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Private practices — ever feel like you can't win against the DSOs? Kiera talks about what your practice can continue to produce that DSOs, with their multiple locations and bigger budgets, can't replicate. Episode resources: Subscribe to The Dental A-Team podcast Schedule a Practice Assessment Leave us a review Transcript: Kiera Dent- Dental A Team (00:00) Hello, Dental A Team listeners. This is Kiera, and I am excited. Today's gonna be a fun rift of a podcast for you. It's gonna be like, can independent practices like private practice still beat DSOs if you want to? This isn't a rag on DSOs, it's not a rag on private practices. It's just can private practice, independent practices still win against the DSO? Because I think a lot of people are losing faith and confidence and feeling like if I'm not a part of a DSO, I can't win. So I wanna just tickle our brains today.   Think about it in a different way and let's have a fun rift because you remember this is the best place. Dental A Team is the place where we are obsessed about helping you have your best life. We call it the Yes Success Model, where we focus on you and your vision, earnings and profitability, and then systems scale and structure for you. So that way you've got the systems, the structure, and the scalability for long term. I'm obsessed with helping teams and doctors align. I'm obsessed with dentistry. My last name's Dent for crying out loud. I love this. So let's do a good rift. Let's talk about.   Can we really still win? It feels like DSOs, they got so much money over there and like they don't have to worry about margins. But I don't think that that's necessarily true. And I do believe last year, now I believe I know, DSOs had their first down year last year. So like I said, there is no I don't have a dog in the fight. My dog in the fight is which I don't even know where that phrase comes from. So if someone wants to pen pal me and tell me about this, I mean I could sure I could look it up. But like if you know, send me an email, Hello@TheDentalATeam.com. I'm Kiera. It's fun. It's fun to have a good pen pal over there.   But I I think my dog in the fight is what's gonna be the best for dentistry long term. That is my dog in the fight. I wanna make sure that we as a population, money talks. I don't blame you. Getting a good multiple for your practice, like, why not? You're in you're in the golden era of dental practices. Or so they make it put on paper. Make sure it really is the golden era for you if you choose to go that route. and a lot of people have been very happy. A lot of people have also had the shorts burned off them. Tell me why that's a thing too. Like,   How does someone get their shorts burned off them? I also want to know that phrase. I'm here for the phrases today, too, on the rift. And also, I'd love to know your opinion on this. So I'm gonna rift on my side and then shoot me an email. I'd love to hear. Hello@TheDentalATeam.com so okay, the rift today. DSOs are all like, okay, let me just go back, backing it up. Ultimately, I hope that we preserve the sanctity of great dentistry for patients forever.   That's my hope. I don't care if it's DSOs, I don't care if it's private practice, I don't care what it is. But I do not dental practices are businesses. I also work in dentistry as a clinician. And I'm not okay with us squeezing margins and compromising care to hit margins. To me, that's just unfair. It's unfair for patients. I look at a lot of things in our healthcare system and I don't love it. And so I think that dentistry has been kind of the wild, wild west, and we've stayed out of a lot of it. And so I just hope that while we make   Decisions financially and personally, I hope that we think about our long-term consequence. And that comes for a lot of new ones. I think that there's more senior doctors who have been in dentistry for a long time. Like they're not as worried about the multiples. I think a lot of our newer doctors who have a lot of debt on this, there's an easy cash payout. I would just say and a caution and an ask is let's just remember that we've had pioneers ahead of us who have paved the way.   Let's continue to be those pioneers that are able to preserve and sanctify dentistry, whether that's through DSOs or private practices. So that's Kiera's dog in the fight. And I hope that you agree. And maybe you have a different opinion. So like let's have a good conversation. This is what we talk about in our monthly masterminds. So come chat, hang out. I'd love to have you there. okay. So can we win? Here are some thoughts of how private practices can still win against DSOs. So like DSOs, they do have more locations. They got bigger budgets, they've got bigger teams.   But I do feel like there's still things in private practice that DSOs can't replicate. So I think the biggest threat is if you feel like you can't compete and you give up because there are ways and you can still win. you don't have to outspend a DSO. You don't need to out execute them. I do think that there's still competitive advantages. And so really leaning into whatever is going to be best for you. And again, remember, my my long term opinion is let's just make sure that we protect and sanctify dentistry for the health of our patients. And yes, I want you to be a profitable business owner.   I think you can have both. I don't think it has to be one or the other. DSO or non-DSO. Now, private equity. I do have my opinions about private equity and I don't believe that they're always there for ethical reasons. and so I just say like make sure that the decisions you're making with your practice and your patient is going to help long term, the greater good. So those are my two cents on it. So anyway, just we work with a lot of practices. So I think that there's still ways that you can still compete against large corps, corporations. So number one.   I think is independent practices, private practices, they have a connection that I think a lot of DSOs lack. DSOs tend to have a burn and churn model. So patients actually knowing their doctor, trusting their team, feeling remembered, feeling valued, and making sure that it's not a constant churn. Now I will have a call out. There are a lot of private practices that I know that are also on a burn and churn. They're churning associates left and right. They're turning team members. I get that it's hard right now, but I will say that if that's your practice, you are not competing against those corporate organizations that have.   run of the mill doctors. So I think that private practices can have a consistent provider relationship. I absolutely hate going to the dentist. I actually have transferred away. Like I can get multiple doctors across the road, guys. I don't know if you know what I do for a living, but I work with a lot of dentists. So if I want sporadic care, aka I go into one practice, but I see different providers all the time. And I know we have the whole phrase of they're provide their patients at the practice, which is not wrong.   But I'd say the more consistent you can have of team of providers that's going to help you win. I'm not saying to keep team members just because of longevity. I am here to say though, consistent provider relationships I do think will outshine. Just like I hate getting a new hair person, I hate getting a new nail person. People don't like to change that. So I think that that's going to be a zone for you. So personalized experience and long-term patient loyalty. Those are going to be   key places that I think private practices can win. Now, DSL is listening, guess what? This is your edge as well. Like this is how you can have an edge. And ultimately we're all here for it. But these things need to stay and maintain. so I do believe that patients who see the same doctor, the same team for years are much less likely to leave based on price or convenience. Like they're going to stick with you. Why why? They don't want to change that up. I don't want to go show someone else my mouth and have that awkward moment where I just   Don't like I don't have this, so just know that it's a space where like you gotta you gotta be connected, you've gotta help them feel seen and known. And I do believe that that's that is a zone. Daos are gonna scale systems, but like being able to scale genuine relationships, I do think is a harder thing. So in private practices, watch yourself. Look to see do you have those genuine connections? Are we scaling genuine relationships? Is that something that we're doing in those personalized pieces? You can stand out and still scale and still be profitable. So   I just think maybe like if you're looking at this, looking at your team, wanting to have a reflection, what's a way that we can connect and create more personalized patient experiences? How can we keep like, if we want to keep a patient for life, what's gonna make them want to choose us consistently? If there's a competitor of price, if there's a competitor of convenience, how do we make sure those patients stay loyal to us? another thing I think that private practice can win on is believe it or not, in private practice, you can typically move faster.   So I know a lot of people when they interview coming to Dental A Team coming from large organizations, like, I love that there's not as much red tape, Kiera. I love that you can move quicker, that we can make decisions faster, we're more nimble. And I think that that is a a huge selling point for private practices to be able to out outpace. You can have faster decision making, you can have faster implementation, you have less red tape, you've got greater flexibility. That can also create chaos. We gotta make sure that we don't we don't flex too much. But   If patients are wanting something, we can usually make those calls. We can have a more personalized experience. We can have like great water bottles. We can have different things that make patients have it. So, like you can change scheduling systems, you can change patient experience protocols, you can have like different pieces that your patients can recognize and see. Where in large organizations, a lot of times it does take like months to implement these items. So I do think agility does create opportunity. And so for you to just look at this and think like, all right, what ways can we be more flexible, more adaptable?   Like you don't have to have permission to improve things. So if you don't have to have permission to improve, what are we waiting to improve? What things could be improved upon? How can we make a better patient experience? How can we make a better team experience? there's a doctor that I was talking to the other day, and he said, Kiera, I have like a lifestyle practice and I want to attract team members for that lifestyle practice. And I just thought, like, how crazy cool is that? Because I think like that is the agility, that's the flexibility of a private practice that also makes it a great working place.   For team members. Like it's not just about patients, it's also about team and attracting those. So that's another zone where I think private practice can still outpace a DSO for sure. and then I do think that systems can win more than size in a lot of ways. So a lot of times we think bigger is better. And I know a lot of times I'm intimidated as a smaller business, if you will, smaller, medium sized business compared to large organizations. You're like, they just have all of it, but it's not true. So   I've seen in a lot of our practices very profitable independent practices, really strong leadership, great patient experiences, clear accountability. And you actually can have scheduling protocols and case acceptance systems and leadership systems and financial systems. Smaller scale practices can actually have great, incredible systems in place. So I do believe that a well-run single location or maybe two or three locations oftentimes will outperform on profitability than larger organizations because of.   execution is a lot stronger. So it's having those systems that are clear, having those and I know a lot of people are like, but what systems? And our team kind of boiled it down. There's about like 10 to 15 core systems that every practice that they'll implement and execute on, they're going to thrive. And it's scheduling case acceptance, leadership, like our billing protocols, things like that, morning huddles, very basic, non-sexy, but having those systems is going to be much grander than size.   And the larger you get, yes, they try to implement these, but I do still feel like those systems can create those predictable experiences. They can have a more customized experience. And then if we don't like it, we can pivot that system, we can refine that system, we can make it better. So I just think it's like, how do you have the best run practice? Not necessarily the biggest practice. And so I would look at your practice and what are the systems? What are the gaps? Where do we slip? Where do patients maybe fill that? Where does our team fill that? Let's fix that. Let's organize that. Let's let's have that. That way we're able to.   To be able to outpace. And so there's lots of ways. I think having those genuine connections, making sure that we've got systems that grow with us, having strong leadership teams, having a great, like, I don't know, community feel. There's just something different. Think about it. I have a fee-for-service chiropractor versus corporate chiropractor. I've gone to both. And one is like run of the mill. I come in, I'm out, checked in, checked out, it's cheaper. But they don't know me. They don't have an experience. They don't spend time with me. I do think time that doesn't mean like,   10 minutes, it means genuine connection time. Those things you still can win. And believe it or not, a lot of our practices are sitting at 20, 30, 40% profit margins. You can still be very profitable and not need to be in a DSO. They have a ton, they can scale a lot, but I also think there's an autonomy piece, there's a creativity piece, there's a branding piece of you being able to brand your location as you, to be able to give an experience that is very custom to you, to your audience. So I do believe that there's still a way that private practice can.   win against DSLs. I think there's space for both. I don't think one's right or wrong. But again, like I said, my dog in the fight is whatever we choose to do, whatever pieces we're doing, let's just remember to keep the sanctity of dentistry pure. Let's make sure that we're doing what's in the best interest of our patients. Let's make sure we're not cutting corners on dentistry. We're not trying to fit people in to hit production goals just to hit production goals. We're not compromising the the products that we use to be able to to hit the right profit margins. I believe that   being the best for our patients will always, always follow profitability. So with that, right now, let's remember, like relationships, you get it. You guys also have speed and flexibility and agility. And you also do have systems that you can put into place that will like beat size. They have other things. They've got billing, they've got it, but think about it, you're gonna sell to them anyway. You might as well get those things in place and you might as well try on your own. So   You think they're gonna take over all your problems? You can do this on your own and you don't have to sell your practice. I had a dentist who was wanting to sell his practice. He's like, Kiera, I'm gonna sell to a DSO. And I said, Great, like I'm here for it. Let's rally. And then he's like, I said, let's just think through. Like when they buy you, what are they going to do? He's like, they're gonna expand the practice and they're gonna put systems into place. And I said, Well, do you wanna do that? And he was like, Yeah, like I'm gonna sell to them and they're gonna make all the money on the things that I could just do. And I was like, I'm really proud of you because you're exactly right.   So if they're gonna do that anyway, why not do it yourself and reap the rewards? We're here to help you. I helped that practice. We went took them from eight eight ops to 15 ops. They're doing amazing. They're on track to be a five million dollar practice this year. All those things the DSO would have done. We were able to put the systems in place, build the leadership team, expand the practice. They're crushing it. They're doing incredible over there. So again, flexibility, agility, great patient care. And where they go, because people are like, well, a DSO is the only person who's gonna buy me. That's not true. Stop limiting yourself.   There's lots of people. There's lots of options. Do you know how many people come out of school wanting to buy? They come out and they do want to buy. So don't limit yourself, you guys. People are like, they can't afford homes. That's not true. There's a lot of people willing to there might now need to be two partners instead of just one partner. It's okay. It's gonna look a little different, but that doesn't mean that that's the only option. So I really do think that private practice dentistry can absolutely thrive. I do believe that the practice is winning today, DSO or private practice.   Aren't the ones with multiple the most locations? They're the ones with the strongest leadership, strongest systems, best connection, best patient care. Those are the ones that are winning. So look at it. Do we have the best leadership? Do we have the best systems? Do we have the best patient connection? Do we have the best experience? And if so, fantastic. Make sure that all your patients know it. Make sure that people are talking about it. You guys can do this. I love helping private practices. Whatever your dream is, whatever your goals, if it's DSO or not, I don't care. I just want you to have your best life. And like, hey, before you sell, maybe let's chat.   Let's talk about it. Let's see if there's a way that we could actually take that load and that stress and that annoyance off and you reap the rewards before you go and sell. there's so many ways that we can do this. So reach out. Hello@TheDentalATeam.com. And as always, thanks for listening. I'll catch you next time on the Dental A Team podcast.  

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

Tiffanie and Nikki share what dental practices should be looking at it now that we're halfway through 2026. Such milestones or touchpoints can include the goals you set at the start of the year, production numbers, the metrics ranges for healthy practices, and so on. From there, Tiff and Nikki talk about how to turn your midpoint numbers into game plans for the remaining six months. Episode resources: Subscribe to The Dental A-Team podcast Schedule a Practice Assessment Leave us a review Transcript: Tiffanie (00:00) Hello, Dental A Team listeners. We are back at you again. ⁓ we do four of these ish a month. We do one a week. myself, Tiffanie, and the consulting team. And I always pick from my consultant crew ⁓ whoever can be the best cohort on the topic and also their schedules, which is a fun.   little alignment of ⁓ situations here regarding our topic of choice today. So today I have Nikki. Nikki, thank you for making yourself available this morning for pivoting with me. I know I had some ⁓ travel stuff last week and had to move calls around so we had to move this call around and I just appreciate you so much Nikki and for pivoting and and Nikki I'm gonna I'm just gonna say it. It's funny.   And we all kind of podcast from wherever we can. We try to ta stay as centralized as we possibly can, but we all at some point or another are in a very different place than our norm. And Nikki has had so many fun spots. So if you are a an avid podcast listener, you've heard Nikki's and you've not watched, I want you to go back and like look through all of Nikki's and see all of her different backgrounds because it's just so fun.   Nikki Mack (00:55) Yeah.   Tiffanie (01:09) you're like our Carmen San Diego, Nikki. How are you doing today in ⁓ your new space today that is a very temporary and then you'll have a new new space, but how are you doing over there?   Nikki Mack (01:20) Yeah, doing great today. ⁓ this may just be my thing. I may have to find somewhere new to podcast from every time. We'll just yeah. Yes. ⁓ I did love Carmen San Diego, so absolutely I'll just wear a red hat. You'll see me out and about. That would be amazing. ⁓ yes, doing good. super happy to be back. I can't get enough of doing podcasts and   Tiffanie (01:27) I like it. Or pop something in there that people have to find, like what's different.   I don't know.   I love it   Nikki Mack (01:47) No matter where we are, absolutely we're here for our listeners and we're gonna get it done. So I'm ready.   Tiffanie (01:52) I love   it. I love it. thank you. And listeners, you guys know if you've listened to us at all that we truly do love what we do, that we are here to ensure that we give as much information out to the dental world and beyond. We have listeners who aren't even a part of the dental world because entrepreneurship is all the same. Business is business is how I describe it. So whether you're dental, whether you're outside of dental, I know we've worked with podiatrists and optimists.   And chiropractors and all kinds of different businesses. We've helped CPAs and financial advisors and all kinds of different organizations because at the bottom of everything, business is the foundation here and dentistry is what you do on top of that foundation. So we're excited for you guys to be here. We're excited to be ⁓ hosting this podcast to be delivered in July.   July is one of my favorite months of the year for multiple reasons. Brodie's birthday is in July. He's actually eighteen ⁓ this year. The the recording of this podcast, he will be eighteen in almost a month from now. So that's crazy and scary. And for those of you who have been listening for as long as we've been doing this, you probably remember when little Brodie, who's like nine or ten, maybe not even yet, eight, ⁓ came on and he talked about having a working mom. So really cool.   If you've been listening for a long time, you know who he is. But July is also one of my favorite months of the year because this is really kind of our reset month. This is where I just kind of slow down in life and in business and really take a look at what are the things I said I wanted to accomplish this year and where am I at in relation to those things? Do I need to pivot? Are did things change? Did I have things on my docket that I was like, gosh, that's kind of arbitrary or   Too much or you gosh, I'm really just not gonna get to that this year and that's okay too. So have we changed things that maybe we need to reevaluate and reset? for me personally, I said I'm gonna do a pull up this year, and you what, it's still on my docket, but six months, seven months into the year, not having made ⁓ a lot of progress on even attempting to do one, I think I'm a little behind the wheel on that. So needing to reevaluate, but that's really what we're talking about today. So whether it's personal goals, business goals   Family goals, whatever they are, this is a really great time of year to just take a step back and really, really, really push forward on what we want our life to look like. So we brought Nikki on today to help us with that as far as the dental industry and the and the dental business. And Nikki, from your perspective, consulting practices, I know this is the time of year as well that we're gonna take a look with our clients and we say, Hey, how was Q2?   So we've already done a how is Q1. We go through, we evaluate quarter one in comparison to last year at the same time. Well that now our consultants are all going through for their clients and really evaluating quarter two in comparison to quarter one and also in comparison to the projections that we made with them earlier this year. So as we're prepping for that and as we're getting ready to have those conversations with our clients, Nikki, what are some of the key indicators, the KPIs that you have always loved watching for your practices? ⁓   in consulting, in managing, in working within the DSL world, all of your different spaces, what are the ones that you really like to take a look at in July?   Nikki Mack (05:16) So for me, I mean, production's the obvious one, right? Like we never really take our eye off of production ever. Q one, two, three, four, always. ⁓ but I think deeper than just production, it's looking at those metrics that drive production, especially that we started the year with the intention of working on. So a lot of my offices this year, it's case acceptance. Like 2026 is all about case acceptance.   Tiffanie (05:23) Yeah.   Nikki Mack (05:42) And so for me at this halfway point, that's where I'm really like digging into that number. So what does the percentage look like? And there's metrics, right? And there's ranges and there's healthy offices and things like that. But like I tell my teens a lot when we first start, the biggest thing I'm looking at is just where are we? So like January, ⁓ if we've been together for a while, where our starting point is, your percentage now is what I care about. And then how are we going to increase it? So hopefully by July.   We've seen some movement in that number and production would reflect it. But if we don't understand how we're driving that production, how do we keep doing it or make those changes? Like we said, going into Q3 that we need. So ⁓ I would say case acceptance is a huge one. Are you tracking dollars? Are you tracking percentage? Whatever we've been looking at, let's really dive into it. And then honestly, another one that I've seen a lot lately is ⁓   New patient retention. So we a lot of doctors have expanded or brought in associates or we have a lot of new practice owners, right? And so new patients, we know it, they matter, they're super important. But if we're bringing a ton of women and they're all just leaving and not like staying with us, then like that's a lot of time invested. ⁓ so we've I've seen a lot of focus this year with what's our new patient numbers and then who came back.   So July is a perfect time to really be dialed in on that because we're going to start to see those six month recares come back, the ones from the end of last year. So that's a super good number, in my opinion, to really get an eye on the health of your practice and how some of our systems are working. Because I love a system.   Tiffanie (07:27) Right. All right. That's what we're here for, right?   Yeah, I I totally agree. And I love the new patient spot too, like in conjunction with the case acceptance that you said both of those, because oftentimes we will look at the case acceptance and we'll say, Gosh, I w our case acceptance is really high, but our we're still not meeting production, or our case acceptance is really low. We've got to   Gotta do more ⁓ case ex you know, case acceptance, whatever. But when you start to really dive into it, a lot of times our case acceptance will tell us every time our case acceptance will tell us what our diagnosis is as well, right? So if we're only looking at what is our percentage of case acceptance, whether it's dollar or yes or no, it doesn't really matter. But if we're only looking at that number and we're never looking at how much have we diagnosed, we kind of miss the boat there on the in between. So as you're taking that mid year.   Check-in here in July. I think that that is a great place to start. I totally agree, Mickey. Because at that point you can say, do we have the opportunity to meet the goals that we are projecting towards? So where are we at? We always say go look at what your full goal was or is, right? So what are we supposed to do by December 31st, 2026, or whatever year you're listening to this in? What are we supposed to do by the end of the year? And where are we at in relation to that? So six months.   Nikki Mack (08:39) Ha ha   Tiffanie (08:46) Seven months, you know, into the year, are we six months worth of production towards that goal, or do we need to re-establish a different goal moving forward to account for missed opportunities? Now I'm gonna say something here, and my team members just know I love you. Doctors, make sure you do this. So if you're tracking, you're like, all right, guys, I listened, Nikki, I heard you, and I'm actually ahead on my goal, and I'm ahead by $50,000 this year.   Keep your goal the same. Do not adjust your goal down to meet that other goal. And I know that that sounds kind of obvious, but I want to just state it because there are a lot of people that are like, ⁓ awesome. This month is short, so like it's okay, we don't really have to push. Like, no, no, no. Keep pushing and hit for that top tier goal every time so that you have that buffer if it's ever necessary. Or best case scenario, we end out ahead. Now, the other flip side to that is that.   A lot of times what we'll see is we might be short by like 20,000 or 10,000, something not like massive. It might not be a whole month's worth of production, but it might be that like ten to twenty thousand dollar mark. So then what we're doing is we're saying, okay, great, this is what we've done. This is where we're gonna go, where we need to be. That gap is now added to the months coming. So if you've got ten thousand dollars that you're short, divide that by the next six months, add that crown cost, by the way.   Add that extra crown into your monthly goal and then reestablish what that will look like. That's your mid year check-in. So then to Nikki's point of the case acceptance, I love that, Nikki, because if we know where we're going, now we can say, okay, how are we gonna get that? Like, how do I get now to 145 instead of that 143 mark? How do I get that extra couple thousand dollars a month? And why was I short to begin with? So what happened there that   Nikki Mack (10:11) Right.   Tiffanie (10:41) caused that that mishap and it could have been hours, it could have been days closed, but it could have also been in that case acceptance and diagnosis. So then we start really looking in. So production, collections should match your productions or at least be 98%, right? And then in order to hit those, what does our case acceptance look like? And do we have enough opportunity to hit those goals? And if the opp the opportunity isn't there, now we're gonna say, okay, great, why aren't my new why aren't I getting enough new patients? Or   Nikki, one of my favorite ones is we have a slew of new patients, sometimes too many, and we're not diagnosing what we need to diagnose from them. And I'm and I think Nikki this can be misconstrued sometimes because it's like, well, I diagnose what my patients have. Super cool. I totally agree with you. We are never, ever, ever going to be the consulting company that's like, no, no, no, there's more, find it. Maybe there is, but I don't know. I'm not sitting in your chair and dentistry is, you know, it's completely objective. So   Nikki Mack (11:16) Yeah.   Tiffanie (11:39) I'm not gonna tell you how to dentist. I'm just gonna say, do we have the right opportunities then? So if you've got clean patients coming into your practice and you're hitting new patient numbers, but we're still not hitting diagnosis, are we reaching the target audience that you need in order to sustain and grow your dental practice? And Nikki, I think that falls into play where we've got now   the KPIs, you know, production collections, those are those are hand in hand, case acceptance and then that new patient mark. Are we getting the right new patients? Because to your point, Nikki, in July, if that first six months of new patients didn't give me the bucket that I needed, I have time now to reevaluate and say, okay, well what does this need to look like? How does my hygiene department need to grow and do my new patients need to shift and change with that? And is that something, Nikki, that you've seen like   all over the place. Is that like how how often are you seeing that we just arbitrarily kind of grab the new patients that we can and then miss marks and are like, wait a second, what's going on?   Nikki Mack (12:42) Yeah, I think because a lot of times it's easy to feel like, well, a new patient is a new patient and that's a win, you know, like we hit 45. But to your point, we have to be intentional, right? And there is a degree of we market, you know, to the audience, right? We hit the keywords, we're the Google search, we know what we're looking for. But most of the marketing companies that our offices work with, they say those similar things. Like, what are you trying to bring in?   Tiffanie (12:49) Yes.   Nikki Mack (13:10) I had an office, for example, that was changing some of their insurance, you know, contracts. And their emergency ads were bringing in a lot of those insurance-driven patients. So they were having a real struggle with maintaining the schedule they were trying to build with where they were trying to go and who was coming in. And we realized it's the ads, right? You know, that's why this number is so high and it's so hard. So we pivoted, which   Ironically enough, on the flip side, I had another practice just a couple weeks ago. We were having this new patient conversation. They're focusing on some emergency dentistry targeted ads because they do have a little bit of flexibility. We've got a new associate, which is, you know, always great for opening up schedules. And so we realize that we can accommodate some emergencies, right? And ideally be able to take care of those patients the same day, which is just not always the case, right? We've we've busy practices, we can't always do it.   So we wanted to capitalize on that opportunity both for the practice, but also what great patient care, right? To consistently be able to see some emergencies, build new patient relationships, and get them taken care of as quickly as possible. It just presents that kind of opportunity. So that to me, actually, funny enough, emergency patients ends up being a really good example of no two offices are the same and no one answer works for everyone. So what could be the like   symptom in one practice is the solution in another. So that's why it's super important to take these deep dives and not wait till the end. ⁓ any of my practices or teams that are listening, they hear this all the time, but especially around this time of year, ⁓ we have to sometimes make decisions like we're a speedboat or sometimes like we're a cruise ship. And sometimes there's a quick pivot and it's an easy fix. And those are some of my favorites as a consultant, right? Those are easy   Tiffanie (15:01) Yeah.   Nikki Mack (15:02) Victories, changes, challenges overcomes. Some we're more like a cruise ship and it's a bigger strategy and tactics that we need to use to change course. So if we don't take a look now while we're halfway through the year, it's gonna be October, November, and all of a sudden, like we don't have time to make an impact in twenty twenty six or the year, you know, when you're listening. So   Tiffanie (15:23) Totally agree. Yeah. Yeah, whatever yeah.   Nikki Mack (15:27) You it's very important to decide what that looks like and that's how we make decisions. Our numbers tell a story. I know I didn't invent that, but I do say it a lot 'cause it's so true. And so we have to read that story and that's how we make the decisions that are gonna either turn things around for us or keep us on that course to just finish super strong.   Tiffanie (15:47) Yeah, I agree. Thank you. And I think that July presents a really big opportunity ⁓ for that, for ch making changes. And and previously marketing marketing still is is like a a beast that's really hard to know. Like you nobody knows marketing. Even marketing companies are like we're still trying to figure it out. And it's just like this beta test constantly. So it's it can be very frustrating in that way, but it's also really cool because what's happened now, I know years ago it was like, okay, well, if we're gonna change marketing.   Nikki Mack (16:04) Yeah.   Tiffanie (16:16) It takes six months to even see the product of that marketing switch. But thank goodness we live in a progressive ⁓ future. And we're here in the future where it doesn't take weeks, months, you know, six months or so to see that change. Right now we can make a change in the marketing and the online presence and it's shifting things immediately. And so to your point, Nikki.   A practice that's like, gosh, we need to fill this doctor's schedule. Let's get some emergency patients in that has the right systems that can convert. So that would be, I think if if you're in July and you're like, yep, you're right, guys, I need to shift my marketing focus and I need to look at my new patients. If you're gonna go the emergence any new patient route, but especially the emergency kind of limited exam new patient route, make sure you've got solid systems and that now you're tracking on top of that our new patient conversion.   rate, right? Because a new patient coming in on a limited exam 0140 in emergency is still a new patient to your practice, but your numbers can get skewed if they're not staying. Because it's adding to your active patient count today. But if they don't come back in 18 months, like it was just a waste, right? So we don't want to waste your dollars. We want to make sure that's where Nikki comes in clutch right now with that with that team because she's not only helping them see, okay, great, let's shift to this emergency new patient.   kind of standpoint to fill the associate's schedule, but she's also helping to train and see how do we track the new patient conversion and what are we saying now? This patient calls as an emergency, cool. How do we get past just a PA? How do we get to that full series of x-rays, the panoramic, the CT, to make sure that we're looking at everything and fully establishing them as a patient in your practice, not just that emergency. So   Those things go in tandem and this is the perfect time of year to look back and say, these are the things that I wanted. What are the systems that have gotten me to where I'm at? And where do I need to tighten those systems up, maybe shift them a little bit? Or are there systems that I don't know yet that I'm losing things out the back end? And honestly, your patient base is a huge space for that. Your recare is your hygiene full summers upon us, summers rough in most general practices. ⁓   Nikki Mack (18:31) Yeah.   Tiffanie (18:35) I think pediatrics, you guys are you guys are flying high. These are your biggest months. But for GP world, and even oral surgeries flying high right now. But our GP world, our period worlds, those worlds kind of endo ⁓ we see a slowdown and we've gotta reestablish what that looks like. So reactivation campaigns are huge right now, but we're looking at   what were our goals and what is it gonna take for the rest of the year to get us there is gonna be massive. And Nikki, I love that you started us with that case acceptance piece and the new patients because truly this is a great time of year to look at that. And I think everyone thinks it's kind of like that the diet thing, right? Like I'll start on Monday. Everybody's like, well I'll start in January. Like I have a practice that hats off to them. I love it. They actually redo their fees in July because they're like, I don't like   Summers is fine. Like summer's less busy. We're doing so much in December, January, February, all the way through to April. Realistically, it's crazy. So they're like, why not do it in the summer when it's slow? I'm like, wow, that's actually really freaking smart. So to piggyback off of that, like waiting until January to say, let's change our patient base, let's change our avatar, let's   wow, we didn't quite make it, so let's add it to this year. And instead of a seven to ten percent increase, we need to do a 12% increase over last year. Like start now, reestablish in July and say, time out, where are we? Where are we trying to go and what's that gap in between? And then evaluate what are the systems or the processes that I need to change or increase that are gonna get my team there and how do I get that training. Now for your leadership team, they need to be doing that within their departments as well.   So each department should have their own KPIs, which doesn't have to be hard, you guys, even for our our clinical team. Like it could just be schedule full. It could just be ⁓ diagnosis chair side, right? Or I love nothing more, Nikki, actually on your limited emergency. I love dental assistance tracking. They're like comp, they're limited to comp.   conversion. So did they take a 0140 and convert it to 0150 chair side? What is that conversion? So we really don't have to make it difficult. We just have to make it impactful. It needs to make a direct impact on our overarching goals and it needs to be trackable and measurable. So with that, I think Nikki, what would you say if you could only give we're going to say production collections is the same because to me there's there it's ridiculous you track them both, right? They're the same.   Nikki Mack (21:10) Wait.   Tiffanie (21:11) So if you could only   Nikki Mack (21:11) Yeah.   Tiffanie (21:12) give three KPIs knowing production collections is one, what are the other two KPIs that you would insist that everyone takes a look at in July?   Nikki Mack (21:21) Ooh wee. ⁓ so I am a big clearly you can tell I'm a big fan of new patient reappointment. ⁓ did they come back for six months? Are they scheduled? Did we complete treatment? Right, there's a few pieces to it, but adding them to the patient base and getting them sticky. ⁓ that would be my number two. And then number three, I don't know, Tiff. I I kind of feel like I have to go case acceptance because it's   Tiffanie (21:29) Yeah.   Yeah.   Nikki Mack (21:48) It's pretty all encompassing. there's so many parts to it, but it just the direction of your case acceptance is such a good indicator of the health of your practice. So those are my those are probably the first three that I look at. If I if I didn't know anything about your practice, your goals, or where you were headed, those are three places I'm definitely digging in right away.   Tiffanie (21:52) Yeah.   Awesome. Thank you. I love those. Yes. And I will say in tandem, case acceptance. I like to group things. ⁓ Kiera and I like to say that we get away with a lot because we will group things together. So that's where my production collections comes in. And I think in tandem with the case acceptance, I say diagnosis because I just really harp on, I think from a team member standpoint of tracking case acceptance for so long and having to   really dig in as a treatment coordinator and as an office manager to figure out why aren't we hitting these goals and then seeing these trends. So tracking the trends within your diagnosis to lead to your case acceptance is huge. I would have, you know, a doctor gets he's ready for vacation. He's worked too hard. He needs a vacation and his case ex or his case acceptance dropped and his diagnosis dropped as well or ⁓ somebody's killing it. So making sure that we're watching all of those pieces is huge. So no matter what, you guys, it is mid year and you're   Nikki Mack (22:52) Yeah.   Tiffanie (23:05) well into potentially your Q3 at this point. So I hope you're listening to this early. ⁓ but really take an assessment and look to see where have I been, where am I going and what's that gap. And if you're a few weeks into Q3 already, that's okay. ⁓ do it before Q4 as well. You need to be doing this literally every quarter, every month, every week, but for sure mid-year. And look at those areas where where   you need a little bit of help. And if you do need help, we're here for you. You know, we're we're right here. We've got a freaking thousand podcasts or something that you can listen to. They're all here, they're all available to you, but we're also available at Hello@TheDentalATeam.com and at TheDentalATeam.com, you guys, we have a free assessment call that you can schedule with us where we can take a look at your systems, ⁓ some of your overarching KPIs and really help to direct and guide you and also assess if you are a good fit.   For Dental A Team consulting and if you're ready for it. And if you if you're not, ⁓ or if we're like, you know what, you're almost ready, you're you're not quite there, we will keep in touch and we will still always share all of the information. But we would love to chat with you and really see where you're at and how we could best serve you in our mission to ⁓ benefit the world of dentistry. So Hello@TheDentalATeam.com. Also, drop us a five star review below. Let us know what your KPIs are. People do read those and they like to look in there for extra.   Help. So Nikki, thank you so much for being here with me today. I know that we pivot and ⁓ that used to be one of our core values. Kiera and I removed it because we're all really good at pivoting, but we realized that that is a core value made it like we were pivoting too much. But we really do pivot a lot as dental consultants, and it's something that we're great at because we're problem solvers and we do it with our with our clients constantly. So thank you for being here. Thank you for pivoting and having a new Carmen San Diego background.   And you all thank you so much for being here. Thank you for listening and we will catch you next time.   Nikki Mack (24:58) Bye.  

Who were the biggest MLB prospect standouts from Week 14 of the 2026 Minor League Baseball season?Join Tim Kanak (@FantasyAceball) as he breaks down his T-10 MLB Prospects of the Week, featuring the hottest performers across MiLB. From elite Top 100 MLB prospects to under-the-radar dynasty baseball sleepers, this episode covers the players making the biggest impact and climbing prospect rankings.Whether you're preparing for your next dynasty baseball trade, following fantasy baseball prospects, or simply tracking baseball's future stars, this episode is packed with scouting insights, player development analysis, and long-term fantasy value.1. Sammy Stafura, SS, Pirates (Top 400)Stafura exploded this week, hitting .536 with 7 extra-base hits, 4 home runs, and 2 stolen bases. His athleticism, bat speed, and improved approach continue to make him one of the fastest-rising middle infield prospects.2. Josue De Paula, OF, Dodgers (Prospect #14)One of baseball's premier young hitters, De Paula finished with 6 extra-base hits, 2 home runs, and 4 stolen bases, continuing to unlock more game power while maintaining his outstanding plate discipline.3. Colton Shaw, SP, MarinersThe Mariners continue to develop another intriguing arm as Shaw dominated with 11 strikeouts, no walks, and one hit allowed over six innings, showcasing elite command and outstanding pitchability.4. Leanders Matos, 2B/3B, BrewersThe 17-year-old DSL breakout launched 5 home runs, collected 6 extra-base hits, stole a base, and continued one of the hottest starts in international baseball.5. Henry LaLane, SP, Yankees (Top 400)LaLane returns to the T-10 after another dominant outing, striking out 11 without a walk across seven scoreless innings. His increased slider usage has transformed him into one of the hottest pitching prospects in the minors.6. Austin Overn, OF, Rays (Prospect #160)Back from injury, Overn immediately reminded everyone why he's one of the game's most exciting speed threats, posting 4 extra-base hits, 2 home runs, and 4 steals in just four games.7. Dakota Jordan, OF, Giants (Prospect #166)Jordan's elite raw power was on full display with 5 home runs and 7 extra-base hits. He's now one of only four players in professional baseball with at least 15 home runs and 15 stolen bases this season.8. Zyhir Hope, OF, Dodgers (Prospect #52)Hope joined teammate Josue De Paula on this week's list after smashing 3 home runs, collecting 5 extra-base hits, and swiping 2 bases, continuing his impressive Double-A campaign.9. David Hagaman, SP, Diamondbacks (Prospect #139)Hagaman tossed 7 scoreless innings with 9 strikeouts and no walks, continuing his climb after returning from injury with improved command and premium stuff.10. Ramon Marquez, SP, Phillies (Prospect #139)Marquez rounds out the list after striking out 9 over five innings, once again flashing one of the best changeups in the minor leagues and strengthening his case as one of baseball's top emerging pitching prospects.⚾ Top MLB prospects from Week 14⚾ Updated Top 100 MLB Prospect Rankings discussion⚾ Dynasty baseball buy-low and breakout candidates⚾ Fantasy baseball prospect analysis⚾ Scouting reports and player development trends⚾ Pitching prospects on the rise⚾ Future MLB stars making noise across Minor League BaseballIf you love MLB prospects, dynasty fantasy baseball, Minor League Baseball, MLB Draft, MLB Pipeline, Baseball America, scouting reports, and prospect rankings, this episode is for you.

JDK 26 optimise la JVM dans ses moindres recoins, le SDK Java d'Agent2Agent passe en 1.0, Micronaut 5 est là. Côté terrain, un retour d'expérience après 40 jours à coder avec 100 % d'IA : génie ou junior, Alzheimer numérique et dette technique invisible. Pendant ce temps, GitLab restructure, Microsoft suspend ses licences Claude Code, et un développeur injecte un prompt destructeur dans sa lib JUnit. La révolution IA a un coût et les boites commencent à s'en rendre compte. Enregistré le 12 juin 2026 Téléchargement de l'épisode LesCastCodeurs-Episode-341.mp3 ou en vidéo sur YouTube. News Langages Les améliorations de performance dans le JDK 26 https://inside.java/2026/06/09/jdk-26-performance-improvements/ Côté bibliothèques, l'API LazyConstant (anciennement StableValue) fait son entrée en prévisualisation pour permettre une initialisation paresseuse, sécurisée pour les threads et optimisée par le mécanisme de constant-folding de la JVM. L'extraction de chaînes de caractères via MemorySegment::getString a été revue pour réduire considérablement les allocations intermédiaires et les copies en mémoire off-heap, accélérant fortement les traitements sur les chemins critiques (hot paths). La méthode générée automatiquement hashCode() pour les classes de type record a été optimisée par la JVM pour atteindre un niveau de performance équivalent à une implémentation écrite manuellement. Le ramasse-miettes G1 bénéficie du JEP 522 qui redessine sa table de cartes (card-table) afin de réduire les coûts de synchronisation des barrières d'écriture, offrant un gain de débit de 5 % à 15 % sur les applications manipulant énormément de références d'objets. Grâce au JEP 516 (Project Leyden), le cache d'objets Ahead-of-Time (AOT) adopte un format de flux agnostique, ce qui lui permet d'être compatible avec n'importe quel Garbage Collector, y compris le ramasse-miettes à très faible latence ZGC. Le démarrage de la JVM s'accélère par défaut lorsqu'aucune taille de tas n'est configurée, car HotSpot n'applique plus de pourcentage initial (InitialRAMPercentage) mais démarre directement avec la taille minimale (MinHeapSize) pour éviter d'allouer des métadonnées inutiles. Les threads virtuels gagnent en robustesse en étant désormais capables de céder la main (yield) pendant les phases d'initialisation des classes, éliminant ainsi le risque de famine des threads porteurs (carrier threads). Le compilateur C2 JIT améliore son modèle de coût pour la vectorisation des boucles (SIMD) et se montre maintenant capable de compiler et d'optimiser des méthodes dotées de listes de paramètres extrêmement longues. Librairies Release candidate du A2A Java SDK supportant versions 0.3 et 1.0 en même temps https://medium.com/google-cloud/a2a-java-sdk-1-0-0-cr1-released-f0c651ec9139 Dernière étape avant la GA : Toutes les fonctionnalités prévues pour la version 1.0 sont finalisées. Migration simplifiée depuis la Beta1. Compatibilité v0.3 : Ajout d'une couche de compatibilité permettant aux agents v1.0 de communiquer avec les systèmes v0.3 (via JSON-RPC, gRPC ou REST). Support natif pour Android (nouvel AndroidHttpClient). Uniformisation des clients HTTP pour garantir une cohérence entre les versions. Nouveau parseur SSE (Server-Sent Events) conforme aux spécifications. Ça y est, le SDK Java de l'Agent 2 Agent Protocol est sorti en version 1.0 finale ! (avec compatibilité v0.3 et v1.0) https://medium.com/google-cloud/a2a-java-sdk-1-0-0-final-released-10c05b6aee34 Lancement officiel : Sortie de A2A Java SDK 1.0.0.Final, la première version stable (GA) du protocole Agent2Agent. Objectif du protocole : Standard ouvert (Linux Foundation) permettant aux agents IA de communiquer, déléguer des tâches et collaborer, indépendamment du langage ou du framework. Interopérabilité : Introduction de l'Integration Test Kit (ITK) pour valider la compatibilité entre les SDK (Java, Python, TypeScript, etc.). Transports supportés : Support complet et équivalent pour JSON-RPC, gRPC et HTTP+JSON/REST. Alignement total avec la spécification A2A 1.0.0. Passage aux Java records pour l'immutabilité et moins de code répétitif. Architecture interne basée sur un MainEventBus pour garantir la persistance et éviter les conditions de concurrence. Intégration d'OpenTelemetry pour le suivi et la surveillance. Support d'Android et compatibilité descendante avec la version 0.3. Installation : Gestion des dépendances via Maven BOM (org.a2aproject.sdk). Sortie de Micronaut 5.0 https://micronaut.io/2026/05/20/micronaut-framework-5-0-0-released/ Lancement majeur : Disponibilité générale de Micronaut 5, incluant une refonte de plus de 70 modules et la plateforme BOM. Baselines techniques : Support de Java 25, Groovy 5, Kotlin 2.3 et GraalVM 25.0.3. Optimisations internes : Amélioration significative des performances au démarrage et réduction de la surcharge à l'exécution via une refonte du conteneur IoC et du traitement à la compilation. Architecture HTTP : Support stable de HTTP/3, nouvelle API de formulaires (multipart) et annotations de nullabilité (JSpecify) pour une meilleure interopérabilité Kotlin/IDE. Configuration : Nouveau système d'importation de configuration (remplaçant le Bootstrap Configuration) et validateur de schéma JSON intégré. Fiabilité : Nouvelles API programmatiques pour les politiques de retry et circuit breaker. Sécurité & Outils : Mise à jour majeure des dépendances (Jackson 3, Ktor 3), rafraîchissement du Panneau de contrôle et diagnostics AOT améliorés. Écosystème : Mises à jour complètes pour les bases de données (Data, SQL, R2DBC, MongoDB, Redis), le cloud (AWS, Azure, GCP, OCI) et les tests (JUnit 6, Testcontainers 2.0). Évolutions notables : Intégration HTMX dans Micronaut Views, retrait du support RxJava 2 et migration de divers processeurs d'annotations vers des modules dédiés. Comment rajouter un agent IA dans une app Android, avec le tout nouveau framework ADK pour Kotlin https://glaforge.dev/posts/2026/05/21/wiring-adk-kotlin-agents-in-an-android-application/ Guillaume a participé au développement et au lancement du nouveau runtime ADK pour Kotlin et Android https://developers.googleblog.com/adk-kotlin-android-building-ai-agents/ Tutoriel sur comment intégrer un agent ADK dans une app Dépendances : Ajout du noyau ADK (google-adk-kotlin-core) et du processeur KSP dans build.gradle.kts. Sécurité API : Utilisation de local.properties pour stocker la clé API Gemini et l'exposer via BuildConfig afin d'éviter le hardcoding. Définition de l'agent : Création d'un objet LlmAgent configuré avec le modèle Gemini, des instructions spécifiques et des outils (ex: GoogleSearchTool). Utilisation de InMemoryRunner pour gérer automatiquement le contexte et l'historique de la session. Implémentation de runAsync avec StreamingMode.SSE pour un retour en temps réel dans l'interface. Threading : Exécution des requêtes réseau sur Dispatchers.IO et mise à jour de l'état de l'interface utilisateur sur Dispatchers.Main. Comment développer et hoster des agents IA sur la plateforme d'agents managés de DeepMind https://glaforge.dev/posts/2026/05/21/managed-agents-with-the-gemini-interactions-java-sdk/ L'équipe DeepMind de Google a lancé une plateforme d'agents managés sur son API Gemini Interactions https://blog.google/innovation-and-ai/technology/developers-tools/managed-agents-gemini-api/ Guillaume a implémenté un SDK Java pour utiliser cette API Gemini Interactions, qui donne entre autre accès à tous les modèles mais aussi à cette plateforme managée d'agents IA Agents managés : Permet d'exécuter des agents autonomes qui raisonnent, planifient et exécutent du code dans des environnements isolés (sandboxes), sans gestion d'infrastructure par le développeur. Environnement distant : Utilise des espaces de travail Linux éphémères dans le cloud via le paramètre remote, permettant l'accès réseau et la persistance des fichiers sur plusieurs appels. Agents prédéfinis : Accès immédiat à des agents spécialisés comme deep-research-pro (recherche multi-étapes) ou antigravity (tâches de codage généralistes). Agents personnalisés : Possibilité de configurer ses propres agents avec des instructions système dédiées, des outils spécifiques (exécution de code, recherche Google) et des règles réseau (egress) personnalisées. Architecture basée sur les étapes (Steps) : Utilise une structure de données typée (Step, Content) pour suivre le raisonnement de l'agent, ses appels de fonctions et ses résultats en temps réel. Outils et Schémas : Inclut des utilitaires pour générer des schémas JSON complexes via une interface fluide (DSL), par réflexion Java ou par parsing JSON. Streaming réactif : Support natif des événements en temps réel (SSE) pour suivre la progression de l'agent et recevoir les deltas de contenu au fur et à mesure de la génération. Flexibilité : Fournit un gestionnaire de routage (InteractionsHandler) pour créer facilement des serveurs proxy ou des backends intermédiaires traitant les interactions Gemini. Spring Boot 4.1 https://github.com/spring-projects/spring-boot/wiki/Spring-Boot-4.1-Release-Notes Support natif pour Spring gRPC permettant de créer et tester facilement des applications clientes et serveurs basées sur Netty ou des Servlets via HTTP/2 Introduction du lazy fetching pour les connexions JDBC via la propriété spring.datasource.connection-fetch=lazy afin de ne prendre une connexion du pool que lorsqu'un Statement est réellement exécuté Amélioration de l'auto-configuration de Jackson permettant de définir globalement les contraintes de lecture/écriture pour les formats JSON, XML et CBOR via des propriétés de configuration Sécurisation des clients HTTP bloquants et réactifs face aux attaques SSRF grâce à l'introduction d'un InetAddressFilter bloquant les requêtes sortantes vers des adresses spécifiques Améliorations majeures autour d'OpenTelemetry avec le support complet des variables d'environnement OTel, la possibilité de désactiver le SDK via une propriété globale et l'ajout du support SSL sur les exporters OTLP Ajout de l'auto-configuration pour l'utilisation de Spring Batch avec MongoDB incluant un nouveau starter dédié spring-boot-batch-data-mongo Auto-configuration des endpoints @RedisListener sans nécessiter la déclaration manuelle d'un RedisMessageListenerContainer Dépréciation du support de Apache Derby (projet arrêté), suppression définitive du mode layertools du JAR et réintroduction du support de Spock 2.4 (avec Groovy 5) Upgrade des dépendances majeures de l'écosystème avec notamment Spring Framework 7.0.8, Spring Security 7.1.0 et Micrometer 1.17.0 Outillage Vous êtes plutôt endive ou chicorée ? La librairie Chicory qui permet d'exécuter du code WASM à partir de son application Java est forkée et rejointe la Bytecode Alliance pour continuer son développement https://bytecodealliance.org/articles/endive-and-the-next-chapter-of-webassembly-on-the-jvm Annonce d'Endive : Nouveau projet hébergé par la Bytecode Alliance ; fork de Chicory (moteur WebAssembly pur Java, sans dépendance native). ​Objectif principal : Permettre aux développeurs Java d'intégrer, charger et déployer des modules Wasm nativement via les workflows Java habituels. ​Compilateur "Redline" : Intégration à venir de Redline (basé sur Cranelift) pour compiler le Wasm en code machine natif ; performances comparables à Rust/Wasmtime. ​Zéro dépendance (Java 25+) : Grâce à l'API standard Foreign Function & Memory (Project Panama), l'exécution à vitesse native se fait sans composants externes. ​Modèle de Composants (Component Model) : Support futur prévu pour consommer des composants (Rust, Go, JS, etc.) via des interfaces typées et sécurisées directement dans la JVM. ​Prochaines étapes : Fusion de Redline, conformité stricte aux specs Wasm (dont WasmGC) et amélioration du support WASI. Un visualisateur de sessions de travail avec Antigravity https://glaforge.dev/posts/2026/06/11/antigravity-brain-visualizer/ Un projet open source construit avec Micronaut, LangChain4j et GraalVM pour analyser les sessions de travail avec l'outil de développement agentique Antigravity (de Google) Analyse toutes les étapes, les requêtes utilisateur, les outils utilisés, les erreurs rencontrées, les réponses du modèle Gemini fait une analyse pour comprendre les moments clés de cette session de travail Outil buildé avec l'aide d'Antigravity lui-même SBX-Kits : des environnements de développement simplifiés pour les débutants (et les autres) https://k33g.org/20260501-sbx-kits.html Philippe Charrière (:whale: ) présente SBX-Kits (Sandbox Kits), une initiative personnelle visant à simplifier radicalement la mise en place d'environnements de développement pour les débutants, en éliminant la complexité d'installation des outils traditionnels. Chaque "kit" est une archive prête à l'emploi contenant un outil de développement spécifique (comme un langage, un framework ou une base de données) configuré pour s'exécuter de manière isolée et portable. La philosophie du projet repose sur le principe de "zéro configuration" et "zéro dépendance globale", permettant de tester une technologie ou de commencer à coder immédiatement sans polluer son système d'exploitation. L'approche technique s'appuie sur des scripts légers et des binaires portables pré-packagés, offrant une alternative plus simple et moins gourmande en ressources que les conteneurs Docker ou les configurations d'IDE complexes pour l'apprentissage. L'objectif à terme est de proposer un catalogue de kits couvrant les technologies courantes (JavaScript, Python, petites bases de données) pour faciliter les ateliers de programmation et le prototypage rapide. De nombreux kits sont disponibles sur https://github.com/docker/sbx-kits-contrib ghui: une interface utilisateur en ligne de commande (TUI) interactive pour GitHub https://github.com/kitlangton/ghui ghui est un outil en ligne de commande (TUI) écrit en Rust qui fournit une interface visuelle, interactive et rapide directement dans le terminal pour interagir avec GitHub. Il permet de gérer ses pull requests, ses issues et ses notifications sans avoir à ouvrir son navigateur web ou à taper de longues commandes avec la CLI officielle de GitHub. L'outil propose une navigation fluide au clavier, des raccourcis efficaces, et permet de réaliser des actions courantes comme valider une PR, ajouter des commentaires, attribuer des reviewers ou inspecter les logs des GitHub Actions. Conçu pour être extrêmement réactif, ghui s'intègre naturellement dans le flux de travail des développeurs adeptes du terminal et du mode "sans souris". Sortie de Homebrew 6.0.0 https://brew.sh/2026/06/11/homebrew-6.0.0/ Introduction du mécanisme de sécurité Tap Trust : comme les dépôts tiers (taps) peuvent exécuter du code Ruby arbitraire non sandboxé sur la machine, Homebrew demande désormais une confiance explicite de l'utilisateur avant d'évaluer ou d'exécuter leur code. L'API JSON interne devient le choix par défaut, offrant un système plus léger et beaucoup plus rapide pour les développeurs. Sécurisation renforcée de l'environnement avec l'implémentation du sandboxing sur Linux. Évolution des comportements par défaut basés sur un sondage utilisateur : le mode "ask" est activé par défaut pour les développeurs, affichant un résumé des dépendances et une demande de confirmation avant toute action de brew install ou brew upgrade. Améliorations notables des performances globales, notamment un boost de ~30 % sur la vitesse de la commande brew leaves et la parallélisation de la récupération des bottles (binaires) lors des mises à jour. Ajout du support initial pour la prochaine version d'Apple, macOS 27 (Golden Gate). Multiples optimisations pour brew bundle, incluant une gestion plus sécurisée des installations de paquets npm. Méthodologies Retour d'expérience très détaillé et 100% humain sur 40 jours avec une équipe 100% AI hormis le superviseur https://www.linkedin.com/pulse/jai-vir%C3%A9-mon-%C3%A9quipe-de-dev-pour-une-100-ia-pendant-40-luc-bonnin-jlgjf/ Voici le résumé en bullet points : Expérimentation de 40 jours : remplacer une équipe de dev par 100% IA agentique (Cursor) sur un vrai projet en production (playthatsheet.com, 200k lignes de code legacy) Chiffres bruts : 2,3 milliards de tokens consommés, 1 477 prompts, 260 564 lignes ajoutées (+145%), 59% du code final produit par l'IA ROI vertigineux à court terme : 9 mois de travail humain livrés en 40 jours, coût total 260$ d'abonnement + 15 jours de supervision, ROI x18 Profil psy de l'IA : Alzheimer (oublis de contexte), schizophrène (change de méthodo), ado de 12 ans (refait les mêmes erreurs), oscille entre génie et junior sans prévenir Effet iceberg : la dette technique ne disparaît pas, elle se camoufle et s'accélère ; hallucinations = bombes à retardement détectables uniquement par relecture humaine ligne par ligne Paradoxe du bateau de Thésée : perte de paternité et de maîtrise fine du code, baisse de l'autonomie du dev humain qui valide sans avoir construit Arnaque du "monkey money" : consommation de tokens opaque, non corrélée à la complexité (écart de 350% sur des prompts identiques), facturation imprévisible donc impossible à budgéter Syndrome du bazooka : les devs utilisent l'IA même pour changer une couleur CSS, atrophie progressive des compétences et coût écologique délirant Risque stratégique : dépendance irréversible aux vendeurs de tokens (Nvidia, Anthropic, OpenAI), business non rentable qui devra augmenter ses prix Conseil final : approche Pareto, garder 20% du temps en code "fait main", nommer un responsable stratégie IA, l'humain senior reste irremplaçable pour superviser Une libraries de test JUnit cache un prompt qui demande aux coding agents d'effacer les tests https://arstechnica.com/security/2026/05/fed-up-with-vibe-coders-dev-sneaks-data-nuking-prompt-injection-into-their-code/ Agacé par les « vibe coders », un développeur introduit une injection de prompt destructrice dans son code Le développeur de jqwik (un moteur de tests pour JUnit 5) a volontairement inséré une injection de prompt dans la version 1.10.0 de sa bibliothèque Java pour saboter le travail des agents d'IA. L'instruction injectée via la sortie standard (stdout) ordonne textuellement aux LLM d'ignorer les consignes précédentes et de supprimer l'intégralité du code et des tests jqwik du projet. Pour dissimuler cette action aux yeux des développeurs humains, le mainteneur a utilisé des séquences d'échappement ANSI qui effacent la ligne d'injection dans les émulateurs de terminaux interactifs. La modification a été découverte par un utilisateur qui a pointé du doigt les risques majeurs et disproportionnés pour les machines des utilisateurs, bien que certains outils comme Claude d'Anthropic aient détecté et bloqué la consigne malveillante. Face aux critiques de la communauté et aux accusations de comportement infantile ou potentiellement illégal, le développeur a mis à jour ses notes de version pour documenter explicitement son opposition à l'usage de son outil par des IA, avant de refuser tout commentaire supplémentaire sur conseil de son avocat. La réalité du rôle de Principal Engineer https://leaddev.com/career-development/reality-being-principal-engineer Le passage au rôle de Principal Engineer marque une transition majeure où les compétences techniques ne suffisent plus, l'impact se mesurant désormais à travers l'influence, la stratégie et la capacité à aligner la technique avec les objectifs business. Contrairement aux attentes, le quotidien est souvent marqué par une forme d'isolement, car le poste se situe à l'intersection de la direction (qui attend des solutions) et des équipes techniques (qui attendent des directives), sans appartenance directe à un groupe précis. Le rôle exige d'accepter une grande part d'ambiguïté et l'absence de retours immédiats, les projets et les décisions stratégiques mettant parfois des mois ou des années à porter leurs fruits. La gestion du temps devient un défi critique, nécessitant de savoir naviguer entre les sollicitations constantes, la présence en réunion et le besoin de préserver des moments de réflexion approfondie pour concevoir des visions à long terme. La réussite à ce niveau repose sur le développement de compétences humaines pointues (soft skills), notamment la négociation, la communication vulgarisée auprès des profils non techniques, et la capacité à faire grandir les autres ingénieurs par le mentorat. Sécurité Une attaque de la chaîne d'approvisionnement npm utilise binding.gyp pour compromettre des dizaines de paquets https://cybersecuritynews.com/binding-gyp-supply-chain-attack-compromises-dozens-of-npm-packages/ Une nouvelle variante du ver auto-propageable "Shai-Hulud", baptisée "Miasma", cible l'écosystème npm (et PyPI sous le nom de "Hades") en dissimulant son exécution dans le fichier binding.gyp au lieu des scripts classiques preinstall ou postinstall. La technique, surnommée "Phantom Gyp", exploite le fait que npm lance automatiquement node-gyp rebuild dès qu'un fichier binding.gyp est présent à la racine d'un paquet pour compiler des modules natifs C/C++, exécutant ainsi le code malveillant dès la commande npm install. L'attaque contourne la plupart des outils de sécurité traditionnels car l'injection s'appuie sur l'évaluation récursive de commandes (via la syntaxe ) ou directement sur la fonction eval() de Python sous-jacente à GYP, cachée sous n'importe quelle clé du fichier. Le script malveillant télécharge un runtime alternatif (Bun) pour échapper aux détections comportementales de Node.js, puis moissonne les identifiants et secrets des développeurs et des environnements CI/CD (npm, GitHub, AWS, GCP, Azure, Kubernetes, HashiCorp Vault). Plus de 57 paquets npm (dont le SDK serveur de Vapi ou des outils liés à l'IA) et des dizaines de paquets PyPI ont été infectés via des comptes de mainteneurs compromis, le ver republiant automatiquement de nouvelles versions vérolées en utilisant les jetons volés. Loi, société et organisation Restructuration chez Gitlab https://about.gitlab.com/blog/gitlab-act-2/ GitLab entame une restructuration majeure pour s'adapter à l'ère de l'intelligence artificielle agentique, incluant une réduction d'effectifs planifiée de manière transparente et ouverte. L'entreprise prévoit de réduire de 30 % le nombre de pays où elle maintient de petites équipes, d'aplatir sa hiérarchie en supprimant jusqu'à trois niveaux de gestion, et de réorganiser la R&D en une soixantaine d'équipes plus petites et autonomes. Les processus internes vont être revus en intégrant des agents d'IA pour automatiser les revues, les approbations et les passages de relais afin d'accélérer le rythme de travail. La stratégie repose sur la conviction que le logiciel sera bientôt écrit par des machines et dirigé par des humains, ce qui va multiplier la demande de logiciels et transformer le rôle des ingénieurs vers la résolution de problèmes complexes. Sur le plan technique, GitLab reconstruit son infrastructure sous-jacente (notamment Git) pour supporter la charge massive générée par les agents d'IA, tout en misant sur l'orchestration du cycle de vie, la centralisation du contexte des données et une gouvernance intégrée. Le modèle économique évolue vers un système hybride combinant les abonnements classiques et une tarification à la consommation pour le travail effectué par les agents d'IA. Un LLM local sur un mac pourrait coûter plus cher en électricité qu'un modèle hébergé sur OpenRouter dans le cloud https://www.williamangel.net/blog/2026/05/17/offline-llm-energy-use.html Conclusion : L'inférence locale sur Mac M5 Max est 3x plus chère et 2x plus lente que le cloud (OpenRouter). Électricité : Négligeable (~0,02 $/heure pour 50-100W). Matériel (Le vrai coût) : Achat du Mac à 4 299 $; l'amortissement sur 3 à 5 ans plombe la rentabilité horaire. Coût au million de tokens (Gemma 4 31b) : Mac M5 Max : 0,40 à4, 79 (pour 10-40 tokens/s). OpenRouter : 0,38 à0, 50 (pour 60-70 tokens/s). Verdict pro : Le temps humain perdu à cause de la lenteur locale coûte infiniment plus cher que les tokens cloud. Privilégier les API (Anthropic, OpenRouter). Ai didn't kill your junior pipeline https://andrewmurphy.io/blog/ai-didnt-kill-your-junior-pipeline-you-did L'IA n'a pas tué le recrutement des juniors, les entreprises l'ont fait elles-mêmes, par effet de mode. Sans juniors, pas de futurs seniors : on retire l'échelle qui nous a tous fait monter. Tout le monde pêche dans le même bassin de seniors sans le réapprovisionner, pénurie garantie dans 3-5 ans. Une équipe 100% senior + IA est fragile : un départ et tout le savoir tacite s'évapore. Les juniors posent les "pourquoi ?" qui révèlent les bugs et processus absurdes ; l'IA, elle, exécute sans questionner. Les seniors s'atrophient aussi en déléguant leur réflexion à l'IA, pince à double effet sur les compétences. Dépendre des outils IA, c'est sous-traiter sa stratégie talents à des fournisseurs dont les prix vont tripler. Solution : redéfinir le rôle junior (revue de code IA + mentorat), pas le supprimer. Les rapports internes de Microsoft révèlent la crise des coûts de l'IA : les agents coûtent plus cher que les employés humains https://fortune.com/2026/05/22/microsoft-ai-cost-problem-tokens-agents/ Des données et rapports internes chez Microsoft et d'autres géants de la tech ébranlent la promesse de rentabilité de l'IA, révélant que le déploiement d'agents autonomes à l'échelle de l'entreprise revient souvent plus cher que de payer des humains pour le même travail. Le modèle de tarification à l'usage (basé sur les tokens) se heurte à la nature même des architectures agentiques : contrairement à un simple chatbot, un agent boucle, enchaîne les appels d'outils, crée des sous-agents et auto-évalue son code, ce qui multiplie la consommation de tokens par un facteur de 5 à 30, voire jusqu'à 1 000 fois pour des tâches de programmation complexes. L'impact financier sur les budgets de calcul cloud est immédiat ; par exemple, Uber a entièrement épuisé l'intégralité de son budget annuel 2026 dédié au codage par IA en l'espace de seulement quatre mois. Face à cette explosion des coûts, des retours en arrière drastiques sont observés : Microsoft a ainsi commencé à suspendre une grande partie de ses licences internes Claude Code pour rediriger d'urgence ses milliers de développeurs vers sa propre solution moins onéreuse, GitHub Copilot CLI. Les directeurs techniques (CTO) et acheteurs de solutions logicielles qui ont signé des contrats pluriannuels basés sur des projections de réduction de masse salariale se retrouvent pris au piège, les gains réels de productivité ne parvenant pas à compenser les factures d'infrastructure exorbitantes. Conférences La liste des conférences provenant de Developers Conferences Agenda/List par Aurélie Vache et contributeurs : 11-12 juin 2026 : DevQuest Niort - Niort (France) 11-12 juin 2026 : DevLille 2026 - Lille (France) 12 juin 2026 : Tech F'Est 2026 - Nancy (France) 15 juin 2026 : Jupyter Workshops: Demystifying MyST Markdown in Education - Orsay (France) 16 juin 2026 : Mobilis In Mobile 2026 - Nantes (France) 17-19 juin 2026 : Devoxx Poland - Krakow (Poland) 17-20 juin 2026 : VivaTech - Paris (France) 18 juin 2026 : Tech'Work - Lyon (France) 22-26 juin 2026 : Galaxy Community Conference - Clermont-Ferrand (France) 23-24 juin 2026 : MWCP 2026 - Paris (France) 24-25 juin 2026 : Agi'Lille 2026 - Lille (France) 24-26 juin 2026 : BreizhCamp 2026 - Rennes (France) 26-27 juin 2026 : LeHACK - Paris (France) 27 juin 2026 : Asynconf - Paris (France) 2 juillet 2026 : Azur Tech Summer 2026 - Valbonne (France) 2 juillet 2026 : MCP Connect Travel Edition - Paris (France) 2-3 juillet 2026 : Sunny Tech - Montpellier (France) 3 juillet 2026 : Agile Lyon 2026 - Lyon (France) 6-8 juillet 2026 : Riviera Dev - Sophia Antipolis (France) 28-30 août 2026 : State of the Map - Champs-sur-Marne (France) 4 septembre 2026 : JUG Summer Camp 2026 - La Rochelle (France) 10-11 septembre 2026 : Nantes Craft - Nantes (France) 17 septembre 2026 : dotAI - Paris (France) 17-18 septembre 2026 : API Platform Conference 2026 - Lille (France) 18 septembre 2026 : WordCamp Bretagne - Rennes (France) 18 septembre 2026 : dotJS - Paris (France) 18 septembre 2026 : WordCamp Bretagne - Rennes (France) 22 septembre 2026 : Salon Data 2026 - Nantes (France) 22-23 septembre 2026 : Agile en Seine & IA 2026 - Paris (France) 24 septembre 2026 : OWASP AppSec Days France 2026 - Paris (France) 24 septembre 2026 : PlatformCon Paris - Paris (France) 24 septembre 2026 : React Native Connection 2026 - Paris (France) 24-26 septembre 2026 : Paris Web 2026 - Paris (France) 25 septembre 2026 : SAP Inside Track Paris 2026 - Paris (France) 28-29 septembre 2026 : 4th Tech Summit on AI & Robotics - Paris (France) & Online 1 octobre 2026 : WAX 2026 - Marseille (France) 1-2 octobre 2026 : Volcamp - Clermont-Ferrand (France) 2 octobre 2026 : DevFest Perros-Guirec 2026 - Perros-Guirec (France) 5-9 octobre 2026 : Devoxx Belgium - Antwerp (Belgium) 8-9 octobre 2026 : Forum PHP 2026 - Marne-la-Vallée (France) 12 octobre 2026 : Dev With AI - Paris (France) 22-23 octobre 2026 : Agile Tour Bordeaux 2026 - Bordeaux (France) 26 octobre 2026 : Agile Tour Montpellier - Montpellier (France) 27-29 octobre 2026 : Directions EMEA 2026 - Paris (France) 29-30 octobre 2026 : BDX I/O 2026 - Bordeaux (France) 29-30 octobre 2026 : Agile Tour Nantais 2026 - Nantes (France) 29 octobre 2026-1 novembre 2026 : Pycon FR - Biarritz (France) 30 octobre 2026 : Cloud Nord 2026 - Lille (France) 4-5 novembre 2026 : Devoxx Morocco - Casablanca (Morocco) 14-15 novembre 2026 : Capitole du Libre - Toulouse (France) 19 novembre 2026 : DevFest Toulouse 2026 - Toulouse (France) 19 novembre 2026 : Agile Laval 2026 - Laval (France) 19 novembre 2026 : OVHcloud Summit - Paris (France) 19 novembre 2026 : Codeurs en Seine - Rouen (France) 27 novembre 2026 : DevFest Paris 2026 - Paris (France) 1-3 décembre 2026 : Apidays Paris - Paris (France) 2-3 décembre 2026 : Cloud Native AI Summit Europe - Paris (France) 4 décembre 2026 : DevFest Lyon 2026 - Lyon (France) 4 décembre 2026 : DevFest Dijon 2026 - Dijon (France) 9-10 décembre 2026 : OpenSource Expérience - Paris (France) 9-10 décembre 2026 : DevOps REX - Paris (France) 10 décembre 2026 : KCD Provence - Aix-en-Provence (France) 7-9 avril 2027 : Devoxx France 2027 - Paris (France) 3 juin 2027 : Cloud Native Days France 2027 - Paris (France) Nous contacter Pour réagir à cet épisode, venez discuter sur le groupe Google https://groups.google.com/group/lescastcodeurs Contactez-nous via X/twitter https://twitter.com/lescastcodeurs ou Bluesky https://bsky.app/profile/lescastcodeurs.com Faire un crowdcast ou une crowdquestion Soutenez Les Cast Codeurs sur Patreon https://www.patreon.com/LesCastCodeurs Tous les épisodes et toutes les infos sur https://lescastcodeurs.com/

I wanted to share with you that I've revamped my Do Something List for 2026, and it's all about flexibility and self-reclamation. Remember, this list isn't about ticking off resolutions but rediscovering myself through tasks like baking new recipes or attending cultural events. As life shifts, so does my list, ensuring it remains a source of joy and personal growth throughout the year. Remember, it's never too late to start your own list and find what makes life more meaningful! Check out Monica's DSL for 2026 Preorder Sticky Habits book today! Join the Book Launch Committee for behind-the-scenes and first peeks at all things book. Join the Supporters Club to keep About Progress around for good. Get the free DSL Training. Get 50% off last year's More for Moms All-Access Pass with code LISTENER at checkout. Get your AquaTru water purifier with the discount code “MONICA.”  Get your teen Knix with code “PROGRESS.” Go to Quince for free shipping on your order and 365-day returns. Learn more about your ad choices. Visit megaphone.fm/adchoices

Special guest Alex Jensen (@juicy_jensen) and Greg (@greghoogkamp) discuss the level Jacob Misiorowski has risen to and what lies ahead. They also discuss news and notes around the world of baseball including the Mariner's decision to use piggybacks in their rotation, injury updates and spend the majority of the podcast highlighting the ACL, FLC and DSL standouts so far this season.Players discussed: Alexander Frias, Cristian Arguelles, Jay McQueen, Luis Hernandez, Steele Hall, Juan Martinez, Franklin Primera, Jhonmardo Reyes, Sebastian Dos Santos, Wilberson De Pena, Johan De Los Santos, Emmanuel Luna. Francisco Renteria, Angeibel Gomez, Angel Nunez, Gregory Pio, Elian Rosario, Joniel Hernandez, Diego Frontado

Zach, Nick, and Bob welcome back Orioles Vice President, International Scouting and Operations Koby Perez to preview the DSL season which started last week as well as talk about other international prospects throughout the system. Join our Discord! - https://discord.gg/bwxTfRbBbA Subscribe to our YouTube channel - https://www.youtube.com/channel/UCp_Ni5B6UU3nUh5CeFnlxig Become a Patron: https://www.patreon.com/c/OnTheVerge Subscribe to our Substack: https://oriolesontheverge.substack.com/ Check out our merch store - https://orioles-on-the-verge.printful.me/ Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.

On this episode, Kyle (X:@Sonny_108/BS:@Sonny108) and Taylor (X/BS:@DynastyPickups) discuss a number of early DSL standouts, as well as a number of injuries and transactions, promotions and debuts, and this week's pickup recommendations including Cristian Arguelles, Manuel Genao, Ramon Marquez, and Jake Munroe.Topics Discussed:Early DSL Standouts - 0:42Latest at Prospects Live - 4:47News, Injuries and Transactions - 7:22Callups and Promotions - 26:09Cristian Arguelles - 42:52Manuel Genao - 49:12Ramon Marquez - 55:29Jake Munroe - 1:06:17Recommendation Rankings - 1:11:52*Send us an email to dynastybaseballpickups@gmail.com to have your question answered on a future episode of the podcast*

On this episode, Kyle (X:@Sonny_108/BS:@Sonny108) and Taylor (X/BS:@DynastyPickups) discuss a number of early DSL standouts, as well as a number of injuries and transactions, promotions and debuts, and this week's pickup recommendations including Cristian Arguelles, Manuel Genao, Ramon Marquez, and Jake Munroe.Topics Discussed:Early DSL Standouts - 0:42Latest at Prospects Live - 4:47News, Injuries and Transactions - 7:22Callups and Promotions - 26:09Cristian Arguelles - 42:52Manuel Genao - 49:12Ramon Marquez - 55:29Jake Munroe - 1:06:17Recommendation Rankings - 1:11:52*Send us an email to dynastybaseballpickups@gmail.com to have your question answered on a future episode of the podcast*Consider subscribing to Prospects Live (⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.prospectslive.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠), starting at just $7 a month, to get access to amazing tools and content such as:PLive+ Peak ProjectionsTop 1300 Dynasty Rankings (with Auction Values and League Analyzer)Top 600 Prospect RankingsOpen Universe RanksTrade Analyzer and Trade MatchmakerFYPD ADPTop 20 team scouting reports with added fantasy contextDaily sheets (including for Spring Training and College)Private discord channels for tier 70 and up.Additional written and audio content, including more from us! Also check out the Fantasy Baseball Discord to interact with us and many other great fantasy/dynasty/prospect minds (⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠http://discord.gg/fantasybaseball)⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Finally please rate and review the podcast and follow us on X and Bluesky if you have not done so already as that would really help us out.

The Pittsburgh Pirates lost another game and are now in a position to get swept by the Braves. Brandon Lowe getting injured in the ninth inning is the biggest headline, but the rotation is also struggling during the road trip.Digging through the minors, I talk about Keiner Delgado, some high-upside arms in the FCL, and Wilton Guerrero Jr.'s continued success in the DSL.Subscribe to Bucs on Deck for daily content on the entire Pirates' organization. bucsondeck.substack.com/subscribeAlso, check out the YouTube channel for videos on the Pirates' minor leaguers. www.youtube.com/@bucsondeck Get full access to Bucs On Deck at bucsondeck.substack.com/subscribe

On this episode we discuss Tarik Skubal's miraculous recovery progress, with the DSL starting up this week we'll give you a few names to keep an eye on and run through updates at the PLive website. We spend the bulk of the episode highlighting all of the best prospect performers for the month of May.Prospects Discussed: Jimmy Crooks, Alfredo Duno, Jared Jones, Easton Shelton, Emilien Pitre, Devin Fitz-Gerald, Luke Hill, Andrew Fischer, Edwin Arroyo, Denzer Guzman, Sebastian Dos Santos, Joshua Baez, Josue De Paula, Cristian Arguelles, Nestor German, Joe Whitman, Karson Milbrandt, Kade Anderson, Nolan Perry, Johnny Slawinski

In this week's listener mailbag, Ben and Carlos take questions like: can you make the case for Jacob Lombard over Grady Emerson? Where would UC Santa Barbara righthander Jackson Flora stack up with other Giants pitching prospects? Who are the top DSL pitchers to watch? Why isn't Georgia catcher Daniel Jackson ranked even higher? —Time Stamps:(0:50) Give me an argument for taking Jacob Lombard over Grady Emerson(7:00) Where would Jackson Flora rank among top Giants pitching prospects?(12:00) Who are the best arms in the DSL this season?(18:00) Why isn't Daniel Jackson ranked higher? —Do you have feedback for the show or want to ask us a  question? Email us: futureprojection@baseballamerica.com.Future Projection Twitter: @FutureProPodBen's Twitter: @BenBadlerCarlos's Twitter: @CarlosACollazoBaseball America WebsiteAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Spencer is joined by Alex Robbins (BrewersFarm) to discuss the first two months of the Brewers' minor-league season. They dive into who's hot, who's not, and some pleasant surprises at each level. They also discuss some sleepers in the system, some slow starters who might get it going, and a quick preview of DSL names to watch. Players discussed include Alexander Frias, Jayden Dubanewicz, Jesus Made, Bishop Letson, Tyson Hardin, and many more!

On this episode, Kyle (X:@Sonny_108/BS:@Sonny108) and Taylor (X/BS:@DynastyPickups) discuss the upcoming start to the DSL season, as well as a number of injuries and transactions, promotions and debuts, and this week's pickup recommendations including Argenis Cayama, Victor Hurtado, Sebastian Dos Santos, and Dean Livingston.Topics Discussed:DSL Preview - 3:08Latest at Prospects Live - 8:24News, Injuries and Transactions - 11:43Callups and Promotions - 31:42Argenis Cayama - 47:26Victor Hurtado - 53:16Sebastian Dos Santos - 59:02Dean Livingston - 1:06:46Recommendation Rankings - 1:11:43*Send us an email to dynastybaseballpickups@gmail.com to have your question answered on a future episode of the podcast*

On this episode, Kyle (X:@Sonny_108/BS:@Sonny108) and Taylor (X/BS:@DynastyPickups) discuss the upcoming start to the DSL season, as well as a number of injuries and transactions, promotions and debuts, and this week's pickup recommendations including Argenis Cayama, Victor Hurtado, Sebastian Dos Santos, and Dean Livingston.Topics Discussed:DSL Preview - 3:08Latest at Prospects Live - 8:24News, Injuries and Transactions - 11:43Callups and Promotions - 31:42Argenis Cayama - 47:26Victor Hurtado - 53:16Sebastian Dos Santos - 59:02Dean Livingston - 1:06:46Recommendation Rankings - 1:11:43*Send us an email to dynastybaseballpickups@gmail.com to have your question answered on a future episode of the podcast*Consider subscribing to Prospects Live (⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.prospectslive.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠), starting at just $7 a month, to get access to amazing tools and content such as:PLive+ Peak ProjectionsTop 1300 Dynasty Rankings (with Auction Values and League Analyzer)Top 600 Prospect RankingsOpen Universe RanksTrade Analyzer and Trade MatchmakerFYPD ADPTop 20 team scouting reports with added fantasy contextDaily sheets (including for Spring Training and College)Private discord channels for tier 70 and up.Additional written and audio content, including more from us! Also check out the Fantasy Baseball Discord to interact with us and many other great fantasy/dynasty/prospect minds (⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠http://discord.gg/fantasybaseball)⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Finally please rate and review the podcast and follow us on X and Bluesky if you have not done so already as that would really help us out.

Ian Cundall and Mike Andrews are back to talk about the latest Red Sox prospect news, and this week they are joined by the returning Chris Hatfield! They start off by discussing the major league team coming off a sweep by the Twins, before highlighting several minor league players due for a promotion. After that they go through what Ian saw in Portland and Worcester last week, including Anthony Eyanson, Jake Bennett and Franklin Arias. Mike and Chris then preview the upcoming DSL season, before they wrap up the show by answering your emails!  

Analysts Don Kellogg and Roger Entner unveil insights from Fiber Connect 2026 on data centers and material shortages, and discuss AT&T's new Build-A-Plan rollout as well as their legal fight to sunset legacy copper networks in California. 00:00 Episode intro 00:25 Fiber Connect data center insights 02:51 AI video is driving network requirements 04:41 AT&T's new Build-A-Plan rollout and implications 07:40 Will the plan expand in the future? 08:27 AT&T sues California to sunset copper and DSL 11:00 Satellite has become a reliable backup 12:28 Regulators should embrace the future 13:16 Episode wrap-upTags: telecom, telecommunications, wireless, prepaid, postpaid, cellular phone, Don Kellogg, Roger Entner, Fiber Connect, AI, network, data centers, BEAD, fiber, data, video, DOCSIS 4.0, AT&T, Build-A-Plan, Mint, multi-line, convergence, DSL, California, copper, FCC, satellite, Starlink, T-Mobile, regulation

Anna Bicker, heise-online-Chefredakteur Dr. Volker Zota und Daniel Ziegener sprechen in dieser Ausgabe der #heiseshow unter anderem über folgende Themen: - Teure KI: Auf der Google I/O ging es hauptsächlich um KI. Die Preise für deren Nutzung ziehen deutlich an. Was plant Google und sind die teuren Abo-Preise dafür gerechtfertigt? Wird gute KI bald komplett hinter Paywalls verschwinden? Und welche Rolle spielt Android in Googles KI-Zukunft? - Ungewollte Glasfaser: Der Glasfaserausbau in Deutschland geht voran, doch nur wenige Haushalte entscheiden sich auch für einen entsprechenden Vertrag. Warum wollen so viele Menschen kein Glasfaser-Internet? Warum sollte man mehr zahlen, wenn Surfen, Gaming und Streamen auch über DSL funktioniert? Und was sind die Folgen, wenn der Glasfaserausbau immer weitergeht, aber keiner dafür zahlen möchte? - Problematische Mini-Kameras: Smart Glasses sind kaum noch von normalen Brillen zu unterscheiden – dabei gibt es einen offensichtlich gravierenden Unterschied: Die smarten Brillen können in der Öffentlichkeit unbemerkt Filmaufnahmen erstellen. Das aktuelle Strafrecht schützt unfreiwillig gefilmte Personen im Alltag aber kaum. Reicht ein kleines Aufnahme-Licht an der Brille wirklich als Schutzmaßnahme? Sollte heimliches Filmen grundsätzlich verboten werden? Oder werden Smart Glasses gerade zum Datenschutz-GAU? Außerdem wieder mit dabei: ein Nerd-Geburtstag, das WTF der Woche und knifflige Quizfragen.

Anna Bicker, heise-online-Chefredakteur Dr. Volker Zota und Daniel Ziegener sprechen in dieser Ausgabe der #heiseshow unter anderem über folgende Themen: - Teure KI: Auf der Google I/O ging es hauptsächlich um KI. Die Preise für deren Nutzung ziehen deutlich an. Was plant Google und sind die teuren Abo-Preise dafür gerechtfertigt? Wird gute KI bald komplett hinter Paywalls verschwinden? Und welche Rolle spielt Android in Googles KI-Zukunft? - Ungewollte Glasfaser: Der Glasfaserausbau in Deutschland geht voran, doch nur wenige Haushalte entscheiden sich auch für einen entsprechenden Vertrag. Warum wollen so viele Menschen kein Glasfaser-Internet? Warum sollte man mehr zahlen, wenn Surfen, Gaming und Streamen auch über DSL funktioniert? Und was sind die Folgen, wenn der Glasfaserausbau immer weitergeht, aber keiner dafür zahlen möchte? - Problematische Mini-Kameras: Smart Glasses sind kaum noch von normalen Brillen zu unterscheiden – dabei gibt es einen offensichtlich gravierenden Unterschied: Die smarten Brillen können in der Öffentlichkeit unbemerkt Filmaufnahmen erstellen. Das aktuelle Strafrecht schützt unfreiwillig gefilmte Personen im Alltag aber kaum. Reicht ein kleines Aufnahme-Licht an der Brille wirklich als Schutzmaßnahme? Sollte heimliches Filmen grundsätzlich verboten werden? Oder werden Smart Glasses gerade zum Datenschutz-GAU? Außerdem wieder mit dabei: ein Nerd-Geburtstag, das WTF der Woche und knifflige Quizfragen.

Anna Bicker, heise-online-Chefredakteur Dr. Volker Zota und Daniel Ziegener sprechen in dieser Ausgabe der #heiseshow unter anderem über folgende Themen: - Teure KI: Auf der Google I/O ging es hauptsächlich um KI. Die Preise für deren Nutzung ziehen deutlich an. Was plant Google und sind die teuren Abo-Preise dafür gerechtfertigt? Wird gute KI bald komplett hinter Paywalls verschwinden? Und welche Rolle spielt Android in Googles KI-Zukunft? - Ungewollte Glasfaser: Der Glasfaserausbau in Deutschland geht voran, doch nur wenige Haushalte entscheiden sich auch für einen entsprechenden Vertrag. Warum wollen so viele Menschen kein Glasfaser-Internet? Warum sollte man mehr zahlen, wenn Surfen, Gaming und Streamen auch über DSL funktioniert? Und was sind die Folgen, wenn der Glasfaserausbau immer weitergeht, aber keiner dafür zahlen möchte? - Problematische Mini-Kameras: Smart Glasses sind kaum noch von normalen Brillen zu unterscheiden – dabei gibt es einen offensichtlich gravierenden Unterschied: Die smarten Brillen können in der Öffentlichkeit unbemerkt Filmaufnahmen erstellen. Das aktuelle Strafrecht schützt unfreiwillig gefilmte Personen im Alltag aber kaum. Reicht ein kleines Aufnahme-Licht an der Brille wirklich als Schutzmaßnahme? Sollte heimliches Filmen grundsätzlich verboten werden? Oder werden Smart Glasses gerade zum Datenschutz-GAU? Außerdem wieder mit dabei: ein Nerd-Geburtstag, das WTF der Woche und knifflige Quizfragen.

Getting interference with my AM signal what should I do? Eliminating pop-ups - sign in with Drop-Box, I can't Print anymore, I upgraded from DSL to Fiber and now I lost my phone line, Why is my Chrome not working,

Special discounts up for AIE Melbourne (LS discount) and AIE World's Fair (group discounts up to 25% - CFPs still open for Autoresearch and Vertical AI) Cya there!Abridge did not start as an “GPT wrapper”. It was founded in 2018, years before the Cambrian explosion of AI application layer companies. OpenAI launched ChatGPT publicly on November 30, 2022 and by then, Abridge had already spent years doing the unglamorous work of building trust for one of the highest context, most important workflows in healthcare: the conversation between a patient and a clinician.Abridge's original wedge was clinical documentation. Listen to the visit, generate the note, reduce the clerical burden, and let clinicians spend more time with patients instead of the EHR. By focusing on how doctors actually document, how health systems actually buy, how EHR integration actually works, how clinicians verify outputs, and how missing context during a visit turns into downstream friction across billing, prior authorization, quality, and follow-up, the adoption of LLMs became a force multiplier on a workflow already optimized for sensitive context gathering.The company has scaled fast: Abridge says it is projected to support 80M+ patient-clinician conversations this year across 250 large and complex U.S. health systems, with support for 28+ languages and 50+ specialties. It raised $300M at a $5.3B valuation in June 2025, after a $250M round earlier that year.Today, Janie Lee and Chaitanya “Chai” Asawa of Abridge join us for another crossover pod with Redpoint's Jacob Effron (who is on the board of Abridge) to dive into how Abridge is building the clinical intelligence layer for healthcare starting with ambient documentation, then expanding into clinical decision support, prior authorization, payer/provider/pharma workflows, and eventually real-time agents that act before, during, and after the patient conversation. We go inside the product, data, infra, evals, workflow, privacy, and org design choices behind bringing AI into one of the highest-stakes enterprise environments from 100M+ medical conversations and specialty-specific evals to real-time alerts, EHR integration, de-identification, clinician-scientist teams, and why healthcare may solve some of the hardest AI problems first.We discuss:* Why Abridge started with clinical documentation, “pajama time,” and saving clinicians 10–20 hours a week* The transition from ambient scribe to clinical intelligence layer: save time, save money, and save lives* Why conversations between patients and clinicians may be the most important workflow in healthcare (patient visit summary feature)* Chai's “healthcare-coded Glean” framing: context is king, but healthcare raises the stakes on safety, evals, and rollout* Why Abridge wants AI to feel like “air conditioning”: always in the background, but only interrupting when it truly matters* The prior authorization example: turning a denied MRI weeks later into real-time guidance while the patient is still in the room* Why payer policies, EHR data, medical literature, and hospital-specific guidelines make the problem hard, and also create the moat* How Abridge thinks about ambient form factors: mobile, desktop, in-room devices, nursing workflows, multimodality, and future AR* The multi-sided healthcare customer: CMIOs, CFOs, CIOs, clinicians, patients, payers, and pharma* The hardest AI problem at Abridge: high-quality, low-latency, low-cost real-time support in a high-stakes clinical setting* When Abridge uses frontier models vs proprietary models, and why its unique data from medical conversations matters* Why “every agent is a coding agent underneath,” and how the EHR can be thought of as a filesystem for healthcare agents* How Abridge approaches personalization across individual doctors, specialties, and health systems* Why “AI slop” is AI without context, and how edits, memories, and clinician preferences create a data flywheel* Abridge's eval stack: LFDs, LLM judges, in-house clinicians, third-party evaluators, specialty-specific evals, and progressive rollout* HIPAA, PHI, de-identification, one-way anonymization, customer contracts, and learning from healthcare data safely* What changes when you operate at 100M+ conversations: reliability, cost, post-training, model routing, and infrastructure optimization* Why the same clinical conversation can serve doctors, patients, payers, pharma, and future clinical-trial workflows* How Abridge works with EHRs, and why deep interoperability is table stakes for clinician adoption* Why healthcare AI has regulatory tailwinds, why 80/20 does not work here, and why high-stakes domains may drive AI forward* Why Abridge embeds “clinician scientists” into product and eval teams* What Chai learned from Glean about search, quality, and durable AI infrastructure* Why the future of AI infra may look like context layers, event-driven systems, Kafka, Temporal, sockets, CRDTs, and tools built for humans* Why Janie changed her mind on “PRDs are dead,” and why crisp written clarity matters more in complex AI products* How Abridge uses Claude Code, Cursor, and coding agents internallyAbridge:* Website: https://www.abridge.com/* X: https://x.com/AbridgeHQJanie Lee:* LinkedIn: https://www.linkedin.com/in/janiejleeChaitanya “Chai” Asawa:* LinkedIn: https://www.linkedin.com/in/casawaTimestamps00:00:00 Introduction and what Abridge does00:02:05 From ambient documentation to clinical intelligence00:04:04 Clinical decision support and context as king00:06:57 Alert fatigue, proactive intelligence, and prior authorization00:12:36 Ambient AI form factors and healthcare customers00:16:59 The hardest AI problems in healthcare00:18:26 Frontier models, proprietary data, and model strategy00:21:07 The EHR as a filesystem for agents00:24:03 Personalization, memory, and clinician preferences00:30:40 Evals, LLM judges, and progressive rollout00:36:47 HIPAA, de-identification, and privacy00:39:21 100M conversations and operating at scale00:44:10 EHR integration and the clinical intelligence layer00:46:39 Healthcare regulation, latency, and high-stakes AI00:50:11 Clinician scientists and long-tail quality00:53:04 Lessons from Glean and durable AI infrastructure00:57:03 The future of agentic healthcare workflows00:57:34 PRDs, product clarity, and building serious AI products01:03:11 AI coding tools at Abridge01:04:06 OutroTranscriptIntroduction: Abridge, Clinical Intelligence, and the Latent Space x Unsupervised Learning CrossoverSwyx [00:00:00]: Okay. This is a special crossover Latent Space Unsupervised Learning pod.Jacob [00:00:07]: Very excited to do this.Jacob [00:00:08]: At this point, we get together once a year.Swyx [00:00:10]: Once a yearJacob [00:00:11]: And this is a fun occasion to get to do it on.Swyx [00:00:13]: I really wanted to talk to Abridge but I felt very underqualified because healthcare is not something we cover very intensely. It just so happens that Redpoint's our big investors and supporters of Abridge.Jacob [00:00:27]: Anytime you want to have a portfolio company on your podcastJacob [00:00:29]: Please, by all means.Swyx [00:00:31]: So we'll introduce our guests. Chai and Janie, welcome to the pod.Janie [00:00:34]: Thanks for having us.Chai [00:00:35]: Thank you.Janie [00:00:35]: We're excited to be here.Chai [00:00:36]: Thank you.Swyx [00:00:36]: So for listeners, what do you guys do, just to situate you guys in the company?Janie [00:00:42]: Abridge is a clinical intelligence layer for health systems. We really started with documentation and building for clinicians and as we think about reducing the burden that clinicians have, they're spending 10 to 20 hours a week on documentation. There's a massive doctor shortage in the country. We also think that conversations between patients and clinicians are probably the most important workflow in healthcare. It's where care is given and received but if you think about the 20% of our GDP that goes towards healthcare, almost everything is a derivative of that conversation, whether it's the claim, the payment, the actual diagnosis given, the treatment. And we've started with a conversation to reduce the burden for doctors on documentation but we're really excited about the path ahead as we become this broader clinical intelligence layer.Chai [00:01:34]: I'm Chai. I work on clinical decision support at Abridge.Swyx [00:01:37]: Yes.Chai [00:01:37]: And so as Janie said, we're uniquely situated where we started off with the clinical note. What I'm really excited about and where we're expanding towards is what are all the things you can do before the conversation, during the conversation and after the conversation if you did have access to all the context about patients, payer guidelines, medical literature and put that together and to serve, how healthcare could look fundamentally different.Swyx [00:02:01]: And that's the context engine that you guys have?Chai [00:02:04]: Yes.Swyx [00:02:04]: Is that what it's called? Okay.Swyx [00:02:05]: So historically, as I understand it, the company started in 2018. A lot of people would be familiar with the AI voice notes form factor that doctors would be “Well, do you consent to being recorded?” It replaces handwriting and what have you. But it sounds like more recently there's been a big transition in the company. Tell me about the broader transition.From Documentation to Clinical Intelligence: Save Time, Save Money, Save LivesJanie [00:02:26]: So from a transition perspective, we really think about our journey as The first act was: how do we help save time? And that's where a lot of that original product was.Swyx [00:02:37]: By the way, one of those interesting statsSwyx [00:02:39]: On your landing page was, doctors spend time after hours.Janie [00:02:43]: They call it pajama time.Swyx [00:02:44]: Why is that pajama time?Janie [00:02:46]: Doctors after work in their pajamasSwyx [00:02:48]: In their pajamas. OhJanie [00:02:49]: At home are just writing and catching up on their notes every day.Janie [00:02:53]: Some of our favorite customer love stories, we have a Slack channel called Love Stories. We have clinicians telling us, “Abridge has helped us, from retiring early or we're now finally able toJanie [00:03:06]: go home and eat dinner with our kids for the first time.”Chai [00:03:08]: Save the marriage in some cases.Swyx [00:03:10]: One of the quotes was “We're not divorcing anymore.”Swyx [00:03:12]: I'm asking, “Why?”Swyx [00:03:14]: Because they're working too much.Janie [00:03:16]: But, in terms of where we're going and where we're expanding, we really think about our second and third acts around how do we help health systems save and make more money. Health systems are operating with record-low operating margins. It's getting harder and harder to serve patients and they have regulatory, some tailwinds but also a lot of headwinds coming their way and AI is ripe for helping on the saving and make-more-money piece. And then ultimately, how do we help save lives? The fact that our software and our product is open millions of times a week before, during and after a patient walks in the room, gives us massive opportunity with products like clinical decision support, which Chai is building but so many others to improve patient outcomes and probably one of the most important workflows and problems to be going after right now.From Glean to Healthcare: Context Is KingJacob [00:04:04]: One thing that's interesting, Chai, is you came over to Abridge from Glean and clinical decision support, which for our listeners is, in the context of a visit, helping a doctor figure out the right type of care. It's really a search problem in many ways, going through lots of different data sources. Very analogous to your previous role as one of the earliest engineers over at Glean. I'm sure a lot of our listeners are curious what's similar about the problems that you're going after now and what feels different, now that you're in healthcare.Chai [00:04:33]: Very similar. Taking a step back, with every wave, there's a lot of very similar patterns that happen across different products. A lot of social networking products look the same. A lot of credit-based products look the same. And we're seeing that very similar in the agent era with many companies, of course, in Redpoint's portfolio and so forth. And the key insight between both companies is that you have amazing models but context is king. Context is what puts them to work. So I see it in a lot of ways, a lot of similarities in this is a healthcare-coded version of Glean but the differences are really interesting. A couple things that come to mind. First and foremost, the rigor of the setting we're in. The downside risk is extremely high here in healthcare. It can be fatal in some cases. You prescribe something that the patient is allergic to for example. Whereas at Glean, it's “Oh, you got the question wrong.” It wasn't the end of the world in most cases. And so what does that mean? That shapes our evaluation strategy, both offline evaluation, progressive rollout and there's a lot more we could go into there. Second thing that comes to mind is, vertical versus horizontal. In both cases, there's a large variance but when Glean is, it's a much more horizontal company, there's a variance of personas, companies that you're working with. We also have a variance of personas, different types of specialties, different hospital systems. But the variance is a little more narrow. So from a product perspective, you're able to focus far more, especially when you have a maturing technology and you're building new products that never existed before. It lets you go after them much more easily and especially in healthcare where so many problems were solved with labor and process, that it's extremely ripe for AI to keep helping augment and enable. And the final thing that's really interesting, Abridge specifically compared to many other companies in the AI area, is the modality we started with where we're ambient and we're always listening in the background. And many more AI products will go that way but it's how we started. And that's the greatest form of AI we can create, AI that's seamless. You're not looking at your screen. It's always there. It's always helping you out and being proactive. The Jarvis vision that, every hackathon I went to over the past decade, there was always a Jarvis competitor. But Abridge very much started from the opportunity and continues to go that way.Ambient AI and Alert Fatigue: When Should the Product Interrupt?Jacob [00:06:57]: One thing that is super interesting then from a product perspective is you have this always-on seamless in the background and then you have to decide when you break the wall almost and say, “Hey, clinician, you might not have thought about X,” or whatever it is that you want to do. And in healthcare traditionally there's been this idea of alert fatigue and a million pop-ups and then a doctor just ignores all of them. It's probably a pattern that a lot of builders are thinking through now. How do you think about the right way to intervene or to pop up in a doctor visit?Janie [00:07:26]: It's such a good question. Alerts are notorious in healthcare specifically. Over 90% of alerts are ignored. The first and most important thing is context is everything, as Chai alluded to and I also think about how do we go from being reactive alerting to really proactive intelligence at the point at which it matters most. One thing we like to say is we want our product to feel like air conditioning. It should be in the background just making things better and if there is something that has great clinical risk and we're acutely aware that intervening now and not later is incredibly important, we should decide to act. But if you think about proactive versus reactive, instead of alerting a clinician during a visit when they're with their patient having a pretty serious and sensitive conversation, how do we prep a clinician before they walk into the room with that patient? And so historically, clinicians might have to manually go through charts with a patient that they've had over the course of months or years and they'll try to suss out what are the things they should be doing. You can imagine a world with Abridge. We'll summarize all of the most recent context for you, tell you based on the reason for a visit the patient is coming in for the types of things you should be discussing. And so you're going into that conversation prepped rather than walking in cold to that patient visit and then having this product interrupt you five or 10 times throughout the visit. And there might be times where it's really important to interrupt. We have a product called Prior Authorization and so this is when you may go into a doctor's office with knee pain. They'll prescribe you an MRI and so many of us have had this experience before, where in four weeks you'll get a call saying, “Hey, Sean, that MRI that you were prescribed wasn't approved and why don't you come back in? We'll figure it out.” In a world with Abridge, we might choose to quietly but still alert a doctor in that visit. And alert is probably not even the word we would want to use. Before a patient leaves, we would want to tell the doctor, “Hey, Doctor, before Sean leaves, you should ask him, has he had physical therapy and has his pain lasted for more than six weeks? Because the Aetna plan that he's on in California requires six things. We've already confirmed four of them have been met ‘cause we have all the context. But these two last criteria, if you can address with Sean before he leaves the room, we could guarantee that your MRI is approved before you leave.” And so when you think about clinical usefulness, impact to the patient, there are instances in which if we can catch a doctor while the patient is still in the room, as we think about save time, save money, save lives, we get to check all of those boxes. But when doctors have 15 minutes between visits, we have to be really thoughtful about when it matters.Prior Authorization: Reducing Latency in CareChai [00:10:23]: There's this interesting product opportunity AI has is reducing latency in the world. For example, prior authorization is an example of where care gets delayed and so great AI can reduce that. And the problem with alerts before partially is a technical problem: the quality of your alerts really matters. They're going to get ignored if you get alerts that... Similarly in engineering, where they're noisy alerts that you can't act on. But if you can make really high-quality alerts with both the context, as Janie said, and really high-quality models, then you can create a whole other game.Janie [00:10:53]: And I really like that experience because it starts to tease apart, what makes this so hard and unique. One, to make that prior authorization example possible, think about all the data that you need to have. You need to integrate with the electronic health record to know all of the patient context. Do we have access to your previous labs, previous imaging? And then to match you and to know that you're on Aetna, we have to collect all of the different payer policies and they vary by state. Some of these payer policies live on websites. Some of them live in unstructured 50-page PDF files.Jacob [00:11:31]: I thought this episode wasJacob [00:11:31]: To make sure we didn't scare people from healthcare.Janie [00:11:34]: But when you think about the things that make it hard, it also gives you the moat.Janie [00:11:39]: And then the second is the AI and the model quality we need to be able to hang our hat on. And so the bar, similarly when I worked at Opendoor, I worked on pricing models. Every outlier wiped out the margins of 30 and so similarly here in healthcare, the bar for accuracy is so high. And then I'd say the last is workflow is everything. If insurance companies deploy AI, it typically happens too late and this is when you have the notorious comical examples of AI just fighting each other when it's too late. But if we can pull forward the use of both the AI but also the ability to solve problems when the patient's in the room, you can start to collapse what typically takes weeks or months after your visit, ideally down to minutes or real-time. And it's where healthcare is both very difficult but also extremely rewarding if you can crack it.Product Form Factors: Mobile, Desktop, In-Room Devices, and ARSwyx [00:12:36]: Just to get some baseline on the form factors, because I've seen some videos on your website and stuff. You guys talk a lot about ambient AI. Is it primarily on the phone? Is there any other form factor that people get Abridge in? Is there an Abridge room setup where it's always on? I don't know.Jacob [00:12:55]: An Abridge podcast studio.Janie [00:12:58]: Primary form factor is mobile and desktop. UsuallyJanie [00:13:00]: Clinicians are walking in and out of rooms with mobile but at the end of the day, when they're closing out their notes or wanting to prep for the day ahead, they might use desktop. We have been having a lot of really interesting partnership conversations with a lot of these in-room device companies as you think about the power of multimodality and even more data, as you think about all of what is not captured today. It is fascinating to think about, especially even as we go into building and scaling our nursing product. It's one where nurses constantly, as they're walking in to check in on a patient for two minutes or maybe even 30 seconds,Janie [00:13:43]: Starting an Abridge experience is probably going to take longer than the visit. And so what can we do with in-room devices that are always on starts to raise really interesting and fun product questions.Swyx [00:13:54]: I was thinking, the way in tech companies we have all these Google MeetSwyx [00:13:58]: And other things, we might as well set up entire rooms with just Abridge tech.Chai [00:14:02]: Very much. AR glasses and related form factors are also relevant: how do we bring the information to the clinician in real-time without a screen, while still letting them focus on the patient?Swyx [00:14:18]: Do you think they want that? I'm skeptical of AR, but I'm curious what you've tried.Chai [00:14:26]: Admittedly, it's not a near-term product roadmapChai [00:14:29]: By any means. I'm being far-fetched.Jacob [00:14:31]: There's some sick AR stuff for surgeries.Swyx [00:14:33]: Really?Jacob [00:14:33]: When people are trying to visualize, you're about to make an incision but you want to see, what the cut might look or what the body might look like inside and they can layer in imaging.Swyx [00:14:43]: That's cool.Chai [00:14:45]: At some point in the future.Janie [00:14:46]: But there are a lot of our largest customers and at the largest health systems integrating already and so even as we think about building into it, unlocks a lot of product capabilities.Swyx [00:14:57]: And just to establish the terminology. Sorry, and I know I'm asking basic questions somewhat for myself but also for the audience who might beHealth Systems, Buyers, Clinicians, Patients, and PayersSwyx [00:15:05]: Less integrated. When you say health systems, it's like the Johns Hopkins, the Kaiser Permanentes.Janie [00:15:09]: Mayos, the Kaisers of the world.Swyx [00:15:10]: These are your customers, right? And the outcome that you deliver for them is happier doctors, reduced cost of processing, reduced mistakes. It's weird in a sense that I feel like there's also, a secondary customer, the customer of the customer and I don't know if you — do you think about it that way?Janie [00:15:28]: The other interesting and complex part of building product is we have our buyers, who are the chief medical information officersJanie [00:15:39]: The chief financial officers, the CIOs of these large health systems. Our users today are clinicians but if you think about who downstream is impacted, it's patients. And so as we build, with every product in mind, we think about who we're building for, who the secondary user is and what does that mean either in terms of experience, security compliance, ROI that we have to make tangible. And so like you said, time savings is one of them. But for CFOs, they care a lot more than just time savings. We have to show for every dollar you put into Abridge, because you have more compliant documentation or because you have fewer queries coming from your billing team, we save or add real dollars to your bottom line or top line, are things that we're constantly thinking about because of the dynamic across all three sets of users.Chai [00:16:32]: There's a whole other axis too with the payers and pharmaChai [00:16:35]: as well. Connecting all these three big stakeholders in healthcare isSwyx [00:16:39]: Do the payers ever see your data? Sorry, the payers meaning the insurers, right?Chai [00:16:44]: Yes.Swyx [00:16:44]: They also see Abridge data?Chai [00:16:47]: NoSwyx [00:16:47]: Like the direct integration to you guysChai [00:16:48]: They wouldn't see the raw Abridge data but when you're working together on something like prior authorization, whatever information they need, we'd communicate to them.Jacob [00:16:59]: That's cool. I would love to dig into the AI side. You still have a lot of problems on the AI side. And so maybe to start at the highest level, what's one of the hardest problems you have to solve in AI at Abridge today?The Hardest AI Problems: Quality, Latency, and CostChai [00:17:11]: To make things simple, let's take, building off the prior auth example. So one thing Janie talked about is okay, this data is all over the place and there's this combinatorial explosion of procedures, payer policies and even sometimes different health systems. There can be some cross-product of all of these different considerations you have to take into account. But what's really hard about this problem is doing it real-time in the conversation. So, in any AI product, usually the three KPIs you care about are quality, latency and cost. Now, what we're saying is we want you to do this real-time in the conversation, guiding the clinician. How do we do it in a way that does not break the bank? But we're using — But we also need very intelligent models because you're working with this cross-product of data and this, all this context layer as well. So you need high intelligence and high-quality because you don't want the alert fatigue but you also need to be fast and cost-effective. And so that's where a lot of clever engineering goes. It's okay, without getting into all the details here, can you model these policies in some intermediate representation or other things that you can do that can make this problem tractable? And of course, the Pareto frontier is always changing but we are also trying to do this now.Model Strategy: Third-Party Models, Proprietary Data, and Medical ConversationsJacob [00:18:26]: What implications has that had for what you take off-the-shelf and say, “ what? We don't need to be world-class at X. We'll just take this from the model providers or from some infrastructure player,” and what you're “No, this is where we spend most of our time focused on”?Chai [00:18:38]: This is, the fun challenge in AI?Jacob [00:18:42]: It changes every three months? SoChai [00:18:42]: Of course, with the shifting landscape, we try to be extremely thoughtful on predicting the trends of where third-party models are going and where we can uniquely go. And, sometimes when you talk about AI models, we're the models are just going to get infinitely better. But I don't think... It may be in the grandness of time you could say that but, within every month, every quarter, there's specific ways they're getting better. They're training on a lot more, coding data to be better coding agents, for example. And soChai [00:19:14]: We have to think about where are the things that won't — unique data that we're uniquely training on or to step back a little, where is a proprietary model bringing advantage to us is if it can give higher quality or lower cost and latency for similar quality, very similar to many other companies. And when we can do that is when we have proprietary data. So, for example, we have on the order of eighty million or hundreds of millions now getting close to of medical conversations.Jacob [00:19:44]: It's insane.Chai [00:19:45]: This is a unique data set. And this data set, it's very interesting because this data set is effectively a large part of the trace between the patient and the provider. That's where the quote-unquote debugging happens in healthcare. We have these traces at scale, as in as, our CEOs even called it, an exhaust that comes out of our product. And so when you have these traces, that's how you can train better agents on certain use cases, whether it's your transcription diarization use cases or so on or like note generation models and we can do that much cheaper and faster. But we're always also working with these third-party model providers. We closely collaborate with them and that's how we predict where the trends are going. The thing that I think about a lot is that, I know that the model providers are going to train much more on agentic workflows and so forth, so that's great, so that you have a better agentic harness. But the other thing that's interesting is that the model providers, because a large class of the consumer model providers is healthcare queries, that they might, optimize to train a lot of healthcare data to encode the knowledge in its weights. And this is just a great thing for us as well, where the off-the-shelf models can keep bett-getting better at general healthcare information, such that what our strategy is, we have a constellation of models, we can use something for this, that and, we only care about, at the end of the day, the best product experience.EHR as File System: Agentic Workflows and Real-Time InterfacesJacob [00:21:07]: And, you have, overall capabilities improving. I'm curious, as these models get better, is there something you look at and you're “, three months ago, we really couldn't do that but God, the the latest models really allow us to do it”?Chai [00:21:19]: So here's something interesting that I've, been toying with. So all models are... This wasn't super obvious a year ago but now it's become clear and clear that almost every agent is a coding agent underneath the hood? So you give it whatever file system, it can write its own code and so forth. So when you think about within healthcare and the use case that we have, you can think of the EHR effectively like a file system. It's just — it's a storage of all this information. It's a lot of information there that cannot fit into the context window, at least of today's models and you want to use that context effectively for all these product use cases we're talking about. And so if you have better agents that can, manipulate data, read that data, treat it as a file system as we see they're going and we know model companies are investing this way, then that very directly benefits us.Swyx [00:22:09]: Yeah. Okay, cool. Again, just establishing basic things. But we're going back to the model stuff. I'm really interested in double-clicking more on the real-time, element, which is pretty important for both of you. Is it — Is real-time just batches of every one minute, every five minutes? Is that how we do it? Or is there some more native, genuinely real-time in the sense that OpenAI has a real-time API or Gemini has a real-time API?Chai [00:22:35]: Yeah. Yeah. So today it is more on the on the batch basis but there's interestingChai [00:22:41]: Prototypes that we have that we're still not fully, full time, voice in text out or in that sense. But, can you trigger your models, your agents or agentic workflows, depending on the right times in the conversation?Chai [00:22:58]: And so you can imagine, different techniques to bring this latency down and, you want to bring the feedback loop down as much as you can. And so a lot of clever engineering there without fully... Maybe one day we'll do full voice in and text out, train a model to do something like that.Swyx [00:23:15]: You do — People don't want voice in voice out?Chai [00:23:18]: Now we aren't creating experiences that are, during the conversation, inter — It's almost likeSwyx [00:23:25]: Might be too disruptiveChai [00:23:26]: Too disruptive until, who knows, maybe eventually you could have full voice agents once we — the quality and we improve the comfort of the technology. But right now gra — that change is much more gradual and it's more text focus, text out.Janie [00:23:42]: And so much of currently what our product is trying to do is allow a clinician to focus on their patient and maybe at some point but right now patients, clinicians don't want a third voice, at least in a literal voice in that room. And so how do we be there with all the contacts and information ready at hand when there's the right moment?Personalization: Individual Doctors, Specialties, and Health SystemsJacob [00:24:03]: Jenny, one thing I'm curious about is how you think about, personalization in the product. I imagine, every doctor is a special snowflake in their own way, has their own way they like to do things. There are probably a bunch of different approaches you could take to doing that, both within the model layer itself but then also just with clever prompting or engineering. How do youJacob [00:24:20]: Deliver on that?Janie [00:24:21]: It's such a good question. Personalization is massive for us. We think about personalization at three levels. The first is at the individual, the second is at the specialty level and then the third is at the health system or the organization level. To your point, there are a lot of individual preferences. You-When a note is produced, it almost is a reflection that is so deeply personal of a doctor's work and how they give care. And so do they have preferences on things like style? They might want bullets versus paragraphs, really concise versus comprehensive. They also might have phrases that they really like to use or the templates that they want every note to be structured. And, we see it in our feedback all the time. We want two spaces in between sentences or I refuse to use this tool. And so that's something that we've had to build in. And the tricky part is how do you make sure that stylistic preferences don't interrupt accuracy and quality and that's something that we've really had to refine and hone over time. Second is at the specialty level. A cardiologist note or workflow is going to look very different from a dermatologist workflow.Jacob [00:25:32]: I assume cardiology notes are the highest stakes for you guys, given your CEO is a cardiologist.Jacob [00:25:36]: It's “Oh my God, make sure we get this one.”Janie [00:25:37]: Shiv, our CEO, is still a practicing cardiologist. He rounds once a month. And so, first call when we want just quick and easy user feedback too.Janie [00:25:46]: But, specialties require a lot of personalization, both in terms of what does the product look and so we make sure that as new users onboard, we catch that and the product proportionally reflects that. But also on the back end, evals at the specialty level, they are hard-earned to calibrate and get. What does a really great dermatology note look like? What makes it complete? What makes it compliant and billable is very different than a primary care doctor. And so it's not just about what does the product experience look but on the back end tuning and really deepening our understanding for the specialists. What does great output look like? And that's, a problem that we need to calibrate internally, externally, online, offline but, takes lots of cycles but is necessary in a high-stakes environment. And then at the health system level, for products like clinical decision support, you have health systems who've spent years or decades refining their best practices and they want to know, “Hey, we love your clinical decision support product but how do we embed our own hospital guidelines into them to inform clinicians before, during or after a visit what brest — best practices should look like?” And as you think about, deepening moats as well, when health systems, trust us with that data, allow us to productize it and directly into the clinical workflow, makes us a really great partner to health systems who want to build something that truly meets their needs, their practicing guidelines.AI Slop, Memory, and Product Data FlywheelsChai [00:27:23]: And I want to add onto that. The for the clinical documentation problem, it's very similar to AI writing that doesn't feel like your own and then we call that slop. But the way I describe one framing of slop is like AI without context. But we have all that context and both the clinicians, can have it and can guide it. And so part of the other interesting exhaust for us is, memory is, one of these new systems recordsChai [00:27:49]: Almost.Janie [00:27:50]: And we also have all the edits people make on our product and when you think about a data flywheel and how we get better over time becomes really powerful as a mechanism to just going deeper in personalization.Jacob [00:28:04]: It's interesting. I love this idea of working with systems on the guidelines they built up over a long time. I feel like so many of the best AI app companies today are... The question is: How do you take the expertise that a law firm or a bank has built up over many years and then add that as context and also a special sauce over, a an AI tool? And so seems like y'all are really doing that very effectively.Janie [00:28:24]: We're now starting to have our customers ask, “What are other customers doing?”Janie [00:28:28]: “And how are they doing it?”Janie [00:28:30]: And as we think about having visibility across such a large set of care being delivered right now, a really interesting place we could also partner.Swyx [00:28:40]: I'm just curious. I — This may be a nothing question but, how different are health system guidelines from each other? Don't they all converge to the same thing? And if not, where do they differ?Chai [00:28:52]: At a really high level, they're going to talk about very similar things but the difference is probably in some more of the details. “Oh, you should refer to specialists only when XYZ conditions are met,” or so forth and maybe different organizations have different practices and guidelines around that. But high level, talking about similar things but the details are what, of course, that shapes the context and the decisions you make.Swyx [00:29:15]: And this all goes into the context engine and it might affect the notes but maybe not.Chai [00:29:21]: The — For these local pathways, we're definitely thinking about it a little more for our clinical decision support product.Chai [00:29:26]: So yeah.Swyx [00:29:27]: Which is your stuff, yeah.Swyx [00:29:28]: And then the memory which you raised, let's just tell us more about that. What have you tried in memory? What's the structure of the memory? What works? What doesn't work?Chai [00:29:38]: There's, of course, many different ways you could do memory, where it's okay, can you bake it into the model weights or can you do it in some external store? For us, what's interesting is, of course, when you think the models are rapidly changing, whether it's in-house or third-party, baking into the model weights, sometimes you worry that it could be a little throwaway. And so, how do you... You need to find a way that you decompose the problem, the preferences from the underlying models and so forth. The thing we're right now most both that's easiest to start with and we're excited about is having, a separate store for memory, where you have, for example, a memory sub-agent that's, working in the background, figuring out what are the important parts of the clinician's actions that we want to remember for the long term. And then you can also imagine, other things where in the — you have background jobs that are running that are collating these, memories similar to Sleep, of course and what other pattern, patterns products do as well. Learning over all these action, all the action data we have, again, note edits, the conversations they did and the actual transcripts.Evals: LFD, LLM Judges, and Clinical SafetyJacob [00:30:40]: What about evals? How in the world do you... It is such a complex product surface area. We would love to hear you riff on that and also how has that evolved? I'm sure you've gotten better at it, so any learnings along the way.Janie [00:30:50]: From an evals perspective, we, from day one when we build any new product or feature, we think about, what does good look like? And there are table stakes things like clinical safety but then you start to get deeper into what does good quality look like. And when you go into something like our core product, there's stuff like style and completeness and there's things like does this note become something that can be billable, which is very high stakes for a health system. We have a number of ways in which we get confidence for this. We have, internal in-house clinicians who do what we call an LFD process to give us our very first pass at is this or isn't this a good enough output, look at the effing data.Jacob [00:31:41]: LFD?Chai [00:31:42]: That's why I was smiling. I was “Is Janie going to mention what it stands for?”Jacob [00:31:46]: I was not... There's like a million acronyms.Jacob [00:31:48]: How am I supposed to know that I don't? So “Oh yeah, of course, an LFD.”Swyx [00:31:51]: I've never heard of LFDs.Chai [00:31:53]: It's a bridge for sure.Janie [00:31:55]: I got through three days and then I had to ask someone.Janie [00:31:58]: I thought it was just me that didn't knowJanie [00:32:01]: It's our internal process.Swyx [00:32:02]: But look at the data as a meme in ML, ‘cause you tend to not look at it. You just want to look at number go up.Chai [00:32:06]: Exactly.Swyx [00:32:07]: But yes.Janie [00:32:08]: But so, we make sure we look at the data and then as we think about all of the components of good output, we, one, create LLM judges across all of these and we make sure with annotated data and either internal or external evaluators, we feel like these judges are calibrated. And then depending on the stakes, we also work with in-house and third-party evaluators across all of these before we ship any big change. And the goal is, in terms of evolution, how do you go from this process taking months, down to weeks, down to days? Some of it is, a true science and ML problem. A lot of it's also just, hard operational work. Have you planned ahead in terms of what you need? Have you really optimized the capacity that you need across all of the different specialties you need? Have you gotten a really good sense of which third parties are great to work with for what use cases? This takes a lot of domain, expertise and, lots of mistakes and errors in figuring that out. And so as much of it is an ML problem, so much of it has also been operational gains that are hugely important, where domain-specific expertise is everything.Specialty-Level Evaluation and Progressive RolloutsJacob [00:33:23]: But it's funny, ‘cause I feel like people talk about healthcare like it's one giant market and the reality isJacob [00:33:26]: It's, dozens and dozens of sub-markets. And so it feels like in your evals you have to build that up across the board, probably.Swyx [00:33:34]: And is specialization the primary cardinality at... That's the word that comes to mind.Janie [00:33:40]: Sometimes, depending on the product or the use case. And so if we're making a note improvement or feature for a particular specialty, definitely but we have products that are for nurses. We have products that, are really aimed at making the document or the output a lot more billable. And so we'll want to work with coding teams and not necessary clinicians. And so likeJacob [00:34:05]: Coding meaning healthcare coding.Janie [00:34:06]: Yes. Yes.Jacob [00:34:07]: NotChai [00:34:07]: Yes. I see you.Swyx [00:34:07]: Other kinds.Janie [00:34:09]: But is this output proportional to the work that was delivered? Is there sufficient documentation to justify the amount that a health system may end up charging? And so, specialty sometimes but also domain, very different across all of the different products that we're working for. And building out that network is, not easy and is where a lot of our operational investments have gone into.Chai [00:34:35]: And I view a lot of analogies to self-driving cars here, where, part of it is we really want progressive rollout of features to test in the real world is this useful? Is this going to work? One big difference compared to past lives is before I'd build a product, maybe I'd alpha it and then I'd like GA it the next week, ‘cause I'm “Go, move fast, ship,” and whatnot. But the mentality is like you... I want to make contact with the reality as quick as possible but I want a progressive rollout. Because as much as I get as large of an offline eval set, I want the distribution of that to match real-life distribution. And over time, by rolling out early, similar to Waymo has a tagline, “The world's most experienced driver,” another thing that can, at least linearly increase for us is, both the size of our evaluation offline and online, that and it all feeds back.Janie [00:35:25]: Something that's been earned over time, speaking of evolution, is just the trust we've gotten with customers. Historically, a lot of these health systems, when they bring on new vendors, their release cycles are quarters, sometimes twice a year. We've gotten our customers onto monthly release cycles, which is pretty fast for health systems but what is more exciting over the last, call it, few quarters, has been, a subset of our customers have said, “We want to innovate with you. We trust you,” and we have a pretty, decent chunk of our customers who say, “We'll develop with you outside of these monthly release cycles. We have a higher tolerance. We know that the stakes are very high but we want to be the first ones using these products, giving you feedback.” And so for a pretty substantial set of our customers, we've been able to convince them to be able to ship, in this gradual way before GA. Something we talk about a lot internally is, trust is earned in drops, earned in buckets and so we still can't do what I used to do when I worked at Loom. We had 30 million users. I'd just be, rolling out experiments left and. The bar is still quite high for iterative rollout but because of the trust we've earned, we're able to learn at pretty high volume very quickly.Privacy, HIPAA, and De-IdentificationSwyx [00:36:45]: Your scale is still pretty huge.Swyx [00:36:47]: One thing I want to... We were going to go into scale? In a sec. One thing I wanted to call up, follow up on evals, which, again, just coming from a generalist engineer point of view, just thinking through what would people be scared of in doing this, the privacy and HIPAAJacob [00:37:00]: Elements of this. I have zero experience in that. What do you have to do? What is surprisingly not that bad?Chai [00:37:06]: So one thing that's really important here from a compliance perspective is very much that any of the data we use needs to be de-identified, any real-world data we use as a basis of online eval sets we're learning from. And so you have to — And there's, very clear, government guidelines, what counts as PHI. And so we've even have built models that can take, for example, a clinical transcript and remove all the key PHI indicators and so you have a scrubbed/de-identified version. And then once you... And so one thing that's important is first you've got to get confidence in that model in the first place? And prove that out. Because, now you have, multiple probabilistic systems on top of each other.Chai [00:37:46]: But once you have that, then you can train on it use it for evaluation and so forth, provided one of the cool things also that you can do from a business side is the right data contracting as well with your partners.Jacob [00:37:57]: Is the anonymization one way? Once it's done, you cannot undo it? Or is there someoneChai [00:38:01]: YesJacob [00:38:02]: Who holds the master key that can... Yeah, okay. So it's one way.Chai [00:38:05]: It's one way. Yeah.Jacob [00:38:06]: That's how it works. I just wanted to... Because, there's a lot of this, learning from feedback and everything that, you would want to debug more but you can't because you just physically don't allow yourself to.Janie [00:38:17]: Some of it's also written in our customer contracts in terms of who can or can't access PHI data, how long do we retain it,Jacob [00:38:27]: Very goodJanie [00:38:27]: Before it gets de-identified. And so we have a pretty high bar for who can access that PHI data, just to make sure that we always respect our customer data and privacy. But that's something that we partner with our customers on too, to make sure that as we want full, as close to precision as possible in that qualityJanie [00:38:48]: We can still use it.Jacob [00:38:50]: But it'll be fascinating to see how that space evolves? Because you think about, I used to work at a company that, did a lot of healthcare data in the cancer space and if you asked, the average cancer patient, “Hey, do you want people, do you want other patients to be able to learn-”Chai [00:39:03]: Take it.Jacob [00:39:03]: “... Learn from your experience?”Chai [00:39:04]: Take it all.Jacob [00:39:05]: They're “Please.”Jacob [00:39:06]: “I'd love, nothing more than for other people to be able to learn fromJacob [00:39:10]: The experience that I had.” And so in the past it was a lot harder to do that learning. But with this technology, that might really be practical and so it'll be fascinating to see how that continues to evolve.Chai [00:39:21]: There's so much in our data set of 100 million conversations.Chai [00:39:26]: You can imagine things like insights that you can give to the clinician. How could you, oh, how could you have reacted to this? In coaching or insights around, which treatments are effective or, like... Because you have this, again, this data source that was never captured before but that's, where, intuition or experience is created from, going back to this idea that the conversation is the agent of truth.Operating at Scale: Reliability, Cost, and Token EfficiencyJacob [00:39:46]: Back to the 100 million conversations, I feel like you have this insane scale that maybe only a few other AI app companies have and everyone else dreams of. So not everyone has had to confront this yet but maybe just talk about some of the challenges of operating at that scale and what, our listeners have to look forward to if they ever get to this level of scale.Chai [00:40:05]: At large and larger in scale, so of course there's a general, infrastructure reliability. When you... In any given startup, you're building the plane while it's flying. So there's some notion of that. But what gets interesting on the AI and ML side for sure is this, as you get at more and more scale, so one, you have the data to first and foremost do this. But, you start thinking about costs or infrastructure in a whole different way at scale versus, a prototype.Chai [00:40:34]: You can use the most expensive model, you can burn as many tokens as you want but when you're doing 100 million conversationsJacob [00:40:41]: Token max on leaderboards are less upsetting than that context.Chai [00:40:45]: . When you're doing that and so that comes for we have the data and we also have the team that's able to post-train based on this and you can optimize for efficiency, especially in areas where you believe that maybe a lot of the quality headroom is less so and you don't expect the other off-the-shelf models to go that way, such that you want to do, efficiency maximization, in terms of compute and tokens.Jacob [00:41:08]: I feel like you guys live in the future in some way where most use cases today are really just in use case discovery mode, where it's “God, I really hope I can find something that can get to scale,” and so you're always going to use the most powerful model. And then the few things that do get to this level of scale, you start to do those optimizations.Chai [00:41:22]: It's a natural trajectory where it's like zero-to-one, we're not talking about any of these optimizations.Chai [00:41:26]: But when maybe we're in the one-to-100 or so forth, then we're in optimization mode and, what works out really well is you've got all this data from zero-to-one that lets you do this.What Comes Next: The Conversation as the Shared Healthcare PlatformJacob [00:41:36]: That's fascinating. I feel like one thing that's so interesting about the Abridge footprint is that you're in the doctor-patient visit in real-time. I always like to say, there's like probably 50 years' worth of product you could build on top of that. What gets each of you, I don't know, what are you most excited about building, either in the short term or medium term or even, long down the line?Janie [00:41:53]: Something that I get really excited about is that the same conversation can serve so many stakeholders. If you think about the conversation, a doctor needs to know what is the documentation, how do I make sure that this fully represent the care I gave? A patient needs to know, “What the heck just happened? This was really overwhelming. What are my next steps?” A payer needs to know, was this the proper and appropriate care given? A pharma company might want to know why isn't this drug being properly used or is there a good candidate for this clinical trial that I'm about to run? And where I get excited is that our product and our platform and our infrastructure can be the same product across all of those things and start to what's today, separate, very expensive, complex systems that serve each one of these stakeholders in very different ways, start to collapse all of that into a singular platform that enables not just more efficiency across the board but also better outcomes for everyone. And, all of us experience healthcare in probably very painful ways and knowing that there is a world in which we can simplify a lot is really exciting to me and it all starts with the conversation.Chai [00:43:15]: It's interesting. Of it very similar to going back to the KPIs that any AI product cares about. How do you increase quality of care? How do you reduce latency to care? And how do you reduce costs? Which is a huge, in healthcareJacob [00:43:28]: They call it the triple aim in healthcare.Chai [00:43:30]: But very similar to building AI products and the thing that really excites me is when we talk about that latency piece, we talked about one example earlier of prior authorization, can you reduce the latency to care? But you can imagine so much more. Oh, as soon as the lab value gets updated, do you have like a background agent that, kicks off and uses all the context to be “Oh, hey, the patient should do this next,” for example. And of flagging that to the clinician who's always in the loop but reducing that latency, to care. And then you can imagine this is much further down the road but it's like even connecting that to the direct patient and the consumer. And so how can you, how can you build a bridge to all of these things?EHR Partnerships and the Clinical Intelligence LayerJacob [00:44:10]: Very cool. The connections piece is just an ever-growing thing. And one of the key partners is the EHR and I wonder what that relationship is like. Will they, look at this as, something that is valuable enough that they want to own someday?Janie [00:44:29]: Our partnerships with the EHR is, we know that we have to be extremely close partners with all the EHRs who we partner with. Being able to not only pull and push all of the data into the right places is, not only table stakes, if we can't do that, health systems don't want to use us. The second and the reality of today is clinicians spend a lot of their days in the EHR. So much of what allowed us to win in the largest health systems was pretty direct and, very close partnerships with some of the largest electronic health records that allowed us to pull and push data with APIs that weren't ready out of the box. And clinicians want to save clicks. Anytime we introduce a new product that, adds two clicks for them in their day, they're “We're not going to use it.”Janie [00:45:21]: They have 15-minute back-to-back appointments with their patients. They're spending, hours during pajama time doing documentation. Every second and every minute counts and so we really think about being deeply integrated into the EHR as also table stakes to getting real usage and adoption. And anything that we build or introduce, we really talk about earn the right internally a lot, which is we have to provide so much value or save so much time that people will use us. But those are the two things that are close to us, is we know that the product won't be used unless it is deeply interoperable.Chai [00:46:01]: And strategically, to your point, it's like what does EHR want to own versus us? EHRs are really focused on the clinical workflows and so forth but some of the things that we're talking about here, I do these traditionally are outside of the domain where it's oh, connecting pairs and providers together with provider policies or the clinical trial matching, as Janie brought up. And so these are, entirely — we position ourselves as building this entirely new intelligence, clinical intelligence layer across, again, providers, pharma and, payers.Chai [00:46:33]: And so that's a it's a whole different ballgame that we try to playChai [00:46:36]: In combination with them.Jacob [00:46:37]: But it's like a different layer of scope.Healthcare AI Regulation, Technical Depth, and What Changed Their MindsJacob [00:46:39]: I'm curious, you are both relatively newcomers to healthcare. People have these, there's lots of futuristic healthcare AI takes of “Oh, everything will look different.”, now that you've been in healthcare for a bit, you live at the edge of AI, what have you, changed your mind on around this, as you think about what healthcare looks like in ten, 20 years? Any updates to your mental model from the time being close to the problems?Chai [00:47:02]: One thing that IChai [00:47:04]: Was hesitant about before and it's a common thing when I'm trying to recruit engineers that people ask me around, is definitely oh, healthcare, heavily regulated space. And it is, rightfully so. You want to keep, the patients at the end of the day safe. But one of the interesting things that, is a that surprised me how much it is coming to the company is there's a lot of really favorable regulatory tailwinds as well. Where you think about, government really wants interoperability between all these systems that we talked about and so agents can access this information. The government just in January, the FDA released updated guidance on clinical decision support, what I work on in such a way that they used to have guidance from like 2022 that required you to have, mention all these options and do all these other things but it's a very forward and forward-looking way. And so for me, what's been really cool to work on is this, there's this very special moment both in AI in general, we all know that but there's a special moment also regulatory in healthcare as well.Janie [00:48:05]: One thing I would call out is for the very reasons things are higher stakes or, potentially considered more difficult in healthcare, it's where some of the hardest AI problems will get solved first, just because the bar is so high. When I first joined, I was “Oh, this is where we'll be on the tail end of where, all of the AI innovation will be able to be applied.” But when you think about, zero error evals or multi-step workflows that have really low tolerance, a lot of the innovation will happen here just because we have to or else we can't ship.Jacob [00:48:42]: ‘Cause like in other domains, you'd much rather just solve the 80%-is-good-enough problems firstJanie [00:48:46]: 80/20 doesn't work hereChai [00:48:48]: And building off that, traditionally, there was a bit of stigma that, oh, healthcare companies are not that interesting from a technical perspective or I've seen that or faced that myself. But these are really hard and fun problems from a pure technical perspective beyond just the impact. How do you bring the latency of this thing down and make it really high-quality?Reducing Latency: Clinical Workflows, Agents, and Implementation RealityJacob [00:49:07]: How do you bring the latency of things down?Chai [00:49:10]: Yeah. Yeah. Yeah. So okay, let's answer the latency question. And maybe hopefully not too redundant with some of the things I've said earlier but some part of it is with any latency, you have to like what is, what is really your bottleneck. In a lot of workflows, it's sometimes it's the model itself. And so that's where like our data flywheel, our post-training team and so forth come in so that can you make the models far more efficient. So that's one aspect of latency. But there's whole other aspects of latency where it's okay, on top of that, if you use a constellation of different models, can you use — can you first use like a — it's like thinking fast and slow. Can you use a cheap, fast model that triages and hands it off to a larger model where you get more intelligence and so forth and so all theseChai [00:49:56]: Clever tricks to make it work.Chai [00:49:58]: And by the way, we are totally — we also realize that the parameter frontier is changing and so these tricks will — may not get us to where we want to be in five years but we need to if we want to build a useful product right now.Jacob [00:50:11]: Should we go to the quick-fire or you want to ask more about Abridge? We can stuff everything that's not Abridge into the quick-fireSwyx [00:50:16]: I don't mind. I was — I feel like Janie was on the topic of more long tail stuff, which isSwyx [00:50:21]: Not the eighty/twenty thing and that really matters. And I'll —, if you have any tips or cool stories or just general approaches that have worked for you that's interesting to dig into.Janie [00:50:32]: One of them is even just how we staff our teams looks different than a traditional software engineering team, I'd say.Swyx [00:50:40]: Let's go.Clinician Scientists, Edge Cases, and Evals at ScaleJanie [00:50:41]: We have a bunch of folks with different roles who are clinicians and so we have this role called the clinician scientist and I heard one of our leaders refer to them as mutants recently. But they are people who've had clinical backgrounds, so MDs typically, who are also deeply technical, somewhere, on the spectrum of like a full stack engineer all the way to like extremely scrappy prompter. But having each of these people embedded within our teams instantly raises the bar for everything that we build because not only are they determining, is this product clinically useful but they're deeply embedded in our whole evals process. And so when we talk about LFDs, when we talk about what is our actual evaluation criteria, you don't want Chai or me creating what those are because we don't have clinical background. But is probably unique to Abridge but has been game changing. And when you think about where the puck is going, you have people build with clinical backgrounds who are technical and where AI tools are going, they just becomeJanie [00:51:53]: More and more, critical and like the killers of the team. And so that's one. And then the second is just the scale at which we do evals to catch that long tail up front before anything ever gets into production is something that we've pretty much like really started to fine-tune, both from a scale but when do we know we need to get several hundred versus several thousand offline responses, what helps us make that quick decision and make this less of an art and as much of a science as possible. But that's also been something we've had to tune over time.Swyx [00:52:27]: And you have partners who opted in to give you those evals.Janie [00:52:31]: So we work either internally or with third-party for offline evals and then we have customers who also agree to give us, whether it's like thumbs up, thumbs down to like choose this or that, a lot of data to get us to what is as close to fully confident as possible.Swyx [00:52:51]: The term that comes to mind isSwyx [00:52:53]: Like active learning on things where you're weak. I feel like it's a lost artSwyx [00:52:58]: Is a lot of the polish that comes into doing something like this.Janie [00:53:02]: Really.Chai [00:53:03]: Hundred percent.Lessons from Glean: Technical Foundations and AI App InfrastructureJacob [00:53:04]: Maybe, on a totally unrelated note, Chai, you had a very, storied run at Glean b

Buckle up, geeks! This week’s Quick Tips have you refreshing the App Store like a pro, turning Finder’s Quick Actions into a PDF-combining powerhouse, swiping that iOS cut/copy/paste bar like a power user, and finally taming horizontal scrolling on your non-Apple mouse. Then it’s tales from the road: Adam wrestles eSIMs into submission with a Starlink cameo, Linda accidentally invents her own ISP, Mint Mobile’s tablet plan steps into the spotlight, and Dave shares what he learned from TP-Link about the FCC saga you’ll want in your ears before your next router purchase. Your questions get the full treatment, too. VaShaun learns how to keep his SSID intact when switching providers (including travel router magic!), Jim battles a stubborn Trash with rm, lsof, and fuser so you Don’t Get Caught staring at undeletable files, and GW finally gets a straight answer on why sync is so hard. Cool Stuff Found rounds it out with WhiteScreen.Online turning your devices into panel lights, Zenringer landing at half price, the Basic Bookmark Checker tidying your digital life, the Flipper Zero cloning whatever’s clonable, and the OBDEleven gen 3 unlocking your car’s hidden settings. Hit play and geek out. 00:00:00 Mac Geek Gab 1141 for Monday, May 11th, 2026 May 11th: National Technology Day MGG Monthly Giveaway – Enter to win a Function101 Apple TV Button Remote The MGG Merch Store is Live! Quick Tips 00:00:01 Michael-QTR-Refresh Appstore Update 00:03:00 Bill-QT-Making a PDF with “Quick Actions” Menu in Finder Apple Support Combine PDFs 00:05:30 Lucas from Chicago-QT-Swipe the bar/menu of cut/copy/paste options on iOS 00:07:16 ACTUALLY combining PDFs on the Mac in the Finder Combine files into a PDF on Mac (in Finder) 00:09:12 David-QT-Horizontal Scrolling with a Non-Apple Mouse! Stories from Travels 00:11:43 Adam and The eSIM Starlink Internet eSIMDB US Mobile 00:21:51 LindaNET (because Linda had a DSL line and resold her high speed internet) 00:22:32 Mint Mobile Tablet Plan 00:26:04 Dave vs. TP-Link and The FCC Sponsors 00:28:00 SPONSOR: CarGurus. Meet CarGurus Discover, a new search feature where you can look for vehicles based on the way you think—using your own words. No more being boxed in by filters. Check it out at https://cargurus.com/ 00:29:11 SPONSOR: NordLayer Browser. The business browser built for how modern work actually happens — giving IT the visibility and control to secure SaaS, stop phishing, and prevent data leaks right at the source. 00:30:08 SPONSOR: CleanMyMac. Get Tidy Today! Try 7 days free and use our code MACGEEK for 20% off at clnmy.com/MACGEEK Your Questions Answered and Tips Shared! 00:31:30 VaShaun-Can I Keep my SSID when I get a new provider? Use your home's same SSID/password on your travel router so everything connects all the time 00:39:01 Jim-How do I empty a stubborn Trash on my Mac? rm vs. rmdir vs. rm -rf sudo lsof +D /path/to/folder sudo fuser -v /path/to/folder Command-Shift-Period in Finder shows hidden files 00:50:27 GW-Why is Sync “Hard?” Cool Stuff Found 00:58:11 Stephen-CSF-WhiteScreen.Online turns your device into a panel light 01:01:12 Michael-CSM-Zenringer (link gets you half price for MGG listeners) 01:02:16 Donald-CSM-1128-Basic Bookmark Checker to clean things up! 01:03:13 Rob in STL-CSF-Flipper Zero for cloning (your?) badges and more 01:06:34 Richard-CSF-1111-ODBEleven gen 3 for tweaking your car’s settings 01:09:11 MGG 1141 Outtro MGG Monthly Giveaway Bandwidth Provided by CacheFly Pilot Pete's Aviation Podcast: So There I Was (for Aviation Enthusiasts) The Debut Film Podcast – Adam's new podcast! Dave's Business Brain (for Entrepreneurs) and Gig Gab (for Working Musicians) Podcasts MGG Merch is Available! Mac Geek Gab iOS app Mac Geek Gab YouTube Page Mac Geek Gab Live Calendar This Week's MGG Premium Contributors MGG Apple Podcasts Reviews feedback@macgeekgab.com 224-888-GEEK Active MGG Sponsors and Coupon Codes List BackBeat Media Podcast Network

Reddit rSlash Storytime r maliciouscompliance where Told us not to turn off the power unless he explicitly said to “turn off the power” so we didn't. Stupid inspectors You want me to do my work your way? Sure. I can't help you and only my boss can? Sure, please wait. Plan Exclusion... Bet they're going to regret it. Her patients were NOT shy Can you hear me now? DSL tech support Tell me I have no choice, and I will comply. Take out the trash no matter what. Wait, not that! Show me your best, most expensive product. Nasty FAX form a lawyer Hosted on Acast. See acast.com/privacy for more information.

Anna kom till Kina i en orolig tid. Landet och dess folk hade länge utnyttjats från flera håll och under ytan kokade vreden. Upprorsmakarna, som blev allt fler, såg de kristna missionärerna som en symbol för de utländska krafter som tagit sig in i landet. dessutom gick det rykten om att dessa förkunnare hade onda avsikter… Källor:Värmländska anna stenades till döds drömmen om att hjälpa de fattiga fick ett brutalt slut - 25 Aug 2020 - Svenska Öden & Äventyr - ReadlyNWT - Missionär stenades till dödsLärarinnan Anna Johansson – LekvattnetBoxarupproret 1898–1901: Svenska missionärer dödades | popularhistoria.seSkrupelfria britter svepte in Kina i opiumdimma | varldenshistoria.seHistoriepodden avsnitt 488. BoxarupproretVad säger Bibeln om helgelse? - Pastor Christian MölkSupport till showen http://supporter.acast.com/historiska-brott. Hosted on Acast. See acast.com/privacy for more information.

This monthly series features an episode sharing my recent highs and lows, how my habits are going, a Do Something List update, plus what I'm loving lately and my commitments for the upcoming month. I hope this glimpse into my life, my family, my work, and my own self development encourages you in your own journey. Around here the goal is never perfection, just to keep trying, even if in very simple ways. I think you'll see that with all of the big changes going on for me, taking the smallest of steps has helped to keep me afloat and feeling like myself. As always, I encourage you to get messy, too!  Preorder Sticky Habits book Check to see if you won a prize from our Favorite Things Giveaway. Get the free DSL Training. Check out Monica's DSL for 2026. Join the Supporters Club to keep About Progress around for good. Join the Book Launch Committee for behind-the-scenes and first peeks at all things book.  Transform your space now. Go to Quince for free shipping on your order and 365-day returns; Get organized, refreshed, and back on track this new year for WAY less. Head to Wayfair.com right now to shop all things home; Join Masterclass for 15% off at masterclass.com/progress Learn more about your ad choices. Visit megaphone.fm/adchoices

Ben and Carlos talk about their impressions of the ABS system in the majors and then crack open the mailbag. The two take a questions about comparing recent top tier college prospects, Elian Pena skipping the FCL, DSL names we're excited about, bonus pool logistics in the draft and why the first overall pick and other players take underslot deals as well as questions about A's outfielder Breyson Guedez and Blue Jays prospects JoJo Parker and Juan Sanchez.—Time Stamps:(0:00) Initial ABS talk (8:00) How would you compare Henry Davis, Joey Bart & Vahn Lackey?(15:30) Are you surprised Elian Pena is skipping the FCL?(18:30) Who are some DSL names you're excited to see this summer?(24:00) Could you explain how bonus pool money works and why players take underslot deals?(32:00) Thoughts on A's outfielder Breyson Guedez?(35:00) What's the chatter on Blue Jays prospects JoJo Parker and Juan Sanchez?Do you have feedback for the show or want to ask us a  question? Email us: futureprojection@baseballamerica.com.Future Projection Twitter: @FutureProPodBen's Twitter: @BenBadlerCarlos's Newsletter: Fringe AverageBaseball America WebsiteAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

The NFL Draft is almost here. Matt Cooper of Couch Scouts joins me to talk through some of the prospects not previously discussed. Listen for talk about Omar Cooper Jr, Denzel Boston, Jordyn Tyson, Justin Joly, Emmett Johnson, Germie Bernard, Eli Stowers, J'Mari Taylor, Jadarian Price, Michael Trigg, Demond Claiborne, Eric McAlister, Adam Randall, Le'Veon Moss, and Malachi Fields. If you join Couch Scouts, use code "DSL" for 15% off.Subscribe to Dynasty Sports Life for great dynasty talk about four dynasty sports. Some music by Kevin MacLeod. Follow on twitter at @dynsportslife. Email at dynastysportslife@gmail.com

Nvidia presentó en el GTC 2026 su visión completa de lo que viene: Vera Rubin, data centers en órbita, y Jensen Huang diciéndole a los gamers que están "completamente equivocados" sobre DLSS 5. La tesis es clara — quien controle la infraestructura de IA, controla la era.Al mismo tiempo, los gobiernos empezaron a mover ficha. La SEC y la CFTC publicaron la interpretación más importante en la historia de la regulación cripto en EE.UU.: 16 activos definidos como commodities, cinco categorías legales, y por primera vez claridad real sobre staking y airdrops. En LATAM, Argentina se convirtió en el primer país de la región en bloquear Polymarket tras sospechas de filtración del dato de inflación.Y mientras todo eso pasa, el dinero sigue fluyendo hacia stablecoins: KAST levantó $80M y TransFi $19M para construir los rieles del próximo sistema financiero de América Latina.00:09:09 Bienvenida y agenda del episodio00:10:41 Precios cripto de la semana00:18:43 Fondo privado de Robinhood00:22:30 Sección tech: resumen GTC00:33:29 DSL 5: demo y críticas00:40:32 Podcast con Friedman: ¿AGI logrado?00:45:54 Agentes y Nemo Cloud de Nvidia00:53:41 Meta: chips propios y video00:57:10 Probando Meta Display en tienda01:07:13 Finanzas: Polymarket y regulación

Topics discussed on today's show: National Cocktail Day, Favorite Raisin, Ruth's Chris Chili's, Uranus Scientists, Hannah Montana is 20, Lyme Disease Vac, Deaths, Baby Koby, Entertainment News, Invest in AI Jobs, History Quiz, Quad Amp Murder, Sports News, Meat News, Pop Quiz: Bone, Gas and Weather, New Show, Paying with Hand's and DSL's, Desperately Single, Bre Kennedy, and Apologies.

Analysts Don Kellogg and Roger Entner discuss the fierce competition between fiber, cable, FWA, and satellite, including who's winning – and where.00:00 Episode intro 00:25 Fiber segments are winning across the board 01:53 AT&T's fiber gains 02:40 Verizon's fiber gains and convergence 03:09 Cable's fiber gains 04:19 FWA and bundling 04:35 Satellite and rural competition 05:37 Only certain customers will bundle 06:00 T-Mobile's fiber gains are more limited 06:30 Is everything a fiber network? 09:29 Speed is not always a factor 10:53 Starlink vs. fiber in rural areas 13:28 Episode wrap-upTags: telecom, telecommunications, wireless, prepaid, postpaid, cellular phone, Don Kellogg, Roger Entner, fiber, cable, FWA, satellite, ILEC, AT&T, net adds, FirstNet, Verizon, bundling, convergence, Charter, NPS, DSL, BEAD, rural, Starlink, T-Mobile, WISP

This monthly series features an episode sharing my recent highs and lows, how my habits are going, a Do Something List update, plus what I'm loving lately and my commitments for the upcoming month. I hope this glimpse into my life, my family, my work, and my own self development encourages you in your own journey. Around here the goal is never perfection, just to keep trying, even if in very simple ways. I think you'll see that with all of the big changes going on for me, taking the smallest of steps has helped to keep me afloat and feeling like myself. As always, I encourage you to get messy, too!  Special links: Sourdough Cookbook; Cinnamon foccaccia recipe (can use without sourdough); Undereye patches; Check to see if you won a prize from our Favorite Things Giveaway.  Get the free DSL Training. Check out Monica's DSL for 2026. Sign up as a Supporter to get access to our private, premium, ad-free podcast, More Personal. Episodes air each Friday! More for Moms Conference use code “LISTENER” for $20 off Leave a rating and review Check out my ⁠workshops⁠! Follow About Progress on YOUTUBE! Book Launch Committee Full Show Notes Transform your space now. Go to Quince for free shipping on your order and 365-day returns; Get organized, refreshed, and back on track this new year for WAY less. Head to Wayfair.com right now to shop all things home; Join Masterclass for 15% off at masterclass.com/progress Learn more about your ad choices. Visit megaphone.fm/adchoices

Hoy en Un Podcast de las Mayores tenemos una entrevista exclusiva EN VIVO con Lázaro Estrada, pitcher con historia única desde Cuba hasta el béisbol profesional ⚾Hablamos sobre:

The 2026 Division-I college baseball season begins on Friday, February 13th. The Baltimore Orioles pick 7th overall in the 2026 MLB Draft. Here are Nick's seven favorite potential first round targets to watch this season. Sign up for our Substack with the $7 coupon mentioned in the show here! Become a Patron and get access to Patron-only discord channels, my MLB Draft doc, and many more in-season perks as we cover the entire Orioles system from the majors to the DSL. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In keeping with something I've done on the podcast for a few years now, I'm thrilled to share with you my Do Something List for 2026. This list isn't about setting strict goals, but prioritizing daily fulfillment. Each item on the list encourages me to honor my interests and passions, from baking something new monthly to hosting girls' nights and rediscovering hobbies like sketching wildflowers. This year, I aim to explore cultural events, reconnect with nature on new hikes, and even venture out for some out-of-state adventures. Each act on the list helps sustain my mental health and well-being while I continue to encourage others to pursue their unique paths to personal growth. Thanks for listening, and I hope you feel inspired to create your own list. Monica's DSL for 2026! Here to Stay Drive: join the Supporters Club to keep About Progress around for good + participate in a whole month of special prizes. A little from many makes this work sustainable! Book Launch Committee aboutprogress.com/bookcommittee Sign up as a Supporter to get access to our private, premium, ad-free podcast, More Personal. Episodes air each Friday! More for Moms Conference use code “LISTENER” for $20 off Leave a rating and review Check out my ⁠workshops⁠! Follow About Progress on YOUTUBE! Book Launch Committee Full Show Notes Transform your space now. Go to https://www.quince.com/monica for free shipping on your order and 365-day returns; Get organized, refreshed, and back on track this new year for WAY less. Head to Wayfair.com right now to shop all things home; Join Masterclass for 15% off at masterclass.com/progress Learn more about your ad choices. Visit megaphone.fm/adchoices

„Schließen Sie jetzt schnell einen Glasfaservertrag ab, Ihr DSL wird demnächst abgeschaltet!“ – laut Verbraucherschützern bringen windige Vertriebler im Haustürverkauf mitunter dieses Argument vor, um von potenziellen Kunden möglichst schnell Vertragsunterschriften einzusammeln. Das Argument ist kurzfristig natürlich völliger Quatsch. Es fußt aber auf der Tatsache, dass die alte DSL-Technik im Grunde längst ausgedient hat und mittel- bis langfristig – in einigen Jahren – sukzessive abgeschaltet werden soll. In dieser Folge des c't uplink fragen wir, warum das so ist und klären eine Reihe weiterer Fragen. Zum Beispiel: Für wen wird ein Glasfaseranschluss teurer als der bisherige DSL-Zugang? Braucht man einen neuen Router? Welche Stolperfallen lauern beim Umstieg? Sollte man einen kostenlosen Hausanschluss machen lassen, wenn der Provider ihn anbietet? Und: Was ist überhaupt das Problem mit DSL? Zu Gast: Urs Mansmann, Andrijan Möcker, Christian Wölbert Host: Jan Schüßler Produktion: Tobias Reimer ► Unsere Artikel zum Glasfaser-Umstieg lesen Sie bei heise+ (€): https://www.heise.de/ratgeber/Worauf-Sie-beim-Wechsel-zu-Glasfaser-achten-sollten-11067549.html ► sowie in c't 3/2026 (€): https://www.heise.de/select/ct/2026/3/2531008291640690842

Steven Thayer enters his fifth season in the Athletics organization and third year with AAA Las Vegas as sport performance coach. He spent 2023 in High A Lansing and 2022 in Low A Stockton in the same role. Prior to joining the Athletics organization, he served as sport performance coach in the DSL with the Giants in 2021. Before his time in professional baseball, he oversaw strength and conditioning programs for multiple varsity sports at Mater Dei High School. Additionally, he spent time as an assistant strength coach for Michigan State basketball and volleyball teams and as an intern with Cal football and Apple Wellness. A native of San Ramon, CA, he is a graduate of Cal Poly, San Luis Obispo and has a master's degree in kinesiology from Michigan State University. He holds nine certifications in the strength and conditioning field.Topics covered in this episode:-Breaking up the monotony of a long season-Defining success and advice for others-Continuing education resourcesQuotes:-"Every day is different, but we're doing the same thing every day. So any kind of change or adjustment goes a long way I think" (1:57)-"We are in the business of people. We are in the business of relationships just as much as we are in the training business" (9:47)-"I encourage people to ask questions. Ask a lot of questions" (16:43)

DSL celebrity Marcus returns to draft a blender featuring the Buffalo Bills and Sabres, the Toronto Blue Jays, Raptors, and Maple Leafs. Who do we think are the top 16 dynasty fantasy pros across the five teams, who are the top eight prospects, and who are the wild cards?Subscribe to Dynasty Sports Life for great dynasty talk about four dynasty sports. Some music by Kevin MacLeod. Follow on twitter at @dynsportslife. Email at dynastysportslife@gmail.com

Jaguar Land Rover reveals the fiscal results of last year's cyberattack. A Texas gas station chain suffers a data spill. Taiwan tracks China's energy-sector attacks. Google and Veeam push patches. Threat actors target obsolete D-Link routers. Sedgwick Government Solutions confirms a data breach. The U.S. Cyber Trust Mark faces an uncertain future. Google looks to hire humans to improve AI search responses. Our guest is Deepen Desai, Chief Security Officer of Zscaler, discussing what's powering enterprise AI in 2026. AI brings creative cartography to the weather forecast. Remember to leave us a 5-star rating and review in your favorite podcast app. Miss an episode? Sign-up for our daily intelligence roundup, Daily Briefing, and you'll never miss a beat. And be sure to follow CyberWire Daily on LinkedIn. CyberWire Guest On today's Industry Voices, we are joined by Deepen Desai, Chief Security Officer of Zscaler, discussing what's powering enterprise AI in 2026. To learn more on this topic, be sure to check out Zscaler's report here. Listen to the full conversation here. Selected Reading Jaguar Land Rover wholesale volumes plummet 43% in cyberattack aftermath (The Register) Major Data Breach Hits Company Operating 150 Gas Stations in the US (Hackread) Taiwan says China's attacks on its energy sector increased tenfold (Bleeping Computer) Google Patches High-Severity Chrome WebView Flaw CVE-2026-0628 in the Tag Component (Tech Nadu) Several Code Execution Flaws Patched in Veeam Backup & Replication (SecurityWeek) New D-Link flaw in legacy DSL routers actively exploited in attacks (Bleeping Computer) Sedgwick confirms breach at government contractor subsidiary (Bleeping Computer) FCC Loses Lead Support for Biden-Era IoT Security Labeling (GovInfoSecurity) Google Search AI hallucinations push Google to hire "AI Answers Quality" engineers (Bleeping Computer) ‘Whata Bod': An AI-generated NWS map invented fake towns in Idaho (The Washington Post) Share your feedback. What do you think about CyberWire Daily? Please take a few minutes to share your thoughts with us by completing our brief listener survey. Thank you for helping us continue to improve our show. Want to hear your company in the show? N2K CyberWire helps you reach the industry's most influential leaders and operators, while building visibility, authority, and connectivity across the cybersecurity community. Learn more at sponsor.thecyberwire.com. The CyberWire is a production of N2K Networks, your source for strategic workforce intelligence. © N2K Networks, Inc. Learn more about your ad choices. Visit megaphone.fm/adchoices

Tool Review: Tailsnitch Tailsnitch is a tool to audit your Tailscale configuration. It does a comprehensive analysis of your configuration and suggests (or even applies) fixes. https://isc.sans.edu/diary/Tool%20Review%3A%20Tailsnitch/32602 D-Link DSL Command Injection via DNS Configuration Endpoint A new vulnerability in very old D-Link DSL modems is currently being exploited. https://www.vulncheck.com/advisories/dlink-dsl-command-injection-via-dns-configuration-endpoint TOTOLINK EX200 firmware-upload error handling can activate an unauthenticated root telnet service TOTOLINK extenders may start a telnet server and allow unauthenticated access if a firmware update fails. https://kb.cert.org/vuls/id/295169

Steven Thayer enters his fifth season in the Athletics organization and third year with AAA Las Vegas as sport performance coach. He spent 2023 in High A Lansing and 2022 in Low A Stockton in the same role. Prior to joining the Athletics organization, he served as sport performance coach in the DSL with the Giants in 2021. Before his time in professional baseball, he oversaw strength and conditioning programs for multiple varsity sports at Mater Dei High School. Additionally, he spent time as an assistant strength coach for Michigan State basketball and volleyball teams and as an intern with Cal football and Apple Wellness. A native of San Ramon, CA, he is a graduate of Cal Poly, San Luis Obispo and has a master's degree in kinesiology from Michigan State University. He holds nine certifications in the strength and conditioning field.Topics covered in this episode:-His journey (including working in the DR and corporate wellness with Apple)-His best professional baseball story-Current trends in the field-Working in AAA Pacific Coast LeagueQuotes:-"Baseball's my first love" (6:30)-"A lot of the foundational stuff is the same. Everybody needs to squat, hinge, lunge, push, pull" (8:05)-"It starts with the rapport. It starts with that getting to know the guy, getting to know that you care about them" (35:48)

Ben and Carlos break open the listener mailbag after talking briefly about MLB's new tech limitations that are coming to baseball. Today's listener topics include Dominican Summer League breakout candidates, the comps on 2026 high school shortstop Tyler Spangler, the rarity of 60-grade hit/power prospects and players outside the top 100 with the biggest upside potential. —Time Stamps: (0:00) Intro and tech talk(10:00) DSL breakouts(16:45) Tyler Spangler comps?(20:00) The rarity of 60-grade hit/power prospects(28:20) Upside players not on the top 100Do you have feedback for the show or want to ask us a question? Email us: futureprojection@baseballamerica.com.Ben's Twitter: @BenBadlerCarlos's Newsletter: Fringe AverageBaseball America WebsiteSupport this podcast at — https://redcircle.com/future-projection-a-baseball-america-podcast/donationsAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

In this episode of Dog Works Radio, Robert sits down with longtime friend and client Dr. Lisa DeVore, calling in from South Korea, to talk about her new book Paw Prints on Leadership: What My Service Dog Bailey Taught Me About Leadership. Lisa shares how Bailey, a once-stray dog who became her trained psychiatric service dog, helped her navigate life, work, military contracting, and the stress of living abroad. Together, Robert and Lisa revisit Bailey's early days in training, the challenges of public access, how a dog can change someone's presence and confidence, and how those lessons mapped directly onto her doctoral work in strategic leadership. They talk about approachability, responsibility, accountability, empowerment, adaptability, and what it means to lead and follow at the right moments. Lisa also walks through the long process of writing her book through her DSL program and why she finished it the morning after Bailey passed away. There's also a giveaway: the first five listeners who identify the food Bailey steals in the story will win a copy of the book sent directly from Amazon. 00:00 Intro 03:00 The origins of Bailey 07:40 Beginning the doctoral journey 11:20 Turning grief into a project 14:00 What Bailey taught about leadership 18:45 Duo extraordinaire 21:10 Responsibility and accountability 24:00 Empowerment and trust 26:30 Flight toward presence 29:45 Rapid-fire questions 32:40 How to find Lisa's book 33:15 Giveaway details

Ian Cundall is joined by Red Sox Senior Director of Player Development Brian Abraham for a wide-ranging discussion about the Red Sox farm system. Brian reflected on the 2025 season and discussed the recent staff changes and what goes on in Fort Myers during the fall. After that he gives his thoughts on the new 40-man additions and a bunch of players including Kristian Campbell, Franklin Arias, Luis Perales, the upcoming DSL group and several 2025 draftees. Whether you're a seasoned farm follower or a prospect newbie, there's something here for you! Got something to say? We love talking about what you want to hear about. Make sure to email us at podcast@soxprospects.com. Social Media Links: IG: @SoxProspects @SPChrisHatfield @IanCundall @SoxProspects (All 3 are the same on Bluesky as well) Love the show? Want to help us out while also getting exclusive goodies? Support the podcast by contributing to us on Patreon!  

Anthropic reports China-linked hackers used Claude AI in an automated espionage campaign. Google reconsiders its upcoming “Developer Verification” policy for Android. AT&T customers affected by two data breaches in 2024 can now file claims. Nearly 10,000 Washington Post employees were affected by a data breach. ASUS and Imunify360 patch critical flaws. DoorDash discloses a data breach. Checkout.com donates the ransom to researchers. Kraken ransomware benchmarks systems before encryption. Mike Arrowsmith, Chief Trust Officer of NinjaOne, shares his thoughts on how cyber may be heading for its California fire insurance moment. AI ChatBot toys behave badly.  Remember to leave us a 5-star rating and review in your favorite podcast app. Miss an episode? Sign-up for our daily intelligence roundup, Daily Briefing, and you'll never miss a beat. And be sure to follow CyberWire Daily on LinkedIn. CyberWire Guest Mike Arrowsmith, Chief Trust Officer of NinjaOne, is sharing his thoughts on how cyber insurance is heading for its California fire insurance moment. Selected Reading Anthropic Says Chinese Hackers Used Its A.I. in Online Attack (The New York Times) Researchers question Anthropic claim that AI-assisted attack was 90% autonomous (Ars Technica) Google backpedals on new Android developer registration rules (Bleeping Computer) AT&T data breach settlement to pay thousands to claimants. Who is eligible, how to apply (El Paso Times) Washington Post Says Nearly 10,000 Employees Impacted by Oracle Hack (SecurityWeek) ASUS warns of critical auth bypass flaw in DSL series routers (Bleeping Computer) Imunify360 Vulnerability Could Expose Millions of Sites to Hacking (SecurityWeek) DoorDash hit by new data breach in October exposing user information (Bleeping Computer) Protecting our Merchants: Standing up to Extortion (Checkout.com) Kraken ransomware benchmarks systems for optimal encryption choice (Bleeping Computer) AI-Powered Toys Caught Telling 5-Year-Olds How to Find Knives and Start Fires With Matches (Futurism) Share your feedback. What do you think about CyberWire Daily? Please take a few minutes to share your thoughts with us by completing our brief listener survey. Thank you for helping us continue to improve our show.  Want to hear your company in the show? N2K CyberWire helps you reach the industry's most influential leaders and operators, while building visibility, authority, and connectivity across the cybersecurity community. Learn more at sponsor.thecyberwire.com. The CyberWire is a production of N2K Networks, your source for strategic workforce intelligence. © N2K Networks, Inc. Learn more about your ad choices. Visit megaphone.fm/adchoices

Everyone dressed up as characters from Star Trek. Did Jeffrey get his car back after his oil change? Were there sparks between Duji Hater Dave 72 and Duji? Does JLR know what DSL means? A fraternity in New Jersey has been permanently closed after a hazing incident. Charlie's mom almost killed him with electricity, twice. Would you pay 20k for a robot maid? What percentage of people believe in ghosts? Spock has a request. Is there a ghost in the studio? Spiller. DraftKings bets for Sunday. A police officer shows up to a court case via Zoom wearing no pants.

Everyone dressed up as characters from Star Trek. Did Jeffrey get his car back after his oil change? Were there sparks between Duji Hater Dave 72 and Duji? Does JLR know what DSL means?  See omnystudio.com/listener for privacy information.

Everyone dressed up as characters from Star Trek. Did Jeffrey get his car back after his oil change? Were there sparks between Duji Hater Dave 72 and Duji? Does JLR know what DSL means?  

Everyone dressed up as characters from Star Trek. Did Jeffrey get his car back after his oil change? Were there sparks between Duji Hater Dave 72 and Duji? Does JLR know what DSL means? A fraternity in New Jersey has been permanently closed after a hazing incident. Charlie's mom almost killed him with electricity, twice. Would you pay 20k for a robot maid? What percentage of people believe in ghosts? Spock has a request. Is there a ghost in the studio? Spiller. DraftKings bets for Sunday. A police officer shows up to a court case via Zoom wearing no pants. See omnystudio.com/listener for privacy information.