Bohemian novelist and short-story writer (1883–1924)
POPULARITY
Categories
238 episodes with kafka
54 episodes with kafka
53 episodes with kafka
46 episodes with kafka
21 episodes with kafka
12 episodes with kafka
21 episodes with kafka
38 episodes with kafka
36 episodes with kafka
12 episodes with kafka
10 episodes with kafka
10 episodes with kafka
29 episodes with kafka
15 episodes with kafka
11 episodes with kafka
9 episodes with kafka
14 episodes with kafka
16 episodes with kafka
8 episodes with kafka
16 episodes with kafka
12 episodes with kafka
21 episodes with kafka
9 episodes with kafka
9 episodes with kafka
18 episodes with kafka
8 episodes with kafka
8 episodes with kafka
11 episodes with kafka
8 episodes with kafka
12 episodes with kafka
17 episodes with kafka
9 episodes with kafka
![Engenharia de Dados [Cast]](https://ivyfm.s3.amazonaws.com/i320/1212346.jpg)
7 episodes with kafka
8 episodes with kafka
8 episodes with kafka
12 episodes with kafka
4 episodes with kafka
9 episodes with kafka
9 episodes with kafka
10 episodes with kafka
15 episodes with kafka
13 episodes with kafka
14 episodes with kafka
7 episodes with kafka
4 episodes with kafka
10 episodes with kafka
7 episodes with kafka
7 episodes with kafka
12 episodes with kafka
5 episodes with kafka
4 episodes with kafka
7 episodes with kafka
6 episodes with kafka
11 episodes with kafka
11 episodes with kafka
9 episodes with kafka
6 episodes with kafka
7 episodes with kafka
5 episodes with kafka
5 episodes with kafka
6 episodes with kafka
6 episodes with kafka
10 episodes with kafka
10 episodes with kafka
3 episodes with kafka
10 episodes with kafka
5 episodes with kafka
5 episodes with kafka
7 episodes with kafka
5 episodes with kafka
7 episodes with kafka
6 episodes with kafka
7 episodes with kafka
5 episodes with kafka
2 episodes with kafka
4 episodes with kafka
5 episodes with kafka
4 episodes with kafka
5 episodes with kafka
4 episodes with kafka
3 episodes with kafka
3 episodes with kafka
5 episodes with kafka
8 episodes with kafka
8 episodes with kafka
6 episodes with kafka
6 episodes with kafka
4 episodes with kafka
3 episodes with kafka
6 episodes with kafka
5 episodes with kafka
7 episodes with kafka
19 episodes with kafka
2 episodes with kafka
4 episodes with kafka
3 episodes with kafka
3 episodes with kafka
7 episodes with kafka
2 episodes with kafka
5 episodes with kafka
10 episodes with kafka
3 episodes with kafka
3 episodes with kafka
3 episodes with kafka
5 episodes with kafka
2 episodes with kafka
4 episodes with kafka
6 episodes with kafka
3 episodes with kafka
4 episodes with kafka
4 episodes with kafka
10 episodes with kafka
7 episodes with kafka
3 episodes with kafka
3 episodes with kafka
5 episodes with kafka
5 episodes with kafka
3 episodes with kafka
4 episodes with kafka
3 episodes with kafka
2 episodes with kafka
4 episodes with kafka
4 episodes with kafka
8 episodes with kafka
4 episodes with kafka
4 episodes with kafka
6 episodes with kafka
2 episodes with kafka
3 episodes with kafka
3 episodes with kafka
3 episodes with kafka
4 episodes with kafka
3 episodes with kafka
5 episodes with kafka
6 episodes with kafka
8 episodes with kafka
3 episodes with kafka
2 episodes with kafka
3 episodes with kafka
4 episodes with kafka
5 episodes with kafka
2 episodes with kafka
2 episodes with kafka
3 episodes with kafka
4 episodes with kafka
4 episodes with kafka
7 episodes with kafka
2 episodes with kafka
2 episodes with kafka
3 episodes with kafka
4 episodes with kafka
2 episodes with kafka
2 episodes with kafka
3 episodes with kafka
3 episodes with kafka
3 episodes with kafka
5 episodes with kafka
5 episodes with kafka
5 episodes with kafka
3 episodes with kafka
2 episodes with kafka
2 episodes with kafka
2 episodes with kafka
2 episodes with kafka
2 episodes with kafka
2 episodes with kafka
3 episodes with kafka
2 episodes with kafka
5 episodes with kafka
5 episodes with kafka
48 episodes with kafka
7 episodes with kafka
2 episodes with kafka
2 episodes with kafka
3 episodes with kafka
3 episodes with kafka
2 episodes with kafka
6 episodes with kafka
2 episodes with kafka
3 episodes with kafka
AI artist Mick Mahler has a counterintuitive take: the more powerful the machines get, the less the technology actually matters. Showing delightful examples of his own art, from jazz-playing spiders to a Kafka-inspired beetle film, he explains how creators can use new technology to serve their vision (not replace it). The real question — the one that separates meaningful work from AI slop — is the one only you can answer. Hosted on Acast. See acast.com/privacy for more information.
"In a just world, Mark Waid is the bar, rather than a notable comics writer." ---------- The saying goes that he who is known as an early riser can get out of bed whenever he wants. Similarly, Action Comics #1094, "Our Superboy At War," pushes the limits of what "liberal" storytelling can depict before it crosses the line into conservative jingoism. Because ostensible progressive Mark Waid wrote it, the actions of the hero within must be just and good. The line drawn here is blurry, and what it divides is less important (although just as obscured) as where it ends. And where it begins. ---------- Special thanks to our Lovable Sidekicks: Better Possible Futures, Kourtney Smith, Walt Lewellyn, Kafka, The Black Casebook's Very Own Nightwing, JD Lunt, Ambird, Mr. Pig from the Intervention, Travis Armstrong, Chris Marks, Wirecats, Sheeee-itttt, VoidTek, Mars Hottentot, Richard Bell, TakoTuesday, Joseph, and Knife Money ---------- Email: collectiveactioncomics@gmail.com Instagram: https://www.instagram.com/collectiveactioncomics Twitter: https://twitter.com/CAComixPod Bluesky: https://bsky.app/profile/cacomixpod.bsky.social Patreon: https://www.patreon.com/collectiveactioncomics
(Insight Meditation Society - Retreat Center) Dolls, loss, love and the narrative cure. Kafka consoles a girl with a series of letters about a lost doll. An excursion into four different and indispensable tasks for any contemplative practitioner: (i) Calming & stabilzing (ii) Dis-identification and decentering (iii) Deep Inquiry and thorough contemplative Investigation (iv) The bigger Picture – gaining an universal perspective on the personal
Dharma Seed - dharmaseed.org: dharma talks and meditation instruction
(Insight Meditation Society - Retreat Center) Dolls, loss, love and the narrative cure. Kafka consoles a girl with a series of letters about a lost doll. An excursion into four different and indispensable tasks for any contemplative practitioner: (i) Calming & stabilzing (ii) Dis-identification and decentering (iii) Deep Inquiry and thorough contemplative Investigation (iv) The bigger Picture – gaining an universal perspective on the personal
This interview was recorded for the GOTO Book Club.http://gotopia.tech/bookclubEkaterina Gorshkova - Apache Kafka Engineer at SOFTEC & Author of "Kafka for Architects"Viktor Gamov - Principal Developer Advocate at Confluent & Co-Author of "Kafka in Action"Check out more here:https://gotopia.tech/episodes/440RESOURCESEkaterinahttps://www.linkedin.com/in/ekaterina-gorshkova-978bb6https://medium.com/@katyagorshkovaViktorhttps://bsky.app/profile/gamussa.devhttps://x.com/gAmUssAhttps://github.com/gamussahttps://www.linkedin.com/in/vikgamovhttps://gamov.ioLinks45% off discount code (expires on 25 May 2026): GOTOKGKafkaAffiliate link: https://hubs.la/Q044HgTvhttps://current.confluent.io/londonDESCRIPTIONApache Kafka has evolved far beyond a simple message broker — it has become a foundational layer for modern enterprise software. In this GOTO Book Club episode, Ekaterina Gorshkova, author of "Kafka for Architects", shares how her decade-long journey with Kafka — starting in a Czech bank's integration team in 2015 — shaped her understanding of what it really takes to design Kafka-based systems at scale. The conversation covers core architectural decisions, real-world patterns for enterprise integration, the role of Kafka Streams, and how to avoid the classic pitfalls of building systems that "only three engineers understand".The episode also looks forward: Ekaterina and host Viktor Gamov explore how Kafka is increasingly becoming the connective tissue for AI-driven systems, acting as an orchestration layer between intelligent agents, real-time data, and business workflows. Her book's central argument is that while AI and tooling change fast, the fundamental knowledge of how to design robust, event-driven systems is durable and career-proof. Kafka for Architects is framed not just as a technical manual, but as a roadmap for architects who want to get Kafka right from day one — requirements, design, testing, and all.RECOMMENDED BOOKSEkaterina Gorshkova • Kafka for Architects • https://amzn.to/42mDarUDylan Scott, Viktor Gamov & Dave Klein • Kafka in Action • https://amzn.to/4vJ3KcjViktor Gamov, Tartakovsky, Rasputnis & Fain • Enterprise Web Development • https://amzn.to/3CezL0RShapira, Palino, Sivaram & Petty • Kafka: The Definitive Guide • https://amzn.to/3RPtdLPBill Bejeck • Kafka Streams in Action • https://amzn.to/3CGJiiMBlueskyInstagramLinkedInFacebookCHANNEL MEMBERSHIP BONUSJoin this channel to get early access to videos & other perks:https://www.youtube.com/channel/UCs_tLP3AiwYKwdUHpltJPuA/joinLooking for a unique learning experience?Attend the next GOTO conference near you! Get your ticket: gotopia.techSUBSCRIBE TO OUR YOUTUBE CHANNEL - new videos posted daily!
Live from the SoulWords House on Rosh Chodesh Sivan, Rabbi Shais Taub, Rabbi Dovid Bashevkin, and Bruce Backman come together for a raw and wide-ranging conversation honoring the yahrzeit of Franz Kafka. Why does Kafka continue to speak so powerfully to the Jewish soul?What is the spiritual root of alienation?Why do sensitive souls often feel like strangers in the world?And what happens when Kafka's writings are read through the lens of Torah and Chassidus? לעילוי נשמת אנשיל בן חנוך EMERGENCY RELIEF FOR THE REITER FAMILY: Our community is heartbroken by the tragic passing of Devora Leah Reiter ע"ה, a 47-year-old mother of nine children who spent her life helping others. Please open your heart to this urgent cause. 100% goes directly to the family: https://charidy.com/reiterfamilyfund/..
Mock-интервью с Николаем Лебедевым - DevOps/SRE-инженер, 17 лет в Linux, 4 года AWS EKS. Stack: Terraform, Flux, Cassandra, Kafka, Vault, SOPS. Два часа - много практики, много каверзных вопросов. ЧТО СПРАШИВАЛИ ☁️ AWS: EKS и IRSA, VPC с нуля (CIDR, multi-AZ, multi-region), managed K8s vs self-hosted, Elasticache, Golden Signals и метрики SRE.
Bestuurskundige Paul Frissen ziet ons land al een tijd heen en weer schommelen tussen populisme en technocratisch beheer. En dat is heel iets anders dan het beoefenen van de kunst van politiek in een land van minderheden en pluralisme in opvattingen en overtuigingen. Zijn nieuwe boek analyseert hoe dat komt en hoe de beoefening van die kunst de ruimte kan terugvinden die zij verdient. Jaap Jansen en PG Kroeger praten met Frissen over zijn boek De neutrale staat, pleidooi voor conservatief pluralisme. *** Deze aflevering is mede mogelijk gemaakt met donaties van luisteraars die we hiervoor hartelijk danken. Word ook vriend van de show! Heb je belangstelling om in onze podcast te adverteren of ons te sponsoren? Zend ons een mailtje en wij zoeken contact. *** De staat moet ons beschermen tegen de revolutionaire tijdgeest, zegt Paul Frissen. Hij moet daarin zelf geen politieke voorkeur aan de dag leggen maar vooral het pluralisme behoeden en behouden. Pluralisme is immers een levendig en uitdagend karakteristiek van de Nederlandse cultuur en politiek: "Iedereen is hier ergens minderheid." Daarom moet de staat het pluralisme dat al die vormen van eigenheid beschermt en ruimte geeft helpen bewaren. De staat zelf moet daarbij neutraal blijven. Meer verkeersregelaar of scheidsrechter. De spelregels met gezag handhaven, zonder te bepalen wat de inhoud en de uitkomst van het spel moet zijn. Want het strafrecht en het geweldsmonopolie van de staat zijn machtige wapens die alleen zorgvuldig en terughoudend ingezet mogen worden. Anders is voor je het weet de rechtsstaat verloren. Populisten vinden dit soort evenwicht en ingetogenheid maar onzin, noteert Frissen. Radicalen willen alles en wel nu. "Sentimenten die meteen bevredigd moeten worden." Populistische stromingen – of ze nu radicaal-rechts zijn of radicaal-links - zijn revolutionair van aard. Zij gaan uit van een welhaast onvermijdelijke apocalyps die het bedrijven van politiek juist onmogelijk maakt. Bij de een is dat een dreigende beschavingsondergang door 'omvolking', bij een ander is dat de planetaire verwoesting door klimaatcatastrofes. Allebei weigeren ze stil te staan bij de politieke matiging van het pluralisme. Dat is volgens hen 'het systeem', gestuurd door 'de elite' die zich tegen hun revolutionaire omwenteling verzet. Als de wereld op instorten staat, legitimeert dat voor radicalen ongeremd gedrag en beleid dat het alledaagse maatschappelijk verkeer moet verstoren. Denk aan de eis van 'noodwetten'. De ‘spontanité' van de Franse revolutie zien we nu terug in fakkeloptochten en het besmeuren van kunstwerken; ruiten ingooien; online bedreigingen; trekker-parades. Erupties in groepsverband van individuele emoties, die voortdurend gevoed moeten worden. Maar een échte oplossing is ook weer niet de bedoeling, blijkt uit bijvoorbeeld PVV-gedrag. Er moet voor hen vooral géén afgewogen migratiebeleid komen. Dat arbeidsmigratie een politieke keuze is en migratie ons niet hoeft te overkomen als een soort natuurverschijnsel, moet buiten beeld blijven. En ook al is het radicale aspect aan de linkerzijde minder dominant, bijvoorbeeld bij Extinction Rebellion ziet Frissen die romantische beleving van identiteit en ondergangsstemming eveneens. Het antwoord uit beleidspartijen rond 'het midden' blijft meestal steken in technocratisch beheer, klaagt hij. Het koesteren van pluralisme is daar verstatelijkt in plaats van een krachtbron. In de verzuiling waren verschillen de maatschappelijke basis. De behoefte aan gelijkheid - in kansen, voorzieningen en posities - bracht vanaf de jaren ‘60 en ‘70 uniformering. Verschillen werden een probleem dat via diagnostisering, monitoring, protocollen en compensatiemechanismen beheersbaar kon worden gemaakt. Wat Alexis de Tocqueville zo fraai 'mild despotisme' noemde. Beheersing en de technocratie van regelstelsels werd essentieel, dat hadden Michel Foucault en Jürgen Habermas goed gezien. Frissen toont zich geïnspireerd door de Franse denker Claude Lefort, die erop wijst dat niet beheersing en technocratisch management, maar creatieve botsingen de essentie van de democratie vormen. Dat technocratie en de gedachte dat deze door wetenschappelijke kennis, feiten en inzichten gestuurd kan worden nogal feilbaar is, zagen we in bij Covid-19. Opvallend was nu juist hoe wendbaar en flexibel het improviseren in de samenleving ook toen uiteindelijk weer bleek te zijn. Zelfs bij grote verschillen in aanpak tussen verschillende landen in Europa liep het overal uit op ‘doormodderen’ en improviseren. Wie pluralisme meer ruimte wil geven, krijgt van Frissen huiswerk mee. Afschaffen van regels? Met het behoud van protocollen en steeds meer toezicht leidt dat tot niets. De rechter op de stoel van de politiek zetten - denk aan een Constitutioneel Hof of aan het Urgenda- arrest – leidt tot nog meer detailsturing, governance voorschriften en protocollen. In de kern gaat het volgens Frissen om de bereidheid verschil te accepteren. "De participatiesamenleving was een interessante gedachte. Maar alleen als je dan wel variëteit accepteert. En niet meteen Kamervragen gaat stellen als scholen of ouderenzorg in Maastricht anders te werk gaan dan op Walcheren." Hij verzucht: "Maakbaarheid wás een linkse zaak ooit, maar die lijkt inmiddels overal te zijn doorgedrongen.” *** Verder luisteren 323 - Paul Frissen en het gevaarlijke verlangen naar de integrale oplossing 210 - Herman Tjeenk Willink over het verval van de democratische rechtsorde 474 – Parlementair historicus Joop van den Berg: “De democratie is in groot gevaar. Je moet niet denken: het loopt wel los" 226 - In het oog van de orkaan: Roel in 't Veld over wat er mis is met politiek en bestuur 445 - Chaos en onrecht in het sociale stelsel 152 - De 19e-eeuwse wortels van FvD en PVV 60 - Coen Brummer & Daniël Boomsma: De canon van het sociaal-liberalisme 34 - 140 jaar Anti-Revolutionaire Partij 385 - Jan de Koning en het verschil tussen een greppel en de laatste gracht 300 - Ethische politiek: het bijzondere Nederland met zijn 'moreel hoogstaande opvattingen' 296 - Doe effe normaal man! De macht der gewoonte in de Nederlandse politiek 57 - Alexis de Tocqueville 70 - 'Voorzitter, het is Kafka!' - Leven en werk van Franz Kafka *** Tijdlijn 00:00:00 – Deel 1 00:28:01 – Deel 2 01:03:46 – Deel 3 01:22:43 – EindeSee omnystudio.com/listener for privacy information.
Hoy hablamos de la obra de Franz Kafka, los sueños y pesadillas recurrentes en sus relatos y novelas, mundos en los que las leyes racionales se transforman de forma surrealista y laberíntica y el individuo experimenta una sensación constante de extrañamiento. Esa atmósfera onírica no fue un mero recurso estético; contribuyó a modificar la manera en que la cultura moderna comprendió la subjetividad, el inconsciente y la fragilidad psicológica del individuo contemporáneo. También leemos su breve relato: "Un sueño", como ejemplo de la relación de su literatura con el mundo onírico. Aquí podéis encontrarlo para su lectura: https://ciudadseva.com/texto/un-sueno-kafka/Esperamos que disfruteis del programa tanto como nosotros, buenas noches Navegantes...
Special discounts up for AIE Melbourne (LS discount) and AIE World's Fair (group discounts up to 25% - CFPs still open for Autoresearch and Vertical AI) Cya there!Abridge did not start as an “GPT wrapper”. It was founded in 2018, years before the Cambrian explosion of AI application layer companies. OpenAI launched ChatGPT publicly on November 30, 2022 and by then, Abridge had already spent years doing the unglamorous work of building trust for one of the highest context, most important workflows in healthcare: the conversation between a patient and a clinician.Abridge's original wedge was clinical documentation. Listen to the visit, generate the note, reduce the clerical burden, and let clinicians spend more time with patients instead of the EHR. By focusing on how doctors actually document, how health systems actually buy, how EHR integration actually works, how clinicians verify outputs, and how missing context during a visit turns into downstream friction across billing, prior authorization, quality, and follow-up, the adoption of LLMs became a force multiplier on a workflow already optimized for sensitive context gathering.The company has scaled fast: Abridge says it is projected to support 80M+ patient-clinician conversations this year across 250 large and complex U.S. health systems, with support for 28+ languages and 50+ specialties. It raised $300M at a $5.3B valuation in June 2025, after a $250M round earlier that year.Today, Janie Lee and Chaitanya “Chai” Asawa of Abridge join us for another crossover pod with Redpoint's Jacob Effron (who is on the board of Abridge) to dive into how Abridge is building the clinical intelligence layer for healthcare starting with ambient documentation, then expanding into clinical decision support, prior authorization, payer/provider/pharma workflows, and eventually real-time agents that act before, during, and after the patient conversation. We go inside the product, data, infra, evals, workflow, privacy, and org design choices behind bringing AI into one of the highest-stakes enterprise environments from 100M+ medical conversations and specialty-specific evals to real-time alerts, EHR integration, de-identification, clinician-scientist teams, and why healthcare may solve some of the hardest AI problems first.We discuss:* Why Abridge started with clinical documentation, “pajama time,” and saving clinicians 10–20 hours a week* The transition from ambient scribe to clinical intelligence layer: save time, save money, and save lives* Why conversations between patients and clinicians may be the most important workflow in healthcare (patient visit summary feature)* Chai's “healthcare-coded Glean” framing: context is king, but healthcare raises the stakes on safety, evals, and rollout* Why Abridge wants AI to feel like “air conditioning”: always in the background, but only interrupting when it truly matters* The prior authorization example: turning a denied MRI weeks later into real-time guidance while the patient is still in the room* Why payer policies, EHR data, medical literature, and hospital-specific guidelines make the problem hard, and also create the moat* How Abridge thinks about ambient form factors: mobile, desktop, in-room devices, nursing workflows, multimodality, and future AR* The multi-sided healthcare customer: CMIOs, CFOs, CIOs, clinicians, patients, payers, and pharma* The hardest AI problem at Abridge: high-quality, low-latency, low-cost real-time support in a high-stakes clinical setting* When Abridge uses frontier models vs proprietary models, and why its unique data from medical conversations matters* Why “every agent is a coding agent underneath,” and how the EHR can be thought of as a filesystem for healthcare agents* How Abridge approaches personalization across individual doctors, specialties, and health systems* Why “AI slop” is AI without context, and how edits, memories, and clinician preferences create a data flywheel* Abridge's eval stack: LFDs, LLM judges, in-house clinicians, third-party evaluators, specialty-specific evals, and progressive rollout* HIPAA, PHI, de-identification, one-way anonymization, customer contracts, and learning from healthcare data safely* What changes when you operate at 100M+ conversations: reliability, cost, post-training, model routing, and infrastructure optimization* Why the same clinical conversation can serve doctors, patients, payers, pharma, and future clinical-trial workflows* How Abridge works with EHRs, and why deep interoperability is table stakes for clinician adoption* Why healthcare AI has regulatory tailwinds, why 80/20 does not work here, and why high-stakes domains may drive AI forward* Why Abridge embeds “clinician scientists” into product and eval teams* What Chai learned from Glean about search, quality, and durable AI infrastructure* Why the future of AI infra may look like context layers, event-driven systems, Kafka, Temporal, sockets, CRDTs, and tools built for humans* Why Janie changed her mind on “PRDs are dead,” and why crisp written clarity matters more in complex AI products* How Abridge uses Claude Code, Cursor, and coding agents internallyAbridge:* Website: https://www.abridge.com/* X: https://x.com/AbridgeHQJanie Lee:* LinkedIn: https://www.linkedin.com/in/janiejleeChaitanya “Chai” Asawa:* LinkedIn: https://www.linkedin.com/in/casawaTimestamps00:00:00 Introduction and what Abridge does00:02:05 From ambient documentation to clinical intelligence00:04:04 Clinical decision support and context as king00:06:57 Alert fatigue, proactive intelligence, and prior authorization00:12:36 Ambient AI form factors and healthcare customers00:16:59 The hardest AI problems in healthcare00:18:26 Frontier models, proprietary data, and model strategy00:21:07 The EHR as a filesystem for agents00:24:03 Personalization, memory, and clinician preferences00:30:40 Evals, LLM judges, and progressive rollout00:36:47 HIPAA, de-identification, and privacy00:39:21 100M conversations and operating at scale00:44:10 EHR integration and the clinical intelligence layer00:46:39 Healthcare regulation, latency, and high-stakes AI00:50:11 Clinician scientists and long-tail quality00:53:04 Lessons from Glean and durable AI infrastructure00:57:03 The future of agentic healthcare workflows00:57:34 PRDs, product clarity, and building serious AI products01:03:11 AI coding tools at Abridge01:04:06 OutroTranscriptIntroduction: Abridge, Clinical Intelligence, and the Latent Space x Unsupervised Learning CrossoverSwyx [00:00:00]: Okay. This is a special crossover Latent Space Unsupervised Learning pod.Jacob [00:00:07]: Very excited to do this.Jacob [00:00:08]: At this point, we get together once a year.Swyx [00:00:10]: Once a yearJacob [00:00:11]: And this is a fun occasion to get to do it on.Swyx [00:00:13]: I really wanted to talk to Abridge but I felt very underqualified because healthcare is not something we cover very intensely. It just so happens that Redpoint's our big investors and supporters of Abridge.Jacob [00:00:27]: Anytime you want to have a portfolio company on your podcastJacob [00:00:29]: Please, by all means.Swyx [00:00:31]: So we'll introduce our guests. Chai and Janie, welcome to the pod.Janie [00:00:34]: Thanks for having us.Chai [00:00:35]: Thank you.Janie [00:00:35]: We're excited to be here.Chai [00:00:36]: Thank you.Swyx [00:00:36]: So for listeners, what do you guys do, just to situate you guys in the company?Janie [00:00:42]: Abridge is a clinical intelligence layer for health systems. We really started with documentation and building for clinicians and as we think about reducing the burden that clinicians have, they're spending 10 to 20 hours a week on documentation. There's a massive doctor shortage in the country. We also think that conversations between patients and clinicians are probably the most important workflow in healthcare. It's where care is given and received but if you think about the 20% of our GDP that goes towards healthcare, almost everything is a derivative of that conversation, whether it's the claim, the payment, the actual diagnosis given, the treatment. And we've started with a conversation to reduce the burden for doctors on documentation but we're really excited about the path ahead as we become this broader clinical intelligence layer.Chai [00:01:34]: I'm Chai. I work on clinical decision support at Abridge.Swyx [00:01:37]: Yes.Chai [00:01:37]: And so as Janie said, we're uniquely situated where we started off with the clinical note. What I'm really excited about and where we're expanding towards is what are all the things you can do before the conversation, during the conversation and after the conversation if you did have access to all the context about patients, payer guidelines, medical literature and put that together and to serve, how healthcare could look fundamentally different.Swyx [00:02:01]: And that's the context engine that you guys have?Chai [00:02:04]: Yes.Swyx [00:02:04]: Is that what it's called? Okay.Swyx [00:02:05]: So historically, as I understand it, the company started in 2018. A lot of people would be familiar with the AI voice notes form factor that doctors would be “Well, do you consent to being recorded?” It replaces handwriting and what have you. But it sounds like more recently there's been a big transition in the company. Tell me about the broader transition.From Documentation to Clinical Intelligence: Save Time, Save Money, Save LivesJanie [00:02:26]: So from a transition perspective, we really think about our journey as The first act was: how do we help save time? And that's where a lot of that original product was.Swyx [00:02:37]: By the way, one of those interesting statsSwyx [00:02:39]: On your landing page was, doctors spend time after hours.Janie [00:02:43]: They call it pajama time.Swyx [00:02:44]: Why is that pajama time?Janie [00:02:46]: Doctors after work in their pajamasSwyx [00:02:48]: In their pajamas. OhJanie [00:02:49]: At home are just writing and catching up on their notes every day.Janie [00:02:53]: Some of our favorite customer love stories, we have a Slack channel called Love Stories. We have clinicians telling us, “Abridge has helped us, from retiring early or we're now finally able toJanie [00:03:06]: go home and eat dinner with our kids for the first time.”Chai [00:03:08]: Save the marriage in some cases.Swyx [00:03:10]: One of the quotes was “We're not divorcing anymore.”Swyx [00:03:12]: I'm asking, “Why?”Swyx [00:03:14]: Because they're working too much.Janie [00:03:16]: But, in terms of where we're going and where we're expanding, we really think about our second and third acts around how do we help health systems save and make more money. Health systems are operating with record-low operating margins. It's getting harder and harder to serve patients and they have regulatory, some tailwinds but also a lot of headwinds coming their way and AI is ripe for helping on the saving and make-more-money piece. And then ultimately, how do we help save lives? The fact that our software and our product is open millions of times a week before, during and after a patient walks in the room, gives us massive opportunity with products like clinical decision support, which Chai is building but so many others to improve patient outcomes and probably one of the most important workflows and problems to be going after right now.From Glean to Healthcare: Context Is KingJacob [00:04:04]: One thing that's interesting, Chai, is you came over to Abridge from Glean and clinical decision support, which for our listeners is, in the context of a visit, helping a doctor figure out the right type of care. It's really a search problem in many ways, going through lots of different data sources. Very analogous to your previous role as one of the earliest engineers over at Glean. I'm sure a lot of our listeners are curious what's similar about the problems that you're going after now and what feels different, now that you're in healthcare.Chai [00:04:33]: Very similar. Taking a step back, with every wave, there's a lot of very similar patterns that happen across different products. A lot of social networking products look the same. A lot of credit-based products look the same. And we're seeing that very similar in the agent era with many companies, of course, in Redpoint's portfolio and so forth. And the key insight between both companies is that you have amazing models but context is king. Context is what puts them to work. So I see it in a lot of ways, a lot of similarities in this is a healthcare-coded version of Glean but the differences are really interesting. A couple things that come to mind. First and foremost, the rigor of the setting we're in. The downside risk is extremely high here in healthcare. It can be fatal in some cases. You prescribe something that the patient is allergic to for example. Whereas at Glean, it's “Oh, you got the question wrong.” It wasn't the end of the world in most cases. And so what does that mean? That shapes our evaluation strategy, both offline evaluation, progressive rollout and there's a lot more we could go into there. Second thing that comes to mind is, vertical versus horizontal. In both cases, there's a large variance but when Glean is, it's a much more horizontal company, there's a variance of personas, companies that you're working with. We also have a variance of personas, different types of specialties, different hospital systems. But the variance is a little more narrow. So from a product perspective, you're able to focus far more, especially when you have a maturing technology and you're building new products that never existed before. It lets you go after them much more easily and especially in healthcare where so many problems were solved with labor and process, that it's extremely ripe for AI to keep helping augment and enable. And the final thing that's really interesting, Abridge specifically compared to many other companies in the AI area, is the modality we started with where we're ambient and we're always listening in the background. And many more AI products will go that way but it's how we started. And that's the greatest form of AI we can create, AI that's seamless. You're not looking at your screen. It's always there. It's always helping you out and being proactive. The Jarvis vision that, every hackathon I went to over the past decade, there was always a Jarvis competitor. But Abridge very much started from the opportunity and continues to go that way.Ambient AI and Alert Fatigue: When Should the Product Interrupt?Jacob [00:06:57]: One thing that is super interesting then from a product perspective is you have this always-on seamless in the background and then you have to decide when you break the wall almost and say, “Hey, clinician, you might not have thought about X,” or whatever it is that you want to do. And in healthcare traditionally there's been this idea of alert fatigue and a million pop-ups and then a doctor just ignores all of them. It's probably a pattern that a lot of builders are thinking through now. How do you think about the right way to intervene or to pop up in a doctor visit?Janie [00:07:26]: It's such a good question. Alerts are notorious in healthcare specifically. Over 90% of alerts are ignored. The first and most important thing is context is everything, as Chai alluded to and I also think about how do we go from being reactive alerting to really proactive intelligence at the point at which it matters most. One thing we like to say is we want our product to feel like air conditioning. It should be in the background just making things better and if there is something that has great clinical risk and we're acutely aware that intervening now and not later is incredibly important, we should decide to act. But if you think about proactive versus reactive, instead of alerting a clinician during a visit when they're with their patient having a pretty serious and sensitive conversation, how do we prep a clinician before they walk into the room with that patient? And so historically, clinicians might have to manually go through charts with a patient that they've had over the course of months or years and they'll try to suss out what are the things they should be doing. You can imagine a world with Abridge. We'll summarize all of the most recent context for you, tell you based on the reason for a visit the patient is coming in for the types of things you should be discussing. And so you're going into that conversation prepped rather than walking in cold to that patient visit and then having this product interrupt you five or 10 times throughout the visit. And there might be times where it's really important to interrupt. We have a product called Prior Authorization and so this is when you may go into a doctor's office with knee pain. They'll prescribe you an MRI and so many of us have had this experience before, where in four weeks you'll get a call saying, “Hey, Sean, that MRI that you were prescribed wasn't approved and why don't you come back in? We'll figure it out.” In a world with Abridge, we might choose to quietly but still alert a doctor in that visit. And alert is probably not even the word we would want to use. Before a patient leaves, we would want to tell the doctor, “Hey, Doctor, before Sean leaves, you should ask him, has he had physical therapy and has his pain lasted for more than six weeks? Because the Aetna plan that he's on in California requires six things. We've already confirmed four of them have been met ‘cause we have all the context. But these two last criteria, if you can address with Sean before he leaves the room, we could guarantee that your MRI is approved before you leave.” And so when you think about clinical usefulness, impact to the patient, there are instances in which if we can catch a doctor while the patient is still in the room, as we think about save time, save money, save lives, we get to check all of those boxes. But when doctors have 15 minutes between visits, we have to be really thoughtful about when it matters.Prior Authorization: Reducing Latency in CareChai [00:10:23]: There's this interesting product opportunity AI has is reducing latency in the world. For example, prior authorization is an example of where care gets delayed and so great AI can reduce that. And the problem with alerts before partially is a technical problem: the quality of your alerts really matters. They're going to get ignored if you get alerts that... Similarly in engineering, where they're noisy alerts that you can't act on. But if you can make really high-quality alerts with both the context, as Janie said, and really high-quality models, then you can create a whole other game.Janie [00:10:53]: And I really like that experience because it starts to tease apart, what makes this so hard and unique. One, to make that prior authorization example possible, think about all the data that you need to have. You need to integrate with the electronic health record to know all of the patient context. Do we have access to your previous labs, previous imaging? And then to match you and to know that you're on Aetna, we have to collect all of the different payer policies and they vary by state. Some of these payer policies live on websites. Some of them live in unstructured 50-page PDF files.Jacob [00:11:31]: I thought this episode wasJacob [00:11:31]: To make sure we didn't scare people from healthcare.Janie [00:11:34]: But when you think about the things that make it hard, it also gives you the moat.Janie [00:11:39]: And then the second is the AI and the model quality we need to be able to hang our hat on. And so the bar, similarly when I worked at Opendoor, I worked on pricing models. Every outlier wiped out the margins of 30 and so similarly here in healthcare, the bar for accuracy is so high. And then I'd say the last is workflow is everything. If insurance companies deploy AI, it typically happens too late and this is when you have the notorious comical examples of AI just fighting each other when it's too late. But if we can pull forward the use of both the AI but also the ability to solve problems when the patient's in the room, you can start to collapse what typically takes weeks or months after your visit, ideally down to minutes or real-time. And it's where healthcare is both very difficult but also extremely rewarding if you can crack it.Product Form Factors: Mobile, Desktop, In-Room Devices, and ARSwyx [00:12:36]: Just to get some baseline on the form factors, because I've seen some videos on your website and stuff. You guys talk a lot about ambient AI. Is it primarily on the phone? Is there any other form factor that people get Abridge in? Is there an Abridge room setup where it's always on? I don't know.Jacob [00:12:55]: An Abridge podcast studio.Janie [00:12:58]: Primary form factor is mobile and desktop. UsuallyJanie [00:13:00]: Clinicians are walking in and out of rooms with mobile but at the end of the day, when they're closing out their notes or wanting to prep for the day ahead, they might use desktop. We have been having a lot of really interesting partnership conversations with a lot of these in-room device companies as you think about the power of multimodality and even more data, as you think about all of what is not captured today. It is fascinating to think about, especially even as we go into building and scaling our nursing product. It's one where nurses constantly, as they're walking in to check in on a patient for two minutes or maybe even 30 seconds,Janie [00:13:43]: Starting an Abridge experience is probably going to take longer than the visit. And so what can we do with in-room devices that are always on starts to raise really interesting and fun product questions.Swyx [00:13:54]: I was thinking, the way in tech companies we have all these Google MeetSwyx [00:13:58]: And other things, we might as well set up entire rooms with just Abridge tech.Chai [00:14:02]: Very much. AR glasses and related form factors are also relevant: how do we bring the information to the clinician in real-time without a screen, while still letting them focus on the patient?Swyx [00:14:18]: Do you think they want that? I'm skeptical of AR, but I'm curious what you've tried.Chai [00:14:26]: Admittedly, it's not a near-term product roadmapChai [00:14:29]: By any means. I'm being far-fetched.Jacob [00:14:31]: There's some sick AR stuff for surgeries.Swyx [00:14:33]: Really?Jacob [00:14:33]: When people are trying to visualize, you're about to make an incision but you want to see, what the cut might look or what the body might look like inside and they can layer in imaging.Swyx [00:14:43]: That's cool.Chai [00:14:45]: At some point in the future.Janie [00:14:46]: But there are a lot of our largest customers and at the largest health systems integrating already and so even as we think about building into it, unlocks a lot of product capabilities.Swyx [00:14:57]: And just to establish the terminology. Sorry, and I know I'm asking basic questions somewhat for myself but also for the audience who might beHealth Systems, Buyers, Clinicians, Patients, and PayersSwyx [00:15:05]: Less integrated. When you say health systems, it's like the Johns Hopkins, the Kaiser Permanentes.Janie [00:15:09]: Mayos, the Kaisers of the world.Swyx [00:15:10]: These are your customers, right? And the outcome that you deliver for them is happier doctors, reduced cost of processing, reduced mistakes. It's weird in a sense that I feel like there's also, a secondary customer, the customer of the customer and I don't know if you — do you think about it that way?Janie [00:15:28]: The other interesting and complex part of building product is we have our buyers, who are the chief medical information officersJanie [00:15:39]: The chief financial officers, the CIOs of these large health systems. Our users today are clinicians but if you think about who downstream is impacted, it's patients. And so as we build, with every product in mind, we think about who we're building for, who the secondary user is and what does that mean either in terms of experience, security compliance, ROI that we have to make tangible. And so like you said, time savings is one of them. But for CFOs, they care a lot more than just time savings. We have to show for every dollar you put into Abridge, because you have more compliant documentation or because you have fewer queries coming from your billing team, we save or add real dollars to your bottom line or top line, are things that we're constantly thinking about because of the dynamic across all three sets of users.Chai [00:16:32]: There's a whole other axis too with the payers and pharmaChai [00:16:35]: as well. Connecting all these three big stakeholders in healthcare isSwyx [00:16:39]: Do the payers ever see your data? Sorry, the payers meaning the insurers, right?Chai [00:16:44]: Yes.Swyx [00:16:44]: They also see Abridge data?Chai [00:16:47]: NoSwyx [00:16:47]: Like the direct integration to you guysChai [00:16:48]: They wouldn't see the raw Abridge data but when you're working together on something like prior authorization, whatever information they need, we'd communicate to them.Jacob [00:16:59]: That's cool. I would love to dig into the AI side. You still have a lot of problems on the AI side. And so maybe to start at the highest level, what's one of the hardest problems you have to solve in AI at Abridge today?The Hardest AI Problems: Quality, Latency, and CostChai [00:17:11]: To make things simple, let's take, building off the prior auth example. So one thing Janie talked about is okay, this data is all over the place and there's this combinatorial explosion of procedures, payer policies and even sometimes different health systems. There can be some cross-product of all of these different considerations you have to take into account. But what's really hard about this problem is doing it real-time in the conversation. So, in any AI product, usually the three KPIs you care about are quality, latency and cost. Now, what we're saying is we want you to do this real-time in the conversation, guiding the clinician. How do we do it in a way that does not break the bank? But we're using — But we also need very intelligent models because you're working with this cross-product of data and this, all this context layer as well. So you need high intelligence and high-quality because you don't want the alert fatigue but you also need to be fast and cost-effective. And so that's where a lot of clever engineering goes. It's okay, without getting into all the details here, can you model these policies in some intermediate representation or other things that you can do that can make this problem tractable? And of course, the Pareto frontier is always changing but we are also trying to do this now.Model Strategy: Third-Party Models, Proprietary Data, and Medical ConversationsJacob [00:18:26]: What implications has that had for what you take off-the-shelf and say, “ what? We don't need to be world-class at X. We'll just take this from the model providers or from some infrastructure player,” and what you're “No, this is where we spend most of our time focused on”?Chai [00:18:38]: This is, the fun challenge in AI?Jacob [00:18:42]: It changes every three months? SoChai [00:18:42]: Of course, with the shifting landscape, we try to be extremely thoughtful on predicting the trends of where third-party models are going and where we can uniquely go. And, sometimes when you talk about AI models, we're the models are just going to get infinitely better. But I don't think... It may be in the grandness of time you could say that but, within every month, every quarter, there's specific ways they're getting better. They're training on a lot more, coding data to be better coding agents, for example. And soChai [00:19:14]: We have to think about where are the things that won't — unique data that we're uniquely training on or to step back a little, where is a proprietary model bringing advantage to us is if it can give higher quality or lower cost and latency for similar quality, very similar to many other companies. And when we can do that is when we have proprietary data. So, for example, we have on the order of eighty million or hundreds of millions now getting close to of medical conversations.Jacob [00:19:44]: It's insane.Chai [00:19:45]: This is a unique data set. And this data set, it's very interesting because this data set is effectively a large part of the trace between the patient and the provider. That's where the quote-unquote debugging happens in healthcare. We have these traces at scale, as in as, our CEOs even called it, an exhaust that comes out of our product. And so when you have these traces, that's how you can train better agents on certain use cases, whether it's your transcription diarization use cases or so on or like note generation models and we can do that much cheaper and faster. But we're always also working with these third-party model providers. We closely collaborate with them and that's how we predict where the trends are going. The thing that I think about a lot is that, I know that the model providers are going to train much more on agentic workflows and so forth, so that's great, so that you have a better agentic harness. But the other thing that's interesting is that the model providers, because a large class of the consumer model providers is healthcare queries, that they might, optimize to train a lot of healthcare data to encode the knowledge in its weights. And this is just a great thing for us as well, where the off-the-shelf models can keep bett-getting better at general healthcare information, such that what our strategy is, we have a constellation of models, we can use something for this, that and, we only care about, at the end of the day, the best product experience.EHR as File System: Agentic Workflows and Real-Time InterfacesJacob [00:21:07]: And, you have, overall capabilities improving. I'm curious, as these models get better, is there something you look at and you're “, three months ago, we really couldn't do that but God, the the latest models really allow us to do it”?Chai [00:21:19]: So here's something interesting that I've, been toying with. So all models are... This wasn't super obvious a year ago but now it's become clear and clear that almost every agent is a coding agent underneath the hood? So you give it whatever file system, it can write its own code and so forth. So when you think about within healthcare and the use case that we have, you can think of the EHR effectively like a file system. It's just — it's a storage of all this information. It's a lot of information there that cannot fit into the context window, at least of today's models and you want to use that context effectively for all these product use cases we're talking about. And so if you have better agents that can, manipulate data, read that data, treat it as a file system as we see they're going and we know model companies are investing this way, then that very directly benefits us.Swyx [00:22:09]: Yeah. Okay, cool. Again, just establishing basic things. But we're going back to the model stuff. I'm really interested in double-clicking more on the real-time, element, which is pretty important for both of you. Is it — Is real-time just batches of every one minute, every five minutes? Is that how we do it? Or is there some more native, genuinely real-time in the sense that OpenAI has a real-time API or Gemini has a real-time API?Chai [00:22:35]: Yeah. Yeah. So today it is more on the on the batch basis but there's interestingChai [00:22:41]: Prototypes that we have that we're still not fully, full time, voice in text out or in that sense. But, can you trigger your models, your agents or agentic workflows, depending on the right times in the conversation?Chai [00:22:58]: And so you can imagine, different techniques to bring this latency down and, you want to bring the feedback loop down as much as you can. And so a lot of clever engineering there without fully... Maybe one day we'll do full voice in and text out, train a model to do something like that.Swyx [00:23:15]: You do — People don't want voice in voice out?Chai [00:23:18]: Now we aren't creating experiences that are, during the conversation, inter — It's almost likeSwyx [00:23:25]: Might be too disruptiveChai [00:23:26]: Too disruptive until, who knows, maybe eventually you could have full voice agents once we — the quality and we improve the comfort of the technology. But right now gra — that change is much more gradual and it's more text focus, text out.Janie [00:23:42]: And so much of currently what our product is trying to do is allow a clinician to focus on their patient and maybe at some point but right now patients, clinicians don't want a third voice, at least in a literal voice in that room. And so how do we be there with all the contacts and information ready at hand when there's the right moment?Personalization: Individual Doctors, Specialties, and Health SystemsJacob [00:24:03]: Jenny, one thing I'm curious about is how you think about, personalization in the product. I imagine, every doctor is a special snowflake in their own way, has their own way they like to do things. There are probably a bunch of different approaches you could take to doing that, both within the model layer itself but then also just with clever prompting or engineering. How do youJacob [00:24:20]: Deliver on that?Janie [00:24:21]: It's such a good question. Personalization is massive for us. We think about personalization at three levels. The first is at the individual, the second is at the specialty level and then the third is at the health system or the organization level. To your point, there are a lot of individual preferences. You-When a note is produced, it almost is a reflection that is so deeply personal of a doctor's work and how they give care. And so do they have preferences on things like style? They might want bullets versus paragraphs, really concise versus comprehensive. They also might have phrases that they really like to use or the templates that they want every note to be structured. And, we see it in our feedback all the time. We want two spaces in between sentences or I refuse to use this tool. And so that's something that we've had to build in. And the tricky part is how do you make sure that stylistic preferences don't interrupt accuracy and quality and that's something that we've really had to refine and hone over time. Second is at the specialty level. A cardiologist note or workflow is going to look very different from a dermatologist workflow.Jacob [00:25:32]: I assume cardiology notes are the highest stakes for you guys, given your CEO is a cardiologist.Jacob [00:25:36]: It's “Oh my God, make sure we get this one.”Janie [00:25:37]: Shiv, our CEO, is still a practicing cardiologist. He rounds once a month. And so, first call when we want just quick and easy user feedback too.Janie [00:25:46]: But, specialties require a lot of personalization, both in terms of what does the product look and so we make sure that as new users onboard, we catch that and the product proportionally reflects that. But also on the back end, evals at the specialty level, they are hard-earned to calibrate and get. What does a really great dermatology note look like? What makes it complete? What makes it compliant and billable is very different than a primary care doctor. And so it's not just about what does the product experience look but on the back end tuning and really deepening our understanding for the specialists. What does great output look like? And that's, a problem that we need to calibrate internally, externally, online, offline but, takes lots of cycles but is necessary in a high-stakes environment. And then at the health system level, for products like clinical decision support, you have health systems who've spent years or decades refining their best practices and they want to know, “Hey, we love your clinical decision support product but how do we embed our own hospital guidelines into them to inform clinicians before, during or after a visit what brest — best practices should look like?” And as you think about, deepening moats as well, when health systems, trust us with that data, allow us to productize it and directly into the clinical workflow, makes us a really great partner to health systems who want to build something that truly meets their needs, their practicing guidelines.AI Slop, Memory, and Product Data FlywheelsChai [00:27:23]: And I want to add onto that. The for the clinical documentation problem, it's very similar to AI writing that doesn't feel like your own and then we call that slop. But the way I describe one framing of slop is like AI without context. But we have all that context and both the clinicians, can have it and can guide it. And so part of the other interesting exhaust for us is, memory is, one of these new systems recordsChai [00:27:49]: Almost.Janie [00:27:50]: And we also have all the edits people make on our product and when you think about a data flywheel and how we get better over time becomes really powerful as a mechanism to just going deeper in personalization.Jacob [00:28:04]: It's interesting. I love this idea of working with systems on the guidelines they built up over a long time. I feel like so many of the best AI app companies today are... The question is: How do you take the expertise that a law firm or a bank has built up over many years and then add that as context and also a special sauce over, a an AI tool? And so seems like y'all are really doing that very effectively.Janie [00:28:24]: We're now starting to have our customers ask, “What are other customers doing?”Janie [00:28:28]: “And how are they doing it?”Janie [00:28:30]: And as we think about having visibility across such a large set of care being delivered right now, a really interesting place we could also partner.Swyx [00:28:40]: I'm just curious. I — This may be a nothing question but, how different are health system guidelines from each other? Don't they all converge to the same thing? And if not, where do they differ?Chai [00:28:52]: At a really high level, they're going to talk about very similar things but the difference is probably in some more of the details. “Oh, you should refer to specialists only when XYZ conditions are met,” or so forth and maybe different organizations have different practices and guidelines around that. But high level, talking about similar things but the details are what, of course, that shapes the context and the decisions you make.Swyx [00:29:15]: And this all goes into the context engine and it might affect the notes but maybe not.Chai [00:29:21]: The — For these local pathways, we're definitely thinking about it a little more for our clinical decision support product.Chai [00:29:26]: So yeah.Swyx [00:29:27]: Which is your stuff, yeah.Swyx [00:29:28]: And then the memory which you raised, let's just tell us more about that. What have you tried in memory? What's the structure of the memory? What works? What doesn't work?Chai [00:29:38]: There's, of course, many different ways you could do memory, where it's okay, can you bake it into the model weights or can you do it in some external store? For us, what's interesting is, of course, when you think the models are rapidly changing, whether it's in-house or third-party, baking into the model weights, sometimes you worry that it could be a little throwaway. And so, how do you... You need to find a way that you decompose the problem, the preferences from the underlying models and so forth. The thing we're right now most both that's easiest to start with and we're excited about is having, a separate store for memory, where you have, for example, a memory sub-agent that's, working in the background, figuring out what are the important parts of the clinician's actions that we want to remember for the long term. And then you can also imagine, other things where in the — you have background jobs that are running that are collating these, memories similar to Sleep, of course and what other pattern, patterns products do as well. Learning over all these action, all the action data we have, again, note edits, the conversations they did and the actual transcripts.Evals: LFD, LLM Judges, and Clinical SafetyJacob [00:30:40]: What about evals? How in the world do you... It is such a complex product surface area. We would love to hear you riff on that and also how has that evolved? I'm sure you've gotten better at it, so any learnings along the way.Janie [00:30:50]: From an evals perspective, we, from day one when we build any new product or feature, we think about, what does good look like? And there are table stakes things like clinical safety but then you start to get deeper into what does good quality look like. And when you go into something like our core product, there's stuff like style and completeness and there's things like does this note become something that can be billable, which is very high stakes for a health system. We have a number of ways in which we get confidence for this. We have, internal in-house clinicians who do what we call an LFD process to give us our very first pass at is this or isn't this a good enough output, look at the effing data.Jacob [00:31:41]: LFD?Chai [00:31:42]: That's why I was smiling. I was “Is Janie going to mention what it stands for?”Jacob [00:31:46]: I was not... There's like a million acronyms.Jacob [00:31:48]: How am I supposed to know that I don't? So “Oh yeah, of course, an LFD.”Swyx [00:31:51]: I've never heard of LFDs.Chai [00:31:53]: It's a bridge for sure.Janie [00:31:55]: I got through three days and then I had to ask someone.Janie [00:31:58]: I thought it was just me that didn't knowJanie [00:32:01]: It's our internal process.Swyx [00:32:02]: But look at the data as a meme in ML, ‘cause you tend to not look at it. You just want to look at number go up.Chai [00:32:06]: Exactly.Swyx [00:32:07]: But yes.Janie [00:32:08]: But so, we make sure we look at the data and then as we think about all of the components of good output, we, one, create LLM judges across all of these and we make sure with annotated data and either internal or external evaluators, we feel like these judges are calibrated. And then depending on the stakes, we also work with in-house and third-party evaluators across all of these before we ship any big change. And the goal is, in terms of evolution, how do you go from this process taking months, down to weeks, down to days? Some of it is, a true science and ML problem. A lot of it's also just, hard operational work. Have you planned ahead in terms of what you need? Have you really optimized the capacity that you need across all of the different specialties you need? Have you gotten a really good sense of which third parties are great to work with for what use cases? This takes a lot of domain, expertise and, lots of mistakes and errors in figuring that out. And so as much of it is an ML problem, so much of it has also been operational gains that are hugely important, where domain-specific expertise is everything.Specialty-Level Evaluation and Progressive RolloutsJacob [00:33:23]: But it's funny, ‘cause I feel like people talk about healthcare like it's one giant market and the reality isJacob [00:33:26]: It's, dozens and dozens of sub-markets. And so it feels like in your evals you have to build that up across the board, probably.Swyx [00:33:34]: And is specialization the primary cardinality at... That's the word that comes to mind.Janie [00:33:40]: Sometimes, depending on the product or the use case. And so if we're making a note improvement or feature for a particular specialty, definitely but we have products that are for nurses. We have products that, are really aimed at making the document or the output a lot more billable. And so we'll want to work with coding teams and not necessary clinicians. And so likeJacob [00:34:05]: Coding meaning healthcare coding.Janie [00:34:06]: Yes. Yes.Jacob [00:34:07]: NotChai [00:34:07]: Yes. I see you.Swyx [00:34:07]: Other kinds.Janie [00:34:09]: But is this output proportional to the work that was delivered? Is there sufficient documentation to justify the amount that a health system may end up charging? And so, specialty sometimes but also domain, very different across all of the different products that we're working for. And building out that network is, not easy and is where a lot of our operational investments have gone into.Chai [00:34:35]: And I view a lot of analogies to self-driving cars here, where, part of it is we really want progressive rollout of features to test in the real world is this useful? Is this going to work? One big difference compared to past lives is before I'd build a product, maybe I'd alpha it and then I'd like GA it the next week, ‘cause I'm “Go, move fast, ship,” and whatnot. But the mentality is like you... I want to make contact with the reality as quick as possible but I want a progressive rollout. Because as much as I get as large of an offline eval set, I want the distribution of that to match real-life distribution. And over time, by rolling out early, similar to Waymo has a tagline, “The world's most experienced driver,” another thing that can, at least linearly increase for us is, both the size of our evaluation offline and online, that and it all feeds back.Janie [00:35:25]: Something that's been earned over time, speaking of evolution, is just the trust we've gotten with customers. Historically, a lot of these health systems, when they bring on new vendors, their release cycles are quarters, sometimes twice a year. We've gotten our customers onto monthly release cycles, which is pretty fast for health systems but what is more exciting over the last, call it, few quarters, has been, a subset of our customers have said, “We want to innovate with you. We trust you,” and we have a pretty, decent chunk of our customers who say, “We'll develop with you outside of these monthly release cycles. We have a higher tolerance. We know that the stakes are very high but we want to be the first ones using these products, giving you feedback.” And so for a pretty substantial set of our customers, we've been able to convince them to be able to ship, in this gradual way before GA. Something we talk about a lot internally is, trust is earned in drops, earned in buckets and so we still can't do what I used to do when I worked at Loom. We had 30 million users. I'd just be, rolling out experiments left and. The bar is still quite high for iterative rollout but because of the trust we've earned, we're able to learn at pretty high volume very quickly.Privacy, HIPAA, and De-IdentificationSwyx [00:36:45]: Your scale is still pretty huge.Swyx [00:36:47]: One thing I want to... We were going to go into scale? In a sec. One thing I wanted to call up, follow up on evals, which, again, just coming from a generalist engineer point of view, just thinking through what would people be scared of in doing this, the privacy and HIPAAJacob [00:37:00]: Elements of this. I have zero experience in that. What do you have to do? What is surprisingly not that bad?Chai [00:37:06]: So one thing that's really important here from a compliance perspective is very much that any of the data we use needs to be de-identified, any real-world data we use as a basis of online eval sets we're learning from. And so you have to — And there's, very clear, government guidelines, what counts as PHI. And so we've even have built models that can take, for example, a clinical transcript and remove all the key PHI indicators and so you have a scrubbed/de-identified version. And then once you... And so one thing that's important is first you've got to get confidence in that model in the first place? And prove that out. Because, now you have, multiple probabilistic systems on top of each other.Chai [00:37:46]: But once you have that, then you can train on it use it for evaluation and so forth, provided one of the cool things also that you can do from a business side is the right data contracting as well with your partners.Jacob [00:37:57]: Is the anonymization one way? Once it's done, you cannot undo it? Or is there someoneChai [00:38:01]: YesJacob [00:38:02]: Who holds the master key that can... Yeah, okay. So it's one way.Chai [00:38:05]: It's one way. Yeah.Jacob [00:38:06]: That's how it works. I just wanted to... Because, there's a lot of this, learning from feedback and everything that, you would want to debug more but you can't because you just physically don't allow yourself to.Janie [00:38:17]: Some of it's also written in our customer contracts in terms of who can or can't access PHI data, how long do we retain it,Jacob [00:38:27]: Very goodJanie [00:38:27]: Before it gets de-identified. And so we have a pretty high bar for who can access that PHI data, just to make sure that we always respect our customer data and privacy. But that's something that we partner with our customers on too, to make sure that as we want full, as close to precision as possible in that qualityJanie [00:38:48]: We can still use it.Jacob [00:38:50]: But it'll be fascinating to see how that space evolves? Because you think about, I used to work at a company that, did a lot of healthcare data in the cancer space and if you asked, the average cancer patient, “Hey, do you want people, do you want other patients to be able to learn-”Chai [00:39:03]: Take it.Jacob [00:39:03]: “... Learn from your experience?”Chai [00:39:04]: Take it all.Jacob [00:39:05]: They're “Please.”Jacob [00:39:06]: “I'd love, nothing more than for other people to be able to learn fromJacob [00:39:10]: The experience that I had.” And so in the past it was a lot harder to do that learning. But with this technology, that might really be practical and so it'll be fascinating to see how that continues to evolve.Chai [00:39:21]: There's so much in our data set of 100 million conversations.Chai [00:39:26]: You can imagine things like insights that you can give to the clinician. How could you, oh, how could you have reacted to this? In coaching or insights around, which treatments are effective or, like... Because you have this, again, this data source that was never captured before but that's, where, intuition or experience is created from, going back to this idea that the conversation is the agent of truth.Operating at Scale: Reliability, Cost, and Token EfficiencyJacob [00:39:46]: Back to the 100 million conversations, I feel like you have this insane scale that maybe only a few other AI app companies have and everyone else dreams of. So not everyone has had to confront this yet but maybe just talk about some of the challenges of operating at that scale and what, our listeners have to look forward to if they ever get to this level of scale.Chai [00:40:05]: At large and larger in scale, so of course there's a general, infrastructure reliability. When you... In any given startup, you're building the plane while it's flying. So there's some notion of that. But what gets interesting on the AI and ML side for sure is this, as you get at more and more scale, so one, you have the data to first and foremost do this. But, you start thinking about costs or infrastructure in a whole different way at scale versus, a prototype.Chai [00:40:34]: You can use the most expensive model, you can burn as many tokens as you want but when you're doing 100 million conversationsJacob [00:40:41]: Token max on leaderboards are less upsetting than that context.Chai [00:40:45]: . When you're doing that and so that comes for we have the data and we also have the team that's able to post-train based on this and you can optimize for efficiency, especially in areas where you believe that maybe a lot of the quality headroom is less so and you don't expect the other off-the-shelf models to go that way, such that you want to do, efficiency maximization, in terms of compute and tokens.Jacob [00:41:08]: I feel like you guys live in the future in some way where most use cases today are really just in use case discovery mode, where it's “God, I really hope I can find something that can get to scale,” and so you're always going to use the most powerful model. And then the few things that do get to this level of scale, you start to do those optimizations.Chai [00:41:22]: It's a natural trajectory where it's like zero-to-one, we're not talking about any of these optimizations.Chai [00:41:26]: But when maybe we're in the one-to-100 or so forth, then we're in optimization mode and, what works out really well is you've got all this data from zero-to-one that lets you do this.What Comes Next: The Conversation as the Shared Healthcare PlatformJacob [00:41:36]: That's fascinating. I feel like one thing that's so interesting about the Abridge footprint is that you're in the doctor-patient visit in real-time. I always like to say, there's like probably 50 years' worth of product you could build on top of that. What gets each of you, I don't know, what are you most excited about building, either in the short term or medium term or even, long down the line?Janie [00:41:53]: Something that I get really excited about is that the same conversation can serve so many stakeholders. If you think about the conversation, a doctor needs to know what is the documentation, how do I make sure that this fully represent the care I gave? A patient needs to know, “What the heck just happened? This was really overwhelming. What are my next steps?” A payer needs to know, was this the proper and appropriate care given? A pharma company might want to know why isn't this drug being properly used or is there a good candidate for this clinical trial that I'm about to run? And where I get excited is that our product and our platform and our infrastructure can be the same product across all of those things and start to what's today, separate, very expensive, complex systems that serve each one of these stakeholders in very different ways, start to collapse all of that into a singular platform that enables not just more efficiency across the board but also better outcomes for everyone. And, all of us experience healthcare in probably very painful ways and knowing that there is a world in which we can simplify a lot is really exciting to me and it all starts with the conversation.Chai [00:43:15]: It's interesting. Of it very similar to going back to the KPIs that any AI product cares about. How do you increase quality of care? How do you reduce latency to care? And how do you reduce costs? Which is a huge, in healthcareJacob [00:43:28]: They call it the triple aim in healthcare.Chai [00:43:30]: But very similar to building AI products and the thing that really excites me is when we talk about that latency piece, we talked about one example earlier of prior authorization, can you reduce the latency to care? But you can imagine so much more. Oh, as soon as the lab value gets updated, do you have like a background agent that, kicks off and uses all the context to be “Oh, hey, the patient should do this next,” for example. And of flagging that to the clinician who's always in the loop but reducing that latency, to care. And then you can imagine this is much further down the road but it's like even connecting that to the direct patient and the consumer. And so how can you, how can you build a bridge to all of these things?EHR Partnerships and the Clinical Intelligence LayerJacob [00:44:10]: Very cool. The connections piece is just an ever-growing thing. And one of the key partners is the EHR and I wonder what that relationship is like. Will they, look at this as, something that is valuable enough that they want to own someday?Janie [00:44:29]: Our partnerships with the EHR is, we know that we have to be extremely close partners with all the EHRs who we partner with. Being able to not only pull and push all of the data into the right places is, not only table stakes, if we can't do that, health systems don't want to use us. The second and the reality of today is clinicians spend a lot of their days in the EHR. So much of what allowed us to win in the largest health systems was pretty direct and, very close partnerships with some of the largest electronic health records that allowed us to pull and push data with APIs that weren't ready out of the box. And clinicians want to save clicks. Anytime we introduce a new product that, adds two clicks for them in their day, they're “We're not going to use it.”Janie [00:45:21]: They have 15-minute back-to-back appointments with their patients. They're spending, hours during pajama time doing documentation. Every second and every minute counts and so we really think about being deeply integrated into the EHR as also table stakes to getting real usage and adoption. And anything that we build or introduce, we really talk about earn the right internally a lot, which is we have to provide so much value or save so much time that people will use us. But those are the two things that are close to us, is we know that the product won't be used unless it is deeply interoperable.Chai [00:46:01]: And strategically, to your point, it's like what does EHR want to own versus us? EHRs are really focused on the clinical workflows and so forth but some of the things that we're talking about here, I do these traditionally are outside of the domain where it's oh, connecting pairs and providers together with provider policies or the clinical trial matching, as Janie brought up. And so these are, entirely — we position ourselves as building this entirely new intelligence, clinical intelligence layer across, again, providers, pharma and, payers.Chai [00:46:33]: And so that's a it's a whole different ballgame that we try to playChai [00:46:36]: In combination with them.Jacob [00:46:37]: But it's like a different layer of scope.Healthcare AI Regulation, Technical Depth, and What Changed Their MindsJacob [00:46:39]: I'm curious, you are both relatively newcomers to healthcare. People have these, there's lots of futuristic healthcare AI takes of “Oh, everything will look different.”, now that you've been in healthcare for a bit, you live at the edge of AI, what have you, changed your mind on around this, as you think about what healthcare looks like in ten, 20 years? Any updates to your mental model from the time being close to the problems?Chai [00:47:02]: One thing that IChai [00:47:04]: Was hesitant about before and it's a common thing when I'm trying to recruit engineers that people ask me around, is definitely oh, healthcare, heavily regulated space. And it is, rightfully so. You want to keep, the patients at the end of the day safe. But one of the interesting things that, is a that surprised me how much it is coming to the company is there's a lot of really favorable regulatory tailwinds as well. Where you think about, government really wants interoperability between all these systems that we talked about and so agents can access this information. The government just in January, the FDA released updated guidance on clinical decision support, what I work on in such a way that they used to have guidance from like 2022 that required you to have, mention all these options and do all these other things but it's a very forward and forward-looking way. And so for me, what's been really cool to work on is this, there's this very special moment both in AI in general, we all know that but there's a special moment also regulatory in healthcare as well.Janie [00:48:05]: One thing I would call out is for the very reasons things are higher stakes or, potentially considered more difficult in healthcare, it's where some of the hardest AI problems will get solved first, just because the bar is so high. When I first joined, I was “Oh, this is where we'll be on the tail end of where, all of the AI innovation will be able to be applied.” But when you think about, zero error evals or multi-step workflows that have really low tolerance, a lot of the innovation will happen here just because we have to or else we can't ship.Jacob [00:48:42]: ‘Cause like in other domains, you'd much rather just solve the 80%-is-good-enough problems firstJanie [00:48:46]: 80/20 doesn't work hereChai [00:48:48]: And building off that, traditionally, there was a bit of stigma that, oh, healthcare companies are not that interesting from a technical perspective or I've seen that or faced that myself. But these are really hard and fun problems from a pure technical perspective beyond just the impact. How do you bring the latency of this thing down and make it really high-quality?Reducing Latency: Clinical Workflows, Agents, and Implementation RealityJacob [00:49:07]: How do you bring the latency of things down?Chai [00:49:10]: Yeah. Yeah. Yeah. So okay, let's answer the latency question. And maybe hopefully not too redundant with some of the things I've said earlier but some part of it is with any latency, you have to like what is, what is really your bottleneck. In a lot of workflows, it's sometimes it's the model itself. And so that's where like our data flywheel, our post-training team and so forth come in so that can you make the models far more efficient. So that's one aspect of latency. But there's whole other aspects of latency where it's okay, on top of that, if you use a constellation of different models, can you use — can you first use like a — it's like thinking fast and slow. Can you use a cheap, fast model that triages and hands it off to a larger model where you get more intelligence and so forth and so all theseChai [00:49:56]: Clever tricks to make it work.Chai [00:49:58]: And by the way, we are totally — we also realize that the parameter frontier is changing and so these tricks will — may not get us to where we want to be in five years but we need to if we want to build a useful product right now.Jacob [00:50:11]: Should we go to the quick-fire or you want to ask more about Abridge? We can stuff everything that's not Abridge into the quick-fireSwyx [00:50:16]: I don't mind. I was — I feel like Janie was on the topic of more long tail stuff, which isSwyx [00:50:21]: Not the eighty/twenty thing and that really matters. And I'll —, if you have any tips or cool stories or just general approaches that have worked for you that's interesting to dig into.Janie [00:50:32]: One of them is even just how we staff our teams looks different than a traditional software engineering team, I'd say.Swyx [00:50:40]: Let's go.Clinician Scientists, Edge Cases, and Evals at ScaleJanie [00:50:41]: We have a bunch of folks with different roles who are clinicians and so we have this role called the clinician scientist and I heard one of our leaders refer to them as mutants recently. But they are people who've had clinical backgrounds, so MDs typically, who are also deeply technical, somewhere, on the spectrum of like a full stack engineer all the way to like extremely scrappy prompter. But having each of these people embedded within our teams instantly raises the bar for everything that we build because not only are they determining, is this product clinically useful but they're deeply embedded in our whole evals process. And so when we talk about LFDs, when we talk about what is our actual evaluation criteria, you don't want Chai or me creating what those are because we don't have clinical background. But is probably unique to Abridge but has been game changing. And when you think about where the puck is going, you have people build with clinical backgrounds who are technical and where AI tools are going, they just becomeJanie [00:51:53]: More and more, critical and like the killers of the team. And so that's one. And then the second is just the scale at which we do evals to catch that long tail up front before anything ever gets into production is something that we've pretty much like really started to fine-tune, both from a scale but when do we know we need to get several hundred versus several thousand offline responses, what helps us make that quick decision and make this less of an art and as much of a science as possible. But that's also been something we've had to tune over time.Swyx [00:52:27]: And you have partners who opted in to give you those evals.Janie [00:52:31]: So we work either internally or with third-party for offline evals and then we have customers who also agree to give us, whether it's like thumbs up, thumbs down to like choose this or that, a lot of data to get us to what is as close to fully confident as possible.Swyx [00:52:51]: The term that comes to mind isSwyx [00:52:53]: Like active learning on things where you're weak. I feel like it's a lost artSwyx [00:52:58]: Is a lot of the polish that comes into doing something like this.Janie [00:53:02]: Really.Chai [00:53:03]: Hundred percent.Lessons from Glean: Technical Foundations and AI App InfrastructureJacob [00:53:04]: Maybe, on a totally unrelated note, Chai, you had a very, storied run at Glean b
Java 26 est là, GraalVM cartonne chez Trivago (43 à 12 réplicas !), OpenJDK interdit le code généré par LLM, Spring et Quarkus enchaînent les releases. Côté IA : ADK 1.0, A2A, Lyria 3 chante (mal ?), Yann LeCun lance Ami Labs et ses World Models. Mythos d'Anthropic fait trembler la sécu, Claude Code a leaké son source, et les git worktrees envahissent vos terminaux. Bonus : la mort annoncée de l'IDE, vagues de licenciement chez Oracle et Block, et nos voix toutes clonées. Bon week-ends de mai ! Enregistré le 7 mai 2026 Téléchargement de l'épisode LesCastCodeurs-Episode-340.mp3 ou en vidéo sur YouTube. News Langages Retour d'expérience d'une migration vers graalVM chez Trivago https://medium.com/graalvm/inside-trivagos-graalvm-migration-native-image-for-graphql-at-scale-912bca9df841 La passerelle GraphQL de Trivago (point d'entrée de tout le trafic vers 48 microservices) souffrait de pics de timeout au démarrage JVM Résultats spectaculaires après migration vers GraalVM Native Image : réduction des réplicas de 43 à 12, CPU de 15 à 5 cœurs, images Docker plus légères Obstacles techniques : incompatibilité Log4j → migration vers Logback, remplacement de Mockk par Testcontainers, compilation CI/CD très gourmande Netflix DGS et d'autres librairies manquaient de support GraalVM → l'équipe a contribué des correctifs upstream en open source Approche recommandée : commencer par les services les moins complexes, investir massivement dans les tests automatisés À la 14e migration, le processus était si rodé qu'il allait plus vite que la toute première tentative OpenJDK Interim Policy on Generative AI - https://openjdk.org/legal/ai OpenJDK adopte une politique intérimaire interdisant toute contribution incluant du contenu généré par des LLMs, modèles de diffusion ou systèmes deep-learning Le périmètre est large : code source, texte, images dans les dépôts Git, pull requests GitHub, emails, pages wiki et issues JBS Les contributeurs peuvent utiliser les outils d'IA de manière privée pour comprendre, déboguer et relire le code OpenJDK, mais ne peuvent pas contribuer le contenu généré Trois risques justifient cette politique : surcharge des relecteurs face au code plausible mais incorrect, risques de sûreté/sécurité pour une plateforme critique, et risques de propriété intellectuelle (l'OCA exige que les contributeurs possèdent les droits IP de leurs contributions) Même éditer partiellement du code AI-généré ne le rend pas acceptable à la contribution Oracle, sponsor corporatif d'OpenJDK, travaille sur une politique complète à soumettre au Governing Board GraalVM Native Image et la Closed-World Assumption en Java https://pvs-studio.com/en/blog/posts/java/1357/ Un bon article de rappel du contexte de closed world en Java GraalVM Native Image compile les applications Java en exécutables natifs statiques, sans JVM au runtime. La JVM fonctionne en monde ouvert : les classes sont chargées à la demande, les appels sont des références symboliques résolues dynamiquement. Native Image impose la "closed-world assumption" : tous les chemins d'exécution doivent être connus à la compilation. Les fonctionnalités dynamiques Java (réflexion, proxies, chargement de classes) créent des chemins cachés invisibles à l'analyse statique. C'est pourquoi Native Image exige des fichiers de configuration explicites pour la réflexion, les proxies, les ressources et la FFM API. L'article illustre le problème avec la Foreign Function & Memory API pour appeler printf natif : fonctionne sur JVM, échoue en Native Image sans config. Inclure tout le bytecode accessible serait inutilisable : binaire géant, compilation très lente, et la réflexion nécessite des métadonnées précises. La configuration n'est pas un défaut de conception mais une conséquence logique du passage du dynamique au statique. Java 26 : les nouveautés https://foojay.io/today/java-26-whats-new/ Java est le langage de la JVM, publié tous les 6 mois depuis Java 9 ; Java 26 est une version non-LTS avec 10 JEPs. JEP 500 : protection des champs final modifiés par réflexion profonde, avec des avertissements configurables. JEP 504 : suppression définitive de l'API Applet, plus supportée par les navigateurs. JEP 516 : le cache AOT (Project Leyden) fonctionne désormais avec n'importe quel garbage collector. JEP 517 : support HTTP/3 dans le client HTTP, HTTP/2 reste le défaut mais HTTP/3 est accessible à la demande. JEP 522 : amélioration du débit du GC G1 en réduisant la synchronisation entre threads applicatifs et threads GC. Nouveau support des UUIDv7 via UUID.ofEpochMillis(), naturellement triables et adaptés aux identifiants de bases de données. Process devient AutoCloseable, utilisable dans un try-with-resources. Aucune fonctionnalité en preview n'est graduée en standard ; Structured Concurrency en est à sa 6e preview. Librairies Guillaume a créé une petite librairie Java sans dépendance pour extraire le JSON d'une réponse d'un LLM un peu verbeux https://glaforge.dev/posts/2026/03/22/extracting-json-from-llm-chatter-with-jsonspotter/ Les LLM génèrent souvent du JSON, mais il est parfois entouré de bla-bla et/ou contient des erreurs (ex: commentaires, virgules finales) qui bloquent les parseurs JSON standards. Guillaume a créé une petite librairie légère sans dépendance pour localiser et extraire la structure la plus longue ressemblant à du JSON (même malformé) On peut ensuite passé cette chaîne à un parseur "lénient" (plus tolérant) comme Jackson pour ensuite avoir de bons vieux objets Java fortement typés Librairie dispo sur Maven Central ADK Java sort sa version 1.0 (Agent Development Kit par Google) https://developers.googleblog.com/announcing-adk-for-java-100-building-the-future-of-ai-agents-in-java/ ADK est un framework open source de Google pour créer des agents IA, initialement en Python, maintenant multi-langages (Python, Java, Go, Typescript). Nouvelles fonctionnalités majeures : Outils puissants : GoogleMapsTool, UrlContextTool, ContainerCodeExecutor, VertexAiCodeExecutor, abstraction ComputerUseTool. Architecture de plugins centralisée : Nouveau conteneur App pour gérer les Plugins à l'échelle de l'application (ex: LoggingPlugin, GlobalInstructionPlugin). Context engineering amélioré : Compaction d'événements pour gérer la taille des fenêtres de contexte (résumé et rétention). Human-in-the-Loop (HITL) : Supporte les workflows ToolConfirmation pour approbation humaine des actions d'agent. Services de session et de mémoire : Contrats clairs pour la gestion de l'état (InMemory, VertexAI, Firestore) et la mémoire à long terme. Support Agent2Agent (A2A) : Collaboration native entre agents distants de différents frameworks via le protocole A2A. Dans cet autre article, Guillaume partage comment il a développé l'application Comic Trip montrée dans la vidéo YouTube et qui utilise ADK 1.0 https://glaforge.dev/posts/2026/03/30/building-my-comic-trip-agent-with-adk-java-1-0/ Nouvelle version du SDK Java pour Agent2Agent Protocol, avec le support de la version 1.0 de la spécification https://medium.com/google-cloud/a2a-java-sdk-1-0-0-beta1-released-e83c414b34cc Alignement avec la version 1.0 de la spécification Nouveau groupId org.a2aproject.sdk et package org.a2aproject.sdk Protocoles de transport : support complet et équivalent pour JSON-RPC, gRPC et HTTP+JSON/REST. Gestion des erreurs : introduction de codes d'erreur et détails structurés pour une meilleure observabilité. Optimisation HTTP : ajout d'en-têtes de cache pour les métadonnées des agents (Agent Card). Flexibilité du client HTTP : support par défaut du JDK HttpClient, avec option Vert.x pour les environnements Quarkus. Nouvelles fonctionnalités techniques : méthode DataPart.fromJson() pour la création simplifiée d'objets depuis du JSON brut. Prochaines étapes (v1.0.0.GA) : support simultané des versions 1.0.0 et 0.3.0 du protocole pour assurer l'interopérabilité. JPA 4.0 Milestone 2 : nouvelles fonctionnalités pour Jakarta Persistence https://in.relation.to/2026/04/23/JPA-4-M2/ Jakarta Persistence (JPA) est la spécification standard Java pour le mapping objet-relationnel (ORM), implémentée notamment par Hibernate. JPA 4.0 M2 est la deuxième milestone de la prochaine version majeure de la spécification, annoncée par Gavin King. Construction de requêtes Criteria à partir de chaînes JPQL, offrant plus de flexibilité dans la composition dynamique des requêtes. Nouveaux types d'expressions spécialisés (TextExpression, NumericExpression) pour simplifier l'écriture des requêtes Criteria. Nouvelle interface FetchOption pour contrôler explicitement la stratégie de chargement des associations, dont un BatchSize intégré. Nouvelle annotation @EntityListener qui découple les classes entités de leurs listeners, supprimant les dépendances à la compilation. Les listeners peuvent cibler plusieurs types de callbacks et s'appliquer globalement à toute l'unité de persistance. Introduction de FlushModeType.EXPLICIT et QueryFlushMode pour un contrôle plus fin de la synchronisation avec la base de données. La méta-annotation @Discoverable permet de placer des annotations comme @NamedQuery sur n'importe quelle classe ou interface. Améliorations du DDL via @Index amélioré et clarifications de la spécification via la javadoc. Quarkus 3.35 : tree-shaking, PGO et AOT Semeru https://quarkus.io/blog/quarkus-3-35-released/ Quarkus est un framework Java cloud-natif optimisé pour GraalVM et HotSpot, conçu pour les microservices et les environnements conteneurisés. Nouveau JAR tree-shaking expérimental : analyse des dépendances à la compilation pour supprimer les classes inutilisées. Sur le CLI Quarkus, cela supprime plus de 6 000 classes et économise environ 18 Mo (39,5 %). Support du Profile-Guided Optimization (PGO) pour les builds natifs via quarkus.native.pgo.enabled=true. Le PGO est une fonctionnalité Oracle GraalVM, non disponible dans la Community Edition. Support de l'AOT IBM Semeru : le démarrage passe de ~380 ms à ~190 ms dans les premiers tests. Nouvelle extension quarkus-reactive-transactions : support de @Transactional pour les méthodes Hibernate Reactive retournant Uni. Configuration CORS dédiée pour l'interface de management, indépendante de l'interface HTTP principale. Les tests n'utilisent plus les System Properties pour la propagation de configuration, facilitant la parallélisation future. Le serializer jackson sans reflection n'est pas le default du aux retours de cas limites, encore du travail This Week in Spring - 21 avril 2026 https://spring.io/blog/2026/04/21/this-week-in-spring-april-21-2026 Spring Framework 6.2.18 et 7.0.7 corrigent trois failles de sécurité : DoS via fichiers multipart WebFlux, empoisonnement de cache de ressources statiques, et DoS sur Windows. Le support open source de Spring Framework 5.3.x et 6.1.x est terminé, la migration est recommandée. Spring Data 2026.0.0-RC1 introduit l'upsert (MERGE/INSERT ON CONFLICT) dans l'API Template de Spring Data Relational. Spring Data ajoute un RedisMessageSendingTemplate pour la cohérence avec les listeners Redis, et une optimisation de réinitialisation de caches en un seul appel. Spring AI introduit une Session API (série Agentic Patterns, partie 7) : architecture event-sourcée pour la mémoire des agents IA. La Session API supporte la compaction turn-safe, l'isolation de sous-agents en parallèle, et la persistence JDBC (PostgreSQL, MySQL, MariaDB, H2). Elle vise Spring AI 2.1 (novembre 2026) et remplacera à terme l'API ChatMemory. Spring Vault 4.1.0-RC1 et 4.0.2 sont disponibles. Netflix a présenté son usage de Java, Spring Boot et Spring AI dans une vidéo. This Week in Spring - 28 avril 2026 https://spring.io/blog/2026/04/28/this-week-in-spring-april-28-2026 Cette série hebdomadaire de Josh Long compile les nouveautés de l'écosystème Spring : articles, outils, podcasts et annonces de la communauté. Spring Boot 4 introduit un package natif de résilience org.springframework.resilience avec une nouvelle API de retry qui remplace les approches fragiles via Spring Retry ou Resilience4j. L'API retry native de Spring Boot 4 a des noms d'attributs et sémantiques différents des anciennes bibliothèques, rendant les tutoriels pré-2025 obsolètes et sources de bugs silencieux. Le SDK Spring AI pour Amazon Bedrock AgentCore est disponible en GA : il intègre les capacités AgentCore dans Spring AI via annotations et auto-configuration. Le SDK AgentCore gère automatiquement le contrat runtime AgentCore : endpoint /invocations, health check /ping, SSE avec backpressure. Il offre mémoire court terme (sliding window) et long terme (sémantique, préférences, résumé, épisodique), ainsi que des outils pour navigateur et exécution de code en sandbox. Un plugin Maven (Nullability Maven Plugin) simplifie l'intégration de JSpecify et NullAway pour enforcer la null-safety à la compilation dans les projets Java. Le plugin génère automatiquement les fichiers package-info.java par package et configure le compilateur pour traiter les violations de nullabilité comme des erreurs. Josh Long et Dr. Venkat Subramaniam ont co-présenté à Voxxed Days Amsterdam sur "Intelligent Kotlin", avec un épisode de podcast associé. Cloud Amazon S3 Files https://aws.amazon.com/about-aws/whats-new/2026/04/amazon-s3-files/ Amazon S3 Files est un nouveau service donnant un accès système de fichiers direct aux données stockées dans les buckets S3 Basé sur la technologie Amazon EFS, il supprime la barrière entre stockage objet et interface système de fichiers sans dupliquer les données Débit en lecture pouvant atteindre plusieurs téraoctets par seconde ; des milliers de ressources de calcul peuvent y accéder simultanément Les données restent accessibles via les deux interfaces : S3 API classique et système de fichiers standard, sans migration nécessaire Cas d'usage : agents IA pour la persistance de mémoire entre pipelines, équipes ML sans staging, simplification des data lakes Disponible dans 34 régions AWS Data et Intelligence Artificielle Comment générer de la musique et des clips audio en Java avec le modèle Lyria 3 https://glaforge.dev/posts/2026/03/25/generating-music-with-lyria-3-and-the-gemini-interactions-java-sdk/ Génération musicale avec Lyria 3 (DeepMind) et le SDK Java Gemini Interactions. Lyria 3 : modèle d'IA générative pour créer musique avec paroles ou pistes instrumentales. Utilisation via le SDK Java de l'API Gemini, nécessite une clé API Gemini. Deux versions de modèle Lyria 3 : lyria-3-clip-preview : Clips courts (30s), extraits. lyria-3-pro-preview : Chansons complètes (jusqu'à 3 min), structurées. Personnalisation via les prompts : Fournir ses propres paroles ou les faire générer. Contrôler la structure de la chanson ([Intro], [Verse], [Chorus], [Outro]). Générer des morceaux instrumentaux uniquement. Utiliser des images comme source d'inspiration (modèle multimodal). Sortie : Audio (MP3) et texte (paroles/structure) directement, sans décodage complexe. Facilite l'intégration de la génération musicale dans les applications Java. Les world model, la prochaine étape pour les IA https://www.lepoint.fr/sciences-nature/comment-le-commando-de-yann-le-cun-se-prepare-a-ringardiser-les-geants-mondiaux-de-lia-depuis-paris-OZVUWTDYBNE25C6WF44265ZQKE/ Yann LeCun a quitté Meta FAIR pour créer AMI Labs (Advanced Machine Intelligence) basée à Paris Sa thèse : les LLMs ne mèneront pas à l'intelligence générale, la vraie IA doit partir de la compréhension du monde physique AMI Labs a levé 1,03 milliard de dollars en seed (le plus grand seed round de l'histoire européenne) à 3,5 milliards de valorisation Les world models apprennent à prédire et comprendre la réalité physique plutôt qu'à prédire le prochain token d'une séquence Slogan d'AMI : "Real intelligence does not start in language. It starts in the world." Paris comme base stratégique pour challenger la Silicon Valley dans la prochaine rupture de l'IA Debezium 2026 : résultats du sondage communautaire https://debezium.io/blog/2026/04/27/debezium-2026-survey-results/ Debezium est un outil de Change Data Capture (CDC) open source qui capture les modifications de bases de données en temps réel pour les diffuser vers des systèmes comme Kafka. 98,6% des répondants utilisent Debezium activement ou prévoient de le faire dans l'année, avec 91,3% déjà en production. 63,8% des déploiements tournent sur Kubernetes, 60,9% utilisent Kafka Connect auto-géré, et 17,4% restent sur des VMs ou bare metal. Helm charts est l'approche dominante pour la gestion de configuration, souvent combiné avec GitOps, CI/CD, Ansible ou Terraform. PostgreSQL domine les connecteurs utilisés à 69,6%, suivi de MySQL (33,3%), SQL Server (29%) et Oracle (27,5%). Les volumes de changements capturés vont de 1-25 modifications par minute jusqu'à 1-2 millions par minute selon les environnements. Infinispan rejoint l'écosystème OGX comme fournisseur de stockage vectoriel https://infinispan.org/blog/2026/04/17/infinispan-joins-ogx-ecosystem OGX (anciennement Llama Stack) est un serveur API agentique open source pour construire des applications d'IA complètes. OGX compose des fournisseurs d'inférence, des stores vectoriels, des backends de sécurité, des runtimes d'outils et du stockage de fichiers en un seul serveur déployable. OGX se positionne comme une alternative à l'API OpenAI, déployable sur diverses infrastructures et modèles. OGX cible les workflows RAG (Retrieval-Augmented Generation) et les applications agentiques. Infinispan s'y intègre comme fournisseur de vector IO, apportant recherche vectorielle, par mots-clés et hybride. Je n'ai pas entendu parlé de ce renommage, vous le voyez dans vos deploiements ? Outillage cmux un nouveau terminal basé sur Ghostty spécialisé pour les coding agents https://cmux.com/ Application macOS native construite sur le moteur de rendu Ghostty (libghostty), offrant une accélération GPU pour une fluidité maximale Conçu spécifiquement pour le multitâche et les workflows assistés par IA, avec des onglets verticaux affichant la branche Git, le répertoire et les ports actifs Intègre des notifications qui illuminent les panneaux lorsqu'un agent IA (Claude Code, Codex, etc.) nécessite l'attention de l'utilisateur Propose un navigateur web intégré et scriptable qui peut être affiché en écran scindé à côté du terminal via une API Alternative moderne à tmux, ne nécessitant pas de fichiers de configuration complexes ou de préfixes de touches pour la gestion des vitres et des sessions Supporte nativement tous les agents de codage en ligne de commande et permet l'automatisation via une API socket et une interface CLI dédiée Git Worktree comme un chef https://www.metal3d.org/blog/2026/git-worktree-comme-un-chef/ Article par Patrice Ferlet Git Worktree: Travailler sur plusieurs branches simultanément via des répertoires distincts. Évite git stash ou clones multiples pour le changement de contexte rapide. Méthode "bare" (recommandée): Cloner le dépôt en mode bare (ex: .bare). Lier le dossier racine au dépôt bare via un fichier .git. Configurer le remote tracking pour voir toutes les branches distantes. Ajouter des worktrees pour chaque branche (git worktree add ). Avantages: Économie d'espace, source de vérité unique (un git fetch met tout à jour), hooks/configs partagés, sécurité. Conseils: Ne jamais faire de git checkout à l'intérieur d'un worktree. git fetch --all depuis n'importe quel worktree pour tout mettre à jour. git worktree add --detach pour tester des merges temporaires sans créer de branche. Supprimer: git worktree remove puis git worktree prune. Un script wtree est fourni pour automatiser l'initialisation du setup "bare". Améliore considérablement le workflow. L'IDE meurt et vite https://x.com/jdegoes/status/2036931874057314390?s=46&t=C18cckWlfukmsB_Fx0FfxQ Des leaders techniques prédisent la fin rapide de l'IDE traditionnel, remplacé par des interfaces conversationnelles agentiques Le changement de paradigme : le développeur n'écrit plus des lignes de code mais exprime son intention et supervise des agents autonomes Des outils comme Claude Code, Copilot et Cursor transforment déjà radicalement les workflows de développement quotidiens L'IDE centré sur l'éditeur de code perd sa raison d'être quand l'agent lit, modifie et structure le code de manière autonome La transition est comparable au passage du desktop au mobile : les pratiques établies depuis 30 ans remises en question en quelques mois Le source de Claude Code a leaké via probablement le codemap et un site decrit sont fonctionnement https://ccunpacked.dev/ Le 31 mars 2026, Anthropic a accidentellement inclus les sourcemaps dans un package npm de Claude Code, exposant ~512 000 lignes de TypeScript La fuite n'était pas un piratage mais une erreur humaine : un "*.map" oublié dans .npmignore Le site ccunpacked.dev a été lancé pour analyser et visualiser le code source décompressé Le code révèle un agent background permanent nommé "KAIROS", un mode furtif pour cacher les contributions des employés Anthropic à l'open source, et 44 feature flags cachés Une fonctionnalité inédite "Buddy" (animal de compagnie électronique dans le terminal) et un mode "dream" pour l'idéation continue ont été découverts Anthropic a confirmé : "Aucune donnée client sensible n'était impliquée. Erreur humaine dans le packaging de la release." Gemini CLI passe aux agents https://x.com/srithreepo/status/2039794081925382307?s=46&t=GLj1NFxZoCFCjw2oYpiJpw Gemini CLI, l'agent IA open source de Google pour le terminal, introduit des hooks dans sa boucle agentique Les hooks permettent d'exécuter des scripts automatiquement (scanners de sécurité, vérifications de conformité, logging) à chaque étape de l'agent Lancement de Gemini CLI GitHub Actions : un agent autonome pour les repositories qui peut exécuter des tâches de codage de routine Support des MCP servers pour étendre les capacités et des "Agent Skills" pour des workflows spécialisés Mode agent disponible dans VS Code et IntelliJ avec accès aux outils du système de fichiers et terminal Wispr, le speech to text en local sur macOS http://wispr.stormacq.com/ Wispr est une application macOS de dictée vocale entièrement locale, propulsée par Whisper (OpenAI) sur appareil, sans cloud ni tracking Sébastien Stormacq a développé Wispr en un jour et demi sans écrire une seule ligne de code, grâce à Kiro CLI (agent IA Amazon) Disponible en open source sur GitHub et via Homebrew Détection automatique de la langue, insertion du texte au curseur dans n'importe quelle application via un raccourci global En un mois : 19 releases incluant mode mains-libres, suppression des mots de remplissage, auto-envoi pour les chats, et un outil CLI Exemple concret de développement vibe coding produisant un outil de qualité production sans expertise Swift préalable Comment, Gordon, l'assistant spécialisé en Docker est né https://n9o.xyz/posts/202603-building-gordon/ Nuno Coração (n9o.xyz) détaille comment Gordon, l'assistant spécialisé Docker, a été construit sur docker-agent, le runtime d'agents IA open source de Docker écrit en Go Les agents sont définis en YAML déclaratif et distribués comme des artefacts OCI, sans mise à jour binaire nécessaire L'architecture initiale en essaim de 9 agents spécialisés a été abandonnée au profit d'un agent racine unique avec un prompt soigneusement conçu Le modèle utilisé est Claude Haiku 4.5, suffisant après optimisation des prompts Principe clé "show, then do" : toute action de l'agent nécessite une approbation explicite de l'utilisateur La description des outils impacte fortement la précision du LLM : ajouter des outils peut paradoxalement dégrader les performances existantes Le prompt est une spécification détaillée (identité, patterns d'accès fichiers, règles de sécurité) plutôt qu'une simple instruction IBM Bob https://bob.ibm.com/blog/announcing-ibm-bob-launch IBM Bob assistant IA d'IBM pour coder sur de vraies codebases (lancé avril 2026) 5 modes : Ask, Plan, Code, Advanced (MCP), Orchestrator Détecte la complexité du code en temps réel et propose des refactos Fait des revues de code automatiques sur tes branches/issues GitHub Permet d'écrire en langage naturel directement dans l'éditeur Fonctionne aussi en terminal/CLI et dans les pipelines CI/CD Sécurité : approbation manuelle, .bobignore, checkpoints, pas de training sur tes prompts How I use Claude - 50 tips pratiques https://www.youtube.com/watch?v=mZzhfPle9QU Staff Engineer Meta partage 50 tips après 6 mois d'utilisation intensive de Claude Code Basé sur ~12h/jour d'usage perso et professionnel Couvre tout : bases, workflows avancés, parallélisation Objectif : partager ce qu'il aurait voulu savoir dès le départ Méthodologies Quelqu'un rale sur la non soutenabilité des bases de code écritent avec des agents https://mariozechner.at/posts/2026-03-25-thoughts-on-slowing-the-fuck-down/ Mario Zechner estime que les agents IA font les mêmes erreurs répétitivement sans apprendre, accumulant la complexité à grande vitesse faute de bottlenecks humains Sans vision globale, les agents créent du cargo-cult : les "best practices" de l'industrie appliquées localement sans cohérence architecturale La croissance de la base de code dégrade la capacité des agents à retrouver le code existant → duplication et incohérences croissantes Il cite des pannes AWS et des initiatives qualité Microsoft comme signes préoccupants liés au code généré par IA Solution : réserver les agents aux tâches délimitées et évaluables, garder l'architecture, les APIs et les systèmes critiques écrits à la main Maintenir une revue de code rigoureuse et traiter les humains comme les gardiens finaux de la qualité On m'oblige à utiliser l'IA https://n.survol.fr/n/on-moblige-a-utiliser-lia Éric D. défend l'adoption obligatoire de l'IA comme décision stratégique légitime, comparable au choix du full remote ou de la stack technique Il distingue la décision stratégique (adoption IA) de la méthode d'accompagnement (qui reste collaborative et bienveillante) La compétence IA devient un critère de recrutement : chercher des candidats déjà curieux et explorateurs de ces outils L'alignement culturel sur les pratiques et outils est un prérequis à la cohésion d'équipe Le refus d'adopter certains outils stratégiques peut justifier de ne pas recruter un candidat autrement compétent Encore une metodo SPDD https://martinfowler.com/articles/structured-prompt-driven/ Problème : l'IA accélère le dev individuel mais amplifie ambiguïtés et incohérences à l'échelle d'une équipe. martinfowler SPDD : traiter les prompts comme des artefacts versionnés, révisables et réutilisables plutôt que des échanges jetables. martinfowler Canvas REASONS : 7 dimensions (Requirements, Entities, Approach, Structure, Operations, Norms, Safeguards) pour guider le LLM de l'intention à l'exécution. martinfowler Workflow en 6 étapes : exigences → analyse → contexte → prompt structuré → code → tests unitaires, chaque étape s'appuyant sur la précédente. martinfowler 3 compétences clés : abstraction d'abord, alignement de l'intention, revue itérative. martinfowler Limites : fort ROI sur du code métier complexe, peu adapté aux hotfixes urgents, scripts jetables ou travail créatif/visuel. m Sécurité Le projet Glasswing pour sécuriser les logiciels https://www.anthropic.com/glasswing Anthropic lance Glasswing, une initiative de cybersécurité utilisant Claude Mythos Preview pour identifier des vulnérabilités zero-day 12 partenaires fondateurs dont AWS, Apple, Cisco, CrowdStrike, Google, JPMorganChase, Linux Foundation, Microsoft et NVIDIA Anthropic investit 100 millions de dollars en crédits de modèle et 4 millions en dons aux organisations de sécurité open source Le modèle opère avec une autonomie substantielle, identifiant des milliers de vulnérabilités dans les OS, navigateurs et infrastructures critiques Plus de 40 organisations supplémentaires ont accès pour scanner et sécuriser leurs systèmes Objectif : donner l'avantage aux défenseurs avant que les techniques de hacking assistées par IA ne se généralisent chez les attaquants LinkedIn vous espionne https://frenchbreaches.com/blog/linkedin-est-accuse-de-fouiller-dans-votre-ordinateur-illegalement Scandale "BrowserGate" : LinkedIn injecte du JavaScript qui tente de détecter les extensions Chrome installées sur votre navigateur Le script analysé contient une liste codée en dur de 6 222 extensions Chrome avec identifiants et chemins de fichiers internes Croissance alarmante de la liste ciblée : 38 extensions en 2017 → 461 en 2024 → ~1 000 en mai 2025 → 6 222 début 2026 Les données collectées incluent aussi CPU, RAM, résolution d'écran, timezone et état batterie pour du fingerprinting Certaines extensions ciblées sont liées à la neurodivergence, aux pratiques religieuses ou aux opinions politiques → violation grave du RGPD LinkedIn défend que le scan vise uniquement à détecter les extensions qui pratiquent le scraping de données Post mortem de la supply chain attack sur la librairie NPM axios https://github.com/axios/axios/issues/10636 Le 31 mars 2026, deux versions malveillantes d'axios (1.14.1 et 0.30.4) ont été publiées via un compte mainteneur compromis Vecteur d'attaque : RAT installé via ingénierie sociale ciblée sur la machine personnelle du mainteneur principal La 2FA ne protège pas si la machine de l'utilisateur est compromise : l'attaquant contrôle tout et peut agir comme l'utilisateur Les packages malveillants injectaient plain-crypto-js@4.2.1, un cheval de Troie multi-plateforme (macOS, Windows, Linux) Détection communautaire en ~3 heures, suppression par npm, mesures correctives : rotation complète des credentials Changements préventifs : publication via OIDC, releases immuables, amélioration des pratiques GitHub Actions Passbolt un gestionnaire de mots de passe open source https://lesjoiesducode.fr/passbolt-gestionnaire-de-mots-de-passe-gratuit-open-source-que-votre-equipe-merite-vraiment Gestionnaire de mots de passe open source conçu pour le partage d'identifiants en équipe, utilisé par plus de 50 000 organisations Chiffrement individuel par utilisateur et par version de credential, pas de coffre-fort partagé — architecture zero-knowledge "Forward secrecy" : quand un membre quitte l'équipe, ses copies chiffrées sont automatiquement révoquées sans reset manuel Supporte TOTP, clés SSH, tokens API et champs personnalisés avec piste d'audit complète de tous les accès Édition communautaire entièrement gratuite avec utilisateurs illimités, auto-hébergeable ou cloud Chiffrement OpenPGP nécessitant passphrase + clé privée, avec tokens visuels anti-phishing Loi, société et organisation Anthropic fait un don d'1,5 millions de dollars à la fondation Apache https://news.apache.org/foundation/entry/the-apache-software-foundation-announces-1-5m-donation-from-anthropic Anthropic donne 1,5 million de dollars à l'ASF pour soutenir l'infrastructure, la sécurité et la communauté open source Vitaly Gudanets (CISO d'Anthropic) : "Soutenir l'ASF est un investissement direct dans la résilience et l'intégrité des systèmes dont dépend l'IA moderne" Les fonds financeront les systèmes de build, les processus de sécurité et les services aux projets Apache Ce don est le déclencheur de l'initiative IA responsable à 10 millions de dollars de l'ASF L'infrastructure Apache est invisible mais critique : des systèmes financiers aux plateformes de santé, elle sous-tend l'écosystème logiciel mondial L'ASF lance l'initiative IA responsable https://news.apache.org/foundation/entry/the-apache-software-foundation-launches-10m-responsible-ai-initiative-with-initial-1-75m-donation L'ASF lance une initiative pour une IA responsable dotée d'un budget de 10 millions de dollars sur 3 ans minimum Anthropic est le premier donateur avec 1,5 million de dollars ; Alpha-Omega contribue 250 000 dollars L'initiative fournit aux projets Apache un accès à des modèles IA pour l'expérimentation et la sécurité Elle soutient l'ensemble de la chaîne IA/ML : pipelines de données, infrastructure, frameworks de deep learning Des tracks de conférences, hackathons et bourses de voyage sont prévus pour élargir la communauté Les principes directeurs incluent la supervision humaine, l'intégrité des licences et la sécurité open source Oracle vire 30000 personnes https://rollingout.com/2026/03/31/oracle-slashes-30000-jobs-with-a-cold-6/ Oracle licencie 20 000 à 30 000 employés, 18% de ses effectifs mondiaux. Les salariés ont appris leur licenciement par un simple email à 6h du matin, sans aucun préavis. L'accès à tous les systèmes (Slack, Zoom, badges) a été coupé immédiatement après. But : libérer 8 à 10 milliards de dollars pour construire des centres de données IA. Oracle a déjà contracté 50 milliards de dettes en 2026 pour financer ses projets IA. Paradoxe : l'entreprise affiche un bénéfice record de 6,13 milliards, mais ses liquidités sont dans le rouge. L'action Oracle a perdu plus de la moitié de sa valeur depuis septembre 2025. Et si l'IA n'était qu'un prétexte pour licencier https://eventuallycoding.com/p/ia-licenciements-et-si-l-intelligence-artificielle-n-etait-qu-une-excuse Hugo Lassiège (eventuallycoding) estime que les entreprises utilisent l'IA comme narratif commode pour masquer des erreurs de gestion passées (Block a triplé ses effectifs post-COVID sans croissance des revenus correspondante) Moins de 1% des licenciements technologiques seraient réellement dus à des gains de productivité IA selon les analyses citées Mesurer la productivité des développeurs reste un problème non résolu, mais les entreprises affirment des gains d'efficacité sans preuves Des pressions économiques réelles (inflation, guerres commerciales, coûts énergétiques) sont masquées derrière le discours IA Les restructurations nécessaires sont présentées comme des transformations AI-driven positives pour rassurer les investisseurs Il y voit une fenêtre d'opportunité pour l'Europe pendant que les géants américains se restructurent GitHub Copilot va utiliser les interacitons pour entrainer ses modèles sauf si vous vous délistez https://github.blog/news-insights/company-news/updates-to-github-copilot-interaction-data-usage-policy/ À partir du 24 avril 2026, GitHub utilise par défaut les interactions des utilisateurs Copilot Free, Pro et Pro+ pour entraîner ses modèles Les données collectées incluent le code accepté ou modifié, les snippets envoyés, les noms de fichiers et structures de dépôts, et les retours utilisateurs Les utilisateurs Copilot Business, Enterprise et les dépôts d'entreprise sont exclus de cette collecte de données d'entraînement Opt-out disponible dans les paramètres GitHub > "Privacy" ; les préférences de désactivation préalables sont conservées automatiquement Objectif déclaré : améliorer la précision des modèles sur les langages et cas d'usage du monde réel Grosse percée de Claude Code dans les commits sur GitHub https://aifoc.us/damn-claude-thats-a-lot-of-commits/ Explosion de Claude Code : En six mois, Claude Code est passé de 0,7 % à 4,5 % de tous les commits publics sur GitHub, surpassant tous les autres outils d'IA combinés. Adoption massive des agents IA : Environ 5 % des commits publics sur GitHub sont désormais générés par des agents IA, un chiffre en croissance rapide depuis fin 2025. Domination des bots sur GitHub : Au-delà des commits, les outils d'IA sont omniprésents dans la gestion des pull requests et des problèmes (Copilot et CodeRabbit notamment). Limites méthodologiques : Les données ne concernent que les dépôts publics (les entreprises utilisent massivement des dépôts privés, invisibles ici). Le comptage dépend fortement de la visibilité des signatures (certains outils comme Claude marquent systématiquement leurs commits, d'autres non) L'API de recherche GitHub présente une fiabilité variable à cette échelle. Changement de paradigme : Le développement logiciel vit une transition majeure, comparable au passage du desktop au mobile. L'intégration des agents IA dans le cycle de production n'est plus une expérimentation, mais une réalité opérationnelle à grande échelle. Dysmaths une application pour aider à apprendre les mathématiques et la géométrie lorsque l'on souffre de dyspraxie, dysgraphie https://dysmaths.com/ Application web pour aider les élèves de collège et lycée souffrant de dysgraphie et dyspraxie à faire des maths et de la géométrie Outils de dessin à main levée, géométrie précise (compas, rapporteur, règle) et opérations structurées (fractions, racines, puissances, symboles mathématiques) Export PDF et PNG avec conservation fidèle de l'échelle pour l'impression et la soumission des exercices Options d'accessibilité : police OpenDyslexic, personnalisations d'interface, import d'images et de PDFs Répond à un besoin réel : les outils standards ne sont pas adaptés aux difficultés de coordination et d'organisation spatiale en mathématiques IA ou réalité ? Par Amistory https://www.youtube.com/watch?v=PPYdAhBBF2I L'IA génère des contenus (images, voix, vidéos) de plus en plus indétectables Les arnaques au clonage de voix et deepfakes sont en forte hausse Les faux contenus viraux manipulent l'opinion à grande échelle Le faux n'est plus un accident, c'est devenu un système organisé La société entre dans une ère de doute généralisé sur le réel Comment s'informer quand le réel lui-même peut être simulé ? Conférences La liste des conférences provenant de Developers Conferences Agenda/List par Aurélie Vache et contributeurs : 6-7 mai 2026 : Devoxx UK 2026 - London (UK) 12 mai 2026 : Lead Innovation Day - Leadership Edition - Paris (France) 12-13 mai 2026 : Lyon Craft - Lyon (France) 19 mai 2026 : La Product Conf Paris 2026 - Paris (France) 19-20 mai 2026 : Green Code Challenge - 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) 27 mai 2026 : aMP Day Strasbourg 2026 - Strasbourg (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) 4 juin 2026 : Workplace Intelligence Days - 1ère édition - Lyon (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) 9 juin 2026 : France API 2026 - Paris (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) 15 juin 2026 : Jupyter Workshops: Demystifying MyST Markdown in Education - Orsay (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) 23-24 juin 2026 : MWCP 2026 - Paris (France) 24-25 juin 2026 : Agi'Lille 2026 - Lille (France) 24-26 juin 2026 : BreizhCamp 2026 - Rennes (France) 25-26 juin 2026 : Agile Tour Toulouse 2026 - Toulouse (France) 27 juin 2026 : Asynconf - Paris (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) 28-30 août 2026 : State of the Map - Champs-sur-Marne (France) 4 septembre 2026 : JUG Summer Camp 2026 - La Rochelle (France) 10-11 septembre 2026 : Nantes Craft - Nantes (France) 17 septembre 2026 : dotAI - Paris (France) 17-18 septembre 2026 : API Platform Conference 2026 - Lille (France) 18 septembre 2026 : dotJS - Paris (France) 18 septembre 2026 : WordCamp Bretagne - Rennes (France) 22 septembre 2026 : Salon Data 2026 - Nantes (France) 22-23 septembre 2026 : Agile en Seine & IA 2026 - Paris (France) 24 septembre 2026 : OWASP AppSec Days France 2026 - Paris (France) 24 septembre 2026 : PlatformCon Paris - Paris (France) 24 septembre 2026 : React Native Connection 2026 - Paris (France) 24-26 septembre 2026 : Paris Web 2026 - Paris (France) 28-29 septembre 2026 : 4th Tech Summit on AI & Robotics - Paris (France) & Online 1 octobre 2026 : WAX 2026 - Marseille (France) 1-2 octobre 2026 : Volcamp - Clermont-Ferrand (France) 2 octobre 2026 : DevFest Perros-Guirec 2026 - Perros-Guirec (France) 5-9 octobre 2026 : Devoxx Belgium - Antwerp (Belgium) 12 octobre 2026 : Dev With AI - Paris (France) 27-29 octobre 2026 : Directions EMEA 2026 - Paris (France) 29-30 octobre 2026 : BDX I/O 2026 - Bordeaux (France) 30 octobre 2026 : Cloud Nord 2026 - Lille (France) 4-5 novembre 2026 : Devoxx Morocco - Casablanca (Morocco) 14-15 novembre 2026 : Capitole du Libre - Toulouse (France) 19 novembre 2026 : DevFest Toulouse 2026 - Toulouse (France) 27 novembre 2026 : DevFest Paris 2026 - Paris (France) 1-3 décembre 2026 : Apidays Paris - Paris (France) 4 décembre 2026 : DevFest Lyon 2026 - Lyon (France) 4 décembre 2026 : DevFest Dijon 2026 - Dijon (France) 9-10 décembre 2026 : OpenSource Expérience - Paris (France) 9-10 décembre 2026 : DevOps REX - Paris (France) 10 décembre 2026 : KCD Provence - Aix-en-Provence (France) 7-9 avril 2027 : Devoxx France 2027 - Paris (France) 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/
Kafka'nın kısa ama sarsıcı hikâyesi “Akbaba” üzerinden bu bölümde insanın acıyla kurduğu ilişkiyi konuşuyoruz. Neden bazı şeylerin bize zarar verdiğini bildiğimiz hâlde orada kalmaya devam ediyoruz? Neden bazen acıyı bırakmak, kendimizden vazgeçmek gibi geliyor? Kurtarıcı arayışını, içimizde taşıdığımız yükleri, suçluluk duygusunu ve bizi içten içe tüketen o “akbaba”yı birlikte düşünelim.
Mafia-Oper, Kafka und Wagner: Das Staatstheater Nürnberg startet in seine letzte reguläre Saison im historischen Opernhaus. Danach steht ein Umzug an. Was die kommende Spielzeit noch besonders macht, erzählt Intendant Herzog.
This is a preview of a premium episode. To the listen to the full thing, head over to our Substack: https://designbetterpodcast.com/p/mason-currey At several points in his life, Eli imagined what it would take to become a full-time artist — a photographer or illustrator free from client work. What he didn't realize was that he already had an example of a different path right in front of him: his father, a practicing physician whose published poetry earned recognition from luminaries like John Ashbery. Mason Currey's most recent book explores these alternate paths. He's the author of Daily Rituals, the beloved book that catalogued the working habits of nearly 200 artists, writers, and composers. His new book, Making Art and Making a Living, goes deeper — into the financial realities, the schemes, the compromises, and the surprising strategies that creatives have used to keep their work alive across centuries. What he found is both humbling and strangely reassuring. Virginia Woolf had inherited investments. Kafka had insurance. Chantal Akerman had a cash register she skimmed from. John Cage had Italian game show winnings. And yet, running through all of it is the same question that Mason has been asking about his own life since the day he sat down to write a novel and couldn't: How am I going to pay for this? In this conversation, Mason walks us through the four funding models his book explores — family money, day jobs, patronage, and schemes — and what the lives of creatives from Kafka to Murakami can teach us about building a practice that actually lasts. Bio Mason Currey is the author of the Daily Rituals books, featuring brief profiles of the day-to-day working lives of more than 300 brilliant minds. His latest book, Making Art and Making a Living, was published by Celadon Books on March 31, 2026. Currey lives in Los Angeles and writes Subtle Maneuvers, a twice-monthly newsletter on the creative process. *** Premium Episodes on Design Better This is a premium episode on Design Better. We release two premium episodes per month, along with two free episodes for everyone. New premium subscriber benefit coming soon: we're launching a private Slack community…join now so you get access when it launches! And get a behind-the-scenes pass to every episode with The Roundup, where each week we bring you insights and actionable tactics from recent episodes. Premium subscribers get access to the documentary Design Disruptors and our growing library of books. You'll also get access to our monthly AMAs with former guests, ad-free episodes, discounts and early access to workshops, and our monthly newsletter The Brief that compiles salient insights, quotes, readings, and creative processes uncovered in the show. And subscribers at the annual level now get access to the Design Better Toolkit, which gets you major discounts and free access to tools and courses that will help you unlock new skills, make your workflow more efficient, and take your creativity further. Upgrade to paid
Aujourd'hui, Zohra Bitan, fonctionnaire, Didier Giraud, éleveur de bovins, et Sandrine Pégand, avocate, débattent de l'actualité autour d'Alain Marschall et Olivier Truchot.
Tim Berglund talks to Caleb Grillo (Confluent / WarpStream) about his career in data streaming product management. Caleb's first job: washing windows. Their challenge: reshaping Confluent Cloud's billing and pioneering diskless Kafka to trade latency for huge cost savings.SEASON 2 Hosted by Tim Berglund, Adi Polak and Viktor Gamov Produced and Edited by Noelle Gallagher, Peter Furia and Nurie Mohamed Music by Coastal Kites Artwork by Phil Vo
The episode explores why modern databases keep reinventing the same distributed-systems machinery and argues that a major part of database cost is the operational tax of running replication-heavy systems. Our guest, Almog Gavra, co-founder of Responsive, explains how his team pivoted from operating Kafka Streams as a service to building SlateDB and the “Open Data” manifesto: an object-storage-native LSM foundation that can power multiple database types (vector, time series, logs, key-value) with shared tuning knobs and failure modes. They discuss why distributed-systems complexity is often harder than query engines, how LSM trees provide a tunable tradeoff between read/write/space amplification, caching layers and cost transparency, separating readers/writers, stateless ingest, single-writer availability and fencing via S3 compare-and-set, offloading compaction, and how the architecture enables near-free snapshots. They also cover when this approach doesn't fit: OLTP that can stay on Postgres and ultra-low-latency workloads where cold object-store misses are unacceptable.Chapters:00:00 Introduction08:36 Open Data Manifesto18:34 Specialized vs General25:10 SlateDB Architecture32:51 LSM Trees as Tuning Dial38:58 Tuning Without Overload39:46 Cost Aware Config Knobs41:51 Latency Cost Durability Tradeoffs46:46 Caching Strategies And Layers50:23 Split Readers And Writers52:43 Single Writer Versus Multi Writer55:16 Scaling And Partitioning Writes58:58 Failure Modes And Fencing01:05:23 Compaction As Separate Worker01:09:28 Snapshots And Garbage Collection01:10:25 When Open Data Is Not FitImportant links and references:OpenData: http://github.com/opendata-oss/opendataOpenData manifesto: https://www.opendata.dev/blog/manifestoReach out to Almog: https://www.linkedin.com/in/agavra/ or https://x.com/almoggavraDostovesky paper on LSM: https://nivdayan.github.io/dostoevsky.pdfLatency/Cost/Durability Triad: https://materializedview.io/p/cloud-storage-triad-latency-cost-durabilitySlateDB: https://github.com/slatedb/slatedb"how SSTs work": https://www.bitsxpages.com/p/sorted-string-tables-sst-from-firstFor memberships: join this channel as a member here:https://www.youtube.com/channel/UC_mGuY4g0mggeUGM6V1osdA/joinDon't forget to like, share, and subscribe for more insights!=============================================================================Like building stuff? Try out CodeCrafters and build amazing real world systems like Redis, Kafka, Sqlite. Use the link below to signup and get 40% off on paid subscription.https://app.codecrafters.io/join?via=geeknarrator=============================================================================Database internals series: https://youtu.be/yV_Zp0Mi3xsPopular playlists:Realtime streaming systems: https://www.youtube.com/playlist?list=PLL7QpTxsA4se-mAKKoVOs3VcaP71X_LA-Software Engineering: https://www.youtube.com/playlist?list=PLL7QpTxsA4sf6By03bot5BhKoMgxDUU17Distributed systems and databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4sfLDUnjBJXJGFhhz94jDd_dModern databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4scSeZAsCUXijtnfW5ARlrsNStay Curios! Keep Learning!
Dans son ouvrage, Juliette Mita crée un espace de rencontre entre rap et littérature et fait dialoguer rappeurs et auteurs pour montrer comment leurs univers se répondent et s'enrichissent mutuellement. Sur son compte Instagram Mots Croizés, la journaliste Juliette Mita, influenceuse-entremetteuse, forme des couples improbables en associant rappeurs et auteurs canoniques de la littérature : Shay et Sagan, Jul et Maupassant, Orelsan et Kafka, autant de duos délibérément iconoclastes qui suggèrent une certaine porosité entre les arts. À ceux qui ne jurent que par « Réda », trilogie de titres où le rappeur Lacrim déroule un sanglant récit de vengeance, elle conseille la lecture du Comte de Monte-Cristo. Trahison, incarcération, rétribution : les deux textes présentent en effet, dans les grandes lignes, des trames similaires. D'après Juliette Mita, cette démarche de comparaison vise davantage à sortir Dumas de la naphtaline qu'à légitimer Lacrim. Le rap est légitime selon ses propres codes, et c'est plutôt lui qui pourrait nous permettre d'appréhender des œuvres dont la langue a vieilli. Car le rap est bien « l'expression la plus évoluée de la littérature », dernier héritier d'une longue tradition d'écriture qui traverse aussi bien le roman du XIXè siècle que la chanson à texte de Léonard Cohen et Léo Ferré… La littérature est, à l'origine, orale, et le rap, slamé, scandé, déclamé, nous le rappelle l'autrice. Un dialogue plus qu'une filiation Pour autant, les rappeurs ne sont pas les disciples des écrivains et n'ont aucun devoir de s'intéresser à la littérature. Juliette Mita ne parle ni d'inspiration ni d'intertextualité : nul besoin de prétendre que Dinos a lu Duras pour rapprocher Les pleurs du mal de La douleur. Invitée : Juliette Mita est journaliste et fondatrice de @MotsCroizés. Son ouvrage Rap : littérature 2.0 est publié aux éditions Leduc. Et comme chaque mercredi, Lucie Bouteloup s'amuse à décortiquer pour nous, les expressions de la langue française. Cette semaine c'est l'expression « Être fleur bleue » une expression poétique. Avec Benjamin Rouxel des éditions Le Robert. Programmation musicale : Les artistes : Zuukou Mayzie et Oxmo Puccino avec le titre Dune 2.
Dans son ouvrage, Juliette Mita crée un espace de rencontre entre rap et littérature et fait dialoguer rappeurs et auteurs pour montrer comment leurs univers se répondent et s'enrichissent mutuellement. Sur son compte Instagram Mots Croizés, la journaliste Juliette Mita, influenceuse-entremetteuse, forme des couples improbables en associant rappeurs et auteurs canoniques de la littérature : Shay et Sagan, Jul et Maupassant, Orelsan et Kafka, autant de duos délibérément iconoclastes qui suggèrent une certaine porosité entre les arts. À ceux qui ne jurent que par « Réda », trilogie de titres où le rappeur Lacrim déroule un sanglant récit de vengeance, elle conseille la lecture du Comte de Monte-Cristo. Trahison, incarcération, rétribution : les deux textes présentent en effet, dans les grandes lignes, des trames similaires. D'après Juliette Mita, cette démarche de comparaison vise davantage à sortir Dumas de la naphtaline qu'à légitimer Lacrim. Le rap est légitime selon ses propres codes, et c'est plutôt lui qui pourrait nous permettre d'appréhender des œuvres dont la langue a vieilli. Car le rap est bien « l'expression la plus évoluée de la littérature », dernier héritier d'une longue tradition d'écriture qui traverse aussi bien le roman du XIXè siècle que la chanson à texte de Léonard Cohen et Léo Ferré… La littérature est, à l'origine, orale, et le rap, slamé, scandé, déclamé, nous le rappelle l'autrice. Un dialogue plus qu'une filiation Pour autant, les rappeurs ne sont pas les disciples des écrivains et n'ont aucun devoir de s'intéresser à la littérature. Juliette Mita ne parle ni d'inspiration ni d'intertextualité : nul besoin de prétendre que Dinos a lu Duras pour rapprocher Les pleurs du mal de La douleur. Invitée : Juliette Mita est journaliste et fondatrice de @MotsCroizés. Son ouvrage Rap : littérature 2.0 est publié aux éditions Leduc. Et comme chaque mercredi, Lucie Bouteloup s'amuse à décortiquer pour nous, les expressions de la langue française. Cette semaine c'est l'expression « Être fleur bleue » une expression poétique. Avec Benjamin Rouxel des éditions Le Robert. Programmation musicale : Les artistes : Zuukou Mayzie et Oxmo Puccino avec le titre Dune 2.
Join David Lee Corbo (The Raven) and Top Lobster on Nephilim Death Squad as Thomas the Paranoid American returns for one of the wildest episodes yet! Thomas, 15-year conspiracy & occult comic creator, ex-Disney animator, ex-military, and current Freemason, drops his viral theory: BUGS ARE DEMONS.He traces the 14th-century Middle English origin of “bug” meaning disembodied spirit, hobgoblin, and ghost — not insect — then connects it to biblical Beelzebub (Lord of the Flies), plague locusts that sting like scorpions, worms that don't burn, Exodus flies, and Mesopotamian scorpion-men from the Epic of Gilgamesh.Thomas announces live he is ready to renounce Freemasonry entirely and bend the knee to Christ as King. They break down Masonic boy bride rituals, Albert Pike, the controversial South American photo that triggers every Mason, and why low-level Masonry feels like Rotary Club networking while higher degrees hide darker truths.Plus: Bohemian Grove 2026 updates, general admission tickets still available first to Patreon members at TopLopsa.com, cursed Paranoid American merch (grab it at the Standard Coffee Shop Casino / NDS studio), Thomas's new children's chemtrails book “Connect the Dots” (Magic School Bus style with real research on Morgellons, nanotechnology, Draco star system & Space Preservation Act), eat-the-bugs WEF propaganda, parasites as spiritual conduits, pop culture insect demons in Men in Black (Edgar the Bug), Constantine (Vermin), The Tingler, Alien, Naked Lunch, Spawn's Violator, Nightmare Before Christmas Oogie Boogie, Kafka's Metamorphosis, and more.Thomas also performs at Bohemian Grove every year and is the official Donut convincer. Full episode packed with etymology, scripture, Hopi Ant People, Zoroastrian fly demon Nasu, devil's coach horse beetle, and why killing (or eating) bugs carries spiritual weight.Support the show & get early/ad-free access + Bohemian Grove priority: patreon.com/NephilimDeathSquadTickets & merch: TopLopsa.comThomas's comics, books & cursed merch: paranoidamerican.com 00:00 – Welcome to Nephilim Death Squad 00:45 – Patreon & Bohemian Grove 2026 Tickets Announcement (VIP sold out, General Admission still available for Patrons first) 03:10 – Introducing Thomas “Paranoid American” – 15 years of conspiracy/occult comics, ex-Disney animator, ex-military, current Freemason 05:55 – Thomas drops the bomb: “I'm ready to renounce Freemasonry and bend the knee — Christ is King” 08:40 – Paranoid American merch, cursed merch at the studio, and why it's all “full of lies” 11:20 – Bohemian Grove performance history + Thomas is the official “Donut convincer” 14:30 – How the “Bugs Are Demons” theory was born (flippant comment that went viral) 17:05 – Etymology bombshell: “Bug” originally meant disembodied spirit, hobgoblin, ghost (14th century Middle English) 20:15 – Biblical connections – Beelzebub (Lord of the Flies), plague locusts that sting like scorpions, worms that don't burn, Exodus flies 25:40 – Mesopotamian scorpion-men, Epic of Gilgamesh, and human-insect hybrids 29:50 – Morality of killing bugs – is it okay? Personal stories (cricket torture, son's environmentalism) 35:20 – Parasites as spiritual conduits + demonic possession overlap 39:10 – Pop culture insect demons: Men in Black (Edgar the Bug), Constantine Vermin, The Tingler, Spawn Violator, Oogie Boogie, Kafka's Metamorphosis 45:55 – Hopi Ant People, Zoroastrian fly demon Nasu, Devil's Coach Horse Beetle & more demon-named bugs 51:40 – “Eat the Bugs” WEF agenda + why it feels nefarious 56:30 – Thomas's new children's chemtrails book “Connect the Dots” (Magic School Bus style with real research) 1:01:10 – Chemtrails as spiritual Faraday cage theory 1:05:45 – Deeper Freemasonry talk: Albert Pike, boy bride ritual photo, low-level vs high-level Masonry 1:12:20 – Thomas Edison's Necrophone, bugs in technology, and demons in AI/code 1:18:50 – Closing thoughts + where to find Paranoid American 1:22:30 – Final “Christ is King” moment & outroBecome a supporter of this podcast: https://www.spreaker.com/podcast/nephilim-death-squad--6389018/support.☠️ Nephilim Death Squad — New episodes 5x/week.Join our Patreon for early access, bonus shows & the private Telegram hive.Subscribe on YouTube & Rumble, follow @NephilimDSquad on X/Instagram, grab merch at toplobsta.com. Questions/bookings: chroniclesnds@gmail.com — Stay dangerous.
Anna and Geoff discuss the news that Helen deWitt has turned down the Windham Campbell prize. Are authors expected to do too much publicity? Our book of the week is PEDRO PÁRAMO by Juan Rulfo translated by Douglas J. Weatherford. 'Wuthering Heights located in Mexico written by Kafka' gives a hint - this book is a trip. It broke Anna's brain but Geoff found it richly rewarding once you get into it. Pedro Páramo inspired a generation of Hispanic writers including Gabriel Garcia Márquez and is considered a classic. It's now a Netflix film - but is it too faithful to the book? We needed the Wikipedia plot summary for this one. Read-alikes THE SOUND AND THE FURY by William Faulkner AS I LAY DYING by William Faulkner ONE HUNDRED YEARS OF SOLITUDE by Gabriel Garcia Marquez A SUNNY PLACE FOR SHADY PEOPLE by Mariana Enriquez translated by Megan McDowell HURRICANE SEASON by Fernanda Melchor translated by Sophie Hughes Coming up: LÁZÁR by Nelio Biedermann translated by Jamie Bulloch Follow us! Email: Booksonthegopodcast@gmail.com Instagram: @abailliekaras Substack: Books On The Go Credits Artwork: Sascha Wilkosz
Juan Manuel de Prada nos explica la transformación de Gregorio Samsa en una cucaracha y la metáfora que encierra.
Vandaag bespreken we het boek Het uur van de wolven, van Giuliano da Empoli. Giuliano da Empoli (1973) is een Italiaans-Zwitserse politicoloog, schrijver en journalist. Hij groeide op in verschillende Europese landen, studeerde rechten aan de Sapienza-universiteit in Rome en behaalde een master in politieke wetenschappen aan Sciences Po in Parijs. Da Empoli was onder meer politiek adviseur van de Italiaanse premier Matteo Renzi en locoburgemeester van Cultuur in Florence. Momenteel is hij directeur van de denktank Volta in Milaan en doet hij onderzoek en geeft les aan Sciences Po in Parijs. Da Empoli beweegt zich al jaren in de hoogste kringen van de internationale politiek en staat bekend om zijn scherpe analyses van macht, autoritairisme en de invloed van technologie op de samenleving. Zijn boeken, zoals "De Kremlinfluisteraar" en "Het uur van de wolven", zijn veelgelezen en worden gewaardeerd om hun diepgaande inzichten en verhalende stijl. Hij wordt gezien als een belangrijke stem in het debat over de toekomst van Europa en de democratieën. Zijn achtergrond als adviseur en denker maakt hem tot een opvallende figuur die zowel de academische wereld als de politieke praktijk kent. Het boek "Het uur van de wolven" van Giuliano Da Empoli is een indringend essay dat de gelijktijdige opkomst van autoritairisme en de invloed van tech-giganten en AI op onze samenleving onderzoekt. De kernboodschap is dat we in een tijdperk zijn beland waarin de oude, liberale democratische orde – met vaste spelregels, rechtsstaat en internationale samenwerking – plaatsmaakt voor een nieuwe realiteit. In deze realiteit heersen "wolven": autoritaire leiders zoals Trump, Orbán en Meloni, en tech-magnaten zoals Musk, die chaos en ontregeling gebruiken om hun macht te vergroten. Da Empoli beschrijft hoe deze krachten samenspannen en de democratie, de rechtsstaat en de internationale betrekkingen ondermijnen. Het boek waarschuwt voor de gevaren van deze ontwikkeling en benadrukt hoe slecht de gevestigde orde hierop was voorbereid. De titel "Het uur van de wolven" verwijst naar het moment waarop de wolven – de nieuwe machthebbers – actief worden en de bestaande structuren bedreigen. Het is een oproep om wakker te worden en de schaduwzijde van de macht te herkennen en te bestrijden het boek gaat over de crisis van de democratie en de opkomst van nieuwe, meedogenloze machtsstructuren in politiek en technologie Wat ik mooi vind, is hoe hij met voorbeelden uit de geschiedenis laat zien dat landen en volken dit allemaal al meerdere keren hebben meegemaakt. Inhoud New York, september 2024 Florence, maart 2012 Riyad, november 2024 New York, september 2024 Washington, november 2024 Chicago, november 2017 Montreal, september 2024 Parijs, september 1931 Berlijn, december 2024 Rome, oktober 1998 Lissabon, mei 2023 Lieusaint, december 2024 Intro vergelijk Azteken in de 16e eeuw met de westerse democratieën tegenover de conquistadores van de tech. Zelfvernedering om uiteindelijk vernietigd te worden. Stukje bij beetje leggen de oligarchen de politiek de wil op. Er is geen twijfel mogelijk: het uur van de wolf heeft nu echt geslagen, anders blijft er geen democratie over. Europa lijkt het enige continent waarin de democratie nog wordt verdedigd. New York, september 2024 Begint gelijk ingewikkeld met allerlei ontmoetingen en de algemene vergadering van de VN, Libanon, Israël, Iran, Rusland, Oekraïne, de politiek en ontmoetingen met de dictators rondom deze conflicten. Boek Tony Blair met drie stadia waar politieke leiders doorheen lopen: Als ze net aan de macht zijn, luisteren ze aandachtig. Ze weten dat ze niet veel weten. Na een tijdje overtuigen ze zichzelf dat ze voldoende ervaring hebben opgebouwd en dat ze alles doorhebben. 'Dan heb je geen zin meer om naar anderen te luisteren.' Maar weinigen komen in deze mature fase waarin je tot inzicht komt dat jouw ervaring niet het totaal generaal van politieke kennis is. Dan begin je weer naar anderen te luisteren. Dit gaat vooral over het conflict in Libanon. Oorlog is weer in de mode. In de afgelopen vijf jaar zijn de militaire uitgaven met een derde toegenomen. Een periode waarin aanvallen goedkoper is dan verdedigen. De nucleaire dreiging neemt weer toe. Florence, maart 2012 Mooie start over een werk van Da Vinci over De slag bij Anghiari. p44 over de periodes waarin verdedigen goedkoper is dan aanvallen en perioden waarin aanvallen goedkoper is dan verdedigen. Vergelijk raketten met vliegdekschepen. Cyberaanvallen en chemische en biologische oorlog. Riyad, november 2024 De kroonprins Mohammed bin Salma (MbS). Jong, gestudeerd op een prestigieuze universiteit. Verhaal over gasten in Ritz-Carlton die denken naar een feest gaan maanden worden vastgehouden om voorwaarden van MbS te accepteren om hun schuld te vereffenen (leverde meer dan 100 miljard op). Je moet mensen óf strelen óf uitroeien. De eerste wet van strategisch handelen is krachtdadig optreden. Onbesuisde actie. New York, september 2024 Het bijzondere verhaal over Nayib Bukele die de algemene vergadering toespreekt over zijn actie in El Salvador en iedere met tatoeages vastzet om zo miljoenen mensen te bevrijden van geweld. om uiteindelijk de democratie te ontmantelen. Washington, november 2024 p72 De verandering van het debat van de publieke ruimte naar online waardoor het lijkt dat alles is toegestaan voor eigen gewin. Het bewust creëren van chaos p74 In de nieuwe wereld hebben borgianen een beslissend voordeel. Kennis is een van de grootste vijanden van actie. De juristenpartij (Democraten). Chicago, november 2017 Montreal, september 2024 Parijs, september 1931 Berlijn, december 2024 Rome, oktober 1998 Lissabon, mei 2023 Lieusaint, december 2024 De burgemeester die de sluiproute van Waze die door zijn gemeente stuurt onderuit haalt met maatregelen als verkeerslichten, extra wachtijd van een paar minuten zodat deze sluiproute niet meer sneller is dan de route vervolgen via de snelweg. (nadat er niet naar hem geluistert werd door het bedrijf en het al heel ingewikkeld is om uberhaupt een mens te spreken) Interessant ook de link met de verhalen van Kafka in het Kasteel die de schrijver legt. Interview Buitenhof Opvallende lessen uit het boek voor ons: 00:00 intro - een eerste indruk van het boek en de auteur 03:30 De auteur is een interessante persoon in de toeschouwerrol. 06:20 In de Nederlandse media zijn de wereld van de politiek en de wetenschap gescheiden. 08:10 Wat leer ik uit het verleden voor de huidige tijd. 13:50 Goede voorbereiding op politiek overleg: verdiep je in de geschiedenis. 14:50 De coalitie van techniek en autocratie, dat is levensgevaarlijk. 17:30 Als je aarzelt en niets doet, dan word je onder de voet gelopen. 20:50 De drie stadia van luisteren naar anderen van een politicus. 22:10 In een oorlog is op dit moment, economisch gezien, aanvallen weer goedkoper dan aanvallen. De wapens om een land aan te vallen zijn goedkoper dan het materieel om je te verdedigen tegen een agressor. 29:10 Begin met het definiëren van je kernwaarden als natie. 31:30 De Europese federalisering is niet ver genoeg ontwikkeld. 34:45 Het bijzondere verhaal van Mohammad bin Salman over een bizarre actie die hem meer dan 100 miljard opleverde. 41:50 Het belang om de infrastructuur snel te beheersen bij een staatsgreep, en de link naar de big tech en de Europese afhankelijkheid nu. 43:00 De overeenkomsten van de big tech met Obama en Trump, met de directe invloed op de president, bij de Democraten en Liberalen. 46:45 Eric Schmidt legt zijn taken bij Google neer om Barack Obama te ondersteunen in de verkiezingsstrijd. Door gebruik te maken van data. 47:50 Twee weken na de herverkiezing staakt de antitrustcommissie de gerechtelijke stappen tegen Google. 50:00 Het idee van zelfregulering is een van de grote leugens. Vertrouw bedrijven nooit. 51:30 Een mooi voorbeeld van klein verzet tegen big tech. 53:55 De cirkel rondmaken met het verhaal van Hernán Cortés en de vergelijking met de spiegels en kralen van Big Tech (AI). 55:20 Een democratiecheck doen van de spullen in je (digitale) winkelwagen. Bronnen die we genoemd hebben Ilja Leonard Pfeijffer - Wikipedia Absolute democratie - Ilja Leonard Pfeijffer Alkibiades - Ilja Leonard Pfeijffer Piratenverlichting – David Graeber #boekencast afl 87 Het begin van alles – Wengrow en Graeber #boekencast afl 63 Geschiedenis voor morgen – Roman Krznaric #boekencast afl 112 De goede voorouder – Roman Krznaric #boekencast afl 53 Mohammad bin Salman al-Saoed - Wikipedia (MbS) Jamal Khashoggi - Wikipedia Curzio Malaparte - Wikipedia Man Hacks Google Maps Traffic with 99 phones Hernán Cortés - Wikipedia Onafhankelijk worden van (Amerikaanse en Chinese) Big Tech - een overzicht en actielijst. Cinetree Antifragile – Nassim Nicholas Taleb #boekencast afl 37 Luister naar deze aflevering Beluister hier ons gesprek over het boek Het uur van de wolven. Wat een fijn geschreven boek met confronterende inzichten. In een halfuur delen wij dit boek met jou. Een halfuur met kennis die je tot je neemt terwijl je wandelt, loopt of rijdt, bijvoorbeeld. Video van deze aflevering Bekijk ons gesprek op video https://youtu.be/tL-ep3S5T4Q https://youtu.be/tL-ep3S5T4Q In deze aflevering bespreken we het boek Het uur van de wolven. Wat een bijzonder boek. Een kijkje in de wereld van de toppolitiek. De geschiedenis herhaalt zich. We halen het paard van Troje zelf binnen. Met spiegels en kralen (AI) worden we verleid. We halen hiermee zelf het onheil en daarmee onze ondergang, binnen in Europa. De democratie wordt ondermijnd en afgebroken door de onschuldig uitziende founders die een ander beeld van de toekomst hebben v
Adi Polak talks to Mateo Rojas (LittleHorse) about his career working with Kafka Streams. Mateo's first job: building a real-money policy management platform on early Kafka Streams. His challenge: working at LittleHorse with Kafka as a workflow engine and deciding whether it should be the source of truth.SEASON 2 Hosted by Tim Berglund, Adi Polak and Viktor Gamov Produced and Edited by Noelle Gallagher, Peter Furia and Nurie Mohamed Music by Coastal Kites Artwork by Phil Vo
Dreimal hat Franz Kafka München besucht, in unterschiedlichen Lebensphasen und mit durchwachsenem Erfolg: Zuletzt 1916 für eine Dichterlesung - bei der er angeblich sogar Damen in die Ohnmacht befördert haben soll!
Assertions vs Types: Design by Contract, Deterministic Simulation Testing, and Safety vs Availability (TigerBeetle Vol. 3)In this episode (volume 3), Kai talks with Alex about assertions, type systems, and design by contract, arguing it's not “types vs assertions” but using both: types for cheap, structural guarantees and assertions where types become too costly or obscure logic. Alex defines assertions (in Zig) as a function that crashes the program on false, explains why disabling assertions in production is dangerous, and ties reliable assertion use to deterministic simulation/generative testing to exercise error paths. We discuss aiming for very high assertion density, repeating weak and strong assertions across call sites and callees to form an interlocking “net,” and note you don't need special language features for DBC. We also cover safety vs availability tradeoffs, recovery/isolation boundaries (“let it crash” with recovery), TigerBeetle's approach to correctness, a real cache/hash-table bug caught by an assertion, handling poison-pill failures via fix-forward and frequent releases, control plane vs data plane performance tactics for assertions, and why Alex doesn't use AI to write TigerBeetle code.Chapters:00:00 Welcome and Intro01:33 Assertions Versus Types03:05 Silver Bullets Origins08:10 When Types Get Costly11:06 What Is an Assertion?12:40 Never Disable Assertions15:30 Testing and Error Paths19:52 Simulation Testing Harness22:50 Where to Assert Everywhere27:01 Redundant Contracts Benefits33:08 No Language Features Needed38:01 Visibility and Abstractions40:47 Boundaries and Integration44:01 Safety Versus Liveness Setup44:31 Safety vs Availability Tradeoffs46:16 Let It Crash Philosophy47:13 Isolation and Recovery Boundaries48:02 TigerBeetle vs IDE Priorities53:48 Always Assertions Pattern55:46 Cascading Failures in Clusters57:57 Fix Forward and Fast Releases01:02:27 Worst TigerBeetle Bug Story01:07:00 Control Plane vs Data Plane01:09:50 Assertion Performance Tactics01:15:18 AI Limits for Safety Systems01:18:55 Closing Advice on AssertionsFor memberships: join this channel as a member here:https://www.youtube.com/channel/UC_mGuY4g0mggeUGM6V1osdA/joinDon't forget to like, share, and subscribe for more insights!=============================================================================Like building stuff? Try out CodeCrafters and build amazing real world systems like Redis, Kafka, Sqlite. Use the link below to signup and get 40% off on paid subscription.https://app.codecrafters.io/join?via=geeknarrator=============================================================================Database internals series: https://youtu.be/yV_Zp0Mi3xsPopular playlists:Realtime streaming systems: https://www.youtube.com/playlist?list=PLL7QpTxsA4se-mAKKoVOs3VcaP71X_LA-Software Engineering: https://www.youtube.com/playlist?list=PLL7QpTxsA4sf6By03bot5BhKoMgxDUU17Distributed systems and databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4sfLDUnjBJXJGFhhz94jDd_dModern databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4scSeZAsCUXijtnfW5ARlrsNStay Curios! Keep Learning!
In this episode, Catherine, Co-Founder, and CEO of Kernel, reveals the magic behind Kernel's approach using uni-kernels and micro VMs, enabling browser startups in just milliseconds. We talk about the technical challenges of agent-based web interactions, how Cash App leverages Kernel for live QA of e-commerce sites, the intricacies of handling authentication, and the future potential of attaching virtual GPUs for optimal performance. Whether you're an AI developer or fascinated by the backbone of internet automation, don't miss this insightful conversation. Chapters:00:00 Introduction to AI Agent Challenges00:35 Welcome and Episode Overview01:58 Guest Introduction and Background02:39 The Problem Statement and Kernel's Origin07:21 Infrastructure for AI Agents16:09 Kernel's Technical Architecture20:33 Use Cases and Real-World Applications24:38 Challenges and Future Directions27:20 Running on Bare Metal: Optimizing Browser Instances28:02 Challenges in Infrastructure Optimization30:34 Headful Browsers and Human Interaction30:58 Recording and Debugging Browser Sessions33:42 Preventing Misuse of Browser Automation39:55 Handling Authentication and Secure Access44:57 Bot Detection and Good Actor Automations48:56 Future Developments and GPU Integration52:41 Conclusion and Final ThoughtsImportant links:- Homepage to go get a free account (no credit card required) and just try us out: https://www.kernel.sh/ - Our chromium on Unikernels OSS repo: https://news.ycombinator.com/item?id=43705144- The blog post where in it we benchmarked ourselves against all others and ranked the fastest browser infrastructure in the world: https://www.kernel.sh/blog/fast For memberships: join this channel as a member here:https://www.youtube.com/channel/UC_mGuY4g0mggeUGM6V1osdA/joinDon't forget to like, share, and subscribe for more insights!=============================================================================Like building stuff? Try out CodeCrafters and build amazing real world systems like Redis, Kafka, Sqlite. Use the link below to signup and get 40% off on paid subscription.https://app.codecrafters.io/join?via=geeknarrator=============================================================================Database internals series: https://youtu.be/yV_Zp0Mi3xsPopular playlists:Realtime streaming systems: https://www.youtube.com/playlist?list=PLL7QpTxsA4se-mAKKoVOs3VcaP71X_LA-Software Engineering: https://www.youtube.com/playlist?list=PLL7QpTxsA4sf6By03bot5BhKoMgxDUU17Distributed systems and databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4sfLDUnjBJXJGFhhz94jDd_dModern databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4scSeZAsCUXijtnfW5ARlrsNStay Curios! Keep Learning!
For memberships: join this channel as a member here:https://www.youtube.com/channel/UC_mGuY4g0mggeUGM6V1osdA/joinTigerStyle with matklad Vol. 2 Systems EngineeringChapters:00:00 Introduction to Alex and His Journey00:06 The Importance of Culture and Principles00:25 Weekly Releases and Quality Optimization00:45 Static Allocation Explained01:01 Alex's Passion for Programming01:25 Welcome and Introduction to the Show01:40 Alex's Background and Career Path04:01 Choosing the Right Language for Systems Programming07:12 Mental Models and Programming Philosophy20:19 Test-Driven Development and Quality42:00 Weekly Releases as a Force Multiplier44:49 Monoliths vs Microservices: The Core Idea47:05 The Importance of Engineering Process47:37 Designing a Scalable Chat Application49:36 Achieving Simplicity in System Design52:25 Static Allocation Explained01:13:59 Balancing Safety and Availability with Assertions01:27:08 The Passion Behind ProgrammingAbout matklad: https://matklad.github.io/aboutFor memberships: join this channel as a member here:https://www.youtube.com/channel/UC_mGuY4g0mggeUGM6V1osdA/joinDon't forget to like, share, and subscribe for more insights!=============================================================================Like building stuff? Try out CodeCrafters and build amazing real world systems like Redis, Kafka, Sqlite. Use the link below to signup and get 40% off on paid subscription.https://app.codecrafters.io/join?via=geeknarrator=============================================================================Database internals series: https://youtu.be/yV_Zp0Mi3xsPopular playlists:Realtime streaming systems: https://www.youtube.com/playlist?list=PLL7QpTxsA4se-mAKKoVOs3VcaP71X_LA-Software Engineering: https://www.youtube.com/playlist?list=PLL7QpTxsA4sf6By03bot5BhKoMgxDUU17Distributed systems and databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4sfLDUnjBJXJGFhhz94jDd_dModern databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4scSeZAsCUXijtnfW5ARlrsNStay Curios! Keep Learning!
This is a preview of a premium episode from our Patreon feed, Paid Costly For Me! Head over to Patreon.com/PodCastyForMe to hear more for just $5 a month. We're back with another commentary track, this time for a movie by a director we almost covered: David Cronenberg. His 1988 identical twin gynecologist horror-tragedy DEAD RINGERS deals with all kinds of themes we love: the nature of the self, of experience, of the body, of morality. It's also got the guy from Soderbergh's KAFKA and the lady from TIGHTROPE and OBSESSION. Listen to this along with the film or just listen on its own - it works either way, in our opinion! "Dead Ringers" by Ron Rosenbaum & Susan Edmiston Thanks as always to Jetski for our theme music and Jeremy Allison for our artwork.
In this episode of Friday Conversation, we sit down with acclaimed author Lincoln Michel, known for Metallic Realms, The Body Scout, and his thought-provoking work across fiction and criticism for a deep dive into the craft, business, and future of storytelling.Lincoln walks us through his writing journey, from discovering his voice through art and literature to navigating MFA programs, publishing, and teaching creative writing. We explore how genre fiction, constraints, and experimentation shape his work, and how he blends sci-fi, noir, and literary influences into something uniquely his own.We also get into the realities of modern publishing, the shrinking “midlist,” and what it means to be an author in a world increasingly influenced by AI. Along the way, Lincoln shares insights on creativity, storytelling structure, and why constraints can actually unlock better writing.Plus, we preview his upcoming novel Haunted Hills, a haunted house concept with a wild twist and talk about influences ranging from Kafka and Calvino to Stephen King and cyberpunk classics.If you're a writer, reader, or just someone who loves great conversations about books, this is one you don't want to miss.Send us a message (I'm not able to reply)Support the showPage Chewing BlogPage Chewing ForumFilm Chewing PodcastSpeculative Speculations Podcast Support the podcast via PayPalSupport the show by using our Amazon Affiliate linkJoin Riverside.fm Co-Hosts:JarrodVarsha ChrisJoseCarl D. Albert (author)Thomas J. Devens (author)Alex French (author)Intro and Outro Music by Michael R. Fletcher (2024-Current)
Dan preps for air travel by bringing Steven Spielberg's strange post-9/11 comedy The Terminal to discuss with Brian. Join as they discuss Tom Hanks' silly voice and tremendous performance, influences of Capra and Tati and Kafka, the messier narrative elements of the story, the provocative and allegorical reflections on immigrant experience, the Spielberg touch, and our unsorted feelings on where the story ends up. Dan's movie reviews: http://thegoodsreviews.com/ Subscribe, join the Discord, and find us on Letterboxd: http://thegoodsfilmpodcast.com/
Michael is a retired New York City detective who somehow found his way from Manhattan to the Balkans. After his time with the NYPD, Michael spent two decades working internationally for the U.S. Department of State, the United Nations, and the Department of Justice; He's seen the best and worst of human behavior — often in the same afternoon.Michael has written Land of Broken Toys: Kosovo, a work of fiction based on real events. The novel is part crime thriller, part dark comedy, and part fish-out-of-water story. There's humor in the absurdities of peacekeeping life — Irish jokes told over Turkish coffee, UN bureaucracy that would make Kafka blush — but beneath the laughter sits something serious—the idea that belief and integrity still matter.This was a spirited conversation! Don't miss it!This episode, like all episodes of If This Is True, brings forth what drives creatives to do what they do. For more of this content and interaction, you can also go to my substack, coolmite25.substack.com. Hosted on Acast. See acast.com/privacy for more information.
Where others see obstacles, Carrie creates opportunities, and makes lasting changes to society and culture.(This episode originally aired on March 9, 2021.)
"Welcome to Harshaneeyam!"Can a work of art ever truly be washed clean of the hands that funded it? Is it possible to create a masterpiece in the shadow of a monster? Today, we are exploring these haunting questions through the lens of The Director—the latest novel by the brilliant German author Daniel Kehlmann. Set against the harrowing backdrop of the late 1930s and 1940s, the story follows the legendary film director G.W. Pabst as he returns to Nazi-occupied Austria.Pabst finds himself ensnared by the propaganda machine of Joseph Goebbels, the Nazi Minister of Enlightenment and Propaganda, who wielded absolute control over the German press and arts to serve the regime's twisted ideology. Pabst believes his creative genius can transcend such a system, but at what cost?Upon its original release in Germany under the title Lichtspiel, the novel became an immediate sensation, sparking intense literary debates about the moral compromises of artists during the Third Reich. This buzz has only grown as the book enters the global stage, with the English edition published in the year 2025. Now, having been shortlisted for the International Booker Prize 2026, the novel stands as one of the most anticipated and discussed works of contemporary fiction."Joining me today is the man who brought this intricate, tonal masterpiece into English: the award-winning translator Ross Benjamin. We discuss the 'moral acrobatics' of the artist and the 'tonal agility' required to translate one of the most important voices in contemporary German literature. This is a conversation about light, shadow, and the high cost of staying silent."We believe that every great book is an invitation to a new world. Harshaneeyam serves as your gateway to these contemporary global masterpieces, connecting you with the authors and translators who shape our literary landscape.If you enjoy Harshaneeyam please follow the show on Apple, Spotify, or your favourite podcasting platform and leave a review for us. It will help truly help us; and don't forget to Share our podcast link with your other friends who enjoy similar content.To help us provide even more value, head over to our website www.harshaneeyam.com to complete our brief Listener Survey. Your feedback is the secret ingredient that helps us improve and create content tailored to your interests!The Versatility of Kehlmann's VoiceOne of the most striking aspects of Daniel Kehlmann's writing is his ability to shift registers with surgical precision. Ross Benjamin notes that as a translator, the greatest challenge—and thrill—is capturing Kehlmann's "tonal range and agility." In The Director, the narrative moves seamlessly from the slapstick humor of a film set to the chilling, quiet terror of a propaganda ministry.Kehlmann's work is characterized by a "light touch." He avoids the heavy-handedness often found in historical fiction, opting instead for a style that is "sly and inventive." Whether he is writing about the magical realism of Tyll or the cinematic obsession in 'The Director' Kehlmann manages to be intellectually serious without ever losing his propulsive, vivid storytelling.Cinema and the Shadow of PropagandaThe Director follows the life of the legendary film director G.W. Pabst. After a failed stint in Hollywood, Pabst finds himself back in Nazi-occupied Austria. The novel explores the "adventure of not-knowing"—the tragic delusion that an artist can separate their craft from the political machine surrounding them.Ross Benjamin explains that the novel is not just a biography; it is a meditation on the "moral issues" of creation. Pabst believes he can make a masterpiece even under the watchful eye of Joseph Goebbels. The Original German Title Licht Speil - "light play" refers not just to the flicker of the film projector, but to the deceptive game the artist plays with a monstrous regime.The Challenge for the TranslatorTranslating a work of this magnitude requires more than just bilingual fluency; it requires a deep understanding of historical subtext. Ross Benjamin discusses the difficulty of translating the "Propaganda Deutsch" of the era—a language designed to obfuscate and control."There's a specific humor in Kehlmann," Ross shares. "It's often found in the absurdity of the situations." In one scene discussed during the podcast, a group of officials descends on a film set, and the dialogue shifts into a terrifyingly polite, yet deadly, exchange. Capturing that "mask of civility" in English while maintaining the underlying threat is the "invisible labor" of the translator.Why ‘The Director' Matters TodayAs ‘The Director' makes its way to the International Booker Prize shortlist, its relevance is undeniable. It asks a question that resonates in every era: What is the cost of staying silent? Through the lens of 1940s cinema, Kehlmann and Benjamin provide a mirror to our own world, exploring how easily "art for art's sake" can be weaponized by those in power.For listeners of Harshaneeyam, this interview is a deep dive into the "Philosophy of Translation" and the meticulous craft required to bring a German masterpiece into the global literary consciousness.About the Author & TranslatorDaniel Kehlmann Daniel Kehlmann is one of the most successful contemporary German-language authors. Born in Munich in 1975, he rose to international fame with his 2005 novel Measuring the World (Die Vermessung der Welt), which became one of the biggest-selling German novels since WWII. His works frequently blend historical fact with magical realism and philosophical inquiry. He has received numerous awards, including the Kleist Prize and the Thomas Mann Prize. His previous collaboration with Ross Benjamin on the novel Tyll was also a global sensation and a finalist for the International Booker Prize.Ross Benjamin Ross Benjamin is an award-winning translator of German literature based in the United States. His translations include works by Franz Kafka, Friedrich Hölderlin, and Joseph Roth. He is perhaps best known for his definitive translation of The Diaries of Franz Kafka, which was hailed for its faithfulness to Kafka's original, unedited prose. Benjamin's work is noted for its linguistic sensitivity and his ability to capture the specific "musicality" of German authors. He is a recipient of a Guggenheim Fellowship and the Helen and Kurt Wolff Translator's Prize.The Director' in press -A Times Literary Supplement Book of the Year 2025A New York Times Notable Book of 2025A Telegraph Book of the Year 2025A Guardian Book of the Year 2025An Observer Book of the year 2025* Please complete Harshaneeyam Listener Survey using the link below. It would be lovely to have your feedback. Your feedback will help us improve -https://www.harshaneeyam.com/survey/Listener/***Disclaimer: The views and opinions expressed by Interviewees in interviews conducted by Harshaneeyam Podcast are those of the Interviewees and do not necessarily reflect the official policy or position of Harshaneeyam Podcast. Any content provided by Interviewees is of their opinion and is not intended to malign any religion, ethnic group, club, organization, company, individual, or anyone or anything.This podcast uses the following third-party services for analysis: Podtrac - https://analytics.podtrac.com/privacy-policy-gdrp
Between The Covers : Conversations with Writers in Fiction, Nonfiction & Poetry
Excited to share this classic episode from the archives with one of the great short storytellers of our time, Ted Chiang. This conversation happened in 2019 at the studios of KBOO community radio in Portland, Oregon. Blake Crouch speaking of Exhalation, the book we discuss today, says “Ted Chiang has no contemporary peers when it comes to the short story form. His name deserves to be mentioned in the same breath as Carver, Poe, Borges, and Kafka. Every story is a universe. Every story is a diamond. You will inhale Exhalation in a single, stunned sitting, because true genius doesn't come along nearly as often as advertised. This is the real thing.” For the bonus audio archive Ted contributed a reading of his essay “Silicon Valley Is Turning into its Own Worst Fear,” first published at Buzzfeed, an essay exploring the reasons why Silicon Valley might particularly fear superintelligent A.I. and how credible those fears really are. This joins contributions from everyone from N.K. Jemisin to Daniel Jose Older to Vajra Chandrasekera. You can find out how to subscribe to the bonus audio, and about the other potential rewards and benefits of joining the Between the Covers community as a listener-supporter, at the show’s Patreon page.
In honor of the 30th anniversary of David Foster Wallace's Infinite Jest, novelist Hannah Smart once again joins us for a discussion of the ethical limits and critical revaluation of this maximally ambitious and chronically misunderstood novel. A polygenetic and polyphonic novel, Infinite Jest's interlocking themes and characters circle back to the urgent need and paradoxical impossibility of self-forgetting and transcendence within the American psyche ravaged by the grotesqueries of late consumer capitalism and the imperatives of individualism. Infinite Jest builds its literary DNA out from the spiritual seriousness of Dostoevsky, the parables of Kafka, Pynchon conspiracism, and Gassian forebodings of the infantile fascist lurking in the intellectual artifices of the hidden American heart. It is a novel about the deadly pleasures of the culture industry and temptation of the hedonic oblivion promised by advertisers. In this discussion, we focus on what it can still teach us about the hard-won discipline of sustained activity of reading, what's still true about the ethics of individual responsibility, and hold up a comic mirror to the horror of our American political present and besieged future.Follow Hannah on Twitter(X): @fowlinghantodSubscribe to Hannah's Substack: @howlingfantodPreorder Hannah's debut novel, Meat Puppets: https://merchtable.bigcartel.com/product/meat-puppets-by-hannah-smartRead Hannah's LARB piece: https://lareviewofbooks.org/article/nothing-ever-happens-mister-squishy-and-the-year-of-the-sentence-diagram/Please consider becoming a paying subscriber to our Patreon to get exclusive bonus episodes, early access releases, and bookish merch: https://www.patreon.com/MoralMinorityFollow us on Twitter(X).Devin: @DevinGoureCharles: @satireredactedEmail us at: moralminoritypod@gmail.com
Kärlek kommer ibland i en ny skepnad. Mjukisbyxor på jobbet och en cigg i handen. Cigifiering bland kändisar och advokater i comfy-brallor. Trender vi gärna undviker. Pernilla åker tunnelbana och Sofia kramar en bodyguard och undrar varför vet man något om Jesus som barn? Att lyssna med hjärtat och titta upp från telefonen. Hosted on Acast. See acast.com/privacy for more information.
Jay Kreps is the co-founder and CEO of Confluent, the company built around Apache Kafka — the open-source data streaming platform he originally built while at LinkedIn. In this conversation, Jay shares his full journey: how Confluent grew from a scrappy group of engineers with no go-to-market experience into a publicly traded enterprise software company. He makes the case that the difference between what a company can do, and what it must do, is one of the most underrated building levers; illustrated through his years spent pushing Confluent towards a cloud product, in the face of widespread opposition. In this episode, we discuss: Why moving from software engineer to CEO requires almost an entirely new skillset The product marketing pyramid Jay built to explain Kafka to the world How Confluent bludgeoned its way to a cloud-first business when the early product was “embarrassing” The critical difference between what a company can do and what it must do What keeps scaling companies from becoming "Chipotle” References: Amazon Web Services: https://aws.amazon.com/ Apache Kafka: https://kafka.apache.org/ Benchmark: https://www.benchmark.com/ Confluent: https://www.confluent.io/ Jun Rao: https://www.linkedin.com/in/junrao LinkedIn: https://www.linkedin.com/ McKinsey & Company: https://www.mckinsey.com/ MySpace: https://www.myspace.com/ Neha Narkhede: https://www.linkedin.com/in/nehanarkhede Oracle: https://www.oracle.com/ Red Hat: https://www.redhat.com/ Snowflake: https://www.snowflake.com/ Where to find Jay: LinkedIn: https://www.linkedin.com/in/jaykreps/ Twitter/X: https://x.com/jaykreps Where to find Brett: LinkedIn: https://www.linkedin.com/in/brett-berson-9986094/ Twitter/X: https://twitter.com/brettberson Where to find First Round Capital: Website: https://firstround.com/ First Round Review: https://review.firstround.com/ Twitter/X: https://twitter.com/firstround YouTube: https://www.youtube.com/@FirstRoundCapital This podcast on all platforms: https://review.firstround.com/podcast Timestamps: 01:18 Making the leap from engineer to CEO 03:33 The 80% rule: what a CEO actually needs to know 04:54 Scaling different business disciplines 09:31 How Confluent's story began in LinkedIn 12:13 The growing need for scalable data tech 13:37 What the early Kafka product looked like 16:38 Kafka's underwhelming open-source launch 18:38 The blog post that accelerated Kafka's adoption 20:16 Why so many marketing messages fail 28:08 The decision to build Confluent 34:24 Planning to fundraise before building the product 39:19 Confluent's early years: Tough product decisions 47:07 The underrated growth lever question for companies 55:46 Why founder optimism is an overrated trait 1:00:29 What should founders give up as they scale? 1:02:47 Why people become trapped in a failure mindset 1:08:33 The Chipotle problem: Losing excellence at scale
This lecture discusses key ideas from the 20th century philosophical short story writer, essayist, and poet Jorge Luis Borges's essay "Kafka And His Precursors", in which he first examines the idea of literary precursors, and then identifies and briefly discusses six precursors to the works of Franz Kafka, in his view. These are: Zeno's paradoxes of motion an apologue by Han Yu the writings of Kierkegaard Browning's poem "Fears and Scruples" a short story by Léon Bloy Lord Dunsany's short story "Carcassonne" To support my ongoing work, go to my Patreon site - www.patreon.com/sadler If you'd like to make a direct contribution, you can do so here - www.paypal.me/ReasonIO - or at BuyMeACoffee - www.buymeacoffee.com/A4quYdWoM You can find over 4000 philosophy videos in my main YouTube channel - www.youtube.com/user/gbisadler Get Borges' Other Inquisitions here - https://amzn.to/4br9pul
Adi Polak talks to Arvind Suresh (OpenAI) about his career in distributed systems and real-time streaming. Arvind's first job: coding at school. His challenge: turning OpenAI's fragile Kafka setup into a reliable, multi-region streaming backbone.SEASON 2 Hosted by Tim Berglund, Adi Polak and Viktor Gamov Produced and Edited by Noelle Gallagher, Peter Furia and Nurie Mohamed Music by Coastal Kites Artwork by Phil Vo
J.J. and Dr. Vivian Liska border on the sublime in their discussion of the life and thought of this German-Jewish thinker. If you or your business are interested in sponsoring an episode or mini-series, please reach out at podcasts@torahinmotion.org Follow us on Bluesky @jewishideaspod.bsky.social for updates and insights!Please rate and review the the show in the podcast app of your choice.We welcome all complaints and compliments at podcasts@torahinmotion.org For more information visit torahinmotion.org/podcastsVivian Liska is a Professor of German literature and Director of the Institute of Jewish Studies at the University of Antwerp, Belgium. She has published extensively on literary theory, German modernism, and German-Jewish authors and thinkers. Liska's recent books include Giorgio Agamben's Empty Messianism (2008), in German, translated into Hebrew (Resling 2010), When Kafka Says We. Uncommon Communities in German-Jewish Literature (2008) and Fremde Gemeinschaft. Deutsch-jüdische Literatur der Moderne (2011). A Hebrew translation of this book is in the making with Hakibbutz Hameuchad. In 2012, she was awarded the Cross of Honor for Sciences and the Arts from the Republic of Austria. She is the (co-)editor of numerous books, among them the two-volume ICLA publication Modernism (2007), which was awarded the Prize of the Modernist Studies Association in 2008; Contemporary Jewish Writing in Europe: A Guide (2007); Theodor Herzl between Europe and Zion (2007); What does the Veil Know? (2009); The German-Jewish Experience Revisited (2015); and Kafka and the Universal (2016). She is the editor of the book series “Perspectives on Jewish Texts and Contexts” (De Gruyter, Berlin), co-editor of the Yearbook of the Society for European-Jewish Literature, and arcadia. International Journal of Literary Studies. Her most recent book German-Jewish Thought and its Afterlife (Indiana University Press) was published in 2017.
Toronto's most infamous women's prison was meant to rehabilitate women … but its real history tells a much darker story. Heather Marshall dives headfirst into the Mercer Reformatory in her latest novel, Liberty Street. The book follows Emily Radcliffe, a 1960s journalist who goes undercover to expose the prison's harsh conditions and abuse of inmates. Over 30 years later, after the prison's closing, a detective revisits one of the its sinister mysteries … and these intertwining narratives tell a story of female resilience and strength. This week, Heather tells Mattea Roach about the history of the prison, the real journalists that inspired the story and what it means to be an “incorrigible” woman. Liked this conversation? Keep listening:Who was the woman Kafka loved?Emma Donoghue boards a train destined for disasterCheck us out on Instagram @cbcbooks and TikTok @cbcbooks
In this NBN episode, host Hollay Ghadery speaks with Christine Estima about her novel, Letters to Kafka (House of Anansi, 2025). A sweeping, tragic romance and feminist adventure about translator and resistance fighter Milena Jesenská's torrid love affair with Franz Kafka. In 1919, Milena Jesenská, a clever and spirited twenty-three-year-old, is trapped in an unhappy marriage to literary critic Ernst Pollak. Since Pollak is unable to support the pair in Vienna's post-war economy, Jesenská must supplement their income by working as a translator. Having previously met her compatriot Franz Kafka in the literary salons of Prague, she writes to him to ask for permission to translate his story “The Stoker” from German to Czech, becoming Kafka's first translator. The letter launches an intense and increasingly passionate correspondence. Jesenská is captivated by Kafka's energy, intensity, and burning ambition to write. Kafka is fascinated by Jesenská's wit, rebellious spirit, and intelligence. Jesenská and Kafka meet twice for lovers' trysts, but can such an intense connection endure beyond a fleeting affair? In her remarkable debut novel, Christine Estima weaves little-known facts and fiction into a rich tapestry, powerfully portraying the struggles of a woman forced to choose between the roles of wife, lover, and intellectual. CHRISTINE ESTIMA is an Arab woman of mixed ethnicity (Lebanese, Syrian, and Portuguese) and the author of the short story collection The Syrian Ladies Benevolent Society. She has written for the New York Times, The Walrus, VICE, the Globe and Mail, Chatelaine, Maisonneuve, the Toronto Star, and the CBC. Her story “Your Hands Are Blessed” was included in Best Canadian Stories 2023. She was shortlisted for the 2018 Allan Slaight Prize for Journalism and a finalist for the 2023 Lee Smith Novel Prize. Christine has a master's degree from York University and lives in Toronto. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/new-books-network
In this NBN episode, host Hollay Ghadery speaks with Christine Estima about her novel, Letters to Kafka (House of Anansi, 2025). A sweeping, tragic romance and feminist adventure about translator and resistance fighter Milena Jesenská's torrid love affair with Franz Kafka. In 1919, Milena Jesenská, a clever and spirited twenty-three-year-old, is trapped in an unhappy marriage to literary critic Ernst Pollak. Since Pollak is unable to support the pair in Vienna's post-war economy, Jesenská must supplement their income by working as a translator. Having previously met her compatriot Franz Kafka in the literary salons of Prague, she writes to him to ask for permission to translate his story “The Stoker” from German to Czech, becoming Kafka's first translator. The letter launches an intense and increasingly passionate correspondence. Jesenská is captivated by Kafka's energy, intensity, and burning ambition to write. Kafka is fascinated by Jesenská's wit, rebellious spirit, and intelligence. Jesenská and Kafka meet twice for lovers' trysts, but can such an intense connection endure beyond a fleeting affair? In her remarkable debut novel, Christine Estima weaves little-known facts and fiction into a rich tapestry, powerfully portraying the struggles of a woman forced to choose between the roles of wife, lover, and intellectual. CHRISTINE ESTIMA is an Arab woman of mixed ethnicity (Lebanese, Syrian, and Portuguese) and the author of the short story collection The Syrian Ladies Benevolent Society. She has written for the New York Times, The Walrus, VICE, the Globe and Mail, Chatelaine, Maisonneuve, the Toronto Star, and the CBC. Her story “Your Hands Are Blessed” was included in Best Canadian Stories 2023. She was shortlisted for the 2018 Allan Slaight Prize for Journalism and a finalist for the 2023 Lee Smith Novel Prize. Christine has a master's degree from York University and lives in Toronto. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/literature
AI, under the dangerous control of tech oligarchs, is creating a world with shrinking human choice, creativity, and connection. Technology journalist Jacob Ward, author of The Loop: How Technology is Creating a World without Choices and How to Fight Back, describes why restraint and resistance are necessary to fight back against the AI juggernaut. Highlights include: How tech journalism emphasizes novelty and business profits and amplifies tech companies' hype as journalists seek to maintain access to powerful tech leaders; How profit-driven AI exploits a human bias toward fast, easy thinking and decision-making that leads us to outsource our choices and judgment to automated systems; Why AI large language models (LLMs) are like cover bands providing the 'greatest hits' of humanity's past achievement - an 'artificial hive mind' that is biased toward middle-of-the-road, derivative, and unoriginal ideas; How impersonal, unaccountable, 'black box' AI decision-making creates Kafka-esque systems in government services, jobs, and loans - disproportionately harming the least powerful in society; Why AI large language models are 2 to 3 times more biased than the average person across various cultural and demographic dimensions; How AI will increase addiction and social isolation, replacing real-world relationships with flattering, always available chatbot 'friends'; Why our collective sense-making and democratic decision-making will be further threatened by AI - creating even more tightly sealed, individually customized information bubbles that conform to our feelings, not the truth; How many tech oligarchs pushing AI are also involved in genetic engineering projects with the aim of breeding 'optimized' babies; Why tech companies' legal liability and U.S. states' AI regulations are hopeful avenues of AI pushback; Why we need to rediscover the value of restraint and realize that not all innovation is beneficial for humanity and the planet. See episode website for show notes, links, and transcript: https://www.populationbalance.org/podcast/jacob-ward OVERSHOOT | Shrink Toward Abundance OVERSHOOT tackles today's interlocked social and ecological crises driven by humanity's excessive population and consumption. The podcast explores needed narrative, behavioral, and system shifts for recreating human life in balance with all life on Earth. With expert guests from wide-ranging disciplines, we examine the forces underlying overshoot: from patriarchal pronatalism that is fueling overpopulation, to growth-biased economic systems that lead to consumerism and social injustice, to the dominant worldview of human supremacy that subjugates animals and nature. Our vision of shrinking toward abundance inspires us to seek pathways of transformation that go beyond technological fixes toward a new humanity that honors our interconnectedness with all beings. Hosted by Nandita Bajaj and Alan Ware. Brought to you by Population Balance. Subscribe to our newsletter here: https://www.populationbalance.org/subscribe Support our work with a one-time or monthly donation: https://www.populationbalance.org/donate Learn more at https://www.populationbalance.org Copyright 2016-2026 Population Balance
This week on Toilet Radio: Joe and Jordan talk about the Kafka-esque nightmare of modern life. Also, Michale Graves, not content with getting his shows cancelled in DFW strip malls, is now having his shows cancelled in the UK. William Shatner, at 94 years old, is threatening to release a metal album. Dire stuff. But fortunately, Gen X Correspondent Ian talks with Testament's Chuck Billy about the band's upcoming Thrash of the Titans tour, playing with Judas Priest in 1990 when Dave Mustaine tried to quash their pyrotechnic displays, handling vocal effects in a live setting, working with Johnny Z at Megaforce, writing ballads, and dreams of becoming an architect. FOLKS, it’s a good one. Music featured on this episode: Testament – Infanticide A.I. This program is available on Spotify. It is also available on iTunes or whatever they call it now, where you can rate, review, and subscribe. Give us money on Patreon to get exclusive bonus episodes and other cool shit.
This week on SELECTED SHORTS, host Meg Wolitzer presents three stories about problems without solutions. In Elif Batuman's “The Board,” read by Cindy Cheung, the protagonist has found the perfect apartment, but he has to satisfy a Kafka-esque co-op committee. Jesse Eisenberg imagines an irritating sibling with problems of global proportions in ““My Little Sister Texts Me with Her Problems,” read by real-life sisters Lacey Lamar and Amber Ruffin. And a patient is drawn to her therapist—but is this a bad thing? in Esther Freud's “Transference,” read by Claire Danes. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.
Besuch beim Unternehmer und YouTuber Marco Scheel, der durch seinen Betrieb "Nordwolle" führt und zeigt, wie Behörden ihm das Arbeiten schwer machen. Philip & Ulf fragen sich: Wie kann Verwaltung wieder Probleme der Bürger lösen?
This week on The Broski Report, Fearless Leader Brittany Broski discusses the Harry Styles album announcement, recounts French history, and holds a Book Club on Kafka. Watch The Broski Report AD FREE: https://patreon.com/broskireport The OFFICIAL Songs of The Week Playlist: https://open.spotify.com/playlist/3ULrcEqO2JafGZPeonyuje?si=061c5c0dd4664f01
© 2020-2026 Ivy Podcast Discovery LLC
