RMatteo FarciJohan MiFrankMichael SpagonIsabel TeoMasaDannyMark SeemanpillaiNicky BrownEstelle蕾 戴倫 阿David Kasakeijan-RossTakuAniek ReindersPhilippАндрій Шпакyeongeun ohPENDENGAR SETIAColumba TierneyHH JorgensenAmina AljehaniCamilleAninda P.A.F拓也 高山匠海 杉本 Iga KomarJaime NoriegaEdmund TanАндрей ТутаевAzharra Al FaridLitaTwindadfrankStephen Wellesley-SquiresAnnayi xue sunEdwin HantsonGabriel Adler

Territory Capture 3.0 continues, and tonight we're diving deep into the new research tree, Galactic Anomalies, service rotations, currency strategy, bugs, UI changes, and the big question every alliance is asking: where should we go next, and what are we actually trying to build? Jules Vern joins the show with a full breakdown of Territory Research, including prisms, refined isogenite, Ascendancy Ciphers, refinery priorities, Vanguard Prism strategy, geodes, relics, loot bonuses, and the long-term value hidden inside the new system. We also cover current bugs, rotating territory services, Junker blueprint rules, missing services, possible service cadence changes, and why the new territory map may be pushing alliances toward more strategic choices than simple progression. Plus, huge congratulations to Griffin and Penguin, Star Trek Cruise giveaway details, and the usual Talking Trek live studio chaos.   00:00 Welcome to Talking Trek Live 08:05 Week Two Territory Check-In 09:24 Galactic Anomalies Arrive 13:59 Risk, Reward, and “Hazards 2.0” 20:03 Soft Rollout and Future Expansion 25:30 Cargo Display Change Debate 31:41 Q's Trials Button and UI Complaints 36:00 Community Tools vs Content Creators 39:04 Star Trek Cruise Cabin Giveaway 41:29 Bug Report: Junker BPs and Rotating Services 47:59 Specialty Ship Unlock Rules Explained 52:23 Territory Timer and Mobile UI Bugs 59:59 Dev Communication and Bug Follow-Up 01:02:30 Griffin and Penguin Engagement Announcement 01:08:40 Territory Research Tree Breakdown Begins 01:12:00 Ascendancy Ciphers and Research Paths 01:18:00 Jules Vern's Research Strategy and Cost Math 01:24:01 Infinite Diversity in Research Choices 01:29:30 Territory Currency Grind and Geode Problems 01:36:00 Female Spock, Relics, and Loot Bonuses 01:43:00 Solo vs Alliance Armada Rewards 01:50:00 Territory Services and Blueprint Access 01:58:00 Missing Services and Rotation Concerns 02:06:00 Service Cadence Feedback and Player Surveys 02:12:00 CoTA Placement, Whale Alliances, and Final Thoughts 02:13:48 Closing, Giveaways, and Sign-Off

Get your fill of chlorophyll, when Mike Perrotti joins us to talk about his passion for plants. He shares how a therapist's recommendation to add greenery to combat seasonal depression unexpectedly spiraled into a tropical obsession. Mike describes how he catches himself coveting rare expensive plants and consciously redirects back to the meditative joy of caregiving and cultivation. He also reveals how managing a diverse collection in a low-light apartment requires the same constraint-driven creative thinking as design.Guest BioMike Perrotti (he/him) is a Product Designer at Datadog living in Brooklyn with an assortment of plants and reptiles, a cat, and a loving partner who tolerates it all. He spends his weekends in his lush greenhouse by day and on dark, foggy dancefloors by night. Before moving to New York in 2012, he studied art in Philadelphia, then fell sideways into UI design and frontend development. He later went on to become a major contributor to various design systems, including GitHub's Primer. Along the way he developed a pattern of picking up hobbies and going completely and shamelessly overboard with them. His houseplant hobby snuck up on him and (predictably) spiraled into an obsession.LinksMike on LinkedIn: https://www.linkedin.com/in/michael-perrotti-81402572/Mike on Instagram: https://www.instagram.com/mperrotti_CreditsCover design by Raquel Breternitz.

The great John Moltz returns to the show. Topics include Apple's hardware price hikes in response to the global RAM/SSD shortage, and some spitballing on what we like about the UI changes in the MacOS 27 Golden Gate beta.

A bi-weekly news show informing you on the latest in Bitcoin, privacy and open source tech hosted by Ungovernables, Max and Q. AOB• Holiday season is over• UK no longer melting• Samourai updatesNEWS• GitHub permanently bans Rust Lightning (LDK); Corallo moves Bitcoin dev infra to self-hosted Forgejo - Bitcoin Magazine: Corallo urges Bitcoin projects to exit GitHub after Rust Lightning ban• Sparrow Wallet Apple developer-account scare resolved - Bitcoin Magazine / Craig Raw / Sparrow Wallet• FISA Section 702 lapses for the first time since 2008 - EFF: Victory, 702 Has Expired• Canada's surveillance-and-speech week: Lawful Access bill (C-22) rammed to a June 19 deadline, "Combatting Hate" Act (C-9) gets royal assent June 18 - EFF: Canada Is Forging Ahead with Its Dangerous Surveillance Bill / Canada.ca: stronger hate crime protections become law• US CBDC ban through 2030 heads to the President's desk - House Financial Services Committee• Developer-protection carve-out (BRCA) being sanded down as CLARITY Act stalls; passage odds cut to 50-50 - Bitcoin Magazine: Galaxy Research cuts CLARITY Act passage odds• Bull Bitcoin secures MiCA license in France while keeping self-custody/privacy model - Bitcoin Magazine / Bull Bitcoin• ESMA tells unlicensed crypto providers to wind down as MiCA deadline arrives - ESMA public statement PDF• LND zero-timestamp DoS vulnerability publicly disclosed - Bitcoin Optech #411RELEASESWallets• Envoy v2.3.0-beta - 2026-06-22Redesigned Send flow with inter-account transfers, message signing, Address Explorer, sub-sat fee rates, and custom block-explorer support, plus Passport Prime pairing/Bluetooth reliability fixes. Disclose it is ours.• Zeus v13.1.0 - 2026-06-17Feature release: Cashu multi-mint sends, queue-less Nostr Wallet Connect payments on iOS, CLINK noffer support, and Cashu-token detection in URLs. Strong ecash plus Nostr-native payments stack for a self-custodial Lightning wallet.• Zeus v13.1.1 - 2026-06-26Patch: fixes TLS cert verification for LND/CLN REST over Tor and a currency-converter input bug.• Phoenix 2.8.1 - 2026-06-18Adds an iOS debug screen for signing swap-in transactions and fixes an Android amount-display bug, on updated lightning-kmp.• Blockstream Green iOS 5.5.0 - 2026-06-15Adds support for paying LNURL, BOLT12, and BIP-353 payment instructions, improves Lightning invoice handling, and shows funding fees in the receive flow.• Blockstream Green Desktop 3.4.1 - 2026-06-17Adds BOLT12 offer descriptions and updates GDK to 0.77.6. Secondary to the iOS release, but it reinforces the same BOLT12/BIP-353 direction.• Blink Mobile 3.0.1 - 2026-06-26Pre-release build that continues Blink's self-custodial wallet implementation, including wallet creation, seed backup/recovery, Blink Lightning address, LNURL pay, and Stable Balance with BTC-USD conversion. Mention only as a prerelease / watch item.Lightning / node ops• Core Lightning 26.06.2 - 2026-06-29Point release recommended for minimal OS setups and Docker images lacking root TLS certificates. Late-breaking / record-day item if recording after June 29; not a lead, but useful for node operators.Self-hosting / payments infra• BTCPay Server v2.4.0 - 2026-06-25Major release: multisig wallet setup, loginless passkey auth, and granular per-wallet permissions. Breaking: removes the LNBank and Lightning Charge backends.Bitcoin / wallet infrastructure• Liquid GDK 0.77.6 - 2026-06-16Adds Python 3.14 wheels and fixes singlesig GDK use over Tor that broke after a ureq dependency update. Developer/infrastructure note rather than listener-facing wallet news.Hardware / multisig / inheritance• Nunchuk 2.6.0 - 2026-06-17 (Blog Post: https://nunchuk.io/blog/phased-rollout )Phased rollout of the off-chain inheritance protocol, plus fixes and performance work. Self-custodied estate planning for multisig users.P2P / no-KYC trading• Mostro v0.17.5 - 2026-06-16Anti-abuse bonds harden the Nostr-based P2P marketplace while staying non-custodial. Adds multi-source price aggregation and maker-timeout penalties.• BULL Wallet v6.11.1 - 2026-06-22CSV export, payment notes, auto swap-claim retries, better Electrum reliability, experimental Linux desktop support. • Peach Bitcoin 0.69.0 - 2026-06-25Faster funding-flow sync, but ships a new EU-compliance sign-up step. Worth naming the privacy/compliance tradeoff for a no-KYC-leaning audience.On-chain privacy / coinjoin• Ashigaru Desktop 1.0 - 2026-06-24 Ashigaru Desktop 1.0.0 (June 19, 2026) is the inaugural stable release of the Whirlpool coinjoin desktop wallet, shipping Tx0/zeroleak mixing with Premix/Postmix/Badbank account management, built-in Tor, Mix To for routing postmix funds offline, a receive-only attack-surface reduction model, BIP-329 label import/export, and an offline BIP47 message verifier. It also adds a polished dark-theme UI with live coinjoin progress tracking, full cross-platform packaging (Windows/macOS/Linux plus headless server builds), reproducible builds, and BIP47-signed three-step release verification.• JoinMarket NG 0.33.0 - 2026-06-24Correction/update from earlier 0.32.0 note. 0.33.0 adds forced-address-reuse defences, a config center, TUI send validation, wallet history API work, and fidelity-bond / tumbler fixes.• Wasabi Wallet 2.8.0 - 2026-06-28This release adds P2P sync for compact filters, pay-in-coinjoin, sub-1 sat/vByte fees, payment batching, a Scheme scripting language, Signet support, and Tor forward compatibility. It also broadens platform reach with arm64 Linux, Tails, and Whonix support.• Payjoin Dev Kit payjoin-mailroom 0.1.2 - 2026-06-26Hardens the combined Payjoin Directory and OHTTP Relay with bounded file-descriptor usage, recovery from transient accept errors, database/per-request metrics, and unified mailbox TTL.General wallet / spend• Stack Wallet 2.6.0 - 2026-06-27Adds CakePay gift-card support. EDUCATION• The Fed Is Working on a CBDCThe Rage reports that despite public denials, the Fed is internally researching CBDC design. A concrete surveillance-money piece that pairs directly with the 702 lapse for a “financial panopticon” segment. Also pair with the new ROAD to Housing Act CBDC-ban item: the ban runs only through 2030, which is the punchline.• BIP 110 Fork Simulator / explainerInteractive explainer of the BIP 110 “Reduced Data Temporary Softfork”: activation mechanics, what gets restricted, and how the data-filtering debate could split consensus.• How-To Guide: Running an Ecash MintHands-on Cashu / LNbits + Nutshell mint walkthrough. Ecash is the privacy tech of the moment; a good “run your own mint” counterweight to “just use a wallet.”• Bitcoin Privacy in 2026: A Practical GuideDated 2026-06-04, outside even the grace cutoff, so use as evergreen rather than news. Good “where do listeners actually start” resource.• Bitcoin Optech #411Carries the LND zero-timestamp DoS disclosure plus the usual protocol-discussion roundup. Patch reminder for node runners.• Bitcoin Optech #410Summarises an active dev debate to remove opt-in RBF signaling because it now mainly fingerprints the wallet that created the transaction, plus Sparrow Silent Payments, JoinMarket, and Ark items.KEEP AN EYE ON• EU “Chat Control” final trilogue - Patrick Breyer / fightchatcontrol.euThe fourth and likely final trilogue on the permanent CSAM-scanning regulation is set for Monday 2026-06-29. Member states have reportedly dropped the forced encrypted-message-scanning mandate, but the Council text would still codify “voluntary” scanning and extend the interim derogation, with formal adoption expected in July. Outcome not yet known as of record date.• MiCA enforcement after July 1 - ESMA / Bull BitcoinBull Bitcoin is the self-custody-friendly example to watch, while ESMA's wind-down warning is the enforcement backdrop. After July 1, check which EU-accessible services remain available, which providers stop onboarding, and whether “compliant but non-custodial” survives contact with implementation.TO DONATE TO ROMAN'S DEFENSE FUND: https://freeromanstorm.com/donateHELP GET SAMOURAI A PARDONSIGN THE PETITION ----> https://www.change.org/p/stand-up-for-freedom-pardon-the-innocent-coders-jailed-for-building-privacy-tools DONATE TO THE FAMILIES ----> https://www.givesendgo.com/billandkeonneSUPPORT ON SOCIAL MEDIA ---> https://billandkeonne.org/VALUE FOR VALUEThanks for listening you…

Keiko Shimosato Carreiro was born in the Bay Area … the Boston Bay Area. In Part 1 of this episode, meet Keiko. Today, she's a longtime member of the San Francisco Mime Troupe collective. But her story goes back to her parents' migration to the US from Japan after World War II. Keiko's dad got a Fulbright scholarship to come to this country. He studied medicine. Her mom came to visit the US for one year, but in that time, met Keiko's dad at a Japan Society picnic in Cambridge, Massachusetts. Two weeks after they met, her dad proposed, and roughly a year later, they had their first child—Keiko, named thusly because she was conceived on Cape Cod. Because her dad was doing his residency in Boston, Keiko was born in Cambridge, just across the Charles River. She spent the first 15 years of her life in the Boston area, frequently going to protests of the war in Vietnam with her parents. Keiko also attended "Burn Your Bra" rallies in Harvard Square. Her family comprised the only Asians in Lexington, where they lived. Keiko and her sister were the only Japanese school kids in the district. Perhaps because of that dubious distinction, reporters approached Keiko when she was very young to ask her what she thought of the US war in Vietnam. On the other hand, there were white Americans who welcomed Keiko and her family. Her mom had a sponsor, an American woman who had been a journalist in Japan during WWII. And her dad came here on a Fulbright scholarship, after all. Both had plans to return to their home country, but meeting and considering that Japan was still something of a war-torn country, they decided to settle in Massachusetts. Before they knew it, they had three daughters. We shift the conversation to talk about Keiko and her sisters and their sibling relationships. Keiko sees the demands from her parents on her, as the oldest child, being the highest. Going younger in age among her sisters, the demands lessen. I know this all too well, being the youngest of three boys myself. Keiko and her youngest sister didn't get along when they were young. Maybe it was the seven-year gap in age. But through the experience of Keiko caring for her aging and dying parents, and then losing their middle sister after their parents' passing, the two became and remain close as adults. Midway through her time in high school, Keiko's dad moved his family from the Boston area to Iowa. There was a job opportunity there, but the main reason to leave went back to racism. Her dad didn't feel that he was appreciated at the Boston area hospital where he worked. Going from afternoons in Harvard Square to her parents' new house amid corn fields and barns was a shock, to put it mildly. Keiko also had to say goodbye to her boyfriend back home, which, at 15, was of course devastating. In her new city, the only place Keiko felt comfortable and welcomed was in high school band. She'd been playing flute for some time and wanted to go on to study music in college. There were no Asian grocery stores in Iowa City at the time. Keiko's parents helped to open the first of those. Later, folks from Vietnam, Korea, and some Southeast Asian countries arrived. And when they got there, there were food shopping options available to them. Keiko graduated high school a year early and went to the University of Iowa. Fueled by a desire to escape a cruel world in school, she also started taking college-level courses before graduating high school. The university gave Keiko a full music scholarship to study there, in fact. For fun, she took an acting class amid her music studies at UI. That acting class flipped a switch for Keiko. It felt more like what she wanted to do, more than music did. Eventually, she changed her major to interdisciplinary arts, which freed her up to take classes like dance and creative writing. I take us on a sidebar here, mentioning that I believe it's more important to find something you enjoy doing than it is being quote/unquote good at it. Then Keiko shares the story of how she transported herself from the corn fields of Iowa to the best city in the world—San Francisco. It involves a theater company in Canada. Keiko had married her clown teacher while still in college. He was Canadian, and the theater company called for him. But he wasn't home and so they asked about her. They told her they didn't like to separate couples, so they invited both of them to join. Keiko and her now-ex met this horse-drawn theater company in the middle of its tour (where else?) in California. For the next year-and-a-half, she and her husband traveled with this theater company doing shows of a political nature, mostly around climate issues. That caravan stage company ended up staying in the Bay Area. For Keiko, the thought of returning to Iowa to finish her master's program, especially in the winter, was not attractive at all. She got a job here as an actor in a children's theater company and decided to stay in The City. In 1986, the San Francisco Mime Troupe held auditions for The Dragon Lady's Revenge. Keiko tried out and joined the collective that year. Check back tomorrow for Part 2 of our SF Mime Troupe episode. In it, you'll meet Keiko's friend and fellow collective member Michael Gene Sullivan. We recorded this podcast at the SF Mime Troupe studio in the The Mission in June 2026. Photography by Marcella Sanchez

On this special solo episode, Jeff Mains sets a new agenda for the founder-led audience: futureproofing your company in an AI-driven, fast-changing landscape. This episode dives deep on why traditional business moats—complex code bases, gorgeous interfaces, and integrations—no longer hold up, and what unbreakable moats have emerged: Data, Trust, and Gravity.With frameworks straight from the fire and battle-tested pricing guidance, Jeff Mains shows how to build a company that outlasts market shifts—one that doesn't just survive, but leads. Packed with practical tools and candid stories, this episode is founder intelligence you can actually use.Key Takeaways07:52 Explaining outdated software protections10:11 AI agents and API focus19:49 Community loyalty and cultural moat21:57 Assessing customer reliance and trust31:04 Usage-based billing challenges33:13 Importance of Sales Strategy Adjustment40:33 Discussing pricing strategy questions47:29 Helping Customers Achieve Their Goals50:04 Evaluating customer impact without the company53:29 Letting go and gaining controlTweetable QuotesViral Topic: "Could Your Biggest Customer Rebuild You?": If the smartest team inside, your biggest customer, armed with Claude retool, maybe lovable and and a week of uninterrupted time, decided to rebuild your product or replicate your service, what would they realize? They don't have that you do. — Jeff Mains Slowing Down to Accelerate: "She also had something really counterintuitive to say about when slowing down is actually the most aggressive move you can make, that episode is worth your time as well." — Jeff Mains AI Will Make Beautiful Dashboards Obsolete: "your beautiful interface is invisible functionality, invisible. All that matters is whether your API can do the thing. Your gorgeous dashboard is wallpaper that a machine never looks at." — Jeff Mains Viral Topic: The Power of Community Ecosystems: "When you have a community, your customers aren't just using your product, they're embedded in an ecosystem." — Jeff Mains The Power of Cultural Moats: "That's not a product moat, that is a cultural moat." — Jeff Mains The Chaos of Usage-Based Pricing: "For a lot of SaaS or service companies, jumping straight to usage base without some sort of bridge can be a chaos generator. And chaos, that ain't good, especially when it comes to money." — Jeff Mains Pricing Time Bomb: "If it's the second one, then you're sitting on a pricing time bomb and that's got to be something. Defuse it before, before it's too late." — Jeff Mains SaaS Leadership LessonsDon't Confuse Activity for DefensibilityIf your moat is complex code or UI, you're exposed. Invest in what gets stronger as AI accelerates.Ask What Would Be Gone If You DisappearedIf the only answer is “inconvenience,” you're a vendor, not a partner.Build Moats That CompoundData gets better over time, trust deepens with high-stakes moments, gravity multiplies as processes and identity grow.Own the Transition to Outcome-Focused PricingPer-seat/effort-based pricing punishes efficiency. Build a bridge to usage and outcome models—don't force it overnight.Evolve Faster Than the MarketYour team's learning speed and your own growth determine survivability, not your initial playbook.Recurring Relevance Is the Real MetricAre you needed, or just hard to replace? Build relationships and deliver business outcomes that outpace any AI copycat.Guest Resourceshttps://www.facebook.com/jeffkmains/https://www.linkedin.com/in/jeffkmains/https://x.com/jeffkmainshttps://www.youtube.com/@championleadershiphttps://jeffmains.com/books/https://drive.google.com/file/d/1CPrpxILI2vi_YYJMdv5cwYo-bMlauaLH/view?usp=sharing Episode SponsorThe Futureproof Series - https://www.youtube.com/playlist?list=PLfkXKUPZ5xuOqMPR7_gzGybncTtavyR1NThe Captain's KeysSmall Fish, Big Pond – https://smallfishbigpond.com/ Use the promo code ‘SaaSFuel'Champion Leadership Group – https://championleadership.com/SaaS Fuel ResourcesWebsite - https://championleadership.com/Jeff Mains on LinkedIn - https://www.linkedin.com/in/jeffkmains/Twitter - https://twitter.com/jeffkmainsFacebook - https://www.facebook.com/thesaasguy/Instagram - https://instagram.com/jeffkmains

Episode Summary: Abundance in the Startup Ecosystem with Naseem Sayani In this episode of The Founder's Sandbox, Brenda McCabe sits down with investor, ecosystem builder, and female founder advocate Naseem Sayani to explore how capital, community, and visibility can create a more abundant and equitable startup ecosystem. Naseem shares her journey from management consulting and digital innovation to venture investing, where she became increasingly aware of the disparities women founders face when raising capital. After leaving a successful consulting partnership, she dedicated her career to supporting female entrepreneurs, investing in overlooked founders, and helping reshape how venture capital recognizes opportunity. The conversation dives into practical fundraising advice for women founders, including why founders should "lead with the money, not the empathy," how gender bias shows up in investor questioning, and how pitch decks can be strategically designed to guide investor conversations. Naseem also discusses the research-backed differences between the questions male and female founders receive during fundraising and offers actionable strategies for reframing those interactions. Brenda and Naseem explore several of Naseem's current initiatives, including her podcast The Capital Flex, which amplifies real fundraising stories from women founders, and the newly launched SoCal Women's Health Collective, a community focused on advancing innovation and collaboration in women's health. The discussion also examines the future of healthcare investing, where Naseem advocates for shifting the conversation from "women's health" to precision health—a broader framework that highlights the enormous market opportunities in personalized care, diagnostics, and health solutions that have historically been overlooked. Naseem emphasizes the power of abundance over scarcity, encouraging women to share networks, knowledge, and opportunities rather than competing for limited seats at the table. She argues that true progress will come when more capital flows from traditional funding sources into diverse founder communities, creating better outcomes for investors, founders, and society alike. Captions: 00:09 All right, welcome back to the Founder's Sandbox. I'm Brenda McCabe, your host, now in this fourth season of the Founder's Sandbox. And my mission is quite simple. With the Founder's Sandbox, I have guests that are business owners, service providers, VCs, and corporate directors. 00:36 who like me want to use the power of the enterprise to make change for a better world. And with stories in the sandbox on resilience, scalability, and purpose-driven experiences of my guest, um we have an origin story and we get to really understand what's under the hood of the businesses that my guest, um our owners have. So I'm absolutely delighted to have Naseem Sayani as my guest this month. Welcome, Naseem. 01:06 Thank you. I'm so excited to be here. Yeah. So Naseem and I go back um many, many years. She touches, um checks many, many boxes. Our first encounter was while I was m leading a women's corporate, a women's investment fund. So we invested in women owned companies with a minimum 33 % equity holding of uh the woman uh founder or m C-suite. 01:34 member and Nassim at that time was within Emily Ventures. She's since moved on to other firms and we've we're she's my first port of call when there is a very talented woman founder, particularly in the life science or digital health area. So I was absolutely delighted when the same also launched her own podcast podcast. So we'll get into that in a minute. So let me just make a uh bit more proper. uh 02:04 Introduction to you, Naseem. You're an investor, ecosystem builder, and speaker, as you guys are going to see here. Currently, the operating umbrella for all of the different ventures um that Naseem is orchestrating is Game Changers, World Changers. I love the title of your umbrella. You lead, and I've seen it in real life, commitment to empowering female founders. um 02:34 Her network across the United States and elsewhere is uh has no paragon. She is amazing. She's largely focused in health tech and fintech. And there's something that you've been doing recently, which is really giving a voice to women founders and what's it like raising capital. So with that, we're going to jump into uh our podcast today, abundance in the startup ecosystem. how, why are you doing what you do today? 03:04 Tell us about your origin story. Oh goodness. Yeah. No, back in time, back in time. Back in time. You were a consultant. all. I was. Yeah, we all grew up somewhere. So I like, I like to say I'm a recovering consultant. So I spent many, many, many years in core management consulting. So really problem solving. 03:27 at various strategic levels with Fortune 100s. I was working across healthcare, financial services, consumer, little bits of energy and other things along the way. But it was really those three sectors that I spent most of my time in. And this is early, early 2000s. So digital was a thing, but it wasn't. And we had Facebook that was happening already, you know, early 2000s. But it was in 2007 that we got iPhones in our hands and something shifted. 03:56 Right? It dramatically changed what the words digital strategy might mean. And that's when I started doing very continuously, very core digital strategy work with all of those same clients. And to put this in context, they didn't know what those words meant. Those words didn't mean anything. Like, what is digital strategy? What does it mean to my business? How does it change how I organize? How does it change how I go to market? 04:26 personalization moving from a one to many advertising model to a one to one ad model. That was a, it was a whole new paradigm, right? Of how we might interact and talk to the market as a brand or as a business. And that was all of the work I was doing in mid 2000s. And so it was fascinating to be in the center of, of that much problem solving. Well, that's what it feels like now. 04:54 That's what it feels like now, but at the time we didn't know that we were on the front end of what was this massive transformation, right? We were just doing the work and having fun and a bunch of young people straight out of MBA programs, problem solving. But it was great because you learn so much so quickly when you do that kind of work. And then a couple of years into it, we realized that our clients couldn't really execute against the strategies that we had built because they don't have 05:21 product and tech and UX and UI and scrums don't mean anything. Like this language that we take for granted now was just getting established mid late 2000s. And so we launched a product studio inside of the consulting firm to help them build product and launch experiences. And we, I was living in New York at a time. We launched this product studio out of Los Angeles. And in that same window, I moved back to LA. So I ended up being the person who ran the studio. 05:51 for about two and a half, three years. And so I was doing core strategy work. I was running the product studio and for about three years, every single digital proposal for the firm went through my inbox. Oh my gosh. Globally. it was every single project, every single client, we were tacking on the studio effort onto the back of that proposal. And so I didn't sleep for three years. I worked harder than I've ever worked in those three years. 06:20 But it was tremendous because I got to see so many different things so quickly. And we built a really great studio team. We were doing really great work in that team. And then the firm that I was at got acquired. There was lots of transformation things that happened. And then that product studio went and got acquired by a different management consulting firm and grew up into a full venture incubator. So the products we were building 06:47 ultimately needed different governance, different KPIs, different growth models, different leadership than what the corporate owner was able to do. So now we were, it was a different business. was a spin out turned into a spin out. So the value proposition turned into, we're not just building products. We're actually helping you build the startup that would otherwise put you out of business. That became the thing that we were doing. So now, now we're building startups and we're doing it at scale and we're doing with our clients and we're doing it. Now it's early. 07:17 2010s, 2011, 2012, and we're building startups and in parallel, right? Things like Uber and other things are happening in parallel and we're watching all kinds of change happen in how we engage and what kind of tools we're using across the marketplaces and what's broadly from a digital perspective. And it was great. I got to learn a lot really, really quickly. But in these... 07:43 Rooms right and you can imagine because you've been in these rooms also uh There's not enough women. There's not enough diversity We're building great product, but we don't really cover all the use cases because we're missing women and because we're missing diversity and so I was in parallel trying to meet as much of the I was trying to more founders I wanted to just see what else was out there. So I gravitated towards a lot of the the events that had more women and I was also 08:12 I'd led the diversity efforts inside of the incubator. was always protecting the careers of the women behind me. I held the diversity flag. I was one of three female partners in the incubator of like 50, right? So we were already standing on top of a pretty, on top of pretty small The token women, right? Right, right, exactly. So I was meeting a lot of women outside the building and building great products, building incredible businesses, but like, 08:41 fighting to the nail to raise capital for the companies that they're building. Meanwhile, my day job has capital. We're pouring money into building startups, but I'm surrounded by men all day. So this contrast between my day job and the struggle of what was happening for the female founders outside of the building, this contrast became... just this cognitive dissonance was too much. 09:09 stick too much to handle, right? It just doesn't work. And so I ultimately decided that I had to shift all of my energy, all of my focus. I know how to do things. I know how to build businesses. I know how to think strategically. I know how to problem solve. I can look at a market and hopefully figure it out. So let me just redirect all the energy to actually helping the whole other half of the ecosystem raise some capital and move some money and build some businesses because the boys have all the help that they need, right? 09:39 But the women don't. You left a partner position. I did. Yeah. No, my husband is super excited about that. I left my partner job at the consultancy to go full venture and decide to move some capital. Absolutely. And I started investing. This was 2019-ish and started writing some angel checks, larger angel checks, got deeper in the ecosystem. It was in that window that I met the two women. 10:08 that I launched Emmeline Ventures with. We raised about six and a half, seven million seats, stage focus, healthcare, fintech, sustainability, wrote some fantastic checks. I got some great capital out the door. And then I jumped, as you mentioned, to go do a whole host of other things. Now much more embedded, almost an ecosystem level, so more horizontal than just the vertical of the fund. And it's been great. It's, it's... 10:35 serendipitous that I got involved in the ecosystem in a moment where the female founder ecosystem was growing up the way that it was the women's health ecosystem was growing up the way that it was. And so the notion that I'm a pioneer comes up a lot because people like you've been in this for a while. Like you, you were one of the few people who built this thing. uh 10:57 But it doesn't feel that way to me. But there's a few of us who have been here since the beginning, really crafting this. And so it's really kind of fun to have been in it since the beginning, it feels like. So one of the things that we did with you uh from Ty last year is you did a master class with a cohort of women-owned businesses on how to pitch. Oh, yeah. And granted, I don't want to steal your thunder. 11:27 You're tagline with this, but we go into a room with VC, Rangel Investors. It's largely male. So what are your two or three core messages from that training? I used to it a lot. I attribute it to Naseem Sayani, but it is when women hear this, they're like, oh, I've got to go back and redo my text. 11:54 Yes. Yeah. So there's a couple of things I tell that I coach, I should say, female founders on all the time. One is you need to lead with the money and not the empathy. What we have been conditioned to do as women, and it's not conditioned, it actually comes from a lot of where our core empathy and how women move in the world is. We lead with the emotion. It's how we engage, it's how we build relationships. 12:21 But when you are in a money driven ecosystem, such as venture, you cannot lead with empathy. You have to lead with money. You have to tell me and tell who you're talking to how much this thing is worth. And you have to tell them that quickly. What typically happens with a pitch deck from a female founder is that there are four or five pages on pain. 12:46 pain and stress and how hard it is and just all this stuff that is so, so hard and you don't get to the size of the market until page seven. And that's too far, right? Because by then the people are bored and they don't care. you have do it in a page. Fine. But it's not five pages. Pull the market up sooner. Talk about the size of the opportunity sooner. I don't want any personal stories at the front of that deck. 13:14 The place where I want you to put the personal story is in how you're going to win. Because if you understand the market so closely and this pain point so closely because it happened to you, that is why your hustle is so much stronger. That's why you're after it so much more that your founder market fit comes from that. So put it there. Don't put it into product market fit. That's the wrong place. Right. Founder market fit. 13:42 Put it into founder market fit because that's the reason you're going to win is because you care about it in a way that other people don't care about it. So it's in the wrong place in the story. So put the empathy in the appendix. Nobody cares. I love you, but nobody cares. Lead with the money. That's point number one. Point number two is that there is data. It is validated that women get different questions in pitch meetings than men do. Yes. Harvard research. 14:08 Harvard research proves it. Two out of every three questions that a male founder gets will be about growth and vision and opportunity and how big this market can be. Two out of three questions that a woman gets will be risk related. So, oh my gosh, that CAC number is so high. And oh my goodness, what if you can't find the customers? And oh my goodness, what if somebody else does this? It is prevention. Yeah, it is prevention minded questions. Men get promotion minded questions. And if you're not... 14:37 prepared for that. You will be on the defense of the entire time in that meeting. So you have to practice. You have to spend a weekend with your camuja or your glass of wine or whatever it is and write down all of the prevention questions you might get on your deck, whether it's a main page or a footnote or something on the bottom of page 10. Write down everything. Be horribly brutal. 15:03 and then let it sit for a day, come back and write down your answers. And your intention with the answers is not to answer the question, but to flip that question into a promotion-based response. Excellent. Shift the power dynamic back. As an example, you have a revenue page that has the chart, revenue goes up from zero, year one to year five, and then you've got three bullets, you've got three data points on the right-hand side. Everyone should have a page that looks like this. You've got CAC, you've got AOV, you might have LTV as well. 15:33 More than likely, as a female founder, they're going to ask you about CAC. They're say, oh my God, CAC is $28. That's so high. How are you going to manage that? A male founder is going to get a question on AOV. AOV is $1,200. That's incredible. Can it get to $1,500? That's the difference in the questions. If you get that CAC question, your natural response might be, yeah, it's $28. We're going to work on it. We're going to run some tests. We really think we can get it to $25. 16:03 That might be how we naturally respond. What you should say is, it's $28, but our AOV is $1,200. So we actually think it's performing pretty well. 16:16 Very, very convincing. And that's it. Yes. Yeah. You don't respond to the defensiveness. You redirect them to the data point that actually matters. And you take back the power in that conversation. And it may also be that you have to reformat your deck. Yes. Yes. Yes. 100%. Yeah. So that's the third. That's the third thing is that your deck is a strategic asset. 16:44 Okay. And you should be very thoughtful about what you put in the deck so that you are teeing up the questions that you want to answer. Okay. There's a thing is too much information and there's a thing is too little information. The line in the middle is that if you're putting data on that same page on the right hand side, yes, put AOV at the top, put LTV next, put CAC at the bottom. Don't put CAC at the top because everyone's going look on the top right. It's natural. 17:14 Eyelines go to the top right of a page. Don't put CAC at the top. Put AOV at the top. Is that the biggest number? Put that one at the top. Be very deliberate about where you put data on the page so that you can tee up the questions that you want to get. You can bait the document with the things that you want to answer and set up the conversation that you want to have at least halfway. Right. It takes practice though. It takes practice. It takes practice. Yes. 17:42 And where are you now dedicating a lot of your uh time? We are fellow podcasters. And I know it was some time in the making. So you launched, was it six months ago, the Capital Flex podcast? Yes. Yes. And this is where you hear real stories, right? So to share a bit with my little. Yeah, absolutely. So I launched. 18:08 I launched the Capital Flex in January. I've been working on it since mid last year. It's been living on a post-it on my desk for the last two years, maybe more. So I get a lot of inbound from founders on the crazy things that happen when they're fundraising. A weird conversation, uh a offhand comment, an unfortunate behavior. Just the things that we know happen to women when they're out fundraising. 18:37 So I do a lot of, call myself, I've become this like de facto therapist for the female founder side of the ecosystem. And so I've been making notes on just the crazy that happens. And what I started, what happens is that there's founder, founder in New York and founder in California who are dealing with the same problem, but they don't know each other. So I'm, I'm sharing information across these two women when they should just know each other. So I'll bridge the connect, but you can't, I can't scale that. 19:04 quickly, right? And so instead I said, what if we just talked about it out loud on a podcast and just shared the stories and made it real and not just stories for the sake of, you know, kind of the victim hood that might come with that. That's not the intent. That's not my stance in any case, but I want to share the story so that we know that they're real. And then I want to share the learnings from that experience so that when another founder listens to any one of these episodes, you go, Oh my God, 19:34 Yes, that happens to maybe that's happened to me too. So I'm not alone. It's not just me and it's not personal and three That's a great way to deal with it because that founder dealt with it that way. Maybe I can do that too So it's really the toolkit that comes out of each episode. That was that was the end game And so episode, uh, sorry season one has just wrapped about a week ago 12 20:00 Great conversations, 12 fantastic founders. Each story is just tremendous. You learn a lot. They're very candid. It's very raw. And it's great. it's on Spotify and everywhere you can find it. called the Capital Flex. Everyone should go listen. It's tremendous. And then season two is going to drop in just a few weeks. And we've got another slate of 12 great founders. And we're running. Yeah, that's great. um 20:26 the impulse to actually launch a podcast, and the scene, if I hear you correctly, is you really wanted to amplify and scale these lessons that and experiences that other women founders have lived in their own skin. Yes. And not so much. It's not in a private way, but you really what do you think you're going to get out of this in terms of effectuating change in how people write checks? 20:55 It's visibility on really what's different about the rooms that women walk into versus the rooms that men walk into. It's awareness and accountability on behavior. Because if we know what's happening and we see it happen, we can call it out. Because now there's proof, right? And we go, oh, it's not her being sensitive. These things are really happening. 21:23 And then three, there's a pattern recognition problem in the ecosystem. And there's been so much money has moved in certain ways. there's indicators of success that a lot of the money moves on. But a lot of that is based on a very historical founder profile. And that profile doesn't include women. And it doesn't include people of color. And those levers of success look different in women. And they look different. 21:53 in founders of color. And unless we are understanding the impact of not seeing that we're never going to move capital in bigger ways. And ultimately we're just missing huge opportunities. We are missing massive opportunities spaces because we, our pattern recognition is stopping us from writing checks into spaces that we don't know enough about. So if you were to pitch yourself, right? Or, um, 22:22 game changers, world changers in front of investors. What would be your top line? Do you want to effectuate change over x million of women founders? Are you going international? mean, do a pitch here if you want. I don't know if I'll do that, but I can. So what would be your key guys, right? And yeah, yes. Yeah. So it's how much capital do we shift from? 22:51 from kind of the core buckets, the capital goes to, to founders that they haven't written checks before. Okay. That's, and so that's a big one. Uh, and, and how that, and what's the profile of the check writer? Because we have, there's been a increase in women who run funds in the last 10 years. So there's a lot more diversity in who's running funds, which is great. There's also a lot more diversity in who's building companies. So there's a lot more women, a lot more people of color. 23:20 building companies, but the bulk of the money is still sitting in traditional hands going to traditional profiles of builders. I want both of those things to move. So if we have better awareness of who's building and how they're building and what they look like and how they move in rooms and how they might show up in rooms, then the people writing the profile of who writes the check should also shift, right? It's not just women who should be writing checks to women. Men should be writing checks to women as well. 23:50 So how do we cross the social and gender circles better? capturing that metric, like that's the KPI that I want, is how much capital is going from male-led funds into female-founded teams. That's the metric that I want to be able to track. Because right now what's happening is that all the women-led and diverse funds are who's funding the women-led and diverse founded companies. Say that again. 24:19 for my listeners, because this is important. Women-led and diverse-led funds are the bulk of the money that's funding women-led and diverse founded companies. 24:30 And that cannot persist because we need big capital to go into these companies because they are building fantastic game changing businesses and everybody should make money from what they're doing. And they're going to hit a series a, a series B, a series C, and they're going to need bigger checks. You heard it here on the founder sandbox. Let's, let's, let's change to sectors. largely health tech, fintech. 24:59 Where do you see um greater, where are you focusing your initiatives in terms of um getting more check writers, right? uh Into the ecosystem and where have there been the greatest deficiency in, you you talk about these big product or these sectors, right? That have not addressed women's needs. So, so the answer is the same for both of those. I'm, I'm calling. 25:27 This will come out. This is a semantic problem, but I'm causing I'm calling it precision health. This is a place where I'm spending where I'm spending all of my time where I want to be investing in where the biggest opportunity is is our ability to leverage precision health. This is insight driven health care, whether it's in care delivery, whether it's in diagnostics, whether it's in understanding cancer, whether it's in 25:56 delivering smarter insights based on the type of human we're talking to, that is where we're going to change the game in healthcare. And that's where I want more capital to go and where it should go. Now, we've historically called this women's health, right? You've flipped the term use. So we've called this women's health for a long time. We're still calling it women's health. The problem that we've now uncovered is 26:24 Women's health carries stigma. The language carries stigma. You hear women's health and still I'll have people that say, oh my gosh, no, yeah, we've one or two things. We've made our women's health investment this year. Check the box. We're good. Yeah. Or they'll say, oh, but it's, it's, it's just so niche. We're half of the population. Yeah. Half of the population is not niche. just, I can't even, I have to just look at them and say I'm half the population. 26:55 And then the third thing you'll hear is, there just haven't, we just haven't seen the exit. So we're just not sure that the value is there. And so on the third one, have my, I hope there's two responses. One is, you know, when Google and Facebook and Amazon came to market and we're raising capital, there were no exits for search and e-commerce and social. No, we didn't know what it was. These things were brand new. 27:22 The reason the money went there was because there were behaviors and there was demand and there was money that was moving towards these categories. That was the reason we invested was there was a behavior trend that was shifting. There was a market trend that was shifting. There was something we were after because there was going there was a value pool that we could get after. That's why the money moved. This is what's happening with women's health right now. There is demand. There's behavior. There's money moving there right now. Menopause is a 60 billion dollar category. 27:52 When it comes down to it, women will spend money on their health care and they'll do it out of pocket. They're doing it right now. Right. We have no interest in our grandma's health care. We're absolutely going to go get what we need to solve for the hot flashes and the brain fog and whatever it is. And that's we have the proof now. Now, in January of this year, 2026 at JPMorgan, there was a report that was launched. There's a fantastic team that spent the last year doing the work to reanalyze all of the health care exits for the last 20 years. 28:21 The report is called follow the exits. Okay. And what they did was reclassify all of the exits in healthcare for the last 20 years into three different buckets, assuming that they qualified. If those exits were aligned to conditions that exclusively differently or disproportionately affected women, they bucketed them that way to recast the exits against conditions that affected women. When they're not 28:50 They weren't talked about that way in the market already. But when they did that, they were able to quantify $100 billion of returns that have already made its way back to investors from exits related to companies that built and exited because they were in conditions that exclusively, differently, or disproportionately affected women. 29:13 So people have made money from women's health already. But we don't know how to talk about it. They weren't calling it women's health. They were calling it diagnostics in oncology. It didn't matter that it was breast cancer. It was diagnostics and oncology. So this is why the semantics problem has to get solved and addressed is that when we call it women's health, we're trying to prove a horizontal over and over again, or saying people to believe in this horizontal that has value. And it's not. 29:43 semantics aren't landing. What has been working is just the proof of value in cardiology or in cardiovascular or an autoimmune or in diagnostics where you go, there's money there. Let's go invest because there's money there. And that's the shift that we have to have. And this is why precision health is how I'm phrasing it now is that we need to get after precision health because if I can prove value in precision health because I can solve for cardiovascular disease. 30:11 for women differently than men and there's value in that and I can deliver better services, better care. I can access reimbursement codes. I can do all of these things differently because I understand what a heart attack looks like in a man versus the woman. And that means that they'll end up at their doctor's office and not in the ER, which is more expensive. That's a place we can invest. it's a semantic change, but that's why I'm now calling it precision. I hope it doesn't. 30:38 take another 20 years after the... I don't think it will. I think there's more and more of us talking about it now and actively talking about the semantics differently. And from this Follow the Exits report, what I'm working on, and this is through my role at Women's Health Access Matters with WAM, is that we're taking the data from that report and we're creating assets that founders can use in their pitch decks to actually showcase the exits that line up with the category that they're in. Amazing. 31:07 So here's the market. Here's three exits. I'm good. I have proof. Yeah. So I think we can get there faster. Excellent. So that's a great segue um to yet another initiative that um is near and dear to your heart, which is it's still in a seed stage. Talk to us about the SoCal Women's Collector. Oh, yeah. Yeah. This is brand new. Me and five other fantastic women here in Southern California. 31:37 decided that we wanted to better connect the ecosystem here in SoCal. So from LA to Orange County to San Diego, there are fragmented groups of wonderful humans, all building, researching, investing, et cetera, into women's health, whether they are at universities or at accelerators or independently investing or they're founders, et cetera. So we launched what's called the SoCal Women's Health Collective. 32:04 And we are actively focused on connecting community. So it's budding, it's growing. We're organizing and still setting full strategy. But at the very least, we are bringing people together at events. We're hosting virtual and live events, whether it's happy hours or panels. We're doing things virtually where we're doing coaching sessions with founders. And we're building a mailing list so that we can get this community connected and talking to each other and at least know who else 32:33 is in Southern California touching this category and building in this category. And it's been really incredible because we were just sitting around dinner one night going, my God, couldn't we do this? And now we have a mailing list that's more than 500 people long. And we meet people everywhere that want to be part of it, want to join, want to come to events. And it's really grown. And we're only within a year. It's really only 10 or 11 months old. 33:00 But it's really taken off. It's great. So we can do a lot with it. I love it. And are you at all working with, is it GLG or the Women's Collective? It's an advocacy group in Washington, DC. Yes, the policy group. Yes, the policy group. Yes. So there's a parallel group called Women's Held Advocates, which is the policy and lobbying organization that's focused in Washington. 33:29 that is entirely organized around uh supporting policy initiatives focused on women's health. So across breast cancer, menopause, we now have recently added bone health, fertility, et cetera. We have sub teams under the women's health advocates umbrella that are focused on policy initiatives to get budget lines or policy lines into different things in Congress to make sure there's a tension on women's health across, again, aligned by conditions. 33:58 so that we can get things done in Congress. So, Women's Health Collective and Women's Health Advocates, at least in SoCal, there's high overlap in the leadership across these two groups. So, we can do a lot of good things together because the women on the steering committee for Women's Health Advocates who are in LA are the same women who launched the SoCal Women's Health Collective. It's symbiotic uh and I've attended... 34:26 sessions with both groups and it's a very exciting moment. It's there's bipartisan bills going to Congress on just why are knee replacements for men being birthed at a higher rate than for women? We all have the same. So it's like really just providing the transparency. pulled that thread. It's a whole different podcast. I know. know. know. So anyway, so you heard it here. 34:55 What else? I could go on and on, Naseem, but we have a certain time here. And I just wanted to give you this moment to share with my listeners how to contact you, how to get involved in your multiple initiatives. These will be in the show notes. 35:16 Yeah, no, absolutely. So I love that the so find the podcast. It's on Spotify and Apple and all the places where you listen to your podcast. It's called the Capital Flex. So all the subscribers would be amazing. Come and listen. I love feedback. Tell me what you think. Season two will drop on May 6th and season one is tremendous. So start from the top. The second thing is I also host something called a Founders Coven, which is a monthly meetup for female founders. And it's a virtual session. It's an hour. 35:45 uh once a month and the entire intent is to infuse expertise, insight, education into the female founder half of the ecosystem. So we've had three sessions so far and I was doing these in a previous life and I've now rebooted it this year. We've talked about vibe coding. We've talked about healthcare reimbursement. uh Our next session is in two weeks. We have a exited founder coming to talk about how she built her business and then led to the exit. So 36:12 You can join the coven. It's on my LinkedIn. You can find the sign up sheet so you can join the coven and join us when you can each month. And then I also I do a lot. I'm very active on LinkedIn. I'm always writing and posting. I'm speaking at lots of events so you can find me out in the wild pretty easily also because I tend to be around a lot. So that's the easiest. as a pioneer, this is a question that just came to my mind. You were so in the early stages of social media, right? um 36:42 Would you dare to give an opinion on what is the best type of platform to get your voice out there as a, as a leader, a change leader like you, is it LinkedIn? Is it how it. I believe it's LinkedIn. I tell a lot of founders this, that if you want to build credibility and thought leadership in parallel to building your company, start to build a platform on LinkedIn. Got it. Build a point of view. 37:13 have a point of view on what the future looks like when your company wins and start to talk about it. And it doesn't have to be long. It's a couple of it's like blog posts type things and use headlines, right? Use what's going on in the news and in healthcare to express a point of view and to talk about what it means and what are the implications and what are the so what's of what's going on and how does that tie back to what you're building and why you're building it? Because when investors go out to research a company and to research the founder, 37:40 If they, will look at your data room with a look at financials, a look at what you're building. All of that has to be up to snuff. And then they're going to go research the founder. And if they go on LinkedIn and they see that you're writing and publishing and that you've built an audience and that you're somewhat prolific in terms of communicating a point of view, those things are important. They pay off, right? You go, well, she, she's talking about what she wants and she has a perspective that's valuable because it means that you're really committed to the thing that you're after. And 38:09 And women don't spend enough time building platforms. We don't spend enough time standing on soap boxes talking about the things we care about. And we should be doing it more. And all of your friends should like comment and share every single thing that you post. And so I also tell everyone once you share it, send it to all your friends and tell them they have to comment and post on it. We have to keep building the flywheel. A flywheel. You heard it here on the Founder's Sand. 38:38 All right, we're going to go to the sandbox. I like to close out asking my guests, um what is the meaning to you for the following three terms, which I am passionate about and how I work with my founder clients. What does scalability mean to you? Oh, scalability means an ability to grow and navigate the market. 39:03 in line with market trends and market behaviors. knowing, having a good perspective on what's going on, dynamics in the sector that you're in, and having built enough mobility in how you navigate your organization so that you can turn left, turn right, etc. in line with what's going on outside the business. That's scalability. Perfect. How about resilience? 39:32 Resilience. one is it's that. So one, it's a necessary skill. We'll start there. And two, it's an ability to take in what's going on in the market, not take it personally, reflect and keep going. uh Feedback can come from everywhere. A lot of it from places that maybe aren't relevant and not that useful. So knowing how to filter and listen and then really be able to be open minded and take the good feedback when you get it. 40:02 And I had one founder on my podcast say that she spent a lot of time on the floor while she was fundraising, like curled up in a ball because it was so hard. But she got up again every single time and she raised the capital and she built a business and she can use to do that. And that's resilience. Right. I'm after something big and it matters. So I'm going to get it done. Amazing. Amazing. And I heard you when we were talking about the subtitle for the episode. uh 40:32 Abundance. What's abundant? Why is abundance so important for you? Abundance is really important for me because we have been, we women have been conditioned so badly in scarcity where there's not enough. There's only one, only one of us can win. We can't all win. And so we don't share our networks easily. We don't share our relationship easily. We, we feel like we have to keep things really close because if I share it, then I lose it. 41:02 It's a mentality I hear em from founders and it's a bit generational as well. And I don't want us to do that. I have found more than once that the more I put into the ecosystem, the more I get back. Comes back to me in spades. so opening up our networks, sharing what we know, being open about the learnings, pulling everyone forward with us. All of that is going to... 41:29 it's going to benefit all ships rise. it's how our male counterparts have been doing it for years. The golf course is the golf course for a reason. Right. And so we don't have a golf course, but we do have our networks and we do have our relationships and women are very innate relationship builders. It is superpower territory and we should be using it. And that means abundance. That means not worrying about 41:57 being the only one because you know what? We're building our own tables and we're pulling up more chairs and that's how we're gonna get this done. I love it. have goosebumps just listening to this last part about bandits. Thank you, Naseem. Final question. What does purpose, purpose driven mean to you? No, purpose driven means that 42:21 your the things you are doing and the things that bring you joy are lined up. Amazing. Yeah. And you can make money from it. Yeah. Without the joy, right? Yeah. Make money better. Yeah. Final question to Jeff on here in the sandbox. This was great. Yes. Thank you. Amazing. I really enjoyed listening. Just I enjoy our friendship, our working together on 42:50 Common theme, which is getting more money into the female. This is 100%. Yep. So to my listeners, if you like this episode with the same, so Yanny sign up for the monthly release of the founder sandbox, you can find it on any major streaming platform. You've got to find founders, business owners, corporate directors and service providers that are building resilience, scalable and purpose driven companies with great corporate. 43:19 uh governance. So thank you for joining us today and see you next month. Thank you.

Episode 521 James and Frank obsess over “polish”: the tiny design and packaging details that make apps feel finished. They start with impeccable.style, product.md and design.md (and why agents.md and readme aren't enough), then dig into UI fit‑and‑finish — tray/menu UIs, icon choice, grouping settings and why AI agents still struggle with layout and whitespace. The conversation then moves deep into Windows packaging: WinApp SDK versions, trimming woes with WinRT, ready‑to‑run vs. single‑file self‑contained builds, MSIX tradeoffs, and strange cases where builds bloat with unwanted packages. Key takeaways: give agents the right metadata, expect to hand‑tune UI polish, split architectures, disable R2R for size savings, exclude unnecessary SDK assets, and use Windows Sandbox/WSD for testing. A practical, nitty‑gritty episode for devs who care about the final mile. Follow Us Frank: Twitter, Blog, GitHub James: Twitter, Blog, GitHub Merge Conflict: Twitter, Facebook, Website, Chat on Discord Music : Amethyst Seer - Citrine by Adventureface ⭐⭐ Review Us ⭐⭐ Machine transcription available on http://mergeconflict.fm

Target Startup Chaos, Buggy CoreLinks, andDanfoss EKE400/ICAD Headaches | Advanced Refrigeration PodcastBrett and Kevin open the AdvancedRefrigeration Podcast after a brutal week, with Brett stuck on a transcritical Kevinwith a  startup at a poorly designed downtown store wherethe E3 and roof access are far from the grocery and the rack is on the roof. Heblames electricians for mis-marked and mixed-up comm lines, missingcontrol-power splices, and dangerous live voltages, turning a “simple” startupinto days of rework. They also vent about painfully slow, buggy CoreLinksetup and web UI issues compared to smoother E3 terminal mode. Kevin then walksthrough using a Danfoss EKE400 in CoolConfig for defrost and reheat control, MMRGdisplay jumper/address quirks, Modbus connection ideas, and generating wiringdiagrams. They finish with ICAD failures, O-ring moisture/rust problems, andsolving calibration alarms via process-of-elimination part swapping.

Target Startup Chaos, Buggy CoreLinks, andDanfoss EKE400/ICAD Headaches | Advanced Refrigeration PodcastBrett and Kevin open the AdvancedRefrigeration Podcast after a brutal week, with Brett stuck on a transcritical Kevinwith a  startup at a poorly designed downtown store wherethe E3 and roof access are far from the grocery and the rack is on the roof. Heblames electricians for mis-marked and mixed-up comm lines, missingcontrol-power splices, and dangerous live voltages, turning a “simple” startupinto days of rework. They also vent about painfully slow, buggy CoreLinksetup and web UI issues compared to smoother E3 terminal mode. Kevin then walksthrough using a Danfoss EKE400 in CoolConfig for defrost and reheat control, MMRGdisplay jumper/address quirks, Modbus connection ideas, and generating wiringdiagrams. They finish with ICAD failures, O-ring moisture/rust problems, andsolving calibration alarms via process-of-elimination part swapping.

Steve is back from a Royal Caribbean cruise to Bermuda just in time for the WWDC26 wrap-up, and Kotaro wastes no time YOLOing onto the macOS beta to demo Siri AI live. Things go sideways fast when Siri deletes Kotaro's Redfin emails instead of listing their subjects, sparking a wide-ranging conversation about what it actually means to ship AI features to everyday users. The Trio also maps out their WWDC-inspired side project plans for the year ahead.## Chapters00:08 Introductions 00:56 Side Quest: Steve's Cruise Adventures 13:23 Transitioning to AI and WWDC Discussions 17:23 WWDC26 Highlights 22:04 Kotaro Beta Tests macOS & Siri AI 24:17 Xcode & macOS UI Changes 27:10 Kotaro Tries Out Siri AI Live 31:26 Siri Deletes Kotaro's Emails! 41:04 Exploring Siri AI and User Experience 43:25 WWDC26 Insights and Future Projects 52:25 Wrap-Up & One More Thing... 52:46 Tag ## Show Notes- Steve returns from a 5-night Royal Caribbean cruise to Bermuda: escape rooms, cliff climbing, 17k daily steps, and the best lasagna of his life.- The Trio catches up on WWDC26 after Steve's cruise delay, covering Siri AI, SwiftUI improvements, and Swift Data's new results observer.- Kotaro has already YOLOd the macOS beta onto his laptop and shares first impressions of the more refined, less opinionated UI.- New Xcode hides your current branch but gains liquid glass segment controls and an AI assistant for identifying dead code.- Kotaro demos Siri AI live: it correctly locates the Rocky statue and renders a map widget, building some initial optimism about Apple's AI integration story.- Siri AI then accidentally deletes Kotaro's Redfin emails when he only asked it to list their subject lines.- Siri also fails the classic "how many Rs in Raspberry?" test on the local on-device model.- Steve worries that mainstream Siri AI shipping on devices like his dad's new MacBook Neo will create a new wave of family tech support nightmares.- Steve plans to add conversational health assistant features to BentoFit; Kotaro wants to vibe-code Fave10 using only Xcode AI tools and port Retro Sparkle to Godot.## Links**WWDC26**Videos: https://developer.apple.com/videos/wwdc2026/**One More Thing**AppJawn LLC: https://appjawn.com/Apps: Clipdish, Mio Vino, Minimalist Meditation Timer**PhillyCocoa:** https://phillycocoa.orgIntro music: "When I Hit the Floor", © 2021 Lorne Behrman. Used with permission of the artist.Music licensed through Soundstripe. Code: RLF9P8ZZ6MPKNOGX

In this episode of The GaryVee Audio Experience, I sit down with Naveen, founder of Glance, at Cannes 2026 to talk about agentic commerce — the first time in 30 years the user interface of how people buy things is going to change. We discuss why agents will quietly handle the categories you don't care about so you have more time for the ones you love. I also explain why brands that aren't structuring their websites and content to be read by agents will have no opportunity of being bought.You'll learn about:• What Agentic Commerce Really Is• Why the UI of Shopping Is Changing• How Brands Get Found by Agents• The Late-90s Google Moment Again• Why You Should Participate Early

Is the web dead, or just evolving? Wes Bos breaks down his JS Nation Amsterdam talk on agentic interfaces, why chat won't replace everything, how Web MCP lets agents interact with your existing sites, and what “Clicks and Clankers” really means for the future of UI. Show Notes 00:00 Intro 00:33 Welcome to Syntax! 00:46 Wes's Talk: Agentic Interfaces at JS Nation 01:37 Is the Web Dead? Chat vs. Traditional UI 03:13 No UI, Voice UI, and the Smart Home Vision 04:00 What Is Web MCP and How It Works 05:10 Clicks and Clankers: When to Click vs. Prompt 06:57 The Future of Shopping and the Open Web Problem 08:46 Delegating the Boring Stuff: Groceries and Expense Categorization 11:55 MCP Apps and the Happy Path Problem 12:55 Brought to you by Sentry.io 13:23 Generative UI: Can the LLM Make a Better UI Than You? 14:54 Smart Home Dashboards and the Jarvis Dream 17:24 Is the Web Dead? Final Thoughts Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads Wes: X Instagram Tiktok LinkedIn Threads Scott: X Instagram Tiktok LinkedIn Threads Randy: X Instagram YouTube Threads

For episode 747 of the BlockHash Podcast, host Brandon Zemp is joined by Bret Kenwell, a US Investment and Options Analyst at eToro. eToro is a retail brokerage platform offering access to crypto and traditional asset classes to 40 million users globally. They allow users to trade diverse financial assets like stocks, cryptocurrencies, and ETFs.

Everyone is racing to the same place chasing a limited set of buyers—how will your “AI for BI” product stand out? I've been seeing teams heavily invest in copilots, agents, semantic layers, governance frameworks, and increasingly sophisticated models, yet many still hear the same feedback from sales prospects: “We may just build this ourselves?" Or they don't hear it, but suspect the customer is doing just that.  Whether they actually can DIY the solution is the wrong question. The bigger question is *why they believe they can.* Your product may have a genuine competitive advantage, but your real challenge is that this advantage isn't obvious to buyers. The moat exists, but it is invisible. What makes this relevant is that many capabilities once considered differentiators are rapidly becoming normalized. AI copilots, agentic analytics, governed data, semantic layers, and broad integrations now appear across nearly every platform in the category. As AI accelerates development, sophisticated engineering alone becomes harder to defend as a lasting advantage. So what actually creates a durable moat if the engineering and product seems easy to copy? I explore four areas: proprietary data, trusted relationships, and products that accumulate institutional knowledge remain difficult to replicate. And finally, user experience itself as a strategy. As users increasingly access your intelligence through AI agents rather than dashboards, their experience may become the moat that competitors can't copy. Highlights / Skip to: AI for BI and analytics products is facing a race to commoditization (2:09) Common moats that everyone is using right now and why they fail (3:28) Proprietary data as a moat (9:29) Being embedded in your community as a moat (11:14) Compounding institutional knowledge as a moat (15:22) UX design asa moat even when there is little/no UI to see (18:36) Find the baseline for customer experience to build into later strategies (25:11) Actionable questions to ask your team to move forward on finding your competitive differentiation as a B2B analytics product (28:02)   Links CED: A UX Framework for Designing Analytics Tools That Drive Decision Making

We're excited to have Databricks join us at AIEWF, among hundreds of the top companies in the AI Engineer ecosystem. LS subscribers can use their discount to get past the late bird pricing and access over $50k in sponsor offers! Everyone is still talking about Satya's Frontier Ecosystems post, but few have actually built a (now $175 billion) frontier ecosystem and cloud like our guests today.From open-sourcing the layer above coding agents to rethinking databases for the agent era, Databricks cofounders Matei Zaharia and Reynold Xin are pushing the company beyond the lakehouse into a full data-and-AI operating system. In this episode, Matei and Reynold join swyx at the 2026 Data + AI Summit to unpack Omnigent, LTAP, Lakebase, agent security, open formats, Mosaic, and why databases may matter more than ever once AI agents start doing real work.We go deep on Omnigent: Databricks' open-source meta-harness for combining, controlling, and sharing agents across Claude Code, Codex, Cursor, Pi, custom agents, and internal tools. Matei explains why coding agents and enterprise agents run into the same problems: portability, collaboration, session history, security, spend controls, and the need for a common API above every harness.Then Reynold walks through Databricks' database dream: why CDC is brittle enough to joke that it means “continuous data corruption,” why HTAP has been the holy grail of database engineering, and why Databricks thinks LTAP gets most of the benefits by unifying the storage layer instead of collapsing every query engine. We also cover Databricks' infrastructure scale, the culture behind rapid prototyping, the difference between tech and enterprise customers, Databricks vs Snowflake, whether vector databases should have ever existed, the Mosaic model strategy, Genie, AI Runtime, RL fine-tuning, and the thesis that traditional software gets rewritten once the data is in the right place and agents sit on top.Databricks began as a company for the big data era. The origination of Spark from the Berkeley AMPLab which eventually turned into the product Lakehouse convinced enterprises that they didn't need a separate data lake, warehouse, ML platform, and governance layer. They just needed one open foundation where all of their data could live and be reasoned over.Since then a lot has changed, but data has only become more important. Data is no longer something you keep track of and analyze ad hoc, it's the necessary context agents need in order to act. So the framing has shifted from “where do we put all of our data?” to “how do we expose the right slice of state, history, permissions, and business logic to an AI system at the exact moment it's doing work?”If frontier model performance becomes commoditized, the durable advantage then becomes the company-specific context around them: proprietary data, governed access, operational state, transaction logs, workflows, and feedback loops. Which makes Databricks positioned perfectly.Now coming fresh off the Data + AI Summit 2026, the company is moving just as fast to keep up, announcing Genie One, Omnigent, LTAP, and many more, indicating a central mission in its newer work: Databricks is trying to become the operating system for enterprise agents.Models are getting good enough, but agents are only useful if they have the right context, permissions, memory, state, cost controls, and access to live business data. Fundamentally it appears that significantly better model performance in production is a systems problem, one that data guys like us are remarkably well prepared to solve!We discuss:* Why Databricks built Omnigent as a meta-harness above existing AI agents* Why coding agents and custom enterprise agents need the same infrastructure* The common API for agent sessions, files, streams, tool calls, and cancellation* Why persistent sessions, cloud sandboxes, sharing, search, and collaboration matter* Why Databricks open-sourced Omnigent instead of keeping it proprietary* Databricks' internal agent usage, cloud sandboxes, and coding workflows* The scale of Databricks: 50–60 million virtual machines a day and exabytes before breakfast* Why agent security needs contextual and stateful policies* How an agent could read confidential docs, install a compromised npm package, and leak data* Why spend control matters when an agent can burn $500 reading logs* Startup opportunities around coding-agent analytics, quality, skills, and spend* LTAP, Lakebase, and why Databricks wants to rethink the database stack* OLTP vs OLAP, CDC, and why data pipelines break at 3 a.m.* Why HTAP has historically been the holy grail of database engineering* Why Databricks thinks LTAP is “HTAP done right”* How writing transactional data into column-oriented formats changes analytics* Why agents need live operational context from databases, not just telemetry* How Databricks prototypes strategic systems without endless process* Enterprise vs tech customers, governance, procurement, and DIY culture* The “second system syndrome” risk of rewriting a database engine* Building a database engine from a decade of traces and quadrillions of data points* Why vector databases should never have been a separate category* Why open formats and AI changed the race with Snowflake* The Mosaic story, DBRX, Genie, document parsing models, and specialized model training* Why model customization and RL fine-tuning may become mainstream* Why “get the data there, slap some agent on top” may rewrite traditional softwareMatei Zaharia* LinkedIn: https://www.linkedin.com/in/mateizaharia* X: https://x.com/matei_zahariaReynold Xin* LinkedIn: https://www.linkedin.com/in/rxin* X: https://x.com/rxinDatabricks* Website: https://www.databricks.com* X: https://x.com/databricksTimestamps00:00:00 Introduction00:02:22 Omnigent and the Agent Infrastructure Layer00:08:39 Agent Clouds, Common APIs, and Open Source00:16:52 Databricks Scale and Internal AI Workflows00:18:03 Agent Security, Governance, and Spend Controls00:27:34 LTAP and the Database Dream00:30:30 CDC, HTAP, and Why Data Pipelines Break00:34:05 Lakebase, Parquet, and Live Data for Agents00:36:47 Databricks' Culture of Fast Prototyping00:43:40 The Dream Engine and Rewriting the Database Stack00:51:02 Vector Databases, Query Engines, and LTAP00:52:36 Databricks vs Snowflake00:57:48 Mosaic, DBRX, Genie, and Specialized Models01:03:11 Context, AI Runtime, and RL Fine-Tuning01:06:15 Why Data + Agents May Rewrite Software01:07:09 Closing ThoughtsTranscriptIntroduction: Databricks, Data + AI Summit, and Founder DynamicsSwyx [00:00:00]: Matei and Reynold from Databricks, welcome to Latent Space.Reynold Xin [00:00:06]: Hey, thanks for having us.Swyx [00:00:07]: Yeah.Matei Zaharia [00:00:08]: Yeah, thanks so much.Swyx [00:00:09]: thanks for taking time out. You have your Databricks, Data AI Summit going on. You were just telling me how the first summit that you guys ran was just 50 peopleReynold Xin [00:00:17]: Yeah, it wasSwyx [00:00:17]: in BerkeleyReynold Xin [00:00:18]: little meetup at Berkeley, I thinkMatei Zaharia [00:00:19]: YeahReynold Xin [00:00:19]: put togetherMatei Zaharia [00:00:20]: We were doing these tutorials and, yeah, just teach people Spark.Swyx [00:00:23]: Yeah. obviously now it's like, I think like the headline number's like 100,000 people around the world, 30,000 in person.Swyx [00:00:30]: it's a crazyMatei Zaharia [00:00:31]: AmazingSwyx [00:00:31]: community. Well, I just saw the keynote.Swyx [00:00:35]: Ali's just. Did was it obvious or that back when that Ali would be, like, such a great, like, CEO? LikeReynold Xin [00:00:42]: OhSwyx [00:00:42]: such a great presenter?Reynold Xin [00:00:43]: What do you think?Matei Zaharia [00:00:44]: I think among our group of founders it was clear that, I think he'd be the best at this.Swyx [00:00:50]: Yeah.Matei Zaharia [00:00:50]: And yeah, it turned out great. And he's, he's ramped up on so many topics growing a company. He would just go in and, like, study it and, be talk to all the experts. Like, even if he can't hire the person, learn enough about, like, finance and sales and whatever it was, and, and go from there. Yeah.Swyx [00:01:09]: Yeah.Reynold Xin [00:01:10]: he's obviously very high IQ and a very high EQ, but it wasn't. Like, Ali today is quite different from Ali from, like 10 years ago. I think there's a lot of work that he put in to, get to this point.Swyx [00:01:20]: Yeah. no, to me the most appealing thing about him is that he's funny. And like, it, it's, it'Matei Zaharia [00:01:26]: It's true, yeahSwyx [00:01:26]: it's hard to make jokes about, data warehousesReynold Xin [00:01:30]: About serious topicsSwyx [00:01:31]: securityMatei Zaharia [00:01:32]: YeahSwyx [00:01:32]: what have you.Matei Zaharia [00:01:33]: Oh, yeah. That's for sure.Swyx [00:01:34]: Yeah. So you guys launched a whole bunch of things. I'll, I'll just name check briefly, the stuff because we're not gonna cover everything. Omnigentt, your baby. LTAP, your baby, your dream engine.Swyx [00:01:47]: we're also gonna cover Genie, cover CustomerLake, you acquired PantherMatei Zaharia [00:01:52]: YeahSwyx [00:01:52]: Open Sharing, and there's Unity AI Gateway. A lot of these, I think, like, are things that you would expect a Databricks to do. It's, it's like part of the roadmap. Everyone in your category has similar things. But I think, probably the two of you are leading the two most unique and differentiated initiativesOmnigent and the Agent Infrastructure LayerSwyx [00:02:09]: on, in the landscape. Maybe we'll start with, Omnigentt we'll, we'll, we'll, we'll go into it. I do think that a lot of people are exploring this meta harness concept.Matei Zaharia [00:02:21]: Yeah, totally.Swyx [00:02:21]: What led you to it?Matei Zaharia [00:02:22]: Yeah. There were a couple of, like, converging lines, which I think is a good sign that you need something new. So on the one hand, there's all the coding agent info internally. We have really great, dev infra team. they built something called Isaac, that's like a wrapper on Claude Code and Codex, and, lets you use them either on the web in, like, sandboxes or, just on your dev machine or on your laptop or whatever. And then, they were adding all kinds of stuff there. And we saw all the more advanced engineers like, were building their own workflows with tons of agents, and they were building their own UIs and stuff on top or even on top of that. And then the other one was, like, us building agents. We ship this, like, data science agent called Genie on the research team, which I lead. We also build a lot of internal ones for various things, and then we have all the customer ones. And all of them running into this thing of like, “Oh, I need to switch model and harness and so on,” every few months. Plus the agent is, like, completely useless if you can't share sessions with someone and have history and have search and all this, like, layer on top of it for collaboration. I thought a bit about it from both contexts and, at first people thought it was weird. They're like, “Why are you doing coding agents and custom agents in the same thing?” But I said it's, it's the same problems and, you just wanna build the stuff that lets you deliver the agent, maybe control it if you care about security, and, make it portable across things. And then we prototyped some things as experiments. We saw, yeah, we can make it work, and then we built that for real.Swyx [00:04:06]: I'm wondering if this let's call it architectureMatei Zaharia [00:04:11]: YeahSwyx [00:04:11]: maps to anything in your careers in the past. like I always think about how a lot of things just tie back to operating systems.Swyx [00:04:18]: A lot of operatingMatei Zaharia [00:04:19]: YeahSwyx [00:04:20]: systems tie back to databases,Matei Zaharia [00:04:21]: SoSwyx [00:04:21]: or the other way aroundMatei Zaharia [00:04:22]: so the thing, I do think it ties a lot to, like, network protocols, internet protocol. we alsoSwyx [00:04:29]: Communication between entities.Matei Zaharia [00:04:30]: Yeah. We did stuff with, like, data sharing also, which is probably, most viewers probably won't know unless they'Swyx [00:04:36]: Yeah, open protocol is the term.Matei Zaharia [00:04:37]: Yeah.Swyx [00:04:38]: Open sharing. Open sharing.Matei Zaharia [00:04:38]: Open sharing.Swyx [00:04:39]: Yes.Matei Zaharia [00:04:39]: Yeah. So it's like you have a company, you maintain some table, like let's say like a Walmart or something. They have like the, inventory and what's been sold in each store. And then you also have suppliers, and they would love to produce more things and ship them, like, exactly the moment you need them. So they would love, like, real-time access to your table. So instead of like sending emails around or Excel sheets or phone calls, why can't you share like a view of that table in real time with them? Then they query, they, join it with their data, and they decide what to send. So it's one of these things where you, like you might ask like today since we can vibe code anything so fast, why do we even need to design like protocols or APIs or software? Why can't you just vibe code things on demand? But for this type of interoperability where multiple parties that are moving at different speeds are building stuff and you still want some layer on top to coordinate, you do wanna design it and build it. So it reminds me of that, like agents talking to each other and, users talking to agents and tools.Agent Clouds, Cloud Sandboxes, and Keeping Sessions AliveSwyx [00:05:42]: Reynold, any other comments alternative viewpoints?Reynold Xin [00:05:46]: I think, by the way, we had a debate on exactly which set of benefits would, matter a lot, and I think around the time we decided to do this thing I was telling Matei, “Hey,” it just happened to be there's a particular week that I was coding nonstopSwyx [00:06:00]: from the moment I woke up to, like, the moment I went to bed, I was, like, looking at my Claude sessions, my Codex sessions. And one of the things that was particularly annoying was having to keep my laptop open.Swyx [00:06:12]: I was driving to a doctor's appointment, and I remember because I wanted to make sure the whole thing continues working.Matei Zaharia [00:06:18]: But by the way, it's so comforting to hear you say that because I'm like, “I don't know if I'm a clown and I'm doing this or like.”Swyx [00:06:25]: Yeah. Like honestly, I was driving and I was tethering my laptop to my phone.Matei Zaharia [00:06:29]: huh.Swyx [00:06:29]: Keeping it on the side. Whenever I hit a red light, I started looking at what's going on my laptop.Matei Zaharia [00:06:35]: Yeah.Swyx [00:06:35]: And I just felt that was ridiculous.Matei Zaharia [00:06:37]: Yeah.Swyx [00:06:37]: It felt like we went back to the dark agesMatei Zaharia [00:06:39]: YeahSwyx [00:06:40]: programming. the productivity you gain from all this coding age is amazing, but, yeah.Matei Zaharia [00:06:45]: Have you heard of cloud?Swyx [00:06:47]: Yeah.Swyx [00:06:48]: It was crazy to me.Matei Zaharia [00:06:49]: Oh, the thing you were working on was the sandboxes or was this before that?Swyx [00:06:52]: It was a sandbox.Matei Zaharia [00:06:53]: Okay.Swyx [00:06:54]: I was workMatei Zaharia [00:06:54]: So you were inSwyx [00:06:55]: So I was approaching from a very different angle. I wanted to, “Hey, we're gonna have cloud sandboxes that doesn't shut down. You can get one very quickly,” but not just for running agentic sessions.Matei Zaharia [00:07:06]: Yeah.Swyx [00:07:06]: It's also for running development. So I was personally building that week, and through building that, I ran into all these issues, and then I wroteMatei Zaharia [00:07:15]: YeahSwyx [00:07:15]: a document for Matei, it's like, “Here's my wish list of what the actual environment should do.” And I think he ended up almost implementingMatei Zaharia [00:07:22]: YeahSwyx [00:07:22]: every single one of them.Matei Zaharia [00:07:23]: Yeah, I remember Reynolds saying, ‘cause my first prototype of this had just chats with your agent and he said, “I have to be able to open a shell, like my own shell and like list files and like tail them and stuff.” SoSwyx [00:07:36]: So SSH into a mainframe.Matei Zaharia [00:07:37]: Yeah. it has that now.Swyx [00:07:39]: Tailing my log.Matei Zaharia [00:07:40]: Yeah.Matei Zaharia [00:07:41]: Yeah.Swyx [00:07:41]: And also another thing I think I asked was, I had. I still use cursor for the sole purpose of rendering markdown files.Matei Zaharia [00:07:48]: huh. Yes.Swyx [00:07:49]: So I said, “If you just give me a way to see my markdown files and renderMatei Zaharia [00:07:53]: YeahSwyx [00:07:53]: them properly, I don't need a separate tool anymore.”Matei Zaharia [00:07:55]: Yeah.Swyx [00:07:56]: And I think you also built that in.Matei Zaharia [00:07:57]: Yeah, we, yeah, we did that, yeah. Yeah, we had a lot of engineers building, their own vibe coding setup. But then the other thing they all said is like, “Hey, I built something that's amazing for me, but, like, no one else on the team can use it ‘cause I don't have a server to collaborate.” And this is why we tried to set up, Omnigent, so you can have a server and have the security, set up in there. So, like log in with Google or whatever and, like securely share stuff. which. And that's where we've seen a lot of other agents like hit things. Like people think they prototyped an awesome agent, but it's not allowed to connect to like some really important data or whatever because of the security team.Omnigent Architecture, Open Source, and Common APIsSwyx [00:08:38]: Yeah.Matei Zaharia [00:08:38]: So yeah.Swyx [00:08:39]: Yeah. At this point, so for those watching along on YouTube, we're gonna putting up a image of the structure here, and we can talk a little bit of the architecture. I think I just want to have people understand, ‘cause like when we're talking about software, it can be very abstract and like here is what we're talking about. You've worked out in open source this entire platform and there's a runner component and server component with a uniform API that you've, you've figured out. any other element and obviously you can plug in all this, persistence layers and compute layers. This is a whole cloud. It's an agent cloud.Matei Zaharia [00:09:12]: Yeah. It's, it's got these components to work with it. The, a lot of the action happens like on the machine where you deploy your agent too. So whatever you've got on there, you can run. But yeah, it's, I think it's the minimal thing you want to have hosted, like collaborative agents and to have that server. And one of the reasons we open sourced it is, anyone building agents, this gives them an app they can start with and customize, which we were seeing in Databricks too. Like someone would make a nice, agent app and then other teams would ask, “Oh, can I just use yours for my agent?”Swyx [00:09:45]: Yeah, I think we had like five or six different agentic frameworksMatei Zaharia [00:09:48]: YeahSwyx [00:09:48]: built by every different team. They do all do more or less the same thing. Yeah, you need to. people wanna take something that works in Forkit, and you might as well have something open source. Yeah, which also was another question, which is interesting for Databricks. Like what do you choose to open source? What do you choose to make it proprietary? It's in. this goes back to Spark, right?Matei Zaharia [00:10:05]: Yeah.Matei Zaharia [00:10:06]: One, so one of the reasons to open source something is if you think it's a layer that will there'll be some network effect, it'll benefit from many, people collaborating, on it. So, for example, with Spark, I don't know if when Spark came out, we also focused a lot on letting you have libraries on top. So like there used to be differentSwyx [00:10:28]: EcosystemMatei Zaharia [00:10:28]: distributed computing engines for like machine learning and graph computation. We said they should all be libraries that you can compose. And we made it super easy to add connectors to data sources too. And then we benefit because, we don't have the time to write like connectors to like, 1,000 like different databases and file formats, but we can just use the ones people make, and of course they benefit from joining, this thing. So that's like one of these as it. Another way to think about it is like imagine, we our thing wasn't open. We had some agent hosting thing, but it's not open and then there is an open one. if you're. Which one's gonna win in the long run? So like here, because there is this benefit from like people writing integrations, it'll be, it'll be that. And then there are other things that like you just can't, even deliver as open source that are things the company does. Like for example, how do you make sure you're like streaming, jobs or your Lakebase database doesn't like, lose all your data at night? Well, that requires an operational team that's gonna sit there. There's no way it has to be a service. So like we wanna make sure as a company we're really good at those infra services and then we're as open as we can in terms of like what you build on top.Swyx [00:11:42]: speaking from a benefits, I think we are already seeing pull requestsMatei Zaharia [00:11:45]: YeahSwyx [00:11:45]: of all kinds of ecosystem integration, even though it was only released on Saturday.Matei Zaharia [00:11:50]: Yeah, Saturday. Yeah. So someoneSwyx [00:11:51]: Let's see, let's see what's going on. Yeah, you can look at the merge ones. I asked Sam Nigon this morning aboutMatei Zaharia [00:11:59]: 400 merge already?Matei Zaharia [00:12:00]: Yeah. I think Recent quite, I would guess around half are not from our team. but for example, someone added support for running it on Kubernetesrnetes. people added, many cloud sandboxes, so this can launch a cloud sandbox and run your agent in there, which is great for sharing too, ‘cause it's not, like, on your laptop and someone's, like, running scary code on there. so yeah, many startups have put those in, and, we expect to see more of them. We also have more agent harnesses already. Cursor, CLI, and Antigravity also.The Modern Data Stack and the Emerging AI StackMatei Zaharia [00:12:34]: Yeah. That's all, beautiful. And I, I feel like the last time this happens, there was the rise of the modern data stack.Matei Zaharia [00:12:42]: I don't know if it's that useful. I'm, I'm curious in your postmortem.Matei Zaharia [00:12:46]: I think most peopleSwyx [00:12:47]: AgreeMatei Zaharia [00:12:47]: will agree that it is finally dead. but maybe this arises to a new modern AI stack that, like, does the same thing.Matei Zaharia [00:12:52]: I don't know.Reynold Xin [00:12:54]: I think the modern data stack was a pretty useful thing, probably even up until this day. I think what, maybe for the audience who don't understand the history, I think the modern data stack is effectively decomposed into you need a layer to ingest the data in, you need a layer to transform your data, and then all of this are run, and then you need a layer to maybe visualize your data. And all of this runs on some data warehouse, or later on, as we're doing data warehouse or lakehouse.Reynold Xin [00:13:21]: I think that concepts are all very powerful and very useful. They enable a lot of workloads. What people eventually run into is a question of unification and consolidation is, hey, do you really need to chop all this into different pieces and work with so many different vendors and platforms in order to get, like, a very simple visualization done, right? So I think, like, over time, everybody started realizing that customers are pushing us. We started, we can realize that, so we started building more and more capabilities and trying to consolidate. And at the end of the day now, customers don't have to worry about having me hook up five different systems in orderMatei Zaharia [00:13:55]: YeahReynold Xin [00:13:55]: produce a chart. But the. I think, honestly, something like this is probably happening, in how many different frameworks do you want to hook up together in order to produce, like do a very simple agent.Matei Zaharia [00:14:06]: Just to be clear, I would say the core of this is this common API on top of all the harnesses. So the API is like, you've got an agent session, and you can send in a message or, like, a file. That's what you can send in, and then you get out, these streams as it's streaming text or as it's doing tool calls. And, or the other thing you can send in is you can, like, tell it to cancel a turn. So that's the API. Now, the thing we did is we could get you that on top of, like, cloud code running in a terminal, Codex, Py, OpenAI SDK, all that stuff. We map them all to that same interface. So that is something that you'd have to maintain yourself if you built your own, like, agent orchestrator, and then whenever cloud changes its API, you gotta, tweak your thing or it's gonna lose some messages. So that's the thing that's valuable to maintain. Then on top of that, like, we built a few apps. I think we built a pretty cool UI and stuff, but that's, And we built a security and control piece, which I'm excited about. But it's that common interface, so we don't. We. That doesn't try to be a stack. And in fact, you could plug in your own UI on top of this, server. That, and that's one of the use cases we care a lot about, ‘cause we want to use this in our own products.Compute, Sandboxes, and Databricks ScaleSwyx [00:15:20]: Yeah. It should be everywhere.Matei Zaharia [00:15:22]: Yeah.Swyx [00:15:22]: I think one of those things that is really interesting to me is, like, well, first of all, I'll, I'll endeavor to do everything and not call it the modern AI stack because like it needs a different name.Matei Zaharia [00:15:32]: Yeah.Swyx [00:15:32]: But like, yes, like, so one of the first people that told me about compute, sandboxing was Nikita from Neon.Swyx [00:15:39]: Because a lot of people think about Neon as like, well, it's serverless Postgres with, like, the separation of compute and storage and, instant branching and all those things. But every database company is also a compute company.Matei Zaharia [00:15:51]: Yeah. Yeah.Swyx [00:15:52]: And so he was showing to me his whole, his sandboxing solution. I don't think he have ever launched it.Matei Zaharia [00:15:57]: So our sandbox solution, the reason we could build it so quickly was because we realized if you just take the actual Lakebase architectureSwyx [00:16:05]: YeahMatei Zaharia [00:16:05]: and remove the database from it, by the coming from NeonSwyx [00:16:08]: Exactly, rightMatei Zaharia [00:16:09]: you have this sandboxSwyx [00:16:09]: Every database company has it already, yeah.Matei Zaharia [00:16:11]: Now, there are some differences. For example, in the one to support this particular workflow, it's important to have local persistence,Swyx [00:16:19]: YeahMatei Zaharia [00:16:19]: because you want your state to persist. Your libraries, you don't have to install your library every time, right?Matei Zaharia [00:16:24]: whereas the Neon architecture, because of the separation of storage from compute, you don't need persistent local disk.Swyx [00:16:30]: Yeah.Matei Zaharia [00:16:30]: So there's some differences.Swyx [00:16:32]: Yeah.Matei Zaharia [00:16:32]: But the, at the end of the day, yeah, it's, Yeah, so this is when you run, like, a coding sandbox. Like, if I use it, yeah, we have the dev env internally at Databricks. There's, like, many, like, tens of gigabytes of data just for, like, all the source code and, like, artifacts and stuff that I built, and I want that to come back next time, so.Matei Zaharia [00:16:51]: Yeah.Matei Zaharia [00:16:51]: But yeah.Matei Zaharia [00:16:52]: Before the show, we was talking about some statistics that might be surprising at the adoption.Matei Zaharia [00:16:56]: It could be internal, it could be external, whatever comes to mind, just to impress people the scale this is happening.Swyx [00:17:02]: So we, on the analytics side, I think we launchedReynold Xin [00:17:06]: Maybe 50 or 60 million virtual machines a day across all three clouds, so we're one of the biggest compute orchestrators out there.Reynold Xin [00:17:13]: Stuff for sure for CPU compute.Swyx [00:17:14]: Yeah.Matei Zaharia [00:17:14]: Yeah.Reynold Xin [00:17:15]: the. And all of this process, I think exabytes of data, I joked about depending on which time zone you are, typically before you have breakfast, Databricks would have processed exabytes of data already on that day. and on Neon, it's pretty interesting, too. It's launching, I think, 13 million databasesSwyx [00:17:34]: YeahReynold Xin [00:17:34]: a day now.Swyx [00:17:35]: Yeah, to me that was, like, aReynold Xin [00:17:36]: And that's just likeSwyx [00:17:37]: Like, what do you mean?Matei Zaharia [00:17:38]: Yeah. And that's the point.Reynold Xin [00:17:40]: And a lot of those were thanks to agent- agents and branching experimentationSwyx [00:17:44]: YeahReynold Xin [00:17:44]: because we made it so easy and so quickly, and thanks a lot to Nikita's team, to launch databases. It's, the. So it's changing the way people use databases.Swyx [00:17:54]: Yeah. Okay, we're gonna go into more database talk in a bit, but I wanna make sure we close up anything on Omnigentt. you mentioned, you were excited about the securityOmnigent Security, Contextual Policies, and Spend ControlsSwyx [00:18:03]: control side.Matei Zaharia [00:18:04]: Yeah.Swyx [00:18:04]: a lot of companies are figuring that out right now, as well as the spend side.Matei Zaharia [00:18:08]: Yep.Swyx [00:18:09]: what have you found there?Matei Zaharia [00:18:11]: Yeah, so I spent quite a bit of time talking to internal users, developers, security team, managers, and also lots of customers, and there's a few things. Like, first of all, one thing, that immediately was. became obvious is for security, there's this tension between, like, usability and security. And, the way people do. Like, a lot of coding agents today have very basic things like you can tell me which tool patterns I'll allow or disallow or whatever. It's like yes or no. But that puts you in a very tough spot. So just as an example, like, should my agent be able to read, some confidential documents, or let's say, should it be able to install new packages from npm, which, maybe it's compromised. Yes or no? Like, maybe I wanna allow it. Should my agent be able to publish stuff to the company website? Well, if I'm using it to code on the website, yes. But should it be able to do both, so it can, like grab a confidential document and be prompt injected and leak it? Probably not. So the thing we decided we need is stateful or what we call contextual policies where you keep track of the state of that session. It's not like is it allowed to push to the marketing site or not, but, like, hey, if it did a risky thing, like it installed, a old package from npm, or it read, like, 1,000 confidential docs, then no. Then don't, don't do it. Otherwise, maybe it's okay. That's one example of, like, moving that trade-off so it's both more secure and more useful by having a more powerful engine, essentially. This requires tracking sessions. The other piece that was interesting there is, like, there are these very level events it's doing, and you want some libraries on top that parse them. Like, for example, we have a, MCP server on Google Drive internally. It's got 60 API calls. like, how do I know which of those, like, will share a document with stuff on the internet and which ones won't? It's, it's annoying. So we designed in Omnigentt the policy layer so that it's functions and you can have libraries. Like, someone can make something that maps the level events to high-level ones, and then you write a policy about the high-level things that came out. so and thatSwyx [00:20:25]: This is related to the Panther,Matei Zaharia [00:20:27]: Yeah, Panther is. will help with that. PantherSwyx [00:20:30]: YeahMatei Zaharia [00:20:30]: a similar idea on the event processing side, and it's Python-based versus a weird custom language. this is more, as in realSwyx [00:20:39]: I didn't even know we were good yeah.Matei Zaharia [00:20:41]: Those things are happening, yeah.Swyx [00:20:42]: Yeah.Matei Zaharia [00:20:42]: So yeah, but these are the cool things. I think the contextual or stateful part, and then the way it can be libraries, and that was another reason to make it open source because others will write libraries and, like, we and our customers can use them. And the final thing, because it's stateful, one of the states we track is how much you spent in that session. So I can. I've had, like, I ask an agent to debug something, and it spent $500 because it decided to read a lot of log files and burn a lot of tokens. but I can literally say, “Okay, launch a agent to do this and cap it to spending $5.” Like, ask me for permission if it needs more. And because we're counting that within that session, it'll pop up and tell me, “Okay, you spent five, $5. Do you wanna go on?”Reynold Xin [00:21:27]: So important context here. Matei spent the last five years, a lot of his time was architecting Unity Catalog at DatabricksMatei Zaharia [00:21:34]: YeahReynold Xin [00:21:34]: which is the governance layer for data.Matei Zaharia [00:21:35]: That's right, yeah.Reynold Xin [00:21:36]: And he's combining expertise at that layer together with all the AI governance he knows.Matei Zaharia [00:21:41]: Yeah.Swyx [00:21:41]: DoMatei Zaharia [00:21:41]: But I also spent a lot of time being annoyed by coding agents and getting prompts.Matei Zaharia [00:21:46]: And also as theReynold Xin [00:21:48]: All the aboveMatei Zaharia [00:21:48]: I don't want to end up on the front page as, like, I installed some weird npm package and leakedSwyx [00:21:53]: YeahMatei Zaharia [00:21:53]: all the code, so I'm especially paranoid. But also I have very little time, so I don't want to sit there approving, like, do you want to run a 20-line, bash script, yes or no? so that's why I spend a lot of time figuring out, like, how can I make it as safe as possible and not annoying?Swyx [00:22:10]: Yeah. Is safety and mmm, let's call it security a bigger concern than token maxing or token budgets? which one is, likeMatei Zaharia [00:22:19]: Oh, yeah, they're both there. I don't know. I guess it depends on the type of company you are. So I think, some companies, like, the budget is, limited and, they really care about thatSwyx [00:22:34]: you can be Uber and still be concerned?Matei Zaharia [00:22:36]: Yeah. Oh, yeah, totally. Yeah. If you haveReynold Xin [00:22:38]: for us, securityMatei Zaharia [00:22:39]: YeahReynold Xin [00:22:40]: super paramount.Matei Zaharia [00:22:40]: For us, security is absolutely critical as a, cloud provider. It's, it's the most important thing, and, token maxing, we're not so worried about it yet, but I've seen the Like, for example, I talked to some consulting companies. They have, like, 100,000 employees who are all coding for customers. If those each spend, like, an extra $1,000 a month, that's, that's not fun.Swyx [00:23:04]: YeahMatei Zaharia [00:23:04]: we have, like, only a few thousand engineers.Swyx [00:23:06]: What's the policy in Databricks? Is it just unlimited or what'Matei Zaharia [00:23:08]: It's, it's unlimited, but we do. we use our own product to, like, analyze the traces and stuff, and we have a team that'looking to optimize and to see if anyone's doing something weird. And, we had some really cool insights just from analyzing current traces, like whichSwyx [00:23:24]: YeahMatei Zaharia [00:23:25]: models are better at, say, Rust versus like TypeScript or whatever. So yeah, at least in our code base.Swyx [00:23:31]: Yeah. Amazing. Obviously, I have to ask the token question, obviously.Matei Zaharia [00:23:34]: Yeah.Swyx [00:23:34]: I think it'sReynold Xin [00:23:34]: YeahSwyx [00:23:34]: it's a key thing. But yes, security and control above that, and figuring out a sane layer there you can have some autonomy, but, not too much.Matei Zaharia [00:23:43]: Yeah. Yeah, and we wanna make it super easy. As a engineer, you should set a thing. So in Omnigentt, you can ask your agent, “Set a policy on yourself to do this.” So it can likeSwyx [00:23:52]: But if there's something I should be showingMatei Zaharia [00:23:53]: YeahSwyx [00:23:53]: I don't, I don't see it on the GitHub, but,Matei Zaharia [00:23:55]: Oh, yeahSwyx [00:23:56]: there's justMatei Zaharia [00:23:56]: Well, in the docs there's something.Swyx [00:23:57]: Yeah, this is it.Matei Zaharia [00:23:58]: You can look at it later.Swyx [00:23:59]: Okay. Yeah.Matei Zaharia [00:23:59]: Just look in the docsSwyx [00:24:00]: YeahMatei Zaharia [00:24:00]: contextual policies if you wanna see.Swyx [00:24:04]: I just like to point peopleMatei Zaharia [00:24:05]: look at the built-in policies.Swyx [00:24:06]: Yeah.Reynold Xin [00:24:06]: Yeah.Swyx [00:24:06]: If you want to, follow up on this is exactly where to look, right?Reynold Xin [00:24:10]: Yeah.Matei Zaharia [00:24:10]: Yeah. yeah, and the story of these is, like, I just wrote, like, I wrote a doc with like 10 ideas for things before as you were working on them. Well, that was, like, my wish list of things people asked, and I told the team, like, “Hey, can you do like at least five of these for the launch?” And then they just got back with all of them, so.Swyx [00:24:29]: Oh, wow.Matei Zaharia [00:24:29]: so you can come up with more, but them- some of them are just meant to be examples. really you can intercept, like, any event the agent is making, and you can then either block or force it to ask the user or, like, allow, and you can update state to keepSwyx [00:24:45]: YeahMatei Zaharia [00:24:45]: track stuff.Swyx [00:24:46]: Yeah, ‘cause ultimately you're, I think of you as, like, a systems designer.Swyx [00:24:50]: You let people plug in, right? That's the wholeMatei Zaharia [00:24:51]: YeahSwyx [00:24:52]: modus operandi of what you do.Matei Zaharia [00:24:53]: Yeah.Swyx [00:24:54]: It's likeMatei Zaharia [00:24:54]: And we care a lot about also composab- like, can someone else write a library that others use, whichSwyx [00:24:59]: YeahMatei Zaharia [00:24:59]: this is meant to.Reynold Xin [00:25:00]: There's also a batteries included philosophy hereMatei Zaharia [00:25:03]: YesReynold Xin [00:25:03]: probably very similar to how you did Spark, which is you could just start using.Swyx [00:25:06]: Yeah.Matei Zaharia [00:25:06]: Yeah, that's right. It has to be good out of the box at certain things, and then you can build your own things on top that, like, we don't wanna do. But in Spark, if you just wanna like, I don't know, like read a table or do, like, a aggregation, it should be awesome at that out of the box.Building on Omnigent: Contributions, Startups, and AnalyticsSwyx [00:25:23]: Yeah. People wanna catch up on Omnigentt, they should watch your keynote.Swyx [00:25:26]: they should go through the GitHub and the docs. If they wanted to contribute, or they want to build on this ecosystem what would you call out as the most high-leverage places get involved?Matei Zaharia [00:25:36]: Yeah, do get involved in the Discord and in GitHub. Our team is there, is monitoring, and, some of the things people ask for we just built ourselves. Some of them, we're, we're collaborating with them to build it. and also tell us, likeSwyx [00:25:49]: Yeah, they're gonna be veryMatei Zaharia [00:25:49]: how you would like to use it because I think especially for developers, like, everyone wants it to work their own way, and a really good developer tool, like you have to hear the feedback on all the ways and figure out the abstractions and how to let people customize. So we'd love to hear, like, if you think, “Hey, I, I don't want it to work this way,” tell us. We really just wanna get that compatibility layer across agents and then let you do stuff on top.Swyx [00:26:14]: Yeah. is there any, in terms of like the startup side, I'm, I'm a founder.Swyx [00:26:18]: I wantMatei Zaharia [00:26:18]: YeahSwyx [00:26:18]: I see an opportunity, I wanna get in front of you. What's your request for, like, a startup that, like, I wish someoneMatei Zaharia [00:26:23]: Oh, like you wanna integrate with us?Swyx [00:26:24]: someone was working on this.Matei Zaharia [00:26:26]: Oh, for a startup?Swyx [00:26:27]: Yeah.Swyx [00:26:28]: Like, your, you got your own startup. It's doing well.Matei Zaharia [00:26:30]: Yeah.Swyx [00:26:30]: But like, if you weren't working on your own startup, what is, like, obvious that you should You advise many startups too, obviously.Matei Zaharia [00:26:37]: I do think, just as a company with a lot of engineers, like anything that helps me make sense of how people are usingSwyx [00:26:46]: SpendMatei Zaharia [00:26:46]: coding agents and,Swyx [00:26:48]: Yeah. AnalyticsMatei Zaharia [00:26:48]: spend, but also quality or like you should write, you should add this skill, or you should write this thing, or your agents are really horrible at tasks involving this service, so I go spend time. That would be nice. yeah.Swyx [00:27:00]: Yeah. The closest I've found is, this team, GitAI.Matei Zaharia [00:27:03]: Oh, cool. Yeah.Swyx [00:27:04]: They started with, like, we will just do, code and human attribution, but they're building the analytics layer on top of that.Matei Zaharia [00:27:12]: Yeah.Swyx [00:27:12]: I do think, like, there are a bunch of, like, artificial analysis is obviously,Matei Zaharia [00:27:18]: Yeah, they have their benchmarksSwyx [00:27:18]: doing super wellMatei Zaharia [00:27:19]: YeahSwyx [00:27:19]: with their stuff. so there's, there will be people. I think this is like the domain of consultants first, but then peopleMatei Zaharia [00:27:26]: YeahSwyx [00:27:26]: will build software that, let's say, it's kinda like the management planeMatei Zaharia [00:27:29]: YeahSwyx [00:27:30]: for coding agents.Matei Zaharia [00:27:30]: Yeah, I think there'll be a lot of insights there. You have it in other areas.Swyx [00:27:34]: Okay. Well, and then the other, big thing is your dream engine.LTAP: Lake Transactional/Analytical ProcessingSwyx [00:27:39]: maybe you wanna tell the story of, LTAP.Reynold Xin [00:27:45]: So, and background with. I'm, I'm gonna make people listen to our Ankur Goyal episode where we talked about SingleStore, HTAPMatei Zaharia [00:27:52]: YeahReynold Xin [00:27:52]: and all that history.Matei Zaharia [00:27:52]: Yeah. The LTAP idea is pretty simple. so if people have heard of the, Ankur's, talk about HTAP, it's effectively the world of databases. Sorry, there's like maybe a lot of context needs to be injected here. The world of databasesSwyx [00:28:06]: I am happy to be the database podcast that I'm forcing people to, like, learn your databases, guys.Swyx [00:28:11]: You cannot vibe code with just markdown files.Reynold Xin [00:28:13]: Yeah.Swyx [00:28:13]: Like,Reynold Xin [00:28:14]: It's one of the most important fundamental systems technologies out there. But the world of database effectively split into roughly two halves. There's what we call OLTP databases, which are transactional, and think of your Postgres, your MySQL, your Oracle databases, and the other side is what we call analytics, and sometime might refer to term OLAP. And the difference is on OLTP, you typically have maybe run some transaction on some event that looks up at one specific row. We update that row, right? It's a very oriented data structure. And on analytics, you're trying to reason on the data. You're trying to compute, “Hey, what's my revenue per store? What's my. How's my website doing every day?” And then you, eventually want to probably end up running anal- machine learning on it to predict, “Hey, how will my maybe sales be going in the future?” they are so very different architecture, and everybody start with OLTP databases. Every app, when you become serious enough, that needs more than markdown files, you need to have a database. You want to lose your data, you want to have some transactional consistency. But once you want to reason on the data, if you only have like- A hundred rows, it's probably okay to run it on your Postgres or your own, your MySQL database. But once you have more data and want to run more complicated analysis, the very analysis might crush your Postgres database. So you start doing, getting data out of the OLTP databaseSwyx [00:29:35]: Replication.Reynold Xin [00:29:36]: Replicate them into the analytic systems and just startSwyx [00:29:39]: Yeah, which for people, Elasticsearch is, like, aReynold Xin [00:29:42]: Yeah. So some of them get into Elasticsearch for, like, blocked analysis. A lot of our customers obviously get into Databricks to run more sophisticated things.Swyx [00:29:51]: Yeah.Reynold Xin [00:29:51]: And there's this term called CDC, whichMatei Zaharia [00:29:54]: Change data captureReynold Xin [00:29:55]: change data capture. and what it does, it reads the binlog of the database, and if you don't understand what binlog is, it's fine. The, but it's a little delta of the data, and it reconstructs based on the delta, the state of the database, on the analytics side. But CDC is, like, a very painful thing. It's how standard in the industry, everybody uses it, but, it ends up being. I think many data engineers ends up being waken up at, like, 3:00 a.m, because there's some pipeline thing.Swyx [00:30:22]: my explanation is, like, Airbyte is like a, became a $5 billion company just doing CDC.Reynold Xin [00:30:27]: Yeah, exactly.Reynold Xin [00:30:28]: CDC is, like, a veryMatei Zaharia [00:30:30]: It's hard.Reynold Xin [00:30:30]: It's one of the most boring but one of the most fundamental operations, like, powering modern society.Matei Zaharia [00:30:37]: huh.Reynold Xin [00:30:37]: But it's so brittle that, we joke that it's, should be called continuous data corruption, because you might change your schema on your OLTP database, and then the CDC pipeline fails to handleSwyx [00:30:48]: YeahReynold Xin [00:30:48]: the schema change.Swyx [00:30:49]: Yeah.Reynold Xin [00:30:49]: And then everything goes out.Swyx [00:30:51]: And there's all sorts of tricks that you can do, like, you add in, like, some versioning or whatever, but yeah.Reynold Xin [00:30:55]: Yeah, but it's a very, in general, very complicated. Like, I think at my keynote, I asked the audience put up their hand if they love their CDC pipeline. Only, like, maybe two people put it up. So if single store, like, about maybe a decade ago, I think the industry had this idea, hey, what if I built a single database that can handle both workloads? Now I don't.Swyx [00:31:12]: Which, like, by the way, every database person ever has ever always dreamed about this.Reynold Xin [00:31:15]: Yes. Yes.Reynold Xin [00:31:16]: This is the holy grail of database engineering is why not build a single system that can do both of this? But it ends up just being a lot of compromises. one, I think one of the first issue is that, hey, each. they say Postgres has a massive ecosystem, right? You want to be using the tools that's built for Postgres. And Spark, for example, had a massive ecosystem. There's a lot of libraries you want to use. If you were to create now a new thing, you don't have a ecosystem. You tend to create a new, smaller proprietary API, and you're lacking both, and it's also very difficult to make it performance-wise to be, comparable on either side. So it ends up being sucking on both. And our whole idea of LTAP, it's obviously a wordplay on the term HTAP, is that we think this is HTAP done right. HTAP wants to build a single engine for both. We think you can get 99% of what you need by unifying the storage, and just have a single storage layer. And once you have the single storage layer, if your Postgres databases are writing data in a column-oriented format, everything analytics can just go read that data directly without any delay, right? There's no pipeline in between, so all the data will immediately be available for reasoning analytics. I think I was telling some customers earlier, hey, when we talked about this is gonna be super useful for agents, I at first didn't really believe in it myself, even though we wrote that positioning.Lakebase, Agents, and Live Operational DataMatei Zaharia [00:32:39]: Yeah.Reynold Xin [00:32:40]: But then last night I was having dinner with a Australian customer, and they told me, “Oh, hey, one of the big issue we have is we have all these logs from our services, and we see SLA dips and want to investigate. But then there's no way for those agents to even understand what's going on in the actual databases themselves. All we see is just, like, product telemetry of the database and the services.” It would make those agents 10 times more powerful if understand, for example, who's placing those orders, what is happening, what exactly are they doing. So now I'm sold on our own message.Swyx [00:33:13]: Yeah.Reynold Xin [00:33:14]: I think it's really. It gets you the almost all of the benefits of the HTAP holy grail, which is, hey, make the data available immediately for reasoning analyticsSwyx [00:33:26]: Yeah, I think,Reynold Xin [00:33:27]: without compromiseSwyx [00:33:28]: in the way that humans are generally intelligent and want to have the ability and access to query anythingReynold Xin [00:33:34]: YeahSwyx [00:33:35]: while they do the work, they also need history and need context.Swyx [00:33:38]: And, like, where else does they get context? That's it's an analytical workload.Reynold Xin [00:33:41]: Exactly.Matei Zaharia [00:33:42]: Yeah. Yeah. And I remember when we had incidents with our databases and engineers said, “Well, I can't just run a giant query on it to see what's going on because that's gonna bring down the database and hoard it even more.” Like, that's the stuff that this gets rid of, because you spin up a whole separate fleet of machines that's doing the analytics. You're not overloading, like, the main databaseReynold Xin [00:34:02]: RightMatei Zaharia [00:34:02]: that's still trying to serve stuff.Reynold Xin [00:34:04]: Yeah.Matei Zaharia [00:34:04]: Yeah.Why LTAP Works Now: Parquet, Postgres, and LakebaseSwyx [00:34:05]: So this has been a dream for a while. what had to get done in order to get to today? Like,Reynold Xin [00:34:11]: Yeah.Swyx [00:34:11]: I feel like, you have announced variants of this several times, but it wasn't as clear as LTAP.Reynold Xin [00:34:18]: Yeah.Swyx [00:34:18]: I think LTAP is like Like, okay, we've got it, guys.Matei Zaharia [00:34:21]: This thing, yeah.Reynold Xin [00:34:21]: I was talking to somebody at Meta, and then he was asking me, “Hey, what's the catch? Why is it possible now?” And I think the reality is we took a lot of time to work on the Lakebase architecture. obviously a lot of it came from the Neon team, which is a separation of storage from compute. And it turned out it was just a tiny little step away going from that to this LTAP idea, which is, hey, we just. in the Neon architecture and in Lakebase architecture, we're writing data in oriented format to the open data lake, but in there we're writing in Postgres pages. Ali and I were spending a lot of time debating, hey, can we just change that to write in column-oriented format? And we're just debating, and one day, one of our engineers who's, like, super smart came in, he's like, “Hey, I just prototyped it. It works.”Swyx [00:35:07]: Wait, it's, prototype what?Reynold Xin [00:35:09]: Prototype, instead of storing the data in the data lake in the oriented formatSwyx [00:35:15]: ColumnReynold Xin [00:35:15]: like Postgres pagesSwyx [00:35:15]: YeahReynold Xin [00:35:16]: write them in Parquet.Swyx [00:35:17]: Yeah.Reynold Xin [00:35:18]: and he just made the observation that, hey, our storage fleet has a lot of extra idle CPUs And we could use those CPUs to do the transcoding from row to column, where row is good for OLTP, but column is good for analytics. so let's do that transcoding at that time. And as a matter of fact, once you transcode the data compresses better. So from those services writing to, for example, S3 or other data lake, like object stores, you can write them faster ‘cause now they are now smaller.Matei Zaharia [00:35:49]: Yeah.Reynold Xin [00:35:49]: So there's no overhead, it's no compromise in performanceMatei Zaharia [00:35:52]: Some CPU overhead.Swyx [00:35:54]: Yeah, because,Matei Zaharia [00:35:55]: YeahSwyx [00:35:55]: we had extra CPUs anyway.Matei Zaharia [00:35:56]: We had that fleet anyway, yeah.Swyx [00:35:57]: so the debate ended. it's one of the classics of, tech, issue of a lot of debate, but then somebody went ahead and just tried to prototype it and it worked.Matei Zaharia [00:36:06]: But, like, something this strategicSwyx [00:36:07]: That's rightMatei Zaharia [00:36:07]: and important to the company, I expect there to be, like, a kickoff thing, like a design doc. Nothing like that.Swyx [00:36:13]: Nothing like that.Swyx [00:36:14]: He just. We were debating in many meetingsMatei Zaharia [00:36:17]: Yeah.Swyx [00:36:17]: and then we're just debating whether it's possible or not from first principle.Matei Zaharia [00:36:20]: YeahSwyx [00:36:20]: and then, somebody just did it.Matei Zaharia [00:36:23]: Yeah, if you set yourself up so people do that'll be great. And that happened a bit with Omnigentt too. I think if I just had a doc on, like, we can make these together, everyone would, would think, “Oh, what about this? What about this?” But then you. if you try it out, it helps. And then if you have real users and they bash it and, like, it's still working, or in this case, if you have the workload, what the workload looks like, you can just test the same pattern then.Databricks' Culture of Fast PrototypingSwyx [00:36:47]: Yeah.Matei Zaharia [00:36:47]: Yeah.Swyx [00:36:47]: Tech aside, which is very cool, this is, like, the most important thing, the culture of innovation, and you don't have to ask my permission, you don't have like, do a whole form- formal process, just do it?Matei Zaharia [00:36:59]: Well, especially these days, I think withSwyx [00:37:01]: YeahMatei Zaharia [00:37:01]: AI, it's easier to buildSwyx [00:37:02]: But so, likeMatei Zaharia [00:37:03]: a prototypeSwyx [00:37:03]: I think you are very I made a lot of suite of, like, large companies and, like, I think that at scale, things slow down, and I'm sure you felt it already, but somehow you have this core of people that, like, are exempt. How? I think we hire and we work with really good people, and that's a very important part of it, and empowering them, but also spending a lot of time, maybe us in the trenches matter a lot also.Matei Zaharia [00:37:28]: Yeah, I think, I think first, people can adapt to being in the larger company, so that helps. And we wanna make sure they know that they can try stuff and settle debates and have a lot of examples of how it was done before, or launch a thing in beta or whatever. and then the other thing I do think as a company, like despite the size, we don't launch that many, like, products. We try to keep it pretty coherent. That's, that was the whole, like, theory of the company, was like instead of having, like, 20 Amazon services you need to set up, like a analytics and machine learning stack, you just have one, and it's, like, the same API, the same semantics across all of them, the same copy of the data. So that requires, like, unification. And then we added one more thing at a time. Like, we added storage with Delta Lake. We didn't used to do any storage. Then we added SQL, we added, machine learning platform stuff. So, but yeah, don't, don't do too many, but do those things well and, that also helps, it helps keep it manageable.Reynold Xin [00:38:33]: Yeah. The other thing we encourage a lot is instead of building, boil the ocean for everything, let's figure out how do we do it incrementally, how do we do it very quickly. Like, many of our productsMatei Zaharia [00:38:43]: YeahReynold Xin [00:38:43]: they're built in the span of weeks, and then we go to, hey. Like, usually my first question to whoever team is building is who's the target customer? Who are you working with? Are you on a first-name basis with them? Are you texting with them? I think having that very tight loop,Matei Zaharia [00:38:59]: Can you bring up another launch that comes to mind when, in this thing? I just want to give examples.Reynold Xin [00:39:04]: Omnigentt itself happened that way.Reynold Xin [00:39:05]: Yeah.Matei Zaharia [00:39:06]: Who's the customer? That's a good oneReynold Xin [00:39:34]: storage layer we did. we had, our largest customer at the time said like, “Okay, I need some. I want something in the cloud ‘cause, I. if the rest of our network is compromised, like this thing needs to be separate to store and query the events.” And then, talked to us, he said, “Okay, this is the rate of events per second. This is, like, the freshness I want. Can you do it?” So that was, like, way larger than any workload we had, and we had our, engineer, working on that, Michael Armbrust, and he worked just to make this work. And once it worked for them, it worked for everyone else. Yeah. This was early in the company, probably like four years in or something.Matei Zaharia [00:40:24]: 20- 2018?Swyx [00:40:26]: Yeah, ‘17, ‘18.Matei Zaharia [00:40:28]: Few companiesSwyx [00:40:28]: Do you have other examples?Matei Zaharia [00:40:30]: there'Swyx [00:40:31]: Maybe you have othersMatei Zaharia [00:40:31]: yeah, Clean Room, which is how you share data in a way without sharingSwyx [00:40:35]: YeahMatei Zaharia [00:40:35]: underlying data, but you allow specific operations. Those were done effectively initially just for two customers. I think the industry has a sense of, hey, maybe if you overfit to, like, one or two customers, it's gonna be really bad for you. But I think the, downside of overfitting is much smaller than the upside itself. And if you try to be too ambitious and boil the ocean, it's a much bigger problem.Swyx [00:40:58]: Yeah. Yeah.Matei Zaharia [00:40:58]: ‘Cause you might end up having no customer.Swyx [00:41:00]: Yeah, that's more, that's the more likely outcome.Matei Zaharia [00:41:02]: Yeah.Tech Companies vs. EnterprisesSwyx [00:41:03]: than you can pivot from there. I do think there is such a thing as a bad customer that sometimes you should fire. Yeah.Matei Zaharia [00:41:08]: They could exist sometimes if you drive. well, one of the challenge I think we probably see, and maybe many AI, so newer generation companies are seeing is, so tech companies are very different from tech companies or traditional enterprises.Swyx [00:41:22]: Yeah.Matei Zaharia [00:41:22]: And, if you optimize everything just for tech companies, you might have various challengesSwyx [00:41:27]: OhMatei Zaharia [00:41:27]: scaling them outside of tech companies.Swyx [00:41:28]: Okay, what likeMatei Zaharia [00:41:30]: YeahSwyx [00:41:30]: what like top three differences that you always think about?Reynold Xin [00:41:33]: Governance is a big oneMatei Zaharia [00:41:34]: I think, yeah, a big one is like, yeah, security, data privacy, governance, all that stuff. So usually if you're building some kinda like B2B or developer tool, like your biggest market is gonna be enterprises, but it's just very different. A company that's existed for like, it's had some form of IT for like 30 years, they have so many legacy systems or they operate in a regulated space. whereas a startup or, even like a, like sorta more recent tech company, all the. everything is new and pristine. So yeah, it's just different, and if you've never worked with enterprises or been in one, you just won't know about it.Reynold Xin [00:42:13]: Yeah.Matei Zaharia [00:42:13]: Yeah.Reynold Xin [00:42:13]: And the procurement process is probably quite different. There's far more stakeholders.Matei Zaharia [00:42:17]: Yeah, that is one. Yeah.Matei Zaharia [00:42:18]: Another piece that's interesting is I think some tech companies, people, will say, “Oh, I can build that myself,” right? I'll just build that myself.Matei Zaharia [00:42:27]: So then you go,Reynold Xin [00:42:28]: I don't think people say that about Databricks, butMatei Zaharia [00:42:31]: yeah, it dependsReynold Xin [00:42:32]: They do.Matei Zaharia [00:42:32]: They do?Matei Zaharia [00:42:32]: Yeah, the. Yeah, and it depends on the teams and things. So, but, on the other hand, like many of the enterprises say, “I don't, I never wanna be in the business of building that.” Like, I don't want my, whatever, I'm a retailer or something, I never wannaReynold Xin [00:42:45]: Yeah, sell clothes,Matei Zaharia [00:42:46]: be down because like some weird like nerd like couldn't get streaming pipelines working.Matei Zaharia [00:42:51]: That is not what I'm doing.Reynold Xin [00:42:53]: Yeah.Reynold Xin [00:42:53]: Yeah. This makes them great customers, to be honest, right?Matei Zaharia [00:42:55]: Yeah. But you have to understand that it's hard without having worked there and stuff, like you may not appreciate.Reynold Xin [00:43:01]: Look, I think they're all great. don't get me wrong, they have different challenges. But the, many of the tech companies, for sure there's a lot, far more DIY.Matei Zaharia [00:43:10]: On the flip side, you have people who are. they're very much experts in their domain, like they're building airplanes, they're, designing medicines, whatever, and they just want to bridge the technology, where like they don't wanna learn, databases or whatever. As cool as we think it is, even as interesting as the average software engineer might think it is to read a little bit, like they just never wanna know. They just say, “I have a, giant like, matrix or whatever with my, clinical data, like how do I, how do I like cluster it or whatever?” So yeah.The Dream Engine and Rewriting the Database StackReynold Xin [00:43:40]: Yeah. That's true. Okay, so and then I wanted to build out the dream engine, vision. where does this all lead? So one of the thing we, realized maybe a couple years back is that every single database engine out there, especially on the analytics side, are a decade old. pretty much everything that have reasonable traction are about a decade old. And they all started targeting some very specific narrow use cases, and then over time it's become more and more successful. They have grown in their ambition, and then they try to support more and more use cases. But the fastest way to support those use cases tend to be hacked around the abstractions that were initially created, that were not for those use cases.Matei Zaharia [00:44:23]: Yeah.Reynold Xin [00:44:23]: And then, but you can support them more or less okay. And before it, after 10 years of organic evolution that way, it becomes a gigantic pile of s**t.Reynold Xin [00:44:31]: the. And, but that includes Databricks. And very few company or very few systems, I think, have the gut to say, let's go start from scratch. Let's go back to the drawing board and design, knowing everything we know today after a decade of workloads and probably billions in revenue, let's attempt to rewrite it from scratch and make sure it will work and it can support all of these use cases. So we started doing that, but it's a very ambitious project. by the way, you can search on Wikipedia, there's this thing called second system syndrome.Matei Zaharia [00:45:08]: Yeah, I know that. Yes.Reynold Xin [00:45:09]: Or second system effect.Matei Zaharia [00:45:11]: Every developer must know what a second syndrome is.Reynold Xin [00:45:12]: It's you built your first thing and it works out great, and the second one's bound to fail because you become too ambitious.Reynold Xin [00:45:19]: And then you ask so many requirements.Matei Zaharia [00:45:20]: Or like you think everythingReynold Xin [00:45:21]: YeahMatei Zaharia [00:45:21]: and then you're likeReynold Xin [00:45:22]: You justMatei Zaharia [00:45:22]: you're, “I'm gonna design the perfect system this time.”Reynold Xin [00:45:24]: Yeah. And it turned out it's not perfect, and then it start failing and you're too ambitious, never launch, and you get killed. The, and the engineering team that started this, they were brilliant. I think we hired some of the best database engineers, on the planet into Databricks, and they were brilliant. Thank God it's not their second system. Many of them have built more than two in the past.Matei Zaharia [00:45:44]: Ah, nice.Reynold Xin [00:45:45]: But they were still worried about this, hey, building a database engine from scratch, I think the conventional wisdom is gonna take like five years to mature. This would be a very long-term project. It could fail. I think one of the engineers jokingly said, “Hey, maybe we just call it Reynolds Stream Engine.” If we name after a founder, maybe we then may get canceled or killed. But I think they built something pretty remarkable. they went back to. They changed the way the database engines were built from a paradigm point of view. Usually when y

Your AI code review tools read the diff. They stare at your code. But they never actually run it. So the bugs that only show up at runtime, the broken user flows, the bad query plan, the duplicate submission, sail right past review and land in front of your customers. In this episode, Joe Colantonio sits down with Evan Marshall, founder of Ito and a fifteen year engineer who spent five years in applied cryptography securing hundreds of millions of dollars for millions of people. Evan is taking that ship fast without breaking things discipline and pointing it straight at testing. Ito is an agentic QA platform that builds and runs your actual app on every pull request, navigates it like a real user, exercises the frontend and backend as one system, and brings back real runtime evidence: video replays, logs, the exact lines responsible, and steps to reproduce, posted right in your PR. You will learn: Why static code review misses the bugs that cause real production incidents How Ito spins up ephemeral environments and tests across UI, API, and database Why QA is not disappearing, it is leveling up into a manager and quality strategist role How to keep your test layer separate from your code generation so your signal stays honest The skills testers and engineers need as AI writes more of the code If you are shipping AI generated code at high velocity and your QA cannot keep up, this one is for you. Try Ito on your own code. Your first ten pull requests are reviewed free, no credit card required. Check it out at https://testgld.link/itoai now. And as Joe always says, seeing is believing.

Fraud doesn't usually announce itself with a flashing warning sign. It shows up as a chargeback, a fake account that looks “normal,” or an account takeover that slips through the exact same checkout flow your best customers use. Greg Myers sits down with Tamas Kadar, Co-Founder and CEO of SEON, to unpack how modern fraud actually works and how digital businesses can protect revenue without burying users under friction.Tamas shares the origin story that started with a real loss: a crypto checkout experiment that got hit by fraud almost immediately. That experience turned into years of studying how fraudsters operate and, eventually, into SEON's mission: help businesses prevent fraud, verify identities, and stay compliant in real time using the minimum data points companies already collect, like an email address or phone number, plus hard-to-fake device and digital footprint signals. We dig into when step-up verification makes sense, how to reduce false positives, and why trust and safety teams deserve to be seen as revenue drivers, not cost centers.The conversation goes deep on AI in fraud prevention beyond the buzzwords. Tamas explains where classic machine learning helps, where it breaks, and how LLMs can speed up investigations by summarizing cases, surfacing patterns earlier, and reducing the “five tabs per investigation” problem. We also explore the shift toward headless software, where analysts can ask questions in natural language and get answers from the system of record without clicking through a UI, while still keeping decisions explainable with human-readable rules.We close with what's next: synthetic identities, deepfakes, account takeover, stablecoins and changing payment rails, plus the rise of agentic commerce where good agents and bad bots can blend into the same traffic.

Welcome to Dev Game Club, where this week we complete our series on Tom Clancy's Splinter Cell: Chaos Theory. We talk about some of the late levels before turning to our takeaways. Dev Game Club looks at classic video games and plays through them over several episodes, providing commentary. Sections played: Through Battery (B) and Seoul (T) Issues covered: whether Tim is North Korea, Steam Deck support, the intimate level design of Hokkaido, nightingale floors and ninja, ninja kids, level design that wraps around, changing up the levels, timed sections, cool ideas for the space, connectivity, different types of cameras, a frustrating metric challenge, directing the player, contextual movement and tagging, telegraphing metrics, negative design metrics, finding additional story, the set-up for Seoul, contrast against Battery, escalating the dynamic objectives, the "reversal," upping the ante, getting 100%, the whistle, finding a body as a negative stat, deducing player intent, unconscious witnesses, combining states, layering tools and immersive sims, wanting more guidance to the systems and verbs the player has access to, building tutorial stuff last, executing on the tone, dynamic changes to the plan, leaning into the tech appropriately, communicating enemy AI state clearly without UI, the limited reach of this genre.  Games, people, and influences mentioned or discussed: BioStats, CalamityNolan, OI Interactive, RealmSoft, Clockwork Ambrosia, Michael Patton, Nathan Hiemenz, Ian Clark, Lian Hearn, Across the Nightingale Floor, Teenage Mutant Ninja Turtles, Team Ninja, Vanquish, Platinum Games, Metal Gear Revengeance, Clover Studios, Fallout 3, Skyrim, Jedi Knight, LucasArts, Kevin Kauffman, Matt Tateishi, Jake Stevens, Knute Rockne, Prince of Persia: The Sands of Time, Mysteries of the Sith, White Men Can't Jump, Woody Harrelson, Star Wars: Rogue One, Hal Barwood, Hitman: World of Assassination, Nintendo, Majora's Mask, SW: Republic Commando, Mission: Impossible, Project: Octavia, DOOM (1993), Alien: Isolation, Kirk Hamilton, Aaron Ever, Mark Garcia.  Next time: Psychonauts! Twitch: timlongojr and twinsunscorp  Discord  DevGameClub@gmail.com

Portals and intranets continue to play a critical role in Knowledge Management—whether you're serving internal teams or delivering value directly to clients. In a GenAI-driven world, having a centralized, well-governed home for playbooks, standards, and trusted knowledge is more important than ever.   In this podcast, we spoke with a KM leader who shared insights and lessons learned from building and launching a mature KM platform. The speaker shared practical insights on organizing knowledge, designing sustainable processes, and making thoughtful UI and experience decisions. They also looked back at lessons learned along the way and explored what ongoing governance and maintenance really look like once the platform is live. Moderator: @Brandie Knox - Principal & Creative Director, Knox Design Strategy Speaker: @Caitlin Gibson - Counsel, Debevoise & Plimpton Recorded on 06-17-2026. 

Send us Fan MailGet vidIQ Boost for an exclusive price! https://vidiq.com/podcastWant a 1 on 1 coach? https://vidiq.ink/theboost1on1Join our Discord! https://www.vidiq.com/discordWatch the video: https://youtu.be/_kUOrWhNynwWe break down two YouTube experiments that could change how viewers navigate the app and how creators earn early momentum. We also zoom out into what these UI tests say about control, recommendations and the mental game of posting on YouTube.• Subscriptions tab moving from bottom nav to a top tab on mobile• How UI changes can spike or crater feature usage• Why initial velocity still ties to subscriber behavior• The subscription feed becoming less chronological and more algorithmic• DMs and “invite to chat” showing up inside Subscriptions• YouTube's push toward all-in-one community features• Shorts testing a missing dislike button and a heart icon• Why dislike signals matter for personalization and scam detection• Better intros by watching retention graphs and viewer intent• Avoiding the YouTube Studio refresh spiral after uploadleave a comment below. Let us know what you think about the the the like button being changed and of course the subscription tab being moved on mobile.Make sure you hit that subscribe button, like button. If you're listening to an audio podcast, there will be a link in the show notes to take you over here where you can do the same.

Today we are talking about Test Driven Development, ebooks, and Drupal with guest Oliver Davies. We'll also cover Juicer Social Feed as our module of the week. For show notes visit: https://www.talkingDrupal.com/557 Topics What Is Test Driven Drupal Why Automated Tests Matter How TDD Works AI and Test Quality Balancing Test Coverage When to Write Tests Why Write the Book Why Write an Ebook From Email Course to Ebook Ebook vs Print Tradeoffs Who the Book Helps What You Will Learn Keeping Content Updated Publishing Tools Workflow Lessons and Drupal Changes Podcast and Future Books Mob Programming Explained Free Ebook and Wrap Up Resources Juicer io Drupal 11: The Upgrade Experience I've Been Waiting For codethatships Test-Driven Drupal Sculpin Guests Oliver Davies - oliverdavies.uk opdavies Hosts Nic Laflin - nLighteneddevelopment.com nicxvan John Picozzi - epam.com johnpicozzi Scott Falconer - managing-ai.com scott-falconer MOTW Correspondent Martin Anderson-Clutz - mandclu.com mandclu Brief description: Have you ever wanted to embed social feeds into your Drupal website? There's a module for that. Module name/project name: Juicer Social Feed Brief history How old: created in Mar 2026 by Denis Omerović (drupalchille) Versions available: 1.0.2, that works with Drupal 10.3 or 11 Maintainership Actively maintained (version released today!) No open issues Usage stats: 4 sites Module features and usage This module embeds an aggregated social media feed from Juicer.io directly into Drupal as a configurable block. It natively supports content from Instagram, LinkedIn, Facebook, X (Twitter), TikTok, Bluesky, YouTube, and more. Traditionally, displaying feeds from platforms like Facebook, X, or Instagram requires creating developer accounts, managing rotating OAuth tokens, and keeping up with constantly shifting API restrictions. Juicer handles all API authentication on its platform, shielding your website from sudden breaking changes by individual social networks. To use this module, you will need an active account on Juicer.io. They offer both free and paid tiers depending on how many sources you want to aggregate and how frequently you need the feed to sync. The module is created and maintained by the official Juicer.io team. That should ensure that the module is closely aligned with the product's features and any potential API changes over time. The embedded feed is made available as a Drupal block, to make it easy to control where it should appear on your site. When placing the Juicer block, the UI exposes several user-friendly settings: Feed Slug: Just paste your unique Juicer feed ID to establish the connection. Post Limit: Control exactly how many items populate initially. Source Filtering: If your Juicer account aggregates five networks, but you only want to show LinkedIn posts on a specific page, you can filter down to a single network right inside the block settings. SEO/Semantic Control: You can set titles/subtitles and choose the exact heading level hierarchy ( through ) to ensure your pages remain semantically correct and accessible. I did get a chance to test out the module and the service today, and I can tell you from experience, it's a huge improvement on having to create and pull in feeds directly. I did notice that the block didn't show up in the Drupal Canvas component library, but I was able to determine that two lines of code to declare the block as FullyValidatable were all that was needed. So I opened a Feature Request to add that, and it was merged in and a new release cut in less than an hour. So it's now Drupal Canvas compatible too! It's worth pointing out that the standard Juicer's embed script loads HTMX, which conflicts with the version of HTMX included in Drupal 11 core. As a result, the module fetches feed HTML directly from the Juicer API and includes a minimal HTMX shim to prevent errors. John, you nominated this module, why don't you start us off by telling us about how you got started using it?

Apple is handing parents unprecedented control with new child safety and parental approval features. Rosemary Orchard and Mikah Sargent break down what's changed, how it works, and more iOS goodness during WWDC26 week! Apple's WWDC keynote reveals iOS 27 and platform-wide focus on AI, privacy, and performance Liquid Glass transparency and appearance now user-adjustable for accessibility Toolbars and UI restored for improved navigation and vision support Performance boosts: faster photos, AirDrop, and Spotlight search highlighted Intelligent networking promises smarter Wi-Fi and cellular switching Maps overhaul: more vivid detail with AI and satellite imagery New child safety features: granular app, contact, and website approvals Siri gets personal context, deeper app integration, and smart replies Advanced developer APIs and app intents previewed for on-device AI features Generative photo editing arrives: extend, clean up, and a new Reframe tool System-wide suggestions and information surfacing in Mail, Messages, and calls Quality-of-life updates: independent alarm/ringer/music volumes, swipable now playing Shortcuts Corner: Natural language shortcut creation and changes to automation workflows Hosts: Mikah Sargent and Rosemary Orchard Contact iOS Today at iOSToday@twit.tv. Download or subscribe to iOS Today at https://twit.tv/shows/ios-today Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord. Sponsor: shopify.com/ios

Apple is handing parents unprecedented control with new child safety and parental approval features. Rosemary Orchard and Mikah Sargent break down what's changed, how it works, and more iOS goodness during WWDC26 week! Apple's WWDC keynote reveals iOS 27 and platform-wide focus on AI, privacy, and performance Liquid Glass transparency and appearance now user-adjustable for accessibility Toolbars and UI restored for improved navigation and vision support Performance boosts: faster photos, AirDrop, and Spotlight search highlighted Intelligent networking promises smarter Wi-Fi and cellular switching Maps overhaul: more vivid detail with AI and satellite imagery New child safety features: granular app, contact, and website approvals Siri gets personal context, deeper app integration, and smart replies Advanced developer APIs and app intents previewed for on-device AI features Generative photo editing arrives: extend, clean up, and a new Reframe tool System-wide suggestions and information surfacing in Mail, Messages, and calls Quality-of-life updates: independent alarm/ringer/music volumes, swipable now playing Shortcuts Corner: Natural language shortcut creation and changes to automation workflows Hosts: Mikah Sargent and Rosemary Orchard Contact iOS Today at iOSToday@twit.tv. Download or subscribe to iOS Today at https://twit.tv/shows/ios-today Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord. Sponsor: shopify.com/ios

Apple is handing parents unprecedented control with new child safety and parental approval features. Rosemary Orchard and Mikah Sargent break down what's changed, how it works, and more iOS goodness during WWDC26 week! • Apple's WWDC keynote reveals iOS 27 and platform-wide focus on AI, privacy, and performance • Liquid Glass transparency and appearance now user-adjustable for accessibility • Toolbars and UI restored for improved navigation and vision support • Performance boosts: faster photos, AirDrop, and Spotlight search highlighted • Intelligent networking promises smarter Wi-Fi and cellular switching • Maps overhaul: more vivid detail with AI and satellite imagery • New child safety features: granular app, contact, and website approvals • Siri gets personal context, deeper app integration, and smart replies • Advanced developer APIs and app intents previewed for on-device AI features • Generative photo editing arrives: extend, clean up, and a new Reframe tool • System-wide suggestions and information surfacing in Mail, Messages, and calls • Quality-of-life updates: independent alarm/ringer/music volumes, swipable now playing • Shortcuts Corner: Natural language shortcut creation and changes to automation workflows Hosts: Mikah Sargent and Rosemary Orchard Contact iOS Today at iOSToday@twit.tv. Download or subscribe to iOS Today at https://twit.tv/shows/ios-today Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord. Sponsor: shopify.com/ios

Apple is handing parents unprecedented control with new child safety and parental approval features. Rosemary Orchard and Mikah Sargent break down what's changed, how it works, and more iOS goodness during WWDC26 week! Apple's WWDC keynote reveals iOS 27 and platform-wide focus on AI, privacy, and performance Liquid Glass transparency and appearance now user-adjustable for accessibility Toolbars and UI restored for improved navigation and vision support Performance boosts: faster photos, AirDrop, and Spotlight search highlighted Intelligent networking promises smarter Wi-Fi and cellular switching Maps overhaul: more vivid detail with AI and satellite imagery New child safety features: granular app, contact, and website approvals Siri gets personal context, deeper app integration, and smart replies Advanced developer APIs and app intents previewed for on-device AI features Generative photo editing arrives: extend, clean up, and a new Reframe tool System-wide suggestions and information surfacing in Mail, Messages, and calls Quality-of-life updates: independent alarm/ringer/music volumes, swipable now playing Shortcuts Corner: Natural language shortcut creation and changes to automation workflows Hosts: Mikah Sargent and Rosemary Orchard Contact iOS Today at iOSToday@twit.tv. Download or subscribe to iOS Today at https://twit.tv/shows/ios-today Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord. Sponsor: shopify.com/ios

I've been seeing a recurring pattern with companies selling APIs, MCPs, data feeds, and other developer-focused AI products. While the technology is often sound if not impressive, sales momentum sometimes slows when prospects have to imagine how the product will create value in their own environment. My perspective on this is that the flexibility that makes these tools powerful can also make them harder to evaluate. Flexibility can adversely increase the Invisible Intelligence Gap, and I think certain types of AI-based solutions (LLM) may actually increase this because the boundaries of the product are often so much wider than ever before (if not invisible to the buyer). So, how to close this gap? Well, one way is to build a visual UI that showcases what's possible with your API/feed/data solution. You take the buyer out of the conceptual space and make things concrete. So today, that's what we dig into: when to consider adding a UI, how far you need to go with it, how you can use Copilot/AI agents to help customize these example implementations, and the benefits you might see.  Highlights / Skip to: The challenges of selling API-based analytics and AI products (0:56)  Why this topic matters right now (2:48) The Invisible Intelligence Gap that may be slowing your sales (3:34) Strategies for bridging the Invisible Intelligence Gap with a UI (user interface) layer (7:01) Client case study: the impact and results you may see adding a UI on top of your technical product (14:05) Signs that you should consider adding UI to your technical product (18:23) Leveraging humans' highly developed visual system to help potential customers see the full value of your product (26:24) Conclusion (27:32) Links Invisible Intelligence Gap Azeem Azhar's Exponential View (6/4/26 episode)  

Sure, you didn't miss Anthropic's BIG Opus 4.8 drop.