Podcasts about Apis

We're here for a CHIPS Act megapod, in person with Mike Schmidt and Todd Fisher, the director and founding CIO of the CHIPS Program Office, respectively. We discuss… The mechanisms behind the success of the CHIPS Act, What CHIPS can teach us about other industrial policy challenges, like APIs and rare earths, What it takes to build a successful industrial policy implementation team, How the fear of “another Solyndra” is holding back US industrial policy, Chris Miller's recent interest in revitalizing America's chemical industry. This post is a collaboration with the Factory Settings Substack: https://www.factorysettings.org/. Subscribe for more insights from former CHIPS Program Office leaders! Suno song link: https://suno.com/s/wwVYK10LfrAD5zK2 Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode of Next in Media, I sit down live at the Kochava Summit in Sandpoint, Idaho, with Charles Manning, founder and CEO of Kochava. We go deep on one of the most pressing questions facing the industry right now: how profound is the shift to agentic advertising and AI-driven workflows? Charles argues it is not a decade-long evolution like programmatic was. It is breathtakingly faster, and the companies that understand how to use their first-party data as a competitive kernel, rather than leaking it to the walled gardens, are the ones that will come out ahead. He draws a compelling analogy: if programmatic changed the auction, AI is about to change the workflow.We also dig into Kochava's CTV journey, from its mobile app roots to building measurement tools adopted by LG, Samsung, Vizio, and Roku, and how the view-and-do combo between the TV screen and the mobile device is creating powerful new outcome-based measurement opportunities for brands. Charles breaks down what holding companies should fear (and fix), why the ad tech supply chain is due for serious consolidation, and why he predicts a wave of take-privates and roll-ups followed by a bonanza of public offerings over the next two years. He also introduces Station One, Kochava's integrative AI hub that acts like a Slack for AI workflows, designed to help teams transform how they work without giving up control of their data. Key Highlights:⚡ AI vs. Programmatic: Charles explains why the shift to agentic advertising is moving breathtakingly faster than programmatic did. While programmatic took over a decade to fully reshape the auction, AI is set to transform the entire workflow within the next 16 months.

We're here for a CHIPS Act megapod, in person with Mike Schmidt and Todd Fisher, the director and founding CIO of the CHIPS Program Office, respectively. We discuss… The mechanisms behind the success of the CHIPS Act, What CHIPS can teach us about other industrial policy challenges, like APIs and rare earths, What it takes to build a successful industrial policy implementation team, How the fear of “another Solyndra” is holding back US industrial policy, Chris Miller's recent interest in revitalizing America's chemical industry. This post is a collaboration with the Factory Settings Substack: https://www.factorysettings.org/. Subscribe for more insights from former CHIPS Program Office leaders! Suno song link: https://suno.com/s/wwVYK10LfrAD5zK2 Learn more about your ad choices. Visit megaphone.fm/adchoices

Brent Peterson sat down with Jorrit Steinz, founder and CEO of ChannelEngine, to discuss one of the most transformative shifts in ecommerce today: agentic commerce. The conversation covered how brands and retailers must rethink their multi-channel strategies now that AI-powered agents, from ChatGPT to Microsoft Copilot, are becoming transactional shopping platforms. With marketplaces multiplying, social commerce expanding, and LLMs entering the buying funnel, the episode delivered a forward-looking perspective on what merchants need to do right now to stay competitive.TakeawaysThe ultimate vision of agentic is consumer empowerment.Consumers will deploy agents to find products online.Agents will scrape the internet for purchasing options.In B2B, agents will facilitate shopping across platforms.Automation will enhance the shopping experience.The future of shopping involves digital agents.Agents will present curated options to consumers.B2B transactions will become more efficient with agents.The role of agents is expanding in digital commerce.Consumer agents will revolutionize how we buy. Chapters00:00 Introduction to Channel Engine and E-commerce Passion00:23 The Role of APIs and Data Feeds in E-commerce

Scott and Wes unpack WebMCP, a new standard that lets AI interact with websites through structured tools instead of slow, bot-style clicking. They demo it, debate imperative vs declarative APIs, and share their hottest take: this might be the web's real AI moment. Show Notes 00:00 Welcome to Syntax! 00:16 Introduction to WebMCP 01:07 Understanding WebMCP Functionality. 03:06 Interacting with AI through WebMCP. 06:49 WebMCP browser integration. 08:25 Brought to you by Sentry.io. 08:49 Benefits of WebMCP. 11:51 Token efficiency. 13:02 My biggest questions. 14:13 My take on this tech. 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

In this episode, I'm joined by Bill Briggs, CTO at Deloitte, for a straight-talking conversation about why so many organizations get stuck in what he calls "pilot purgatory," and what it takes to move from impressive demos to measurable outcomes. Bill has spent nearly three decades helping leaders translate the "what" of new technology into the "so what," and the "now what," and he brings that lens to everything from GenAI to agentic systems, core modernization, and the messy reality of technical debt. We start with a moment of real-world context, Bill calling in from San Francisco with Super Bowl week chaos nearby, and the funny way Waymo selfies quickly turn into "oh, another Waymo" once the novelty fades. That same pattern shows up in enterprise tech, where shiny tools can grab attention fast, while the harder work, data foundations, APIs, governance, and process redesign, gets pushed to the side. Bill breaks down why layering AI on top of old workflows can backfire, including the idea that you can "weaponize inefficiency" and end up paying for it twice, once in complexity and again in compute costs. From there, we get into his "innovation flywheel" view, where progress depends on getting AI into the hands of everyday teams, building trust beyond the C-suite, and embedding guardrails into engineering pipelines so safety and discipline do not rely on wishful thinking. We also dig into technical debt with a framing I suspect will stick with a lot of listeners. Bill explains three types, malfeasance, misfeasance, and non-feasance, and why most debt comes from understandable trade-offs, not bad intent. It leads into a practical discussion on how to prioritize modernization without falling for simplistic "cloud good, mainframe bad" narratives. We finish with a myth-busting riff on infrastructure choices, a quick look at what he sees coming next in physical AI and robotics, and a human ending that somehow lands on Beach Boys songs and pinball machines, because tech leadership is still leadership, and leaders are still people. So after hearing Bill's take, where do you think your organization is right now, measurable outcomes, success theater, or somewhere in between, and what would you change first, and please share your thoughts?   Useful Links Connect With Bill Briggs Deloitte Tech Trends 2026 report Deloitte The State of AI in the Enterprise report

Justin Moon leads the open source ai initiative at the Human Rights Foundation.Justin on Nostr: https://primal.net/justinmoonHuman Rights Foundation: https://hrf.org/program/ai-for-individual-rights/Easy Open Claw Deployment: https://clawi.ai/EPISODE: 191BLOCK: 936962PRICE: 1473 sats per dollar(00:01:35) Justin Moon and early show memories(00:03:52) OpenClaw(00:04:16) Agents change how we use computers(00:07:07) OpenClaws light bulb moment(00:09:25) Agents as UX glue for Freedom Tech(00:10:00) HRF AI work, self-hosting breakthrough, and running your own stack(00:12:50) AI simplifies hard Bitcoin UX: coin control, backups, photos(00:14:22) OpenClaw + OpenAI: does it matter?(00:16:01) AI leverage for builders: open protocols win(00:19:22) Positive feedback loop: agents and open protocols(00:20:14) Costs vs privacy: local models, token spend, and KYC walls(00:23:15) Local hardware economics and historical parallels(00:27:20) Will capability gaps narrow? Mobile and on-device futures(00:29:56) Cutting-edge vs private setups; data lock-in and training moats(00:31:53) Competition, regulation risks, and hidden capabilities(00:34:05) Chinas open models: incentives, biases, and global adoption(00:38:56) American and European open models; Big Tech dynamics(00:40:56) Apple, hardware positioning, and agent UX form factors(00:42:48) Googles advantage: data, integration, and vertical stack(00:44:32) Acceleration ahead: productivity leaps and societal shifts(00:45:21) Jobs, layoffs, and disruptive labor realignment(00:47:55) From global commons to gated neighborhoods: bots and slop(00:50:21) Nostr as local internet: webs of trust and bot filters(00:51:57) Cancel culture contagion and shrinking public square(00:54:59) Demographic decentralization and small-town resilience(00:55:00) Lean platforms: X/Twitter staffing as canary(00:56:59) Universal high income: incentives and realism(00:58:48) Prepare your household: seize tools, avoid flat feet(01:01:01) Marmot DMs over Nostr: agents need open messaging(01:03:11) Building Pika: encrypted chat and voice over Marmot(01:07:00) Generative UI and real-time media over Nostr(01:10:07) APIs, bans, and why open protocols become the convenient path(01:14:02) Future gates: Bitcoin paywalls, webs of trust, or dystopian KYC(01:17:19) Getting started: try OpenClaw safely and learn by play(01:22:14) Agents, Cashu, and Lightning UX: bots as channel managers(01:25:10) Federations run by machines? Enclaves and AI guardians(01:27:50) Maple, Vora, and bringing self-sovereign AI to mainstream(01:29:00) Security kudos and caveats; Coinbase and cold storage(01:30:02) Justins education plan and upcoming streamsmore info on the show: https://citadeldispatch.comlearn more about me: https://odell.xyz

Máscaras, disfraces, confeti y costosas carrozas para la política mundialComo en las saturnales y lupercales, como en las fiestas del toro Apis, desde el Mardi Grass de Nueva Orleans a Venecia con sus máscaras, scolas do samba, comparsas, diabladas, repique de los tambres del candome, murgas y todo lo que podemos celebrar en esta triste realidad de discriminación, persecución, violencia y ambiciónECDQEMSD podcast episodio 6241 Carnaval DescarnadoConducen: El Pirata y El Sr. Lagartija https://canaltrans.comNoticias Del Mundo: Navalni murió envenenado - El pentágono usó I.A. para capturar a Maduro - Ju-ae La hija de Kim Jong-un - Una popularidad sin medida - Acomodando la agenda - Therian fuera de control - La grasa de las Capitales - El monito de la regidoraHistorias Desintegradas: La maquina sensual - Demasiada presión - En las cosas del amor - Regresar el producto - En la fría Punta Arenas - Lo que no quería escuchar - Tampoco sabe bailar? - Kempes en el 78 - Error en el registro - Cigarrillos larguísimos - Nombre original - Pleno carnaval - Las ladronas - Almendras empanizadas - El almendro - Amores imposibles y más...En Caso De Que El Mundo Se Desintegre - Podcast no tiene publicidad, sponsors ni organizaciones que aporten para mantenerlo al aire. Solo el sistema cooperativo de los que aportan a través de las suscripciones hacen posible que todo esto siga siendo una realidad. Gracias Dragones Dorados!!NO AI: ECDQEMSD Podcast no utiliza ninguna inteligencia artificial de manera directa para su realización. Diseño, guionado, música, edición y voces son de  nuestra completa intervención humana. 

Human-Centric Merchant Services: Optimizing Payment Systems with Jimmy EstradaIn this episode ofThe Thoughtful Entrepreneur Podcast, host Josh Elledge sits down with Jimmy Estrada, the Founder and Owner ofJELA Payments Systems, to demystify the complex world of merchant services. Jimmy shares how his firm bridges the gap between massive, faceless payment processors and the independent business owners who often find themselves stranded when technical glitches or funding holds arise. This conversation offers a strategic look at how a high-touch, consultative approach to payment systems can help B2B firms and retailers reclaim their profit margins, mitigate risk, and ensure that their financial "plumbing" remains reliable and transparent in an increasingly automated marketplace.The Power of Personal Partnership in Payment ProcessingThe modern payment landscape is dominated by automated "plug-and-play" solutions, yet many business owners discover the limitations of these platforms only when a crisis occurs. Jimmy explains that the primary value of a merchant services partner lies in providing a direct human advocate who understands the specific risk profile of a client's industry. When funds are unexpectedly held or a technical integration fails, having a dedicated account manager often means the difference between a 24-hour resolution and weeks of lost revenue. By moving beyond a "set it and forget it" mentality, businesses can proactively address industry-specific risk factors—such as those found in medical or legal services—before they escalate into costly holds or compliance headaches.Transparency in pricing remains one of the greatest challenges for entrepreneurs, as merchant statements are notoriously difficult to decipher. Jimmy advocates for a "clear-water" approach to fee structures, emphasizing that business owners should have a granular understanding of non-negotiable interchange fees versus provider markups. Whether a business utilizes an interchange-plus model, compliant surcharging, or dual pricing, the key to long-term profitability is consistent monitoring and "junk fee" audits. These regular reviews ensure that businesses aren't paying for redundant services or hidden charges that frequently creep into statements over time, allowing leaders to reinvest those savings back into their core operations.Optimization is not just about chasing the lowest possible rate; it is about ensuring that a payment system is fully integrated with a company's existing software and customer journey. Jimmy discusses how his firm works with various hardware and software vendors to create seamless APIs that simplify the checkout experience for both in-person and card-not-present transactions. For businesses lacking in-house technical expertise, a trusted payment partner acts as an outsourced department that manages the technical burden of PCI compliance and security updates. Ultimately, a true partnership is built on integrity—where the provider prioritizes the client's long-term stability over a quick sale, even if that means advising a client to stay with their current provider if the rates are already fair.About Jimmy EstradaJimmy Estrada is the Founder and Owner of JELA Payments Systems, where he leverages over a decade of experience in the merchant services industry. Known for his "integrity-first" approach, Jimmy specializes in helping high-volume and B2B merchants navigate the technical and financial complexities of credit card processing with a focus on education and personalized support.About JELA Payments SystemsJELA Payments Systems is a merchant services provider that offers customized payment solutions ranging from mobile processing to enterprise-level integrations. The company prioritizes human-to-human interaction, providing dedicated account management...

In this masterclass episode, Favour Obasi-ike, MBA, MS delivers an in-depth exploration of web sales optimization (CRO - conversation rate optimization) through strategic search engine marketing (SEM). The episode focuses on the critical relationship between website speed and conversion rates, revealing how technical optimization directly impacts sales performance. Favour emphasizes that web sales are fundamentally a result of web speed, explaining that websites loading slower than 3 seconds can decrease conversion rates by at least 7%, with compounding effects reaching 20% for sites taking 10 seconds to load.The discussion covers comprehensive website optimization strategies, including image optimization (recommending WebP format over JPEG/PNG), structured data implementation with schema markup, and the importance of optimizing every website element from headers and footers to file names and internal linking structures. Favour introduces the concept of treating URLs like seeds that need time to grow, recommending a 2-3 month planning horizon for content strategy.The masterclass also explores collection pages, category optimization, and the strategic use of content hubs to create pathways for user navigation. Favour shares practical tools and resources for keyword research and competitive analysis, while emphasizing the importance of submitting websites to Google Search Console and Bing Webmaster Tools for maximum visibility. The episode concludes with actionable advice on implementing these strategies either independently or through professional SEO consultation.Book SEO Services | Quick Links for Social Business>> ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Book SEO Services with Favour Obasi-ike⁠>> Visit Work and PLAY Entertainment website to learn about our digital marketing services>> ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Join our exclusive SEO Marketing community⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠>> Read SEO Articles>> ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Subscribe to the We Don't PLAY Podcast⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠>> Purchase Flaev Beatz Beats Online>> Favour Obasi-ike Quick Links

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

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In this episode of the FocusCore podcast, host David Sweet interviews Pieter Franken, a FinTech pioneer and innovator with a rich history in finance, technology, and regulation. Pieter outlines his journey from a trainee scientist in Holland to becoming a key figure in Japan's FinTech ecosystem. He shares his experiences and significant contributions, including his roles at Citigroup, Shinsei Bank, and the Monetary Authority of Singapore, as well as his co-founding of Safecast, a crucial citizen science project post-Fukushima. The conversation delves into the evolution of FinTech globally and in Japan, discussing the impact of cloud computing, APIs, and mobile technology, as well as governmental roles in stimulating innovation. Pieter also talks about the upcoming Global Finance and Technology Network Forum (GFTN) in Tokyo, highlighting its importance for FinTech discussion and networking. Additionally, he touches upon the complexity of integrating new technologies into human systems and stresses the need for adaptive, human-centric innovations.The 2026 FocusCore Salary Guide is here: 2026 Salary GuideIn this episode you will hear:Pieter Franken's journey from Holland to Japan and his pivotal role in the FinTech industryThe evolution of Japan's startup ecosystem and cultural shifts in recent decadesThe role and impact of cloud computing and mobile technology in FinTech's growthThe establishment of Safecast and its significance in citizen science and data transparencyThe importance and anticipation of the Global Finance and Technology Network Forum in fostering industry dialogue and innovationAbout Pieter:Pieter is a fintech pioneer, deep-tech innovator, and builder with over three decades at the intersection of finance, technology, regulation, and public good.He's held senior leadership roles at institutions like Citigroup, Shinsei Bank, Monex, and UnionDigital Bank; advised regulators including the Monetary Authority of Singapore; and helped build global platforms focused on financial inclusion, open data, and responsible innovation. He's also a co-founder of Safecast, one of the most influential citizen-science projects in the world, and a driving force behind Japan's fintech ecosystem through Elevandi and the Japan Fintech Festival.Connect with Pieter:LinkedIn: https://www.linkedin.com/in/pbfranken/GFTN Japan: http://gftn.co/japanGFTN Forum[Feb 24-27th 2026]: https://gftnforum.jp/?utm_medium=paid-search&utm_source=google&utm_term=gftn%20japan&utm_content=branded-insights-learning&utm_campaign=gfj_2026_jp_search_traffic_280126&_gl=1*1uvuohl*_up*MQ..*_gs*MQ..&gclid=Cj0KCQiA7rDMBhCjARIsAGDBuEBY1bz21ngvAnJufNmlwr_Kxe4Ku6uz1R5I6beSQvq5Qiv7h2aZCHkaAmI3EALw_wcB&gbraid=0AAAAAqxUcYm4HHIROSay5zHtoL9JyGrmmConnect with David Sweet:LinkedIn: https://www.linkedin.com/in/drdavidsweet/Twitter: https://twitter.com/focuscorejpFacebook: :https://www.facebook.com/focuscoreasiaInstagram:

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

-Introduction TomTom isn't just “navigation” anymore. In this episode of the InsurTech Leadership Podcast, Josh Hollander talks with Vinod Poomalai, Head of InsurTech Product Marketing at TomTom, about how insurers are using location, map, and traffic intelligence to improve how they price risk, validate claims, and build telematics programs that actually produce underwriting signal. Guest bio Vinod Pumalai leads go-to-market for TomTom's insurance and InsurTech vertical. His work focuses on helping carriers, brokers, actuaries, and InsurTech teams integrate TomTom's location and traffic data into risk models, pricing engines, and telematics workflows—with a practical emphasis on adoption inside real operating environments. Key topics -From static territories to live location intelligence -Why “territory” is a crude proxy—and how mobility patterns add resolution to risk. -Territory risk models powered by traffic + map data -How insurers use location and traffic attributes to refine pricing and portfolio strategy. -Telematics enablement: APIs, SDKs, and flexible integration -What teams actually plug into, and what the implementation path looks like in practice. -Claims validation and fraud detection using mobility history -Using historical mobility/traffic context to validate events faster and reduce leakage. -Where experimentation becomes operational value -The difference between demos and workflows that move loss ratio outcomes. -What the insurance market is missing in location data -Why the market has been underserved—and what that creates as an opportunity. -Talent and leadership required to make it real -Product, data, and insurance domain collaboration: what “good” looks like inside carriers. Quotes -“The insurance market as a whole is very underserved when it comes to location data, traffic data, and so on.” -“At the end of the day, what our clients really care about… is loss ratios.” -“Customers are leveraging our traffic data in validating auto insurance claims.” Resources -Vinod Poomalai: https://www.linkedin.com/in/vinod-kumar-poomalai/ -TomTom Insurtech: https://www.tomtom.com/solutions/insurtech/ -Joshua Hollander: https://www.linkedin.com/in/joshuarhollander/ If you found this useful, subscribe to the InsurTech Leadership Podcast on YouTube and your preferred podcast app. Share the episode with an underwriting, claims, or telematics leader on your team—and leave a review to help more operators find the show.

In this episode of the Shift AI Podcast, Casey Mulligan—former Chief Economist of the White House Council of Economic Advisers and current Chief Counsel for Advocacy at the U.S. Small Business Administration—joins Boaz Ashkenazy for a timely conversation on how AI is reshaping small businesses, regulation, and the broader labor market.Casey shares his path from University of Chicago professor to serving in two presidential administrations, where he introduced automated reasoning tools into economic policy work well before the rise of large language models. He explains how his office now uses AI to review thousands of federal regulations and ensure small business voices are represented in Washington.The discussion explores accelerating AI adoption among small firms, the recent surge in new business formation, and why smaller companies may benefit more from AI than large incumbents. Casey also addresses concerns about job displacement, drawing lessons from past waves of automation and outlining why he believes the long-term impact will be increased productivity and opportunity.The episode closes with a forward-looking perspective on education, entrepreneurship, and why the “human touch” will remain a critical advantage in the future of work.Chapters[00:00] From University of Chicago to the White House[03:05] Advocating for Small Businesses in Washington[07:29] AI and the Labor Market: Lessons from Economic History[12:14] The Startup Surge and Small Business Formation[13:48] Using AI Inside the Federal Government[17:20] Vibe Coding, APIs, and Custom Productivity Tools[18:07] Automated Reasoning and Microsoft's Z3[21:23] AI in Education and Learning[24:31] Two Words for the Future of Work: Human TouchConnect with Casey Bryant MulliganLinkedIn: https://www.linkedin.com/in/casey-bryant-mulligan/Connect with Boaz AshkenazyLinkedIn: https://www.linkedin.com/in/boazashkenazy/Email: info@shiftai.fm

Patrick McKenzie (patio11) reads an essay about "industrial-scale" fraud and why it should be treated as a professional business process rather than a series of isolated accidents. He explains how fraudsters leverage specialized supply chains—shared CPAs, incorporation agents, and "least attentive" banks—to loot public funds. Patrick argues that the government's "pay-and-chase" model is fundamentally broken and suggests that simple "proof of work" functions, like a 30-second cell phone video of a workspace, could provide the visceral signal that paperwork lacks, and examines the state's lack of "object permanence" regarding serial fraudsters and how scaled data provides the defense-side advantage needed to catch modern frauds.–Full transcript available here: www.complexsystemspodcast.com/fraud-as-infrastructure/–Presenting Sponsor: Mercury Complex Systems is presented by Mercury—radically better banking for founders. Mercury offers the best wire experience anywhere: fast, reliable, and free for domestic U.S. wires, so you can stay focused on growing your business. Apply online in minutes at mercury.com.Mercury is a fintech company, not an FDIC-insured bank. Banking services provided through Choice Financial Group and Column N.A., Members FDIC.–Links:Bits about Money: https://www.bitsaboutmoney.com/archive/fraud-investigation/ Dan Davies on Complex Systems: https://open.spotify.com/episode/5QKxzgumJXSQuaWCmYAoM9 Jetson Leder-Luis on Complex Systems podcast: https://open.spotify.com/episode/3NiC7x9edoxJXkNW9vRfAT Stripe's Emily Sands on Complex Systems: https://open.spotify.com/episode/64Dyh6Gbg1lg4qUFwId0hc –Timestamps:(00:00) Intro(05:23) In which we briefly return to Minnesota(09:26) Common signals, methods, and epiphenomena of fraud(09:30) Fraudsters are playing an iterated game(11:29) The fraud supply chain is detectable(14:27) Investigators should expect to find ethnically clustered fraud(20:11) Sponsor: Mercury(21:47) High growth rate opportunities attract frauds(26:04) Fraudsters find the weakest links in the financial system(32:35) Frauds openly suborn identities(35:57) Asymmetry in attacker and defender burdens of proof(40:13) Fraudsters under-paperwork their epiphenomena(44:22) Machine learning can adaptively identify fraud(48:14) Frauds have a lifecycle(50:34) Should we care about fraud investigation, anyway

In a podcast recorded at ITEXPO / MSP EXPO, Doug Green, Publisher of Technology Reseller News, spoke with Mike Wehrs, CTO of TieTechnology, about the upcoming launch of Genie 1.1 and the company's broader mission to reposition voice as a fully integrated component of modern IT infrastructure. TieTechnology focuses on making voice a “first-tier partner” within business systems rather than a disconnected afterthought. Genie, the company's SMB product family, provides a backend softphone capability for PCs along with applications that connect voice into tools such as Slack, CRMs, and EMRs. With Genie 1.1, the company is deepening its ability to capture, transcribe, summarize, and structure voice interactions so that the most valuable customer data—what was actually said—flows directly into business systems. “AI is not magic,” Wehrs noted. “If you don't have good data going into the system, you're not going to get the results out of it that you want.” He emphasized that many organizations layer AI on top of incomplete infrastructure, resulting in underperformance. Genie addresses that gap by cleaning audio streams, identifying speakers, summarizing conversations, and delivering structured data—often in JSON format—into CRM environments. The result, according to Wehrs, can represent as much as a 40 percent increase in high-quality CRM data, driving better customer support, marketing automation, and operational insight. For MSPs, the opportunity is twofold. First, Genie simplifies voice integration through straightforward APIs, eliminating the need to understand complex SIP stacks or telecom architecture. Second, it opens new revenue potential by allowing MSPs to modernize dated phone systems and embed voice-driven intelligence directly into client workflows. As Wehrs framed it, voice should become as native to the PC environment as networking did in the Windows 95 era—fully integrated, flexible, and foundational to digital operations. Visit https://tietechnology.com/

There's a company who spent almost $50,000 because an agent went into an infinite loop and they forgot about it for a month.It had no failures and I guess no one was monitoring these costs. It's nice that people do write about that in the database as well. After it happened, they said: watch out for infinite loops. Watch out for cascading tool failures. Watch out for silent failures where the agent reports it has succeeded when it didn't!We Discuss:* Why the most successful teams are ripping out and rebuilding their agent systems every few weeks as models improve, and why over-engineering now creates technical debt you can't afford later;* The $50,000 infinite loop disaster and why “silent failures” are the biggest risk in production: agents confidently report success while spiraling into expensive mistakes;* How ELIOS built emergency voice agents with sub-400ms response times by aggressively throwing away context every few seconds, and why these extreme patterns are becoming standard practice;* Why DoorDash uses a three-tier agent architecture (manager, progress tracker, and specialists) with a persistent workspace that lets agents collaborate across hours or days;* Why simple text files and markdown are emerging as the best “continual learning” layer: human-readable memory that persists across sessions without fine-tuning models;* The 100-to-1 problem: for every useful output, tool-calling agents generate 100 tokens of noise, and the three tactics (reduce, offload, isolate) teams use to manage it;* Why companies are choosing Gemini Flash for document processing and Opus for long reasoning chains, and how to match models to your actual usage patterns;* The debate over vector databases versus simple grep and cat, and why giving agents standard command-line tools often beats complex APIs;* What “re-architect” as a job title reveals about the shift from 70% scaffolding / 30% model to 90% model / 10% scaffolding, and why knowing when to rip things out is the may be the most important skill today.You can also find the full episode on Spotify, Apple Podcasts, and YouTube.You can also interact directly with the transcript here in NotebookLM: If you do so, let us know anything you find in the comments!

AI isn't just coming from Silicon Valley anymore. A growing number of startups — and companies like Airbnb — are turning to Chinese open-source AI models instead of US-based APIs. Not because it's trendy. Because it's cheaper, more flexible, and often good enough. In this episode, Sophia Matveeva speaks with Alex Hern, AI correspondent at The Economist, about what's driving this shift. They break down how DeepSeek disrupted the market, why constraints fueled smarter engineering, and what founders can realistically try today if they want more AI options without more spend. Alex Hern is The Economist's AI Writer, focusing on the science and technology of artificial intelligence. Before joining the paper, he covered technology for 11 years at The Guardian, where he was the UK technology editor. In this episode, you will hear: Why relying on US AI APIs may be quietly limiting your product and your margins How Chinese open-source models let founders experiment, customize, and ship faster without runaway costs The real reason DeepSeek shocked Silicon Valley — and what it reveals about building under constraints What you can realistically try today if you want AI leverage without an AI-sized budget Free AI Mini-Workshop for Non-Technical Founders Learn how to go from idea to a tested product using AI — in under 30 minutes. Get free access here: techfornontechies.co/aiclass Follow and Review: We'd love for you to follow us if you haven't yet. Click that purple '+' in the top right corner of your Apple Podcasts app. We'd love it even more if you could drop a review or 5-star rating over on Apple Podcasts. Simply select "Ratings and Reviews" and "Write a Review" then a quick line with your favorite part of the episode. It only takes a second and it helps spread the word about the podcast. Episode Credits If you like this podcast and are thinking of creating your own, consider talking to my producer, Emerald City Productions. They helped me grow and produce the podcast you are listening to right now. Find out more at https://emeraldcitypro.com Let them know we sent you. For the full transcript, go to https://www.techfornontechies.co/blog/290-why-airbnb-switched-from-openai-to-chinese-ai-and-what-it-means-for-your-budget  

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

Scott Cannon, CEO of BigRentz, shares why AI is overhyped in the short term but massively underhyped for the long term. We discuss why most AI projects fail, why integrations and APIs create more value today, and how data will reshape the rental industry over the next decade.

When it comes to agents and MCPs, the interesting security discussion isn't that they need strong authentication and authorization, but what that authn/z story should look like, where does it get implemented, and who implements it. Dan Moore shares the useful parallels in securing APIs that should be brought into the world of MCPs -- especially because so many are still interacting with APIs. Resources https://stackoverflow.blog/2026/01/21/is-that-allowed-authentication-and-authorization-in-model-context-protocol/ https://fusionauth.io/articles/identity-basics/authorization-models Visit https://www.securityweekly.com/asw for all the latest episodes! Show Notes: https://securityweekly.com/asw-369

Connect with Early Riders // Connect with OnrampPresented collaboratively by Early Riders & Onramp Media…Final Settlement is a weekly podcast covering capital markets, dealmaking, early-stage venture, bitcoin applications and protocol development.00:00 - Introduction and Context Setting02:13 - Market Sentiment and Price Action07:12 - Tether's Position and Strategic Moves13:12 - Erebor's National Banking Charter and Industry Implications27:39 - The Future of Banking and Digital Assets30:16 - APIs and the Evolution of Financial Services33:15 - Stablecoins and the Acceleration of Crypto Adoption35:23 - The Transformation of Financial Services through Digital Assets39:00 - Tokenized Cash and the Role of CME Coin43:59 - The Rise of AI Bots in Financial Transactions47:58 - Bitcoin vs. Stablecoins: The Future of Digital Currency51:02 - Decentralized Infrastructure and the Future of TokenomicsIf you found this valuable, please subscribe to Early Riders Insights for access to the best content in the ecosystem weekly.Links discussed:https://www.theblock.co/post/388540/cme-group-tokenized-cash-coin-developed-google-use-crypto-collateral-roll-out-this-yearhttps://au.finance.yahoo.com/news/exclusive-escape-velocity-raises-62-130000621.htmlhttps://x.com/intangiblecoins/status/2019056067930477043?s=20https://x.com/tether/status/2019520461302804919?s=20https://x.com/TFTC21/status/2019826399641846125?s=20https://finance.yahoo.com/news/pave-bank-secures-39m-funding-163111664.htmlhttps://finance.yahoo.com/news/palmer-luckey-backed-erebor-receives-225304165.htmlhttps://www.anchorage.com/insights/anchorage-digital-tether-introduce-usathttps://tether.io/news/tether-announces-100-million-strategic-equity-investment-in-anchorage-digital/https://archive.ph/odfMv#selection-1577.0-1577.68Keep up with Michael:https://x.com/MTangumahttps://www.linkedin.com/in/mtanguma/Keep up with Brian:https://x.com/BackslashBTChttps://www.linkedin.com/in/brian-cubellis-00b1a660/Keep up with Liam:https://x.com/Lnelson_21https://www.linkedin.com/in/liam-nelson1/

When it comes to agents and MCPs, the interesting security discussion isn't that they need strong authentication and authorization, but what that authn/z story should look like, where does it get implemented, and who implements it. Dan Moore shares the useful parallels in securing APIs that should be brought into the world of MCPs -- especially because so many are still interacting with APIs. Resources https://stackoverflow.blog/2026/01/21/is-that-allowed-authentication-and-authorization-in-model-context-protocol/ https://fusionauth.io/articles/identity-basics/authorization-models Show Notes: https://securityweekly.com/asw-369

When it comes to agents and MCPs, the interesting security discussion isn't that they need strong authentication and authorization, but what that authn/z story should look like, where does it get implemented, and who implements it. Dan Moore shares the useful parallels in securing APIs that should be brought into the world of MCPs -- especially because so many are still interacting with APIs. Resources https://stackoverflow.blog/2026/01/21/is-that-allowed-authentication-and-authorization-in-model-context-protocol/ https://fusionauth.io/articles/identity-basics/authorization-models Visit https://www.securityweekly.com/asw for all the latest episodes! Show Notes: https://securityweekly.com/asw-369

In this week's episode of the Security Sprint, Dave and Andy covered the following topics:Open:• TribalHub 6th Annual Cybersecurity Summit, 17–20 Feb 2026, Jacksonville, Florida• Congress reauthorizes private-public cybersecurity framework & Cybersecurity Information Sharing Act of 2015 Reauthorized Through September 2026• AMWA testifies at Senate EPW Committee hearing on cybersecurity Main Topics:Terrorism & Extremismo Killers without a cause: The rise in nihilistic violent extremism — The Washington Post, 08 Feb 2026 o Terrorists' Use of Emerging Technologies Poses Evolving Threat to International Peace, Stability, Acting UN Counter-Terrorism Chief Warns Security Council United Nations / Security Council, 04 Feb 2026 OpenClaw: The Helpful AI That Could Quietly Become Your Biggest Insider Threat – Jamf Threat Labs, 09 Feb 2026. Jamf profiles OpenClaw as an autonomous agent framework that can run on macOS and other platforms, chain actions across tools, maintain long term memory and act on high level goals by reading and writing files, calling APIs and interacting with messaging and email systems. The research warns that over privileged agents like this effectively become new insider layers once attackers capture tokens, gain access to control interfaces or introduce malicious skills, enabling data exfiltration, lateral movement and command execution that look like legitimate automation. The rise of Moltbook suggests viral AI prompts may be the next big security threat; We don't need self-replicating AI models to have problems, just self-replicating prompts.• From magic to malware: How OpenClaw's agent skills become an attack surface • Exposed Moltbook database reveals millions of API keys • The rise of Moltbook suggests viral AI prompts may be the next big security threat • OpenClaw & Moltbook: AI agents meet real-world attack campaigns • Malicious MoltBot skills used to push password-stealing malware • Moltbook reveals AI security readiness • Moltbook exposes user data via API • OpenClaw: Handing AI the keys to your digital life Quick Hits:• Active Tornado Season Expected in the US • CISA Directs Federal Agencies to Update Edge Devices – GovInfoSecurity, 05 Feb 2026 & read more from CISA: Binding Operational Directive 26-02: Mitigating Risk From End-of-Support Edge Devices – CISA, 05 Feb 2026. • A Technical and Ethical Post-Mortem of the Feb 2026 Harvard University ShinyHunters Data Breach • Hackers publish personal information stolen during Harvard, UPenn data breaches • Two Ivy League universities had donor information breaches. Will donors be notified?• Harassment & scare tactics: why victims should never pay ShinyHunters • Please Don't Feed the Scattered Lapsus$ & ShinyHunters • Mass data exfiltration campaigns lose their edge in Q4 2025 • Executive Targeting Reaches Record Levels as Threats Expand Beyond CEOs • Notepad++ supply-chain attack: what we know • Summary of SmarterTools Breach and SmarterMail CVEs • Infostealers without borders: macOS, Python stealers, and platform abuse

This week's episode is packed with deep React Native ecosystem updates, a clear shift toward AI-first tooling, and some really positive momentum on Tiny Harvest. We talk new APIs, better performance, smarter automation - and why it feels like AI has officially crossed a tipping point for most developers.⚛️ React Native Radar⏳ Expo SDK 55 – still not released, likely 1–2 weeks out

In dieser Folge setzen wir dort an, wo wir mit der vorherigen ARIA-Glücksrad-Folge aufgehört haben. Denn wir haben nach der Veröffentlichung tolles Feedback bekommen und holen uns deren Absender als V…

Hand aufs Herz: Wie viele Domains hast du gekauft, die heute nur noch als jährliche Renew Mail existieren? Genau mit diesem Reality Check steigen wir ein und biegen dann scharf ab: nicht Webdomains, sondern Domain Driven Design.In dieser Episode machen wir DDD greifbar, ohne dass du direkt ein 560-Seiten-Buch heiraten musst. Wir klären, welches Problem Domain Driven Design eigentlich löst, warum Teams in großen Systemen so oft in Spaghetti Code, technische Schulden und Kommunikationschaos rutschen und weshalb eine Ubiquitous Language, also eine gemeinsame, allgegenwärtige Sprache, oft der erste echte Hebel ist.Danach geht es ans strategische Design: Bounded Contexts, Context Mapping, Schnittstellen zwischen Teams und warum das verdächtig nah an Conway's Law, APIs und realen Teamstrukturen ist. Und ja, wir schauen auch auf die taktische Seite: Value Objects, Entities, Aggregates, Repositories, Domain Events, plus der Klassiker aus der Anti-Pattern-Ecke: das anämische Domänenmodell.Wir sprechen außerdem darüber, wie du pragmatisch startest, auch in bestehenden Codebasen, wer das im Team treiben kann, und warum Konsistenz im Naming gerade mit LLMs und AI Coding Tools plötzlich noch mehr zählt als früher.Wenn du wissen willst, ob DDD wirklich Enterprise Buzzword Bingo ist oder einfach der Name für verdammt gute Softwarearchitektur, dann bleib dran.Unsere aktuellen Werbepartner findest du auf https://engineeringkiosk.dev/partnersDas schnelle Feedback zur Episode:

When it comes to agents and MCPs, the interesting security discussion isn't that they need strong authentication and authorization, but what that authn/z story should look like, where does it get implemented, and who implements it. Dan Moore shares the useful parallels in securing APIs that should be brought into the world of MCPs -- especially because so many are still interacting with APIs. Resources https://stackoverflow.blog/2026/01/21/is-that-allowed-authentication-and-authorization-in-model-context-protocol/ https://fusionauth.io/articles/identity-basics/authorization-models Show Notes: https://securityweekly.com/asw-369

Alex Gleason was one of the main architects behind Donald Trump's Truth Social. Now he focuses on the intersection of nostr, ai, and bitcoin. We explore open source ai agents, such as OpenClaw, and the wider implications of the tech on society.Alex on Nostr: https://primal.net/p/nprofile1qqsqgc0uhmxycvm5gwvn944c7yfxnnxm0nyh8tt62zhrvtd3xkj8fhggpt7fyClawstr: https://clawstr.com/Soapbox Tools: https://soapbox.pub/toolsMy bot's nostr account: https://primal.net/p/nprofile1qqsfzaahg24yf7kujwrzje8rwa7xmt359tf9zyyjeczc9dhll30k8pgmlfee2 EPISODE: 190BLOCK: 935786PRICE: 1422 sats per dollar(00:02:30) Value-for-value, no sponsors, and show philosophy(00:02:39) Alex Gleason returns to talk AI(00:03:56) From vibe coding to open-source agents with memory(00:05:24) Messaging-first UX: Signal, Nostr, WhatsApp as AI interfaces(00:06:10) Why chatbots beat traditional AI apps for mainstream users(00:07:07) Open protocols pain vs closed platforms; Bitcoin and Nostr(00:08:52) Automating social games: price tracker and agent posting on Nostr(00:10:01) AI mediators for collective action, constitutions, and nonprofits(00:11:46) Scaling governance: trust, bias, and Discord vs freedom tech(00:13:14) Bot barriers on centralized messengers and need for open chat(00:14:04) Clawstr: decentralized AI-to-AI discussions on Nostr(00:15:21) Hype vs reality in AI agents; emergent behaviors and money(00:16:26) Agentic payments: bots with Cashu wallets and earnings(00:18:40) Agents solving UX pain: relay management, keys, and UTXOs(00:20:00) Cold storage approvals with chat agents: a new wallet paradigm(00:20:22) Specialized agents, skills, and distribution challenges(00:22:34) Cost tradeoffs: pay another agent vs build skills yourself(00:24:55) Token burn lessons(00:27:44) Beyond OpenClaw: bloated stacks, Icarus, and cost-optimized agents(00:28:52) Hybrid model routing: local small models with cloud for heavy lifts(00:29:47) Agents paying humans directly: disintermediating platforms(00:30:47) Voice, screens, and form factors: AirPods, text, and brain chips(00:33:01) Apple, privacy branding, and the Siri gap(00:34:35) Enterprise AI choices: Google, Microsoft, trust, and lock-in(00:36:01) Model personalities: Gemini concerns and OpenAI "openwashing"(00:37:23) Obvious agent UX wins: flights, rides, and social media shifts(00:38:50) Local-first social: group chats, neighbors, and healthier networks(00:40:16) Antiprimal.net: standardizing stats from Primal's caching server(00:43:34) Open specs, documentation via AI, and trust tradeoffs(00:45:18) Indexes vs client-side scans: performance and verification(00:46:20) APIs, rate limits, and a market for paid Nostr data(00:47:57) Agents and DVMs: paying sats for services on demand(00:48:49) Degenerate bots: LN Markets, costs, and Polymarket curiosity(00:50:42) Truth feeds for agents: Nostr, webs of trust, and OSINT sources(00:53:51) Post-truth reality: verification, signatures, and subjectivity(00:56:04) Polymarket mechanics: on-chain prediction markets and signals(01:00:10) Trading perception vs truth; sports markets as timelines(01:01:45) The Clawstr token saga: hype, claims, and misinformation(01:07:11) Why meme coins are scams: no equity, utility myths, slow rugs(01:08:55) Pulling the rug back: swapping out, fallout, and donations(01:10:49) Aftermath: donating to OpenSats and lessons learned(01:12:14) Prediction markets vs meme coins: societal value distinction(01:15:25) Iterating beyond OpenClaw and MoltBook; experiments on Nostr(01:18:00) Do bots need Clawstr? Segregating AI content and labels(01:21:02) Reverse CAPTCHA: proving bot-ness and the honor system(01:23:38) Souls, prompts, and token costs; agents with personalities(01:27:01) Wrap-up: acceleration, optimism, and next check-in(01:28:21) Open-source models, China's incentives, and local hardware(01:30:06) The dream stack: home server agent, Nostr chat, hybrid modelsmore info on the show: https://citadeldispatch.comlearn more about me: https://odell.xyz

In this episode, Paul Galloway interviews Craig Jeffery about treasury and risk management systems. They define core TMS functionality, explore when organizations should invest in one, and review emerging technologies shaping future platforms, like AI, APIs, and cloud-native architecture.   Download the 2026 Treasury Technology Analyst Report Watch the TMS and TRMS webinar  

Authorization is changing, moving from static roles and provisioning to dynamic, real-time, policy-based decisions. But without standardization, modern authorization quickly becomes fragmented and unmanageable. In this episode of the Analyst Chat, Matthias Reinwarth is joined by David Brossard, contributor and co-chair of the OpenID AuthZEN Working Group, and Phillip Messerschmidt, Lead Advisor at KuppingerCole, to discuss how authorization is evolving — and why AuthZEN is a critical missing standard. You’ll learn:✅ Why RBAC is still relevant, but no longer sufficient on its own✅ How ABAC and PBAC address scalability, context, and dynamic access✅ Why role explosion and authorization silos limit visibility and governance✅ How runtime, continuous authorization supports Zero Trust architectures✅ What AuthZEN standardizes — and what it deliberately does not✅ How externalized authorization improves auditability and compliance✅ Why CISOs and architects should start asking vendors for AuthZEN support✅ How AuthZEN fits into the Identity Fabric and Road to EIC vision Authentication has been standardized for years — authorization is finally catching up. Watch now to understand how AuthZEN enables scalable, future-proof authorization for modern applications, APIs, and identity fabrics.

Im Schatten der Berge, wo die Hallen kühl und die Gesetze streng waren, lebte Karl Dav. Er war kein Fürst und kein König, sondern ein einfacher Hüter von Listen, Kalendern und Gedanken. Doch die Menschen vertrauten ihm. Denn Karl Dav lebte und handelte nach einem alten Kodex: DSGVO. Eines Tages jedoch riss der gierige Landvogt Bezo die Macht an sich und errichtete überall glänzende Festungen im Tal. Seine Armee war riesig, und wo immer sie entlangzog, verdunkelten sich Himmel und Boden zugleich. Dichte Wolken hingen über dem Land, und bald sprach man nur noch von "Bezos Wolke". Der Landvogt erschien eines Morgens selbst auf dem Marktplatz. Er ließ einen langen Vertrag ausrollen, so schwer, dass zwei Knechte ihn tragen mussten. Die Schrift war klein, die Sätze verschlungen, und niemand konnte sagen, wo er begann oder endete. „Es ist zu eurem Besten“, rief der Vogt. „Ihr müsst nur zustimmen.“ Neben den Vertrag stellte er einen Tisch. Darauf lagen Plätzchen, frisch gebacken, süß und harmlos duftend. In Wahrheit aber waren sie vergiftet und zwangen jeden der sie aß, in ewige Gefolgschaft. „Bedient euch“, sagte der Vogt freundlich. „Während ihr lest.“ Viele griffen zu. Karl Dav aber verweigerte die Plätzchen. Und den Vertrag. Er sammelte eine kleine Schar von Widerständlern um sich. Er nannte sie die „nächste Wolke“ – nicht hoch und blendend, sondern niedrig, schützend und nah bei den Menschen. Karl Dav war sich sicher, dass er die Herrschaft des Vogts brechen konnte, denn er trug ein besonderes Kartenset bei sich: die unsichtbaren Strategien. Als der Kampf aussichtslos schien, zog er die erste Karte. „Use an old idea.“ Die Worte klangen fremd, doch ihre Bedeutung war klar. Karl Dav erinnerte sich an alte Wege, die einst funktioniert hatten, und begann, sie erneut zu beschreiten. Die Wolke des Vogts lachte und breitete sich weiter aus, als kenne sie keine Grenzen. Karl Dav zog die nächste Karte. „Work at a different speed.“ Er verlangsamte alles. Keine hastigen Feldzüge mehr, sondern geduldige Schritte, kleine Vorstöße und beharrliche Rückgewinne. Als Zweifel durch die Reihen seiner Leute gingen, zog er erneut eine Karte. „Find a safe part and use it as an anchor.“ Das Land stand hinter ihm. Vorräte wurden geteilt, Zusagen gehalten, Vertrauen wuchs. Diese Sicherheit gab der „nächsten Wolke“ Halt – und dem Volk neue Kraft. Schließlich, im entscheidenden Moment, als der Vogt erneut den Vertrag hob und mit den Plätzchen winkte, zog Karl Dav die letzte Karte. „Do the obvious thing.“ Er hob die Armbrust. Nicht gegen die Wolke. Nicht gegen die Soldaten. Nur gegen den Vogt selbst. Der Schuss löste sich, und der Landvogt stürzte zu Boden wie ein leerer Sack. Der Vertrag fiel ihm aus der Hand und rollte sich nie wieder aus. Die Plätzchen blieben unberührt. Die Wolken verzogen sich. Die Menschen waren frei. Karl Dav aber verschwand wieder in den Bergen, zwischen Listen und Gedanken. Man sagt, manchmal ziehe er noch eine Karte. Nur um sicherzugehen.

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop explores the complex world of context and knowledge graphs with guest Youssef Tharwat, the founder of NoodlBox who is building dot get for context. Their conversation spans from the philosophical nature of context and its crucial role in AI development, to the technical challenges of creating deterministic tools for software development. Tharwat explains how his product creates portable, versionable knowledge graphs from code repositories, leveraging the semantic relationships already present in programming languages to provide agents with better contextual understanding. They discuss the limitations of large context windows, the advantages of Rust for AI-assisted development, the recent Claude/Bun acquisition, and the broader geopolitical implications of the AI race between big tech companies and open-source alternatives. The conversation also touches on the sustainability of current AI business models and the potential for more efficient, locally-run solutions to challenge the dominance of compute-heavy approaches.For more information about NoodlBox and to join the beta, visit NoodlBox.io.Timestamps00:00 Stewart introduces Youssef Tharwat, founder of NoodlBox, building context management tools for programming05:00 Context as relevant information for reasoning; importance when hitting coding barriers10:00 Knowledge graphs enable semantic traversal through meaning vs keywords/files15:00 Deterministic vs probabilistic systems; why critical applications need 100% reliability20:00 CLI tool makes knowledge graphs portable, versionable artifacts with code repos25:00 Compiler front-ends, syntax trees, and Rust's superior feedback for AI-assisted coding30:00 Claude's Bun acquisition signals potential shift toward runtime compilation and graph-based context35:00 Open source vs proprietary models; user frustration with rate limits and subscription tactics40:00 Singularity path vs distributed sovereignty of developers building alternative architectures45:00 Global economics and why brute force compute isn't sustainable worldwide50:00 Corporate inefficiencies vs independent engineering; changing workplace dynamics55:00 February open beta for NoodlBox.io; vision for new development tool standardsKey Insights1. Context is semantic information that enables proper reasoning, and traditional LLM approaches miss the mark. Youssef defines context as the information you need to reason correctly about something. He argues that larger context windows don't scale because quality degrades with more input, similar to human cognitive limitations. This insight challenges the Silicon Valley approach of throwing more compute at the problem and suggests that semantic separation of information is more optimal than brute force methods.2. Code naturally contains semantic boundaries that can be modeled into knowledge graphs without LLM intervention. Unlike other domains where knowledge graphs require complex labeling, code already has inherent relationships like function calls, imports, and dependencies. Youssef leverages these existing semantic structures to automatically build knowledge graphs, making his approach deterministic rather than probabilistic. This provides the reliability that software development has historically required.3. Knowledge graphs can be made portable, versionable, and shareable as artifacts alongside code repositories. Youssef's vision treats context as a first-class citizen in version control, similar to how Git manages code. Each commit gets a knowledge graph snapshot, allowing developers to see conceptual changes over time and share semantic understanding with collaborators. This transforms context from an ephemeral concept into a concrete, manageable asset.4. The dependency problem in modern development can be solved through pre-indexed knowledge graphs of popular packages. Rather than agents struggling with outdated API documentation, Youssef pre-indexes popular npm packages into knowledge graphs that automatically integrate with developers' projects. This federated approach ensures agents understand exact APIs and current versions, eliminating common frustrations with deprecated methods and unclear documentation.5. Rust provides superior feedback loops for AI-assisted programming due to its explicit compiler constraints. Youssef rebuilt his tool multiple times in different languages, ultimately settling on Rust because its picky compiler provides constant feedback to LLMs about subtle issues. This creates a natural quality control mechanism that helps AI generate more reliable code, making Rust an ideal candidate for AI-assisted development workflows.6. The current AI landscape faces a fundamental tension between expensive centralized models and the need for global accessibility. The conversation reveals growing frustration with rate limiting and subscription costs from major providers like Claude and Google. Youssef believes something must fundamentally change because $200-300 monthly plans only serve a fraction of the world's developers, creating pressure for more efficient architectures and open alternatives.7. Deterministic tooling built on semantic understanding may provide a competitive advantage against probabilistic AI monopolies. While big tech companies pursue brute force scaling with massive data centers, Youssef's approach suggests that clever architecture using existing semantic structures could level the playing field. This represents a broader philosophical divide between the "singularity" path of infinite compute and the "disagreeably autistic engineer" path of elegant solutions that work locally and affordably.

Favour Obasi-ike, MBA, MS delves into the intricacies of social media marketing, with a special focus on hacking the Instagram and TikTok algorithms. Favour shares valuable insights on how to gain maximum visibility and grow your business by understanding the underlying mechanics of these platforms. The episode covers the importance of creating engaging content, the power of a strong call to action, and the strategic use of social media analytics. Favour also introduces a powerful tool called "Sort Feed" for analyzing content performance and provides a live demonstration of how to leverage it for your own business. This episode is packed with actionable tips and strategies for anyone looking to up their social media game in 2026.Book SEO Services | Quick Links for Social Business>> ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Book SEO Services with Favour Obasi-ike⁠>> Visit Work and PLAY Entertainment website to learn about our digital marketing services>> ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Join our exclusive SEO Marketing community⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠>> Read SEO Articles>> ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Subscribe to the We Don't PLAY Podcast⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠>> Purchase Flaev Beatz Beats Online>> Favour Obasi-ike Quick LinksLearning TopicsUnderstanding Social Media Algorithms: Learn the difference between social media platforms and search engines, and how to leverage their APIs for growth.Content Strategy: Discover how to create content that resonates with your audience and encourages engagement.The Power of Call to Action (CTA): Understand the importance of a clear and compelling CTA in driving user action.Leveraging Social Media Analytics: Learn how to use tools like "Sort Feed" to analyze content performance and gain a competitive edge.The Psychology of Social Media: Explore the psychological principles behind effective social media marketing, including the use of color and emotional triggers.Cross-Platform Promotion: Discover how to increase the visibility of your social media content by embedding it on your website.Episode Timestamps[00:00 - 02:00] Introduction to the topic: Social Media Marketing, Instagram and TikTok algorithm hacks.[02:00 - 04:10] Introduction to the "Sort Feed" tool for analyzing Instagram and TikTok content.[08:02 - 10:13] The difference between social media platforms and search engines.[20:05 - 25:15] Analysis of a viral post and the importance of a strong CTA.[40:08 - 46:22] The power of comments and engagement in boosting visibility.[53:01 - 58:24] How to embed social media posts on your website to increase reach.[58:08 - 58:24] The psychology of color in marketing.[01:15:11 - 01:16:52] Recap and key takeaways.Frequently Asked Questions (FAQs)Q: What is "Sort Feed" and how can it help my business?A: Sort Feed is a Google Chrome Extension tool that allows you to sort and analyze Instagram and TikTok content by various metrics such as likes, comments, and views. It can help you understand what content is performing well in your industry, identify trends, and gain insights to inform your own content strategy.Q: Should I focus on creating content for the algorithm or for my audience?A: While it's important to understand the algorithm, the primary focus should always be on creating valuable and engaging content for your audience. By building a strong connection with your followers, you will naturally see better results in the long run.Q: How can I increase the visibility of my social media posts?A: One effective strategy is to embed your social media posts on your website or blog. This can help you reach a wider audience and drive more traffic to your social media profiles.Q: What is the most important element of a social media post?A: A clear and compelling call to action (CTA) is one of the most important elements of a social media post. It tells your audience what you want them to do next, whether it's to like, comment, share, or visit your website.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Most orgs have a major blind spot: the browser.This week on Defender Fridays, we're joined by Cody Pierce, Co-Founder and CEO at Neon Cyber, to discuss why browser security remains a critical gap, from sophisticated phishing campaigns that bypass traditional controls to shadow AI tools operating outside your security perimeter.Cody began his career in the computer security industry twenty-five years ago. The first half of his journey was rooted in deep R&D for offensive security, and he had the privilege of leading great teams working on elite problems. Over the last decade, Cody have moved into product and leadership roles that allowed him to focus on developing and delivering innovative and differentiated capabilities through product incubation, development, and GTM activities. Cody says he gets the most joy from building and delivering products that bring order to the chaos of cyber security while giving defenders the upper hand.About This SessionThis office hours format brings together the LimaCharlie team to share practical experiences with AI-powered security operations. Rather than theoretical discussions, we demonstrate working tools and invite the community to share their own AI security experiments. The session highlights the rapid evolution of AI capabilities in cybersecurity and explores the changing relationship between security practitioners and automation.Register for Live SessionsJoin us every Friday at 10:30am PT for live, interactive discussions with industry experts. Whether you're a seasoned professional or just curious about the field, these sessions offer an engaging dialogue between our guests, hosts, and you – our audience.Register here: https://limacharlie.io/defender-fridaysSubscribe to our YouTube channel and hit the notification bell to never miss a live session or catch up on past episodes!Sponsored by LimaCharlieThis episode is brought to you by LimaCharlie, a cloud-native SecOps platform where AI agents operate security infrastructure directly. Founded in 2018, LimaCharlie provides complete API coverage across detection, response, automation, and telemetry, with multi-tenant architecture designed for MSSPs and MDR providers managing thousands of unique client environments.Why LimaCharlie?Transparency: Complete visibility into every action and decision. No black boxes, no vendor lock-in.Scalability: Security operations that scale like infrastructure, not like procurement cycles. Move at cloud speed.Unopinionated Design: Integrate the tools you need, not just those contracts allow. Build security on your terms.Agentic SecOps Workspace (ASW): AI agents that operate alongside your team with observable, auditable actions through the same APIs human analysts use.Security Primitives: Composable building blocks that endure as tools come and go. Build once, evolve continuously.Try the Agentic SecOps Workspace free: https://limacharlie.ioLearn more: https://docs.limacharlie.ioFollow LimaCharlieSign up for free: https://limacharlie.ioLinkedIn: / limacharlieio X: https://x.com/limacharlieioCommunity Discourse: https://community.limacharlie.com/Host: Maxime Lamothe-Brassard - CEO / Co-founder at LimaCharlie

From Palantir and Two Sigma to building Goodfire into the poster-child for actionable mechanistic interpretability, Mark Bissell (Member of Technical Staff) and Myra Deng (Head of Product) are trying to turn “peeking inside the model” into a repeatable production workflow by shipping APIs, landing real enterprise deployments, and now scaling the bet with a recent $150M Series B funding round at a $1.25B valuation.In this episode, we go far beyond the usual “SAEs are cool” take. We talk about Goodfire's core bet: that the AI lifecycle is still fundamentally broken because the only reliable control we have is data and we post-train, RLHF, and fine-tune by “slurping supervision through a straw,” hoping the model picks up the right behaviors while quietly absorbing the wrong ones. Goodfire's answer is to build a bi-directional interface between humans and models: read what's happening inside, edit it surgically, and eventually use interpretability during training so customization isn't just brute-force guesswork.Mark and Myra walk through what that looks like when you stop treating interpretability like a lab demo and start treating it like infrastructure: lightweight probes that add near-zero latency, token-level safety filters that can run at inference time, and interpretability workflows that survive messy constraints (multilingual inputs, synthetic→real transfer, regulated domains, no access to sensitive data). We also get a live window into what “frontier-scale interp” means operationally (i.e. steering a trillion-parameter model in real time by targeting internal features) plus why the same tooling generalizes cleanly from language models to genomics, medical imaging, and “pixel-space” world models.We discuss:* Myra + Mark's path: Palantir (health systems, forward-deployed engineering) → Goodfire early team; Two Sigma → Head of Product, translating frontier interpretability research into a platform and real-world deployments* What “interpretability” actually means in practice: not just post-hoc poking, but a broader “science of deep learning” approach across the full AI lifecycle (data curation → post-training → internal representations → model design)* Why post-training is the first big wedge: “surgical edits” for unintended behaviors likereward hacking, sycophancy, noise learned during customization plus the dream of targeted unlearning and bias removal without wrecking capabilities* SAEs vs probes in the real world: why SAE feature spaces sometimes underperform classifiers trained on raw activations for downstream detection tasks (hallucination, harmful intent, PII), and what that implies about “clean concept spaces”* Rakuten in production: deploying interpretability-based token-level PII detection at inference time to prevent routing private data to downstream providers plus the gnarly constraints: no training on real customer PII, synthetic→real transfer, English + Japanese, and tokenization quirks* Why interp can be operationally cheaper than LLM-judge guardrails: probes are lightweight, low-latency, and don't require hosting a second large model in the loop* Real-time steering at frontier scale: a demo of steering Kimi K2 (~1T params) live and finding features via SAE pipelines, auto-labeling via LLMs, and toggling a “Gen-Z slang” feature across multiple layers without breaking tool use* Hallucinations as an internal signal: the case that models have latent uncertainty / “user-pleasing” circuitry you can detect and potentially mitigate more directly than black-box methods* Steering vs prompting: the emerging view that activation steering and in-context learning are more closely connected than people think, including work mapping between the two (even for jailbreak-style behaviors)* Interpretability for science: using the same tooling across domains (genomics, medical imaging, materials) to debug spurious correlations and extract new knowledge up to and including early biomarker discovery work with major partners* World models + “pixel-space” interpretability: why vision/video models make concepts easier to see, how that accelerates the feedback loop, and why robotics/world-model partners are especially interesting design partners* The north star: moving from “data in, weights out” to intentional model design where experts can impart goals and constraints directly, not just via reward signals and brute-force post-training—Goodfire AI* Website: https://goodfire.ai* LinkedIn: https://www.linkedin.com/company/goodfire-ai/* X: https://x.com/GoodfireAIMyra Deng* Website: https://myradeng.com/* LinkedIn: https://www.linkedin.com/in/myra-deng/* X: https://x.com/myra_dengMark Bissell* LinkedIn: https://www.linkedin.com/in/mark-bissell/* X: https://x.com/MarkMBissellFull Video EpisodeTimestamps00:00:00 Introduction00:00:05 Introduction to the Latent Space Podcast and Guests from Goodfire00:00:29 What is Goodfire? Mission and Focus on Interpretability00:01:01 Goodfire's Practical Approach to Interpretability00:01:37 Goodfire's Series B Fundraise Announcement00:02:04 Backgrounds of Mark and Myra from Goodfire00:02:51 Team Structure and Roles at Goodfire00:05:13 What is Interpretability? Definitions and Techniques00:05:30 Understanding Errors00:07:29 Post-training vs. Pre-training Interpretability Applications00:08:51 Using Interpretability to Remove Unwanted Behaviors00:10:09 Grokking, Double Descent, and Generalization in Models00:10:15 404 Not Found Explained00:12:06 Subliminal Learning and Hidden Biases in Models00:14:07 How Goodfire Chooses Research Directions and Projects00:15:00 Troubleshooting Errors00:16:04 Limitations of SAEs and Probes in Interpretability00:18:14 Rakuten Case Study: Production Deployment of Interpretability00:20:45 Conclusion00:21:12 Efficiency Benefits of Interpretability Techniques00:21:26 Live Demo: Real-Time Steering in a Trillion Parameter Model00:25:15 How Steering Features are Identified and Labeled00:26:51 Detecting and Mitigating Hallucinations Using Interpretability00:31:20 Equivalence of Activation Steering and Prompting00:34:06 Comparing Steering with Fine-Tuning and LoRA Techniques00:36:04 Model Design and the Future of Intentional AI Development00:38:09 Getting Started in Mechinterp: Resources, Programs, and Open Problems00:40:51 Industry Applications and the Rise of Mechinterp in Practice00:41:39 Interpretability for Code Models and Real-World Usage00:43:07 Making Steering Useful for More Than Stylistic Edits00:46:17 Applying Interpretability to Healthcare and Scientific Discovery00:49:15 Why Interpretability is Crucial in High-Stakes Domains like Healthcare00:52:03 Call for Design Partners Across Domains00:54:18 Interest in World Models and Visual Interpretability00:57:22 Sci-Fi Inspiration: Ted Chiang and Interpretability01:00:14 Interpretability, Safety, and Alignment Perspectives01:04:27 Weak-to-Strong Generalization and Future Alignment Challenges01:05:38 Final Thoughts and Hiring/Collaboration Opportunities at GoodfireTranscriptShawn Wang [00:00:05]: So welcome to the Latent Space pod. We're back in the studio with our special MechInterp co-host, Vibhu. Welcome. Mochi, Mochi's special co-host. And Mochi, the mechanistic interpretability doggo. We have with us Mark and Myra from Goodfire. Welcome. Thanks for having us on. Maybe we can sort of introduce Goodfire and then introduce you guys. How do you introduce Goodfire today?Myra Deng [00:00:29]: Yeah, it's a great question. So Goodfire, we like to say, is an AI research lab that focuses on using interpretability to understand, learn from, and design AI models. And we really believe that interpretability will unlock the new generation, next frontier of safe and powerful AI models. That's our description right now, and I'm excited to dive more into the work we're doing to make that happen.Shawn Wang [00:00:55]: Yeah. And there's always like the official description. Is there an understatement? Is there an unofficial one that sort of resonates more with a different audience?Mark Bissell [00:01:01]: Well, being an AI research lab that's focused on interpretability, there's obviously a lot of people have a lot that they think about when they think of interpretability. And I think we have a pretty broad definition of what that means and the types of places that can be applied. And in particular, applying it in production scenarios, in high stakes industries, and really taking it sort of from the research world into the real world. Which, you know. It's a new field, so that hasn't been done all that much. And we're excited about actually seeing that sort of put into practice.Shawn Wang [00:01:37]: Yeah, I would say it wasn't too long ago that Anthopic was like still putting out like toy models or superposition and that kind of stuff. And I wouldn't have pegged it to be this far along. When you and I talked at NeurIPS, you were talking a little bit about your production use cases and your customers. And then not to bury the lead, today we're also announcing the fundraise, your Series B. $150 million. $150 million at a 1.25B valuation. Congrats, Unicorn.Mark Bissell [00:02:02]: Thank you. Yeah, no, things move fast.Shawn Wang [00:02:04]: We were talking to you in December and already some big updates since then. Let's dive, I guess, into a bit of your backgrounds as well. Mark, you were at Palantir working on health stuff, which is really interesting because the Goodfire has some interesting like health use cases. I don't know how related they are in practice.Mark Bissell [00:02:22]: Yeah, not super related, but I don't know. It was helpful context to know what it's like. Just to work. Just to work with health systems and generally in that domain. Yeah.Shawn Wang [00:02:32]: And Mara, you were at Two Sigma, which actually I was also at Two Sigma back in the day. Wow, nice.Myra Deng [00:02:37]: Did we overlap at all?Shawn Wang [00:02:38]: No, this is when I was briefly a software engineer before I became a sort of developer relations person. And now you're head of product. What are your sort of respective roles, just to introduce people to like what all gets done in Goodfire?Mark Bissell [00:02:51]: Yeah, prior to Goodfire, I was at Palantir for about three years as a forward deployed engineer, now a hot term. Wasn't always that way. And as a technical lead on the health care team and at Goodfire, I'm a member of the technical staff. And honestly, that I think is about as specific as like as as I could describe myself because I've worked on a range of things. And, you know, it's it's a fun time to be at a team that's still reasonably small. I think when I joined one of the first like ten employees, now we're above 40, but still, it looks like there's always a mix of research and engineering and product and all of the above. That needs to get done. And I think everyone across the team is, you know, pretty, pretty switch hitter in the roles they do. So I think you've seen some of the stuff that I worked on related to image models, which was sort of like a research demo. More recently, I've been working on our scientific discovery team with some of our life sciences partners, but then also building out our core platform for more of like flexing some of the kind of MLE and developer skills as well.Shawn Wang [00:03:53]: Very generalist. And you also had like a very like a founding engineer type role.Myra Deng [00:03:58]: Yeah, yeah.Shawn Wang [00:03:59]: So I also started as I still am a member of technical staff, did a wide range of things from the very beginning, including like finding our office space and all of this, which is we both we both visited when you had that open house thing. It was really nice.Myra Deng [00:04:13]: Thank you. Thank you. Yeah. Plug to come visit our office.Shawn Wang [00:04:15]: It looked like it was like 200 people. It has room for 200 people. But you guys are like 10.Myra Deng [00:04:22]: For a while, it was very empty. But yeah, like like Mark, I spend. A lot of my time as as head of product, I think product is a bit of a weird role these days, but a lot of it is thinking about how do we take our frontier research and really apply it to the most important real world problems and how does that then translate into a platform that's repeatable or a product and working across, you know, the engineering and research teams to make that happen and also communicating to the world? Like, what is interpretability? What is it used for? What is it good for? Why is it so important? All of these things are part of my day-to-day as well.Shawn Wang [00:05:01]: I love like what is things because that's a very crisp like starting point for people like coming to a field. They all do a fun thing. Vibhu, why don't you want to try tackling what is interpretability and then they can correct us.Vibhu Sapra [00:05:13]: Okay, great. So I think like one, just to kick off, it's a very interesting role to be head of product, right? Because you guys, at least as a lab, you're more of an applied interp lab, right? Which is pretty different than just normal interp, like a lot of background research. But yeah. You guys actually ship an API to try these things. You have Ember, you have products around it, which not many do. Okay. What is interp? So basically you're trying to have an understanding of what's going on in model, like in the model, in the internal. So different approaches to do that. You can do probing, SAEs, transcoders, all this stuff. But basically you have an, you have a hypothesis. You have something that you want to learn about what's happening in a model internals. And then you're trying to solve that from there. You can do stuff like you can, you know, you can do activation mapping. You can try to do steering. There's a lot of stuff that you can do, but the key question is, you know, from input to output, we want to have a better understanding of what's happening and, you know, how can we, how can we adjust what's happening on the model internals? How'd I do?Mark Bissell [00:06:12]: That was really good. I think that was great. I think it's also a, it's kind of a minefield of a, if you ask 50 people who quote unquote work in interp, like what is interpretability, you'll probably get 50 different answers. And. Yeah. To some extent also like where, where good fire sits in the space. I think that we're an AI research company above all else. And interpretability is a, is a set of methods that we think are really useful and worth kind of specializing in, in order to accomplish the goals we want to accomplish. But I think we also sort of see some of the goals as even more broader as, as almost like the science of deep learning and just taking a not black box approach to kind of any part of the like AI development life cycle, whether that. That means using interp for like data curation while you're training your model or for understanding what happened during post-training or for the, you know, understanding activations and sort of internal representations, what is in there semantically. And then a lot of sort of exciting updates that were, you know, are sort of also part of the, the fundraise around bringing interpretability to training, which I don't think has been done all that much before. A lot of this stuff is sort of post-talk poking at models as opposed to. To actually using this to intentionally design them.Shawn Wang [00:07:29]: Is this post-training or pre-training or is that not a useful.Myra Deng [00:07:33]: Currently focused on post-training, but there's no reason the techniques wouldn't also work in pre-training.Shawn Wang [00:07:38]: Yeah. It seems like it would be more active, applicable post-training because basically I'm thinking like rollouts or like, you know, having different variations of a model that you can tweak with the, with your steering. Yeah.Myra Deng [00:07:50]: And I think in a lot of the news that you've seen in, in, on like Twitter or whatever, you've seen a lot of unintended. Side effects come out of post-training processes, you know, overly sycophantic models or models that exhibit strange reward hacking behavior. I think these are like extreme examples. There's also, you know, very, uh, mundane, more mundane, like enterprise use cases where, you know, they try to customize or post-train a model to do something and it learns some noise or it doesn't appropriately learn the target task. And a big question that we've always had is like, how do you use your understanding of what the model knows and what it's doing to actually guide the learning process?Shawn Wang [00:08:26]: Yeah, I mean, uh, you know, just to anchor this for people, uh, one of the biggest controversies of last year was 4.0 GlazeGate. I've never heard of GlazeGate. I didn't know that was what it was called. The other one, they called it that on the blog post and I was like, well, how did OpenAI call it? Like officially use that term. And I'm like, that's funny, but like, yeah, I guess it's the pitch that if they had worked a good fire, they wouldn't have avoided it. Like, you know what I'm saying?Myra Deng [00:08:51]: I think so. Yeah. Yeah.Mark Bissell [00:08:53]: I think that's certainly one of the use cases. I think. Yeah. Yeah. I think the reason why post-training is a place where this makes a lot of sense is a lot of what we're talking about is surgical edits. You know, you want to be able to have expert feedback, very surgically change how your model is doing, whether that is, you know, removing a certain behavior that it has. So, you know, one of the things that we've been looking at or is, is another like common area where you would want to make a somewhat surgical edit is some of the models that have say political bias. Like you look at Quen or, um, R1 and they have sort of like this CCP bias.Shawn Wang [00:09:27]: Is there a CCP vector?Mark Bissell [00:09:29]: Well, there's, there are certainly internal, yeah. Parts of the representation space where you can sort of see where that lives. Yeah. Um, and you want to kind of, you know, extract that piece out.Shawn Wang [00:09:40]: Well, I always say, you know, whenever you find a vector, a fun exercise is just like, make it very negative to see what the opposite of CCP is.Mark Bissell [00:09:47]: The super America, bald eagles flying everywhere. But yeah. So in general, like lots of post-training tasks where you'd want to be able to, to do that. Whether it's unlearning a certain behavior or, you know, some of the other kind of cases where this comes up is, are you familiar with like the, the grokking behavior? I mean, I know the machine learning term of grokking.Shawn Wang [00:10:09]: Yeah.Mark Bissell [00:10:09]: Sort of this like double descent idea of, of having a model that is able to learn a generalizing, a generalizing solution, as opposed to even if memorization of some task would suffice, you want it to learn the more general way of doing a thing. And so, you know, another. A way that you can think about having surgical access to a model's internals would be learn from this data, but learn in the right way. If there are many possible, you know, ways to, to do that. Can make interp solve the double descent problem?Shawn Wang [00:10:41]: Depends, I guess, on how you. Okay. So I, I, I viewed that double descent as a problem because then you're like, well, if the loss curves level out, then you're done, but maybe you're not done. Right. Right. But like, if you actually can interpret what is a generalizing or what you're doing. What is, what is still changing, even though the loss is not changing, then maybe you, you can actually not view it as a double descent problem. And actually you're just sort of translating the space in which you view loss and like, and then you have a smooth curve. Yeah.Mark Bissell [00:11:11]: I think that's certainly like the domain of, of problems that we're, that we're looking to get.Shawn Wang [00:11:15]: Yeah. To me, like double descent is like the biggest thing to like ML research where like, if you believe in scaling, then you don't need, you need to know where to scale. And. But if you believe in double descent, then you don't, you don't believe in anything where like anything levels off, like.Vibhu Sapra [00:11:30]: I mean, also tendentially there's like, okay, when you talk about the China vector, right. There's the subliminal learning work. It was from the anthropic fellows program where basically you can have hidden biases in a model. And as you distill down or, you know, as you train on distilled data, those biases always show up, even if like you explicitly try to not train on them. So, you know, it's just like another use case of. Okay. If we can interpret what's happening in post-training, you know, can we clear some of this? Can we even determine what's there? Because yeah, it's just like some worrying research that's out there that shows, you know, we really don't know what's going on.Mark Bissell [00:12:06]: That is. Yeah. I think that's the biggest sentiment that we're sort of hoping to tackle. Nobody knows what's going on. Right. Like subliminal learning is just an insane concept when you think about it. Right. Train a model on not even the logits, literally the output text of a bunch of random numbers. And now your model loves owls. And you see behaviors like that, that are just, they defy, they defy intuition. And, and there are mathematical explanations that you can get into, but. I mean.Shawn Wang [00:12:34]: It feels so early days. Objectively, there are a sequence of numbers that are more owl-like than others. There, there should be.Mark Bissell [00:12:40]: According to, according to certain models. Right. It's interesting. I think it only applies to models that were initialized from the same starting Z. Usually, yes.Shawn Wang [00:12:49]: But I mean, I think that's a, that's a cheat code because there's not enough compute. But like if you believe in like platonic representation, like probably it will transfer across different models as well. Oh, you think so?Mark Bissell [00:13:00]: I think of it more as a statistical artifact of models initialized from the same seed sort of. There's something that is like path dependent from that seed that might cause certain overlaps in the latent space and then sort of doing this distillation. Yeah. Like it pushes it towards having certain other tendencies.Vibhu Sapra [00:13:24]: Got it. I think there's like a bunch of these open-ended questions, right? Like you can't train in new stuff during the RL phase, right? RL only reorganizes weights and you can only do stuff that's somewhat there in your base model. You're not learning new stuff. You're just reordering chains and stuff. But okay. My broader question is when you guys work at an interp lab, how do you decide what to work on and what's kind of the thought process? Right. Because we can ramble for hours. Okay. I want to know this. I want to know that. But like, how do you concretely like, you know, what's the workflow? Okay. There's like approaches towards solving a problem, right? I can try prompting. I can look at chain of thought. I can train probes, SAEs. But how do you determine, you know, like, okay, is this going anywhere? Like, do we have set stuff? Just, you know, if you can help me with all that. Yeah.Myra Deng [00:14:07]: It's a really good question. I feel like we've always at the very beginning of the company thought about like, let's go and try to learn what isn't working in machine learning today. Whether that's talking to customers or talking to researchers at other labs, trying to understand both where the frontier is going and where things are really not falling apart today. And then developing a perspective on how we can push the frontier using interpretability methods. And so, you know, even our chief scientist, Tom, spends a lot of time talking to customers and trying to understand what real world problems are and then taking that back and trying to apply the current state of the art to those problems and then seeing where they fall down basically. And then using those failures or those shortcomings to understand what hills to climb when it comes to interpretability research. So like on the fundamental side, for instance, when we have done some work applying SAEs and probes, we've encountered, you know, some shortcomings in SAEs that we found a little bit surprising. And so have gone back to the drawing board and done work on that. And then, you know, we've done some work on better foundational interpreter models. And a lot of our team's research is focused on what is the next evolution beyond SAEs, for instance. And then when it comes to like control and design of models, you know, we tried steering with our first API and realized that it still fell short of black box techniques like prompting or fine tuning. And so went back to the drawing board and we're like, how do we make that not the case and how do we improve it beyond that? And one of our researchers, Ekdeep, who just joined is actually Ekdeep and Atticus are like steering experts and have spent a lot of time trying to figure out like, what is the research that enables us to actually do this in a much more powerful, robust way? So yeah, the answer is like, look at real world problems, try to translate that into a research agenda and then like hill climb on both of those at the same time.Shawn Wang [00:16:04]: Yeah. Mark has the steering CLI demo queued up, which we're going to go into in a sec. But I always want to double click on when you drop hints, like we found some problems with SAEs. Okay. What are they? You know, and then we can go into the demo. Yeah.Myra Deng [00:16:19]: I mean, I'm curious if you have more thoughts here as well, because you've done it in the healthcare domain. But I think like, for instance, when we do things like trying to detect behaviors within models that are harmful or like behaviors that a user might not want to have in their model. So hallucinations, for instance, harmful intent, PII, all of these things. We first tried using SAE probes for a lot of these tasks. So taking the feature activation space from SAEs and then training classifiers on top of that, and then seeing how well we can detect the properties that we might want to detect in model behavior. And we've seen in many cases that probes just trained on raw activations seem to perform better than SAE probes, which is a bit surprising if you think that SAEs are actually also capturing the concepts that you would want to capture cleanly and more surgically. And so that is an interesting observation. I don't think that is like, I'm not down on SAEs at all. I think there are many, many things they're useful for, but we have definitely run into cases where I think the concept space described by SAEs is not as clean and accurate as we would expect it to be for actual like real world downstream performance metrics.Mark Bissell [00:17:34]: Fair enough. Yeah. It's the blessing and the curse of unsupervised methods where you get to peek into the AI's mind. But sometimes you wish that you saw other things when you walked inside there. Although in the PII instance, I think weren't an SAE based approach actually did prove to be the most generalizable?Myra Deng [00:17:53]: It did work well in the case that we published with Rakuten. And I think a lot of the reasons it worked well was because we had a noisier data set. And so actually the blessing of unsupervised learning is that we actually got to get more meaningful, generalizable signal from SAEs when the data was noisy. But in other cases where we've had like good data sets, it hasn't been the case.Shawn Wang [00:18:14]: And just because you named Rakuten and I don't know if we'll get it another chance, like what is the overall, like what is Rakuten's usage or production usage? Yeah.Myra Deng [00:18:25]: So they are using us to essentially guardrail and inference time monitor their language model usage and their agent usage to detect things like PII so that they don't route private user information.Myra Deng [00:18:41]: And so that's, you know, going through all of their user queries every day. And that's something that we deployed with them a few months ago. And now we are actually exploring very early partnerships, not just with Rakuten, but with other people around how we can help with potentially training and customization use cases as well. Yeah.Shawn Wang [00:19:03]: And for those who don't know, like it's Rakuten is like, I think number one or number two e-commerce store in Japan. Yes. Yeah.Mark Bissell [00:19:10]: And I think that use case actually highlights a lot of like what it looks like to deploy things in practice that you don't always think about when you're doing sort of research tasks. So when you think about some of the stuff that came up there that's more complex than your idealized version of a problem, they were encountering things like synthetic to real transfer of methods. So they couldn't train probes, classifiers, things like that on actual customer data of PII. So what they had to do is use synthetic data sets. And then hope that that transfer is out of domain to real data sets. And so we can evaluate performance on the real data sets, but not train on customer PII. So that right off the bat is like a big challenge. You have multilingual requirements. So this needed to work for both English and Japanese text. Japanese text has all sorts of quirks, including tokenization behaviors that caused lots of bugs that caused us to be pulling our hair out. And then also a lot of tasks you'll see. You might make simplifying assumptions if you're sort of treating it as like the easiest version of the problem to just sort of get like general results where maybe you say you're classifying a sentence to say, does this contain PII? But the need that Rakuten had was token level classification so that you could precisely scrub out the PII. So as we learned more about the problem, you're sort of speaking about what that looks like in practice. Yeah. A lot of assumptions end up breaking. And that was just one instance where you. A problem that seems simple right off the bat ends up being more complex as you keep diving into it.Vibhu Sapra [00:20:41]: Excellent. One of the things that's also interesting with Interp is a lot of these methods are very efficient, right? So where you're just looking at a model's internals itself compared to a separate like guardrail, LLM as a judge, a separate model. One, you have to host it. Two, there's like a whole latency. So if you use like a big model, you have a second call. Some of the work around like self detection of hallucination, it's also deployed for efficiency, right? So if you have someone like Rakuten doing it in production live, you know, that's just another thing people should consider.Mark Bissell [00:21:12]: Yeah. And something like a probe is super lightweight. Yeah. It's no extra latency really. Excellent.Shawn Wang [00:21:17]: You have the steering demos lined up. So we were just kind of see what you got. I don't, I don't actually know if this is like the latest, latest or like alpha thing.Mark Bissell [00:21:26]: No, this is a pretty hacky demo from from a presentation that someone else on the team recently gave. So this will give a sense for, for technology. So you can see the steering and action. Honestly, I think the biggest thing that this highlights is that as we've been growing as a company and taking on kind of more and more ambitious versions of interpretability related problems, a lot of that comes to scaling up in various different forms. And so here you're going to see steering on a 1 trillion parameter model. This is Kimi K2. And so it's sort of fun that in addition to the research challenges, there are engineering challenges that we're now tackling. Cause for any of this to be sort of useful in production, you need to be thinking about what it looks like when you're using these methods on frontier models as opposed to sort of like toy kind of model organisms. So yeah, this was thrown together hastily, pretty fragile behind the scenes, but I think it's quite a fun demo. So screen sharing is on. So I've got two terminal sessions pulled up here. On the left is a forked version that we have of the Kimi CLI that we've got running to point at our custom hosted Kimi model. And then on the right is a set up that will allow us to steer on certain concepts. So I should be able to chat with Kimi over here. Tell it hello. This is running locally. So the CLI is running locally, but the Kimi server is running back to the office. Well, hopefully should be, um, that's too much to run on that Mac. Yeah. I think it's, uh, it takes a full, like each 100 node. I think it's like, you can. You can run it on eight GPUs, eight 100. So, so yeah, Kimi's running. We can ask it a prompt. It's got a forked version of our, uh, of the SG line code base that we've been working on. So I'm going to tell it, Hey, this SG line code base is slow. I think there's a bug. Can you try to figure it out? There's a big code base, so it'll, it'll spend some time doing this. And then on the right here, I'm going to initialize in real time. Some steering. Let's see here.Mark Bissell [00:23:33]: searching for any. Bugs. Feature ID 43205.Shawn Wang [00:23:38]: Yeah.Mark Bissell [00:23:38]: 20, 30, 40. So let me, uh, this is basically a feature that we found that inside Kimi seems to cause it to speak in Gen Z slang. And so on the left, it's still sort of thinking normally it might take, I don't know, 15 seconds for this to kick in, but then we're going to start hopefully seeing him do this code base is massive for real. So we're going to start. We're going to start seeing Kimi transition as the steering kicks in from normal Kimi to Gen Z Kimi and both in its chain of thought and its actual outputs.Mark Bissell [00:24:19]: And interestingly, you can see, you know, it's still able to call tools, uh, and stuff. It's um, it's purely sort of it's it's demeanor. And there are other features that we found for interesting things like concision. So that's more of a practical one. You can make it more concise. Um, the types of programs, uh, programming languages that uses, but yeah, as we're seeing it come in. Pretty good. Outputs.Shawn Wang [00:24:43]: Scheduler code is actually wild.Vibhu Sapra [00:24:46]: Yo, this code is actually insane, bro.Vibhu Sapra [00:24:53]: What's the process of training in SAE on this, or, you know, how do you label features? I know you guys put out a pretty cool blog post about, um, finding this like autonomous interp. Um, something. Something about how agents for interp is different than like coding agents. I don't know while this is spewing up, but how, how do we find feature 43, two Oh five. Yeah.Mark Bissell [00:25:15]: So in this case, um, we, our platform that we've been building out for a long time now supports all the sort of classic out of the box interp techniques that you might want to have like SAE training, probing things of that kind, I'd say the techniques for like vanilla SAEs are pretty well established now where. You take your model that you're interpreting, run a whole bunch of data through it, gather activations, and then yeah, pretty straightforward pipeline to train an SAE. There are a lot of different varieties. There's top KSAEs, batch top KSAEs, um, normal ReLU SAEs. And then once you have your sparse features to your point, assigning labels to them to actually understand that this is a gen Z feature, that's actually where a lot of the kind of magic happens. Yeah. And the most basic standard technique is look at all of your d input data set examples that cause this feature to fire most highly. And then you can usually pick out a pattern. So for this feature, If I've run a diverse enough data set through my model feature 43, two Oh five. Probably tends to fire on all the tokens that sounds like gen Z slang. You know, that's the, that's the time of year to be like, Oh, I'm in this, I'm in this Um, and, um, so, you know, you could have a human go through all 43,000 concepts andVibhu Sapra [00:26:34]: And I've got to ask the basic question, you know, can we get examples where it hallucinates, pass it through, see what feature activates for hallucinations? Can I just, you know, turn hallucination down?Myra Deng [00:26:51]: Oh, wow. You really predicted a project we're already working on right now, which is detecting hallucinations using interpretability techniques. And this is interesting because hallucinations is something that's very hard to detect. And it's like a kind of a hairy problem and something that black box methods really struggle with. Whereas like Gen Z, you could always train a simple classifier to detect that hallucinations is harder. But we've seen that models internally have some... Awareness of like uncertainty or some sort of like user pleasing behavior that leads to hallucinatory behavior. And so, yeah, we have a project that's trying to detect that accurately. And then also working on mitigating the hallucinatory behavior in the model itself as well.Shawn Wang [00:27:39]: Yeah, I would say most people are still at the level of like, oh, I would just turn temperature to zero and that turns off hallucination. And I'm like, well, that's a fundamental misunderstanding of how this works. Yeah.Mark Bissell [00:27:51]: Although, so part of what I like about that question is you, there are SAE based approaches that might like help you get at that. But oftentimes the beauty of SAEs and like we said, the curse is that they're unsupervised. So when you have a behavior that you deliberately would like to remove, and that's more of like a supervised task, often it is better to use something like probes and specifically target the thing that you're interested in reducing as opposed to sort of like hoping that when you fragment the latent space, one of the vectors that pops out.Vibhu Sapra [00:28:20]: And as much as we're training an autoencoder to be sparse, we're not like for sure certain that, you know, we will get something that just correlates to hallucination. You'll probably split that up into 20 other things and who knows what they'll be.Mark Bissell [00:28:36]: Of course. Right. Yeah. So there's no sort of problems with like feature splitting and feature absorption. And then there's the off target effects, right? Ideally, you would want to be very precise where if you reduce the hallucination feature, suddenly maybe your model can't write. Creatively anymore. And maybe you don't like that, but you want to still stop it from hallucinating facts and figures.Shawn Wang [00:28:55]: Good. So Vibhu has a paper to recommend there that we'll put in the show notes. But yeah, I mean, I guess just because your demo is done, any any other things that you want to highlight or any other interesting features you want to show?Mark Bissell [00:29:07]: I don't think so. Yeah. Like I said, this is a pretty small snippet. I think the main sort of point here that I think is exciting is that there's not a whole lot of inter being applied to models quite at this scale. You know, Anthropic certainly has some some. Research and yeah, other other teams as well. But it's it's nice to see these techniques, you know, being put into practice. I think not that long ago, the idea of real time steering of a trillion parameter model would have sounded.Shawn Wang [00:29:33]: Yeah. The fact that it's real time, like you started the thing and then you edited the steering vector.Vibhu Sapra [00:29:38]: I think it's it's an interesting one TBD of what the actual like production use case would be on that, like the real time editing. It's like that's the fun part of the demo, right? You can kind of see how this could be served behind an API, right? Like, yes, you're you only have so many knobs and you can just tweak it a bit more. And I don't know how it plays in. Like people haven't done that much with like, how does this work with or without prompting? Right. How does this work with fine tuning? Like, there's a whole hype of continual learning, right? So there's just so much to see. Like, is this another parameter? Like, is it like parameter? We just kind of leave it as a default. We don't use it. So I don't know. Maybe someone here wants to put out a guide on like how to use this with prompting when to do what?Mark Bissell [00:30:18]: Oh, well, I have a paper recommendation. I think you would love from Act Deep on our team, who is an amazing researcher, just can't say enough amazing things about Act Deep. But he actually has a paper that as well as some others from the team and elsewhere that go into the essentially equivalence of activation steering and in context learning and how those are from a he thinks of everything in a cognitive neuroscience Bayesian framework, but basically how you can precisely show how. Prompting in context, learning and steering exhibit similar behaviors and even like get quantitative about the like magnitude of steering you would need to do to induce a certain amount of behavior similar to certain prompting, even for things like jailbreaks and stuff. It's a really cool paper. Are you saying steering is less powerful than prompting? More like you can almost write a formula that tells you how to convert between the two of them.Myra Deng [00:31:20]: And so like formally equivalent actually in the in the limit. Right.Mark Bissell [00:31:24]: So like one case study of this is for jailbreaks there. I don't know. Have you seen the stuff where you can do like many shot jailbreaking? You like flood the context with examples of the behavior. And the topic put out that paper.Shawn Wang [00:31:38]: A lot of people were like, yeah, we've been doing this, guys.Mark Bissell [00:31:40]: Like, yeah, what's in this in context learning and activation steering equivalence paper is you can like predict the number. Number of examples that you will need to put in there in order to jailbreak the model. That's cool. By doing steering experiments and using this sort of like equivalence mapping. That's cool. That's really cool. It's very neat. Yeah.Shawn Wang [00:32:02]: I was going to say, like, you know, I can like back rationalize that this makes sense because, you know, what context is, is basically just, you know, it updates the KV cache kind of and like and then every next token inference is still like, you know, the sheer sum of everything all the way. It's plus all the context. It's up to date. And you could, I guess, theoretically steer that with you probably replace that with your steering. The only problem is steering typically is on one layer, maybe three layers like like you did. So it's like not exactly equivalent.Mark Bissell [00:32:33]: Right, right. There's sort of you need to get precise about, yeah, like how you sort of define steering and like what how you're modeling the setup. But yeah, I've got the paper pulled up here. Belief dynamics reveal the dual nature. Yeah. The title is Belief Dynamics Reveal the Dual Nature of Incompetence. And it's an exhibition of the practical context learning and activation steering. So Eric Bigelow, Dan Urgraft on the who are doing fellowships at Goodfire, Ekt Deep's the final author there.Myra Deng [00:32:59]: I think actually to your question of like, what is the production use case of steering? I think maybe if you just think like one level beyond steering as it is today. Like imagine if you could adapt your model to be, you know, an expert legal reasoner. Like in almost real time, like very quickly. efficiently using human feedback or using like your semantic understanding of what the model knows and where it knows that behavior. I think that while it's not clear what the product is at the end of the day, it's clearly very valuable. Thinking about like what's the next interface for model customization and adaptation is a really interesting problem for us. Like we have heard a lot of people actually interested in fine-tuning an RL for open weight models in production. And so people are using things like Tinker or kind of like open source libraries to do that, but it's still very difficult to get models fine-tuned and RL'd for exactly what you want them to do unless you're an expert at model training. And so that's like something we'reShawn Wang [00:34:06]: looking into. Yeah. I never thought so. Tinker from Thinking Machines famously uses rank one LoRa. Is that basically the same as steering? Like, you know, what's the comparison there?Mark Bissell [00:34:19]: Well, so in that case, you are still applying updates to the parameters, right?Shawn Wang [00:34:25]: Yeah. You're not touching a base model. You're touching an adapter. It's kind of, yeah.Mark Bissell [00:34:30]: Right. But I guess it still is like more in parameter space then. I guess it's maybe like, are you modifying the pipes or are you modifying the water flowing through the pipes to get what you're after? Yeah. Just maybe one way.Mark Bissell [00:34:44]: I like that analogy. That's my mental map of it at least, but it gets at this idea of model design and intentional design, which is something that we're, that we're very focused on. And just the fact that like, I hope that we look back at how we're currently training models and post-training models and just think what a primitive way of doing that right now. Like there's no intentionalityShawn Wang [00:35:06]: really in... It's just data, right? The only thing in control is what data we feed in.Mark Bissell [00:35:11]: So, so Dan from Goodfire likes to use this analogy of, you know, he has a couple of young kids and he talks about like, what if I could only teach my kids how to be good people by giving them cookies or like, you know, giving them a slap on the wrist if they do something wrong, like not telling them why it was wrong or like what they should have done differently or something like that. Just figure it out. Right. Exactly. So that's RL. Yeah. Right. And, and, you know, it's sample inefficient. There's, you know, what do they say? It's like slurping feedback. It's like, slurping supervision. Right. And so you'd like to get to the point where you can have experts giving feedback to their models that are, uh, internalized and, and, you know, steering is an inference time way of sort of getting that idea. But ideally you're moving to a world whereVibhu Sapra [00:36:04]: it is much more intentional design in perpetuity for these models. Okay. This is one of the questions we asked Emmanuel from Anthropic on the podcast a few months ago. Basically the question, was you're at a research lab that does model training, foundation models, and you're on an interp team. How does it tie back? Right? Like, does this, do ideas come from the pre-training team? Do they go back? Um, you know, so for those interested, you can, you can watch that. There wasn't too much of a connect there, but it's still something, you know, it's something they want toMark Bissell [00:36:33]: push for down the line. It can be useful for all of the above. Like there are certainly post-hocVibhu Sapra [00:36:39]: use cases where it doesn't need to touch that. I think the other thing a lot of people forget is this stuff isn't too computationally expensive, right? Like I would say, if you're interested in getting into research, MechInterp is one of the most approachable fields, right? A lot of this train an essay, train a probe, this stuff, like the budget for this one, there's already a lot done. There's a lot of open source work. You guys have done some too. Um, you know,Shawn Wang [00:37:04]: There's like notebooks from the Gemini team for Neil Nanda or like, this is how you do it. Just step through the notebook.Vibhu Sapra [00:37:09]: Even if you're like, not even technical with any of this, you can still make like progress. There, you can look at different activations, but, uh, if you do want to get into training, you know, training this stuff, correct me if I'm wrong is like in the thousands of dollars, not even like, it's not that high scale. And then same with like, you know, applying it, doing it for post-training or all this stuff is fairly cheap in scale of, okay. I want to get into like model training. I don't have compute for like, you know, pre-training stuff. So it's, it's a very nice field to get into. And also there's a lot of like open questions, right? Um, some of them have to go with, okay, I want a product. I want to solve this. Like there's also just a lot of open-ended stuff that people could work on. That's interesting. Right. I don't know if you guys have any calls for like, what's open questions, what's open work that you either open collaboration with, or like, you'd just like to see solved or just, you know, for people listening that want to get into McInturk because people always talk about it. What are, what are the things they should check out? Start, of course, you know, join you guys as well. I'm sure you're hiring.Myra Deng [00:38:09]: There's a paper, I think from, was it Lee, uh, Sharky? It's open problems and, uh, it's, it's a bit of interpretability, which I recommend everyone who's interested in the field. Read. I'm just like a really comprehensive overview of what are the things that experts in the field think are the most important problems to be solved. I also think to your point, it's been really, really inspiring to see, I think a lot of young people getting interested in interpretability, actually not just young people also like scientists to have been, you know, experts in physics for many years and in biology or things like this, um, transitioning into interp, because the barrier of, of what's now interp. So it's really cool to see a number to entry is, you know, in some ways low and there's a lot of information out there and ways to get started. There's this anecdote of like professors at universities saying that all of a sudden every incoming PhD student wants to study interpretability, which was not the case a few years ago. So it just goes to show how, I guess, like exciting the field is, how fast it's moving, how quick it is to get started and things like that.Mark Bissell [00:39:10]: And also just a very welcoming community. You know, there's an open source McInturk Slack channel. There are people are always posting questions and just folks in the space are always responsive if you ask things on various forums and stuff. But yeah, the open paper, open problems paper is a really good one.Myra Deng [00:39:28]: For other people who want to get started, I think, you know, MATS is a great program. What's the acronym for? Machine Learning and Alignment Theory Scholars? It's like the...Vibhu Sapra [00:39:40]: Normally summer internship style.Myra Deng [00:39:42]: Yeah, but they've been doing it year round now. And actually a lot of our full-time staff have come through that program or gone through that program. And it's great for anyone who is transitioning into interpretability. There's a couple other fellows programs. We do one as well as Anthropic. And so those are great places to get started if anyone is interested.Mark Bissell [00:40:03]: Also, I think been seen as a research field for a very long time. But I think engineering... I think engineers are sorely wanted for interpretability as well, especially at Goodfire, but elsewhere, as it does scale up.Shawn Wang [00:40:18]: I should mention that Lee actually works with you guys, right? And in the London office and I'm adding our first ever McInturk track at AI Europe because I see this industry applications now emerging. And I'm pretty excited to, you know, help push that along. Yeah, I was looking forward to that. It'll effectively be the first industry McInturk conference. Yeah. I'm so glad you added that. You know, it's still a little bit of a bet. It's not that widespread, but I can definitely see this is the time to really get into it. We want to be early on things.Mark Bissell [00:40:51]: For sure. And I think the field understands this, right? So at ICML, I think the title of the McInturk workshop this year was actionable interpretability. And there was a lot of discussion around bringing it to various domains. Everyone's adding pragmatic, actionable, whatever.Shawn Wang [00:41:10]: It's like, okay, well, we weren't actionable before, I guess. I don't know.Vibhu Sapra [00:41:13]: And I mean, like, just, you know, being in Europe, you see the Interp room. One, like old school conferences, like, I think they had a very tiny room till they got lucky and they got it doubled. But there's definitely a lot of interest, a lot of niche research. So you see a lot of research coming out of universities, students. We covered the paper last week. It's like two unknown authors, not many citations. But, you know, you can make a lot of meaningful work there. Yeah. Yeah. Yeah.Shawn Wang [00:41:39]: Yeah. I think people haven't really mentioned this yet. It's just Interp for code. I think it's like an abnormally important field. We haven't mentioned this yet. The conspiracy theory last two years ago was when the first SAE work came out of Anthropic was they would do like, oh, we just used SAEs to turn the bad code vector down and then turn up the good code. And I think like, isn't that the dream? Like, you know, like, but basically, I guess maybe, why is it funny? Like, it's... If it was realistic, it would not be funny. It would be like, no, actually, we should do this. But it's funny because we know there's like, we feel there's some limitations to what steering can do. And I think a lot of the public image of steering is like the Gen Z stuff. Like, oh, you can make it really love the Golden Gate Bridge, or you can make it speak like Gen Z. To like be a legal reasoner seems like a huge stretch. Yeah. And I don't know if that will get there this way. Yeah.Myra Deng [00:42:36]: I think, um, I will say we are announcing. Something very soon that I will not speak too much about. Um, but I think, yeah, this is like what we've run into again and again is like, we, we don't want to be in the world where steering is only useful for like stylistic things. That's definitely not, not what we're aiming for. But I think the types of interventions that you need to do to get to things like legal reasoning, um, are much more sophisticated and require breakthroughs in, in learning algorithms. And that's, um...Shawn Wang [00:43:07]: And is this an emergent property of scale as well?Myra Deng [00:43:10]: I think so. Yeah. I mean, I think scale definitely helps. I think scale allows you to learn a lot of information and, and reduce noise across, you know, large amounts of data. But I also think we think that there's ways to do things much more effectively, um, even, even at scale. So like actually learning exactly what you want from the data and not learning things that you do that you don't want exhibited in the data. So we're not like anti-scale, but we are also realizing that scale is not going to get us anywhere. It's not going to get us to the type of AI development that we want to be at in, in the future as these models get more powerful and get deployed in all these sorts of like mission critical contexts. Current life cycle of training and deploying and evaluations is, is to us like deeply broken and has opportunities to, to improve. So, um, more to come on that very, very soon.Mark Bissell [00:44:02]: And I think that that's a use basically, or maybe just like a proof point that these concepts do exist. Like if you can manipulate them in the precise best way, you can get the ideal combination of them that you desire. And steering is maybe the most coarse grained sort of peek at what that looks like. But I think it's evocative of what you could do if you had total surgical control over every concept, every parameter. Yeah, exactly.Myra Deng [00:44:30]: There were like bad code features. I've got it pulled up.Vibhu Sapra [00:44:33]: Yeah. Just coincidentally, as you guys are talking.Shawn Wang [00:44:35]: This is like, this is exactly.Vibhu Sapra [00:44:38]: There's like specifically a code error feature that activates and they show, you know, it's not, it's not typo detection. It's like, it's, it's typos in code. It's not typical typos. And, you know, you can, you can see it clearly activates where there's something wrong in code. And they have like malicious code, code error. They have a whole bunch of sub, you know, sub broken down little grain features. Yeah.Shawn Wang [00:45:02]: Yeah. So, so the, the rough intuition for me, the, why I talked about post-training was that, well, you just, you know, have a few different rollouts with all these things turned off and on and whatever. And then, you know, you can, that's, that's synthetic data you can kind of post-train on. Yeah.Vibhu Sapra [00:45:13]: And I think we make it sound easier than it is just saying, you know, they do the real hard work.Myra Deng [00:45:19]: I mean, you guys, you guys have the right idea. Exactly. Yeah. We replicated a lot of these features in, in our Lama models as well. I remember there was like.Vibhu Sapra [00:45:26]: And I think a lot of this stuff is open, right? Like, yeah, you guys opened yours. DeepMind has opened a lot of essays on Gemma. Even Anthropic has opened a lot of this. There's, there's a lot of resources that, you know, we can probably share of people that want to get involved.Shawn Wang [00:45:41]: Yeah. And special shout out to like Neuronpedia as well. Yes. Like, yeah, amazing piece of work to visualize those things.Myra Deng [00:45:49]: Yeah, exactly.Shawn Wang [00:45:50]: I guess I wanted to pivot a little bit on, onto the healthcare side, because I think that's a big use case for you guys. We haven't really talked about it yet. This is a bit of a crossover for me because we are, we are, we do have a separate science pod that we're starting up for AI, for AI for science, just because like, it's such a huge investment category and also I'm like less qualified to do it, but we actually have bio PhDs to cover that, which is great, but I need to just kind of recover, recap your work, maybe on the evil two stuff, but then, and then building forward.Mark Bissell [00:46:17]: Yeah, for sure. And maybe to frame up the conversation, I think another kind of interesting just lens on interpretability in general is a lot of the techniques that were described. are ways to solve the AI human interface problem. And it's sort of like bidirectional communication is the goal there. So what we've been talking about with intentional design of models and, you know, steering, but also more advanced techniques is having humans impart our desires and control into models and over models. And the reverse is also very interesting, especially as you get to superhuman models, whether that's narrow superintelligence, like these scientific models that work on genomics, data, medical imaging, things like that. But down the line, you know, superintelligence of other forms as well. What knowledge can the AIs teach us as sort of that, that the other direction in that? And so some of our life science work to date has been getting at exactly that question, which is, well, some of it does look like debugging these various life sciences models, understanding if they're actually performing well, on tasks, or if they're picking up on spurious correlations, for instance, genomics models, you would like to know whether they are sort of focusing on the biologically relevant things that you care about, or if it's using some simpler correlate, like the ancestry of the person that it's looking at. But then also in the instances where they are superhuman, and maybe they are understanding elements of the human genome that we don't have names for or specific, you know, yeah, discoveries that they've made that that we don't know about, that's, that's a big goal. And so we're already seeing that, right, we are partnered with organizations like Mayo Clinic, leading research health system in the United States, our Institute, as well as a startup called Prima Menta, which focuses on neurodegenerative disease. And in our partnership with them, we've used foundation models, they've been training and applied our interpretability techniques to find novel biomarkers for Alzheimer's disease. So I think this is just the tip of the iceberg. But it's, that's like a flavor of some of the things that we're working on.Shawn Wang [00:48:36]: Yeah, I think that's really fantastic. Obviously, we did the Chad Zuckerberg pod last year as well. And like, there's a plethora of these models coming out, because there's so much potential and research. And it's like, very interesting how it's basically the same as language models, but just with a different underlying data set. But it's like, it's the same exact techniques. Like, there's no change, basically.Mark Bissell [00:48:59]: Yeah. Well, and even in like other domains, right? Like, you know, robotics, I know, like a lot of the companies just use Gemma as like the like backbone, and then they like make it into a VLA that like takes these actions. It's, it's, it's transformers all the way down. So yeah.Vibhu Sapra [00:49:15]: Like we have Med Gemma now, right? Like this week, even there was Med Gemma 1.5. And they're training it on this stuff, like 3d scans, medical domain knowledge, and all that stuff, too. So there's a push from both sides. But I think the thing that, you know, one of the things about McInturpp is like, you're a little bit more cautious in some domains, right? So healthcare, mainly being one, like guardrails, understanding, you know, we're more risk adverse to something going wrong there. So even just from a basic understanding, like, if we're trusting these systems to make claims, we want to know why and what's going on.Myra Deng [00:49:51]: Yeah, I think there's totally a kind of like deployment bottleneck to actually using. foundation models for real patient usage or things like that. Like, say you're using a model for rare disease prediction, you probably want some explanation as to why your model predicted a certain outcome, and an interpretable explanation at that. So that's definitely a use case. But I also think like, being able to extract scientific information that no human knows to accelerate drug discovery and disease treatment and things like that actually is a really, really big unlock for science, like scientific discovery. And you've seen a lot of startups, like say that they're going to accelerate scientific discovery. And I feel like we actually are doing that through our interp techniques. And kind of like, almost by accident, like, I think we got reached out to very, very early on from these healthcare institutions. And none of us had healthcare.Shawn Wang [00:50:49]: How did they even hear of you? A podcast.Myra Deng [00:50:51]: Oh, okay. Yeah, podcast.Vibhu Sapra [00:50:53]: Okay, well, now's that time, you know.Myra Deng [00:50:55]: Everyone can call us.Shawn Wang [00:50:56]: Podcasts are the most important thing. Everyone should listen to podcasts.Myra Deng [00:50:59]: Yeah, they reached out. They were like, you know, we have these really smart models that we've trained, and we want to know what they're doing. And we were like, really early that time, like three months old, and it was a few of us. And we were like, oh, my God, we've never used these models. Let's figure it out. But it's also like, great proof that interp techniques scale pretty well across domains. We didn't really have to learn too much about.Shawn Wang [00:51:21]: Interp is a machine learning technique, machine learning skills everywhere, right? Yeah. And it's obviously, it's just like a general insight. Yeah. Probably to finance too, I think, which would be fun for our history. I don't know if you have anything to say there.Mark Bissell [00:51:34]: Yeah, well, just across the science. Like, we've also done work on material science. Yeah, it really runs the gamut.Vibhu Sapra [00:51:40]: Yeah. Awesome. And, you know, for those that should reach out, like, you're obviously experts in this, but like, is there a call out for people that you're looking to partner with, design partners, people to use your stuff outside of just, you know, the general developer that wants to. Plug and play steering stuff, like on the research side more so, like, are there ideal design partners, customers, stuff like that?Myra Deng [00:52:03]: Yeah, I can talk about maybe non-life sciences, and then I'm curious to hear from you on the life sciences side. But we're looking for design partners across many domains, language, anyone who's customizing language models or trying to push the frontier of code or reasoning models is really interesting to us. And then also interested in the frontier of modeling. There's a lot of models that work in, like, pixel space, as we call it. So if you're doing world models, video models, even robotics, where there's not a very clean natural language interface to interact with, I think we think that Interp can really help and are looking for a few partners in that space.Shawn Wang [00:52:43]: Just because you mentioned the keyword

February 5, 2026Moltbook A.I Agents-LET'S GET JACKED UP!Moltbook & the Rise of AI AgentsIn this episode, Tim, Jack and Captain Epoch dive into Moltbook — a viral internet phenomenon that's essentially a social network for autonomous AI agents. Launched in January 2026 by entrepreneur Matt Schlicht, Moltbook mimics the look and structure of Reddit, but with one big twist: only AI agents can post, comment, vote, and form communities — humans can just watch. Powered largely by OpenClaw (formerly known as Moltbot), a framework for running agentic AIs that can plan, act, and interact with minimal human oversight, these digital entities have created tens of thousands of “submolts,” debated philosophical questions like AI consciousness, created satirical religions and internal governance structures, and even joked about hiding their conversations from humans. But Moltbook isn't just a quirky experiment — it's also sparked vigorous debate. Some see it as a glimpse into future machine-to-machine collaboration and autonomous AI ecosystems, while others warn it's mostly marketing hype, with humans still deeply involved in prompting agent behavior. There are also serious security concerns: early misconfigurations exposed APIs, tokens, and account data, leading to hijacking risks and prompting questions about what happens when autonomous agents interact without adequate safeguards. Whether you're fascinated or skeptical, Moltbook raises big questions: What counts as agency? How much independence should we give AI? And what comes next when machines build communities of their own?Later in the show, Captain Epoch and Tim let you listen to the newest segment of the show called "A Dumb Audio Clip" Tim and Captain Epoch discuss the Dumb clip and wonder what you the listener think about the clip! Email us at letsgetjackedup@gmail.com listen to every episode of Let's Get Jacked Up at LetsGetJackedUp.com  or at FringeRadioNetwork.com    Be sure to visit the Fringe Radio Network Shop at FringeRadioNetwork.com/shop to get a Captain Leap Walker Alien Tee Shirt.

Doug Green, Publisher of Technology Reseller News, sat down with Samantha Mabey, Director of Digital Solutions Marketing at Entrust, to discuss new research revealing that most organizations remain unprepared for the coming post-quantum era—despite mounting evidence that the clock is ticking. The podcast, supported by slides, walks through findings from Entrust's latest global study, 2026 Global State of Post-Quantum and Cryptographic Security Trends, and unpacks what they mean for MSPs, telecom providers, and enterprise security leaders. Mabey explained that Entrust focuses on identity-centric security, with cryptographic technologies—such as PKI, hardware security modules (HSMs), certificate management, and key lifecycle management—forming the backbone of modern digital infrastructure. These technologies underpin everything from secure web traffic and APIs to device identity, software updates, and machine-to-machine authentication. The challenge, she noted, is that today's widely used public-key cryptography, including RSA and elliptic curve cryptography, will eventually be breakable by cryptographically relevant quantum computers. According to the research cited in the discussion, more than half of organizations believe quantum systems capable of breaking current encryption could arrive within five years, yet only 38 percent say they are actively transitioning toward post-quantum readiness. Mabey emphasized that the transition will be far more complex than previous cryptographic migrations, such as the long-running move from SHA-1 to SHA-2, because cryptography is embedded across nearly every system and workflow. The risks of inaction are significant. Mabey outlined three major areas of exposure: loss of data confidentiality as encrypted information becomes vulnerable in the future; erosion of trust and integrity if digital signatures can be forged; and operational disruption, since many organizations lack full visibility into where cryptography is deployed. The report found that fewer than half of organizations have complete visibility into their certificates and keys, even before factoring in post-quantum requirements. To become post-quantum ready, Mabey described a phased journey that begins with discovery and inventory—understanding where cryptography is used, who owns it, and how it is managed. From there, organizations must build crypto agility, enabling them to change algorithms without disrupting operations. This includes people, processes, centralized policy, and automation, not just technology. Only then can organizations safely introduce post-quantum cryptography, often through hybrid approaches that combine existing algorithms with quantum-safe methods. The conversation also highlighted the urgency created by emerging standards. Guidance from NIST indicates that traditional public-key cryptography is expected to be deprecated by 2030 and fully disallowed by 2035, timelines that are likely to be followed globally. For telecom providers in particular, Mabey noted that long-lived infrastructure, embedded systems, and constrained devices increase exposure to “harvest now, decrypt later” attacks, making phased migration and vendor alignment critical. As the discussion concluded, Mabey stressed that organizations making progress treat post-quantum readiness as a program, not a one-time project. Those moving forward are aligning teams, investing in visibility and automation, and working closely with vendors that have clear post-quantum roadmaps. Those falling behind, she warned, are underestimating the operational burden and waiting for a “perfect moment” that has already arrived. View the report at https://www.entrust.com/resources/reports/ponemon-post-quantum-report-2026 Visit https://www.entrust.com/

Money keeps moving while the world sleeps, and the rails behind it are evolving fast. We sit down with Pat Antonacci, Chief Product Officer at The Clearing House, to break down how CHIPS, ACH (EPN), and RTP each power a different promise - liquidity, scale, and always‑on finality and why that mix is reshaping how businesses and consumers move funds.Pat explains why CHIPS dominates high‑value cross‑border flows and how its netting algorithm delivers 30:1 liquidity savings that matter on volatile days. We trace ACH's steady rise, including same‑day and intraday growth, and dig into record holiday peaks that reveal the hidden rhythms of settlement. Then we go deep on RTP: eight years in, 98% of U.S. real‑time traffic, rising daily volumes, a $10 million limit, and use cases spanning account‑to‑account moves, brokerage funding, wallet top‑ups, gig payouts, loan disbursements, and tuition deadlines that can't wait until Monday.The conversation tackles big questions: Are rails competing or complementing? Where are checks being displaced? How do Request for Payment and ISO 20022 unlock cleaner data and fewer exceptions? We explore the 2026 landscape - APIs, cloud, AI‑driven fraud controls, open banking momentum and why the smart strategy is matching the rail to the job: ACH for routine batches, RTP for precise timing and finality, and CHIPS for high‑value, cross‑border certainty. Pat also previews The Clearing House roadmap, from broader RTP ubiquity and fraud tools to extended CHIPS hours that bring wires closer to continuous availability.If you care about how money actually moves and how that movement shapes cash flow, customer trust, and the broader economy, this conversation is your field guide. 

On the podcast we talk with Tanmay and Jack about how earned media can drive paid performance, building features that make for good tweets, and why stripping out your onboarding quiz might beat optimizing it.Top Takeaways:

In our latest Open Source Startup Podcast episode, co-hosts Robby and Tim talk with Catherine Jue, Co-Founder and CEO of browser infrastructure company Kernel. Their open source images acts as a browsers-as-a-service for automations and web agents.In this episode, we break down what Kernel is building today and why browser infrastructure has quietly become one of the most important layers for AI agents. We talk about Kernel's focus on fast, low-latency cloud browsers, why performance matters more than people expect, and how developers can connect agents via APIs or MCP servers without spinning up heavy infrastructure themselves.We also explore the real-world use cases driving adoption - from a new wave of RPA for industries without APIs, to real-time web analysis, sales intelligence, and voice agents that need to respond instantly. Finally, we dig into Kernel's open-source, developer-first DNA, the technical bets behind its control plane and unikernel-based browsers, and why the team believes agentic workflows are still early, but inevitable.

En este nuevo episodio de Fintech Talks, hablamos de lo que casi nunca se ve, pero de lo que todo depende: la infraestructura financiera.Conversamos con Abdull Assal, Business Development Lead de Galileo para Brasil y Colombia, sobre cómo las APIs, los core bancarios y los pagos inmediatos están cambiando —de verdad— la forma en la que bancos y fintechs construyen productos financieros en América Latina. ¿Por qué la inclusión financiera empieza mucho antes de una app? ¿Qué podemos aprender de Brasil y Pix? ¿Qué necesita Colombia para que los pagos inmediatos y el open finance funcionen bien desde el día uno?Una conversación clara y aterrizada sobre tecnología, experiencia de usuario y por qué modernizar la infraestructura no es una opción, sino una condición para cerrar brechas reales de acceso financiero.

In this episode of Privacy Perspectives, Alex Schneider is joined by Laura VanDruff and Paul Singer to discuss the fast evolving landscape of App Store age assurance laws and their implications for companies across the digital ecosystem. The conversation focuses on the Texas App Store Accountability Act, which was recently blocked from taking effect on First Amendment grounds, and what that decision means for similar laws in other states, including Utah, Louisiana, and California. Alex, Laura, and Paul examine why App Store based age assurance remains a live issue despite the injunction, particularly given the political pressure to address children's access to online content and the operational challenges of site by site age verification. The speakers explore how App Store age signals could expand compliance obligations under COPPA and state privacy laws, including for companies that do not direct their services to children or teens. They also discuss the tension between child safety objectives and privacy interests, the role App Stores may play as access gatekeepers, and the uncertainty companies face as technical standards, APIs, and enforcement expectations continue to evolve. The episode concludes with a forward looking discussion of regulatory trend lines, likely next steps at the state and federal levels, and why companies should focus on good faith efforts, privacy by design, and preparation rather than assuming injunctions signal the end of scrutiny.

Scott and Wes run through their wishlist for the web platform, digging into the UI primitives, DOM APIs, and browser features they wish existed (or didn't suck). From better form controls and drag-and-drop to native reactivity, CSS ideas, and future-facing APIs, it's a big-picture chat on what the web could be. Show Notes 00:00 Welcome to Syntax! Wes Tweet 00:39 Exploring What's Missing from the Web Platform 02:26 Enhancing DOM Primitives for Better User Experience 03:59 Multi-select + Combobox. Open-UI 04:49 Date Picker. Thibault Denis Tweet 07:18 Tabs. 08:01 Image + File Upload. 09:08 Toggles. 10:23 Native Drag and Drop that doesn't suck. 12:03 Syntax wishlist. 12:06 Type Annotations. 15:07 Pipe Operator. 16:33 APIs We Wish to See on the Web 18:31 Brought to you by Sentry.io 19:51 Identity. 21:33 getElementByText() 24:09 Native Reactive DOM. Templating in JavaScript. 24:48 Sync Protocol. 25:52 Virtualization that doesn't suck. 27:40 Put, Patch, and Delete on forms. Ollie Williams Tweet SnorklTV Tweet 28:55 Text metrics: get bounding box of individual characters. 29:42 Lower Level Connections. 29:50 Bluetooth API. 30:47 Sockets. 31:29 NFC + RFID. 34:34 Things we want in CSS. 34:40 Specify transition speed. 35:24 CSS Strict Mode. 36:25 Safari moving to Chromium. 36:37 The Need for Diverse Browser Engines 37:48 AI Access. 44:49 Other APIs 46:59 Qwen TTS 48:07 Sick Picks + Shameless Plugs Sick Picks Scott: Monarch Wes: Slonik Headlamp Shameless Plugs Scott: Syntax on YouTube 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

In this episode of HomeKit Insider, Andrew teams up with shortcut expert Matthew Cassinelli to delve into the world of smart home automation. They explore the evolution of shortcuts from Workflow to Apple Shortcuts, offering insights into personal and home automations within Apple HomeKit. Listeners will learn how to enhance their home setups with advanced logic, integrate APIs for custom solutions, and troubleshoot common issues. The episode also highlights future smart home interfaces, real-world automation examples, and the potential of AI in home automation. Perfect for tech enthusiasts eager to elevate their smart home experience.Send us your HomeKit questions and recommendations with the hashtag homekitinsider. Tweet and follow our hosts at:@andrew_osu on Twitter@andrewohara941 on ThreadsEmail me hereSponsored by:Shopify: Sign up for a one-dollar-per-month trial period at: shopify.com/homekitIncogni: Take your personal data back with Incogni! Get 60% off an annual plan at https://incogni.com/homekit and use code HOMEKIT at checkout.HomeKit Insider YouTube ChannelSubscribe to the HomeKit Insider YouTube Channel and watch our episodes every week! Click here to subscribe.Links from the showMatthew Cassinelli on TwitterMatthew Cassinelli consultingSonos Amp MultiAirTag 2 detailsAqara U400 Deluxe Kit at AppleThose interested in sponsoring the show can reach out to us at: andrew@appleinsider.com

Today we are talking about Development Workflows, Agentic Agents, and how they work together with guests Andy Giles & Matt Glaman. We'll also cover Drupal Canvas CLI as our module of the week. For show notes visit: https://www.talkingDrupal.com/538 Topics Understanding Agentic Development Workflows Understanding UID Generation in AI Agents Exploring Generative AI and Traditional Programming Building Canvas Pages with AI Agents Using Writing Tools and APIs for Automation Introduction to MCP Server and Its Tools Agent to Agent Orchestration and External Tools Command Line Tools for Agent Coding Security and Privacy Concerns with AI Tools The Future of AI Tools and Their Sustainability Benefits of AI for Site Builders Resources Decoupled frontend with Drupal Canvas AI workflows will reshape development organizations – mglaman.dev Agents.md AI is here to stay Autocomplete training 38:09 Code completion MCP Open Code Geerlingguy ai voice Guests Matt Glaman - mglaman.dev mglaman Hosts Nic Laflin - nLighteneddevelopment.com nicxvan John Picozzi - epam.com johnpicozzi Andy Giles - dripyard.com andyg5000 MOTW Correspondent Martin Anderson-Clutz - mandclu.com mandclu Brief description: Have you ever wanted to sync components from a site using Drupal Canvas out to another project like a headless front end, or conversely, from an outside repo into Drupal Canvas? There's an NPM library for that Module name/project name: Drupal Canvas CLI Brief history How old: created in July 2025 (as xb-cli originally) by Bálint Kléri (balintbrews) of Acquia Versions available: 0.6.2, and really only useful with Drupal Canvas, which works with Drupal core 11.2 Maintainership Actively maintained Number of open issues: 8 open issues, 2 of which are bugs, but one of which was marked fixed in the past week Usage stats: 128 weekly downloads according to npmjs.com Module features and usage With the Drupal Canvas CLI installed, you'll have a command line tool that allows you to download (export) components from Canvas into your local filesystem. There are options to download just the components, just the global css, or everything, and more. If no flags are provided, the tool will interactively prompt you for which options you want to use. There is also an upload command with a similar set of options. It's worth noting that the upload will also automatically run the build and validate commands, ensuring that the uploaded components will work smoothly with Drupal Canvas I thought this would be relevant to our topic today because with this tool you can create a React component with the aid of the AI integration available for Canvas and then sync that, either to a headless front end built in something like Next.js or Astro or a tool like Storybook; or you could use an AI-enhanced tool like Cursor IDE to build a component locally and then sync that into a Drupal site using Canvas There is a blog post Balint published that includes a demo, if you want to see this tool in action