Podcasts about SLA

With the increasing incidence of colorectal cancer in those less than 50 years of age, one must wonder how many patients present with a Stage IV diagnosis. Take a deep dive with us discussing the management of metastatic colorectal cancer by joining our team and guests, Drs. Cathy Eng, Michael D'Angelica, and Nina Sanford.Hosts: - Dr. Janet Alvarez - General Surgery Resident at New York Medical College/Metropolitan Hospital Center- Dr. Wini Zambare – General Surgery Resident at Weill Cornell Medical Center/New York Presbyterian- Dr. Philip Bauer, Assistant Professor of Surgery, Division of Colon and Rectal Surgery, The Ohio State University Wexner Medical Center, Arthur G. James Cancer Hospital-  Dr. J. Joshua Smith MD, PhD, Chair, Department of Colon and Rectal Surgery at MD Anderson Cancer Center Guest Speakers:- Dr. Michael D'Angelica MD, FACS – Hepatopancreatobiliary Surgery, Memorial Sloan Kettering Cancer Center, Enid A. Haupt Chair in Surgery, Vice Chair, Education- Dr. Cathy Eng MD, FACP - Division of Hematology and Oncology, Vanderbilt-Ingram Cancer Center, David H. Johnson Endowed Chair in Surgical and Medical Oncology, Professor of Medicine, Hematology and Oncology, VICC Associate Director for Strategic Relations and Research Partnerships, Executive Director, Young Adult Cancers Program - Dr. Nina Sanford, MD – Radiation Oncology, UT Southwestern Medical Center, Chief of Gastrointestinal Radiation Oncology Service, Associate Professor Learning Objectives:1.     Review the epidemiology, prognosis, and common metastatic patterns of metastatic colorectal cancer (mCRC).2.     Discuss the role of systemic chemotherapy and targeted therapies in the first- and subsequent-line treatment of mCRC, including the impact of molecular biomarkers such as MSI/MMR, RAS, BRAF, and HER2.3.     Evaluate the indications and timing of surgical and locoregional therapies for metastatic colorectal cancer, particularly in patients with liver-limited or oligometastatic disease.4.     Describe the multidisciplinary management of mCRC, including the roles of radiation therapy, systemic therapy sequencing, and palliative interventions to optimize outcomes and quality of life.References:Singh, M., Morris, V. K., Bandey, I. N., Hong, D. S. & Kopetz, S. Advancements in combining targeted therapy and immunotherapy for colorectal cancer. Trends Cancer 10, 598–609 (2024). PubMed Link: https://pubmed.ncbi.nlm.nih.gov/38821852/Napolitano, S. et al. BRAFV600E mutant metastatic colorectal cancer: Current advances in personalized treatment and future perspectives. Cancer Treat. Rev. 134, (2025). PubMed Link: https://pubmed.ncbi.nlm.nih.gov/40009904/Ciardiello, F. et al. Clinical management of metastatic colorectal cancer in the era of precision medicine. CA. Cancer J. Clin. 72, 372–401 (2022). PubMed Link: https://pubmed.ncbi.nlm.nih.gov/35472088/Kim, S. Y. & Kim, T. W. Current challenges in the implementation of precision oncology for the management of metastatic colorectal cancer. ESMO Open 5, e000634 (2020). PubMed Link: https://pubmed.ncbi.nlm.nih.gov/32188714/Biller, L. H. & Schrag, D. Diagnosis and Treatment of Metastatic Colorectal Cancer: A Review. JAMA 325, 669–685 (2021). PubMed Link: https://pubmed.ncbi.nlm.nih.gov/33591350/Smith, J. J. et al. Genomic stratification beyond Ras/B-Raf in colorectal liver metastasis patients treated with hepatic arterial infusion. Cancer Med. 8, 6538–6548 (2019). PubMed Link: https://pubmed.ncbi.nlm.nih.gov/31503397/Saadat, L. V. et al. Hepatic Artery Infusion Chemotherapy Compared to Transarterial Radioembolization For Unresectable Colorectal Liver Metastases. Ann. Surg. 10.1097/SLA.0000000000006851 doi:10.1097/SLA.0000000000006851. PubMed Link: https://pubmed.ncbi.nlm.nih.gov/?term=10.1097/SLA.0000000000006851 (Linked via DOI search as the direct PMID is still indexing)Xiao, A. & Fakih, M. KRAS G12C Inhibitors in the Treatment of Metastatic Colorectal Cancer. Clin. Colorectal Cancer 23, 199–206 (2024). PubMed Link: https://pubmed.ncbi.nlm.nih.gov/38825433/André, T. et al. Pembrolizumab in Microsatellite-Instability–High Advanced Colorectal Cancer. N. Engl. J. Med. 383, 2207–2218 (2020). PubMed Link: https://pubmed.ncbi.nlm.nih.gov/33264544/Morris, V. K. et al. Treatment of Metastatic Colorectal Cancer: ASCO Guideline. J. Clin. Oncol. 41, 678–700 (2023). PubMed Link: https://pubmed.ncbi.nlm.nih.gov/36252154/Xu, Z. et al. Treatments for Stage IV Colon Cancer and Overall Survival. J. Surg. Res. 242, 47–54 (2019). PubMed Link: https://pubmed.ncbi.nlm.nih.gov/31071604/Smith, J. J. & D'Angelica, M. I. Surgical Management of Hepatic Metastases of Colorectal Cancer. Hematol. Oncol. Clin. North Am. 29, 61–84 (2015). PubMed Link: https://pubmed.ncbi.nlm.nih.gov/25475573/Strickler, J. H. et al. Tucatinib plus trastuzumab for chemotherapy-refractory, HER2-positive, RAS wild-type unresectable or metastatic colorectal cancer (MOUNTAINEER): a multicentre, open-label, phase 2 study. Lancet Oncol. 24, 496–508 (2023). PubMed Link: https://pubmed.ncbi.nlm.nih.gov/37142372/Kruijssen, D. E. W. van der et al. Upfront resection versus no resection of the primary tumor in patients with synchronous metastatic colorectal cancer: the randomized phase III CAIRO4 study conducted by the Dutch Colorectal Cancer Group and the Danish Colorectal Cancer Group. Ann. Oncol. 35, 769–779 (2024). PubMed Link: https://pubmed.ncbi.nlm.nih.gov/38852675/Hitchcock, K. E., Romesser, P. B. & Miller, E. D. Local Therapies in Advanced Colorectal Cancer. Hematol. Oncol. Clin. North Am. 36, 553–567 (2022). PubMed Link: https://pubmed.ncbi.nlm.nih.gov/35562258/Hitchcock, K. E. et al. Alliance for clinical trials in Oncology (Alliance) trial A022101/NRG-GI009: a pragmatic randomized phase III trial evaluating total ablative therapy for patients with limited metastatic colorectal cancer: evaluating radiation, ablation, and surgery (ERASur). BMC Cancer 24, 201 (2024). PubMed Link: https://pubmed.ncbi.nlm.nih.gov/38350888/Adam, R. et al. Liver transplantation plus chemotherapy versus chemotherapy alone in patients with permanently unresectable colorectal liver metastases (TransMet): results from a multicentre, open-label, prospective, randomised controlled trial. The Lancet 404, 1107–1118 (2024). PubMed Link: https://pubmed.ncbi.nlm.nih.gov/39306468/Elez, E. et al. Encorafenib, Cetuximab, and mFOLFOX6 in BRAF-Mutated Colorectal Cancer. N. Engl. J. Med. 392, 2425–2437 (2025). PubMed Link: https://pubmed.ncbi.nlm.nih.gov/40444708/***Fellowship Application Link: https://forms.gle/QSUrR2GWHDZ1MmWC6Please visit https://behindtheknife.org to access other high-yield surgical education podcasts, videos and more.  If you liked this episode, check out our recent episodes here: https://behindtheknife.org/listenBehind the Knife Premium:General Surgery Oral Board Review Course: https://behindtheknife.org/premium/general-surgery-oral-board-reviewTrauma Surgery Video Atlas: https://behindtheknife.org/premium/trauma-surgery-video-atlasDominate Surgery: A High-Yield Guide to Your Surgery Clerkship: https://behindtheknife.org/premium/dominate-surgery-a-high-yield-guide-to-your-surgery-clerkshipDominate Surgery for APPs: A High-Yield Guide to Your Surgery Rotation: https://behindtheknife.org/premium/dominate-surgery-for-apps-a-high-yield-guide-to-your-surgery-rotationVascular Surgery Oral Board Review Course: https://behindtheknife.org/premium/vascular-surgery-oral-board-audio-reviewColorectal Surgery Oral Board Review Course: https://behindtheknife.org/premium/colorectal-surgery-oral-board-audio-reviewSurgical Oncology Oral Board Review Course: https://behindtheknife.org/premium/surgical-oncology-oral-board-audio-reviewCardiothoracic Oral Board Review Course: https://behindtheknife.org/premium/cardiothoracic-surgery-oral-board-audio-reviewDownload our App:Apple App Store: https://apps.apple.com/us/app/behind-the-knife/id1672420049Android/Google Play: https://play.google.com/store/apps/details?id=com.btk.app&hl=en_US

J'ai découvert le travail de Stéphanie Pillonca à l'occasion de la sortie du film Un invincible été, en mai 2023.Un mois plus tard, pour la deuxième édition du festival photographique Réflexivité(s), j'obtiens l'accord du producteur afin de le projeter au cinéma Le Cigalon, à Cucuron. Le film me bouleverse.Son titre, emprunté à Albert Camus, « Au milieu de l'hiver, j'ai découvert en moi un invincible été », résonne autrement dans le Luberon. L'écrivain a vécu ici, à Lourmarin. En plein mois de juillet, dans la salle obscure, ils sont douze. Douze aficionados qui rient et pleurent. Le guichetier me glisse, presque complice : « C'est un succès total. »Dans Un invincible été, Stéphanie signe le portrait d'Olivier Goy, entrepreneur confronté à la maladie de Charcot, la SLA. Elle nous confie :« Au début, j'ai dit non. Je ne voulais pas faire un film sur une maladie. Ça me semblait déjà vu. Puis j'ai rencontré Olivier. Et sa manière de parler de sa vie m'a fait changer d'avis ». Elle comprend alors qu'il ne veut pas parler de sa maladie. Il veut parler de vie afin de transformer son histoire en action utile pour les autres.Le tournage devient une course contre la montre, sa parole décline. Le dernier jour de tournage, juste après le clap de fin, il ne parlera plus. Et Stéphanie ajoute, avec un sourire qu'on devine : « Quoique… depuis, il n'a jamais autant parlé. »*Ce film s'inscrit dans une œuvre plus vaste où Stéphanie aborde les situations humaines les plus sensibles avec une précision documentaire qui n'efface jamais la délicatesse.Après des études de comédie au Conservatoire de Toulon, Stéphanie Pillonca rejoint La Classe Libre du Cours Florent à Paris. Elle commence devant la caméra, apprend le rythme, la lumière, la tension d'un plateau. Entre 1998 et 2001, elle travaille à la télévision, chroniqueuse, animatrice, notamment pour Exclusif. Elle passe par la Star Academy. Elle observe. Elle absorbe. Mais très vite, ce n'est plus l'exposition qui l'intéresse. C'est le regard.En vingt ans, elle construit une œuvre qui refuse le spectaculaire pour lui préférer l'intime. Documentaire ou fiction, peu importe la frontière. Ce qui l'attire, ce sont les trajectoires fragiles, les endroits où la vie vacille.Elle filme une femme qui réapprend à marcher après un traumatisme.Une communauté religieuse isolée du monde.Des parents confrontés au handicap.Des enfants que l'on attend.Des adolescents que la justice regarde déjà autrement.Toujours la même attention, avec toujours la même retenue.En fiction, elle ne change pas de cap. Elle s'empare de l'autisme, du déni de grossesse, du cancer du sein, du handicap à l'adolescence. Non pour illustrer un sujet de société, mais pour raconter des vies traversées par ces réalités.Dans Handigang, elle choisit un casting inclusif.Dans Les Randonneuses, elle accompagne six femmes sur un sentier de montagne qui devient un chemin intérieur.Dans Je te promets, adaptation française de This Is Us, elle travaille la nuance, l'émotion tenue, le lien invisible entre les êtres.Ce qu'elle cherche, ce n'est pas le réel brut. C'est la vérité émotionnelle. Regarder là où d'autres préfèrent détourner les yeux et le faire sans pathos.Son dernier long métrage de fiction, Jours d'après, marque une étape forte dans sa carrière. Le travail est fait, elle attend la sortie du film et la promotion. Elle adore la promotion.*Olivier Goy a enregistré sa voix avant de la perdre définitivement. Il s'exprime désormais avec sa voix de synthèse.Studio Revolver à Boulogne BillancourtEnregistrement au Club We Are ParisProducteur et animateur Boris PierreHébergé par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.

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

What if your TPA jobs, SLA deadlines, and communication tools were all managed in one place?In this episode, Jason McLaughlin, CEO, and Austin McLaughlin, COO, of Complitrac break down how their custom-built platform is helping restoration companies stay ahead of strict SLA requirements and improve visibility across TPA programs.They discuss why the restoration industry is still struggling with outdated, disconnected technology and how modern platforms can automate SLA tracking, send real-time alerts, and consolidate multiple systems into one dashboard.You'll also hear how better communication, transparent updates, and user-friendly tools can improve both policyholder experience and operational efficiency.

What if your TPA jobs, SLA deadlines, and communication tools were all managed in one place?In this episode, Jason McLaughlin, CEO, and Austin McLaughlin, COO, of Complitrac break down how their custom-built platform is helping restoration companies stay ahead of strict SLA requirements and improve visibility across TPA programs.They discuss why the restoration industry is still struggling with outdated, disconnected technology and how modern platforms can automate SLA tracking, send real-time alerts, and consolidate multiple systems into one dashboard.You'll also hear how better communication, transparent updates, and user-friendly tools can improve both policyholder experience and operational efficiency.

In dieser Folge geht es wieder um die Hernienchirurgie. Zu Gast ist Prof. René Fortelny. Im Mittelpunkt stehen präperitoneale und retromuskuläre Techniken, ihre Ergebnisse im klinischen Alltag sowie die Frage, welche Mesh-Strategien und Zugangswege heute wirklich überzeugen.Ausgehend von einer großen prospektiven Serie zur präperitonealen Ventralhernienreparation aus den USA diskutieren wir Evidenz, Technikdetails und praktische Implikationen für die moderne Bauchwandchirurgie.Moderation: Christoph PaaschGast: Prof. Rene FortelnyHeniford BT, Ross SW, Wormer BA, Walters AL, Lincourt AE, Colavita PD, Kercher KW, Augenstein VA. Preperitoneal Ventral Hernia Repair: A Decade Long Prospective Observational Study With Analysis of 1023 Patient Outcomes. Annals of Surgery. 2018. doi:10.1097/SLA.0000000000002966.

Jeudi 22 Janvier 2026HISTOIRELes nouveaux martyrs - XVIIIe-XXe Siècle | Pierre M. DELPUEn dialogue avec Catherine BRICEEditions Passés composésLa modernité n'a pas aboli les martyrs. Autrefois morts pour Dieu, aujourd'hui morts pour la nation, pour la vertu, pour la morale, bref, pour “la cause”. Du XVIIIe siècle, Pierre M. Delpu revient sur ce phénomène en s'attachant à la figure des nouveaux martyrs, femmes et hommes, en France, mais aussi en Espagne, en Italie et plus généralement dans l'Europe entière.Historien des révolutions sur-européennes du XIXe siècle, Pierre M. Delpu est chargé de recherches du FRS-FNRS, rattaché à l'Université Libre de Bruxelles. Il travaille sur le martyre politique.

That data centers need insurance goes without saying. Any commercial building will typically be insured against standard damages and risks, but data centers are far more complicated than an average warehouse or office. One area that has emerged of late is the idea of “SLA” insurance, or Service Level Agreement insurance. Parametrix is one of the pioneering companies in this realm, and we discuss where the idea came from and why it is growing drastically in popularity.

Before Patty Hearst appeared as an actress in John Waters' movies, she captivated America on the silver screen as a hostage terrorized by the Symbionese Liberation Army. When the newspaper heiress was kidnapped by the radical organization in 1974, the country sympathized with her plight. But after just a few months, the SLA's guns weren't pointing at Patty anymore. Suddenly, Patty was firing her own weapons during fistfights and bank robberies as a member of the same terrorist group that once kept her locked in a closet. In court, Patty claimed she was brainwashed and that she played along for her own safety. It's true that Patty Hearst gave the performance of a lifetime — but we still don't know which part of her life was the performance. This episode contains themes that may be disturbing to some listeners, including graphic depictions of violence. To learn more about listener data and our privacy practices visit: https://www.audacyinc.com/privacy-policy Learn more about your ad choices. Visit https://podcastchoices.com/adchoices

Welcome to the CanadianSME Small Business Podcast, hosted by Maheen Bari. In this episode, we explore how IT success is no longer measured by uptime and ticket closures alone, but by employee experience, productivity, and real business outcomes. Our guest is Syriac Joswin, Chief Revenue Officer at Synoptek. A Wharton graduate and global transformation leader, Syriac shares how Synoptek's Managed Experience Provider MxP model connects technology performance directly to measurable business impact. Key Highlights The Evolution of IT Priorities: Why employee experience and business alignment now outweigh traditional SLA metrics.   Defining Managed Experience: How providers measure and manage end user experience across the entire tech stack.   Outcomes Over Outputs: How shifting from tasks completed to value delivered transforms daily service delivery.   From Insight to Action: How Synoptek converts experience data into integrated, organization wide improvements.   MxP in the AI Era: How the Managed Experience model will evolve as AI reshapes enterprise technology. Special Thanks to Our Partners: UPS: https://solutions.ups.com/ca-beunstoppable.html?WT.mc_id=BUSMEWA Google: https://www.google.ca/ A1 Global College: https://a1globalcollege.ca/ ADP Canada: https://www.adp.ca/en.aspx For more expert insights, visit www.canadiansme.ca and subscribe to the CanadianSME Small Business Magazine. Stay innovative, stay informed, and thrive in the digital age! Disclaimer: The information shared in this podcast is for general informational purposes only and should not be considered as direct financial or business advice. Always consult with a qualified professional for advice specific to your situation.

Annahmeverzugslohn nach unwirksamer Kündigung – Wie § 11 KSchG das wirtschaftliche Risiko im Kündigungsschutzprozess neu justiert (LAG Niedersachsen, 5 SLa 465/25)Unwirksame Kündigung – und trotzdem kein voller Annahmeverzugslohn?Das Landesarbeitsgericht Niedersachsen (Urt. v. 11.12.2025 – 5 SLa 465/25) setzt ein deutliches Signal für die Praxis:Auch wenn eine außerordentliche und eine hilfsweise ordentliche Kündigung unwirksam sind, kann der Annahmeverzugslohn erheblich gekürzt werden – wenn der Arbeitnehmer nicht unverzüglich eine neue Beschäftigung sucht.Im entschiedenen Fall verlangte ein Produktionshelfer Annahmeverzugslohn für den Zeitraum 07.11.–31.12.2024. Das LAG bestätigte zwar die Unwirksamkeit der Kündigungen, kappte jedoch den Anspruch bereits zum 15.12.2024, weil der Arbeitnehmer sich erst am 5. Dezember beworben hatte. Nach Auffassung des Gerichts hätte er spätestens Mitte Dezember eine neue Stelle finden können.Die eigentliche Bedeutung der Entscheidung liegt jedoch nicht im Einzelfall von wenigen Wochen – sondern in ihrer Übertragbarkeit auf langjährige Kündigungsschutzverfahren. Denn:Annahmeverzug läuft grundsätzlich bis zum rechtskräftigen Abschluss des Verfahrens.In der Praxis dauern Verfahren häufig 12–24 Monate.Der Annahmeverzugslohn bildet regelmäßig den wirtschaftlichen Hebel für hohe Abfindungsforderungen.Das LAG durchbricht diese Logik:Wer als Arbeitnehmer nicht zeitnah sucht, riskiert die Anrechnung eines hypothetischen Verdienstes – und damit den Verlust seines zentralen Verhandlungsinstruments.§ 615 Satz 2 BGB§ 11 Nr. 2 KSchG („böswilliges Unterlassen anderweitigen Erwerbs“)Einordnung in die aktuelle Rechtsprechung des BAG, insbesondere Urt. v. 27.02.2025 – 5 AZR 127/24Wann beginnt die Bewerbungspflicht nach einer Kündigung?Was bedeutet „böswillig“ im Sinne des § 11 KSchG?Wie verändert diese Entscheidung die Vergleichsdynamik im Kündigungsschutzprozess?Was müssen Arbeitgeber konkret vortragen, um sich auf § 11 KSchG berufen zu können?Welche Risiken entstehen, wenn beide Seiten lediglich „abwarten“?Annahmeverzug ist kein Automatismus.Er setzt Mitwirkung voraus – auf beiden Seiten.Wer nicht handelt, verliert Verhandlungsmacht.

Sorry, ich hatte die falsche Folge hochgeladen. Nun der richtige Beitrag!In dieser Folge „3 von 3“ stelle ich drei aktuelle arbeitsgerichtliche Entscheidungen vor:Landesarbeitsgericht Berlin-Brandenburg, Urteil vom 07.11.2025 – 12 SLa 876/25Fristlose Kündigung eines Rabbiners wegen sexueller Belästigung bestätigt.Arbeitsgericht Berlin, Urteil vom 30.01.2026 – 21 Ca 13264/25Fristlose Kündigung eines Direktors unwirksam (Fristversäumnis), ordentliche Kündigung wirksam.Bundesarbeitsgericht, Urteil vom 29.01.2026 – 8 AZR 49/25Kopftuch bei Tätigkeit als Luftsicherheitsassistentin zulässig; Entschädigung nach § 15 AGG.1. ⁠ Lexikon zu den KündigungsgründenHomepage:⁠⁠⁠⁠⁠⁠⁠Rechtsanwalt Andreas Martin - Arbeitsrecht in Marzahn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Anwalt für Arbeitsrecht Berlin ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

Catastrophic cloudbursts are already reshaping Copenhagen. Instead of hiding the problem underground in massive pipes, what if rainwater could be used to improve everyday life in the city?  In this episode, host Michael Booth meets Mette Skjold, CEO and senior partner at landscape architecture studio SLA, to explore the transformation of Bispeparken, a former stretch of anonymous lawn turned into a nature-based climate adaptation project. Designed to manage extreme rainfall, the park uses bioswales, terrain and planting to slow and store water, while creating new spaces for play, rest and community life.  The conversation shows how landscape architecture can turn billion-euro flooding risks into a quality-of-life bonus, and why starting with nature may be the key to building more resilient, liveable cities.  Guest: Mette Skjold, CEO & Senior Partner, SLA  Host: Michael Booth  Let's Talk Architecture is a podcast by Danish Architecture Center. Sound edits by Munck Studios.

"Byen er en samling af menneskelige individer, der drømmer om at være en skov." Ordene er den italienske plantefilosof Emanuele Coccias. Arkitektur er ifølge italieneren aldrig et anliggende mellem mennesket og dets omverden alene: Det er et anliggende mellem arter lige fra mennesker, dyr og bakterier til de alger og planter, hvis fotosyntese i første omgang gjorde og gør vores verden åndbar. "Den levende verden er en verden af arkitekter," som han skriver. På Bloom 2025 gik Coccia i dialog med naturdesigner og arkitekt Stig L. Andersson. Fra hver deres side af skellet mellem filosofi og arkitektur bidrager de til en aktuel naturfilosofisk strømning, der forsøger at gentænke naturens rolle i vores byer og landskaber. I århundreder har mennesket tæmmet, beskåret og ødelagt natur for at gøre plads til urbanisering. Men i en tid med klima- og miljøkriser går flere tænkere, arkitekter og landskabsdesignere nu til byarkitekturen på måder, der lader den forme og medskabe byens rum. I sin naturfilosofi har Coccia gjort sig til fortaler for en 'mellemartslig arkitektur', der ikke længere tænker på naturen som et stykke ikke-civilisation uden for bymurene, men giver naturen plads til at vokse sammen med byerne og samtidig har planternes og dyrenes livsvilkår for øje. Som grundlægger af det naturbaserede design-studio SLA har Andersson de seneste årtier sat sit unikke præg på et utal af byrum, parker og landskaber, hvor naturen sættes i den æstetiske forgrund. Spørger man ham, har arkitekturen i alt for lang tid beskæftiget sig med 'det byggede' og overset 'det groede' miljø. Med inspiration fra alt fra Immanuel Kants æstetik til Niels Bohrs komplementaritetsprincip tegner Andersson konturerne af en ny naturfilosofisk tilgang til arkitektur og design, der lader det målbare og det rationelle vige for en mere sanselig oplevelse af naturen. En tilgang, der ifølge Andersson skal skabe en "by, der føles og fungerer som en skov". Hør to at tidens store naturtænkere undersøge arkitekturens rolle i naturkrisernes tid.

What does autonomous IT really look like when you move beyond the slideware and start wiring systems together in the real world? At Dynatrace Perform in Las Vegas, I sat down with Pablo Stern, EVP and GM of Technology Workflow Products at ServiceNow, to unpack exactly that. Pablo leads the teams focused on CIOs and CISOs, building the workflows and security products that sit at the heart of modern IT organizations. From service desks and command centers to risk and asset management, his remit is clear: enable AI to work for people, not the other way around. We began with ServiceNow's deepening multi-year partnership with Dynatrace. While the announcement made headlines, Pablo was quick to point out that the real story starts with customers. This collaboration is rooted in a shared goal of helping joint customers reduce outages, improve SLA adherence, and shrink mean time to resolution. The vision of autonomous IT operations is not about hype. It is about connecting observability data with deterministic workflows so that insight can evolve into coordinated, system-level action. Pablo walked me through the maturity curve he sees emerging. First came AI-powered insight, summarizing data and surfacing signals from noise. Then came task automation, drafting knowledge articles, paging teams, triggering predefined playbooks. The next step, and the one that excites him most, is orchestrated autonomy. That means stitching together skills, agents, and workflows into systems that can drive end-to-end outcomes. It is a journey measured in years, not months, and it depends as much on digitizing process and building trust as it does on technology. We also explored root cause analysis, still one of the biggest time drains in IT. By combining Dynatrace's AI-driven observability with ServiceNow's workflow engine, enterprises can automate forensic steps, correlate events faster, and shorten the time spent on major incident bridges where teams debate ownership. Even incremental improvements in accuracy can save hours when incidents strike. Trust, of course, remains central. Pablo was candid that full self-healing systems are still some distance away. What we will see first is relief automation, controlled failovers, scripted actions suggested by machines but approved by humans. Over time, as confidence grows and processes become fully digitized, the balance will shift. Beyond the technology, a consistent theme ran through our conversation. Outcomes have not changed. Enterprises still want higher availability, faster resolution, better employee experiences. What is changing is the how. ServiceNow is reimagining its platform to deliver those outcomes at a much higher standard, not through incremental tweaks, but through rethinking workflows for an AI-first world. From design partnerships with banks building pre-flight change checks, to internal teams acting as the toughest customers, this was a grounded, practical conversation about where autonomous operations are headed and what it will take to get there. If you are a CIO, CISO, or IT leader wondering how to move from theory to execution, this episode offers a clear-eyed look behind the curtain.      

Three weeks. No verified ransom demand. No authenticated contact. A family publicly willing to pay — and silence on the other end. True Crime Today's Tony Brueski examines the criminal history patterns that suggest Nancy Guthrie's abduction may have never been about money at all.Using three landmark cases — the obsessive targeting of John Lennon by Mark David Chapman, Danny Rolling's calculated performance in Gainesville, and the SLA's abduction of Patty Hearst where demands were designed to humiliate rather than collect — Tony builds a framework for understanding what happens when a perpetrator's goal is power, pain, or control rather than a payday.The crime scene evidence is deliberate and specific. The silence is telling. And the investigative framework changes completely depending on which kind of crime this actually is.Daily coverage. Real analysis. No noise.Join Our SubStack For AD-FREE ADVANCE EPISODES & EXTRAS!: https://hiddenkillers.substack.com/Want to comment and watch this podcast as a video? Check out our YouTube Channel. https://www.youtube.com/channel/UC8-vxmbhTxxG10sO1izODJg?sub_confirmation=1Instagram https://www.instagram.com/hiddenkillerspod/Facebook https://www.facebook.com/hiddenkillerspod/Tik-Tok https://www.tiktok.com/@hiddenkillerspodX Twitter https://x.com/TrueCrimePodThis publication contains commentary and opinion based on publicly available information. All individuals are presumed innocent until proven guilty in a court of law. Nothing published here should be taken as a statement of fact, health or legal advice.#NancyGuthrie #SavannahGuthrie #NancyGuthrieMissing #CelebrityKidnapping #TrueCrimeToday #TrueCrimePodcast #FBIInvestigation #GuthrieCase #KidnappingMotive #TrueCrime

Welcome to this episode of The New Warehouse Podcast, where Kevin chats with Audrey Djiya, CEO and Co-Founder of Handled. Audrey shares how her team is tackling one of the most overlooked areas of logistics: what happens after the customer clicks 'buy'. Handled is building an AI-powered operations platform focused on eliminating the manual, fragmented, and reactive work that dominates post-purchase workflows. From disconnected systems to return chaos and SLA blind spots, the conversation explores how automating manual tasks can shift 3PLs from constant firefighting to proactive execution.Learn more about Sonaria here. Follow us on LinkedIn and YouTube.Support the show

We talk about Tabletop Gaming Live, our first convention event of the year, and chat in Millie Lavelle about RPG plans for EK Games Expo00.00.40: Introductions00.02.53: World of Gaming: Mantic Games announce a Ghost in the Shell RPG (not to be confused with Ghost in the Shell Arise - or "Ghost in the Machine"?! What was Matthew on?!); Cohors Cthulhu Larium Adventures is out (we divert into how often phasers are "locked" in Start Trek); SLA industries Savage Worlds edition announced; Tunnels and Trolls beta quickstart released; late pledges on The Cosmographers Atlas showcase some beautiful illustration; Ryan Dancy leaves AEG after AI commentary.00.53.08: Matthew's Tabletop Gaming Live Report01.04.33: Interview: Millie Lavelle on UK Games Expo epic games01.19.02: Next time and Goodbye Effekt is brought to you by Effekt Publishing. Music is by Stars in a Black Sea, used with kind permission of Free League Publishing.Like what we do?Sign up for updates on Tales of the Old West via our website and download Tales of the Old West QuickDraw available for free on DriveThru. The core rules are now available on DriveThru too.Put our brand on your face! (and elsewhere)Buy pdfs via our DriveThru Affiliate linkLeave a review on iTunes or PodchaserFind our Actual Play recordings on effektap ★ Support this podcast on Patreon ★

Op 16 januari 2024 wordt het Drentse dorp Weiteveen wakker in een nachtmerrie: het echtpaar Sam en Ineke is doodgeschoten na een slepend conflict met hun buurman over het huis dat zij van hem kochten. Wat begint als een zakelijk meningsverschil over verborgen gebreken, ontaardt in een ruzie die steeds grimmiger en dreigender wordt.Sla nu je slag bij mattsleeps.com! Gebruik de code MOORDZAKEN voor korting op de collectie en 30% korting op het Original en Hybrid Pro matras.Er zit een monster in ons allemaal - The Bride - 5 maart in de bioscoop: kijk hier de trailer!Luister je graag naar onze podcast? Je kunt ons een fooi (elk gewenst bedrag, anoniem, eenmalig of maandelijks) geven via: Fooienpod.com/moordzakenDat waarderen wij natuurlijk zeer, bedankt!Ben je nabestaande van een moord of vermissing en je wilt contact met lotgenoten, kijk dan eens op de website van Federatie Nabestaanden Geweldslachtoffers (FNG Nederland) of op de besloten Facebook-pagina “Nabestaanden moord & vermissingen”. Blijf luisteren & volg ons!Insta: @MoordzakenPodcastTwitter (X): @MoordzakenPodYouTube-kanaal: @MoordzakenPodcast

In dieser Folge sprechen wir über die Assoziation von kleinen Leistenhernien mit dem Auftreten eines chronischen postoperativen Schmerzsyndroms aus dem Patientenkollektiv des Herniamedregisters.Moderation: Felix RühlmannGast: PD Henry HoffmannBesprochene Publikation:Hoffmann H, Walther D, Bittner R, Köckerling F, Adolf D, Kirchhoff P. Smaller Inguinal Hernias are Independent Risk Factors for Developing Chronic Postoperative Inguinal Pain (CPIP): A Registry-based Multivariable Analysis of 57, 999 Patients. Ann Surg. 2020 Apr;271(4):756-764. doi: 10.1097/SLA.0000000000003065. PMID: 30308610.

Robotic surgery has moved from novelty to norm, and in this episode of Behind the Knife, Drs. James Jung and Joey Lew sit down with urologic pioneer and Medtronic CMO Dr. Jim Porter to dissect how we got here, what the data really say about “the death of laparoscopy,” and where competing robotic platforms like Hugo may take the field next. From ergonomics and education to economics and global access, they tackle both the hype and the hard questions around robotics as the future of minimally invasive surgery.Hosts: ·      James Jung, MD, PhD, Assistant Professor of Surgery, Duke University·      Joey Lew, MD, MFA, Surgical resident PGY-3, Duke University, @lew__actuallyLearning Goals: By the end of this episode, listeners will be able to:·      Describe key clinical, ergonomic, and educational drivers behind the rapid adoption of robotic surgery in the United States and globally.·      Summarize current evidence comparing robotic and laparoscopic approaches for common procedures, including where outcomes are equivalent, inferior, or clearly superior.·      Explain how surgeon ergonomics, trainee experience, and video-based learning influence practice patterns and learning curves in minimally invasive surgery.·      Discuss the role of cost, reimbursement structures, and market competition (e.g., Medtronic Hugo vs da Vinci) in shaping robotic adoption across different health systems.·      Anticipate how next-generation, task- or organ-specific robotic platforms may further change standards of care in minimally invasive surgery.References:·      Violante T, Ferrari D, Novelli M, Larson DW. The Death of Laparoscopy - Volume 2: A Revised Prognosis. A retrospective study. Ann Surg. 2025 Jun 16. doi: 10.1097/SLA.0000000000006792. Epub ahead of print. PMID: 40518997. https://pubmed.ncbi.nlm.nih.gov/40518997/·      Yu Yoshida, Yoshiro Itatani, Takehito Yamamoto, Ryosuke Okamura, Koya Hida, Kazutaka Obama, Single-incision plus one robot-assisted surgery (SIPORS) using the Hugo robotic-assisted surgery (RAS) system for rectal cancer, Annals of Coloproctology, 10.3393/ac.2025.00787.0112, 41, 6, (586-591), (2025). https://pubmed.ncbi.nlm.nih.gov/41486916/Please visit https://behindtheknife.org to access other high-yield surgical education podcasts, videos and more.  If you liked this episode, check out our recent episodes here: https://behindtheknife.org/listenBehind the Knife Premium:General Surgery Oral Board Review Course: https://behindtheknife.org/premium/general-surgery-oral-board-reviewTrauma Surgery Video Atlas: https://behindtheknife.org/premium/trauma-surgery-video-atlasDominate Surgery: A High-Yield Guide to Your Surgery Clerkship: https://behindtheknife.org/premium/dominate-surgery-a-high-yield-guide-to-your-surgery-clerkshipDominate Surgery for APPs: A High-Yield Guide to Your Surgery Rotation: https://behindtheknife.org/premium/dominate-surgery-for-apps-a-high-yield-guide-to-your-surgery-rotationVascular Surgery Oral Board Review Course: https://behindtheknife.org/premium/vascular-surgery-oral-board-audio-reviewColorectal Surgery Oral Board Review Course: https://behindtheknife.org/premium/colorectal-surgery-oral-board-audio-reviewSurgical Oncology Oral Board Review Course: https://behindtheknife.org/premium/surgical-oncology-oral-board-audio-reviewCardiothoracic Oral Board Review Course: https://behindtheknife.org/premium/cardiothoracic-surgery-oral-board-audio-reviewDownload our App:Apple App Store: https://apps.apple.com/us/app/behind-the-knife/id1672420049Android/Google Play: https://play.google.com/store/apps/details?id=com.btk.app&hl=en_US

If you've ever wondered how Oracle Database really works inside AWS, this episode will finally turn the lights on.   Join Senior Principal OCI Instructor Susan Jang as she explains the two database services available (Exadata Database Service and Autonomous Database), how Oracle and AWS share responsibilities behind the scenes, and which essential tasks still land on your plate after deployment.   You'll discover how automation, scaling, and security actually work, and which model best fits your needs, whether you want hands-off simplicity or deeper control.   Oracle Database@AWS Architect Professional: https://mylearn.oracle.com/ou/course/oracle-databaseaws-architect-professional/155574 Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, Anna Hulkower, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode.   ------------------------------------------------------------   Episode Transcript:   00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started! 00:26   Lois: Hello and welcome to the Oracle University Podcast! I'm Lois Houston, Director of Communications and Adoption with Customer Success Services, and with me is Nikita Abraham, Team Lead: Editorial Services with Oracle University.  Nikita: Hi everyone! In our last episode, we began the discussion on Oracle Database@AWS. Today, we're diving deeper into the database services that are available in this environment. Susan Jang, our Senior Principal OCI Instructor, joins us once again.  00:56 Lois: Hi Susan! Thanks for being here today. In our last conversation, we compared Oracle Autonomous Database and Exadata Database Service. Can you elaborate on the fundamental differences between these two services?     Susan: Now, the primary difference is between the service is really the management model. The Autonomous is fully-managed by Oracle, while the Exadata provides flexibility for you to have the ability to customize your database environment while still having the infrastructure be managed by Oracle.   01:30 Nikita: When it comes to running Oracle Database@AWS, how do Oracle and AWS each chip in? Could you break down what each provider is responsible for in this setup?  Susan: Oracle Database@AWS is a collaboration between Oracle, as well as AWS. It allows the customer to deploy and run Oracle Database services, including the Oracle Autonomous Database and the Oracle Exadata Database Service directly in AWS data centers.   Oracle provides the ability of having the Oracle Exadata Database Service on a dedicated infrastructure. This service delivers full capabilities of Oracle Exadata Database on the Oracle Exadata hardware. It offers high performance and high security for demanding workloads. It has cloud automation, resource scaling, and performance optimization to simplify the management of the service.  Oracle Autonomous Database on the dedicated Exadata infrastructure provides a fully Autonomous Database on this dedicated infrastructure within AWS. It automates the database management tasks, including patching, backups, as well as tuning, and have built-in AI capabilities for developing AI-powered applications and interacting with data using natural language. The Oracle Database@AWS integrates those core database services with various AWS services for a comprehensive unified experience.  AWS provides the ability of having a cloud-based object storage, and that would be the Amazon S3. You also have the ability to have other services, such as the Amazon CloudWatch. It monitors the database metrics, as well as performance. You also have Amazon Bedrock. It provides a development environment for a generative AI application.   And last but not the least, amongst the many other services, you also have the SageMaker. This is a cloud-based platform for development of machine learning models, a wonderful integration with our AI application development needs.  03:54 Lois: How has the work involved in setting up and managing databases changed over time?  Susan: When we take a look at the evolution of how things have changed through the years in our systems, we realize that transfer responsibility has now been migrated more from customer or human interaction to services. As the database technology evolves from the traditional on-premise system to the Exadata engineered system, and finally to the Autonomous Database, certain services previously requiring significant manual intervention has become increasingly automated, as well as optimized.  04:34 Lois: How so?  Susan: When we take a look at the more traditional database environment, it requires manual configuration of hardware, operating system, as well as the software of the database, along with initial database creation. As we evolve into the Exadata environment, the Exadata Database, specifically the Exadata cloud service, simplifies provisioning through web-based wizard, making it faster and easier to deploy the Oracle Database in an optimized hardware.     But when we move it to an Autonomous environment, it automates the entire provisioning process, allowing users to rapidly deploy mission-critical databases without manual intervention, or DBA involvement. So as customers move toward Autonomous Database through Exadata, we have fewer components that the customer needs to manage in the database stack, which gives them more time to focus more on important parts of the business.  With the Exadata Database, it provides a co-management of backup, restore, patches and upgrade, monitoring, and tuning. And it allows the administrator the ability to customize the configuration to meet their very specific business needs. With Autonomous Database, it's now fully automated and it's a greater responsibility is shift toward the service. With Autonomous Database on dedicated infrastructure, it provides that fine-grained tuning more for Oracle to help you perform that task.  06:15 Nikita: If we narrow it down just to Oracle and AWS for a moment, which parts of the infrastructure or day-to-day ops are handled by each company behind the scenes?  Susan: When we take a look at Oracle Database@AWS, it operates under a shared responsibility model, dividing the service responsibilities between AWS, as well as Oracle, as well as you, the customer.   The AWS has the data center. Remember, this is where everything is running. The Oracle Database@AWS, the Oracle Database infrastructure may be managed by Oracle and run in OCI, but is physically located within the AWS regions, as well as the availability zones and the AWS data centers.  The AWS infrastructure, in this case, is AWS's responsibility to secure the environment, including the physical security of the data center, the network infrastructure, and the foundational services like the compute, the storage, and the networking, all within AWS.  The next thing of who's responsible for the shared responsibility, it's Oracle. And that would be the hardware. We provide the hardware. While the hardware may physically reside in the AWS data center, Oracle's Cloud Infrastructure operational team will be the one managing this infrastructure, including software patching, infrastructure update, and other operations through a connection to OCI. This means Oracle handles the provisioning, as well as the maintenance of any of the underlying Exadata infrastructure hardware.  When we take a look at the next thing that it manages, it is also responsible besides the infrastructure of the Exadata. It is also the ability to manage the hardware, the environment of that hardware through the database control plane. So Oracle manages the administration and the operational for the Oracle Database@AWS service, which resides in OCI. So this includes the capabilities for management, upgrade, and operational features.  08:37 Nikita: And what are the key things that still remain on the customer's plate?   Susan: If you are in an Exadata environment or in an Autonomous environment, it is you, the customer, who is responsible for most of the database administration operation, as well as managing the users and the privileges of the user to access the database. No one knows the database and who should be accessing the data better than you.  You will be responsible for securing the applications, the data of the database, which now allows you to define who has access to it, control the data encryption, and securing the application that interacts with the Oracle Database@AWS.  09:29 Lois: Susan, we've talked about both Autonomous Database and Exadata Database Service being available on Oracle Database@AWS, but what's different about how each works in this environment, and why might someone pick one over the other?  Susan: Both databases, even though they run on the same Exadata Cloud Infrastructure, both can be deployed on both public cloud, as well as the customer data center, which is Oracle Cloud@Customer.  The Autonomous Database is a fully managed, completely automated environment. And this provides a capability of having a fully Autonomous Database Service running on a dedicated Oracle Exadata Infrastructure within your AWS data center.  The Exadata is a service that is provided and managed by Oracle and is physically running in the AWS data center, but is designed for mission critical workload and includes RAC environment, Real Application Cluster, offering a high performance availability and full feature capability that is similar to other Exadata environment, such as those running in our customers' data center.  The primary difference is really between the two services. When you take a look at the Exadata, the customer only pays for the compute resources that is used. Autoscaling can be used for a variety or variable resources, the workload, to automatically scale to the compute resources up or down when required.  The Autonomous Database also has automatic optimization for data warehousing, transaction processing, as well as JSON workload. The Exadata service, the customer again, also pays for the compute resources that they allocate. But that's the key thing. The customer can initiate the scaling because it's very specific to the workload that is needed.  So when you take a look at the two database services, one gives the ability to let Oracle fully manage it, including the scaling capability. The other, the Exadata, provides you the capability of having the environment that it's running on the infrastructure be managed by Oracle that adds a database administrator. You may wish to have a little bit more granular control of how you want the database to not only be scaling, but how you wish to customize how the database will be running.  12:10 Nikita: Focusing on Autonomous Database for a moment, what should teams know about how it actually runs within AWS?   Susan: The Autonomous Database on the Oracle Database@AWS brings the power of the Oracle's self-managing, self-securing, and self-repairing database into your AWS environment.  It provides the capability of the database automatically, automates many of the traditional, complex, and time-consuming database management tasks, such as the provisioning of the database, the patching, the backing up, and the scaling, and the performance tuning, reducing the need for any manual intervention by the database administrator.  Running the Autonomous Database in your AWS region enables low latency access for your AWS applications and services that is deployed within AWS, thus improving performance and response time. With the Autonomous Database, it automates many of the traditional things that is now automatically done by Oracle. It also supports integration with various AWS services, such as the ability of the not in addition to AIM, but the cloud formation, the CloudWatch for monitoring and the S3 for the storage.  You can easily migrate existing Exadata workload, including those running on Oracle RAC to AWS with minimum or no change to any of your databases or applications. In addition, there's a really powerful capability and feature of the database is called zero ETL, and that's zero extract, transformation, and load.  It's an integration capability with services like your Amazon Redshift, enabling near real time analytics and machine learning on your transactional database that is stored within the Autonomous Database on in your AWS environment. So with the Autonomous Database, it checks off many of the boxes for automatic capability, securing, tuning, as well as scaling the database.  With the Autonomous Database in the Dedicated Exadata Infrastructure, the Exadata Cloud Infrastructure resource represents the physical system, which can be expanded with storage, as well as compute services, the compute host. This now provides the ability to have an isolated zone for the highest protection from other tenants. The data is stored on a dedicated server only for one customer. That would be you.  14:56 Lois: Could you explain the role of Autonomous VM? What are its primary benefits?  Susan: The virtual machine or as we refer to them as the cluster, includes the grid infrastructure and provides a private network isolation. This provides you the capability of having custom memory, core, and storage allocation.  The Oracle Grid Infrastructure includes the Oracle Clusterware, which manages the cluster, as well as the servers, and ensure that the database can failover to another server in case of any failure.  15:34 Be a part of something big by joining the Oracle University Learning Community! Connect with over 3 million members, including Oracle experts and fellow learners. Engage in topical forums, share your knowledge, and celebrate your achievements together. Discover the community today at mylearn.oracle.com.  15:55 Nikita: Welcome back! Susan, what is the Autonomous Container Database?  Susan: With the Autonomous Container Database, and you need that if you're going to create an Autonomous Database, you need to provision that within your Autonomous Exadata VM Cluster. It serves as a container to hold or to house one or more Autonomous Databases.  This allows multiple Autonomous Databases to coexist in the same infrastructure while still being logically separated. And this allows for the separation of databases based on their intended use. Think of a database for production. Think of a database for development. Think of a database for testing. You may have different database versions within the same infrastructure.  This isolation makes it easier for you to be able to meet your SLA, your Service Level Agreement, any long-term backups you may have, very specific encryption key needs to prevent issues from one database impacting another. So, the ability to have everything be isolated and secure is still grouping it in a manner that will meet your business needs.  17:08 Lois: Looking at Exadata Database Service specifically, what are some standout advantages for customers who deploy it on Oracle Database@AWS? Is there anything in particular they should get excited about in terms of performance or integration with AWS?  Susan: The Exadata Database Service is running on a dedicated Exadata Infrastructure that's deployed within your AWS data center. It delivers the same Exadata service experience in cloud control planes as the Oracle Cloud Infrastructure, allowing you to leverage existing skills and processing across your multi-cloud environment.  It addresses the data resiliency, or residency rather. And that's the scenario where many of our customers has the need. You have a need because of your security compliance to have the data local to you. By having the Exadata Database in your Oracle Database@AWS, it is running in your data center. So, this addresses that very important need, data residency, to have it close to you.  It also allows for seamless integration with other AWS services and applications. So now you have a capability of a hybrid cloud architecture leveraging the benefit of both Oracle Exadata and your AWS system. It has built-in high availability, the RAC application cluster, as well as Data Guard, a capability of addressing disaster recovery capability.  This also provides the ability for you to scale your compute, as well as your storage and your I/O resources independently. So as mentioned with Exadata, you have flexibility of how you want your database to be running individually. So just like the Autonomous, the Exadata Database checks off many of the boxes for running a mission-critical with high availability, highly redundant hardware and software features, along with extreme performance, scalability, and reliability.  This now allows you to run your AI environment, your online transaction processing, your analytic workload on any scale on the Exadata Infrastructure running in the Oracle Cloud. And in this case, running in your data center.  19:45 Nikita: If a business suddenly needs more capacity, how does scaling work with Exadata Database Service versus Autonomous Database on Oracle Database@AWS?   Susan: So with the Exadata scaling, you now can scale to meet expected demands so you know at certain point I will need more. I will then ask it to scale at that point when I will assign it-- and I'm using an example, I will assign it three computer cores all the time. But there may be demands. Think of your end of the quarter, end of the year processing that you may need more. So, you are enabling the compute cores to scale at the time you need it.  And what's cool is it will then, when it's no longer needed, it will then scale back down to the original three cores that you assign. So, you only pay for the enabled cores. But what's very cool about the Autonomous is that it is real-time scaling. So, with Autonomous, now you have the capability using Autonomous Database since it is self-tuning, self-monitoring, the Autonomous Database actually monitors the workload requirement and scales to match the workload demand.  Once the minimum level of the compute is defined and enabled, the automatic scaling is set. Autonomous Database will adjust to the consumption when it's needed, and it will scale back down when it's not. So though the Exadata is pretty cool, it will scale up and down on the workload demand.  This is with the Autonomous is even more powerful. It is real-time scaling based on that usage at that moment. Built-in automatic increase to meet the workload demands when it spikes and it automatically scales back when it's not needed.  A very powerful capability with all of our Oracle databases, the ability, even with traditional, to allow you to define what you may need with Exadata scaling for peak demands, as well as Autonomous scaling for real-time consumption and scaling when needed.  When you look at all of our options, one of the key things to bear in mind is a phrase that we use: performance scale as more servers are added. And what this is really saying is Oracle's automated scaling ability for the database, it basically has the ability to maintain or improve its performance under increased workload by automatically adding computational resources when needed.  This process is also known as horizontal scaling. It involves adding more servers, compute instances, to a cluster to share the processing load. And it has that capability automatically.  22:53 Nikita: There's so much more we can discuss about Oracle Database@AWS, but let's pause here for today! Thank you so much Susan for joining us.  Lois: Yeah, it's been really great to have you, Susan. If you want to dive deeper into the topics we covered today, go to mylearn.oracle.com and search for the Oracle Database@AWS Architect Professional course. Until next time, this is Lois Houston…  Nikita: And Nikita Abraham, signing off!  23:23 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We'd also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.

Après avoir investigué la situation, la santé publique arrive à la conclusion qu’il n’y aurait pas de situation exceptionnelle à Charlevoix par rapport au nombre de cas plus élevés de SLA. Entrevue avec Dr Philippe Robert, directeur de la santé publique du CIUSSS de la Capitale-Nationale au Québec. Regardez aussi cette discussion en vidéo via https://www.qub.ca/videos ou en vous abonnant à QUB télé : https://www.tvaplus.ca/qub ou sur la chaîne YouTube QUB https://www.youtube.com/@qub_radioPour de l'information concernant l'utilisation de vos données personnelles - https://omnystudio.com/policies/listener/fr

Rapport de la commission Gallant : le procureur en chef affirme que ça va changer des choses, mais… | Des sièges vides aux Jeux olympiques fâchent Mario Dumont | Disparition de Nancy Guthrie: est-elle encore en vie? | Concentration de SLA à Charlevoix: pas de facteur déterminant, selon la santé publique Dans cet épisode intégral du 17 février, en entrevue : Mario Dumont, en direct de Milan-Cortina. Me Simon Tremblay, procureur en chef de la commission Gallant. Me Vicky Powell, avocate criminaliste. André Gélinas, sergent-détective à la retraite au SPVM. Dr Philippe Robert, directeur de la santé publique du CIUSSS de la Capitale-Nationale au Québec. Une production QUB Février 2026Pour de l'information concernant l'utilisation de vos données personnelles - https://omnystudio.com/policies/listener/fr

Après avoir investigué la situation, la santé publique arrive à la conclusion qu’il n’y aurait pas de situation exceptionnelle à Charlevoix par rapport au nombre de cas plus élevés de SLA. Entrevue avec Dr Philippe Robert, directeur de la santé publique du CIUSSS de la Capitale-Nationale au Québec. Regardez aussi cette discussion en vidéo via https://www.qub.ca/videos ou en vous abonnant à QUB télé : https://www.tvaplus.ca/qub ou sur la chaîne YouTube QUB https://www.youtube.com/@qub_radioPour de l'information concernant l'utilisation de vos données personnelles - https://omnystudio.com/policies/listener/fr

Connect to John Gilroy on LinkedIn   https://www.linkedin.com/in/john-gilroy/ Want to listen to other episodes? www.Federaltechpodcast.com Cybersecurity is a rapidly evolving field, where every effective defense technique is quickly noticed and adapted to by malicious actors. The real question is how fast each side of this ongoing cat-and-mouse game can respond. Let us take an example of web applications. In the decade-long slog of the cloud, federal users migrated to web-based applications protected by Web Application Firewalls (WAFs). firewalls. As that method matured, malicious observers noted that the Application Programming Interface (API) allowed these software programs to communicate and exchange data. Voila, another attack vector was born. During today's interview, Joe Henry from Akamai Technologies notes that 80% of their customers report API attacks. Henry details a curious term called "Broken-Object Level Authorization." In this attack, an application fails to check if a user is authorized to access specific data objects. The ID is manipulated, and the malicious actor gets access. Akamai's API Security performs behavioral analysis beyond WAFs, flags PII exposure, and supports a zero-trust posture. Software developers talk about a "shift left"; we apply that to the Akamai approach. They have a worldwide network of Points of Presence (POPs) and data centers where they can observe attacks as they develop. It is so strong that it provides fail-open resilience with a 100% SLA. Akamai provides a State of the Internet Report (quarterly). If you would like to stay connected with the next manifestation of attack, consider subscribing or visiting their website to stay informed about the latest trend

Wendy y Bruno están en su casa, pero no están libres. Un brazalete en el tobillo les recuerda cada día que son los principales sospechosos de la desaparición de su propia hija: Magdiel, de cinco años.00:00:00 - 00:02:12 | Introducción00:02:13 - 00:09:48 | Magdiel desapareció00:09:49 - 00:17:41 | Detenida al ir a denunciar00:17:42 - 00:27:47 | Declaró contra su esposo00:27:48 - 00:37:42 | Arrestados 4 años despuésLa primera parte de esta historia fue el testimonio de Bruno. Ahora habla Wendy. Desde aquella mañana en que abrió los ojos, levantó las cobijas y su hija ya no estaba, hasta los cinco días que la tuvieron detenida cuando fue ella misma a denunciar la desaparición. Los cuatro años de búsqueda sin respuestas. La declaración que dio contra su propio esposo bajo presión de una activista. El segundo arresto, frente a su hija menor, en plena calle. Los seis meses en una prisión donde la llamaban "la que descuartizó a su hija". Y el regreso a casa, hace apenas dos semanas, con un brazalete y la misma pregunta sin respuesta.Esta es una historia sobre el dolor de no saber. Sobre un sistema que prefiere dar culpables a seguir buscando. Sobre dos padres que perdieron a su hija y, después, también su libertad.Magdiel sigue desaparecida. Y la Fiscalía sigue mirando hacia ellos.Para ver episodios exclusivos, entra aquí: https://www.patreon.com/Penitencia_mx¿Quieres ver los episodios antes que nadie? Obtén acceso 24 horas antes aquí: https://www.youtube.com/channel/UC6rh4_O86hGLVPdUhwroxtw/joinVisita penitencia.comSíguenos en:https://instagram.com/penitencia_mx  https://tiktok.com/@penitencia_mx  https://facebook.com/penitencia.mx  https://x.com/penitencia_mx  Spotify: https://spotify.link/jFvOuTtseDbApple: https://podcasts.apple.com/mx/podcast/penitencia/id1707298050Amazon: https://music.amazon.com.mx/podcasts/860c4127-6a3b-4e8f-a5fd-b61258de9643/penitenciaRedes Saskia:https://www.youtube.com/@saskiandr - suscríbete a su canalhttps://instagram.com/saskianino  https://tiktok.com/@saskianino  https://x.com/saskianino

The Five-Phase Sales Solution Cadence: Facts, Benefits, Applications, Evidence, Trial Close When you've done proper discovery—asked loads of questions about where the buyer is now and where they want to be—you earn the right to propose a solution. But here's the kicker: sometimes the right move is to walk away. If you force a partial or wrong-fit solution, you might "grab the dough" short-term, but you'll torch trust and reputation—the two assets that don't come back easily.  Below is a search-friendly, buyer-proof cadence you can run in any market—**Japan vs **United States, SME vs enterprise, B2B services vs SaaS—especially post-pandemic when procurement teams want clarity, proof, and outcomes, not fluffy feature parades. How do you know if your solution genuinely fits the buyer (and when should you walk away)? You know it fits when you can map your solution to their stated outcomes—and prove it—without twisting the facts. If the buyer needs an outcome you can't deliver, the ethical (and commercially smart) play is: "We can't help you with that." In 2024–2026, buyers are savvier and more risk-aware. They'll check reviews, ask peers, and sanity-test claims through AI search tools and internal stakeholder scrutiny. In high-trust cultures (including Japan) and high-compliance industries (finance, health, critical infrastructure), a wrong-fit sale becomes a reputational boomerang. The deal closes once; the story travels forever. Do now: Write a one-page "fit test": buyer outcomes → your capability → evidence. If any outcome can't be supported, qualify out fast.  What does "facts" mean in a modern B2B sales conversation? Facts are the provable mechanics—features, specs, process steps, constraints—and the proof that they work. Facts aren't the goal; they're the credibility scaffolding. Salespeople often drown here: endless micro-detail, endless Q&A, endless spreadsheets. Yes, analytical buyers (engineering-led firms, CFO-led committees) will pull you into the weeds—but remember: they aren't buying the process. They're buying the outcome from the process. Bring facts that de-risk the decision: implementation timelines, security posture (SOC 2/ISO), uptime/SLA history, integration limits, and measurable performance benchmarks. Then move on before you get stuck. Do now: Prepare a "facts pack" with 5–7 proof points (not 57 features). Use it to earn trust, then pivot to outcomes.  How do you turn features into benefits buyers will actually pay for? Benefits are the "so what"—the measurable results the buyer gets because the feature exists. If you can't link a feature to an outcome, it's just trivia. A weight, colour, dimension, workflow, dashboard, or AI model is not valuable by itself. It becomes valuable when it improves a KPI: reduced cycle time, fewer defects, higher conversion, lower churn, faster onboarding, better safety, tighter compliance. This is where classic sales thinking still holds up—think **SPIN Selling and the buyer's implied needs: pain, impact, and value. In a tight 2025 budget environment, "nice-to-have" benefits die quickly; "must-have" outcomes survive. Do now: For every top feature, write one sentence: "This enables ___, which improves ___ by ___ within ___ days." If you can't fill the blanks, drop the feature from your pitch.  What is the "application of benefits" and how do you make it real inside their business? Application is where benefits turn into daily operational reality—what changes in workflow, decisions, and results.This is the "rubber meets the road" layer. Don't just say "we improve productivity." Show where it lands: which meetings get shorter, which approvals disappear, which roles stop firefighting, which customers get served faster, which errors are prevented, and what leaders see weekly on dashboards. Compare contexts: a startup may care about speed and cash runway; a multinational may care about governance, change management, and multi-region rollouts. A consumer business might chase conversion and NPS; a B2B industrial firm might chase downtime reduction and safety incidents. Do now: Build a simple "Before → After" map for their week: processes eliminated, expanded, improved—and who owns each change.  What counts as credible evidence (and what "proof" actually convinces buyers)? Credible evidence is specific, comparable, and close to the buyer's reality—same industry, similar scale, similar constraints. "Trust me" is not evidence. Bring proof that survives scrutiny: reference customers, quantified case studies, independent reviews, pilot results, and implementation artefacts (plans, timelines, adoption metrics). The closer the comparison company is to the buyer, the more persuasive it becomes. This is also where storytelling matters: not hype—narrative. Who was involved? What went wrong? What changed? What were the numbers before and after? Analysts like **Gartner or **Forrester can help with category credibility, but a near-peer success story usually seals confidence. Do now: Collect 3 "mirror case studies" (similar buyer profiles) and write them as short stories: problem → actions → results → lessons.  How do you do a trial close without sounding pushy or sleazy? A trial close is a simple comprehension-and-comfort check that invites objections early—before you ask for the order. Done right, it's calm, not clingy. After you've walked through facts → benefits → application → evidence, ask: "How does that sound so far?" Then shut up. Silence is a tool. If they raise objections, good—interest is alive, and you can add pinpoint proof. If they say nothing (or go vague), start worrying: they may have already mentally deleted you as an option. This is the moment to clarify, re-anchor to outcomes, and confirm next steps in the sales cycle. Do now: Use one trial close per phase. Treat objections as data, not drama, and log them into your CRM as themes to address.  Conclusion: the cadence that keeps you credible and gets you paid This five-phase cadence works because it respects how adults buy: they need proof, relevance, and a clear path from "today" to "better." Keep the sequence tight—facts, then benefits, then application, then evidence, then a trial close—and you'll avoid the two killers of modern selling: feature-dumps and wishful thinking.  Author credentials Dr. Greg Story, Ph.D. in Japanese Decision-Making, is President of Dale Carnegie Tokyo Training and Adjunct Professor at Griffith University. He is a two-time winner of the Dale Carnegie "One Carnegie Award" (2018, 2021) and recipient of the Griffith University Business School Outstanding Alumnus Award (2012). As a Dale Carnegie Master Trainer, Greg is certified to deliver globally across all leadership, communication, sales, and presentation programs, including Leadership Training for Results.  He has written several books, including three best-sellers — Japan Business Mastery, Japan Sales Mastery, and Japan Presentations Mastery — along with Japan Leadership Mastery and How to Stop Wasting Money on Training. His works have been translated into Japanese, including Za Eigyō (ザ営業), Purezen no Tatsujin (プレゼンの達人), Torēningu de Okane o Muda ni Suru no wa Yamemashō (トレーニングでお金を無駄にするのはやめましょう), and Gendaiban "Hito o Ugokasu" Rīdā (現代版「人を動かす」リーダー).  Greg also publishes daily business insights on LinkedIn, Facebook, and Twitter, and hosts six weekly podcasts. On YouTube, he produces The Cutting Edge Japan Business Show, Japan Business Mastery, and Japan's Top Business Interviews, which are widely followed by executives seeking success strategies in Japan. 

La constante de Marchetti est un concept issu de l'urbanisme et de la géographie humaine, proposé par l'ingénieur et chercheur italien Cesare Marchetti dans les années 1990. Elle décrit une observation étonnamment stable à travers les époques et les civilisations :? Les êtres humains consacrent en moyenne environ une heure par jour aux déplacements quotidiens, notamment entre leur domicile et leur lieu de travail.Cette durée — environ 60 minutes aller-retour — semble remarquablement constante, que l'on vive dans une ville antique, une métropole moderne ou une mégapole contemporaine.D'où vient cette idée ?Marchetti a analysé des données historiques et contemporaines portant sur :Les villes romainesLes cités médiévalesLes villes industriellesLes métropoles modernesIl a constaté que, malgré des moyens de transport très différents (marche, cheval, tramway, voiture, métro, train), le temps moyen quotidien de déplacement reste proche d'une heure.Ce n'est pas la distance parcourue qui est constante, mais bien le temps accepté.Pourquoi cette constance ?L'explication principale est biologique et psychologique.Les humains semblent avoir une tolérance limitée au temps passé en déplacement. Au-delà d'un certain seuil, les trajets deviennent perçus comme trop fatigants, trop coûteux mentalement et socialement.Autrement dit, nous organisons inconsciemment nos choix de vie autour de ce budget-temps :Choix du logementChoix du travailChoix du mode de transportSi un trajet dépasse trop souvent ce seuil, les gens cherchent à déménager, changer d'emploi ou modifier leurs habitudes.Une conséquence surprenanteQuand les moyens de transport deviennent plus rapides, on ne réduit pas forcément le temps de trajet…? On augmente la distance parcourue.Exemples :Avec la marche : on habite près du travail.Avec le train ou la voiture : on peut vivre plus loin.Avec les transports rapides : les villes s'étalent.Résultat : les villes s'agrandissent, mais le temps de trajet moyen reste proche d'une heure.Ce que cela révèle sur nos sociétésLa constante de Marchetti suggère que :Le progrès technique ne libère pas automatiquement du tempsIl transforme surtout l'organisation de l'espaceL'étalement urbain est en partie une conséquence directe de transports plus rapidesElle remet en question l'idée que des transports toujours plus performants réduisent mécaniquement la contrainte des déplacements.La constante de Marchetti affirme que l'être humain accepte un budget quotidien de transport d'environ une heure, quelles que soient l'époque et la technologie.Une idée simple, mais puissante, qui montre que certaines limites ne sont pas techniques… mais profondément humaines. Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.

In surgical residency, cases are long, expectations are high, and patient care always comes first. But then you become a parent, and suddenly you're navigating pregnancy risk, parental leave, and lactation logistics in a system that wasn't built for it. We've made meaningful progress over the past decade, but too many trainees still face becoming parents in residency without clear, consistent support. In this episode, join Dr. Kara Button with Dr. Arielle Kanters and Dr. Sarah Shubeck as they ask: How do we build a surgical workforce that's skilled, resilient—and genuinely supported as surgeon-parents? They'll share practical, evidence-based steps programs can take to make parental support the norm—not the exception.Hosts:Kara Button, DO — General Surgery Resident, Maine Medical Center; Behind the Knife Surgical Education FellowArielle Kanters, MD — Colorectal Surgeon; Associate Program Director, Colorectal Surgery Fellowship, Cleveland ClinicDr. Sarah Shubeck, MD — Assistant Professor of Surgery; Breast Surgical Oncologist, University of ChicagoReferences:Bamdad MC, Hughes DT, Englesbe M. Safe and supported pregnancy: A call to action for surgery chairs and program directors: A call to action for surgery chairs and program directors. Ann Surg. 2022;275(1):e1-e2. doi:10.1097/SLA.0000000000005181 https://pubmed.ncbi.nlm.nih.gov/34433187/Castillo-Angeles M, Atkinson RB, Easter SR, et al. Pregnancy during surgical training: Are residency programs truly supporting their trainees? J Surg Educ. 2022;79(6):e92-e102. doi:10.1016/j.jsurg.2022.06.011 https://pubmed.ncbi.nlm.nih.gov/35842402/Castillo-Angeles M, Smink DS, Rangel EL. Perspectives of general surgery program directors on paternity leave during surgical training. JAMA Surg. 2022;157(2):105-111. doi:10.1001/jamasurg.2021.6223 https://pubmed.ncbi.nlm.nih.gov/34851404/Kanters AE, Shubeck SP. The importance of parental leave and lactation support for surgeons. Clin Colon Rectal Surg. 2023;36(5):333-337. doi:10.1055/s-0043-1764288 https://pubmed.ncbi.nlm.nih.gov/37564351/Kling SM, Slashinski MJ, Green RL, Taylor GA, Dunham P, Kuo LE. Parental leave experiences for the non-childbearing general surgery resident parent: A qualitative analysis. Surgery. 2024;176(5):1320-1326. doi:10.1016/j.surg.2024.04.035 https://pubmed.ncbi.nlm.nih.gov/38910045/Mann H, Glazer T. Current state of safe pregnancy policies for the US surgical trainee. OTO Open. 2024;8(3):e172. doi:10.1002/oto2.172 https://pubmed.ncbi.nlm.nih.gov/39036338/Rangel EL, Smink DS, Castillo-Angeles M, et al. Pregnancy and motherhood during surgical training. JAMA Surg. 2018;153(7):644-652. doi:10.1001/jamasurg.2018.0153 https://pubmed.ncbi.nlm.nih.gov/29562068/Rangel EL, Castillo-Angeles M, Easter SR, et al. Incidence of infertility and pregnancy complications in US female surgeons. JAMA Surg. 2021;156(10):905-915. doi:10.1001/jamasurg.2021.3301 https://pubmed.ncbi.nlm.nih.gov/34319353/https://www.nytimes.com/2019/12/20/science/doctors-surgery-motherhood-medical-school.htmlhttps://behindtheknife.org/podcast/family-leave-during-surgical-training-a-discussion-with-abs-president-dr-jo-buyskePlease visit https://behindtheknife.org to access other high-yield surgical education podcasts, videos and more.  If you liked this episode, check out our recent episodes here: https://behindtheknife.org/listenBehind the Knife Premium:General Surgery Oral Board Review Course: https://behindtheknife.org/premium/general-surgery-oral-board-reviewTrauma Surgery Video Atlas: https://behindtheknife.org/premium/trauma-surgery-video-atlasDominate Surgery: A High-Yield Guide to Your Surgery Clerkship: https://behindtheknife.org/premium/dominate-surgery-a-high-yield-guide-to-your-surgery-clerkshipDominate Surgery for APPs: A High-Yield Guide to Your Surgery Rotation: https://behindtheknife.org/premium/dominate-surgery-for-apps-a-high-yield-guide-to-your-surgery-rotationVascular Surgery Oral Board Review Course: https://behindtheknife.org/premium/vascular-surgery-oral-board-audio-reviewColorectal Surgery Oral Board Review Course: https://behindtheknife.org/premium/colorectal-surgery-oral-board-audio-reviewSurgical Oncology Oral Board Review Course: https://behindtheknife.org/premium/surgical-oncology-oral-board-audio-reviewCardiothoracic Oral Board Review Course: https://behindtheknife.org/premium/cardiothoracic-surgery-oral-board-audio-reviewDownload our App:Apple App Store: https://apps.apple.com/us/app/behind-the-knife/id1672420049Android/Google Play: https://play.google.com/store/apps/details?id=com.btk.app&hl=en_US

You're in the middle of surgical residency, and you realize you need more than a few weeks away from clinical responsibilities. Maybe you need more time to be a parent, recover from an illness, care for family, learn a new skill, or simply create space to reflect and reset. What if you could complete five years of training over six calendar years by spreading that time out in a way that fits your life?  Join Dr. Kara Button with Dr. Joe Buyske, and Dr. Bridget Olson as they break down the 5-in-6 pathway including how it works, who it's for, and the real-world logistics that matter.Hosts:Kara Button, DO — General Surgery Resident, Maine Medical Center; Behind the Knife Surgical Education FellowJo Buyske, MD — President & CEO, American Board of SurgeryDr. Bridget Olsen, MD — General Surgery Resident, Maine Medical CenterReferences: Bamdad MC, Hughes DT, Englesbe M. Safe and supported pregnancy: A call to action for surgery chairs and program directors: A call to action for surgery chairs and program directors. Ann Surg. 2022;275(1):e1-e2. doi:10.1097/SLA.0000000000005181 https://pubmed.ncbi.nlm.nih.gov/34433187/Castillo-Angeles M, Atkinson RB, Easter SR, et al. Pregnancy during surgical training: Are residency programs truly supporting their trainees? J Surg Educ. 2022;79(6):e92-e102. doi:10.1016/j.jsurg.2022.06.011 https://pubmed.ncbi.nlm.nih.gov/35842402/Castillo-Angeles M, Smink DS, Rangel EL. Perspectives of general surgery program directors on paternity leave during surgical training. JAMA Surg. 2022;157(2):105-111. doi:10.1001/jamasurg.2021.6223 https://pubmed.ncbi.nlm.nih.gov/34851404/Kanters AE, Shubeck SP. The importance of parental leave and lactation support for surgeons. Clin Colon Rectal Surg. 2023;36(5):333-337. doi:10.1055/s-0043-1764288 https://pubmed.ncbi.nlm.nih.gov/37564351/Kling SM, Slashinski MJ, Green RL, Taylor GA, Dunham P, Kuo LE. Parental leave experiences for the non-childbearing general surgery resident parent: A qualitative analysis. Surgery. 2024;176(5):1320-1326. doi:10.1016/j.surg.2024.04.035 https://pubmed.ncbi.nlm.nih.gov/38910045/Mann H, Glazer T. Current state of safe pregnancy policies for the US surgical trainee. OTO Open. 2024;8(3):e172. doi:10.1002/oto2.172 https://pubmed.ncbi.nlm.nih.gov/39036338/Rangel EL, Smink DS, Castillo-Angeles M, et al. Pregnancy and motherhood during surgical training. JAMA Surg. 2018;153(7):644-652. doi:10.1001/jamasurg.2018.0153 https://pubmed.ncbi.nlm.nih.gov/29562068/Rangel EL, Castillo-Angeles M, Easter SR, et al. Incidence of infertility and pregnancy complications in US female surgeons. JAMA Surg. 2021;156(10):905-915. doi:10.1001/jamasurg.2021.3301 https://pubmed.ncbi.nlm.nih.gov/34319353/https://www.nytimes.com/2019/12/20/science/doctors-surgery-motherhood-medical-school.htmlhttps://behindtheknife.org/podcast/family-leave-during-surgical-training-a-discussion-with-abs-president-dr-jo-buyskePlease visit https://behindtheknife.org to access other high-yield surgical education podcasts, videos and more.  If you liked this episode, check out our recent episodes here: https://behindtheknife.org/listenBehind the Knife Premium:General Surgery Oral Board Review Course: https://behindtheknife.org/premium/general-surgery-oral-board-reviewTrauma Surgery Video Atlas: https://behindtheknife.org/premium/trauma-surgery-video-atlasDominate Surgery: A High-Yield Guide to Your Surgery Clerkship: https://behindtheknife.org/premium/dominate-surgery-a-high-yield-guide-to-your-surgery-clerkshipDominate Surgery for APPs: A High-Yield Guide to Your Surgery Rotation: https://behindtheknife.org/premium/dominate-surgery-for-apps-a-high-yield-guide-to-your-surgery-rotationVascular Surgery Oral Board Review Course: https://behindtheknife.org/premium/vascular-surgery-oral-board-audio-reviewColorectal Surgery Oral Board Review Course: https://behindtheknife.org/premium/colorectal-surgery-oral-board-audio-reviewSurgical Oncology Oral Board Review Course: https://behindtheknife.org/premium/surgical-oncology-oral-board-audio-reviewCardiothoracic Oral Board Review Course: https://behindtheknife.org/premium/cardiothoracic-surgery-oral-board-audio-reviewDownload our App:Apple App Store: https://apps.apple.com/us/app/behind-the-knife/id1672420049Android/Google Play: https://play.google.com/store/apps/details?id=com.btk.app&hl=en_US

En Chine, on estime qu'il existe aujourd'hui environ 4 000 noms de famille différents réellement en usage.Selon les sources et la façon de compter (variantes d'écriture, noms minoritaires, noms composés à deux caractères), on trouve des estimations allant d'environ 3 100 patronymes courants jusqu'à 6 000+ au total. Historiquement, la Chine a pourtant connu près de 12 000 noms recensés dans les textes anciens, mais une grande partie a disparu ou s'est fondue dans d'autres.En France, c'est l'inverse : la diversité est immense. On parle généralement de 1,2 à 1,5 million de noms de famille distincts si l'on compte toutes les graphies et variantes (ex : Dupont/Dupond, ou les noms avec/sans accents), et de plusieurs centaines de milliers de noms réellement portés de façon significative.En Chine, c'est un phénomène très frappant, mais il s'explique assez bien.1) Les noms chinois se sont fixés très tôtEn Chine, le nom de famille (姓) existe depuis l'Antiquité et structure la société en clans et lignages. Le système est donc ancien, stable et très codifié.En Europe, au contraire, les noms se sont fixés tard : beaucoup de gens n'avaient pas de patronyme héréditaire avant le Moyen Âge ou même l'époque moderne. Résultat : plus de diversité.2) Beaucoup de noms ont été “absorbés”Au fil des siècles, lors de guerres, migrations ou changements de dynastie, des familles ont souvent abandonné un nom rare pour adopter un nom plus commun ou prestigieux (par protection, par intégration sociale, ou pour se fondre dans la population).Cela a “compressé” la diversité des patronymes.3) Standardisation administrativeL'État impérial chinois a été très tôt un État bureaucratique : recensements, registres, examens… Les noms ont été normalisés, et les variantes locales ont souvent été uniformisées. Ce qui est rare, mal enregistré ou trop complexe finit par disparaître.4) Des noms très courts, donc moins de possibilitésLa plupart des noms chinois sont à un seul caractère : Wang, Li, Zhang…Les noms à deux caractères existent, mais sont minoritaires. Moins de combinaisons = plus de concentration. Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.

Foundations of Amateur Radio On your amateur radio journey, you'll likely discover that many transceivers run on 13.8 volt DC, give or take. For example my FT-857d requires 13.8 volt plus or minus 15 percent, with a negative ground, and a current draw of 22 ampere, more on that later. In other words, the power supply needs to be between about 11.7 and 15.9 volts, the same voltage that runs most vehicles with some wiggle room for fluctuating alternator charging cycles. While some radios will absolutely fit in your car, there's plenty where that just isn't the case, even though they're set-up for a 13.8 volt power supply. You might think of it as an anachronism, a few steps removed from spark gap transmitters, but there's more to the story. Most residential power grids run on AC power, at varying voltages and frequencies between 50 and 60 Hz. Across the world there's eight different AC voltages in use between 100 and 240 volts. Some countries use more than one combination and I haven't even looked at three phase power. Perhaps 13.8 volt DC isn't looking quite as odd. With this revelation comes the need to actually have 13.8 volt available in your shack. Converting your grid power to something you can plug your gear into requires some form of transformation, typically achieved with a power supply. Efficient, cheap and plentiful, the switch mode power supply is the most common. Built to a price, they're also often noisy, not just the fan, but noisy from a radio emissions perspective. Amateur radio has very sensitive receivers and as a result you can often hear, or see if you have a waterfall display, RF birdies, a sound reminiscent of a budgie whistling, every 100 kHz or so across the whole radio spectrum. Not something most other equipment cares about, so you're often left to fend for yourself in figuring out how to deal with this phenomenon. There's plenty of filtering techniques and circuits to be found and some of them even work, but for my money, I'd spend it on a power supply that doesn't make noise in the first place. A regulated power supply maintains a constant output voltage or current, regardless of variations in load or input voltage. An unregulated power supply can wander all over the place. Adjustable power supplies allow you to set the voltage, amperage, or both, sometimes with knobs, sometimes using external controls. At this point you might decide that this is all too hard and you want to do away with all this complexity and use a Sealed Lead Acid, or SLA battery, after all, that's what the 13.8 volt is based on, but then you'll need to charge it. Similarly, picking any battery technology requires some form of charging. Another word for charger is: power supply, often a switch mode one, and likely not filtered in any way that matters to you, since batteries, and for that matter solar power inverters, are unlikely to care about RF birdies. I will make mention of linear power supplies. When I started on this journey, this was the strong recommendation from my peers as the most desirable option. Although they're significantly less efficient than switch mode power supplies, only 30 percent versus better than 80 percent, from an RF perspective, they're extremely quiet. Of course, the lack of efficiency reveals itself in the form of heat, which necessitates the application of cooling, from a fan, often a very noisy fan. One potential source of power supply is a computer power supply unit or PSU. Before you go down that route, consider that they're intended for installation inside a case, often generate various voltages at very specific current draws and are not typically known for being RF quiet. After weighing up all the variables, I chose a laboratory grade switch mode current limiting adjustable power supply. It's set to 13.8 volt and it sits on my desk doing its thing. Rated at 1 to 15 volts at 40 ampere, it's now as old as I am in amateur radio terms, well and truly a teenager, it's also overkill, by quite a margin. Remember when I mentioned that my FT-857d is rated at drawing 22 ampere? As a QRP or low power station I typically use my transmitter set to 5 watt, but even when others use it at full power, I have never ever seen it draw more than 12 ampere. That's not to say that it can't draw 22, I've just never seen it. As a benefit of having such a massive overkill in the specifications of my power supply, I can power more than one radio and not notice. Not that they're all transmitting at the same time, or using more than 5 watt, it just doesn't matter. I previously discussed setting a standard for coax connectors in the shack, the same is true for deciding what to pick for power supply connectors. In my case I chose Anderson Powerpole connectors. Pins come in 15, 30 and 45 ampere ratings, are genderless and housings are available in many different colours. When I say genderless, it means that you can join two identical connectors. Within my shack, I use the RACES or ARES Powerpole wiring standard and every single 13.8 volt connection uses it. If I get new gear that uses some other connector, I'll cut the power supply wire in half and terminate both the power supply and the cut off cable using Powerpole connectors. That way my gear will connect to my own power supply and I'll have a universal adaptor cable when I need it. Over the years I've collected an impressive array of adaptors using this method and it's helped immensely when sharing gear with other amateurs. Word of warning, make sure you get positive and negative the right way around when you join your Powerpole connectors, and make sure that you have the red and black housings the right way around too, you can thank me later. If you do this more than a few times, I'd recommend that you spend the money on a proper crimping tool. It makes the experience So. Much. Better. To avoid many of the pitfalls of interference whilst connecting power and coax to the same radio, try hard to avoid running both in parallel, or worse, joined to each other. Instead, attempt to run them in different directions and only cross at right angles if you have to. One thing to consider is the ability to switch everything off immediately. To that end I have a power switch on my desk that isolates all power to the equipment. You'll notice that I have not said anything about grounding or earthing, that's on purpose. Your laws and mine are not the same. Similarly, information you'll find online rarely, if ever, describes the jurisdiction it applies to, so, look at your own rules and implement accordingly. I'm Onno VK6FLAB

In this inspiring episode of Education Matters, NaShawn Edwards, the founder of Sunrise Learning Academy joins us to to discuss what it truly means to serve students who don't fit the traditional educational mold. NaShawn shares her personal journey navigating undiagnosed ADHD, advocating for six children, and fostering youth who've experienced trauma. Her experiences shaped her vision for a school where confidence, connection, and individualized support come first. Episode Highlights Early Educational Experiences: NaShawn Edwards reflects on transitioning from a small, conservative private school to a large public school in Indianapolis, the challenges she faced, and the relationships that shaped her—especially with supportive school staff like her principal and the janitor. Parenting and Advocacy:With six children, five boys and one girl, NaShawn describes learning styles and personalities as unique as fingerprints. Her hands-on involvement in PTA and school committees was driven by her desire to see real change and advocate for her kids' needs. Classroom Challenges:She discusses how classroom sizes, peer conflict, and chaotic environments contributed to declining confidence and academic progress in her children, particularly those with ADHD and autism. This led her to explore new educational models focused on small class sizes and relationship-driven learning. Foster Care Realities:As a foster parent, NaShawn Edwards encountered children academically behind—some unable to spell their own names in elementary school—making her acutely aware of systemic issues and underscoring the need for trauma-informed instruction. The Birth of Sunrise Learning Academy:Inspired by her foster care experiences, especially working with a young boy not ready for kindergarten, NaShawn Edwards started SLA to deliver individualized, trauma-informed education, and integrate therapeutic services like counseling, speech, and occupational therapy into the school day. Model of Sunrise Learning Academy:SLA integrates standardized curriculum, faith foundations, trauma-informed strategies, and hands-on projects. Multi-age classrooms foster mentorship and leadership, and the intentionally small school atmosphere allows teachers to quickly respond to student needs. Addressing Misconceptions: NaShawn Edwards tackles misconceptions about trauma-impacted learners and emphasizes that her students are resilient and capable—not “bad off.” Push-in therapeutic services at SLA help students and families regain time and access needed support during the school day. Community Collaboration:SLA prioritizes collaboration over competition with other schools. Partnerships with local charter and private schools mean shared resources and joint events, like safe-sitter classes and basketball leagues, strengthening the wider educational community. Bridging Gaps:Resources, tutoring, and relationship-building are key to SLA's strategy so that no child slips through the cracks—whether academic, emotional, or social support is needed. Did you find this episode informative? Help us out! Leave a review Share it with your friends Give us a 5 Star rating on your podcatcher of choice For more information about school choice and your school choice options, visit our website at https://www.i4qed.org

In this episode, we are joined by Alessio Pagliai and Stepan Drunks. Brought to you by Sovol (https://jle.vi/sovol) and OctoEverywhere (https://octoeverywhere.com/welcome?id=podcast).

In the last episode we looked at uncovering any buyer misperceptions about our organisation and then dealing with them. How did that go? Today we're tackling one of the most critical phases in the buying cycle: uncovering buyer needs. Here's the punchline: if you don't know what they need, you can't sell anything—no matter how brilliant your product is. And buyer needs aren't uniform. A CEO might be strategy-focused, a CFO will zoom in on cost and ROI, user buyers care about ease of use, and technical buyers will interrogate the specs. That's the directional truth—then your questioning skills do the real work. How do you uncover buyer needs without guessing or pitching too early? You uncover buyer needs by analysing what you're looking for before you start asking questions or showing slides. Most salespeople do the opposite: they rock up, pitch hard, and hope something sticks. That's basically dumb. In Japan, especially, buyers often default to "safer" decisions—keep the incumbent, do nothing, delay, or create consensus through internal alignment (think nemawashi and ringi-style approvals). In the US or Australia, you might get faster objections; in Japan you'll often get silence, hesitation, or "we'll consider it." Same meaning: risk management. So don't wing it. Prepare a needs map first, then design questions that locate the priority need and the real decision logic across stakeholders. Answer card / Do now: Map needs first, question second. Don't pitch until you know what "success" looks like for thisbuyer. What is a buyer's "Primary Interest" and why does it matter more than product features? Primary Interest is the outcome the buyer cares about—not the tool, not the features, not your brochure. Buyers buy results: more revenue, improved efficiency, better safety, higher quality, greater flexibility, stronger ROI. If you spend the whole meeting talking about the "tool," you've missed the point. This is where B2B sellers get trapped—especially in tech, consulting, HR services, and industrial solutions. Features are easy to copy; outcomes are what justify budget. In a multinational procurement team, Primary Interest might be "standardisation across APAC," while an SME founder might want "cashflow certainty in the next 90 days." Same category, totally different language. Your job is to find the onehigh-priority outcome that makes the decision obvious, and keep coming back to it. Answer card / Do now: Translate your offering into a single measurable outcome the buyer cares about (time saved, risk reduced, revenue gained). What "Buying Criteria" do executives and procurement teams actually use? Buying Criteria are the must-haves that determine whether your solution is even allowed into the final decision. These are the basics: budget fit, required features, approvals, implementation effort, after-sales support, location constraints, quantity, quality, security, integration requirements, and vendor reliability. In enterprise deals, this often becomes a checklist: legal, IT, finance, procurement, and the business unit all have veto points. In Japan, buying criteria can heavily favour "proven suppliers" and "low disruption." In the US, you may see more appetite for a challenger vendor—if the business case is strong. In regulated sectors (finance, healthcare, infrastructure), criteria can be as much about governance and auditability as it is about performance. Quick checklist you can use in discovery: Budget range and approval path Non-negotiable features / specs Support expectations (SLA, training, local coverage) Timeline and resourcing constraints Answer card / Do now: Get the buyer's must-have criteria early—before you invest weeks chasing a deal you can't qualify into. How do you handle "Risk vs Reward" when buyers prefer doing nothing? Risk vs Reward is where deals stall—because "no decision" feels safer than change. In Japan, the safest move is often sticking with the current supplier or system. That inertia is brutal for salespeople. But here's the twist: doing nothing isn't free—it carries an opportunity cost. The buyer may lose market position, miss a turning point, or let a competitor strengthen their foothold. Post-pandemic, many firms tightened governance and became more cautious, even while digital transformation accelerated (a messy paradox in the 2020s). To shift this, you must quantify the return versus investment. If you can't provide credible numbers—time saved, defects reduced, revenue impact, risk mitigation—you're asking them to "trust you," which is not a strategy. Use conservative ranges if you must, but bring maths. Answer card / Do now: Reframe "no action" as a cost. Quantify the loss of delay in plain numbers the CFO can defend. Why should salespeople always ask "why" after an objection or hesitation? Because the first objection is often a symptom—not the real reason. I was talking to a President recently and he pushed for added value or a discount. The lazy move would've been to concede. Instead, I asked "why." Turns out headquarters required a form showing how he improved the supplier's offer. That's not a price objection—it's an internal process requirement. If I'd rushed in, I might have offered too much and trained the buyer to negotiate unnecessarily. This is universal. In a startup, "it's too expensive" might mean "we're unsure you'll deliver." In a conglomerate, it can mean "legal hasn't cleared this category." Asking "why" turns vague resistance into a solvable problem. And it keeps you from negotiating against yourself. Answer card / Do now: When you hear an objection, ask "why" once more than feels polite. You're not pushing—you're diagnosing. What is "Individual Motive," and how does it influence B2B buying decisions? Individual Motive is the emotional driver behind the business logic—and it's always there, even in "rational" organisations. People buy for personal reasons: recognition, promotion, job security, a bonus, avoiding embarrassment, beating internal rivals, gaining influence, or creating a quick win. Human nature is reliable: we prioritise our own needs first, company needs second—even when we don't admit it out loud. In Japan, this may show up as reputation protection and consensus safety. In Western firms, it may show up as "I want to be the person who drove this transformation." Either way, ignoring Individual Motive makes your message flat. It also explains why two buyers in the same company can want completely different things. The CFO may want downside protection; the user buyer wants simplicity; the project sponsor wants a career win. Answer card / Do now: Identify the personal win for each stakeholder—then connect it to the business outcome without sounding manipulative. Conclusion Uncovering buyer needs isn't a "nice-to-have." It's the foundation of selling. If you analyse needs across Primary Interest, Buying Criteria, Risk vs Reward, and Individual Motive, you stop guessing, stop pitching prematurely, and start having the conversations that actually move decisions—especially in high-inertia markets like Japan.

With the SLA in tatters, Patricia Hearst goes on the run. The FBI gets an unusual tip, one that promises a breakthrough.Be the first to know about Wondery's newest podcasts, curated recommendations, and more! Sign up now at https://wondery.fm/wonderynewsletterListen to American Scandal on the Wondery App or wherever you get your podcasts. Experience all episodes ad-free and be the first to binge the newest season. Unlock exclusive early access by joining Wondery+ in the Wondery App, Apple Podcasts or Spotify. Start your free trial today by visiting wondery.com/links/american-scandal/ now.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Trička s Leninem a vlajky se srpem a kladivem jsou od ledna trestné. Je zákaz propagace komunismu cestou, jak zločineckou ideologii ve společnosti marginalizovat? „Nacismus má v sobě prvek nenávisti, mimo jiné rasové nenávisti. A komunismus má třídní nenávist,“ souhlasí někdejší senátor a studentský vůdce Martin Mejstřík. „Jsou různé podoby komunismu, je to mnohotvárný fenomén. Řada komunistů kritizovala diktatury,“ nesouhlasí v pořadu Pro a proti politolog Ondřej Slačálek.

Patricia Hearst confronts a deadly new reality. The Hearst family tries to strike a deal with the SLA.Be the first to know about Wondery's newest podcasts, curated recommendations, and more! Sign up now at https://wondery.fm/wonderynewsletterListen to American Scandal on the Wondery App or wherever you get your podcasts. Experience all episodes ad-free and be the first to binge the newest season. Unlock exclusive early access by joining Wondery+ in the Wondery App, Apple Podcasts or Spotify. Start your free trial today by visiting wondery.com/links/american-scandal/ now.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

2025 年，大模型行业经历了一个明显的转折点。从 DeepSeek moment 开始，中美两端都感受到了一次「被重新洗牌」的冲击，经过一年的时间，中国的大模型，正在真实地进入全球使用场景之中。一份由 OpenRouter 与 Andreessen Horowitz 联合发布的报告 《State of AI:An Empirical 100 Trillion Token Study with OpenRouter》 (https://openrouter.ai/state-of-ai?utm_source=chatgpt.com)显示：2024 年，中国开源模型在全球调用中的占比只有约 1.2%，而到了 2025 年，这一比例已经上升至接近 30%。 这些被大量调用的中国模型，究竟是被谁在使用？在数据隐私、合规与地缘政治压力下，美国企业是否有所顾虑？中国模型出海，流量、影响力和商业化之间是否正在出现错位？节目邀请了大模型出海的一线观察者 Forrest Xu，从 OpenRouter、Inference Partner、企业级部署结构、开源与闭源模型的商业路径等多个层面，系统拆解了中国模型出海的真实运行机制。 本期人物 Forrest Xu，大模型出海从业者 丁教 Diane，「声动活泼」联合创始人、「科技早知道」主播 主要话题 [03:28] OpenRouter 是什么平台？为什么中国模型会在这里大量出现？ - OpenRouter 是以价格为核心的模型路由平台，强调“自动选择最便宜方案” - 美国市场缺乏持续更新的高质量开源模型 - 中国厂商在开源模型的性能与成本上形成明显优势 [07:08] OpenRouter 与 Inference Partner 的本质区别是什么？ - 路由平台：用户不知道具体模型，随时切换，几乎没有品牌忠诚度 - Inference Partner：与企业签署合同，明确模型、算力位置和数据归属 [14:10] 美国企业真正担心的是模型来自中国吗？ - 企业最核心的关切是数据是否出境、是否可控 - 只要数据留在美国或指定地区，模型来源并非首要问题 - 地缘政治压力更多体现在法务和合规层面 [16:12] 企业客户在合同中最常见的三项核心要求是什么？ - 数据不得进入中国大陆 - 输入和输出数据不得用于再训练 - 数据需可按需删除，并具备明确 SLA （service level agreement ） [22:40] 中国开源模型被大量使用，但为什么模型厂商本身并不赚钱？ - 开源模型几乎不直接产生收入 - 利润主要集中在推理服务商和中间层 - 模型厂商更多是在积累品牌、声誉和长期生态位置 [26:53] 推理模型的爆发，与中国开源模型之间有什么关系？ - 推理能力的扩散高度依赖开源社区 - 推理正在从“高端能力”变成“默认能力” - 中国开源模型在这一轮技术演进中起到了关键推动作用 [34:38] 开源模型未来是否存在新的商业化路径？ - 探讨通过授权 (licensing) 的方式实现“开源但收费” - 在西方企业环境中，license 合规具有现实可行性 - 开源并不必然等于无法商业化 GEO/AEO 闭门会 Untitled https://media24.fireside.fm/file/fireside-uploads-2024/images/4/4931937e-0184-4c61-a658-6b03c254754d/euIOMejd.png 在上一期节目中，我们收到了很多关于 AEO（Answer Engine Optimization） 的评论和反馈。 1 月 11 日， CES 结束之后我们将在硅谷组织一场小规模闭门交流，邀请在这个领域有较早探索的公司和产品以及AI平台与搜索引擎相关嘉宾一起来讨论： • AEO 在 AI 产品出海中的最新实践 • ChatGPT、Perplexity 等 AI 搜索入口的变化 • Reddit 等技术社区在 AEO 中的角色 • 当前阶段哪些方法有效，哪些值得谨慎对待 这将是一场以交流和讨论为主的闭门会，如果你有兴趣参与，请通过下方链接填写报名表。 我们将根据报名情况 定向发出邀请。

Are you chasing AI because it sounds good, or because it actually solves a real problem in your freight brokerage? And are you protecting your data, your margins, and your relationships while you do it?  Let's hear the real cost of AI in transportation and why proprietary AI and workflow automation can be a smarter long-term play in this episode with Herbert Orellana of Avanza BPO & Nearshoring and Sterling Engelhard of Data Gurus Group.  We discuss AI adoption in freight brokerage, data security, private AI models, why optimizing your internal processes has to come before automation if you want real ROI, nearshore AI development, how logistics-focused teams can help control costs and protect data ownership, and why 90% of brokerage work can be automated—but the human touch is still the competitive edge that builds trust with shippers and carriers!   Herbert Orellana Co‑Founder and COO of Avanza began his career at GlobalTranz in Scottsdale, AZ, where he thrived in a fast‑paced, tech‑driven freight brokerage environment. At Avanza, Herbert has been pivotal in scaling operations in Honduras, replicating the high‑pressure, SLA‑driven culture of American 3PLs within a nearshore model. His focus is on strategy and client success, partnering with 3PLs and FreightTech companies to design and build high‑performing nearshore teams that drive growth and efficiency.   Sterling Engelhard Seasoned technology leader with 15+ years of experience building and scaling engineering, data, and product teams across North America and LATAM. He is an expert in nearshore software delivery models, helping companies accelerate roadmaps, reduce costs, and improve quality through high-performing LATAM talent. Sterling has successfully scaled multiple software startups, shaping technical strategy, optimizing delivery operations, and leading digital transformation for enterprise clients.   Connect with Herbert and Sterling Website: https://avanzabpo.com/ / https://datagurus.tech/  LinkedIn: https://www.linkedin.com/in/herbert-orellana-099a1abb/ / https://www.linkedin.com/in/sterlingengelhard/  

Slaðan Stojković je kroz svoju 23-nju karijeru sa uspehom nastupao za brojne klubove u bivšoj Jugoslaviji, a najdublji trag ostavio je u BKK Radnicki i Slobodi iz Tuzla.Sankcije Jugoslaviji uskratile su ga nastupa za vece klubove u Evropi iako je vise puta bio prvi strelac lige.Uzivajte u razgovoru!00:00:00 Uvod 00:04:30 Radnicki00:10:00 EL00:15:00 Odrastanje 00:23:00 Prelazak za Beograd 00:40:00 Tuzla 00:48:50 Pancevo00:52:50 Poljska00:57:50 Borovica01:06:00 Anegdote01:12:00 Israel /Kipar 01:15:00 Poslenji ples01:21:30 Najtezi protivnik01:27:30 Najtalenat01:32:00 Plan za posle01:33:45 Benefiti01:34:20 Savet za mlade01:36:10 Top 5Thumbnail designer:https://instagram.com/design33_mk?igshid=MzRlODBiNWFlZA==Pratite nas na društvenim mrežama!Instagramhttps://www.instagram.com/jaomile_podcast/Facebook https://www.facebook.com/JAOMILEPODCASTTikTokhttps://www.tiktok.com/@jaomile_podcastTwitter https://twitter.com/mileilicGost: Slaðan StojkovićDatum:  17.12. 2025. Autor i domaćin: Mile IlićLokacija: Studio Long MileProdukcija: Jao Mile#jaomilepodcast #sladjanstojkovic #crvenazvezda #kkpartizan #NikolaJovic #nba  #nikolajokic #abaliga #jokic #bogdanovic #euroleague #doncic #nikolatopic #micic #bkkradnicki

Join the Refrigeration Mentor Hub here Learn more about Refrigeration Mentor Customized Technical Training Programs at www.refrigerationmentor.com/courses This episode is about the importance of curiosity when your out on service calls to help in troubleshooting. Curiousity is a trait that will open doors to dig deeper, leverage resources, inspect things more thoroughly and get to know the complex systems we work on so much more. Here, we cover troubleshooting techniques, specifically focusing on dealing with proof failures and SLA alarms. The more you learn, the more refrigeration technicians will fast-track your careers and become confident, skilled professionals. In this episode, we cover: -Importance of curiosity in refrigeration -Refrigeration training programs  -Proof failure troubleshooting example -Advice for on-call technicians -Investing in yourself and continuous learning Helpful Links & Resources: Episode 250. Service Call Stories and Troubleshooting Tips with Andrew Freeburg Episode 333. Training and Troubleshooting Tips to Level Up Your Career Episode 295. A Compressor Story: The Key to Faster Troubleshooting  

professorjrod@gmail.comPrinters and multifunction devices are more than just simple office tools—they're intricate systems combining optical, thermal, mechanical, and networked computing components. In this episode, we decode printer technology and its critical role in business operations, highlighting how these devices impact IT skills development and technology education. From unboxing to output, we explore the key decisions that keep your pages moving smoothly while safeguarding your data. Whether you're preparing for CompTIA exams or seeking practical IT certification tips, this episode offers valuable insights into managing printer technology within your IT infrastructure.Instructional Downloadable Resource Guidehttps://www.professorjrod.com/downloadsWe start with fit-for-purpose buying—matching speed, DPI, trays, duplexing, and duty cycle to real workloads—then move to placement and environment, where airflow, humidity, and power quality determine whether a fleet runs smoothly or jams at 4:58 p.m. Firmware strategy matters more than most shops admit: back up configs, schedule updates, and never interrupt a flash. On connectivity, we compare USB simplicity against Ethernet and Wi‑Fi flexibility, then layer in drivers and PDLs—PCL for speed, PostScript for precision, XPS for Windows pipelines—plus the color logic of CMYK. You'll hear clean exam clues for the A+ and practical tells for real-world triage, like when a single user's issue is just a preference and not a driver.Inside the box, we translate the seven-step laser process into actionable troubleshooting: charging, exposing, developing, transferring, fusing, and cleaning each leave fingerprints—smears, ghosting, or blank pages—that point straight to the failing part. We round out the print tech tour with inkjet (thermal vs piezo), thermal printers (direct vs transfer), and impact units for multipart forms. Then we head to the network, where DHCP reassignments, wrong ports, and spooler crashes derail entire floors. Print servers centralize power and risk, and mobile/cloud printing adds discovery quirks and new attack surfaces.Security is the blind spot: printers hold disks, address books, and cached jobs. We lay out the must-haves—PIN or badge release, secure erase, firmware signing, role-based access, and segmentation—so confidential pages don't land in the wrong tray and default passwords don't become open doors. We finish with ethics, because technicians handle sensitive data and trust is the real SLA. If you want sharper troubleshooting, stronger security, and higher A+ exam confidence, this one's a field guide you'll use tomorrow.Enjoyed the deep dive? Follow @ProfessorJRod, share this episode with your IT team, and leave a review so more techs can find it.Support the showArt By Sarah/DesmondMusic by Joakim KarudLittle chacha ProductionsJuan Rodriguez can be reached atTikTok @ProfessorJrodProfessorJRod@gmail.com@Prof_JRodInstagram ProfessorJRod

Laurens en Stefan gaan verder. Vanuit de Bajes dit keer, want wie kwam daar als een bezetene aangespeerd in z'n bolide? Lars Boom.Geen Roubaix of Tour, maar het was cross wat de klok sloeg. Over trainen achter de smart, winnen in Monopoli, en natuurlijk de reconstructie van die ene dag in Treviso. 29 januari 2008. De dag dat de Belgische hegemonie doorbroken werd en Lars Boom wereldkampioen cyclocross bij de elite werd.En hoe zat het ook alweer met die nieuwe Merry en de dat uitbrekende achterkantje in de sneeuw? Je hoort het allemaal, in weer een nieuwe aflevering van de Live Slow Ride Fast podcast.

This is a free preview of a paid episode. To hear more, visit smokeempodcast.substack.comNancy and Sarah discuss political writer Olivia Nuzzi, who became the main character on Twitter this week when an excerpt from her new memoir, published in Vanity Fair, coincided with a bombshell story by ex-fiancee Ryan Lizza. The scandal included cameos by broadcaster Keith Olbermann, politician Mark Sanford, and Livvy, a pop-music persona Nuzzi created at 16. Nuzzi is a talented journalist who's appeared on this podcast. Last year, she lost her gig at New York magazine after news hit about an entanglement with Robert F. Kennedy Jr. This sordid new chapter created a feeding frenzy among (many less talented) journalists, but Sarah and Nancy try to push past the schadenfreude to understand how we got here: the little girl drawn to the spotlight, fluent in the double-speak of politicians, and the ambitious young woman who lost both parents by 30. Also discussed:* That time Patty Hearst and the SLA hid out in Disneyland* The magic of the open road* “I like messy people.”* A political profile vs. a celebrity profile* Rule #1: Don't sleep with your sources.* Rule #2: Do not look through your significant other's stuff* The Keith Olbermann of it all* Vanity Fair and glamour of the 90s* Remember that time a governor from New Jersey was caught having sex with a dude, and it became a whole giant scandal? Yeah, us neither* More Monmouth Musings could use a better name …* Livvy, the “morally bankrupt” and “undeniably infectious” pop tartlet* The dirty-girl era of Ke$ha and Lady Gaga* The exhibitionism of the iPhone * Sarah will lay her chips on Nuzzi's futurePlus, Sarah can see alcoholism in people's eyes, Nancy reconnects with a former flame, a nearly unbelievable story about a 38-year-old unopened letter from Ken Kesey and much more!Make your world bigger. Become a paid subscriber.

When nineteen-year-old Patty Hearst was kidnapped from her apartment in February 1974, everyone assumed the heiress had been abducted for the purposes of ransom. However, in the days that followed, Hearst's kidnappers, the Symbionese Liberation Army (SLA), made themselves known when they sent a letter demanding the Hearst family provide food to every needy family in California. For nearly two months, the SLA held Patty Hearts captive, or so it seemed to the public. But when the group's demands were met and Hearst was given the opportunity to leave, the teenager shocked the world when, rather than flee her captors, she joined their ranks in support of their cause. Hearst's decision set in motion a chain of events that resulted in several acts of explosive violence and forever changed the way we think about victims of kidnapping. Yet in all the analysis of the case over the last fifty years, one question remains unanswered, and possibly unanswerable: Was Patty Hearst a willing accomplice to the SLA or was she a brainwashed victim trying to survive a traumatic ordeal?Thank you to the Amazing Dave White (of BRING ME THE AXE PODCAST) for research and writing assistance!

When nineteen-year-old Patty Hearst was kidnapped from her apartment in February 1974, everyone assumed the heiress had been abducted for the purposes of ransom. However, in the days that followed, Hearst's kidnappers, the Symbionese Liberation Army (SLA), made themselves known when they sent a letter demanding the Hearst family provide food to every needy family in California. For nearly two months, the SLA held Patty Hearts captive, or so it seemed to the public. But when the group's demands were met and Hearst was given the opportunity to leave, the teenager shocked the world when, rather than flee her captors, she joined their ranks in support of their cause. Hearst's decision set in motion a chain of events that resulted in several acts of explosive violence and forever changed the way we think about victims of kidnapping. Yet in all the analysis of the case over the last fifty years, one question remains unanswered, and possibly unanswerable: Was Patty Hearst a willing accomplice to the SLA or was she a brainwashed victim trying to survive a traumatic ordeal?Thank you to the Amazing Dave White (of BRING ME THE AXE PODCAST) for research and writing assistance!

When nineteen-year-old Patty Hearst was kidnapped from her apartment in February 1974, everyone assumed the heiress had been abducted for the purposes of ransom. However, in the days that followed, Hearst's kidnappers, the Symbionese Liberation Army (SLA), made themselves known when they sent a letter demanding the Hearst family provide food to every needy family in California. For nearly two months, the SLA held Patty Hearts captive, or so it seemed to the public. But when the group's demands were met and Hearst was given the opportunity to leave, the teenager shocked the world when, rather than flee her captors, she joined their ranks in support of their cause. Hearst's decision set in motion a chain of events that resulted in several acts of explosive violence and forever changed the way we think about victims of kidnapping. Yet in all the analysis of the case over the last fifty years, one question remains unanswered, and possibly unanswerable: Was Patty Hearst a willing accomplice to the SLA or was she a brainwashed victim trying to survive a traumatic ordeal?Thank you to the Amazing Dave White (of BRING ME THE AXE PODCAST) for research and writing assistance!ReferencesAssociated Press. 1974. "SLA commandos rob bank, shoot 2." Los Angeles Times, April 15: 1.Caldwell, Earl. 1974. "Miss Hearst says she joins terrorists." New York Times, April 4: 1.Conant, Jane Eshleman. 1974. "Guns point at 'Tania' in bank." San Francisco Examiner, April 16: 1.Cook, Stephen. 1976. "Doctor: I wasn't harsh with Patty." San Francisco Examiner, January 15 : 1.—. 1975. "Patty falling apart and must leave jail, her lawyer says." San Francisco Examiner, September 29: 1.Curtain, Andrew. 1974. "New offer to Patty's captors." San Francisco Examiner, February 23: 1.Fosburgh, Lacey. 1974. "Miss Hearst: an unlikely revolutionary." New York Times, April 7: 1.Hager, Philip, and Daryl Lembke. 1974. "Kidnappers may offer 'deal' for Hearst girl." Los Angeles Times, February 9: 1.Hager, Philip, and Dick Main. 1974. "$2 million for food pledged by Hearst." San Francisco Examiner, February 19: 1.Hearst, Patricia. 1974. "Transcript of Patricia Hearst's diatribe on 'SLA expropriation'." San Francisco Examiner, April 25: 4.Kendall, John. 1974. "'Never afraid of death,' defiant Miss Hearst declares on tape." Los Angeles Times, June 8: 1.Linder, Douglas. n.d. The Patty Hearst Tapes. Accessed June 22, 2025. https://www.famous-trials.com/pattyhearst/2209-tapes.Martinez, Al, and Robert Kistler. 1974. "Suspected SLA hideout stormed, 5 die." Los Angeles Times, May 18: 1.Nordheimer, Jon. 1974. "Miss Hearst is now Tania, but how and why?" New York Times, May 26: 160.San Francisco Examiner. 1974. "Father agree--it's Patty's voice." San Francisco Examiner, February 12: 18.—. 1974. "Her voice: 'Mom, Dad, I'm ok'." San Francisco Examiner, February 12: 1.—. 1974. "'It's terrible, vicious,' father says." San Francisco Examiner, April 16: 1.—. 1975. "Patty asked to join the SLA, Rolling Stone article says." San Francisco Examiner, September 29: 2.—. 1974. "'People in Need' opens with chaos, violence." San Francisco Examiner, February 23: 1.—. 1974. "The public's reaction to the kidnapping." San Francisco Examiner, February 17: 20.—. 1974. "5 victims in shootout at suspected SLA hideout." San Francisco Exminer, May 18: 1.2020. The Crimes That Changed Us. Performed by Sebastian Smith.Symbionese Liberation Army. n.d. "SLA Communique." UMKC Famous Trials. Accessed June 19, 2025. https://www.famous-trials.com/pattyhearst/2328-sla-communique.Toobin, Jeffrey. 2017. American Heiress: The Wild Saga of the Kidnapping, Crimes and Trial of Patty Hearst. New York, NY : Anchor Books.Turner, Wallace. 1974. "Graddaughter of Hearst abducted by 3." New York Times, February 6: 1.—. 1974. "Note says terrorists hold Miss Hearst." New York Times, February 8: 1.United Press International. 1976. "Jury acquits Steve Soliah." Daily Breeze (Torrence, CA), April 28: 6.Waugh, Dexter. 1974. "Key groups offer help to free Patty." San Francisco Examiner, February 14: 1.Waugh, Dexter, and Don West. 1979. "'Nothing wrong with being Patty Hearst'." San Francisco Examiner, February 1: 1.Subscribe to SiriusXM Podcasts+ to listen to new episodes of Morbid ad-free. Start a free trial now on Apple Podcasts or by visiting siriusxm.com/podcastsplus.

When nineteen-year-old Patty Hearst was kidnapped from her apartment in February 1974, everyone assumed the heiress had been abducted for the purposes of ransom. However, in the days that followed, Hearst's kidnappers, the Symbionese Liberation Army (SLA), made themselves known when they sent a letter demanding the Hearst family provide food to every needy family in California. For nearly two months, the SLA held Patty Hearts captive, or so it seemed to the public. But when the group's demands were met and Hearst was given the opportunity to leave, the teenager shocked the world when, rather than flee her captors, she joined their ranks in support of their cause. Hearst's decision set in motion a chain of events that resulted in several acts of explosive violence and forever changed the way we think about victims of kidnapping. Yet in all the analysis of the case over the last fifty years, one question remains unanswered, and possibly unanswerable: Was Patty Hearst a willing accomplice to the SLA or was she a brainwashed victim trying to survive a traumatic ordeal?Thank you to the Amazing Dave White (of BRING ME THE AXE PODCAST) for research and writing assistance!ReferencesAssociated Press. 1974. "SLA commandos rob bank, shoot 2." Los Angeles Times, April 15: 1.Caldwell, Earl. 1974. "Miss Hearst says she joins terrorists." New York Times, April 4: 1.Conant, Jane Eshleman. 1974. "Guns point at 'Tania' in bank." San Francisco Examiner, April 16: 1.Cook, Stephen. 1976. "Doctor: I wasn't harsh with Patty." San Francisco Examiner, January 15 : 1.—. 1975. "Patty falling apart and must leave jail, her lawyer says." San Francisco Examiner, September 29: 1.Curtain, Andrew. 1974. "New offer to Patty's captors." San Francisco Examiner, February 23: 1.Fosburgh, Lacey. 1974. "Miss Hearst: an unlikely revolutionary." New York Times, April 7: 1.Hager, Philip, and Daryl Lembke. 1974. "Kidnappers may offer 'deal' for Hearst girl." Los Angeles Times, February 9: 1.Hager, Philip, and Dick Main. 1974. "$2 million for food pledged by Hearst." San Francisco Examiner, February 19: 1.Hearst, Patricia. 1974. "Transcript of Patricia Hearst's diatribe on 'SLA expropriation'." San Francisco Examiner, April 25: 4.Kendall, John. 1974. "'Never afraid of death,' defiant Miss Hearst declares on tape." Los Angeles Times, June 8: 1.Linder, Douglas. n.d. The Patty Hearst Tapes. Accessed June 22, 2025. https://www.famous-trials.com/pattyhearst/2209-tapes.Martinez, Al, and Robert Kistler. 1974. "Suspected SLA hideout stormed, 5 die." Los Angeles Times, May 18: 1.Nordheimer, Jon. 1974. "Miss Hearst is now Tania, but how and why?" New York Times, May 26: 160.San Francisco Examiner. 1974. "Father agree--it's Patty's voice." San Francisco Examiner, February 12: 18.—. 1974. "Her voice: 'Mom, Dad, I'm ok'." San Francisco Examiner, February 12: 1.—. 1974. "'It's terrible, vicious,' father says." San Francisco Examiner, April 16: 1.—. 1975. "Patty asked to join the SLA, Rolling Stone article says." San Francisco Examiner, September 29: 2.—. 1974. "'People in Need' opens with chaos, violence." San Francisco Examiner, February 23: 1.—. 1974. "The public's reaction to the kidnapping." San Francisco Examiner, February 17: 20.—. 1974. "5 victims in shootout at suspected SLA hideout." San Francisco Exminer, May 18: 1.2020. The Crimes That Changed Us. Performed by Sebastian Smith.Symbionese Liberation Army. n.d. "SLA Communique." UMKC Famous Trials. Accessed June 19, 2025. https://www.famous-trials.com/pattyhearst/2328-sla-communique.Toobin, Jeffrey. 2017. American Heiress: The Wild Saga of the Kidnapping, Crimes and Trial of Patty Hearst. New York, NY : Anchor Books.Turner, Wallace. 1974. "Graddaughter of Hearst abducted by 3." New York Times, February 6: 1.—. 1974. "Note says terrorists hold Miss Hearst." New York Times, February 8: 1.United Press International. 1976. "Jury acquits Steve Soliah." Daily Breeze (Torrence, CA), April 28: 6.Waugh, Dexter. 1974. "Key groups offer help to free Patty." San Francisco Examiner, February 14: 1.Waugh, Dexter, and Don West. 1979. "'Nothing wrong with being Patty Hearst'." San Francisco Examiner, February 1: 1.Enjoy new episodes of Morbid ad-free. Learn more about your SiriusXM Podcasts+ subscription by visiting siriusxm.com/podcastsplus.Subscribe to SiriusXM Podcasts+ to listen to new episodes of Morbid ad-free. Start a free trial now on Apple Podcasts or by visiting siriusxm.com/podcastsplus.