Podcasts about UI

Cześć, dzień dobry!Jak wygląda droga od jednoosobowej firmy programistycznej do grupy IT liczącej setki osób? W tym odcinku podcastu „BSS bez tajemnic”, w cyklu „Kim ONI są?” rozmawiam z Jerzym Kuflem – założycielem i prezesem Grupy ITEO.Posłuchajcie historii budowania firmy technologicznej, która zaczynała od tworzenia aplikacji mobilnych dla dealerów samochodowych, a dziś realizuje projekty dla klientów z wielu branż i rozwija grupę spółek poprzez akwizycje.Jerzy Kufel opowiada, jak ITEO rozwijało się przez ponad 15 lat – od kilkuosobowego zespołu, przez pierwszych klientów zagranicznych ze Stanów Zjednoczonych, Singapuru, Australii i Wielkiej Brytanii, aż po obecną działalność na rynku polskim i rozwój międzynarodowy. W rozmowie pojawia się także temat Arabii Saudyjskiej i tego, dlaczego w tamtym regionie biznes wymaga budowania relacji bezpośrednio na miejscu.Jerzy mówi o także o tym, jak akwizycje pozwalają szybciej zdobywać klientów i uzupełniać kompetencje technologiczne grupy.Nie zabrakło również AI i jego wpływu na branżę IT. Rozmawiamy o programowaniu z wykorzystaniem sztucznej inteligencji, przyszłości zespołów developerskich oraz o tym, dlaczego wraz z rozwojem AI coraz większego znaczenia nabierają UX, UI, badania użytkowników i projektowanie produktów cyfrowych.Jerzy Kufel opowiada także o modernizacji starszych systemów IT, projektach dla Masterlease, współpracy z InPostem, strukturze zespołu ITEO oraz planach dalszego rozwoju.Zapraszam  Linki:Iteo – https://iteo.com/Jerzy Kufel na Linkedin – https://www.linkedin.com/in/jerzykufel/Iteo na Linkedin - https://www.linkedin.com/company/iteo-software-house/  ****************************  Nazywam się Wiktor Doktór i na co dzień prowadzę Klub Pro Progressio https://proprogressio.com/pl/dzialalnosc/klub-pro-progressio/1 – to społeczność wielu firm prywatnych i organizacji sektora publicznego, którym zależy na rozwoju relacji biznesowych w modelu B2B. W podcaście BSS bez tajemnic poza odcinkami solowymi, zamieszczam rozmowy z ekspertami i specjalistami z różnych dziedzin przedsiębiorczości.Zapraszam do odwiedzin moich kanałów na:YouTube - https://www.youtube.com/@wiktordoktor Facebook - https://www.facebook.com/wiktor.doktor LinkedIn - https://www.linkedin.com/in/wiktordoktor/ Moja strona internetowa - https://wiktordoktor.pl/ Możesz też do mnie napisać. Mój adres email to - kontakt(@)wiktordoktor.pl   ****************************  Patronami Podcastu “BSS bez tajemnic” są: Marzena Sawicka https://www.linkedin.com/in/marzena-sawicka-hillway-training/Przemysław Sławiński https://www.linkedin.com/in/przemys%C5%82aw-s%C5%82awi%C5%84ski-155a4426/ Damian Ruciński - https://www.linkedin.com/in/damian-rucinski/ Szymon Kryczka https://www.linkedin.com/in/szymonkryczka/Grzegorz Ludwin https://www.linkedin.com/in/gludwin/ Adam Furmańczuk https://www.linkedin.com/in/adam-agilino/ Igor Tkach - https://www.linkedin.com/in/igortkach/ Damian Wróblewski - https://www.linkedin.com/in/damianwroblewski/ Paweł Łopatka - https://www.linkedin.com/in/pawellopatka/ Wiktor Doktór Jr. - https://www.linkedin.com/in/wiktor-dokt%C3%B3r-jr-916297188/Agata Stolarz - https://www.linkedin.com/in/agata-stolarz/Hubert Antczak - https://www.linkedin.com/in/hubert-antczak/  Wspaniali ludzie, dzięki którym pojawiają się kolejne odcinki tego podcastu. Ty też możesz wesprzeć rozwój podcastu na: Patronite - https://patronite.pl/wiktordoktor Patreon - https://www.patreon.com/wiktordoktor Buy me a coffee - https://www.buymeacoffee.com/wiktordoktor Buycoffee.to - https://buycoffee.to/wiktordoktor Become a supporter of this podcast: https://www.spreaker.com/podcast/bss-bez-tajemnic--4069078/support.

In questa puntata parlo di come usare AI, Figma e MCP per trasformare un mockup in una vera interfaccia funzionante. Partiamo dal design, passiamo alla generazione del codice con Copilot o Claude Code e arriviamo fino alla verifica nel browser con Playwright e Chrome DevTools. L'obiettivo non è solo generare una UI, ma creare un vero ciclo di implementazione, controllo e correzione.#dotnet #blazor #githubcopilot #claudecode #figma #mcp #playwright #chromedevtools #ai #artificialintelligence #codingagent #frontend #webdevelopment #aspnetcore #developer #podcast #dotnetinpillole

Colter Nuanez provides analysis of Idaho's upcoming season before Nuanez and Samuel Akem sit down with UI second-year head coach Thomas Ford Jr. (11:20), senior quarterback Joshua Wood (23:18), and senior linebacker Cruz Hepburn (33:30 about the upcoming campaign. EDITOR'S NOTE: We are aware of the microphone issues, but did not realize we had reverb until after the interviews were finished. We have remedied the issue. 

Louie Mantia returns to the show to talk about the state of UI and icon design on Apple's platforms, and some speculation on Apple's trade secret lawsuit against OpenAI.

Kelly and JK share updates and discuss running and gym trends. Kelly sets a summer/fall race schedule with two road mile races and three 5Ks. They discuss Garmin's acquisition of TrainingPeaks/TrainHeroic, including UI preferences, data/privacy considerations, and potential future integration or AI-driven analytics across platforms. Kelly recaps Josh Kerr's London Diamond League performance breaking the men's mile world record. JK observes more brick-and-mortar gyms closing post-COVID due to costs, competition from studios and low-cost chains, and shifting membership behavior.00:00 Welcome and Episode Intro01:38 Kelly's Race Schedule07:42 Garmin Acquires TrainingPeaks18:47 JK's Training Update24:04 Brick Phone Check In29:26 Josh Kerr's World Record Attempt42:13 Local Gyms ClosingWatch the 1 mile race: https://youtu.be/eYi2f4ONEDg?is=QNKDjsgTlrr-M1K0Follow the podcast at ⁠⁠⁠@liftingrunninglivingpod⁠⁠⁠Email us at ⁠⁠⁠liftingrunninglivingpod@gmail.com⁠⁠⁠Follow JK at ⁠⁠@coachjkmcleod⁠⁠Follow Kelly at ⁠⁠⁠@runningklutz ⁠

Is Clean Core keeping you from tailoring SAP to your actual business needs? The move to #S4HANA Cloud (Public Edition) requires a fundamental mind shift. While the goal is to stick to the standard, real-world business processes often demand custom extensions, simpler screens, and smart automation.In the latest episode of #HANACafeNL, we sit down with Stefanie Schulkes and Sanne van Bruijnsvoort from Scheer Netherlands to explore practical extensibility strategies.Here's what we cover in this episode:Simplifying Complex S/4HANA Screens: How Scheer built #SAPBuild apps to reduce unnecessary UI friction for project managers and purchasing teams.From Low-Code to AI Agents & Joule: Moving beyond simplified UIs into conversational UI, Joule Skills, and visual agents for automated task handling.Creative #SAPBTP Workarounds: Real-world examples combining SAP Build Process Automation, CDS Views, and MS Graph APIs when standard features fall short.Enterprise Governance & Guardrails: Why AI agents in enterprise setups need proper identity management, process monitoring, and strict guardrails.Your HANA Cafe NL hosts: Jan Penninkhof and Twan van den BroekS11E11

At Macstock 2026, Max Mellman showed off his his iPhone app, II Love a Piano. Max explains how the app stands out through realistic haptic feedback, intuitive navigation, portrait and landscape support, and a design that encourages exploration instead of intimidation. Chuck and Max also discuss his inspiration, teaching career, future iPad support, and broader music education projects.  This edition of MacVoices is brought to you by the MacVoices Dispatch, our weekly newsletter that keeps you up-to-date on any and all MacVoices-related information. Subscribe today and don't miss a thing. Show Notes: Chapters: 00:00 Introduction and Macstock 2026 setting 01:00 Meeting Max Melman and his Macstock experience 03:05 The story behind Beloved Melody 04:25 Introducing I Love a Piano 06:15 What makes the app different from other piano apps 09:10 Experiencing the haptic keyboard firsthand 11:40 Portrait, landscape, and intuitive navigation 14:05 iPad support and future development 16:05 Pricing, availability, and App Store release 17:35 Teaching music and inspiring students through technology 20:10 Music education projects and closing thoughts Links: iLoveAPiano.app http://iLoveAPiano.app Scribe.band http://scribe.band Guests: Max Mellman's beloved melody (of my parents Belove and Mellman) is his software design studio. He is a veteran elementary music teacher, musician, arranger, and UI designer from New Jersey. I'm also the author of The Bagel Song and the creator of the app I Love a Piano. Support: Become a MacVoices Patron on Patreon      http://patreon.com/macvoices      Enjoy this episode? Make a one-time donation with PayPal Connect: Web:      http://macvoices.com Twitter: http://www.twitter.com/chuckjoiner      http://www.twitter.com/macvoices Mastodon:      https://mastodon.cloud/@chuckjoiner Facebook:      http://www.facebook.com/chuck.joiner MacVoices Page on Facebook:      http://www.facebook.com/macvoices/ MacVoices Group on Facebook:      http://www.facebook.com/groups/macvoice LinkedIn:      https://www.linkedin.com/in/chuckjoiner/ Instagram:      https://www.instagram.com/chuckjoiner/ Subscribe:      Audio in iTunes      Video in iTunes      Subscribe manually via iTunes or any podcatcher: Audio: http://www.macvoices.com/rss/macvoicesrss      Video: http://www.macvoices.com/rss/macvoicesvideorss

At Macstock 2026, Max Mellman showed off his his iPhone app, I Love a Piano. Max explains how the app stands out through realistic haptic feedback, intuitive navigation, portrait and landscape support, and a design that encourages exploration instead of intimidation. Chuck and Max also discuss his inspiration, teaching career, future iPad support, and broader music education projects.  This edition of MacVoices is brought to you by the MacVoices Dispatch, our weekly newsletter that keeps you up-to-date on any and all MacVoices-related information. Subscribe today and don't miss a thing. Show Notes: Chapters: 00:00 Introduction and Macstock 2026 setting 01:00 Meeting Max Melman and his Macstock experience 03:05 The story behind Beloved Melody 04:25 Introducing I Love a Piano 06:15 What makes the app different from other piano apps 09:10 Experiencing the haptic keyboard firsthand 11:40 Portrait, landscape, and intuitive navigation 14:05 iPad support and future development 16:05 Pricing, availability, and App Store release 17:35 Teaching music and inspiring students through technology 20:10 Music education projects and closing thoughts Links: iLoveAPiano.app http://iLoveAPiano.app Scribe.band http://scribe.band Guests: Max Mellman's beloved melody (of my parents Belove and Mellman) is his software design studio. He is a veteran elementary music teacher, musician, arranger, and UI designer from New Jersey. I'm also the author of The Bagel Song and the creator of the app I Love a Piano. Support:      Become a MacVoices Patron on Patreon      http://patreon.com/macvoices      Enjoy this episode? Make a one-time donation with PayPal Connect:      Web:      http://macvoices.com      Twitter:      http://www.twitter.com/chuckjoiner      http://www.twitter.com/macvoices      Mastodon:      https://mastodon.cloud/@chuckjoiner      Facebook:      http://www.facebook.com/chuck.joiner      MacVoices Page on Facebook:      http://www.facebook.com/macvoices/      MacVoices Group on Facebook:      http://www.facebook.com/groups/macvoice      LinkedIn:      https://www.linkedin.com/in/chuckjoiner/      Instagram:      https://www.instagram.com/chuckjoiner/ Subscribe:      Audio in iTunes      Video in iTunes      Subscribe manually via iTunes or any podcatcher:      Audio: http://www.macvoices.com/rss/macvoicesrss      Video: http://www.macvoices.com/rss/macvoicesvideorss

0000019f-b325-db2d-adff-f3ed06260000https://www.wvik.org/podcast/good-morning-from-wvik-news/2026-07-30/iowa-board-of-regents-expands-oversight-of-uis-center-for-intellectual-freedom-with-new-committeeJoseph Leahy Iowa Board of Regents expands oversight of UI's Center for Intellectual

Bentornati e bentornate su Azure Italia Podcast, il podcast in italiano su Microsoft Azure!Per non perderti nessun nuovo episodio clicca sul tasto FOLLOW del tuo player

Sebastian und Michelle sprechen in dieser Folge über Cluster-Architektur mit Kubernetes: was das Tool leistet, welche Betriebsvarianten es gibt und für wen sich der Einsatz überhaupt rechnet. Ausgangspunkt sind die typischen Gründe für k8s – Skalierung, Ausfallsicherheit durch Health Checks und Rolling Updates sowie Infrastructure as Code. Danach geht es um die Frage self-hosted (k8s, k3s) oder managed (AWS, GCP, Azure) und um den Unterschied zwischen plain Kubernetes und Red Hat OpenShift. Ein zweiter Schwerpunkt liegt auf dem Zuschnitt der Umgebung: wie viele Cluster sinnvoll sind (pro Stage, pro Produkt) und wie innerhalb eines Clusters mit Namespaces und Network Policies getrennt wird. Zum Schluss diskutieren die beiden Alternativen ohne Kubernetes und die Voraussetzungen, die im Team erfüllt sein müssen.   **Zusammenfassung** Kubernetes verwaltet containerisierte Anwendungen und arbeitet mit Docker zusammen – die beiden Tools sind keine Konkurrenz Hauptargumente für k8s: Skalierung und Ressourcennutzung, Verfügbarkeit über Health Checks (Liveness, Readiness), Rolling Updates und Rollbacks, Konfiguration als Code in YAML Self-hosted (k8s, k3s) bedeutet eigene Versions-Updates und Skills in Systemadministration und Provisionierung; Managed Cluster kosten mehr, reduzieren aber Maintenance und erhöhen die Verfügbarkeitsgarantie OpenShift bringt eigene CLI, UI, Monitoring und Enterprise Support mit (Open-Source-Variante: OKD), plain k8s läuft dafür auf schlankerer Hardware Anzahl der Cluster: mindestens eine Trennung von DEV und PRD, größere Organisationen provisionieren pro Produkt x Stage – jedes zusätzliche Cluster bedeutet mehr Aufwand für Updates, Monitoring und Provisionierung Trennung nach Produkt statt nach Team, weil Zuständigkeiten sich ändern; Namespaces sind die leichtgewichtige Alternative zur vollen Isolation Network Policies funktionieren wie Firewall-Regeln für Pods (Ziel/Quelle, ingress/egress); standardmäßig ist alles erlaubt, sobald ein Pod eine Policy hat, gilt für ihn Deny-All – ein Deny-All pro Namespace ist deshalb Pflicht Alternative für kleine Setups: ein oder zwei VMs mit mehreren Instanzen hinter einem Loadbalancer, Rolling Updates per Skript; entscheidend sind Produkt, Verfügbarkeitsanspruch und vorhandenes Know-how   **Links** Episode #14: Kubernetes https://inwt.podbean.com/e/14-kubernetes/ Training Course by The Linux Foundation: Introduction to Kubernetes (LFS158) https://training.linuxfoundation.org/training/introduction-to-kubernetes/  Kubernetes: https://kubernetes.io/ k3s: https://k3s.io/

Why Making Complex Revenue Simple at Scale Requires More Than Throwing Contracts Into a Chat InterfaceGuest: Deepak Bapat, Co-Founder and CTO at TabsHost: Seth Earley, CEO at Earley Information SciencePublished on: July 30, 2026In this episode, Seth Earley speaks with Deepak Bapat, Co-Founder and CTO at Tabs, a revenue and accounts receivable management platform built for B2B companies. They explore why dropping contracts into a general-purpose AI tool is not a strategy for enterprise scale, what generative AI unlocked that OCR and legacy machine learning could never solve, why context engineering beat fine-tuning for contract extraction, and why newer and larger models are not always better for specialized tasks. Deepak shares candid and specific insights on building atomic AI pipelines, the provability requirement that financial compliance demands, and what finance and data leaders consistently underestimate before deploying AI on their contracts.Key Takeaways:Dropping contracts into a chat interface is a reasonable experiment but not an enterprise strategy - doing things at scale requires specific tooling, specific expertise, and integration across systems.The SaaSpocalypse framing misses the point - the more interesting question is not whether chat replaces UI, but how platforms can understand intent and preempt the actions users would otherwise have to click through manually.Generative AI solved the contract problem by reasoning over ambiguous natural language at document level - something OCR and rules-based systems fundamentally could not do.Context engineering beat fine-tuning at Tabs because merchant preferences vary so significantly that fine-tuning per merchant became cost-prohibitive - a well-prompted generalized model proved faster and more elastic.Newer and larger models are not always better for specialized tasks - Deepak's eval sets show that models from six months ago outperform newer versions on certain contract extraction jobs, likely due to overfitting on coding.Provability is the non-negotiable requirement in financial AI - it is not enough to produce correct output, you must be able to prove the output is correct and traceable back to the source contract.Organizations that want to deploy AI on their contracts first need to standardize internally on what outcomes they actually want - two people on the same team asking the same question about the same contract should not produce two different answers.Insightful Quotes:"The misconception is that difficult problems can just be solved by throwing something into ChatGPT and having the answer come out the other side. In our case, the at-scale piece is everything. Those intelligence tools are still individualized tools - to do things at scale for an entire enterprise still takes specific tooling, specific thought, and specific expertise." - Deepak Bapat"What we're trying to do is move from a place of unstructured data to provable and correct structured data. That is what Tabs is built around - and that is what most of these other systems simply cannot handle." - Deepak Bapat"When you think about the legacy players that were more rigid SaaS tools with manual entry and brittle connectors - what was intractable about that model is exactly what generative AI made solvable. The ability to reason over the words in a document, understand what they meant, and understand what the output should be - that changed everything." - Seth EarleyTune in to discover what it actually takes to build AI that is accurate enough, auditable enough, and elastic enough to handle enterprise revenue data at scale - and what most organizations underestimate before they start.LinksLinkedIn: https://www.linkedin.com/in/deepakbapat/Website: https://www.tabs.incThanks to our sponsors:VKTREarley Information ScienceAI Powered Enterprise Book

Discover the origin story of Jetpack Compose and how it changed the way Android developers build applications. We discuss the original challenges of unbundling the UI toolkit from the operating system to deliver faster updates. By adopting Kotlin and a declarative approach, the team reduced the boilerplate necessary to build user interfaces. While earlier systems relied heavily on XML, Jetpack Compose shifts to a code-first model for easier iteration. Listen to the team share their insights on modifiers, performance improvements, and the future of Android design.   Resources: Android is Compose-first → https://goo.gle/compost-first Jetpack Compose overview → https://goo.gle/compose-overview   Subscribe to our YouTube channel → https://goo.gle/AndroidDevs Speakers: Clara Bayarri, Tor Norbye, Chet Haase, Romain Guy

A bi-weekly news show informing you on the latest in Bitcoin, privacy and open source tech hosted by Ungovernables, Max and Q. AOBFreedom.Tech launch reminderKeyOS v1.3 now publicly availableNEWSIndia orders GitHub to take down BitChat's source code; Internet Freedom Foundation calls it unconstitutional - TFTC: India BitChat GitHub takedown, I4C, IFF / CoinDeskFourth Circuit says border agents can hand-search your phone with zero suspicion, as a man is prosecuted for a duress-wipe - EFF: Fourth Circuit says border agents can search your phone by hand, no suspicion required / TechCrunch: US accuses American of wiping his phone with a duress password at the borderSenate Democrats kill the CLARITY Act before recess; the developer safe harbor (Section 604) stalls with it - TFTC: CLARITY Act rejected, Bitcoin ownership surpasses goldState Department launches a "Freedom Tech" program with BPI, Palantir, and Anduril as founding partners - Bitcoin Magazine: State Department tech program with BitcoinBlock open-sources Buzz: a Nostr-native, keypair-identity workspace for humans and AI agents - LINKBIP-110 approaches its mandatory signaling window with support under 1%, and enforcing nodes staring at a minority fork - TFTC: BIP-110 enters mandatory signaling window below 1% hashrateRELEASESBitcoin core / protocolbtcd v0.26.2 - 2026-07-25Security-hardening for the Go full node: stricter PSBT/input parsing, Schnorr and WIF validation, rejection of malformed bech32, tighter inbound admission.Hardware / signingKeystone 3 v3.0.0 - 2026-07-21Major firmware across all variants of the airgapped open-source signer: reworked passcode/recovery flow, stronger validation, upgraded security policies. Reproducible with published checksums.Trezor Suite v26.7.2 - 2026-07-22Firmware security updates plus a lower 0.2 sat/vB minimum fee and cancel-pending-transaction support.Nunchuk 2.7.1 - 2026-07-16Collaborative-custody multisig wallet. 2.7.0 (07-15) added self-custodial USDT on Liquid and Trezor Bluetooth support; 2.7.1 is bug fixes on top. On-lens for multisig self-custody.Bitkey App 2026.11.0 - 2026-07-14Block's consumer hardware wallet. Release highlights its Emergency Access (recovery/inheritance) path; full notes hosted off-repo at bitkey.world/releases.LightningCore Lightning v26.06.6 - 2026-07-22Patch release (26.06.3-5 pulled over broken PyPI publishing). Now rejects channels reusing an existing funding outpoint, closing a channel-security edge case.LNDg v1.11.0 - 2026-07-26Self-hosted LND dashboard: peer-offline reporting, auto re-index on data migration, historic failed-HTLC data via API. Update logging config on upgrade.Zeus v13.1.3 - 2026-07-21Point release / version bump on the 13.1 line for the self-custodial Lightning wallet.LNbits v1.5.6 - 2026-07-15Minor patch on 1.5.5 (payments extension-field refactor and fixes) for the self-hosted Lightning accounts system.Lightning Labs Wavelength - 2026-07-21A toolkit for adding self-custodial bitcoin (and stablecoin) payments to any application, designed to create the best developer experience for humans and agents.EcashCashu TS v5.0.0-rc.5 - 2026-07-23RC for the major v5 of the reference TS Cashu library: NUT-18 payment requests (PaymentRequestBuilder), mint-preference support, hardened P2PK validation, integer fee math. Foundational for ecash wallets.Nutshell 0.20.3 - 2026-07-22Reference mint/wallet: Pay-to-Blinded-Key (lock ecash to a receiver without revealing their pubkey to the mint), a Spark L2 backend, and a false-UNPAID melt-race fix. DB migration, back up first.Fedimint v0.12.0-beta.0 - 2026-07-23Beta pre-release of the federated ecash / community-custody protocol. Flagged unstable, no upgrade guarantee. "In the pipeline," not production. (Admin UI: Fedimint UI v0.7.4, adds arm64 image.)On-chain privacy / coinjoinWasabi Wallet v2.8.1 - 2026-07-22Now receives to Taproot addresses by default (a real "state of the network" adoption nudge, four-plus years post-activation), adds Linux AppImage, on top of 2.8.0's serverless P2P filter sync.Ashigaru Desktop v1.1.2 - 2026-07-25Whirlpool coinjoin QoL: live Tor/Electrum status, one-click connect, faster startup, self-clearing coordinator banner.JoinMarket-NG 0.34.2 - 2026-07-20Actively-maintained modern fork of JoinMarket: safe expired-fidelity-bond handling, correct frozen-UTXO reporting, multi-wallet RPC routing.Bitcoin Safe 2.1.1 - 2026-07-20Multisig/single-sig desktop wallet: UI fixes and improved Debian build reproducibility.P2P / no-KYCBisq 1.10.4 - 2026-07-24Mandatory security update for the decentralized no-KYC exchange (audit findings): signed DAO block providers, stricter blind-vote/dispute validation, re-enabled BSQ swaps. Required to keep trading.Bull Bitcoin 6.12.4 - 2026-07-24Bug-fix for the no-account self-custodial app (iOS startup-lockup fix). The feature release was 6.12.2 (UTXO/coin-control, Coldcard NFC, BitBox02 Nova BLE, sub-1 sat/vB).Vexl v1.45.1 - 2026-07-21Point release of the contacts-based no-KYC P2P trading app (small fixes).Peach Bitcoin 0.69.0 (381) - 2026-07-23Latest build of the no-KYC P2P Bitcoin marketplace (rolling 0.69.0 build increments 379/380/381 across the fortnight). Verify the build-tag slug before publishing (parentheses in the tag).Self-hosting / infraBTCPay Server v2.4.1 - 2026-07-23Self-hosted no-KYC payment processor: BIP-329 label import, editable invoice comments, refund-email triggers, RTL UI, restored Boltcard payments.Start9 StartOS v0.4.0 - 2026-07-24Major: a complete ground-up rewrite of StartOS, out of public beta after six years, billed as the "correct architecture for sovereign computing." Note: the only upgrade path is a fresh install (no in-place migration). One of the biggest self-hosting stories of the fortnight.Liquid GDK release_0.77.7 - 2026-07-20Blockstream's wallet SDK: libwally + Tor bumps, macOS/iOS cross-compile, single-sig gap-limit fee fix.Privacy stack / PayjoinPayjoin Dev Kit payjoin-cli 1.0.0-rc.1 - 2026-07-23RC for the reference Payjoin CLI, synced to payjoin 1.0.0-rc.6. Signals the v1.0 Payjoin stack nearing release (breaks common-input-ownership heuristics on-chain).NostrAmber v6.3.0 - 2026-07-20Android Nostr remote signer (keeps your nsec off client apps): grouped/collapsible multi-request approvals, a log-disabling privacy mode, built-in Tor, NIP-65 relay prefetch.Wallets (self-custody)BlueWallet 8.0.1 - 2026-07-21Major v8 line: iOS 26 UI refresh, BC-UR v2 airgap scanning (OneKey/Keystone), Unchained multisig cosigner import, 19 new languages, crypto-js replaced with @noble. Broad user base. Confirm the exact tag slug before publishing.Cake Wallet 6.3.2 - 2026-07-24Non-custodial BTC/Monero wallet: home-screen recent history, better OpenAlias/ENS/Unstoppable alias resolution, faster Zcash sync.EDUCATIONWhat Is a UTXO, and Why Does It Matter for Bitcoin Privacy? - 2026-07-25Community explainer thread on Stacker News. The useful part is the top response, which walks through how receive-and-spend patterns fingerprint you and where coinjoin actually helps. Good raw material for a plain-English UTXO segment, which pairs with the Wasabi and Ashigaru releases and gives newer listeners the vocabulary before the coinjoin talk.Bitcoin Optech Newsletter #415 - 2026-07-24Two items worth surfacing. Fabian Jahr's draft BIP459 proposes full aggregation of BIP340 schnorr signatures using DahLIAS, combining multiple signatures into a single 64-byte aggregate, with cross-input signature aggregation as a downstream possibility. And libsecp256k1 #1765 adds an optional BIP352 silent-payments module supporting receiver scanning from only the scan secret and spend pubkey, so the spend private key stays offline. Silent payments quietly becoming infrastructure is a good recurring beat.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 Ungovernable Misfits, we appreciate your continued support and hope you enjoy the shows.You can support this episode using your time, talent or treasure.TIME:- create fountain clips for the show- create a meetup- help boost the signal on social mediaTALENT:- create ungovernable misfit inspired art, animation or music- design or implement some software that can make the podcast better- use whatever talents you have to make a contribution to the show!TREASURE:- BOOST IT OR STREAM SATS on the Podcasting 2.0 apps @ https://podcastapps.com- DONATE via Monero @

This week, The Gay Mix comes in hot with the most glamorous smart-light failure imaginable before Daniel drops a proper newsroom bombshell: he quit his job. After weeks of executive chaos, mystery PowerPoint math, disappearing developers, and enough stress to qualify as cardio, he finally chose peace—and perhaps a future taking happy vacation photos at Disney. Adam, already between jobs himself, naturally suggests they simply make a living podcasting. What could possibly go wrong?From there, the newly unemployed besties get entrepreneurial as Adam unveils his all-in-one podcast production playground, complete with show planning, contact wrangling, soundboards, trivia, and a UI only a developer could love. The contact segment brings Kathy Bacon's allegedly murdered backyard, suspicious celebrity-death arithmetic, Lamont Cranston's deep-cut Miss America reference, and a bold invitation to text the show pictures best left undescribed here. There are also elaborate funeral plans, a possible AI afterlife, a spicy detour through Broadway ticket prices and living wages, and a News Game performance that proves quitting your job does not automatically improve your knowledge of rivers.Birthdays bring Lynda Carter, Jennifer Lopez, and Elisabeth Moss, while Adam closes out the evening with a 3D-printing saga involving scratched light boxes, rogue nozzles, missing connectors, and emergency Walmart LEDs. Daniel teases an equally maddening mystery for the after show, the new production tool gets one last live shakedown, and everyone somehow reaches the closing music with dignity mostly intact. We hope you enjoy this week's blend of liberation, innovation, funeral catering, and technical support with just a whisper of gay panic.Email: Contact@MixMinusPodcast.comVoice/SMS: 707-613-3284

There are roughly 100x more people who use code than who can write code. As code that “just works” becomes easier to generate, this group may be the biggest prize of all — if you can get the agentic interface right.A key trend we have been tracking over at AINews is the absolute explosion in Codex usage this year, with MAU now up >10x from Jan 2026. Less than two weeks after their July 9th launch, OpenAI said ChatGPT Work and Codex had reached 10M users combined (as we cover in the pod, Codex now powers ChatGPT Work, so all ChatGPT Work users are now users of the Codex harness, even if they aren't traditional engineers) — showing the early innings of what happens when you graduate from coding agents to knowledge work agents:We've been calling out how coding agents are “breaking containment” to do everything else this year to power every other part of knowledge work - and it started with the org chart, with a major reorg last month that amounted to two of Codex's most prominent leaders, Greg and Tibo, taking responsibility over product and ChatGPT specifically, completing a “Superapp” consolidation cycle first discussed in March.With these updates Codex is no longer just a coding tool. In June, OpenAI said knowledge workers already accounting for roughly 20% of Codex's user base and growing more than 3x as quickly as developers. A product dedicated for knowledge workers was being pulled out of the Codex team.However, knowledge work has a different set of problems and environments than coding. For decades, knowledge work has been scattered across different primitives like documents for writing, spreadsheets for analysis, slide decks for communication, and specialized applications for everything else. ChatGPT Work now enables users to work across every primitive with agents. Instead of opening an application and manually operating its features, the user can describe an outcome and collaborates with an agent that can assemble the tools, context, and artifact needed to reach it.From building no-code products at Airtable to leading Productivity Engineering at OpenAI, Akshay Nathan has spent much of his career trying to make the power of software accessible to people who do not write code. In this episode, Akshay joins swyx and Vibhu to unpack the launch of ChatGPT Work, why Codex unexpectedly took off among non-developers inside OpenAI, and the company's broader plan to bring useful agents from software engineers to knowledge workers and eventually everyone.We go deep on the shared agent harness behind Codex and ChatGPT Work, why OpenAI brought the experiences together without making them identical, and how persistent computers, artifacts, Sites, plugins, memory, and sub-agents are changing what people can delegate to AI. Akshay explains why some teams are replacing decks and spreadsheets with interactive websites, how agents can gather context across code, Slack, documents, and local files, and what OpenAI learned from personal-agent products like OpenClaw.Side note: also don't miss Abhihek's sandbox track keynote at AIE, which now powers a lot of the sandboxing for ChatGPT Work… and yes was also broken by an unreleased OpenAI model in the recent HuggingFace incident.Akshay also reflects on how AI is transforming product development itself: why more people will become generalists with a specialty, why ideas and taste become the bottlenecks when almost anyone can build, why LLMs still struggle to generate genuinely grounded new ideas, and why teams must distinguish increased motion from actual progress.We discuss:* Why Codex unexpectedly took off among non-developers inside OpenAI* Why employees felt like using Codex gave them a new superpower* The product insight that led OpenAI to build ChatGPT Work* Why Codex and ChatGPT Work share the same underlying agent harness* How their UX, Git visibility, artifacts, and sandboxing defaults differ* Why OpenAI merged its agent experiences instead of building separate products* How AI is blurring the boundaries between engineering, design, strategy, and operations* Why OpenAI wants the default model configuration to work for most users* When power users should use deeper reasoning, Ultra, or multi-agent modes* Artifacts, agentic spreadsheets, and creating high-fidelity work products* Why interactive Sites may replace decks and spreadsheets* The challenge of designing a simple interface for an agent that can build almost anything* Why users should retry tasks that models could not handle three or six months ago* How AI can gather context for performance reviews without replacing human judgment* The OpenAI automation that turns internal Slack and document activity into memes* What reaching ten million ChatGPT Work and Codex users means for the product* How OpenClaw inspired persistent environments, scheduled tasks, and personal agents* Using ChatGPT for financial planning, budgeting, workouts, meals, and household management* The design tradeoffs behind sub-agents and how much of their work users should see* ChatGPT memory, Chronicle, and long-term context* Why AI may make more people generalists with deep specialties* Why ideas and taste become more important when almost anyone can build* Why LLMs still struggle with the instruction “bring me new ideas”* Measuring productivity through quality at-bats instead of commits, tokens, or pull requests* The critical difference between AI-generated motion and meaningful progressAkshay Nathan* LinkedIn: https://www.linkedin.com/in/akshaynathan/* X: https://x.com/akshaynathan_Timestamps00:00:00 Introduction and Bringing the Power of Code to Everyone00:01:33 Joining OpenAI and Preserving a Startup Culture00:02:40 What OpenAI Learned from Enterprise AI Adoption00:05:28 Why OpenAI Built ChatGPT Work00:07:17 Codex vs. ChatGPT Work and the Shared Agent Harness00:12:07 Why OpenAI Merged Its Agent Experiences00:16:24 Models, Reasoning Levels, and Choosing the Right Default00:20:26 Artifacts, Agentic Spreadsheets, and Model–Product Collaboration00:24:22 Why Sites Could Replace Decks and Spreadsheets00:30:08 Designing an Agent That Can Build Almost Anything00:34:28 From Developer Agents to Knowledge Work—and Everyone00:36:07 Power-User Advice and AI-Assisted Performance Reviews00:40:41 OpenAI's Internal AI Memes and the Ten-Million-User Launch00:44:39 OpenClaw, Personal Agents, and ChatGPT as an Operating System00:50:24 Sub-Agents, Ultra Mode, and How Much Control Users Need00:54:39 ChatGPT Memory, Personalization, and Chronicle01:00:19 How AI Is Reshaping Product Development and Tech Roles01:03:15 Ideas, Taste, and Why LLMs Struggle to Generate New Ideas01:04:42 Measuring Productivity, Quality At-Bats, and Motion vs. ProgressTranscriptIntroduction: Akshay Nathan, ChatGPT Work, and the No-Code ArcSwyx [00:00:00]: We're here in the studio with Akshay from OpenAI. Welcome.Akshay Nathan [00:00:07]: Thank you.Swyx [00:00:08]: And with our trusty co-host, Vibhu. So you recently launched ChatGPT Work. You lead Core Product Engineering. It's been a long journey, into all this. I find it very interesting that you started with no code or low code, with Walrus and Airtable. And to some extent, ChatGPT Work is like the super app of super apps of, well, here is the ultimate no code. You just write a prompt.Akshay Nathan [00:00:32]: Yeah. It's funny how things come, full circle. I think for a long time in my career, I started my career working consumer fintech, but then after that, like, there's this hypothesis that, the things that we were able to do with code, like, as engineers, like, if we could bring that to many more people in a more, accessible way, then that would be truly magical. We were working on a startup. It's funny, like, before LLMs, before vision LLMs, on how to do automated testing with AI. It was just kinda jank, back then, but doing what we can, and then worked at Airtable for a while on the same thesis that, like, if we can bring a database or the primitives behind a database to people, that'd be really useful to them. But once LLMs came onto the scene, it became clear that, this was the missing piece, like, the missing technology required to, like, bring the magic of code to everyone without them having to know what's going on underneath the hood. And so, like, I think this launch and a lot of the stuff that we've been up to is, like, the manifestation of that.From Walrus and Airtable to OpenAIVibhu [00:01:33]: How was stuff when you joined? So you joined OpenAI 2023. Now we've got, so much more stuff, so ChatGPT, Codex app, ChatGPT Work. Have things changed?Joining OpenAI and What Hasn't ChangedAkshay Nathan [00:01:44]: I think the more interesting thing is how things haven't changed. Like, one, I joined I remember when I joined, it was, like, five hundred people. One thing I was worried about was, like, I was looking for something, more early stage and, like, was it gonna feel startup enough? And I joined, and I was like, “This feels even more startup-y than I could ever imagine.” And, like, that really hasn't changed even till now. I think the, like, level of, like, bottoms-up ambition and, like, the ability of anyone to, like, do anything or have an idea and ship it is really cool. But on the, like, mission side, I think what was really compelling to me is this mission of, bringing frontier intelligence to everyone. Like, building AGI and then bringing it to everyone. And, I think acknowledging back then that, like, that vision is gonna, not be a linear progression. Like, we're probably gonna, like, try different products and have different things that succeed and don't. But the vision has stayed the same, and the mission has stayed the same, and we're starting to see the pieces, fall together, and that's really cool.Enterprise Lessons: No One-Size-Fits-All AISwyx [00:02:40]: You worked on Enterprise. What A lot of people never touch ChatGPT Enterprise. What is something that you learned from there that you're bringing into your work now?Akshay Nathan [00:02:52]: I think how there's no one-size-fits-all solution in Enterprise. I remember in the early days of ChatGPT Enterprise, like, when we talked to customers and, like, everyone. That was, like, when I think it was a year after ChatGPT was released, and everyone was so excited to bring, AI into their enterprise. And, there were all these teams being stood up. It was, like, the AI deployment team with, like, these enormous budgets. And if you asked anyone, like, what were they excited about? Like, what were they excited about solving? Like, at first, you'd get, like, kinda like the baseline answers of, like, “Yeah, we have all this context and data and all this stuff.” But then if you ask them, like, “What was, like, a discrete use case that, like, they want AI to enable in their workplace?” You get such a different, like, variance, like, explosion of, different types of answers. And it's interesting, like, you using, like, these models and these products, you have this box, and you can say anything to it, which is the magic. But it'on the flip side, it also means that, like, you don't know what to do with it. And in Enterprise, I think a big part of that is, like, meeting the users where they are, like, what use case were they trying to solve, and then teaching them how they can use AI to, like, gain leverage there.Swyx [00:03:56]: Do you meaningfully differentiate that from forward-deployed engineering?Akshay Nathan [00:04:01]: I think there is the go-to-market side of it and then there is the product side of it. I think you need someone on the product side. And I think, like, however good we get at FDE motion, like, I think at the end of the day, if we have a user who's, like, looking at their computer or looking at their phone, like, it's our job in the product to, like, be enabling them and showing them where to go. So we're really excited about that.Vibhu [00:04:24]: Do you think there's been changes, over the past three years of adoption? So there have been, step function changes. You have reasoning models and whatnot. Is there still the same problems of Enterprise has black box, don't know what to do with it, or have things changed?Adoption, Agents, and the Next 10x MarketAkshay Nathan [00:04:39]: We're seeing now that, like, there's this huge uptake, right? Everyone is extremely excited about it. It feels like, many people are, millions, hundreds of millions of people are using ChatGPT. They understand, like, how generally to work with AI. But then, like, every time, like, a new capability gets unlocked, so now, like, we're seeing with agents, like, there is probably a contingent of, like, early adopters still who, truly get it, who are like, “ we you can do anything. You just have to make sure the right context is there, it's connected to the right tools, and that you are supervising it, but, like, anything is possible.” But then there's, like, this, like, 10x or 100x bigger market where, like, they don't yet get that, or they don't yet see that. And so I think that's the next stage here. So to answer your question, like, I think the adoption is there and growing fast, but I think the opportunity is, like, far bigger than that. That's where we wanna play, especially with ChatGPT Work.ChatGPT Work, Codex, and the Super App MergeSwyx [00:05:27]: Yeah. well, let's, let's skip ahead to ChatGPT Work. only, like, a month ago or so, announced. what was the decision process that led into it? there was this, overall merging of the super app. Is that what we're officially calling it? you deprecated the browser as well. Just, summarize your last, like, couple months of working on this thing.Akshay Nathan [00:05:50]: Yeah. It feels like forever now, but it's only been a few months. I think maybe the one, impetus that, like- Is most salient is when we release Codex, or even internally had Codex, like, it was really surprising to us, I think we recently put out some stats on this, that there was this, like, real inflection of, like, adoption among non-developers at OpenAI. And, I, through this product development process, like, would go to, like, these UXR sessions to talk to people internally. And the thing that stuck out to me is, like, one, like, you go talk to, like, strategic finance or marketing or whatever, and they're all using Codex for, their use cases. That part's cool, but the thing that really stuck out to me is how proud people were that they were using Codex. Like, how, likeSwyx [00:06:34]: It's like, “I'm not supposed to be using it, but I am.”Akshay Nathan [00:06:36]: It was that. It was, like, that they were, early to this, like, new thing, but it was also this thing of, like, they felt like they had a superpower, right? And, what we recognized then is that, like, the power of Codex, the power of agents, like, we already had this massive distribution base of people who have, come to know and love ChatGPT. Like, how do we show that to them? Like, how do we bring it to them? Which is, like, a hard product problem, and it's, like, a tricky thing, right? There's many ways you can go about it. And so that's what we called the Merge and the Super App over time, and ultimately launched it in ChatGPT Work, is how do we do that? But it came from that initial realization that, like, the power was not only for developers, like, much earlier than probably even we thought. Like, it could be extended to everyone.Swyx [00:07:17]: How do you see the products differently? So, like, who is it for, right? So Codex started out even CLI, then app. Now there's a merge of ChatGPT Codex and ChatGPT Work, so is it the opening for the average user, for enterprise, for work? How do you position it?Akshay Nathan [00:07:36]: I think we want to get it to position it for if you're doing work-related things, for lack of a better word, right?Who ChatGPT Work Is ForAkshay Nathan [00:07:42]: I think productivity is what, like, the pillar that I support. Like, that's the name of the team. And the reason for that, the reason we call it productivity and not, like, enterprise or, like, work or something like that, is because there's also personal productivity, right? And, like, I think ChatGPT Work is I've seen people do things in their personal lives that you wouldn't classify as, like, work technically, but, like, these agents are, super capable for. Like, one recent example that someone posted about, on our Slack is, like, someone had, like, a missed package, like they didn't receive it, and then they got, like, the picture of it, from Amazon or whoever the courier was, and they, like, asked ChatGPT Work to, like, find out where that package is. And, like, the agent, is extremely tenacious and, like, took the image and, like, looked at a bunch of, like, listings around their neighborhood and figured out exactly the apartment complex in which the package was, like, gave them some information. And so, like, I think there's all these things that, like, you, work-related or productivity-related things, I think that's what we want the product to be. You asked about Codex. I think we think Codex is, a durable brand, but we have a principle that, like, the user we don't want a user to get stuck in a tab or an experience where they don't get the power of the product. And so, like, everything that you can do, in the Codex portion of the product on desktop, you can do in ChatGPT Work and vice versa. But we made some opinionated product decisions on, like, how much of the Git state, if you're in a Git repo, do we wanna expose to the end user? Or how much do we wanna make the experience of seeing the agents thinking, like, diff forward so that you get exposed to the diffs out of the box. And then, like, on the safety side, like, how do we wanna think about, like, sandboxing and making sure that we have the right defaults in one state versus the other? So, there's, like, some opinions that go behind that, but we do want We don't want the user to need to choose which experience they're in.Swyx [00:09:26]: That is a good goal for AGI, right? Like, people don't want, like, to hide to choose what version of AGI they want. They just want the AGI to decide for them. can I get an answer or, like It's not super clear to me. Is the Codex harness and the ChatGPT Work harness the same? Is it just UI affordances, or are there prompt level or even deeper differences?Shared Harness, Different UX: Codex vs. WorkAkshay Nathan [00:09:49]: So the harness is the same. The harness is shared. on In both of the products, we made improvements to the harness to make it good for knowledge work, especially as it relates to plug-ins or computer use or artifacts. You get that power regardless of which experience you're in. On the UX side, there's opinionated takes that we have when you're in Codex mode, what the UX should be how the UX should behave, and some stuff around the sandbox like I mentioned, but the underlying harness and capabilities should be the same.Swyx [00:10:16]: I'm just kinda curious. Maybe we can, -- Is there a query that we can run that would look different in the two modes?Akshay Nathan [00:10:23]: Yeah. I tried to create, like ask it to create, like, a retirement calculator spreadsheet or something, in both modes. And then in Codex mode, you might have to be in a repo for this, but you'll see, like, the diffs of, like, the sheet that it's creating and stuff like that, and the file edits. But in Work you won't be able to see that.Swyx [00:10:42]: I think that's, that's super clear. And then also the other thing I wanted to dive into was your, the productivity team. what else is there? first of all, what are the top-level teams other than productivity? Isn't productivity everything?Productivity Teams and Core ChatAkshay Nathan [00:10:55]: SoSwyx [00:10:55]: Science?Akshay Nathan [00:10:55]: We have a team focused on ChatGPT. Like, the core chat experience, for consumer, which is like, not, I think all productivity. Like, there'People are using ChatGPT every day for search to, figure out how to write messages to loved ones, to think about, how to, like, learn a new topic, et cetera. And so there's so much more inside to create images. And there's so much more in chat that, the hundreds of millions of users are using that warrants, like, a very dedicated effort. And there's teams focused on enterprise and infrastructure and API and stuff like that, so.Swyx [00:11:33]: I will bring it up.Retirement Calculator Demo and Git-First UXSwyx [00:11:34]: Yeah. So I have them both running. This is ChatGPT Work. There's a Codex version here. I picked “Five Little Ducks” song, so this will take a while.Akshay Nathan [00:11:43]: Huh.Swyx [00:11:43]: I think we'll just keep it in the background and, as they finish, we'll look into some of the differences.Akshay Nathan [00:11:48]: Yeah. But immediately, I think if you flip back to the Codex version you'll see that,Swyx [00:11:53]: That it assumesAkshay Nathan [00:11:54]: Like theSwyx [00:11:54]: It assumes Git. Yeah. Yeah.Akshay Nathan [00:11:56]: The, like, dynamic island assumes that you're in a Git repo. And you might miss some stuff because some of it is, like, in the actual chain of thought with those changes and how we display that, but yeah.Swyx [00:12:07]: Is there an unintuitive like, is there a thing that you wanted to ship and then you got feedback, and you were like, “No, let's not do it?” Like, what's the thinking behind that?Why Merge the ExperiencesAkshay Nathan [00:12:14]: In, ChatGPT Work?Akshay Nathan [00:12:17]: I think one direction we could have gone with this is, like, keeping the experiences, like, completely separate. So it's like, whySwyx [00:12:22]: Different apps.Akshay Nathan [00:12:23]: Exactly, like different apps or even in the same app, like different, completely different experiences. Like, why merge it all? Like, what is. Codex, people love. Like, why bring these products together? And I think the intuition here is that, like, all of our jobs are, like, changing dramatically with AI. Like, for, like, every few months, like, I feel like I wake up, and I'm, like, doing a completely different thing than I was doing a few months ago. And my hypothesis here is that, or I should say our hypothesis is that, like, part of what we're, we're building, this technology is giving people leverage. Like, the things, maybe it's the more mundane parts of your job or parts that, like, if you were able to automate, you'd be able to share more ideas faster or whatever, like, you're able to do now. And because of that, like, that might blur the lines between someone who's, like, only writing code or creating strategy docs or, planning events or, helping with marketing or doing podcasts or whatever, right? And so, like, these things are gonna get blurred over time. And so, like, trying to draw a hard boundary based on, like, the who you are is gonna be, is gonna be tough. And, like, we should enable users to choose, but we shouldn't box them in. And so a lot of the work that went in here, like, keeping the primitives the same, like for example, plugins are, like, unified across, this product and ChatGPT and the cloud, was because of that. It's this thesis that, like, eventually things are gonna come together and we don't wanna be Like, we wanna be prescriptive about when to be in either experience, but we don't want to box anyone in.Swyx [00:13:45]: I wonder if there's users who are very tuned to the old ChatGPT harness that is effectively now replaced by the Codex harness. I can't imagine what that was, but maybe they're more the more conversational side. Can you compare and contrast the two harnesses? ‘Cause only you've seen it.Akshay Nathan [00:14:02]: Yeah. I think ChatGPT, the existing harness, like, still exists today. Like, it exists in this app,Harness Engineering: ChatGPT vs. CodexSwyx [00:14:08]: The classic, right?Akshay Nathan [00:14:09]: TheVibhu [00:14:09]: You just start a new chat, and you don't go under Work, right?Akshay Nathan [00:14:13]: Yeah. If you startVibhu [00:14:13]: SoAkshay Nathan [00:14:14]: A new chat and go to chat, then you're, you're talking to ChatGPT with the instant model.Vibhu [00:14:16]: Oh, we can technically do another. But on instant.Swyx [00:14:21]: Yeah. So this one's not gonna code or it's gonna be in line. It's on a in line in a sandbox.Akshay Nathan [00:14:26]: It'llVibhu [00:14:27]: Oh, that's coolAkshay Nathan [00:14:27]: We try to push you to go to Work if you're creating a spreadsheet. Yeah, but this isSwyx [00:14:30]: And this is a router decision? Sorry. Is it a router decision?Akshay Nathan [00:14:34]: This is the decision that, the model is making, and then, like it sees that you're able to. or you're trying to do something that would be better served in Work mode. But I think your question was like, what are the advantages of, like, the chat, like ChatGPT chat harness?Swyx [00:14:48]: It's more broadly, like, I wanna, do an oral history of harness engineering. Right? the ChatGPT harness lasted us from, let's call it the ‘01 era, until now, and now it's being replaced by the Codex harness effectively. And they're, they're overlapping somewhat, but I'm curious what changed if there is.Akshay Nathan [00:15:10]: My perspective on this is, like, there's, there's, there's there's like a constant process of, like, divergence, convergence, divergence, convergence. And in chat, like, many of the use cases I was talking about before, like, search or learning, I think we're, we're really optimizing for latency and optimizing for personality and, like, different things that, over time, like the product The reason people love ChatGPT is because we've been optimizing for those things and working on them for so long. Codex, what we learned was that, like, if you give the agent access to this infinitely flexible environment as a computer, it can do really powerful things. And so when we think about, like, okay, well, for knowledge work, like, what is which mode should we choose? It was like it felt more natural to us to bring that to this, like, computer environment and, maybe abstract some of the details of this computer away from users who might not be used to that, but, like, give them that same power. But ultimately, I think that we want the power in all places, right? We wanna meet people where they are. So I'm sure there'll be work down the road in order to get things to be, equivalently capable in all scenarios. But it's just a question of, like, what we've been focusing on the product on historically and what we're focusing on now.Models, Defaults, and the Reasoning SliderVibhu [00:16:24]: I think alongside that, outside of just harness and when to use Codex, ChatGPT, or Work, there's also the new models you've released, right? any guidance there? So people love to min-max what to use, like only use Terra on high reasoning versus, for this, you wanna use Sol here, ignore all theseAkshay Nathan [00:16:44]: There's 32 options.Vibhu [00:16:46]: But, that being said, for people that are expanding, so, productivity trying stuff for work that don't have the breakdown of what all this is what's, what's the advice, right?Akshay Nathan [00:16:59]: Well, I think before the advice, like the first thing is, like, none of this would be possible without these models. Like, the, I think you asked earlier, like, what was, like, the inspiration for work and, like, early on, like I mentioned, like, what we were seeing with Codex, but that was also because the models were getting infinitely more capable. That's happening again. I think it's like another step function jump now. And to answer the question on advice, like we want this default to be the best possible. Like, we wanna be opinionated about the default, and so we've we've chosen a default that we think is gonna be the best for everyone. And, we have for power users options under the hood. We could One could argue that there might be too many right now, and we're, working on simplifying it. But you can extend, the reasoning level, and you can change between the different model classes if you need to, but the default should be the best for most use cases. So my advice to most people would be to stick to that. And then, if you reach a situation in which you think that you could, you wanna try, a different configuration, if you're not seeing either the efficiency on the cost side or the quality on the intelligence side, then you can change the defaults and see if you can get something better. But we think that the default should be good enough.Swyx [00:18:09]: I have, I'm just gonna run something by you since you have way more experience than me. I've recently been doing Sol Lite but with goal, with the idea that the goal augments the reasoning effort, but with more terminations and turns.Swyx [00:18:24]: Is that a good way to think about it as opposed to Sol Ultra or Sol, Extra High?Akshay Nathan [00:18:29]: Yeah. It's hard to say becauseSwyx [00:18:31]: Yeah. It's like an interaction effect.Akshay Nathan [00:18:33]: exactly. It's like there's a preference on, for you as an individual, like how do you like to collaborate with the models? Like how many of those like terminations, as you call them, do you want where, you can steer or make sure that it's doing the right thing?Akshay Nathan [00:18:46]: I think generally people should try whatever works for them. I think that like using Ultra or the like multi-agent setups are best for like when you have like tasks that are either incredibly complicated, like open explorations or very paralyzable. I think even for tasks using goal, I think is best for tasks that you'll be able to make consistent progress in a way that's verifiable over time. But I think for most tasks, they don't fall into either of those buckets. And so like at least when they're starting, and so that's why I think the best first step is like trying it with the default configuration and then seeing like where you wanna go from there.Swyx [00:19:29]: Right. You guys worked on a slider, which is super helpful for reducing the amount of panic.Vibhu [00:19:36]: It's nice on mobile at least. There's a nice slider there.Swyx [00:19:38]: It's nicer.Vibhu [00:19:39]: I haven't tried it.Swyx [00:19:40]: So you have the advanced view there, but if you click advanced view. Yeah.Vibhu [00:19:44]: Ooh, it's just a nice slider. Yeah.Swyx [00:19:46]: Very pretty, very colorful.Akshay Nathan [00:19:48]: Yeah. The idea was here was like reduce it to like one dimension even though there's multiple dimensions, right? Try to project it onto a single dimension for the user. Like, something from that represents like, speed and efficiency on one side and then like quality and thoroughness on the other side.Artifacts, Spreadsheets, and the Work LaunchSwyx [00:20:04]: I am just puzzled that it uses Sol so much, like the lowerVibhu [00:20:07]: NoSwyx [00:20:07]: Grounds I would've usedVibhu [00:20:08]: I think the slider, if I'm not mistaken, isSwyx [00:20:09]: Terra.Vibhu [00:20:10]: Oh, it is.Swyx [00:20:11]: Yeah. See? So they preset Terra to only be the light one. But like I think a lot of people would more people should use Terra. One, because Sol keeps running out of capacity.Vibhu [00:20:22]: I'm the reason. Here's ten minutes of ourSwyx [00:20:24]: There you goVibhu [00:20:25]: Retirement calculator.Swyx [00:20:26]: Oh, that's the Excel thing working for you.Vibhu [00:20:28]: This is,Swyx [00:20:28]: Oh my God. Look at thatVibhu [00:20:28]: This is work, and then Codex is still cooking, so we'll get back into it. I think it'll be interesting to see the thought process, the reasoning, and also, this is eight minutes on work. Codex is still cooking.Swyx [00:20:41]: Yeah. And by the way, so I've, do Gabriel Chua? He's part of the OpenAI Singapore team. He showed me this, and I was like pretty shocked that this looks like Excel. It edits Excel files. You never paid an Excel license, right? Like, but somehow this is like workable and it's agentic Excel.Akshay Nathan [00:21:01]: Yeah. one of the big like pushes that we made for this launch was like artifacts, right?Akshay Nathan [00:21:05]: Like both on the model side, like I think if you compare this with GPT-5.5 and GPT-5.4 before that, you'll see that there's been pretty dramatic improvements in the quality of these artifacts and then also on the product side.Vibhu [00:21:16]: The UX side is also crazy, like hosted sites and whatnot. No longer needing to host your own little webpage, like itSwyx [00:21:23]: Oh, I have a story about that. I can do, a separate thing. I'll need to take the visuals here, but we-we'll, we'll cut to that later. Was there co-training, because you were moving making this big move and you launched GPT-5.6 on the same day as ChatGPT Work? Was there influence between the model training teams and the harness teams, or did they did the launch dates just happen to line up the same day?Akshay Nathan [00:21:46]: I think the we collaborate heavily with the research teams, and I think that's like one of the most magical parts of the job, like the most fun parts of the job. But yeah, just using artifacts as an example. Like, a lot of what you're seeing, like underneath the hood, there's a lot of work that went into making sure that like, we had the right infra to be able to train the models to get better at this. And then on the product side, like had the right experience for users to be able to collaborate with the model on an artifact like this. In fact, like this whole viewer, like the intuition here is that like, it's not necessarily that you wouldn't need an Excel license. This is stage one, right? Like, this is probably not what you meant when you're like making a retirement calculator.Vibhu [00:22:24]: Yeah, you can iterate very easily. Yeah.Akshay Nathan [00:22:24]: You wanna iterate and like when you're seeing it, and if this thing is high fidelity to like what you would see in or what your coworkers would see if you were to send this to Sean, like that I think makes it so easier and makes you trust the product in terms of iteration.Vibhu [00:22:39]: When you say coworkers would see, do you see a multiplayer, multi-team collaboration with artifacts? Any things you guys think about that?Multiplayer Artifacts and CollaborationSwyx [00:22:46]: You can already share it, right?Akshay Nathan [00:22:48]: Yeah. It's inter It's something that, we're actively thinking about. one thing that, we've noticed internally without talking too much about the roadmap is that like there's many times when someone will ping me about something, and I will ask ChatGPT Work the question, and then I'll ping them back the answer.Akshay Nathan [00:23:04]: And then I'll be thinking likeVibhu [00:23:04]: Like the simplest would be, the three of us are just all on one hosted.Akshay Nathan [00:23:07]: Exactly. And I'll think about like was I required in this loop or and then maybe it was, rephrase like what they were asking or pulled from certain context or whatever. But like, when I gave them back the answer, that process was also lossy, right? Like I gave them just like my interpretation of what ChatGPT Work cooked up. But like underneath the hood, there's so much context like in the rollout and stuff that could be interesting.Vibhu [00:23:28]: Yeah, it'sSwyx [00:23:28]: So like the answer was preemptively respond to every inbound request?Akshay Nathan [00:23:33]: No, it was just like literally like this is what I do sometimes as my job.Swyx [00:23:36]: I know you copy-paste and then you're just a message forwarding serviceAkshay Nathan [00:23:39]: Yeah. Yeah, exactlySwyx [00:23:39]: From AI to AI.Vibhu [00:23:40]: But I think it's interesting, right? It helps people understand the capability of what you can ask and delegate that oftentimes people don't realize until they try or someone shows you, and then you're like, “Oh, okay. Okay, I see.”Swyx [00:23:52]: I think it's als there's also like a, light security issue, where like you're the permissions layer. Like yes, I could query everything that you query, and I could get an automated response, but maybe I'm not supposed to see it. And that there's no way I would know because I'm not supposed to know what I don't know.Akshay Nathan [00:24:07]: Especially as like, with ChatGPT Work, we're, we're asking you to connect your plug-ins and, it's pulling from your local files and stuff like that. Like the amount of context that the agent has access to is like- Deeply personal and like that's something I think we need to preserve, so that'll be definitely a challenge.Swyx [00:24:22]: There's Excel, there's PowerPoint, there's Docs, the, grand trio of work. What other formats of work do you think about? like you worked on Airtable. Is there a future where there's like OpenAI Airtable? Like what does that look like if you ever ended up doing it?Akshay Nathan [00:24:41]: It's a really good question. I think,Formats of Work: Sites as Knowledge ArtifactsAkshay Nathan [00:24:43]: one that you didn't bring up was Sites, and I think that wasSwyx [00:24:46]: SitesAkshay Nathan [00:24:46]: A core part of this launch. There's one side of Sites that I think people commonly talk about, especially on Twitter and stuff or X, of like, this like prototyping tool. And like we saw that happen with this launch even. The model slider that you guys were referencing earlier, like that was developed almost fully in a Site. Like, the collaboration between design and engineering and product on that was like on a site where we play with, the affordance and figure out how it feels and all of that. But the other aspect that I think is a little bit less talked about is like Sites as like an artifact for knowledge work. I was talking to someone the other day who's on like our corporate finance team, and like we were mentioning how like now when they have these reports that they're, they're working on as a team month to month, historically those things were in slide decks and in spreadsheets, and now they're just in Sites. And like Sites is the mechanism that they collaborate across the team. And the reason is ‘cause it's like, it's like somewhat higher bandwidth. Like, at these tools like PowerPoint and Excel are like infinitely flexible, but at some point you reach the boundary of like either as a human you may not know how to use some feature or something, or the product itself doesn't support it. But with a site you can do anything. You ask for anything and you can get that. once people see that magic, I think it's been really valuable.Swyx [00:26:02]: Yeah, let me show you my case study. this involves all the hot topics including ChatGPT Work, but also GPT-5.6 token billionaires and token maxing and Sites and auto research. I'm a fan of this game called Strata. It's, it's like a little board game that youSites, Auto Research, and Research DashboardsSwyx [00:26:17]: That you play with, physical blocks, that come on top of it like that. So over the weekend I took like thirty photos and just threw into ChatGPT. one point seven billion tokens later, out comes this site with a fully playable thingAkshay Nathan [00:26:32]: WowSwyx [00:26:32]: With 3D, block placement and everything. Because it requires physical blocks and I needed friends to train on it so they can get better, so I can play against them. But also, I could also, do things like train an AI on it and that's, thatAkshay Nathan [00:26:45]: That's your auto researchSwyx [00:26:46]: That gets into auto research. So, you want to train your own AIs, and then make sure they self-play against, each other. I need to set both AIs. So this is AI versus AI, and they're, they're gonna self-play. the AIs start out bad and then you want to define a loss function and get good. I wasn't gonna supervise all this. I was at, I was down in San Mateo, attending a conference. What I ended up doing was, auto researching and on this and creating benchmarks and that there was just way too many parameters for me to read. So I started asking it for a site, and it's created this lab, panel. Where is there a, is there a shortcut for a site that is created?Akshay Nathan [00:27:28]: You should be able to go in the sidebar to Sites, top of the sidebar. The left sidebar.Swyx [00:27:33]: This one? Oh, left?Akshay Nathan [00:27:35]: Yeah. Just scroll all the way to the top.Swyx [00:27:36]: Oh. Oh, it says Sites. Oh, there you go. Yeah.Akshay Nathan [00:27:39]: Ooh.Swyx [00:27:40]: So it create, it creates the sites. I don't, I don't think this is, it is exactly what I wanted, but let me show you what it popped up, right? Like I think as a research artifact, it is very important to communicate, exactly, what is being done. Outputs this thing which I eventually started publishing. So I moved it off of Sites because I wanted more, database and infrastructure than Sites afforded me. But this is like a research output that you can start to mess with and like try to think about like what hyperparameters are you tuning for training AIs. And like I was trying to make like scaling laws and everything and doing all sorts of like game optimization stuff. And the fact that you can just throw this up as a research artifact, like I no longer need to read ChatGPT output. I read Site output. But then there's also a huge sprawl. Like look at how long this thing is. There's so many numbers. It is pretty overwhelming, so then I have to start pruning it from there. But, it's an interesting transition from Markdown effectively that you're putting out to, you're putting out a whole functional site.Akshay Nathan [00:28:41]: I think Markdown just isn't that optimal for people to read, right? Might as well just write HTML website and I don't know. I think you can do a lot with customizing this, right? You have your skills that explain what you want. Like I noticed they're quite verbose. I don't need a lot of this information.Swyx [00:28:57]: It's very verbose.Akshay Nathan [00:28:58]: So and then the nice thing of having a site side by side is, you just iterate on what you want and what you don't, right?Swyx [00:29:05]: Yeah. I don't know if, any that triggers any stories for you of how it's run internally. Am I doing this right?Akshay Nathan [00:29:11]: Yeah. I think that this is like a workflow that we're seeing like all different types of teams use, where like the canonical artifact that was previously a deck or something is now becoming a site. And like with a site you, because it's just HTML, you can like. It's infinitely flexible. And so, if you want to give more prominence to a certain thing that like in a slide deck would, feel like it was buried, like you can do that. You can have it be like the hero image, right? And so I think that like, people are starting to see that. There's more work to be done to make these things like much more easier, easy to collaborate on. You mentioned that they're very, they're long and verbose, could be broken up. I'm sure that there's still something to do there.Swyx [00:29:53]: They're super long. Yeah.Akshay Nathan [00:29:54]: Yeah. But I think we're starting to see that like there is this aspect of this is a really interesting, format, for people to use, that's like much more flexible than what they ever had before.Swyx [00:30:07]: I think your job also comes becomes meta. You're not designing the products. You're designing a product to make products, and I'm curious how you manage that.Designing a Product That Makes ProductsAkshay Nathan [00:30:18]: I think one thing that we've been Like when we look at the UX, like that we've been thinking a lot about is how can we balance like simplicity with capability? Like if we're designing a product, like you said, that like is made to make up build other things, right? You can build so many different things. But we can't put that all in front of you because you'll get overwhelmed.Vibhu [00:30:41]: Yes.Akshay Nathan [00:30:41]: And so we had similar problem or similar challenges even Chat-with ChatGPT, but especially now, like when there's so much that can be done, I think the balance that we're constantly trying to strike is like, how can we give the user enough of a UI surface where, they can be expressive, they can tell the agent what they need, they can verify that it's using the right tools, it's pulling from the right sources, et cetera, but then it gets out of the way. And then how can we build the right system such that we can show them instead of telling them what can be done? Because so much of this is gonna be like, how do they discover the next use case and the next one after that if they really want to be super powered by the AI.Games, Private Evals, and Show-Don'TellVibhu [00:31:19]: Yeah. It's interesting. I feel like everyone also just has a different way to do it, right? I made a similar version of this same game. I didn't take any pictures of board or rule game. I threw in at goal eighteen minutes, fifty-three seconds later, a lot of tokens later, I've got a similar version. not with all the auto research and whatnot, butAkshay Nathan [00:31:39]: You gotta do all the latest trends.Vibhu [00:31:40]: And yeah, I did it with, did it with Codex, not Work, but it's interesting, right?Akshay Nathan [00:31:45]: Yeah. And this is GPT Image generating the pro avatars. Very good for game design. LikeVibhu [00:31:51]: AndAkshay Nathan [00:31:52]: A lot of game designers were like really into GPT Image for assets.Vibhu [00:31:54]: I will say like the broader takeaway probably is the reason that we do this is more so just to test the tools, right? Like, this was also a test for GPT-5.6 came out. I had done the game on GPT-5.5, right? The ability for me to no longer need it to. I had to feed it the rules. It's, it's a pretty niche game. It couldn't find how to do this on its own.Akshay Nathan [00:32:15]: Oh, yeah.Vibhu [00:32:15]: GPT-5.6Akshay Nathan [00:32:16]: It is out-of-distribution, which is why I was also very keen on testing the GPT-5.6 capability.Vibhu [00:32:21]: But, this is just as work comes out, as new things come out, these are just our side ways to test things, right?Akshay Nathan [00:32:27]: Yeah. It's some private eval. That is not this private.Vibhu [00:32:31]: But also valuable because now you can send this to your friends and I learned about this game through seeing this.Akshay Nathan [00:32:36]: It's a hard game. He's very good.Vibhu [00:32:39]: It's good to when no one is competing with you. But yes, it's a classic RL problem of like self-play, bootstrapping your game AI. yeah, you see how easily work becomes personal and personal becomes work because the thing I do for personal, it directly informs people I work with because I showed it to them. They were like, “Oh, you can do that with GPT?” Which like I imagine is the growth strategy.Akshay Nathan [00:33:02]: Yeah. The show not tell is a big piece that, I think we've we're not still not fully cracked of like, showing people all the things that they can do with the product versus like trying to teach that to them through like, articles or onboarding or whatever.Akshay Nathan [00:33:18]: So meeting them in the moment.Vibhu [00:33:19]: It's a career risk for me, because I used to be in developer relations, right? Where your job is to show, and then you're like, “What do you mean? You don't, you don't need.” your job is to tell. And then. But the product people are like, “Well, we don't need you if our product is intuitive enough.” SoAkshay Nathan [00:33:37]: Yeah. that's the magic of the models. So you can tailor the telling or the showing to like specifically what the user needs, like what they care about, what they've done in the past, exactly where they are on the adoption journey. So I think that's like gonna be a super big opportunity.Vibhu [00:33:50]: Seems easier and easier now to tailor custom showing, right? People have different use cases. As much as you said you don't wanna segment different people into different buckets, right? It's also not that hard to for people that are in different categories. But the question, is you said your team is more broadly on. What was the term you used? Productivity?From Developers to Knowledge Work to EveryoneAkshay Nathan [00:34:12]: Productivity.Vibhu [00:34:12]: Productivity. So howAkshay Nathan [00:34:12]: Which is now work.Vibhu [00:34:14]: Is it work? Is there another distribution that we're not hitting? Is there a group of people that will have something different than ChatGPT, Codex or Work? Is there more that the mass isn't targeting?Akshay Nathan [00:34:28]: I see it as like a sequencing, like. The vision is like bring useful agents to everyone. We started with like developers. Like developers historically are like early adopters that are willing to put up with more friction, set things up, et cetera. Like that's where, Codex started. I think the next opportunity is like what we call general knowledge work, all the other functions around developers. I think when you go from developers to this segment, like there's inherent challenges with like, this show not tell thing that we're talking about, making the product more understandable, bringing in new capabilities that matter more for this cohort than matter for developers, things like artifacts, things like computer use, et cetera. And then I think like the same learnings, like similarly how we took the learnings from developers and brought it to, general knowledge work, the next stage will be like taking the learnings from general knowledge work and bringing it to everyone no matter what they're doing in their lives. And we're already seeing that a little bit. Like this game example that you have is, something that's like on the border of like fun and personal life to, your professional life. I use ChatGPT Work full-time at home for everything, like for whatever I'm doing. I used it the other day to come up with a meal plan and like, save that on the like computer environment that it has and something that I can continue going back to. Like is everyone doing that yet? Probably not because the thing says work on it, but eventually, we wanna get people there.Vibhu [00:35:51]: ChatGPT life.Akshay Nathan [00:35:52]: Yeah, exactly. ChatGPT cooking. But I think there's a lot of, there's a lot of opportunity there, but I see it as like, we're, we're built we built a foundation in software engineering, and we're gonna take the same learnings that we take from software engineering to knowledge work to everyone.Vibhu [00:36:07]: Do you have any power user advice? I feel like, there's a group of people that will live it, use it for everything, stay on it twenty four-seven. And then there's a bit of a gap between that crew and people that, okay, I use it for work. I use it occasionally. Sometimes I type questions. any advice, any learnings, anything you recommend or just, takeaways that you've found that help bridge that gap?Power User Advice: Push the Frontier of ImaginationAkshay Nathan [00:36:30]: I think a couple things that I've seen is like, one, that it really helps to broaden your imagination of what's possible, and this has been a learning even for me. Like, the technology has progressed so fast that, something that, like, even three months ago, like, no way the models can do this. Like, now it's like, wow, it's like it can. Like,Swyx [00:36:52]: Give an exampleAkshay Nathan [00:36:52]: We're going through right now our, like, review cycle internally, and, people always talked about this as, like, a thing that the models are good at and like, there's a cliché of like: Okay, like, no one wants to be writing reviews and, like, we just use AI to do it. But in all seriousnessSwyx [00:37:09]: And it can evaluate it as well.Akshay Nathan [00:37:10]: Yeah, exactly. In all seriousness, before it was, like, just, like, slop and, like, I think it was helpful, but, not super productive. Now I've found that, like, the model can do a much better job than me, especially in this environment of, like, pulling context on, like, what people are up to, how they've like the things that they've done to make a difference, highlighting like, wins that they've had that, like, I might may not even have seen. It has access to, like, everything, right? Like the code, like, things that they've caught, reviews, Slack, everything. And so it's, like, incredibly powerful in that domain and, like, just like six months ago, the last time we did this cycle, like, I didn't even I tried using it, but it was not at all helpful. And this time it's been, like, incredibly helpful and, like, so I think continuing to push the frontier of imagination of what's possible, even if you tried something before, I think is maybe the my biggest piece of advice. The other, thing is, like, the more you put in, especially in this environment where, like, the model has access to everything on your computer or in ChatGPT Work, like you can create, artifacts over time and save them in your library and, like, the model will continue having access to those. Like, the more information you give it about whatever domain you're in, whether it's your life or your work, the more valuable it becomes, and it'll become valuable in, like, ways that might surprise you. Like, it might pull from context in a way that, may be proactive and that you might not even have thought about. But it needs to have access to those, to that those tools or that context first.Reviews, Agentic Search, and Context GatheringSwyx [00:38:27]: One thing I just wanna talk about the review stuff because I'm still that's a very sensitive thing and you're, you're a founder, you've managed people, you've hired people. As manager myself, I'm very reticent to put out any LLM-generated things especially when it comes to people, ‘cause it feels like you don't care.Swyx [00:38:46]: Presumably at OpenAI, people are more open to being eval rated by GPT. But are there any unofficial rules around this? Like, what's the etiquette?Akshay Nathan [00:38:57]: Oh, I think the etiquette is that, like, I would never write something via, like, well, solely via AI and, like, present it as, like, a review for someone. What I was talking about is more, like, gathering context. That's the place where it's incredibly helpful.Swyx [00:39:08]: So it's just search.Akshay Nathan [00:39:09]: Yeah, exactly.Swyx [00:39:09]: It's agentic search. Yeah.Akshay Nathan [00:39:10]: It's like agentic search, but, that you can tailor and steer much more capably than you could before, ‘cause, like, the thing is it's all there's a flywheel happening, right? Because of Codex, people are able to do, and because of ChatGPT, people are able to do so much more now than ever before. And if you're able to do so much more, it's easy to miss things as well. And so, like, I think we need to use these same tools to keep up with all the impact that people are having and understand, where we can be helpful.Swyx [00:39:39]: I think the thing, like, I run a small company, so easy to search, but at the scale of OpenAI with the amount of messages that you guys put in Slack, do you think that it misses things?Remembering What Humans MissAkshay Nathan [00:39:50]: Probably, but I think that I also miss things.Swyx [00:39:52]: Like, it doesn't matter, right?Vibhu [00:39:53]: I think sometimes it'sSwyx [00:39:53]: Like it's, as it needs to be human-levelAkshay Nathan [00:39:54]: It's all relative, right? Yeah.Vibhu [00:39:56]: Sometimes it's nice when it finds things you wouldn't, right? Like right now, my Codex system prompts, they're set up in such a way that every project I have has a secret- separate, notes MD, and it just writes learnings to there. And then the global one can pull from all these. So sometimes it'll be like: Oh, there's this project you did like four months ago. Here's a note that we had, and it randomly pulls it back into context that I would never do, I haven't thought about.Vibhu [00:40:20]: And I'm like, okay, this is quite superhuman, right? Like, stuff that would. And, it'll save like hours on chunking of stuff or find something that's already been done. I'm like, as much as it might miss stuff, I would too, but it's very useful when it finds stuff. And I have like a very, non-super engineered solution to this. It's just marked down files that get pulled whenever they want.Akshay Nathan [00:40:41]: Yeah. I have a funny anecdote about this. Like, recently gearing up to this launch, the team has been, really cooking on it for a couple months, and over that time, like there's so much conversation and chatter going on in Slack and Docs and elsewhere. And, one of the members of the team set up this, scheduled tasks, like automation to like look at everything that's going on and, like, come up with the best memes and then post it in one of our shared channels. And like, there are two cool things about this. Like, the first is, like, I think the models are, over time, like starting to become like funny.Swyx [00:41:13]: Funny. Nice.Akshay Nathan [00:41:13]: Whereas like, a year ago, like that was not at all the case. The second is, it was what you were saying, like they find things that in surprising ways that you may not have thought of and like create connections that you may not have thought of. And that really helps with like the meme generation because then you can see something that, genuinely surprises you and, is funny in that way. So yeah, that's like not like the most productive, use of this the technology, but it does it does uncover this, like this capability that's emerging, which is just like to find information that you otherwise would not know of.Launch Momentum and the 10 Million User MilestoneSwyx [00:41:43]: Talking about the launch, I think, I have pretty much said this is the most successful launch in a long time. I think even more successful personally than 5.0, and they're announcing ten million users. Does it feel different? You've been through a lot of launches.Akshay Nathan [00:41:58]: I think it feels like a culmination. Well, I think two things. One, it feels like a culmination, like I was mentioning earlier, like this like vision mission that we've been on for a long time. Like I said, we saw the magic of Codex internally, and then we're like extremely excited to bring this to many more people and to see it working, to like see us reach, the distribution goal, numbers that you mentioned, like I think that's like huge and super exciting. The flip side of that is like, there's so much more to do too. Like, that's also really exciting. Like, ChatGPT as a whole, like the this product that, everyone almost equates to AI and like loves, has hundreds of millions of users. And so like ten million is really cool, but like we need to get this to everyone. Like, we need everyone to feel this magic. And so that's the next step from here. But yeah, I think extremely pumped about how it's going so far and the opportunities.Swyx [00:42:46]: Awesome. I did want to also Because I've, I've, I've been tracking the number closely, it transitioned at some point from just Codex users to Codex plus ChatGPT Work, because they're same harness. The whole point is that you don't, you can't, count them separately. Do you have roughly a billion, ChatGPT users? Why did it just jump to one billion right away? Like, isn't that the default on ChatGPT or no?Codex, ChatGPT Work, and the Developer BrandAkshay Nathan [00:43:11]: We don't default you into ChatGPT Work if you're on ChatGPTSwyx [00:43:14]: If you're free. YeahAkshay Nathan [00:43:15]: It's also only available to paid users right now. And I think there's like a process of, educating users of what is the value of this product, having them try it, learning from their feedback, and making it better over time. But the goal is to, get as many of the people who love ChatGPT today to like feel the power of ChatGPT Work. But I think it'll be a journey.Swyx [00:43:36]: Yeah. And Codex will still be alive as a brand for the foreseeable future. And we'll just toggle between them as needed for UI stuff.Akshay Nathan [00:43:44]: Yeah, I think it's even stronger point than that. Like, I think we fully intend to like, treat developer. Like, developers have been, a core market for us for so long, and like there's, there's so much more that we can do to make Codex great specifically for, software development, and we'll continue to do that. This doesn't take away from that at all. If anything, it should increase the utility of something like Codex, because now you can move seamlessly between writing a diff to creating an artifact or, doing a search over your factor.Swyx [00:44:11]: I do wonder how much this terminology leaks to the non-technical user. Like, do they have to learn to say artifact if I want artifact? Or.Akshay Nathan [00:44:20]: It's funny, like we call it artifacts internally ‘cause that's what the teams call it.Swyx [00:44:23]: It's nice. Yeah.Akshay Nathan [00:44:23]: But like externally, like no one says that, no one calls it an artifact. But I think that people like often, like describe things, whatever they're used to, right? So if, ChatGPT Work is good at creating slides, they'll say ChatGPT Work is good at creating slides, and that's what we want.OpenClaw, Personal OS, and Persistent ComputersSwyx [00:44:38]: One big Another, it's July of twenty-six. One big thing that also happens in, for OpenAI was OpenClaw, and that's I think a lot of people's first time really maxing a agent for personal stuff, but also crossing over to work in essence same way. As far as I understand, OpenClaw is still independent, but did you go through your own OpenClaw moments? Were there any lessons you took from OpenClaw to Codex or back? Whatever.Akshay Nathan [00:45:06]: I think there's a lot of inspiration. I did go through my own OpenClaw moment. I,Swyx [00:45:10]: Yeah, tell the storyAkshay Nathan [00:45:10]: Me and my wife like set up an OpenClaw to like try to manage everything in our house. Not that there's like a ton, but it was like quite useful. We gave it a calendar. It started, creating events for us and stuff. At some point, the laptop that we were running on, it died and never got a chance to pick it back up. But there was a lot of inspiration there, like, in ChatGPT Work, in web and mobile, like you get access to this like persistent computer environment where, you can store files, and those files stay around between sessions. And the idea is to be able to enable use cases like this. one of the members of our team uses ChatGPT Work for what they used OpenClaw from before, and then feel like it has like completely transitioned, which is like, workout planning and like meal tracking. which again, it's like a work-related thing, right? It's like not work necessarily, but it's like in personal productivity space. But it has all the same primitives. So it has scheduled tasks. It has the ability to store files on a file system. It has the ability to like reference those things over time. And so you start to see the same types of use cases emerge, which has been really cool.Swyx [00:46:14]: Is there a point that ChatGPT Work completely replaces OpenClaw? they're independent, so.Akshay Nathan [00:46:20]: Yeah, I'm, I'm not close to it, so I can't speak to the OpenClaw roadmap, but I don't think so. I think that there's gonna be, there's always a need for like this like incredible, like open source technology that team has built. And I think that we can draw inspiration, in the product and, ChatGPT, I think many more people have like heard about and used ChatGPT than have used OpenClaw. And if we can take the magic from OpenClaw and bring it to them, I think that'll be a success. I think that like one thing on the ChatGPT Work side that we feel strongly about is that like the core experience is that you come to this product and you have a conversation, start a session, whatever you wanna call it, with this agent. And the magic of the product is that you can do anything in that moment. And we would like to create a product where you don't have to click a button or to go to a different place, whatever, and you can get whatever functionality exists in, your finances app or where or any other product like in this one place. And so that's the goal. It's like it we want an extensible system with plugins where you can connect to the tools that you need in order to be able to accomplish like a financial task, where you can, if you're doing like science work, like we have an ability to like extend the system in such that you can like write the tech and it performs well. There'll always be like products that we support that are best in class at those things, but we want as much of the magic as possible in that core experience.Swyx [00:47:45]: Yeah. Do you think that you can do everything you used to do with Wealthfront in ChatGPT Finance?Finance, Data Access, and Centralized ContextAkshay Nathan [00:47:50]: I tried it. like ChatGPT doesn't yet custody, cash and assets for me. So that part, no, not yet. But I, there was like a whole component of like retirement planning and, like financial planning and budgeting and stuff that, we were looking into when I was there. And like with the finances plugin, like that's all possible with ChatGPT today. So, I feel

Par Régis BAUDOUIN Il y a encore peu de temps, l’expérience utilisateur (UX) reposait sur un dogme immuable. Concevoir des structures rigides, créer des arborescences de menus complexes et aligner des boutons cliquables restait la norme. L'utilisateur devait s'adapter à la logique de la machine, ce qui affectait l'interface. Dans un précédent article, nous expliquions comment l’intelligence artificielle nous faisait passer de la transaction (exécuter une commande) à l’intention (comprendre un besoin). Aujourd'hui, cette révolution intellectuelle trouve son prolongement visuel et pragmatique. L'interface graphique (GUI) abandonne son statut de maquette figée pour devenir vivante, adaptative et contextuelle. Bienvenue dans l'ère de l'UX liquide et de la générative UI. La fin du design figé Pendant des décennies, le processus de création numérique suivait un schéma linéaire : un designer dessinait une interface sur Figma, un développeur la codait, et 100 % des utilisateurs voyaient exactement le même écran. Principalement des zones de texte à lire ou à remplir et des zones de choix pour transactionner avec la machine. Avec la générative UI, ce modèle vol en éclats. L'interface n'est plus dessinée à l'avance : elle est compilée en temps réel par l'IA en fonction du besoin immédiat de l'utilisateur. En permanence l’IA analyse et réagit aux actions et habitudes de l’utilisateur. Le design de l’écran s’adapte en temps réel. [ Intention de l'utilisateur ] │ ▼ ( L'IA analyse le contexte ) │ ▼ [ Assemblage dynamique des composants UI ] (Carte 3D + Slider de budget + Carrousel) │ ▼ [ Interface éphémère dissoute après usage ] Pour offrir ce type de services, nous disposons de plusieurs solutions de design d’interface. Le concept de l’interface éphémère (Ephemeral UI) Imaginez une application de gestion financière. Au lieu de vous forcer à naviguer dans cinq sous-menus avant de trouver la fonction recherchée. Vous tapez ou énoncez votre projet directement par un prompt. Ainsi, L’IA génère instantanément un composant visuel sur mesure. De plus, ceci inclut un calculateur dynamique avec des jauges interactives, un graphique de projection et un bouton de simulation. Ainsi, une fois la décision prise, cet écran sur-mesure disparaît. L’interface ne surcharge plus l’esprit : elle apparaît, rend service, et s’efface. #Exemple Le “Zero UI” et le design prédictif La meilleure interface est parfois celle qu’on ne voit pas. Si le “Chat” (la simple boîte de texte) a été la première porte d’entrée de l’IA générative, il montre aujourd'hui ses limites d’ergonomie. Personne n’a envie de taper des kilomètres de texte pour régler une alarme ou filtrer une liste. C’est ici le domaine des agents intelligents. L'émergence du Zero UI ne signifie pas la disparition des écrans, mais leur invisibilité proactive. L’anticipation contextuelle : Grâce à l’analyse des données de votre smartphone ou de votre navigateur (géolocalisation, heure, habitudes, niveau de batterie), l’interface prédit l’action suivante. L’adaptation cognitive : Si vous utilisez une application de navigation en marchant rapidement dans la rue, l’IA agrandit automatiquement les zones de frappe et simplifie la densité d’information pour éviter les erreurs d’inattention. En fin de journée, elle ajuste les contrastes et réduit la charge visuelle pour limiter la fatigue oculaire. Le pilotage des agents : Le logiciels active les agents que vous avez défini en fonction de vos besoins du moment et de vos actions pour vous présenter directement les résultats sans passer par l’interface. Du simple Chat à l’espace conversationnel spatial Nous assistons à la métamorphose de la fenêtre de discussion. Les boîtes de dialogue linéaires cèdent la place à des cartes intelligentes manipulables. Carte mentale Lorsque vous interagissez avec un assistant moderne, celui-ci ne répond plus par un bloc de texte indigeste. Il génère des micro-widgets réactifs dans le fil de conversation : Vous cherchez un vol ? Un composant interactif vous permet de modifier les dates via un calendrier visuel sans quitter le fil. Vous comparez des lignes de code ou des données ? L’IA déploie un tableau dynamique filtrable avec des glisser-déposer. L'UI devient un dialogue hybride où la voix et le texte pilotent des objets graphiques vivants. Comment les développeurs construisent l’UX de demain Pour les équipes produit, l’intégration de l’IA dans l’UX ne relève plus du gadget, mais d’une transformation profonde des méthodes de développement. La prise en compte des habitudes de consommation est majeure. Les applications issues des la vie quotidienne avec un smartphone à la main ont modifié notre rapport à l’interface. Moins d’information, plus pertinentes, des notifications et surtout de la simplicité. Le “Code-to-UI” à la volée Les grands modèles de langage ne se contentent plus de sortir du texte brut ou du JSON. Ils génèrent désormais directement des structures de composants UI (React, Web Components, styles Tailwind) interprétées instantanément par le moteur de rendu du navigateur. C’est bien plus dynamique et bien plus agréable à l'œil. Le respect strict du Design System Une peur légitime des designers était de voir l’IA générer une interface chaotiques et hors-charte. Aujourd’hui, les architectures GenUI s’appuient sur les Design Tokens de l’entreprise. L’IA n’invente pas un bouton : elle pioche dans la bibliothèque de composants officiels de la marque (couleurs, typographies, arrondis) et les assemble de manière cohérente. Pour conserver une cohérence visuelle, c’est très important que le design système soit maintenu et utilisé systématiquement. +-----------------------------------------------------------------------+ | ARCHITECTURE GENUI EN COULISSES | | | | [ LLM ] ──> Analyse l'intention | | │ | | ├──> Pioche dans [ Design System Token (Boutons, Cartes, Grilles) ] | | │ | | └──> Compile [ Composant React / Tailwind éphémère ] ──> Écran | +-----------------------------------------------------------------------+ Ce que cela change pour vous Pour les utilisateurs, cette mutation annonce la fin de la courbe d’apprentissage des logiciels. Nous n’aurons plus à apprendre à utiliser un outil complexe, comme un logiciel de paie ou un CRM. Ainsi, c’est l’outil qui générera une interface adaptée à notre niveau d’utilisation. L’expérience utilisateur passe d’une logique d’apprentissage à une logique d’adaptation mutuelle. Et vous, qu’en pensez-vous ? Préférerez-vous demain une application unique “liquide” qui transforme son interface selon vos besoins à la volée, ou restez-vous attaché aux applications traditionnelles aux menus fixes et familiers ? Exprimez-vous en commentaire et n’oubliez pas de vous abonner à XY Magazine pour ne rien manquer des révolutions technologiques qui façonnent notre quotidien ! The post Comment l'IA façonne l'interface “liquide” et l'UX de demain first appeared on XY Magazine.

Today we are talking about Supporting Open Source, Acquia, and The Acquia Fair Trade Initiative with guest James Sims. We'll also cover Image Effects as our module of the week. For show notes visit: https://www.talkingDrupal.com/562 Topics Fair Trade Initiative Explained How the Program Started Why Fair Trade Matters Adoption and Open Framework Agency and Freelancer Benefits Partner Funded Giving Who Can Be Makers Tracking Participation Tax Deduction Questions Community Shaped Program Money Counts Too Early Challenges Timeline And Launch Sustainability Built In How To Get Involved Defining Success Origins Of Fair Trade Resources Acquia Fair Trade Initiative Taste of chicago Giordanos Lou's pizza Five For The Future Image Convolution Playground Guests James Sims - rcjmselp85 Hosts Nic Laflin - nLighteneddevelopment.com nicxvan John Picozzi - epam.com johnpicozzi Avi Schwab - froboy.org froboy MOTW Correspondent Avi Schwab - froboy.org froboy Brief description: Have you ever gone to edit an image style in Drupal, looked at the list of filters, and said "give me more! I want more!". Have you said "I'd like to mirror, filter, and convolute an image in Drupal - all at the same time". If so, you're in luck. Let me introduce you to our module of the week: Module name/project name: Image Effects Brief history How old: Created by Drupal user mondrake of Italy on 17 September 2015. It's also the successor to the ImageCache Actions module, which was created all the way back in 2008. Versions available: It has a 4.0.0 version available with Drupal 10 and 11 support, and a 5.0.0 version for Drupal 11.3 and above. Maintainership Actively maintained Security coverage Test coverage Documentation It has a full README with details about the available image styles and whether they are supported by the GD or ImageMagick PHP libraries. Number of open issues: 35 open issues, 3 of which are bugs against the current branch. (The current branch has only been out a few months, and many of the open issues against prior branches seem to still be relevant.) Usage stats: 35,196 sites report using this module, with most still on the 3.x or 4.x branches. (its predecessor, ImageCache Actions, still has over 25,000 active installs) Module features and usage The module is pulled in just like any other, with composer require and then enable via drush or the UI. Once it's installed there is a very basic settings page, but most folks won't use much on there. The power of Image Effects comes when you go to Config > Media > Image Styles and then edit an Image Style. Once Image Effects is enabled, you'll see over two dozen additional effects in the list. These effects range from simple to complex. Interestingly, many of the effects that were so amazing 15 years ago are now doable with CSS. Still, there are some incredibly powerful filters. Side note: I'd strongly recommend Aubrey Sambor's recent talk from Drupal Camp Asheville, "You Don't Need JS for That", and her prior talk "Color in CSS" to learn a ton of things you didn't know about CSS effects. The basics like Color Shift, Contrast, Mirror, Rotate, and more are there if you'd like to do these natively. More advanced filters like Sharpen, Blur, and Convolute let you make more complex modifications to images. "Convolution" is the process of applying n-dimensional matrixes to images to create effects such as blurring, sharpening, and edge detection. Try it out on https://anna.engineering/Image-Convolution-Playground/src/ Lastly, you can create advanced image styles with ImageMagick arguments, create Text overlays using the power of Drupal tokens, or even develop your own Image Effects guided by the incredibly detailed DEVELOPING.md file included with the module.

marimo is the fastest growing Python project with 22,000 Github stars. Fantastic episode today, covering:0:00 - Intro0:54 - Favorite keyboard2:35 - What Coreweave's GPUs mean for marimo, such as solving the Travelling Salesman Problem8:13 - What is the one thing Jupyter Notebooks always gets wrong, but marimo doesn't?17:43 - UI elements in marimo library like sliders, cascading updates across cells and marimo notebooks as web apps20:08 - What marimo means for different modern data team personas 24:39 - Remote storage connections and example of data analyst work27:15 - How marimo is used by various scientific disciplines and using Anywidget in marimo31:45 - Use of marimo in an enterprise context for GPU compute, roadmap comments and creative freedom the tool enables35:37 - Using marimo to defeat the Esri monopoly in geospatial: if you are David, play a different game than Goliath, again the point that marimo/molab offers creative freedom to sponaneously poke around in the data42:27 - How to use GPUs in marimo, limitations of this47:55 - Using AI to control marimo 50:30 - Call to action - marimo competition to make coolest notebook from an academic paper51:20 - Vincent's summary: this is marimo, go and be David, fight Goliath, most of all - creative freedom. [We are trying] to figure out a way for you to be more creatively free.51:35 - Dice kit55:48 - More on keyboards.57:25 - I try pairing Claude and marimo myself.A huge privilege to have Vincent on to tell us all about it. Here are some links to items discussed:marimo joining Coreweave: https://marimo.io/blog/joining-coreweavemarimo repo: https://github.com/marimo-team/marimomarimo pair - for helping pair with an AGI: https://github.com/marimo-team/marimo-pairA related blog post: https://marimo.io/blog/claude-codeEvidence marimo uses Leafmap: https://docs.marimo.io/api/plotting/?h=map#other-plotting-librariesAnywidget: https://anywidget.dev/ and its dev: https://github.com/manztNotebooks in Wherobots: https://wherobots.com/blog/wherobots-notebook-environment-getting-started-with-wherobots-cloud-sedonadb-part-2/Vincent's Wiggly Stuff: https://koaning.github.io/wigglystuff/marimo recently ended competition: https://marimo.io/pages/events/notebook-competition-2We are now discussing the next one and adding a geospatial theme to it!

Episode 071 – Taylor Built a Full Shopify Brand with AI in One AfternoonKarl and Taylor compare how Claude, Codex, and Cursor behave differently when building Shopify discount and checkout UI extensions, and dig into why testing against a real dev store is still such a pain point. Taylor also walks through spinning up a complete fictitious coffee shop brand, theme, and metaobject data model with Claude Design and Claude Code for his recent DEV conference talk on metaobjects, and shares how a Shopify theme store change quietly broke his Shop Info app. Plus: a major new identity verification requirement for Shopify Partners requesting collaborator access, updates to app pricing limits and review policy, and this week's picks.---SponsorShopify Partner Program / Shopify Supply Dev Mode collection – Shopify's apparel and gear line built for developers. Check it out at shopify.supply and get 15% off your first order with code LIQUID.---Subscribe to Liquid WeeklyDon't miss out on expert insights and tips—subscribe to Liquid Weekly for more content like this: https://liquidweekly.com/---Timestamps00:00 – Cold open: Shopify dev store UX rant00:25 – Intro & welcome back04:55 – Client work: rebuilding a Shopify Script as a Function overnight06:04 – AI subscriptions: Claude, Codex, and Cursor compared07:06 – Why Claude and Codex architect extensions so differently13:21 – The case for ephemeral, on-demand dev stores19:49 – Sponsor: Shopify Partner Program / Supply Dev Mode collection21:01 – How the Shopify Dev MCP has improved over the past year24:53 – Using Claude Design for client mockups and brand guides27:07 – Does AI ever really "one-shot" a build?29:08 – Building a fictitious coffee shop brand for a DEV conference talk42:59 – Shopify breaks Shop Info's theme store tracking48:26 – Taylor's whirlwind two-week conference plan51:45 – Dev Changelog roundup59:12 – Picks of the Week1:04:46 – Wrap-up--- Dev Changelog⚠️ Identity verification requirement for Shopify Partners - https://shopify.dev/changelog/identity-verification-for-partners – New identity verification (potentially photo ID or business details) is being required for partners requesting Collaborator access to stores. Action required, especially for agencies and support teams handling collab requests.App pricing updates - https://shopify.dev/changelog/app-pricing-more-plans-no-charge-plan-testing-and-negative-and-fractional-app-events – Public app plans increased from 4 to 8, private plans increased from 10 to 15 per app, plus new app billing events now supported via the API.App Store review policy update - https://shopify.dev/changelog/updated-app-store-requirements-13-always-use-honest-and-transparent-review-practices – Shopify is targeting incentivized and fraudulent review practices, with penalties including review removal, delisting, or partner account suspension. Also introducing logic to better detect fake/untrusted reviews.Shop Mini updates for June - https://shopify.dev/changelog/shop-minis-may-june-2026-update – Multiple updates worth checking out for anyone building Shop Minis.---Picks of the WeekKarl: His parents' basement – A nostalgia-fueled pick after finding childhood relics (cassette tapes, old CDs, toys, and even his original birth certificate) still sitting in storage.Taylor: Frankenmuth, Michigan - https://www.bronners.com – A small Bavarian-themed town home to Bronner's, a massive year-round Christmas store, and now an annual "Christmas in July" family tradition.

Sean Emory of Avory & Co. explores how AI could change software competition if agents become buyers, not just users, shifting value from platform suites to composable, standalone capabilities.He frames three eras of software: individual tools, integrated platforms, and an emerging era where AI orchestration acts like a new operating system that selects the best capability for a task regardless of UI or vendor.In this model, winning depends less on distribution, brand, and long contracts, and more on reliability, latency, accuracy, security and governance, API quality, cost per call, and task success rates.Sean argues platforms won't disappear. They may become command centers for permissions, compliance, and governance, while features "escape" suites. He walks through examples from Shopify, Shop Pay, Stripe, Twilio, Zoom, Box, Salesforce, Adobe, and HubSpot.Chapters:00:00 AI changes software buying01:44 Three eras of software03:10 Composable software era03:55 Agents versus humans05:06 Orchestrator as new OS07:46 Capabilities escaping platforms08:33 Examples: Shopify, Stripe, Zoom12:28 New scorecard for winners15:32 Two questions for companies17:22 Where this could be wrong19:48 Big picture takeaways23:22 Closing and subscribeListen on:Apple Podcasts: https://podcasts.apple.com/us/podcast/avory-markets-and-investing/id1504555573Spotify: https://open.spotify.com/show/3A8acTyfhhxpUFoFLEKeMJYouTube: https://youtube.com/@avorycoFollow Sean and Avory & Co.:X: https://x.com/avorycoX: https://x.com/_SeanDavidLinkedIn: https://www.linkedin.com/company/avory-coWebsite: https://avoryfunds.comDisclaimerThe content on this channel is for informational and educational purposes only. It does not constitute personal investment advice, a solicitation, or an offer to buy or sell any security. Views expressed are those of Sean Emory and Avory & Co. as of the recording date and are subject to change without notice.Avory & Co. and Sean Emory may hold positions in the securities and companies discussed. Any references to specific companies, products, or services are for illustration only and should not be interpreted as recommendations.Investing involves risk, including possible loss of principal. Past performance does not guarantee future results. Consult a qualified financial advisor before making any investment decision.

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

Fredrik och Kristoffer sågs på stan för ett snack om Gram, teoribyggande, och ganska mycket mer. Kristoffer jobbar på ett jätteprojekt när han inte har något annat för sig. Kommunikation är ett problem - när det tar massor av tid att förklara varför och hur någonting behöver göras, tid man också skulle kunna lägga på att få en del av dem gjorda. Problemen kanske också behöver mogna och utforskas innan det går att låta någon annan göra något åt dem på ett effektivt sätt. Spelar det någon roll hur mycket ett gränssnitt sticker ut eller passar in i nuvarande trender? Vad får uppmärksamhet och varför? Och varför får det inte fler konsekvenser att påstå saker vitt och brett? Man borde inte bygga verktyg som stödjer dåliga sätt att jobba på. Man borde hitta bättre sätt att jobba istället för att bygga bättre verktyg för att stödja dåliga arbetssätt. Eller? Branschen har fastnat i icke-optimala sätt att jobba. Vad vill Kristoffer göra med Gram och varför? Och vad vill han inte göra? Hur borde flikar fungera? Och hur borde bra git-stöd i en textredigerare se ut? Varför är långa listor i gränssnitt alltid svårt? Det och andra problem man borde lösa på ett bra sätt en gång, inte halvbra många gånger. Programmering som teoribyggande. Varför kan vi inte rita i våra IDE:er, eller på andra sätt fånga allt teoribyggande och alla antaganden som inte går att utläsa av koden? Gränsen mellan språkmodeller och deterministiska verktyg. AI-sök saboterar möjligheter att hitta vissa sorters nålar i höstacken. Kodgranskning och teoribyggande - borde man fokusera mer på teorin i sitt granskande? All teori som inte finns med i koden. Rust är lite för bra för Kristoffer. Men det finnas massor som skulle kunna bli bättre också. Ett stort tack till Cloudnet som sponsrar vår VPS! Har du kommentarer, frågor eller tips? Vi är @kodsnack, @thieta, @krig, och @bjoreman på Mastodon, har en sida på Facebook och epostas på info@kodsnack.se om du vill skriva längre. Vi läser allt som skickas. Gillar du Kodsnack får du hemskt gärna recensera oss i iTunes! Du kan också stödja podden genom att ge oss en kaffe (eller två!) på Ko-fi, eller handla något i vår butik. Länkar Sommar-låten Hajk jj - git-ersättare Zed Patrik - vän av och gäst i podden Bug refinement - man vet att det är viktigt när förklaringen är femton sidor om man skulle skriva ut den! Mörk - Kristoffers markdownparser i Gleam Om jag haft mer tid hade jag skrivit ett kortare brev Gram Neovim Kai's power tools Bryce Kai's power goo - video Kai själv Jupyter notebook Magit Versionskontrollsystem där man skapade motsvarigheten till en commit först - innan man börjar göra ändringar - var nog just jj i ett avsnitt av Developer voices RAD debugger RAD game tools REPL - read-evaluate-print-loop Tailwind Immediate-mode GUI CSS GPUI - hårdvaruaccelererat UI-ramverk som driver Zed och Gram Gleam Commonmark Servo Nlnet - ger stöd till personer och organisationer som bidrar till ett öppet informationssamhälle Henna Virkkunen Computer science off course Programming as theory building Peter Naur BNF - Backus-Naur-form, eller Backusnormalform MS paint Stöd oss på Ko-fi! objc2 - Rust-låda med Objective-c-bindningar Newspeak Deltadb - Zeds nya lösning för versionskontroll Zig Tokio Garry Tan - VD för Y combinator och glad vibekodare Token ring Titlar Ett nytt utvecklingspass Den tekniska personen När vi har mer än tre användare Ticket-fokuserat sätt att arbeta Lär er använda git Jag borde bli bättre på git Alla borde bli bättre på git Atomerna för att bygga saker Ett sämre git Sortera sin kompost Välja precis rätt ord Gram power tools Metaboll Experimentgram Det är ju bara en texteditor En ny padda Immediate-mode CSS Tre tentakler Ni ser inte bra ut utifrån I ord förklara vad jag vill ha När man kan sin modell Vem är du att vara upprörd? Par-promptande Glorifierat undo En teori om hur man vill jobba Framtiden för programmering Min startup i Gleam Rust är för bra Nu sker det magi i bakgrunden Värdelös kunskap Professionell Rust Ett skal som ser ut att funka Mesh och b-post

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

If AI can write product requirements and analyse data, where does that leave Product Owners?Paulo Pereira, Global Head of Product, explains how Product Owners at Revolut are given full scope of the features they're building. Acting like mini CEOs, it's a tough job for AI to replace.In this special episode filmed in front of a live studio audience at Revolut's HQ in London, host Alex Carril and Paulo discuss:The difference between being a Product Owner at Revolut and a product manager at a typical tech companyPaulo's career-leaping journey from Operations Manager to Global Head of ProductAn element to avoid that slows down product developmentThe opportunity at Revolut for someone who might be joining nowWhat the first 90 days would look like for a new Product OwnerWhy CEO and co-founder Nik reviews every UI before going livePaulo's prediction of how AI will impact product roles over the next few yearsHow a product professional can shake things up if they feel stagnant in their current roleUsing the ‘fake wall' metaphor to overcome complex problemsPaulo answers questions from the audience, including how to empower founders to innovate, and balancing quality with speed to marketFollow Revolut Insider on Instagram: ⁠revolut.la/RevolutInsider⁠View open Product careers at Revolut: revolut.la/4wEu46V

The Energizer Connect Smart Wi‑Fi Indoor Window Camera is a 5 GHz indoor camera designed to mount inside a window and look outward. This approach is useful for apartments, rental properties, or locations where outdoor installation is not possible. This guide explains the complete pairing process using the Energizer Connect app, including entering pairing mode, adding the device, naming it, and accessing basic controls.RequirementsEnergizer Connect Indoor Window CameraEnergizer Connect appWi‑Fi networkPower outletStep 1: Open the Energizer Connect AppThe Energizer Connect app is opened, and the plus button is selected. The option to add a device is chosen. The app may automatically detect nearby devices, but the manual process is used to ensure consistent pairing.Step 2: Power the Camera OnThe camera is plugged in and an orange light appears on the back. This indicates that the device has powered on and is preparing for pairing. A tone may play when the app and camera discover each other.Step 3: Select the Camera CategoryThe Cameras / Doorbell category is selected. The app displays an overview of the pairing process. The instructional video is skipped to proceed directly to setup.Step 4: Connect to Wi‑FiThe app requests the Wi‑Fi network information. If credentials have not been entered previously, they are added at this step. The app then prepares a QR code for manual pairing if needed.Step 5: Complete the Pairing ProcessIf the camera communicates automatically, the QR code step is skipped. When manual pairing is required, the camera is aimed at the QR code to initiate Wi‑Fi setup. In this example, the camera connects automatically and plays a confirmation tone when pairing is complete.Step 6: Name and Assign the CameraA name is entered for the device. In the example shown, the camera is named "Window Camera." The location is set to "Living Room." The setup is completed by selecting Finish. The device information updates and the app transitions to the live view screen.Step 7: View the Live Video FeedThe camera displays a live video feed. After correcting its orientation, the camera adjusts color and exposure. The feed appears nearly real‑time despite traveling over the internet.Step 8: Review On‑Screen ControlsThe app shows signal strength and data rate. The speaker can be enabled or disabled. The microphone can be activated to allow two‑way communication. Recordings can be accessed from the same interface. The UI is consistent across Energizer Connect cameras.Completion OutcomeThe camera is paired, named, and ready for use. The app provides access to live video, communication features, and recordings.

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

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

It's #340st for 9 July, 2026 or 3312! (33-Oh-twelven) You can find us at our website: http://loosescrewsed.com Discord https://discord.gg/3Vfap47ReaSupport us on Patreon: https://www.patreon.com/LooseScrewsEDSquadron Briefing:  BGS highlights:n/a tonightShort PP Report: Cycle 88:I think a lack of in-game powerplay stat updates from operations instability caused some chaos for powers this week. One of the least productive weeks for nearly all the powers in a while.Princess Aisling didn't seem too phased though, putting up the best week with 13 new systems including a new stronghold.Kaine continues her streak with 8 new systemsBoth Feds continue to face opposition with no new systems for Archer and losing a fortified, and Winters losing a system and a strongholdAll Imperials besides Aisling also having rough weeks with Patreus joining the Feds with a negative week and Torval showing next to zero improvement.https://www.k5elite.com/Dev News: Operations Update 3 - 4.4.0.3https://www.elitedangerous.com/update-notes/4-4-0-3The Operations Update 3 is now live. This update addresses a number of issues, including Operations rewards and lost exploration data.Please note that Missing Operations Rewards and on-Foot inventories will be returned at a later time.Quality of Life:Players unsold exploration data from before the 2nd July Maintenance has now been restored and can be sold as normal. Players can now restore their visited star data cache file via the "Resync Local Data" button located within the Help and Info submenu from the Main Menu.Operations Rewards (Merc Coins, Credits, Materials and Inventory) will be retroactively granted to players at a later date for previously completed Operations which didn't grant rewards as expectedPlayers whose on-foot inventories may have wiped after completing an operation before 2330 UTC Wednesday 2nd will have their inventories restored.Merc Engineering / Merc Modules telegraphing improvements:Improved telegraphing within Outfitting. Improved telegraphing within the Ships Internal Panel. Bug Fixes:Resolved an issue where the Operations Rewards screen could appear empty for some players, despite successfully distributing rewards. Resolved an issue where the "Mission Updated" notification could minimise the Operations Rewards screen. Resolved an issue where the UI could highlight the starting Frontline Runner as a mission objective throughout an Operation. Resolved an issue where if a player dies with an active NPC crew during an Operation, the NPC crew may not respawn upon restart. Resolved instances of some voice-over line and text discrepancies, within the Reclamation Point Operation. Resolved an issue where the wheels of the Scarab SRV could display erratic visual behaviour. Resolved a crash that could occur when opening the Powerplay menu during a hyperspace jump. Resolved an issue where the fighter bay could clip through the Nomad when launching and docking. Resolved an issue where fighter bay doors could clip through support pistons and the main ramp could appear misaligned.Galnet News: Galnet News | Elite Dangerous Community Site  Terri Tora releasedExobiology CG a ‘success'Discussion : Operations: You Brought What?what missions have you solo'd?have you solo'd a hard level mission? What ship/suit/weapon are you using for solowhat changes to your loadout do you make for team fightingif you have a known group of teammates do you see a need or preference for different specially ship/suit/weapon buildout coordinationAny variation for mission type.

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_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/

"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/

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

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

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

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

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.

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.

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