Podcasts about nvl

Watch the full episode on YouTube:We first covered Baseten last year when DeepSeek mania was at peak hype. Now they have raised a monster $13B round and become one of the new cohort of AI Infra decacorns that are (with Nvidia, Intel, and the semis complex) chief beneficiaries of the Inference Inflection. We return to Baseten at the peak of the 2026 edition of Open Weights debate. Ali has published a viral breakdown of Kimi K3:And since you last saw him, Philip has spoken at AI Engineer and written the definitive book on Inference Engineering spotted all over SF:Three years ago, inference engineering barely existed as a category.Today, it is one of the most critical disciplines in AI. Inference engineering inherently tackles a different question than standard model training: “How do you turn those weights from training into a product that is fast, reliable, and affordable at scale?” Focusing on these creates an entirely new optimization problem.In one recent GLM-5.2 experiment, quantizing more of the model actually preserved its benchmark quality while increasing throughput by 20%, because the errors introduced in different layers could cancel each other out.Inference is no longer just the final step after training. It is becoming its own engineering discipline, with its own research problems, infrastructure, and increasingly specialized roles.In this episode, Baseten's Philip Kiely and Ali Taha join swyx and Vibhu to explain what actually happens after a new open model is released and what it takes to turn “we generated a token” into a fast, reliable, production-ready API.We go deep on cache-aware routing, disaggregated prefill and decode, quantization, speculative decoding, KV-cache movement, model parallelism, GPU kernels, and the race to make frontier models up to 10× faster. Philip and Ali explain why inference optimizations can still produce gains of 20%, 100%, or even 200%; how quantization errors can cancel one another out; why identical weights can behave differently across clusters; and how Baseten grafted a Kimi vision encoder onto GLM-5.2 without changing the underlying language model.The conversation then expands beyond LLMs into NVIDIA Dynamo, mega kernels, Rubin, AI-specific chips, local inference, video generation, diffusion versus autoregressive models, and the enormous compute barrier to generating coherent long-form video. Finally, we explore the convergence of training and inference, continual learning through persistent KV cache, and the emerging loop where models help optimize the infrastructure that runs them.We discuss:* What happens when a 200,000-token request enters an inference system* Cache-aware routing and reusing previously computed KV cache* Why prefill and decode are increasingly handled by different GPUs* When dedicated deployments become cheaper and more reliable than shared APIs* How speculative decoding uses a smaller model to accelerate a larger one* Tool calling, structured outputs, and what LLMs actually do* What it takes to support a new open model on day zero* Grafting Kimi's vision encoder onto GLM-5.2* Retrofitting inefficient model layers with components from other architectures* Why models sometimes collapse into repeating the same token* How hardware, kernels, and race conditions create nondeterministic failures* Preserving model fidelity while making inference faster* How quantization errors can cancel each other out* Why inference optimizations still deliver gains of 20%, 100%, and 200%* How optimized serving can make a model up to 10× faster* NVIDIA Dynamo, KV-aware routing, and distributed model serving* Speculative decoding the speculative decoder* Why local AI is about making models less dumb while data-center AI is about making them less slow* Tensor, expert, and pipeline parallelism across GPUs* Hardware-aware model design, auto-tuning, and the case against mega kernels* Rubin and why inference is becoming a systems problem* Whether modern GPUs are evolving into programmable AI ASICs* Why enormous models like Kimi K3 require GB300-class hardware* Why open-source video generation still trails Veo, Kling, and other closed models* The quadratic attention bottleneck behind long-form AI video* Autoregressive video, real-time generation, and compounding quality drift* Why future video systems may combine autoregressive and diffusion architectures* Training for inference and inference for training* Continuous post-training, deployment, evaluation, and improvement loops* How GLM-5.2 helped optimize the kernels serving GLM-5.2 itself* Why faster networking could unlock dramatically faster decoding* Continual learning, KV-cache compaction, and persistent model memoryShow Notes* How to build a day-0 API for Kimi K3* 22580: From GPT2 to Kimi3, ExplainedPhilip Kiely* LinkedIn: https://www.linkedin.com/in/philipkiely* X: https://x.com/philipkiely* Inference Engineering: https://www.baseten.co/inference-engineering/Ali Taha* LinkedIn: https://www.linkedin.com/in/aliestaha/* X: https://x.com/waterloointernTimestamps00:00:00 Introduction and the 200K-Token Prompt00:03:18 Dedicated Deployments, Speculative Decoding, and Tool Calling00:11:26 Launching Production-Ready Open Models00:19:06 Model Retrofits, Failure Modes, and Nondeterminism00:28:22 Quantization and Canceling Errors00:32:15 The Race to 10× Faster Inference00:40:48 Dynamo, Speculation, and Local vs. Data-Center AI00:50:18 Model Parallelism, Auto-Tuning, and Mega Kernels01:00:55 Rubin, GPUs vs. ASICs, and Custom AI Chips01:10:03 Giant Models and the Limits of GPU Memory01:12:42 AI Video, Quadratic Attention, and Autoregressive Generation01:21:47 Audio, Images, and Diffusion Models01:27:32 Training, Self-Optimizing Models, and Continual Learning01:40:06 Closing ThoughtsTranscriptIntroduction: Baseten, Waterloo Intern, and Inference EngineeringSwyx [00:00:00]: Okay, we're here in the studio with Philip, old friend from Inference Engineering, the book, as well as Baseten and everything that you've done, you and I have done before, as well as Ali. Welcome.Ali [00:00:15]: Pleasure to meet you.Swyx [00:00:15]: Waterloo intern.Ali [00:00:16]: Waterloo intern, always.Swyx [00:00:17]: When did you get “Waterloo intern” as a handle?Ali [00:00:19]: As a handle? Oh.Ali [00:00:20]: I think the rebranding happened mid-March. When I saw it was open, I was like, “I have to take it. Up for grabs.”Philip [00:00:26]: The problem is that Ali is really good at his job and is not gonna be an intern much longer.Philip [00:00:30]: So we have to figure out who's gonna get the handle.Ali [00:00:33]: Well, I'll pass the torch over to the next intern.Swyx [00:00:34]: Oh, okay. It can be, like, you just pass it to another Waterloo grad.Ali [00:00:37]: To another Waterloo intern. No, bruh.Philip [00:00:39]: Yeah.Ali [00:00:39]: Intern.Swyx [00:00:40]: Intern, yeah.Ali [00:00:40]: And no.Philip [00:00:41]: You gotta get an intern from Waterloo.Ali [00:00:42]: Yeah, I've gotta get an intern from Waterloo.Swyx [00:00:44]: Right.Ali [00:00:44]: But they have to follow the path.Swyx [00:00:45]: Oh, it could, but it could come from Baseten, so it's like whoever Baseten gets from Waterloo.Ali [00:00:48]: Right.Swyx [00:00:49]: Has the title of Waterloo.Ali [00:00:50]: It stays in the ecosystem.Philip [00:00:51]: Exactly.Ali [00:00:52]: Halfway through the internship, you either get it or you're out.Philip [00:00:55]: You should also do, like, a big graduation ceremony where you change the handle.Ali [00:00:59]: Just say it.Philip [00:00:59]: For everybody.Swyx [00:01:00]: You guys are good at ceremonies, clearly. We had a nice launch of the book, very successful. But before we get into all that, I wanna start off with a fun question for you. Okay, you're an expert inference engineer. What happens when I send a long query, say two hundred thousand tokens into Baseten's inference? What's the process of query through GPU model routing, balancing, all that? What is all the stuff that we don't think about?Long Context Requests, KV Cache, and Cache-Aware RoutingPhilip [00:01:26]: With a long query specifically, the first thing that I'm gonna ask is, “Have you sent me this query before, or at least part of it?” and I really hope you have, because it's gonna be a lot easier for me and a lot cheaper for you. So the first thing that we're gonna look at is some cache-aware routing, where we're going to see, we probably have a number of instances, a number of replicas up serving whatever model you're hitting. We want to send this one to something with, number one, available prefill workers, and number two, ideally some cached input already there so that we can skip prefill on at least part of these two hundred thousand tokens. If you're doing two hundred thousand tokens, it's probably coding or a multi-turn agent or something where you would expect to have that cached. If you don't, we're gonna have to send it to a prefill worker. We've at least on certain models disaggregated prefill and decode, so you're going to have one set of GPUs that's solely going to process the input, create the KV cache, and get you your first token, and then that's going to be passed over to a separate set of GPUs, which is going to run decode. We're going to iteratively make those tokens. We're probably going to have some speculator model in front of that. I'm going to assume that you're doing coding, and because of that, our speculator model, which assumes you're doing coding, is gonna have a high draft token acceptance rate. If I'm wrong and you're asking me to summarize every Harry Potter book, it's gonna be slower. And then we stream that output to you and account for it, charge you, a couple of pennies and say, “Hey, would you like to send another one?”Swyx [00:03:04]: Except Baseten doesn't charge by pennies.Philip [00:03:07]: Well, yeah, we charge. I'm assuming that we're talking about the public model APIs. If you are setting up a dedicated deployment, then yeah, it's not pennies.Public APIs vs. Dedicated DeploymentsSwyx [00:03:18]: Yeah, one of the key differentiators when I was talking with Baseten initially was that people who want very high volume just need to rent by the box, ‘cause then it's up to you to figure out how to saturate the box.Ali [00:03:31]: And more often than not, it's, like, way cheaper if you're pushing, like, millions of tokens per hour, if you just pay per hour instead of pay per token.Philip [00:03:37]: Yeah, they do. I think that we've increasingly seen a lot of demand for the pay per token APIs, just because everyone wants to try open models, and then once they find a use case that's really sticky, then they move over to dedicated.Swyx [00:03:51]: Is there a best practice on when it's time to swap over?Philip [00:03:54]: Couple reasons. Yeah, reliability, that's a big one, right?Ali [00:03:57]: Like, if they have a very specific use case, they want you to train something specifically for them, like they want their own spec dec, for instance, for their own traffic.Swyx [00:04:04]: Spec dec is speculative decoding.Speculative Decoding and Custom SpeculatorsAli [00:04:05]: Speculative decoding, yeah.Swyx [00:04:07]: You have to explain.Ali [00:04:07]: Sorry. Like, speculative decoding is like, if you have a huge model, right? And so the model is going to be generating one token at a time every single turn, every single forward pass. So we attach, like, this little, like, parasite, like this layer that goes on top of the model, and this model just has to predict. It does three very fast autoregressive forward passes, and it will predict, like, three certain tokens, and then you do one forward stage over the entire original model in order to see if those predictions were correct or not, and then you accept them or you reject them. Now, this draft model is traffic specific, so if you, like, Philip said, if you're summarizing Harry Potter books, I can train exclusively that draft model on Harry Potter books, and I can guarantee you that I'm gonna accept the three tokens every single time. And so with that case, I increase your decode speed. I wouldn't be able to provide this to you if you're a shared endpointSwyx [00:04:53]: YeahAli [00:04:53]: ‘cause I have no idea if you're doing Harry Potter, if you're doing coding, if you're doing English. We don't know. Also, there was a thing in the book that mentioned that if they really cared about a specific threshold, chapter four, I think. Do you remember that?Philip [00:05:06]: Yeah. The things that you can do is you can set a specific, like, batch sizing, a specific, like, parallelism strategy if you're trying to optimize for, like, throughput versus latency. You can. Maybe a NVFP4 quant doesn't pass your benchmarks and you wanna run a model at higher precision, you could do that. There's just a bunch of reasons why you might wanna have your own endpoint and the biggest one, of course, just being, like, you don't have to deal with someone else doing a hundred million tokens of benchmarking traffic at the endpoint when you happen to be trying to serve your users.Swyx [00:05:40]: Yeah. I think one thing that is. That is a classic journey. Like, it's people is asking the, what happens when you type Google into the browser. Tool calling, is that just, you're generating JSON or is there more complication beyond that?Tool Calling, JSON, and Structured OutputsAli [00:05:58]: Certain customers that we have, they have their own post-trained models, and so they demand a tool calling that's not just, like parse a file or go find the weather. It's something that's very specific and you have to do post-training on this. And if the post-training on the model is not good or if the quantization after the post-training to get the inference to be fast, the model will struggle reading the JSON file and reading the tool calling. But it doesn't require its own like sandbox. It's not like it's going to use that tool calling to like escape a sandbox or like it doesn't have to be contained. It can just be a normal dedicated deployment. The challenge with tool calling more and more seems to be that the companies want certain tool calling which is a very sensitive thing to train. And because you're dealing with all of the JSON outputs, if it doesn't like close the end of the request in a very certain manner, you end up with a model that did the tool calling and like the thinking and so as a result of that, it didn't see the result and just hallucinated the result as it decoded. That seems to be the most challenging thing with tool calling, not really the sandboxes model.Philip [00:06:56]: Yeah, that's a challenge on the training side and then on the inference side, there's work that you can do to scope the possible output. So we published this at this point close to two years ago, the solution to this problem which is you make a state machine and you use that to constrain the output to a specific format. So this is the structured output problem. If you remember backSwyx [00:07:27]: Yeah, the specific grammar is,Philip [00:07:29]: Yeah, exactlySwyx [00:07:30]: GML had this thing.Philip [00:07:31]: Yeah. So it's like the old-school “make sure this is only JSON”, return only JSON orSwyx [00:07:38]: YeahPhilip [00:07:38]: Grandma's gonna die type of prompts.Swyx [00:07:39]: Is it BNF grammar? At some point OpenAI had released a thing that was like, yeah, if you want to constrain your output, write BNF grammar, back as NOR.Philip [00:07:47]: In our inference system, it's just a specified output format. And you get the guarantee that your output's gonna be structured along that format. And so applying that to tool calls can like help cut down on. You can still call the wrong tool or call no tool. It doesn't solve the certainty problem but it at least solves the output structuring problemSwyx [00:08:10]: YeahPhilip [00:08:10]: Within tool calls.Swyx [00:08:12]: And MCP is just another form of tool, right.Philip [00:08:14]: Yeah, exactly.Swyx [00:08:15]: As far as there's no special thing there.Philip [00:08:16]: The thing I'm always like explaining to people is the LLM is not capable of doing anything. It's only capable of making suggestions of what to do and then if those suggestions are formatted in a certain way and applied to a system that knows what to do with them, then an action occurs.Swyx [00:08:32]: Yeah. Part of the fun stuff is, this is solved outside of tool calling too. Like in an agent loop if the output is not correct or you're right, like reasoning, tool calling was done in the reasoning trace, just be like, “Oh, I don't know what to do. Let me just try again.” And it might get there after a few tries. And on your point of training, sometimes this is harder in smaller models, so you don't have the same exact quality outputAli [00:08:56]: Right.Swyx [00:08:57]: When you just swap from a big model, right?Ali [00:08:59]: Yeah. I will say that, before, I think we need to go back to inference engineering proper.Ali [00:09:04]: But, I had expected that something would replace JSON because it's hard to stream JSON ‘cause JSON must be complete and you must have open and close brackets and everything. So it's hard to parse something or validate something while it's being streamed. So people invented all sorts of things that are like, I forget the name of some of these alternatives, but it's something like TOML, something like YAML. But JSON seems to be dominant still.Philip [00:09:30]: The JSON outputs aren't that long, right? Like you could have a long-- ‘cause tool calls also contain the arguments in them and perhaps for a certain tool you might pass like a very long argument. But my impression of the median tool call is that it's a relatively small number of tokens, right? So I would expect that speculators are generally fairly good at something as formatted as JSON. And so you would have like a pretty fast decode step there and that the streaming wouldn't be as valuable, but maybe I'm wrong about that.Ali [00:10:02]: I think you're also bounded by the software or that the model is gonna integrate with if the software is built with JSON for the tool calls or if the company that you'- if your customer says that this is how our software works and our tools are interfaced with JSON, you can ask them to like, change their software and say like, “Yeah, this is gonna be better for the model.” but like with the right training shouldn't be that much of a difference. Also more profitable if it outputs more tokens probably.Swyx [00:10:25]: Depends on your business model.Swyx [00:10:27]: It really depends. But I will say that, as a writer with like experience a lot with generated output, I do try to move from text to JSON text which is very long JSON, right? Like there's paragraphs in every field because I'm trying to structure it, right?Philip [00:10:44]: Right.Swyx [00:10:44]: I want you to first make factual statements, then make opinions then make bullet point summaries, have dates, have entity references have your sources for references, all these things. Anyway, so these are things that like I think people who really experiment with structural output have to really care about. But, let's, let's recurse up the stack a little bit. Before we started recording, you mentioned something really cool, which is that there's a lot of engineering that-- inference engineering that goes on when a new model provider releases a new model, right? So let's call it GLM-5.2, Kimi K3. I had previously assumed, especially if it's like, well, GLM 5 to 5.1 to GLM-5.2, like that you've supported them before. Is it that much work?What It Takes to Support a New Open ModelAli [00:11:26]: It's a lot of work.Swyx [00:11:28]: Yeah. Okay. So like, a lot of people, all you guys, right whenever a new model launch like, people rush to say like, “Oh, Hugging Face supports this, Fireworks supports this, Spacetime supports this,” and I'm like, “Yeah, of course we support it.” But what goes into that? What goes intoPhilip [00:11:40]: I think it's more than just support it too, right? It benefits the consumer a lot. Like I think it was with Kimi K2.5 or GLM-5.2 the latest, there was an inference war, right? X provider is at 90 tokens a second. The next day we're at 150. The nextSwyx [00:11:55]: I kinda kicked that off with the GLM-5.2.Swyx [00:11:58]: I wrote a Twitter article about. It got like half a million views,Ali [00:12:02]: Based on being numberSwyx [00:12:03]: YeahAli [00:12:04]: Or it's for something else.Swyx [00:12:05]: Yeah. Which,Ali [00:12:06]: Oh my GodSwyx [00:12:07]: Which then got everyone really excited about, hey, how can we, bend tracks a little bit further and,Philip [00:12:14]: There's a difference between support the model, as in I can make a token out of this model, and support a model, as in I have a production-ready API from this model.Philip [00:12:26]: Getting to the point of I can make a token out of this model is not that hard because generally the, open source inference engines, vLLM, SGLang of the world oftentimes even receive weights ahead of time, maintainers do, or the people making the model merge PRs to ensure support. So you generally can, just get it working on the standard open source stack without too much pain in most cases. The challenge is, every inference company is gonna have own proprietary stack. Some open source components, some in-house stuff. And for any arbitrary model, there's going to be some new stuff. Sometimes you get lucky, like K, two five to two six was, like, pretty similar.Quantization, Speculators, and Production ReadinessAli [00:13:16]: Yeah. It was pure continued post-trainingPhilip [00:13:18]: YeahAli [00:13:18]: If I remember correctly.Philip [00:13:19]: Even in those cases, there's still stuff you have to do. You have to redo the quantization work. You're taking the model from. Generally, these models are not released in NVFP4, and we want them to be in NVFP4 for maximum Blackwell compatibility. So we have to perform that quantization, and, calibrate the quantization to make sure that we're not causing any regression in the model's intelligence. And then we also have to train the speculator, as we've talked about. Generally, we have. We have ZDR, zero data retention on our model APIs, so we don't know exactly the traffic that people are sending us, but we know what's popular. We know that coding use cases are popular. We know that agents, agentic use cases are popular. So we can get public data sets that are representative of that traffic and train general speculators. Now, with speculators today, you need to train the speculator using the base model itself because you're getting hidden states out of the model from running inference on these specific prompts, and that is the training data you use to create the speculator. So there's that process which you need the real model weights for. And then there's of course just the process of, standing up all the infrastructure behind it, loading all this stuff, testing it. And then when there's a new model with a newer architecture, I think that, like, the DeepSeek models tend to be the most challenging as they have, like, the most novel architectural stuff going on, model after model. But every new model has something. Kimi K2 had. Oh, sorry, GLM-5.2 hadAli [00:14:53]: Sparse attention.Philip [00:14:54]: Yeah,Ali [00:14:54]: YeahPhilip [00:14:54]: the DSA.Ali [00:14:55]: Right. Which is brought from DeepSeek.Philip [00:14:57]: Yeah. AndAli [00:14:59]: So you can copy-paste then?Philip [00:15:01]: It kindAli [00:15:01]: I don't know how this works.Philip [00:15:02]: So, like we had to, like, build support for that into our runtime. And you're right, like it is really interesting the way that all of these open source labs borrow from each other. For example, like GLM-5.2 doesn't have vision. So something that, Haley, a guy on our team, if we could take a look at this, he, like, grafted the Kimi vision encoder onto GLM-5.2.Retrofitting Vision into GLM-5.2Ali [00:15:27]: We'll be training the projector.Philip [00:15:28]: Exactly. So if you think about, like, the encoder, there's the encoder, which is the part that looks at the image and turns it into latent information, and then there's the projector which likeAli [00:15:38]: You can say latent space. It's okay.Philip [00:15:41]: And then there's the projector that maps it onto, the model itself, and then there's the model weights. You don't wanna mess with the model weights because you run a chance of making the model dumber at something else for the purpose of giving it vision. So instead, Haley started with just a projector, which is only a handful of millions of parameters.Ali [00:16:02]: That would be, yeah.Philip [00:16:02]: Yeah.Ali [00:16:03]: Can you show the training one?Ali [00:16:04]: Like the way it groksPhilip [00:16:05]: YeahAli [00:16:06]: Very interesting.Philip [00:16:06]: And maybeAli [00:16:07]: That right therePhilip [00:16:07]: Maybe Ali, you should take it from here. You've got a betterAli [00:16:10]: Ooh, double the sandPhilip [00:16:11]: Understanding of this than I do.Ali [00:16:11]: Yeah. You can see, like, he. The way he trained this is really cool. At the beginning, he was training it using just like, “Here's a picture of a mountain. Can you describe what's in this mountain?” And that caused it just like the first, learning walls. Like here you can see this all we're trying to teach it is to translate the encoded. Like it's already taken the encoder from Kimi K. It's taken the image. It'Philip [00:16:31]: Yeah. FrozenAli [00:16:31]: FrozenPhilip [00:16:32]: With adapter.Ali [00:16:32]: Exactly.Philip [00:16:33]: Yeah.Ali [00:16:33]: So the brain is frozen and the eyes are frozen. It's just we're tryingPhilip [00:16:37]: AlignAli [00:16:38]: Interconnect between the eye and the brain, right? So the projector. And so you take the tokens and then he's like, “Oh, can you describe what's in this image?” And he's like, “Oh, it's a mountain,” or it's a person or it's a human, whatever the case is. But that didn't cause complete understanding. So he changed it such that every image was associated with a data set of questions. Like, does this image have a white male? Does this image have birds in the top corner? Does this image have a scientist in it? All of that stuff. And it would have to answer questions correctly. And using not just training on describing an image, but being able to answer question, another question, answer over time. Like you can see the grokking, which is like genuinely insane, that retrofitting vision into a large LLM can learn to that extent. And even for images that it doesn't perform well on, for instance, if you ask it a picture of like Stephen Hawking, “Who is this?” Maybe it doesn't get it, but it will say something like, “This is Albert Einstein.” Like it still understandsPhilip [00:17:25]: Close enoughAli [00:17:26]: That this is a scientist who is a man who has, some significant achievements, all that stuff. So that's like really cool.Philip [00:17:32]: Yeah. So, we've covered Hao Tian before, who the author of the LLaVA paper that did this, a while ago. And I think that's very foundational work for anyone who hasn't done vision work before.Ali [00:17:41]: Same with the CLIP and MetaCLIP, where you go from just captioning to building out questionsPhilip [00:17:47]: RightAli [00:17:47]: Off the image and how much better you can get performance.Philip [00:17:50]: Right. Right. Right. Yeah. But what's, what's so exciting about this is if you look at a model like this. Now, this is a little bit more of a research project. It's not. It got to 56% on MMLU Pro, I think. So not quite frontier. But if you're running this model, you haven't suffered any loss on your GLM-5.2 quality. If you don't have an image, it'll just behave exactly the way it used to. And ultimatelyAli [00:18:14]: Which in the inference code you literally do not include the other part, right?Philip [00:18:18]: Yeah. You would just skip the encoder if you don't have an image input.Ali [00:18:22]: Okay.Philip [00:18:22]: Just confirming.Philip [00:18:23]: YeahAli [00:18:23]: Does it affect a lot on the overall inference side? Like you're not adding much, you're adding a very small vision encoder. These are typically likePhilip [00:18:30]: They're super fineAli [00:18:31]: Less than a billion parameters, right?Philip [00:18:32]: Yeah. It's, - There's a little bit less standardization among vision encodersSwyx [00:18:37]: YeahPhilip [00:18:37]: So the support matrix can be a little bit, sparser. But overall, yeah, it's a pretty, it's a pretty minor component of the overall system. And ultimately what you get out of the system is all of a sudden you have Kimi Vision, GLM weights, and DeepSeek attention all in one model.Open Source Model Grafting and Franken-MergesPhilip [00:18:56]: And that's, I think, a lot of the power and beauty of open source, is that you can take all of these different components and combine them together into a system that's better than anyoneSwyx [00:19:05]: YeahPhilip [00:19:05]: Can be individually.Swyx [00:19:06]: People used to say that you would also do Franken-merges where you would take likePhilip [00:19:10]: YeahSwyx [00:19:10]: Layers from each model.Swyx [00:19:11]: Does anyone do that anymore?Ali [00:19:13]: Well, to your point previously when you were mentioning like, the work that goes into supporting a model when it first comes out, like GLM-5.2 or MiniMax M3 or whatever the case is. Sometimes you do have to like, you do have to switch out some things. Like, for instance, the MiniMax M3 head uses full attention, and with full attention you end up with this like insane bottleneck in spec dec ‘cause you're doing auto-regressive token generation for three tokens, and you're doing this like N squared over all of the tokens that are in your sequence. Your KV cache is like very large because it's not sparse, it's not top K. So we find it better to like, okay, we're gonna replace this, we're gonna replace this layer with a layer from another model that's using like GQA, for instance. And then just with the right training, you can get it to have the same acceptance rate. So it is very possible to retrofit layers from other models and very much needed. If a layer is like inefficient, the training just becomes the challenge, like how do you ensure that you train it properly? Which again to your earlier point is like the mesh between training and inference. As in like you need very good training in order to do fast inference. That's like, I feel like more and more becoming true.Swyx [00:20:21]: Yeah. Anything else on the support side when you say like get it to fully production ready?Loop Detection, Race Conditions, and Non-DeterminismPhilip [00:20:26]: Yeah. I think that there's also a question of just, we can test a model to a pretty extensive degree, but we're trying to get it out quickly and then you see a bunch of other people test it and you get interesting results. There was an issue with, GLM briefly where we had some like mode collapses where it would just output the same token over and over again for certain prompts on certain temperatures. Like once you expose an endpoint to the real world, there's going to be, so many more varieties of things given to it that you're able to, discover and patch things. So it's not just a, day zero process, it's then like for the first week, for the first month, if a model remains popular, like how do you both fix bugs and then continue to push the envelope on performance?Ali [00:21:21]: What do you mean you don't want your model outputting S?Swyx [00:21:24]: Is there loop detection on that stuff, by the way? It still happens like quite a lot, which is surprising.Ali [00:21:30]: We have like we, in our endpoint, like if a model was to output the same token like four plus times, we just cut the generation. We say like, “Oh, sorry, this-- Like try again,” or like we will reprocess the request. ‘Cause we know then, like if it, like if, yeah, it's four times the same token, it's probably collapsed.Swyx [00:21:45]: Yeah. Is there a way to opt out in case I really want that?Ali [00:21:48]: You want that?Ali [00:21:50]: I think there's a way that we have to handle it. I'm not exactly certain, but I feel like in certain models, like when they output something like you can imagine, like a table for instance, and so they want, they wanna draw like 12 dashes and 12 dashes. Yeah, I think there's a way for that to happen. I think we only do it on certain tokens. Like we exclude certain special characters.Swyx [00:22:07]: Yeah.Ali [00:22:07]: So we only do it on like certain like S is the most common almost. GLM-5.2Swyx [00:22:11]: OhAli [00:22:11]: And I think it was DSV 4 as well. Like you'd just have like looping issues where like you literallySwyx [00:22:17]: ItAli [00:22:17]: Just have like S.Swyx [00:22:18]: Yeah. Is there a special, something special about S? No, just randomlyAli [00:22:21]: It just seems to be the one token involved.Swyx [00:22:23]: Yeah. And it'Philip [00:22:24]: Is thereSwyx [00:22:24]: And it's only temperature 0Ali [00:22:27]: NoSwyx [00:22:27]: Even at other temperaturesAli [00:22:27]: Even at like 0.9 or whatever, it will still, it will still collapse.Swyx [00:22:30]: That's weird, right?Ali [00:22:30]: It's, it is an inference problem to be honest, like a software problem. Like oftentimes, the image you run will-- like NVIDIA will release an image for instance, and if we will upstream the changes from their latest TensorRT-LLM image into our stack, we'll find that it fixes it. Or oftentimes this will only happen in an inference engine that you're using like SGLang. But if you were to switch to vLLM, that isn't the case. So it seems to be like an extremely like deterministic software issue and not really a model issue. It's not like a weights problem. Like I'- we'll say like, “Oh, it's a problem with the quant. We did PTQ wrong,” right? But that isn't, that doesn't make sense because the same weights used with a different inference engine does not repeat the problem. And sometimes it's, the kernels that are being used in the backend have like these very subtle sometimes race conditions, where if you were to use this model hosted on one cluster, you will never get this problem.Swyx [00:23:19]: Oh my God.Ali [00:23:19]: But if you host it on a different cluster, you will. And the reason is the KV cache transfer from a node to node in that one cluster is using a slower interconnect than the node to node in another cluster. So that exposes the race, whereas in another cluster it doesn't. So then you end up just like, okay, this model is not gonna be hosted on this cluster. We're gonna host it on, another cluster because that cluster exposed that problem. But then it ends up with like, okay, is it the software? Is it the model weights or is it the hardware?Swyx [00:23:42]: There is a thing about this with temperature 0 still not being deterministic, right?Ali [00:23:46]: Right.Swyx [00:23:46]: Mostly because of hardware. Even at temperature 0 same model, you won't always get the same output.Swyx [00:23:52]: Even-- But I'm surprised by the race condition one because, I thought PyTorch was a graph that like guarantees that you at least, execute things in the right order.Ali [00:24:02]: Well, yeah, true. Like I'm not, I'm not saying that there is. Like well, you have things like PTL optimizations where like you can start a kernel before the end of the previous kernel, and that's like ‘cause you want to do that because there'sSwyx [00:24:12]: It's like pipeliningAli [00:24:12]: Expense. Exactly.Swyx [00:24:13]: Yeah.Ali [00:24:13]: But it'- But you don't do it cleanly. Like you overlap a little bit of the execution. No, it is very possible that the kernel itself, like that one block that is supposed to be running in this instance of time, that kernel itself has a race condition. For instance, like a missing barrier. Like often if you're designing a kernel and you want it to make it to be very fast, if you don't test it extensively, you'll, you'll have certain threads access data points from registers before they've been written to by other threadsSwyx [00:24:36]: YeahAli [00:24:36]: For example, because like your barrier is wrong or your synchronization was wrong. But yeah, like the testing itself is very difficult in those like, andSwyx [00:24:42]: And there's no like borrow checkerAli [00:24:45]: What does that mean?Swyx [00:24:46]: Like Rust. Like the. If you're trying to have like memory safety It sounds like a comparable problem.Ali [00:24:52]: Well, yes, but you're working in CUDA, right, NVIDIA GPUs. Like- You just need a higher level language like modular Maybe that's what modular is supposed to do. I don't know.Quantization Quality and Vendor FidelityVibhu [00:25:00]: How do you see keeping quality of the model? So you talked about all these steps of, okay, you gotta do quantization, train your own speculative decoderAli [00:25:07]: RightVibhu [00:25:07]: Run on different hardware. Looking at other model providers, okay, you kicked off a inference speed race on the consumer end. What goes into keeping quality the same across them, right? Sure, you can run benchmarksAli [00:25:22]: YeahVibhu [00:25:22]: But, like, how do you determine how much quantization are there standards? What goes intoPhilip [00:25:27]: There's a few things on quality. Most inference optimizations are lossless. KV caching, for example. You are just recomputing or preventing recomputing the same values. Speculation, of course, if a draft token is wrong, it gets rejected. The main lossy optimization is quantization. And that really comes down to, number one, data format, number two, which parts of the model you choose to quantize, which layers, and number three, like doing a lot of calibration on the quantized weights, to ensure that you're preserving all the outliers. There's other tricks that you can do, though. A big one is long context, ‘cause one thing you asked at, right at the beginning is, “Oh, what's gonna happen if I send a 200,000 token request in?” So with a long input sequence, you need to, store a lot more information. You need to process a lot more tokens. And so even if a model has a context of a certain length, you might, as an inference provider, choose to build an API with a shorter context length, and of course a full length one as well. Because if someone doesn't need the full million token context, for example, you can get them better performance. I don't know if that's exactly like quality of the model. The way that I think about quality is to what degree are we faithfully serving the original model? If you think of a golden implementation of a model that performs exactly the way the model is designed to perform, I think of quality as how close are we getting to that, 100% fidelity of the model.Philip [00:27:13]: You can also, of course, think about quality from the training side and how do you push yourself past 100%. But when I think about purely inference optimizations, it's getting faster while staying as close to that 100% fidelity mark as possible. And certainly our standard internally is that, like you should not be able to tell the difference between our API and a, official API. I think Kimi in particular does a good job of vendor benchmarking hereAli [00:27:41]: YesPhilip [00:27:41]: Where they haveAli [00:27:42]: They released an actual vendor benchmark.Philip [00:27:43]: Exactly, yeah.Ali [00:27:44]: ‘Cause they accused, some people, Amazon? There was some provider that was not doing very well on Kimi's benchmark.Philip [00:27:50]: Yeah.Philip [00:27:51]: So, with Reflect we probablyVibhu [00:27:52]: This was a long time ago, right?Philip [00:27:54]: No.Ali [00:27:54]: Yeah, like threeVibhu [00:27:55]: They alsoAli [00:27:55]: Four, five months agoVibhu [00:27:57]: This also happened with, I don't remember which model, but they pulled out quite a few, and then they started a whole chart about this. It might have beenPhilip [00:28:03]: Kimi Vendor Verifier.Ali [00:28:04]: Yeah.Philip [00:28:05]: Yeah.Ali [00:28:05]: Yeah, ‘cause you, ‘cause you'd be pissed, right? Like if you'Philip [00:28:07]: Yeah.Ali [00:28:07]: If like if I'm a consumer and I'm using like Amazon's endpoint for instance, and I've used Kimi and I'm like, “Oh my God, like this is bad,” I'm not gonna say, “Oh, Amazon quantized the model in a bad way.” I'm gonna say, “Oh, Kimi sucks.” Right?Philip [00:28:17]: Yeah.Ali [00:28:17]: So it seems like that makes sense.Philip [00:28:19]: Yeah, they care. They care.Vibhu [00:28:21]: Justifiably.Ali [00:28:21]: Yeah, justifiably.Vibhu [00:28:22]: This is probably a stupid question, but just checking, has anything improved from main quantization?Philip [00:28:28]: Yeah.Vibhu [00:28:28]: Like, is quantization always strictly worse?Ali [00:28:30]: Well technicallyVibhu [00:28:32]: NoAli [00:28:32]: It's a lossy. QuantizationPhilip [00:28:33]: YeahAli [00:28:33]: Is a lossy, it's a lossy implementation.Philip [00:28:36]: Speed improvesVibhu [00:28:36]: Speed improves.Ali [00:28:37]: It the number, likeVibhu [00:28:38]: No, I' always look for inverse scaling laws.Philip [00:28:40]: Yeah.Ali [00:28:40]: Yeah.Vibhu [00:28:40]: This is something I learned from Noam Brown, where like things that normally act in one direction sometimes do.Philip [00:28:45]: Well, technically when you run a benchmark, because these models are deterministic, sometimes your,Ali [00:28:52]: YeahPhilip [00:28:52]: NVFP4 quant is like, two basis points higher than yourAli [00:28:56]: No, it's noise. It's noise.Philip [00:28:57]: Yeah, exactly. I'm like, yeah, it's, it's within. That's why I always say within margin of error.Philip [00:29:01]: And I stopped saying that because everyone assumes that what is, well, within some margin of error, we're barely inside of that to the worst, so we're saying. But yeah, sometimes it's just like, gives you a higher output score. But like Ali said, that's noise. To my knowledge, you're not necessarily making the results better. You're just trying to, again, like keep your fidelity as close to 100% to the original model.Layer Selection, KL Divergence, and Better QuantizationAli [00:29:27]: There is, to your point, research that we did on MP. I don't know if you are able to pullPhilip [00:29:31]: YeahAli [00:29:32]: A tweet we did. One of our research interns, Joshua, I think it's a tweet on how we have 20% better quantized GLM-5.2 than NVIDIA. Essentially what we found throughout like this month research is, okay, quantization is a lossy. It's. You're compressing the data from, occupying 16 bits to occupying, four bits, for instance. And so you're losing some information, and you're trying to minimize that. And so when I say that I'm gonna quantize the model, my job becomes how do I find the layers that I can quantize, and how to find the layers to not. For instance, with image models, I don't quantize modulation layers, and I don't quantize out projections because those two are. Like out projection is what you see as the user. Modulation is what the model sees or understands. Right, exactly. And so to his paper, do you have the. It doesn't have the. Yeah. It's a long paper. I don't know if I can findVibhu [00:30:25]: If there's a part to search or it's probably in the thread.Ali [00:30:28]: It's probably in the thread.Vibhu [00:30:29]: Yeah.Ali [00:30:29]: But the long and the short is it is very possible that quantizing more of the model makes the results. Like if I have a model that I quantize layers one, five, and 10, and another model where I only quantize layers one and It is possible that the model in which I quantized more information is going to perform better because the quantization errors have canceled out. And so what Joshua showed in his mathematical proof where he had like a verifier in, is that you can predict which layers are going to have quantization errors that will cancel out with each other, and you choose to quantize those layers. And so the result of doing this mathematical quantization is you end up with a model that's 20% more quantized than another provider, so you get 20% more throughput of it because there's more layers than running an NVFP4, and your quality is better than that other quant because the layers that you chose to quantize have their errors cancel out, like one layer skewed to the right one layer skewed to the left, one layer skewed to the right. Your final logits distribution is more similar to the original distribution of the model, so you have better fidelity. And so the way we proved this was with KL divergence. So instead of just scoring on the benchmarks, we scored the KL divergence between the logit distribution of the quantized model and the logit distribution of the original full precision model, and we showed that with this technique we get. If your probability distribution on the logits which token it wants to select is more of the same as the original model, you're probably gonna end up staying true to the original model. So yeah, so it seems like previously before this, it seemed like the industry was, well, the more you quantize, the worse it's gonna be, ‘cause the more loss you introduce. That's not exactly, not necessarily true. So yeah, doesn't improve it, but can cancel out.Philip [00:31:57]: I think it might be this, but reminds me a good bit about pruning where you can prune off certain layers.Philip [00:32:03]: But very interesting. Didn't know this was a whole paper you guys put out.Ali [00:32:06]: It's. Fun fact, it was originally 72 pages, this paper, and then we decidedPhilip [00:32:11]: WowAli [00:32:11]: We can't tell. We couldn't release it. So it's now 45.Swyx [00:32:15]: Still 39 pages, so very substantive. We talked about evals and all these things and, like what's possible in terms of speedup? Like it's like probably like the numberInference Speedups and BenchmarkingSwyx [00:32:25]: Thing that people do wanna care about, and it's something that you wrote about in your post. Like official API is 70 tokens per second, and you push it up to 90. Is that like a normal thing?Philip [00:32:36]: So what's cool about working in inference, the reason that I think inference is going to be a useful place to do engineering for a long time, is that if you look at highly optimized domains like, say, finance, if you're in finance, you measure how much better you got in basis points. It's like, “Oh, I got five basis points better, like twentieth of 1% better,” that's huge news because everything is so optimized. When we publish optimizations, it's 20%, it's 100% it's 200%. So there's still probably like a lot further to go, honestly. Like you'll, you'll know that inference is pretty much solved when researchers start publishing about how they got 1% faster at something.Swyx [00:33:19]: Which by the way, because I am from the finance background, in the ‘70s, that was the margin at the time. When you did quantitative finance research, you would findAli [00:33:27]: And like 20%, tens of percent.Swyx [00:33:29]: That's. Yes.Philip [00:33:29]: Yeah.Swyx [00:33:30]: And now it'Philip [00:33:31]: Tiny fractionsSwyx [00:33:32]: For those people interested, look up Andrew Lo's paper. He had a really interesting illustration of quant, stat arb, distribution, narrowing down from like those kinds of 20% differences in the ‘70s, down to nothing today, which is very cool.Philip [00:33:48]: Exactly, and we're at the beginning of the same type of thing. Now benchmarking is hard. I think anyone will tell you that, and benchmarking provider speeds is hard because there's so many variables that go into it. What hardware are you using? How much load do you have on the system? What's the exact nature of the prompts and input and output sequence lengths? All that stuff. But overall, when you start stacking these improvements, you're looking at multiples. You can look at it. The most common form, of course, is TPS, tokens per second, which is bad naming by us in the industry, ‘cause there's two tokens per second. There's tokens per second, the throughput number, and the latency number.Ali [00:34:31]: TTMT, yeah.Philip [00:34:32]: Like total tokens per second out of the, out of the GPU as a throughput number. Most people only care about tokens per second as the latency number, which we should call ITL, intertoken latency, but we don't.Philip [00:34:44]: Anyway, so you can imagine a standard API without many optimizations for a 1 trillion parameter model operating somewhere in the 30 to 50 tokens per second range for reasonable traffic profile. And we generally see the goal of, pushing to 10X that. But, not necessarily day zero, but by stacking enough optimizations, if you have, say like four optimizations, each of which doubles performance. Or sorry, three optimizations, each of which doubles performance, then you stack that up, that's an 8X gain. That's the order of magnitude that we're working with in this space. We're trying to make things substantially faster, not just go from like 70 to 90.Swyx [00:35:38]: Are you saying you've. You have done that?Philip [00:35:40]: So let's say you have as a reasonable baseline, 30 or 40 tokens per second. You can achieve 10X that. So like on GLM-5.2, if you run it unquantized, perhaps on H100s even, and you're just using an off-the-shelf inference engine with no particular optimizations, no speculator, nothing extra around like KV routing, no disaggregation, you're, you're probably, yeah, looking at that like 30 to 40. You think that's like a reasonable baseline?Swyx [00:36:12]: Right. Right.Philip [00:36:12]: To get to something like 10X, there's a lot of trade-offs that you're making. If we're running at more like a 300, 400 tokens per second range, you are using the best hardware possible. You have a optimized speculator. You have done all of your quantization work. You are Seeing a pretty high cache hit rate. You are running with a reasonably small batch size and a parallelism configuration that is tuned for latency versus throughput, but it is possible. So the spreads that you see if you, like, go on artificial analysis or you go on OpenRouter and you look at, the worst provider to the best provider, oftentimes can hit that range. 10X is of course very aggressive. It's oftentimes maybe more of a four to six times improvement. But that's the performance that makes us really excited, is when we can get these huge gains, not just go from 70 to 90 tokens.Stacking Optimizations: NVFP4, Speculation, and DisaggregationAli [00:37:19]: It's also, like, hardware dependent. Like, ifPhilip [00:37:20]: YeahAli [00:37:20]: If you have a thing where you're serving it on just, like, a node of H100s and then you throw, like, you shard the model across, like, four nodes of B200s. Like, you can definitely increase the speed with just throwing more hardware at it. Like, normalizing for the same exact hardware and the same number of GPUs.Philip [00:37:35]: Yeah. Then you're looking at, like, a two to 4X improvementAli [00:37:38]: Right. RightPhilip [00:37:38]: Depending on the inference optimizations. So yeah, it's. Some of it's, what's the call, and some of it's who's the driver.Vibhu [00:37:46]: If you break down the two to 4X, say the example is run GLM-5.2Ali [00:37:51]: YeahVibhu [00:37:51]: On B200sAli [00:37:53]: YeahVibhu [00:37:53]: Single node, right? What's, like, the cost trade-off for effort to get, like, the last bit of juice out versus what should people just think of, right?Ali [00:38:01]: Spectre quantization. Yeah.Vibhu [00:38:03]: Spectre quantization.Ali [00:38:04]: That's, that's, that's like 95%. LikeVibhu [00:38:06]: And how far does that get you? And how easy is that for the average person to do? So say right I wanna throw the weights of GLM-5.2 on a node of B200s, how easy is it to find speculative decoder- decoder model or already quantized model? How much work goes into it?Philip [00:38:23]: If you're doing it up front, it's quite a lot of work. If you're doing it today, there's going to be people who have published things that you can just, you can just grab some NVFP4 weights. You can grab a speculator. Yeah, if we're thinking about, like, what are the 2Xs we're stacking, going from, BF16 to NVFP4 is, it's not quite a 2X, right? It's like. I think it's about, like, 30 to 40%, from 16 to 8, and then another 30 to 40% multiplied from, 8 to 4. So that doesn't quite get you a 2X, but, like, roughly a 2X. Speculator, roughly a 2X. Disagg on top of that if you're able to get enough hardware and put enough traffic through it, another roughly a 2X. And then you add in some, double-digit percent increase from having just a better runtime with, the latest kernels and stuff behind it. And that's how it stacks up.Ali [00:39:21]: YeahPhilip [00:39:21]: So building each of those, like, building the, quantized weights is, for someone who really knows what they're doing, hours to days of work. Building the speculator, again, like, hours to days of work. And the, disagg setup, hours to days. Well okay, but like once you haveAli [00:39:39]: Once set up. Once set up. YeahPhilip [00:39:40]: Yeah, getting disagg working for the first time, I'm saying, of course, is very difficult.Philip [00:39:44]: The marginal implementationAli [00:39:48]: Like, if you're just grabbing, like if you are a person, like just a normal consumer who has access to, like, a node of B200s and you're wondering, “How can I just host it myself?” You don't need to quantize the model yourself. There's always gonna be, like, an open source quantized checkpoint. NVIDIA's gonna push one out if no one else does. You. Usually, the providers will have their own spec dec that they've trained as well. You don't need to train your own spec dec. You can just use that as well.Philip [00:40:09]: Yeah. Like, GLM-5.2 has its own MTP.Ali [00:40:13]: Right. Right.Vibhu [00:40:14]: What's multi token prediction?Philip [00:40:15]: Yes.Ali [00:40:16]: I'm justVibhu [00:40:16]: Can you explain that?Ali [00:40:16]: I'm just an expert.Ali [00:40:18]: I can do it for you in case I get it wrong?Vibhu [00:40:20]: No.Vibhu [00:40:21]: Yeah, you should correct if we're wrong, but their multi-token prediction can be used for self-speculative decoding.Ali [00:40:27]: I'm not sure. I'm not gonna correct that.Vibhu [00:40:28]: Okay. I'm semi-confident in thatAli [00:40:30]: Okay. YeahVibhu [00:40:30]: But someone can check. But it's useful to paint the story of, okay, not just the average person, but say a company wants to switch from serverless inference I wanna throw this up on. I wanna rent some GPUs, throw it up. These are the steps you take to do significantly faster than just put it behind vLLM.Ali [00:40:48]: Right.Vibhu [00:40:49]: I was waiting for a mention of Dynamo.Vibhu [00:40:51]: I feel like, that's supposed to be the baseline that you measure against.Dynamo, KV Routing, and Disaggregation ToolkitsPhilip [00:40:55]: I would think of Dynamo as less of a box system and more of a toolkit for building with. So when we talk about doing aware routing, when we talk about doing KV offloading, when we talk about doing, PD disaggregation, Dynamo fundamentally is. By the way, Dynamo is an open source library from NVIDIA.Ali [00:41:17]: We've done a pod with KylePhilip [00:41:18]: OkayAli [00:41:19]: Kyle Cranin.Philip [00:41:19]: Cool. So then your listeners know then that it supports all the different inference frameworks. And it is multi hardware, which is interesting.Ali [00:41:28]: But it's just a router, it's not like an optimizer layer.Philip [00:41:30]: Yeah. All it does, like, what Dynamo is good at, it is a library for moving information around your cluster, around your hardware. So if you have, KV cache on one place and you need it to be somewhere else, Dynamo coordinates NIXL for you to move that around.Philip [00:41:49]: That doesn't mean that, like, out of the box, you just say, “Pip install Dynamo,” and then you get, like, a massive performance speed up. It's more of a developer toolkit.Ali [00:42:01]: Yeah. I would have said it would. It comes with a set of defaults that you can then swap out.Philip [00:42:06]: It does. If the industry at large, I think, was, like, rolling out all of these deployments, standard, then I think it would be, like, a credible baseline. But, we've got to, we've got to benchmark against, like, what we're seeing in the wild.Speculative Decoding Methods: Medusa, EAGLE, n-Gram, and Spec-SpecVibhu [00:42:23]: I did wanna talk a little bit more about PD disagg, because that is probably, like, number three after quantized and speculative decoding. In your book though, I was just gonna pull out the book.Philip [00:42:31]: Yeah.Vibhu [00:42:32]: Like section 522 on Medusa, 523 on EAGLEPhilip [00:42:35]: YeahVibhu [00:42:36]: 524 on gram.Philip [00:42:37]: It's 55, would be disaggregationAli [00:42:42]: Yeah. Well, no, I just wanted to dwell a little bitPhilip [00:42:44]: YeahAli [00:42:44]: The other. Like, so what do you choose to include? What do you choose to not to include? Because there was all these other techniques.Philip [00:42:51]: Yeah.Ali [00:42:51]: Are these still relevant? Because I think they came out, like, a year and a half ago maybe.Vibhu [00:42:55]: Medusa is quite old.Philip [00:42:56]: Yeah, Medusa's old.Ali [00:42:58]: It was old.Vibhu [00:42:58]: But is it in the book as a good, here'sPhilip [00:43:01]: BaselineVibhu [00:43:01]: Baseline vanilla understand it?Philip [00:43:02]: Like you should know this.Vibhu [00:43:03]: Like I read the paper, I'm like, “ it makes so much sense.”Philip [00:43:05]: Yeah.Philip [00:43:05]: So with the book, I had a couple goals. One was to give people just a working vocabulary for the space as a whole, and the other was to give them some intuition about how each of these techniques works. As I mentioned in my AI Engineer talk, which is the first public addendum to this, the speculation space has moved much faster than everything else. So yeah, even at the time that I wrote the book Medusa, I very much included as a way for people to understand how the space evolved rather than what the most modern technique is. And now of course, there's DFlash, dSpark. There's, there's newer techniques even than EAGLE, although EAGLE is still very commonly used.Ali [00:43:51]: SpecSpecta.Philip [00:43:52]: Yes. Speculative decoding.Vibhu [00:43:54]: What canAli [00:43:56]: Oh, it's a paper by Tri Dao and it's like, it's doing speculative decodingVibhu [00:44:00]: HuhAli [00:44:01]: For the speculative decoder.Philip [00:44:02]: Oh, in spec- oh my God.Ali [00:44:02]: It's literally just an another. It's like, yeah, that's the most simple way to explain it, and it seems like he got trivial speed ups there. But it seems that the complexity with training, it's almost like in our mind at least, it's almost as complex as training GANs. Like it's like a very delicate balance and oftentimes you, it's just but yeah, it's literally speculative decoding on speculative decoding.Vibhu [00:44:21]: Speculative.Ali [00:44:22]: Yeah. We saw this paper.Vibhu [00:44:24]: It's interesting, right?Ali [00:44:24]: Yeah.Vibhu [00:44:24]: I wouldn't even expect it to be very particular to train, I wouldAli [00:44:29]: Right.Vibhu [00:44:29]: The naive part of me is like, okay, train speculative decoder.Ali [00:44:32]: But like, and it makes sense, like the whole idea of speculative decoding is you. It's like, it's like almost like the iPhone auto predict version but for a normal model, right? Like you're just, you're just, generating three tokens and you're like, okay, I'll do prefill on them. And so you save those three turns for your original model. Now your speculative decoder is doing three turns of auto regression, so why not just have an even smaller model?Ali [00:44:53]: The other question there is what are the size of speculators? So say forPhilip [00:44:58]: Right. It's like a billion parameters.Ali [00:45:01]: Like for MiniMax, it's. Yeah. It's like one layer. It's like one 60th of the original model usually.Philip [00:45:06]: Yeah. I think we should do a paper when we get back to the office.Philip [00:45:10]: SpeculativeAli [00:45:11]: SpeculativePhilip [00:45:11]: Decoding.Ali [00:45:13]: No, it's, it does seem like how, when do you stop? But then it also seems like if you're able to train spec-spec decode for instance, right? Like if you're able to have a small model that is accurately predicts what the intermediate speculator is gonna predict, that is able to predict what the original target model's gonna predict, then why not just use that smallest model directly, right?Vibhu [00:45:34]: Yeah. This isAli [00:45:35]: Like it seems likeVibhu [00:45:35]: Adjacent to the routing problem.Ali [00:45:36]: Right.Vibhu [00:45:36]: Yeah.Ali [00:45:36]: Right.Philip [00:45:37]: The thing with speculators is one of the practical constraints on using them is that you do have to run a small model on the same hardware that you're running the big model on. There is a orchestration and resource competition problem inherent in that, and that is one of the constraints on speculation in general, is that draft tokens cost resources to create and cost software complexity to manage. And so if you have like infinitely recursive speculators, you add in quite a bit of that complexity on the actual implementation within the inference engine as well, not just in the training process.Vibhu [00:46:17]: I was gonna say, I would wonder if you could do similar, like distillation and pruning of, it's the same thing, it's just a model. Can we not just distill a lot of the weights, quantize the speculator, out of my domain? The question that also comes up is, this is all for big server workloads, right? How much of this applies to, say I have this MacBook, I wanna run Gemma really efficiently. Similar problems, not the same?Local AI vs. Data Center InferencePhilip [00:46:45]: Pretty different. I talked to Selo, about this on his podcast a couple weeks ago. The difference between inference engineering for the data center and for production workloads versus inference engineering for local AI, is that we start with fundamentally like different constraints and different goals. With local AI, it's how do I fit this model onto my hardware and then make it less dumb? And with data center influence, it's how do I load this model and then make it less slow? And we care about less dumb, and they care about less slow. But the local AI inference engineering ecosystem, I think has a lot for us to learn from in the data center space. They are experts in various forms of quantization, including dynamic quantization that we just don't touch, in the pruning, in the distillation, in the, layer removal. There'Ali [00:47:42]: Layer removal matters less.Philip [00:47:43]: Yeah. There'Ali [00:47:44]: No one loves pruning really.Philip [00:47:45]: Yeah. Well, but the, but they doVibhu [00:47:46]: Which is surprising, right? But that's, that's a whole different thingPhilip [00:47:48]: Just to fit something on the laptop.Ali [00:47:50]: Right.Philip [00:47:50]: So yeah, it's a, it's an interesting, it's an interesting space. Not necessarily that like their techniques make sense for us to do in the data center, because we have different resources and different goals, but more that the process as well as the openness of that field is something to, admire.Ali [00:48:12]: Yeah. Like to your point, like, certain optimizations that would. Like for instance, Turbo Quantum Sharper, like it made such huge hype on that and we did like a whole deep dive on Twitter and like said, what is it? How does it work? Why is it good or not? And it took off and it was implemented on local devices because your memory bandwidth is so slow on like a MacBook, for instance. But try putting the same thing on like an NVIDIA GPU on a B200 Turbo quant would not be. Like, it would not be used. Like, NVIDIA - Like, NVIDIA made it clear that this is not a good optimization, and we've seen it firsthand where the overhead of doing dequantization, quantization of, in the kernel itself with turbo quant kernel, each end is much slower than the time that you save from doing the bandwidth. ‘Cause on the B200s, you have like 3.5 terabytes per second. You don't need decrease the storage that much. You don't need to do, FP4 KV cache. You don't need to use a requant. There's, there's, there's better optimizations to be made. But on Edge devices, it's extremely important, it's extremely useful. So, seems to be, like, different optimizations there, but then they're all uniquely combined with like all you wanna quantize the model, you wanna do speculative decoding, like certain common prefixes with bothPhilip [00:49:18]: Principles.Ali [00:49:19]: Yeah, exactly. Exactly. Exactly.Philip [00:49:20]: They also do a lot of work on, model parallelism, especially over, heterogeneous topology, where you have, some sparks and they are wired together with, Ethernet, DGX sparks.Ali [00:49:35]: Yeah, this is the Exo Labs guys.Philip [00:49:36]: Yeah. You have, a nu

Ladies and gentlemen, grab your donuts and turn up the volume for the June 5th edition of Good Morning Woodland! We're kicking things off with a huge celebration—26 years of marriage for our host, who shared the "meet cute" story of a college dorm hallway and a 1999 proposal at Elizabeth Park! Speaking of weddings, the playlist is set with everything from Frank Sinatra and Jermaine Jackson to a "popular-at-the-time" Eric Benet track!Now, let's hear it for our stars of the day, including Staff Member of the Year Andrea Constanti and Paraeducator of the Year Sheila Brennan! In the news, things are getting futuristic with gene editing and AI bots taking over the web, though we've still got some "old school" drama with illegal hissing cockroaches in Australia! Over at the sports desk, our golf team swung into 5th place in the NVL, and the softball crew is ready for a big home game against Cromwell!Finally, get ready to "Look for the Good" with a refreshed summer reading program where students can swap traditional book talks for video reflections! Whether you're hitting the Big Dipper fundraiser or charging those Chromebooks for exams, keep it locked here! Now, let's get this party started!

(Breathless, clutching an imaginary trophy) Oh, wow! I'd like to thank the Academy—and the 7:00 a.m. crew at Good Morning Woodland! This May 28th "B-day" is truly special because our seniors have finally returned to the hill from their "fortnight" away.We aren't just a school; we are a record-breaking family celebrating 17 Halo Award nominations for our drama department, proving we have the "Meryl Streeps "of high school theater! From discussing NASA's three moon bases and the Chicago Bears' potential move, to the "Presidents" mascot of Warren G. Harding High, we bring the world to the courtyard. (music starts to fade in) I would be remiss if I didn't note how the baseball and softball teams rallied in their valiant, tight-scoring NVL tournament efforts.I must thank the WRSG and the Big Dipper for the upcoming ice cream social—the secret flavor reveal tomorrow is the cliffhanger of the season! (microphone begins to recede into the floor)To my partners Tomlin, Decker, and Amato—we did it! (speech speeds up trying to get the last few words in) Everyone, please support the Class of 2027 car wash, charge your Chromebooks, and watch out for Saturday's rain. (Music swells) Thank you, Woodland! (Audience erupts in thunderous applause) We're knocking down the door!

Jeff "el Hefe" Samuels is an American Professional Beach Volleyball Player, Entrepreneur, and Model. He is also a spokesperson for Hyundai. The prosperity he is enjoying in his sport is well-documented. From the AVP, FIVB, NVL, to regional tournament wins, this versatile player is fun to watch, wherever the venue may be. He is currently the founder of "Empact," - designed to bring and grow the sport in communities that are underserved in the sport. 01:43 - FUDS, the electric atmosphere, the fellowship, the hecklers and how it compares to other tournaments that are festive 13:13 - Heckling, and understanding your environment, plus, Dave Shaw is a great MC that understands the set and the setting 25:10 - Jordan Lucas, the commentator, and the two sides to the coin, appreciating the pecking order, the personalities 37:10 - Marcus Koppke, the ultimate teammate, the significant improvements 48:11 - Cracking the code of being in the semis, finals or being the winner 51:45 - "Empact," the idea, the creation the growing force

Sponsor OneSkin improves your skincare routine with science-backed skin care products. With over 10,000 five-star reviews and validation from clinical studies, OneSkin has made a name for itself in the skincare industry. If you’re interested in trying OneSkin for yourself, you can get 15% off your order with the code OVERTIRED at oneskin.co/OVERTIRED. Chapters 00:00 Gang Back Together 01:23 Mental Health Corner 01:39 Back Pain Diagnosis 07:09 Dental Insurance Racket 12:34 Post Surge Recovery 19:24 Surgery And Withdrawal 24:36 Sponsor One Skin 26:23 Terminal Widget Reveal 31:24 Widgets And Visualizations 34:51 Release Plans And Review 36:56 Universal Bundle Pricing 37:38 AI Boosts Mark II Sales 39:20 Leaving Oracle Behind 40:03 Ninety Hour Workweeks 41:55 NV Ultra Vaporware Woes 43:17 Missing Collaborators Online 45:09 Dan Peterson Secret App 46:23 The Pit TV Complaints 50:49 ER Nostalgia and Cast 54:01 Season Two and Other Shows 58:33 Gratitude App Picks 01:00:09 AI Tools and Claude Code 01:04:35 Bookshelves and Audiobooks 01:07:10 Wrap Up and Sleep Show Links TerminalWidget Marked 3 Bezel BookShelves Claude app Join the Conversation Merch! Come chat on Discord! Twitter/ovrtrd Instagram/ovrtrd Youtube Get the Newsletter Thanks! You’re downloading today’s show from CacheFly’s network BackBeat Media Podcast Network Transcript Projects and Pitt-falls Gang Back Together Christina: [00:00:00] What’s that? Do you see a podcast update in your feed? Well that’s because you’re back on, on Overtired and, uh, and I’m Christina Warren and I’m joined by, uh, Jeff Severns Guntzel and Brett Terpstra. What do you know? The whole gang is back together. Overtired, everybody what Jeff: Hi everybody. Brett: I need a, we need a party sound. We need a Christina: we do. We need a soundboard. We need a soundboard and we need a, a way to be like what Gangs all here. Some sort of a like a either a a we need a horn. That’s what we need. We need one of those. Those horns they play at at at football games. Jeff: would like that very much. Brett: or that like B. Christina: exactly. Jeff: yeah, Brett: That would really wake people up. Christina: It really would. And, and especially, um, all of us. ’cause I we’re recording this earlier than we ever do. Brett’s been up for a really long time and, uh, I think Jeff is probably like raring to go, but I’m like, I, well now Jeff: raring to go, but I’m warming [00:01:00] up. Christina: Yeah, I, I, I’ve been up since like five 30, so I’m okay too, but yeah. Brett: I wrote an entire shortcuts in shortcut intense interface for my new app this morning, and it’s actually working. I’ve never written for shortcuts before. Christina: Well, Ooh, we will, yeah, you gotta talk to us more about that ’cause I wanna hear more about that. Mental Health Corner Christina: Um, but first I think we should probably do, um, because it’s been a while since we’ve all been together, we should probably do a little bit of a mental health corner. Brett: yeah, Who wants to kick that off? Okay, fine. I will. Jeff: health. Mental health. Silence. Back Pain Diagnosis Brett: I, uh, I, I, my sleep has gotten a little worse than it was before when I told you it was bad. Um, I’m, now, I’m back down to like five hours a night and I just wake up at like 2:00 AM. And like I go to bed by eight or nine and I get up at [00:02:00] 2:00 AM every morning and I just cannot, for the life of me fall back asleep. And for like the first hour I’m up, I’m not even really awake. Um, I’m just kind of sitting on the couch staring at my computer and not be, not able to do anything After about an hour. Um. I, I, I’ll get some coffee, I’ll take my meds and like then it’s kind of like most people’s, like maybe 10:00 AM 11:00 AM um, by, by like 3:00 AM but it’s still wearing me down. Um, I got, so I’ve had back pain, um, for a while now. Uh, I can’t stand up for more than about five minutes and I can’t walk for more than three to five minutes, which has really put a dent in my, um, ability to exercise. And, um, so I finally got, I got an MRI [00:03:00] done, and they. Diagnose me with stenosis, which I think is kind of a, a broad term, but like a couple of the discs in my lower back have collapsed and, um, they, they, they think I can be treated with, uh, with shots and not surgery. Um, so I’m hoping, I’m hoping to get that figured out because, okay, so right now, uh, we, we always go on walks in the wildlife refuge, um, like the wetlands refuge near us, and I love it. We, we see so much cool stuff there and I hadn’t really been able to, but what I found was this little, it’s like. Folded up, it’s like two feet tall, uh, camp chair and it, it’s like a camp stool. And so I carry that with us while we walk and then like every three minutes I’ll like have to set it up on [00:04:00] the side of the trail sit. And if I sit for two minutes, the pain goes away, I can then walk again immediately. Um, but like after, after three to five minutes, like my back freezes up and I, like, I literally, I can’t move anymore. Um, so this little, uh, take carrying a chair and doing it in three minutes stints, um, has at least allowed me to get out and get some green time. But that’s kinda where I’m at. Jeff: What does this little chair look like? Uh Brett: It’s blue Jeff: huh. Brett: and it has four legs and it’s can canvas. Jeff: is it like an adorable little camp chair that you’re supposed to be able to like Brett: I think it’s a toddler’s ch camp chair. Jeff: Excellent. This is the detail I Brett: like, it’s smaller than my butt. Like I’m perching on it, but it’s enough to like get my back, uh, into feeling. Okay. And it’s not too heavy to like carry[00:05:00] Jeff: Show art, but the art, the art is you perching. Just to be really clear. Brett: Yes. My, my 280 pounds pound perched on a two foot camp stool, it’ll be great. Jeff: Wow. Well, I’m glad there’s something like some kind of thing Brett: Yeah, no, it’s actually really good. It’s really good to get the stenosis diagnosis and ’cause for a long time I just assumed because I gained weight, my, my back wouldn’t work anymore, which was depressing. But the more I thought about it, the more I realized I’ve been this heavy before and I have not had this pain. And even after my first like 50 pound sudden weight gain, I didn’t have back pain. So it didn’t make sense that my body just couldn’t handle it, uh, like something else had to be going on. So it was actually much like any diagnosis, I think, um, other than, you know, terminal illness, but for like A [00:06:00] DHD or stenosis or any like mental health condition, it’s a relief to get a diagnosis and find out you weren’t crazy, you weren’t making things up. So yeah, I’m, I’m grateful. Christina: No, I completely like, can, can relate to that. ’cause when I, like with my back, well my cervical spine, um, it was kind of a similar thing. Obviously mine was more acute and it was a different scenario because I got, um, like the, you know, diagnosis relatively quickly, although it still felt like it took longer than, than I wanted it to, to, to get my MRIs and whatnot. Um, but it was similar to you. It was like kind of a relief to be like, oh, okay, so you have like a major problem. This isn’t just you being a wimp and, Brett: Yeah, exactly. Christina: exhilarating pain. Right. Like excruciating pain. Right. And, and just even having that, even knowing, okay, I don’t love that I have to go through [00:07:00] this whole thing. Um, I’m, I’m still like relieved to have a diagnosis and a plan forward. Dental Insurance Racket Brett: Oh, and also I, so I’m on state. Healthcare, and that includes, um, Delta Dental, but it’s this weird version of Delta Dental that nobody in my town accepts. Um, so I have to, I have to drive 45 minutes to get dental care and even then they can’t, he can’t do root canals or anything. And I needed two root canals and that would’ve involved driving two and a half hours or three hours and then going back to the 45 minute away place. And so what I did was I took the extra money I had saved outside of my, like, nest egg savings, but like my working savings. And I paid for a year of actual Delta Dental, um, and started going to a place [00:08:00] just really close to me and, um. It turns out that the best dental health insurance is still shit like it. I don’t know how much dental work you guys get done, but it is, Christina: it’s, it is crappy. Brett: it’s a, it’s, it’s a racket. And I actually watched a YouTube video on why dental insurance is a scam. And it like interviewed Dennis who actually take these like Delta Dental and the Medicaid dentists. Um, and it is truly a scam. And what I found, and this is much the same experience, uh, Christina talked about with her, um, MRII think it was that you did a cash pay. Um, I talked to the dentist and I said, do you have a cash paid discount? And he’s like, oh yeah. And basically. I can just pay cash and do everything for about 60% of the normal cost, and that is better than what [00:09:00] Delta does for me in most cases. Plus, I need so much work that my $2,000 cap with Delta is gone. Christina: Well, I was, I was gonna say like, so when I joined Microsoft, Microsoft used to have really good. Dental insurance, um, respectively speaking as, as good as it can be. But there were still, you know, caps on how much work would be done. But I found like a good person to go to. ’cause I had an incident, um, about a year after I moved to Seattle, maybe less than that, where um, I had to have an emergency root canal and like that sucked. Um, like I went into a normal dentist. She was like, this is what you need. And then I had to like, take an Uber, like over to a guy and see him like that day at like 5:00 PM and I’m like, you know, all like drugged up and, and getting the root canal. And that was not great. And I needed a lot of, of, of work done. Um, and so we split it over like she was a really good dentist and so we split it over. We were like, I was coming close to. The, the end of the calendar year. So she was like, okay, we’re gonna do all of this work and then we will start the next year [00:10:00] when things go forward. And like she knew how to play the system and was like a really good dentist. Well then Micro, then I went to GitHub. GitHub used, um, you know, uh, Delta Dental. And, and that can vary based on plan. Microsoft is apparently on them too. Google also had them on a slightly different plan, and it’s like you never know what you’re getting. And yeah, to your point, because if you need a lot of work done, if you have anything specialized, if you’re, you’re lucky if you get the right plan and you can see a provider in your area, great. But if you don’t, to your point, it is often, this is just fucked up. Like, especially if you’re having to pay out of pocket for it anyway. If it’s part of your employer, you know, benefits, maybe it’s a little different, but it’s like even then it can still wind up being less expensive to just pay the cash stuff than whatever your deductibles are, which have a cap anyway. And, and, and, and, and then, yeah, the, the, the way that the, the Medicaid or, or even insurance pricing works, stuff that they might charge you a very nominal fee for, for like a cleaning or whatever is, or a cavity fill [00:11:00] is gonna be, you know, they’re gonna bill insurance like three or four times that Brett: Right, exactly. So I pay, I pay like 800 bucks for a year of Delta, and that gives me basically $2,000 to work with, plus whatever price they can negotiate. Um, but like you said, like they, they bill three times. Um, so like what still comes out of my like $2,000 pot, um, is higher than I would’ve paid with Christina: If you just paid cash, if you just had an $800 budget, or if you got like, yeah, that’s the thing. Okay. This is an AI app that somebody should build. And I’m saying this hoping that maybe something the audience will, or maybe one of us could vibe code it, because this seems like this would be a relatively easy calculator to do with like certain providers if they, if they, you know, list their things where you could like run the costs and be like, okay, this is, I’m gonna put in this number. This is what my, you know, provider’s fees are. This is what my [00:12:00] insurance thing is. Um, Brett: what my cash pay Christina: this is what my cash pay is. Is it cheaper for me to spend $800 a year on Delta Dental or to just pay cash directly with my, my dentist? Brett: Yeah. Have you as I’ve, as I’ve said to people who have pitched ideas to me in the past, you’re talking about a spreadsheet? Christina: Yes. It is a spreadsheet to be completely out. Yes. But I can now use cloud code to, to to, to, you know, figure out the formula for me is the real thing. Brett: Yeah. There you go. All right. Who’s up? Post Surge Recovery Jeff: Dr. To, um, I can talk, uh, uh, I’m, I mean, I’m doing really well. Uh, I we’re a couple months past, or, you know, a couple months past the operation Metro surge stuff here in January and February, in a little bit of December, but really January. And that was, I’d never kind of experienced like a, a full [00:13:00] taxing of every single person and kind of person I knew and which was amazing. Um, and, uh, and it took a minute when things settled here, um, to, for everybody to kind of figure out what. How to just even enter into the world every day because everything had been driven by what was happening on a almost hourly to hourly basis for, for some time. And, um, and so I kind of moved through that, that period, which was like quite a sort of come down, uh, of adrenaline and, and amygdala sparking. Um, and, and have kind of smoothed a little bit. And, um, and I’m just doing well. I’m having a nice, a nice goal of it right now. Christina: Good. Great to hear. Brett: I, I guess that everything’s relative. Right? Jeff: Yeah. Everything’s relative. Yeah. Yeah. But I think I would call this a nice go of it, uh, even outside the context of comparing [00:14:00] to, to Operation Metro Surge. Brett: that’s, that’s, I, I’m happy for you. That’s awesome. Jeff: I think actually the last time I was on the podcast was with you, Christina, in January right after we had had a raid in our alley, which was even before the surge Christina: You before the big surge, even before Jeff: of an early start. Christina: I was gonna say even before, like I, I, I don’t even know if, if, if the, the, the murder had happened. Um, Jeff: not at all. In fact, we only had 100 extra ice agents here at the time and within a couple of weeks there’d be a woman in front of my house, uh, being pulled out of her car ’cause she was following ice agents and throwing me her phone as she gets tossed into a, into a fucking ice truck. And like it was just, everything happened so fast and so slowly all at the same time. And, and obviously there’s still all sorts of stuff going on, but it is indisputably not what it was in January and February. Brett: I was gonna ask you about that. ’cause like the total number of deportations is only slightly [00:15:00] lower right now than it was during the surge. Um, and they, they removed, they added like, what, 3000 agents and they removed like 800 of them. So, Jeff: they’ve removed way more than Brett: Hey, have they Jeff: oh, yeah. We’re down to, I haven’t, I don’t wanna say the numbers because I haven’t looked at them. We’re, we’re back down to like the high hundreds and we, our baseline is like 1 25. Brett: Okay. Jeff: Yeah. You can tell. Um, it’s, yeah, you can tell. And I, and I’ve been down to the WPO Federal building a a few times, um, which is where ICE was kind of headquartered and there’s just the level of activity there is very low. Um, they had some new vehicles come in at one point about a month ago, but mostly those are replacing rentals that they were using. So it wasn’t like people took it as kind of an indication that they were, you know, staffing up or suiting up again. But it was really just kind of replacing their, their really weird, like sort of duct tape together invasion. Um, it’s kinda like in Iraq when they decided they were gonna [00:16:00] actually armor the Humvees, it was kind of like a little bit of a switch of, of vehicles. Um. Yeah, it’s much different. And like, you know, all the people either in my life or in my community that were in hiding or not, I mean, for the most part, not in hiding anymore vulnerable folks and undocumented folks. And, um, so it’s like, it’s qualitatively and nervous, systemly different Brett: Yeah. Yeah. Jeff: for everybody and still sucks. And there’s still a risk and a threat and, and a horror. And a terror. Brett: Yeah, down here in southern Minnesota, I have not gotten a call to do a food delivery or a grocery delivery for, yeah, a couple months. Um, so yeah, I guess it really has calmed down across the state. Jeff: Yeah. Thank God. I mean, who knows what they’re up to that isn’t as visible, but thank God Brett: exactly. Jeff: over. So yeah, I, I mean it’s, and I actually just had my, my brother’s been in town and every time someone kind of comes to visit, they wanna like. You know, kind of hear or take in what the thing was and you start describing it again, and [00:17:00] now it just, I mean, it felt like a dream at the time. It just felt like, how could this be real? But you were just so in it, like every single person, like you said, Brett, like people were doing grocery deliveries or people were, you know, cooking food for the people that were kind of on the front lines, or you were following ice, or you were dispatching people to follow ice, whatever. It was like every. Single person I could think of as doing something. And uh, and, and so when you try to describe it now, when you look around, especially in my neighborhood where they were all over, um, it it, it seems like, was this, was this real, um, like, was it even real because like, I don’t know, like the end here. ’cause this could go on forever, but I don’t know if any of you saw the footage that went around of a high school called Roosevelt High School, where, uh, where Bovino showed up and there was all this crazy shit and the, the footage of this, um, went around the country and like it was, you know, reposted by freaking everybody that was my son’s school in my neighborhood. And, and so like, it was just this constant thing of like, bovino at my son’s school, binos at my gas station. Like, it was just [00:18:00] utterly insane. And now, and, and every street felt almost, you could feel ice on the streets. Like you would see ghost cars where they had taken people or whatever. You could like, feel ’em on the streets. And so you walk around, you walk around the same streets now, and it’s just birds and kids playing and you’re just like, did that, was that real? Brett: There, there was a tow truck driver that was interviewed who had taken it upon himself to tow those ghost cars for free back to their origin. Um, and just like leave them for people. Jeff: at least, or he would take them in and not charge if you came in for them. And it’s, and that’s just it. Everybody, everybody. It was incredible. It was incredible. Christina: It’s crazy. Jeff: Yeah. All Christina: I hope, I genuinely hope that they’ve lost interest and, and have moved on to other things. Brett: Like Seattle. Christina: yeah. Well, I mean, Seattle is obviously a very different situation and, and that had a, a longstanding, I think, impact. Um, and, and I, I, I. I’ve said this, I said this at the time, people who made that really bad were the [00:19:00] activists who came in outside the so-called activists and putting that in quotation marks who came in, who didn’t even live in the city and agitated things and made things way worse than, than they, than it should have been. Um, but yeah, but I hope that it’s like Seattle, that it just kind of falls like the, the government doesn’t come back and, and continue this, you know, reign of terror. Jeff: Yeah, yeah, yeah, for sure. Surgery And Withdrawal Christina: Um, well, I’ll, I’ll be quick. So I, I had surgery since I guess the last time I was on, Jeff: Sure did. Christina: that went well. Um, the surgery itself, I’m still in some pain, um, in my shoulder after the surgery, uh, which was not like you were fi fixing my cervical spine. But, um, they, uh, I guess however it worked, like I, I think as muscular, um, I, I’ve been going to to to PT for the last few weeks. Um, but I still having some, some shoulder pain. That’s, that’s getting better. Um, the hardest thing was actually some of the medication stuff. So [00:20:00] I, uh, gabapentin, um, I know it’s a lifesaver for a lot of people. I don’t have a good reaction to it. Like I’m one of those people. Like, it, it a, it makes me feel kind of loopy. I don’t like it. B it’s very difficult for me to sleep on it. Um, which, which is a problem and, you know, but, but the big thing is it just kind of makes me like, feel like I’m not kind of in my own head. Like I feel like, don’t know, like, um, altered on it. I, I would say. And so I went off they gabapentin and no one told me, and I am gonna put this as a PSA out there. ’cause I know a lot of people take it. Do not go off of that cold Turkey. Jeff: mm. Christina: They didn’t tell me that. Um, which someone should have, but no one told me that. And it can actually cause seizures if you do other things. But in my case, the real thing was that I had withdrawal. That was some of the worst withdrawal I’ve ever had. In my life ever. And, um, it like awful, like awful, awful, awful to the point that to go off the Gabapentin and they had me on like a, a decent dosage. It [00:21:00] took me a month because I had to keep going basically down like one pill like every week to step down. And, but I mean, I was getting, you know, like, like hot and cold sweats, you know, like feeling like my teeth were gnashing, you know, like nauseous, just like awful, awful stuff. So it took me, you know, a month to go off of that. I had to extend my medical leave in part because of the medication withdrawal stuff, because I was like, I can’t go back to work if I’m gonna be like, still dealing with, with medication bullshit. Um, so, um, that was actually, you know, in some ways like more, uh, of an issue than like recovering from the surgery itself, which was major. Like I, I tried to kind of downplay like what it was, but it was, it was major surgery and um. Um, I’m glad that it’s over. So, you know, onwards and upwards. I’m, I’ve been back at work for a couple weeks. Um, still kind of settling in on that, but, uh, but yeah. Brett: That [00:22:00] withdrawal sounds terrible. Usually you have to do opiates to get that kind of fun. Christina: Yeah, well that was the thing. I saw somebody on, I read it, which of course is anecdotal. I don’t usually look for this stuff, but sometimes you just wanna feel like, okay, is it, is it common for me to have this withdrawal or not? And somebody, and one of the subreddits was like, this was worse than coming off of heroin and I in a jail cell, and I should know because I’ve done that. And I was like, okay, I, I’m not going to equate it at that level, you know, for, for me. But it was definitely like that bad. It was, let me put it this way, it was bad enough that at first I thought. It was the opiate withdrawal because I, they gave me some, some oxy, um, um, contin. Um, and then the doctor was like, no, that’s not a high enough dosage. This is, you know, um, it, it, it probably was gabapentin and, and it, it. What pissed me off is that one of the physician’s assistants or whatever, when I’m telling like my doctor about this, I’m like, okay, if I need another nerve drug, then we need to find something [00:23:00] else. I can go on select so I can go on, you know, something else. But, but I, I clearly can’t stay on this. A, they kind of gaslit me because I’m a woman and obviously my pain and my symptoms can’t be real. So that’s like number one. And that’s just a fact. I don’t care if you’re a male or female doctor, they don’t take you seriously. I’ve complained about that before. Um, b like she had the nerves to say, she was like, well, you know, if the withdrawal is that bad, then why don’t you just stay on the medic medication? It’s not that it, it, it, it’s fine. I’m like, no, it’s not fine. It makes me feel altered. You’re telling me that it’s for nerve pain, that my nerve pain should be fixed if my nerve pain isn’t fixed and if I need something for nerve stuff, then that’s one thing and we could maybe look at an alternative, something that doesn’t make me feel loopy and lets me sleep. But if your suggestion is, oh, to avoid the bad withdrawal, just stay on the drug. I’m sorry, what the fuck are we doing? Um, and, and then the doctor’s like, well, you know, we get this all the time. We never see side effects. And then I looked it up, you know, in the actual drug literature and no, there are side effects exactly like the ones I experienced. So I was like, I recognize that. [00:24:00] I always am usually that like one percentile person who gets like the weird side effect. Like, that’s who I am. I get that. But Brett: crazy. I’ve, I’ve gone off of gabapentin. It sucks. I You’re not crazy at all. Christina: yeah. But, but it just, it just was frustrating to me that like the, the suggestions like, we’ll just stay on it. It’s like, no, like that’s, that’s, that’s not actually gonna be a thing anyway, but onward and upward. Jeff: Yeah. Wow. I’m glad you’re through that. Like Christina: Yeah, me too. Me too. Okay. Sponsor One Skin Christina: Well, I know we have some other topics we wanna get to, but before we do that, um, let’s take a moment to talk about our sponsor of today’s episode One Skin. So, um, you know, I, I’ve gone through a number of different things with my skincare routine over the years. Some have been more effective than other. Um, you know, um, my skin kind of goes back and forth between being too oily and too dry. I’m kind of in a dry [00:25:00] phase right now, and, um, there are tons of products out there that, that promise results. And then you, you get them in the, and they’re, they don’t necessarily work. So, uh, I wanna talk to you about One Skin, which was founded by scientists, and it’s dedicated to longevity. And, um, the, the brand is actually committed to being real science over marketing hype. And so, uh. What they wind up. Uh, what, how, how this works is that they use OSO uh, zero one, which is a proprietary peptide, which is designed to help deactivate the damaged cells that contribute to aging skin. And, um, I’ve been using one skin, um, for a little bit, and I, I’m, I’m liking it. I like how it makes my face feel. Um, I like, um, the fact that, uh, it’s. You know, what the peptides are supposed to do is help basically, uh, support collagen, uh, uh, of production and, and, and strengthening the skin barrier. Um, I’m not alone. There are over 10,005 star reviews and there’s validation from clinical studies and, and it’s making a name for itself in the skincare industry.[00:26:00] So if you are interested in trying one skin for yourself, you can get 15% off your order with the code Overtired at one skin.co/ Overtired. That’s 15% off at one skin. Do co slash Overtired and use that code Overtired. So thank you one skin for supporting our show and check them out. Brett: Awesome. Terminal Widget Reveal Brett: Do you guys, can I tell you about terminal widget? Jeff: Terminal widget. Yes. Set it up. Terminal widget. Brett Terpstra. What’s Brett: so I, I, I wanted, I had scripts running in the background and I wanted a quick way to check them and I thought it should be easy to put. Script output into a, like a widget on the desktop. And I could not find anything that actually worked. Like Shellfish has a widget, but it, it takes minutes to update and it’s flaky and, and the other apps out there [00:27:00] did not work for me. So I thought I would build my own. So I think I started it a month ago. Um, I built a, just something for, you can run a terminal command and update a progress bar or an image or, uh, like sparkline text or just straight up text output from your. Terminal, all kinds of charts and everything, and, and it updates instantly on your desktop, uh, with like a 0.5 to one second delay, uh, which I wasn’t able to find anywhere else. I had to like, use JSON payloads and like basically a cloud kit watcher, um, cloud kit because I did also port it to iOS. And, um, so I can run one command in my terminal or from a script in the background and have my iPhone and my desktop update with progress. Um, I am working [00:28:00] on a watch version of it that is not, I, I have it working in the app, but I wanna make it so it works as a complication. Um, that’s gonna take a little more doing, uh, but this morning and yesterday I spent working on. The Apple script and shortcuts interfaces for it. And I hate designing Apple Script dictionaries, uh, because there’s no, like, there’s no standard for like terminology and there’s no like golden way to do it. And I always end up messing it up even when I do have a plan. This time I think I actually succeeded in building out a dictionary that makes semantic sense and is somewhat. Predictable if you’ve ever written Apples script before, but I also added all of the widgets can be controlled from shortcuts. You just drag in like a chart widget into your shortcut and pass in like a value or like a, a chart of values. It can [00:29:00] do matrices and sign waves and, and line grass and bar charts, and it’s pretty nuts. You can check it out. It’s not available yet, but all of the documentation and all of the screenshots are at Terminal widget app. Um, and I am, I’m pretty impressed with myself and Christina: yeah. Brett: that’s what I’ve been working on while waiting for Mark III to make it through app store reviews so I can finally publish that. I, my latest rejection first, I got rejected, like a couple legitimate. Uh, concerns, but then I had a CLI that I wrote that was embedded in the app bundle and there was an option to create a sim link in your, in your terminal to use the CLI. And this was just a convenience method for like, you give it command line flags and it converts it into URL handlers and they rejected me for Christina: [00:30:00] I was gonna say, I was gonna say, they don’t let you do that. Like what I’ve seen with other apps do is usually there’s like a, um, in the app store is that usually you have to download a helper to install the CL. Brett: right. So what I did, uh, to get past the rejection was completely rip out the binary from the bundle. Uh, if you go to the install cli CLI tool menu item, it simply takes you to a webpage where there’s a, a notarized signed PKG file, or you can install from Homebrew, but it’s completely separate from the app store. And the last rejection said that I was requiring users to download an external app in order to use the app. Which is ridiculous on its face. Like it’s, it’s a convenience method. In no way do you need to download it. Um, there’s no requirement. In fact, it’s almost buried that you would even want it. Um, [00:31:00] and so I argued with the reviewer for a couple days ’cause they were replying like once a day. Um, and then they told me I had to go through a re uh, the appeal process. So I submitted an appeal at four 50 this morning. We’ll see how long that takes now. But in the meantime, terminal Widget is keeping me sane. I’m having a lot of fun with that. Widgets And Visualizations Jeff: I have some terminal widget questions. I’m looking at the site right now. Um, so talk to me about, um, talk to us about your, your initial use case, like was, which you’ve kind of described already, which is you just wanted to be able to check on these scripts Brett: Yeah. I just wanted a progress Jeff: But then Brett Terpstra kicks in ’cause like I just wanted a progress bar and now I’m looking at all the flags and everything else that you could have. You know, I’m curious like of all of the options that are in there, I want you to just share something that might not be intuitive or might not guess you can do. And then I’m curious of like if you have something you’re like, and what I [00:32:00] really want it to be able to do is. Brett: So you can pass it up to a hundred numbers, like a, a list of space or canvas, separated numbers that you can output from whatever script you’re developing. And you can have it, uh, output a sine wave or a um, uh, a waveform. I like the waveform visualization for it. And so you can get like pretty cool visualizations out of. Tabular data basically. And I also just added, um, tabular, like you can, you can give it a CSV file and it’ll generate a table for you. And it really only works well on like the large widget size. Um, but on both, on both iOS and Mac, uh, the tables look pretty good. Jeff: Nice. Christina: That’s awesome. I, I have a, I have a nerdy, uh, well, but less nerdy question. [00:33:00] Um, on the Terminal WIT app website, um, you have like a, a video of a, like, you know, showing off like, um, you know, your, your, your terminal app open and, um, the, the text being typed out. What did you use to create that? Did you use a remotion or did you use something else to generate that Brett: I scripted that, um, I, I wrote if there’s a helper Christina: charm or something? Brett: No, Christina: Okay. Brett: I, it’s a helper. It’s a helper script that it, it clears the screen and then it takes a table of commands and it types the command out with like a jitter delay. So it looks somewhat natural, like typing. And then it actually runs the command in the background. And then once the command’s finished, it clears the screen and does the same thing with the next one. Um, so I can just feed it like a, a, uh, a file with all the commands. I wanna run one per line. Um, and it just types them out and executes them. Jeff: That’s awesome. Christina: Cool. Brett: I know, [00:34:00] like I looked into like using like as, as as cinema. Um, and it just to get that kind of really. Smooth, rapid typing out of it, uh, without, you know, all the backspace and everything. I, it was, I found it difficult to program it to, to code it. And by the time I had it figured out, I figured I should just write my own script for it. Christina: Yeah. There’s, um, there, there’s a, a. Service called Remotion, which can do some of that sort of graphical work, which is what I thought you might’ve used at first. Um, charm has a thing called VHS, which is basically like a CLI home home recorder, which is pretty cool. Um, and I’ve used that before, but yeah, I was just kind of curious, um, what you did, but yeah, you just built your own. That’s awesome. Very cool. Release Plans And Review Christina: Um, now for your, your, when do you think like, because I, I noticed that you have like for for blog book and for terminal widget, you have like coming soon. Is that like, ’cause [00:35:00] you’re still kind of like working on stuff or, um, are you going through review hell with those as well? Brett: I haven’t even tried getting either of those reviewed. Um, blog book I is approved for test flight, um, and anyone who wants in on that can just contact me. It is getting the slowest development out of all my projects right now just because it is, it’s a more niche app that I don’t think is gonna make a ton of money. But, um, mark III is where most of my effort is going. Then I’m working on porting mark three’s, uh, store kit stuff into NV Ultra, and then I can focus on trying to usher terminal widget through app review. Um, I have a feeling that’s going to go very poorly and I may end up just releasing outside the app store, but because it has an iOS Christina: I was gonna say with the iOS component is the hard part. Brett: I kind of have to, so we’ll see what happens. Christina: Yeah. [00:36:00] ’cause I was gonna say, ’cause like, I mean I guess what you could do is if you did something for the iOS F would make it different though. Like if it’s just, ’cause I’m sure it has, it’s working out. It’s pretty much just remote instance that’s showing Brett: No, no, it’s got, it’s a, Christina: you, you built in your own terminal emulator into it. Brett: no, there’s no, no, no, no, no, no. There’s no terminal in this app at all. Like, you use it from whatever terminal or from shortcuts. Um, so it’s all native widgets on both. Christina: right. I was just saying in terms of the app store thing, like, I guess like if since there’s not a native terminal on, on iOS, it’s, I’m assuming that it’s, it’s a remote widget is what I was trying to get at. Brett: Essentially, yes. But if you write a shortcut on iOS that updates the widget, it updates both iOS and Mac os. So it is usable entirely. You could just buy it for iOS and, and it would be a functional app. Christina: okay. Okay. Universal Bundle Pricing Brett: But I do intend, I hope [00:37:00] to sell it as one universal bundle. So you pay like 9 99 and you get the iOS, the Mac, and the watch app without having to buy for every platform separately. Um, I just don’t see it being like such a valuable app that it’s worth making people go through that rigamarole. Christina: right. No, I was just trying to think. Brett: and everyone I’ve shown it to so far has been excited about it and the most common response I get is I will buy this as soon as I figure out what I would use it for. I’m like, yeah, okay. Jeff: Okay, fine. Awesome. AI Boosts Mark II Sales Jeff: And can you talk about how, because the whole world now works in markdown marked, has gotten a bump because I think that’s an amazing story. Brett: Well, yeah, it was. was a few months ago now, maybe six months. Um, my sales just started increasing and I was looking everywhere through all my traffic and all my logs [00:38:00] to figure out where this, where these people were coming from. Um, and it was eventually pointed out to me that if you ask any agent, any AI agent what you should use to view markdown, um, they would point you to Mark two. And it was now, for the last four months, five months, it’s been doing five times the sales year over year. What it was doing, Jeff: How close is it to the highest it ever was? Brett: um, the highest it ever was was actually when it was only 2 99. And Gruber wrote about it. Uh, back in this is like 2000. This was over a decade ago. And, um, back when, like one tweet from Gruber meant like success and that I made that year, I made almost a hundred thousand dollars on it.[00:39:00] Um, this is nowhere near that. This is doing like Jeff: But it’s a highly unexpected bump, right? Like in a delightful, delightful bump. Brett: yeah. It’s doing, it’s doing without even releasing Mark iii, I’m making about half of my former salary off of it. Jeff: Nice. I’m happy for you. Leaving Oracle Behind Brett: Also, uh, one year, um, in two days I’ll be one year out of Oracle and I quite happy about it. Jeff: that’s great. I was wondering about that, Brett: I don’t miss my corporate job. I miss, I miss some aspects, health insurance, paychecks, things like that. But Jeff: that aren’t at all about the content of the job, right? Brett: Well, like that stuff has never mattered all that much to me if I’m happy doing the work. And I really wasn’t happy doing the work. Christina: Well, that’s, that’s the thing. I’m glad that you’re, I’m glad things have been going well. I’m glad that, that the, the agents have, uh, been telling everybody about Mark two. Hopefully they will also tell them [00:40:00] about Mark three. Um. Ninety Hour Workweeks Brett: My, my dentist was doing was doing small talk with me, and he knows I’m a app developer and he asked me, so how many hours a week do you work? And I happen to know the answer because I had just read my timing app report for last week and I said, 90. And he said, oh wow. How much do you make? And he’s like, if you don’t mind me asking. So I told him and uh, it saying it out loud, it’s basically like 20 bucks an hour I get paid. And like, it’s not nothing, but once these apps are out and I can sit back and just make some passive income off of it, I will, I’ll be much Jeff: So it’s 90 because you’re, you’re developing multiple things right now and, and you love it. Brett: I’m pretty much, I’m pretty much on my machine all day except for like an hour for [00:41:00] like getting out, exercising, getting on my recumbent bicycle and an hour for eating. Um, Jeff: Is it time for you to get a trike? I’m serious. Brett: I don’t, I don’t know, I, I actually want to try just getting back on a regular bicycle. Jeff: Hmm. Brett: Um, but I, yeah, like a recumbent tricycle, that’d be pretty awesome. Jeff: dad uses him. He actually just converted one to an to an E-bike. Plus it’s hot now ’cause of DTF St. Louis. Christina: right. Jeff: Awesome. Uh, is that it for your app development because wow, that’s like, uh, quite a, quite a deal. You got anything else in the cooker? Brett: Well, like we talked about blog book. Right? Jeff: Yep. Brett: Okay. Yeah, that’s, that’s what I got. Jeff: Nice. Brett: that’s my big ones. NV Ultra Vaporware Woes Brett: NV Ultra is, um, literally only waiting on me to [00:42:00] get Mark three out and then NV Ultra will be out. And it is well passed a time when it would’ve been a smash hit. Um, when, when Nv, when NVL first started dying before, uh, before something like obsidian really Christina: I was gonna say, if sitting is unfortunately Brett: yeah, they obsidian and five or six other apps have really eaten up market share for, uh, NV Ultra. But it would be nice just to get it published. I have been talking about a replacement for NV for over a decade, and Jeff: Am I gonna get sued if I say this is not your fault. Brett: It’s, it’s not my fault, like none of them have been my fault. Like they’ve all fallen through on me. Um, but I think people don’t believe me anymore when I say it’s coming. In fact, it, in fact, if you ask an AI agent, they will tell you that MB Ultra is vaporware.[00:43:00] Christina: Well, Jeff: a lot ai. Christina: I mean, look at this point, even though yeah, it’s been in beta and you’ve had other things going on. I mean, like it, you know, again, it wasn’t your fault, but, but, but you know, we’ve all been in those situations where you’re like, it’s coming, it’s coming. Or this thing is like, at a certain point you’re like, okay. Like Brett: Yeah. Missing Collaborators Online Brett: Well that there was Bit Writer Christina: TechMate too. Brett: Bit Writer was one that preceded NV Ultra and I was working on that with David Halter, who was a co contributor on VT and. He disappeared. I don’t know if he died or what, but about years ago he just stopped replying to emails, disappeared off of Slack, disappeared from the internet. Just I, and I don’t ha I don’t know his next of kin. I don’t have anyone I can like ask, Hey, whatever happened to David. So if you’re out there, if you’re listening, I’d love to hear from you just to know you’re alive. Just to, just to [00:44:00] check in. Um, I’ve actually had a few people disappear over the last couple months that ha it’s been disconcert when, when you’re used to hearing from someone at least, you know, once a week even. But some of these people were like every day, um, I. Jeff: from them, meaning seeing them somewhere or corresponding or. Brett: Uh, online. These are, these are people I only know online. So like seeing them on Macedon or Facebook or getting emails or text messages from them. Um, a couple of them were in their eighties or nineties, and so it’s not, Jeff: That might be your problem. Brett: it, it’s not out of the realm of the possibility that they have passed on. Um, but some of them were younger than me and one of them has come back after two weeks of messaging, like every other day, like, Hey, are you okay? Haven’t heard from you. Um, finally they’re like, oh, yeah, I’m here. [00:45:00] And offered no explanation for where they’d been or why they went silent, but I didn’t pry either. So. Dan Peterson Secret App Jeff: What is your project with Dan Peterson? That’s on our, our list. Brett: I don’t know if I’m allowed to say a lot about it, but I’ve been working. Dan Peterson is one, the original designer of one password and worked with them for like 20 years before he struck out on his own. And we’ve teamed up, we’re working on a couple things, but one is a a, an IO iOS app that he has put in. I, I don’t even know how many hours into the design of it, like 3D modeling, spline rendering, and um, and then we ported it into an iOS interface. And it is gorgeous. It, it will it when, when it gets to market, which we’re hoping to have it in [00:46:00] testate in time for Max stock in July. Um, it’ll be the best looking app I’ve ever been a part of. It’s gonna be so cool. Jeff: Nice. Christina: That’s awesome. Jeff: Busy time. Brett: Yeah. Jeff: It’s Christina: That’s awesome. Jeff: What else do we got? I mean, Brett, you showed up with a big list. The Pit TV Complaints Christina: I was gonna, is anybody watching anything? Uh, good on TV or rewatching anything? Jeff: I have a serious complaint to put into the world, so I’ve avoided the pit for a long time. Uh, just ’cause I’m, I don’t, I’m not a huge like yeah, Brett: drama. Jeff: it is great. Except are there two separate writing teams for the stars and staff and the people that come in as patients? Because the writing for the people that come in patients is. Awful. They acting sometimes too. Sometimes there’s some people that sell it. I’m only through season one, uh, but I was like, I have been yelling at the tv, uh, about this [00:47:00] for some time. Um, besides also yelling at the TV for the point at which, um, our young friend with a w as a last name Whitaker, who, uh, gets blood all over his face and then they don’t actually immediately clean it up. Um, uh, so I yell at the screen and I like the show, but I yell. I haven’t had a TV show that I’m like, oh, for fuck’s sake now. I mean, I can handle that in The Walking Dead. I can handle that in that kind of movie. But in the ER thing I’m like, come on, you can’t get a writer to handle the patients. I don’t understand. You’ve got an incredible cast, like an incredible cast. Brett: It’s actually all ad-libbed. Jeff: all ad-libs, like the clown. There’s a clown, I won’t give it up, but there’s a, there’s a clown that has been through a mass event and he’s in the, uh, he’s in the ER with his clown makeup on still, and some blood going down his face and at some point he looks around and he goes, what a circus. I just think they, I think, I don’t understand. This confuses me very much [00:48:00] in TV shows when you’re like, okay, you’ve got a great writing team, but clearly you have a separate writing team that is doing just this little job that is actually quite important. So that’s my complaint about the pit. Otherwise, I like it quite a bit. I’m very excited to start season two, probably this weekend. Christina: it’s a good season. It’s a good season. So, yeah, ’cause, because, because I, I, I, um, it, it ended last week and I’m, I’m a big fan of the pit. I will say this, the pit fandom is insane and not in a good way. Like these are people who don’t understand how to watch television shows and don’t understand. Like how television shows work, and, and then also become very entitled about like, how, like their vision of the characters and things should be on a level. Like the last time I’ve seen it, it it’s the same, it’s similar with heated rivalry, but it’s somehow worse because this isn’t like a genre show like that. It’s like low quality for like, you know, middle aged like white women, um, in the suburbs. Um, who, who just like to see two, two hockey players. [00:49:00] You know? Fuck. Um, like, like the pit is actually like, I’m not gonna call it Prestige TV because it’s not er level, but it’s a very good show and it’s extremely well acted. And I think the writing, um, I, I think make a good point about the, uh, the patients not getting as good of storylines as the doctors. But, um, Jeff: no. I don’t need storylines. I Christina: no, I I mean the Jeff: words they Christina: Yeah. Yeah. No, that, that’s, that, that, that that’s what I mean, like, like that, that, that, that I, I, I hear, I hear your Jeff: Because where there’s a patient storyline, those are almost exclusively great. Christina: Yeah, it, so you’re more talking about like, like, like the kind of the background characters, like, kind of like the, the, the one-offs. Yeah, I think, I think that’s fair. Well, a lot of the writing staff and like executive producers are doctors or people who have like, you know, worked, um, extensively in healthcare. And so I, I, I wonder if like, that’s kind of part of it, um, where Brett: they’re really good at writing the doctor’s parts. They’re not so good at Jeff: so good. Oh my God, so Christina: so good at doing the doctor’s parts and, and the procedures. Like they wanna be medically [00:50:00] accurate and like they really, they really are committed to that. There are, um, there are a couple of, I’m trying to think, um, the, the Whitaker thing, I think that was just, I enjoyed that myself. Like the fact that he’s always getting blood Jeff: Oh, I loved the bit, I just couldn’t believe that. I couldn’t believe that through quite, you know, a couple of different bits after that. The blood’s still on his face. I’m like, there has to be a protocol to get blood off your face. Christina: No, there definitely has to be, but I mean, part also one of the running gags first season two. And, and sorry for spoilers, for anyone who hasn’t watched the pit Jeff: Wait, I’m gonna close my ears. Okay. Go ahead. Wave when you’re done. Christina: Rob Robbie can’t pee. And, uh, this wasn’t a real spoiler, but like, but one of the things is like, you know, Robbie’s never able to like, go to the bathroom. Like he can never find a way to pee. So Jeff: I’m back. Brett: you’re safe now. Jeff: I’m back. Christina: you, you’re safe. And I didn’t spoil anything. I was ER Nostalgia and Cast Jeff: The other thing I’ll say about the pit that surprised I did not watch ER and not ’cause out of bad attitude. Uh, it was just a point in my life when I wasn’t watching a lot of tv. Um, I also didn’t realize until I was [00:51:00] like five episodes in that Noah Wiley was a big character in er. I think that’s really cool. Um, Christina: Okay. Okay. I, I understand you weren’t watching TV then, but how did you not realize that Noah Wiley was Jeff: I didn’t know Noah Wiley’s name. Like I, this is just not, I don’t hold names of people. I, you know, I also, on the albums, I love that. I don’t remember song, I don’t know song titles half the time. Um, so I don’t mind You can, you can be very disappointed and express it. And I will accept it. I will receive it. Christina: No, I’m just shocked Jeff: to be better. Christina: because I, I mean, ’cause because I was like 10 years old when ER came out and like, I don’t know, like they were like, that was the number one show on television Jeff: Totally. And I mean, Clooney, come on. I know Clooney. Christina: course Clooney, but, but like, but it was Clooney. It was, but but like the, the, the, the, the original, it was Clooney, it was uh, uh, Sherry Stringfeld, it was um, um, uh, Eric Lesal. It was Juliana Margolis, it was Noah Wiley, and it was Anthony Edwards. So like, Jeff: Oh, my favorite Timber Christina: and I was gonna say ironically going into when er came out, like the, the name was Anthony [00:52:00] Edwards, like, he was like number one on the call sheet, right? Like Clooney I think was like four. Um, and, and then, and then Clooney because he’s a good guy, like blew the fuck up and then still did them a solid and did like a full freaking five years on that show, Jeff: Yeah, which is awesome. Christina: he did not, David, David Caruso, it like David Caruso, who famously like had one, you know, big season of NYPD Blue fucks off to go do a movie career. The movie career implodes, there’s a clause in his contract because A, b, C was so furious about how the way he quit NYPD Blue, that they were like, okay, well you can’t do any television for x number of years. And then his movie career dies and then he has to like come like hat in hand to like CSI Miami. Jeff: Yeah. Yeah. Well I love the pit and this thing that surprised me is the thing I always stayed away from is like I can handle gore in almost every context except real life. And so like I can do all the gore of the Walking Dead. I can do all the gore of Game of Thrones or something, but like, I was like, I don’t know if I want, [00:53:00] yeah. Gore. I love it. I mean, I love it. ’cause I’m fascinated. I’m just fascinated. I’m like, oh, that’s what it looks like when you do that. Like, right. Like you just snip the fingertip off. That’s what it looks like when you do that. Like, Christina: no, Jeff: the first Christina: they show some of the stuff, Jeff: yeah, the first half. I did this every time I covered my face whenever it was like that. And then all of a sudden I could handle it. And I was like, this is fascinating. This is totally Christina: What episode are you, are you up to? How many do you Jeff: I actually, I only have 15 left. I have the last episode left. Um, and unfortunately, like we’ve had, like my brother’s, not unfortunately, my brother’s been, we had stuff every night until late for like three or four days. And I’m so ready to watch that thing. And now, now my wife’s going outta town, so I’m not sure we’ll even see it for another week. It’s making me crazy. Brett: are you watching it together? And you have to wait for her. Jeff: Yeah. Well, and we, and, and sometimes it’s easy for us to find a show together and sometimes there’s just a long dry spell. And so it’s also just like nice. It’s just nice to have a show together always. Um, and so it’s the combination of like, that’s just nice to do and I’m right at the end and I’m just ready to Christina: And you just wanna do that together? [00:54:00] Yeah, no, it makes sense. Season Two and Other Shows Christina: Um, I, I’m, I’m curious to see what you’ll think of season two. Um, I, I, um, it’s, it’s different in some ways. It doesn’t have like the, the, I’m not spoiling anything, but like, it doesn’t have like a big like, catalyzing event, like, like season one does. Um, but I still think it’s, it’s really good TV and, uh, yeah, definitely one of my favorite shows, um, hacks is Back for its final season. That’s definitely one of my favorite Brett: That Jeff: I never Brett: good. I, I finished season one. Um, I think there’s three seasons or is there more? Christina: This, it is now in its fifth season. Yeah. Brett: Okay. Yeah. I, I finished season one and then kind of forgot about it, and then I just saw some trailers for the new season and thought, oh, I should get back into this. It looks, it looks like it, it, it looks like it did well, um, Christina: No, I mean, shrinking. Yeah. Brett: I was gonna say, the new season of shrinking is really good too. Christina: Yeah, it is. Yeah. Um, well, well, uh, bill Lawrence is, is, uh, who created that and he created Scrubs and Spin City and [00:55:00] some other things. Like he’s, he’s really, really, um, good. He also did Rooster, which is now on HBO Max. Um, but, oh, the Scrubs Revival. Speaking of, of new shows, I don’t know if it’s gonna get like renewed because it hasn’t been renewed yet. And so I’m a little bit concerned that it hasn’t been renewed yet, and I only did nine episodes for the first season. But the, the Scrubs reboot, revival, whatever you wanna call it, and I say this is somebody who was a huge scrub fan. I, I don’t consider the, the final season to be scrubs like that. It is not part of Canon to me. Like, I feel like that, that, that wasn’t it, but I thought they actually did an amazing job, um, with the, with the reboot. Like I actually. And, and it was hard for them too because John c McGinley is on Rooster and, um, uh, Judy Reyes is on, um, uh, high Potential. And, um, so, you know, the only like, you know, main characters from the original that they have back in every single episode [00:56:00] are, um, uh, Elliot, JD and Turk. Um, but, uh, and then, and then you see, you know, kind of like, like Carla just isn’t in the office sometimes, but she has some guest appearances. Um, but they actually managed to, to do this, they managed to do like a next generation type of story, but still focused on like the main characters you love, but still kind of bring in like new younger doctors in like a way that I’m genuinely really impressed with how they did it. And, and like it kept the heart and kind of the, the feel of the original, like I, it, it was, I was very, very impressed that they were able to recapture. What made that show so good, um, for, its, I guess they’re calling it its 10th season, but, um, I, I really hope that it comes back because that’s a really good show. Brett: Speaking of reboots, um, they’re rebooting, um, Malcolm in the middle, Jeff: I Christina: Yes, they did. [00:57:00] Yeah. They did a four episode thing. Brett: but what I saw an, I saw Hot ones versus with, um, uh, Frankie Muni and whatever. How Christina: Yeah. Brian Cranston. Who, Brian Cranston. Who, who was, who was the, the father of, of, of Mel King on the pit. Brett: Oh, there you go. Jeff: is so cool. I love her so much. Brett: but anyway, they’re talking about why Dewey wouldn’t come back and basically he was like, I haven’t acted since I was nine. He’s like, he is busy. He is got a life Christina: He’s in grad school, like he went to Harvard and stuff like, like, he’s like, uh, I, which I, I love. And I’m like, okay. You know, I mean, I would’ve loved to see Joey too, but I don’t blame him for being like, no. Brett: Yeah. Jeff: Yeah. Yeah. Brett: neither, neither did the other actors, I don’t think. I think, uh, it, it wasn’t necessary to Christina: no, I was gonna say he wasn’t because Brett: the Yeah, Christina: mean, look, they were able to do Fuller House without the Olson [00:58:00] twins who were a much bigger part of that show Jeff: Fuller Christina: ever was. And, and I, I, I’m not even like defending Fuller house. Like it was, it was fine. It was whatever. But like, even that, you were like, there were enough characters where you’re like, okay, so, so Michelle isn’t here. And that would’ve been weird, to be honest. I don’t think that, like I know that everybody would’ve loved having the cameo, but it’s like, how in the hell are you gonna have the Olson twins, like as adults, even in a cameo on Fuller House without just completely taking you out of the whole thing. You know what I mean? Brett: Yeah. Christina: Like, it just, it just wouldn’t be possible. But Gratitude App Picks Brett: we try to fit in a gude before Jeff: Should we grab, Christina: yeah. Let’s do a gratitude. Brett: Um, I can kick it off. I got one I’m excited about. Um, found this app called Bezel. Um, I needed to do iOS screenshots and I needed to do iOS recordings, and I played around with using Screen flow and screen Studio and Camtasia, and I didn’t like [00:59:00] any of the ways that they recorded iOS movies. And then I found Bezel and I mean, c So screen recording built into iOS, in my opinion, is better than any of the like screen casting apps can do. Um, but bezel, if you, if you hard co hardwire your phone to your computer and turn on screen, mirroring it can record. Perfect. Um. iOS recordings, and it’s really good at just taking screenshots with a single key key command. You get a screenshot with a bezel like the outline of the phone and a desktop background behind it. So I can just hit command S as I like, move through my phone, uh, and then my right hand on my phone, my left hand on my keyboard, and I can get a dozen iOS screenshots in five minutes, and they’re ready to go, like ready to [01:00:00] publish. It’s really nice. Jeff: That’s really awesome. I’m gonna try that. Christina: Same, same. Do you have one Brett, or do you want me to, or uh, Jeff do or do you want me to go. AI Tools and Claude Code Jeff: Uh, I’m happy to go. Um, so this is, this is, uh, an easy one in a way, but I, I wanna be specific about what’s been so useful. So I’ve been using cloud code and vs code forever. I mean for the last, I’d say two or three months. ’cause I’ve got really, really deep into using cloud code actually for qualitative work. Um, but also a totally bananas project I built that has both a. Physical component and a heavy duty code component, which I’ll talk about sometime. Um, but, um, I, and I’ve used the desktop app for cowork and for like just the standard chat and I’ve loved that, but I never used it for cloud code until this latest update, which added like a really amazing interface for cloud code. Um, which is kind of my gratitude is that tab of the desktop app, which like, when you open it up, it gives you like just an awesome little like, work summary of like comedy sessions [01:01:00] you’ve had, how many total tokens you’ve used, like overall the last 30 days, the last seven days, what your peak hour is your longest streak. It has the like GitHub, like little chart that fills in. Um, and, uh, and, and that’s like been really cool to see. Um, and you can also see your usage of various models. It’s just a nice little thing that pops up. And then when you’re actually working, it’s really amazing because you can pull up these sidebars that have like diffs or like a preview or you can just get a terminal open in there. Um, and I have. I have loved that. I still like feel more at home in the VS.

GOOD MORNING, WOODLAND! It's Friday, April 24th, and we are live for an A-DAY at the region! The rain is clearing out because it's game time for Morning Mayhem!In local action, our baseball squad is rolling into a big matchup against the Wolcott! Softball faces Crosby, and Girls Tennis takes on Holy Cross in a tough NVL showdown. Yesterday's Quiz Bowl saw the teachers represent themselves admirably, though they might have focused a bit too much on "ancient history" for the juniors!Headlines from the desk: AI is officially moving in as a bot named Luna starts running retail shops in San Francisco. Plus, Turkey is clamping down on social media for kids under 15!Quick Hits:Best Buddies Prom is tomorrow night at the Prospect Firehouse!Track Team bottle drive hits St. Anthony's Saturday morning!Drowsy Chaperone tickets are on sale now!In the NFL Draft, the Jets, Giants, and Patriots all went big on the offensive line—protection is the name of the game, folks!That's the buzzer! I'm Amato, and we are knocking down the door! Go Hawks!

OH MY GOSH, Woodland! It's April 17th and today's show was a TOTAL BLAST! First off, there's a Penguin Plunge coming May 13th to help the Special Olympics—it's going to be chaos in the courtyard!In the news, they saved a whale named Timmy using giant air cushions to float him back to the ocean! Also, people in Minnesota are having meat raffles for hot dogs and brisket—I want to win a hot dog! Congrats to Alexa Piscatelli for winning first place in the big essay contest, too!Sports are HUGE today! Baseball is taking on Naugatuck and softball plays Oxford! We even met the tennis captains, Jameson and Luke! They told us all about their 3-1 season and how they really want to win the NVL title! They both love BIG serves and playing from the baseline!Don't forget: the library is closed during advisory, senior projects are due Monday, and CHARGE YOUR CHROMEBOOKS! It's a B-day, so let's make it awesome! Byeeee!

GOOD MORNING WOODLAND! It's April 14th, the sun is shining at a balmy 60 degrees, and the news is SIZZLING! First, keep those heads on a swivel—watch out for scam emails trying to hijack your accounts; keep it safe and stick to the books! In the building today, the Media Center hosts a special Holocaust guest speaker for blocks 3A and 4A!In world headlines, the Rock and Roll Hall of Fame just announced a legendary class including Oasis, Wu-Tang Clan, and Iron Maiden! Meanwhile, Meta is going all-in with a $1.6 trillion investment in AI avatars!Now, let's talk SPORTS! Our Boys Tennis team is on a TEAR, shutting out Torrington 5-0! Baseball fought hard but fell 9-6 to a tough St. Paul squad—they'll be back to reclaim the diamond tomorrow!But the MAIN EVENT is right here at home! Our UNDEFEATED Softball team faces a massive Top 10 clash against the Brookfield Bobcats! Senior pitcher Caitlyn and what's called the "best outfield in the NVL" are locked in and ready to defend the nest! GO HAWKS!

On this Thursday, March 5, 2026, Woodland Regional High School's morning broadcast overcame early technical hurdles to deliver a comprehensive report on student excellence.The primary focus remains the upcoming 2026 CASC State Convention at Norwich Free Academy. Woodland's student leaders are poised for significant recognition: Kassie Bailey is a finalist for the Student Leadership Award, while Amari is nominated for the Unsung Hero Award. Furthermore, the school's student government—already a state-level Platinum Council of Excellence—is competing for the association's presidency and the "Top Project" honor. The convention promises a rigorous day of leadership workshops and a service project dedicated to pollinator awareness.In athletics, the community celebrates a historic milestone as the boys' basketball team secured its first-ever NVL championship. Success also extends to the swim and dive team, dance, cheer, and wrestling programs. Finally, the cultural desk highlighted the anticipated March 21st return of K-pop icons BTS on Netflix Live following their military service.Whether through legislative simulation, competitive sports, or environmental stewardship, Woodland Regional continues to demonstrate a steadfast commitment to innovation and leadership.

Good morning! It's a “Monday-ish” Thursday after the recent “Snowmageddon”. Despite technical glitches and power resets, the show is live on YouTube. Shout out to the custodial crew for clearing those immaculate walkways!Today features a shortened schedule for parent night; grab lunch at 11:10 AM and catch the volleyball vs. football game at 11:50 AM. In world news, look west this Saturday for a rare six-planet parade. Also, OpenAI is banning scam accounts, and new eye drops may soon replace reading glasses.In sports, UConn men made history by crushing St. John's 72-40. High school NVL tournaments resume tomorrow after snow delays, and the NFL Combine is currently underway. Don't forget to charge your Chromebooks and stay safe!

Rise and shine, Woodland! It's Thursday, February 19th, and your favorite trio—Amato, Tomlin, and Decker—is back with the high-quality "dulcet sounds" you crave now that those pesky audio issues are fixed.In the local groove, big props to Colby Marsan, our new NVL wrestling champ! And don't miss the boys' basketball squad; they've grabbed the #1 seed in what's becoming a historic season. Feeling hungry? Grab some pizza at Antonio's for the girls' basketball fundraiser.Over at the news desk, Tomlin's digging up the past! They found a 400-year-old Swedish shipwreck and a hidden cannon in England. Plus, Skokie, Illinois is getting a groovy flag makeover, and the sitcom Scrubs is finally making a comeback.Tech-heads, Microsoft's got glass data storage that lasts 10,000 years. And for the trivia buffs, the Jonathan Law mascot is "The Law".Keep those Chromebooks charged and stay classy, Woodland. We're knocking down the door every morning at 7:00! Peace and love

Good morning, Woodland! Jumping into the action this February 18th, and even though we're on the backup camera because of a missing computer, we are ready to roll!The headline of the day: the Woodland Dance Team has officially been crowned the 2026 NVL champions! We're also celebrating our top scholars, Valedictorian Genevieve Bandanza and Salutatorian Emma Bunk. Over in sports, the boys' basketball team just capped off an undefeated regular season, while the girls secured a thrilling win over St. Paul to land their spot in the state tournament.In wilder news, Netflix is developing a Scooby-Doo origin series, and a UPS driver in Branford was actually chased by aggressive turkeys! Looking forward, don't miss the Future Educators meeting on Feb 20th or the Volleyball vs. Football showdown on Feb 26th. Brace yourselves for a fast-moving storm this Friday that could bring anything from sleet to the "apocalypse". We started late, but we're finishing strong—see you tomorrow, Woodland!

Der DAX ist am Mittwoch mit Schwung über 25.000 Punkte zurückgekehrt und schloss 1,12 % höher bei 25.278,21 Punkten. Unterstützung kam am Nachmittag von der Wall Street: Die Tech-Erholung setzte sich fort, der Dow Jones stieg zum Start um 0,5 % auf 49.770 Punkte, der S&P 500 legte 0,6 % auf 6.881 Zähler zu, die Nasdaq gewann 0,8 % auf 22.760 Punkte. Im Fokus stand Nvidia mit mehr als 2 % Plus nach einem mehrjährigen Chip-Vertrag über Millionen aktueller und künftiger KI-Chips mit Meta. Auch Amazon +1,4 % und Alphabet +0,7 % halfen der Stimmung, nachdem zuletzt Sorgen über zu hohe KI-Investitionen gebremst hatten. In Frankfurt fiel Bayer nach einer Vergleichsmeldung um 7 %. Politische Unruhe brachte ein Bericht der Financial Times: EZB-Präsidentin Christine Lagarde könnte ihren Posten vorzeitig räumen, die Amtszeit läuft offiziell bis Oktober 2027. Firmen: Rheinmetall darf die Militärsparte NVL von Lürssen kaufen, inklusive Blohm+Voss, die EU-Kommission sieht keine Wettbewerbsbedenken. Garmin springt über 15 % und erwartet 2026 7,9 Mrd. USD Umsatz sowie 9,35 USD bereinigten Gewinn je Aktie. Citigroup verkauft die frühere Russland-Tochter an Renaissance Capital, der Rückzug soll kapitalneutral sein. Rohstoffe: Gold 5.003,76 USD je Unze +2,59 %, Silber 78,073 USD je Unze +6,32 %. Goldman Sachs sieht darin keinen Rohstoff-Superzyklus.

Der Rüstungskonzern Rheinmetall darf nach einer Entscheidung der EU-Kommission die Militärsparte NVL der Bremer Werftengruppe Lürssen kaufen. Rheinmetall kann damit jetzt offiziell in den Marine-Schiffbau einsteigen.

Reinhard Lüken, Hauptgeschäftsführer beim deutschen Verband für Schiffbau und Meerestechnik (VSM), ist ein erfahrener Experte für das Werften-Geschäft. Seit Jahren warnt er vor einer wachsenden Abhängigkeit der Schifffahrt von der asiatischen, speziell chinesischen, Schiffbau-Industrie. Er fordert deutlich mehr Engagement seitens der hiesigen Politik, um einen fairen Wettbewerb mit den staatlich enorm unterstützen Werften in Fernost zu ermöglichen. Angesichts der welt- und geopolitischen Entwicklungen der jüngeren Vergangenheit sieht er jetzt einen veränderten Realitätssinn in Berlin. Allerdings spricht er sich auch für einen stärkeren europäischen Schulterschluss aus: "Wir werden die Themen nicht alleine national lösen und dürfen nicht den Weg gehen, den wir in den letzten 20 Jahren gegangen sind", sagt Lüken im HANSA Podcast. In der Episode spricht er über den Marine-Schiffbau, der großes Potenzial berge sowie eine große Nachfrage in auch in den anderen Segmenten, in denen die deutschen Werften aktiv sind. Aber: "Nur mit Highend können wir die kritische Messe unter Umständen nicht aufrechterhalten." Eine Verdopplung des Umsatzes der Schiffbau-Industrie hält er dennoch für "nicht überambitioniert", sieht die Branche in einer "absoluten Wachstumsphase". Lüken spricht unter anderem über die Übernahme von NVL aus der Lürsse-Gruppe durch den Rüstungskonzern Rheinmetall, die Querelen um die Meyer Werft sowie seiner Ansicht nach schwierige Aspekte in der Sanierung nach dem Staatseinstieg. Außerdem geht es um das deutsche Vergaberecht ("Der Auftrag nach Spanien war ein Fehler"), Vor- und Nachteile von Staatswerften, die Politik der US-Regierung Trump für die Werften ("Wir können den Amerikanern helfen") und den vieldiskutierten Jones Act sowie die anstehende Nationale Maritime Konferenz in Emden. Von dem Treffen von Politik und maritimer Wirtschaft erwartet er deutlich mehr Konkretes als von der letzten Ausgabe in Bremen vor einigen Jahren: "Unser Anspruch an eine Nationale Maritime Konferenz ist größer", so Lüken, der auf entsprechende Initiativen aus der Wirtschaft sowie Gespräche mit Wirtschaftsministerin Reiche und den Maritimen Koordinator der Bundesregierung, Christoph Ploss, eingeht.

Rise and shine, Woodland! The heat wave has officially split, leaving us in a pristine winter wonderland. Watch your step on those icy driveways—maybe don't go skating on the Naugatuck River! Or Matthies Pond either.Over at the news desk, things are getting heavy. The SS United States is heading to Florida to become a funky coral reef. Meanwhile, Bitcoin and gold are dropping like a bad habit, but Sony is raking in the yen. Can you dig it? In China, Draco Malfoy is the new mascot for the Year of the Horse, and Saudi Arabia is handing out passports to camels!Back at the ranch, show some love to your school counselors. The puzzle challenge has everyone buggin'—1,000 pieces is a lot, man! Big props to the cheer squad for snagging third place at Wolcott. And look for those pyramids, tumbling, and jumps taking them straight into the upcoming competitions like the NVL championship on February 21st.Don't forget Senior Night for basketball. Keep those Chromebooks charged, bring in that PB&J for DECA, and stay groovy, Woodland

Today, the crew investigates a frozen-tundra standoff between a school bus and a garbage truck, sparking the ultimate historical showdown: which specialized vehicle saved us from the Black Plague first?. It's Tuesday—officially the world's "eighth favorite" day—but don't despair!. It's an early dismissal, National School Counseling Week, and the PB&J drive has moved to a high-stakes, "ounce-based" scoring system to avoid further controversy. In global news, Italy is officially sick of your Instagram photos, while Elon Musk prepares to move your data into actual space.Finally, the sports desk hosts NVL and state champion dance captains and they're spilling the tea on an emotional senior night filled with "surreal" goodbyes and tear-jerking letters from their sisters. These legends credit Coach Davis for a total program glow-up, admitting the standards are so high now, they might not have even made the cut as freshmen. Whether they're channeling Michael Jackson in jazz or vibing to Tate McRae in hip-hop, these "Dancing in the Woods" MCs are ready to defend their titles at the NVL tournament on February 12th. Wake up with Woodland—because you never know when you'll need to out-dance a Blue Knight!

Woodland is bracing for “Snowmageddon 2026,” with anonymous sources predicting up to two feet of snow. Locals are already panic-buying bread and milk for "survival French toast". In other news, TikTok has been saved by a new deal, and yodeling is officially recognized as a Swiss cultural treasure.The morning's stars were basketball captains John Napiello. and James Scampolino. Their team is a perfect 11-0, thriving as the "top dogs" of the NVL rather than the "hunters". They attribute their success to a deep bench and a relentless "press" that wears opponents out by the fourth quarter. John recently celebrated a 1,000-point milestone, though he remains focused on a deep playoff run. The duo shouted out "Dog of the Game" Ben Pugles and defensive hustler Marco, setting their sights on NVL and State championships.

It was a bittersweet Monday at Woodland!

Amato gets the camera restarted and reruns the beginning of the show (You can only catch that on Facebook feed). Everybody debates the leftovers of Thanksgiving meals, Elon Musk belives that people can be replaced in 20 years. And Woodland Football wins the NVL!

Big thanks to Cisco for sponsoring this video and sponsoring my trip to Cisco Partner Summit San Diego 2025. This video is a deep dive with Jeetu Patel on why the real AI revolution is happening in infrastructure and networking, not just in GPUs or chatbots. Jeetu explains that we are massively underestimating how much AI infrastructure the world will need. Power becomes the core constraint, GPUs are the core asset, and networking is the force multiplier that lets thousands of GPUs act as one system. He walks through how we went from models on a single GPU → 4–8 GPUs in a server → racks with hundreds of GPUs (like NVL-72 with 500+ GPUs) → clusters of racks, and now “scale across” between data centers when power and real estate are scattered across different regions. The conversation then shifts to edge AI and Cisco Unified Edge: instead of doing all token generation in big data centers, some inference and token generation must move to the edge (branches, factories, hospitals, stadiums, stores) where data is created. Jeetu explains why edge devices need to be plug-and-play, remotely managed, and integrate compute, networking, security and observability in a single platform. He also introduces the idea that AI is now constrained by three big bottlenecks: • Infrastructure • A trust deficit (people don't trust AI yet) • A data gap (models are mostly trained on human internet data, not on rich machine data) Jeetu explains how security becomes a prerequisite for productivity, not a trade-off, and describes Cisco's work with Splunk, open-sourced time-series models, and machine data (logs, metrics, traces) to close the data gap by correlating machine data with human-generated data for better insights. Globally, he talks about the “token generation race” – how every country now cares about having enough AI token generation capacity because it directly links to GDP and national security. He cites huge infrastructure build-outs with partners like G42 in the Middle East, at gigawatt and trillions-of-dollars scale. Finally, Jeetu tackles the “AI will take my job” fear. He outlines three stages of thinking: 1. “AI will take my job.” 2. “Someone who uses AI better will take my job.” 3. “Without AI, I won't be able to do my job.” His message to younger viewers: be excited, adopt AI as a companion, own your learning, and learn fast because AI compresses the time it takes to build skills. // Jeetu Patel's SOCIALS // LinkedIn: / jeetupatel Website: https://www.cisco.com/ X: https://x.com/jpatel41 // David's SOCIAL // Discord: discord.com/invite/usKSyzb Twitter: www.twitter.com/davidbombal Instagram: www.instagram.com/davidbombal LinkedIn: www.linkedin.com/in/davidbombal Facebook: www.facebook.com/davidbombal.co TikTok: tiktok.com/@davidbombal YouTube: / @davidbombal Spotify: open.spotify.com/show/3f6k6gE... SoundCloud: / davidbombal Apple Podcast: podcasts.apple.com/us/podcast... // MY STUFF // https://www.amazon.com/shop/davidbombal // SPONSORS // Interested in sponsoring my videos? Reach out to my team here: sponsors@davidbombal.com // Menu // 0:00 - Coming up 0:33 - "Networking is sexy" 02:24 - Scale up, scale out and scale across explained 04:47 - Cisco and Nvidia partnership 05:55 - Cisco and G42 partnership // Addressing the AI bubble 08:11 - New Cisco Unified Edge 11:08 - Agentic AI in the future 13:05 - Huge demand for networking 13:57 - The three constraints 16:38 - AI in the real world 19:26 - How AI will take jobs away 21:38 - Conclusion Please note that links listed may be affiliate links and provide me with a small percentage/kickback should you use them to purchase any of the items listed or recommended. Thank you for supporting me and this channel! Disclaimer: This video is for educational purposes only.

In today's episode, we had the pleasure of speaking with Stephen Liu, MD, about the potential role for zidesamtinib (NVL-520) for the treatment of patients with advanced non–small cell lung cancer (NSCLC) harboring ROS1 rearrangements. Dr Liu is an associate professor of medicine at Georgetown University, as well as the director of Thoracic Oncology and head of Developmental Therapeutics at the Georgetown Lombardi Comprehensive Cancer Center in Washington, DC. In our exclusive interview, Dr Liu discussed the current standards and challenges for treating patients with ROS1-positive disease, the unique mechanism of action of zidesamtinib, and how positive findings from the phase 1/2 ARROS-1 trial (NCT05118789) may help position this agent in the ROS1-positive NSCLC treatment paradigm.

With the weather getting colder, debates the controversy that swirls around the age old decision--sweatshirt or hoodie (hoodie, ofc). Looks like students are leaning toward keeping the Internet over basic heat and A/C. Tomlin updates the penny shortage while hosting a "Penny War" in Student Government for spirit week. AI could replace teachers. Volleyball drops a tough one in NVL semis, and the NVL CHAMPS, Girls Soccer, swings by: Coach Jillian Jackman and Captains Avery DiGiovanna and Giulianna Celotto.

With all the traffic on Pine Bridge this morning, the crew got off to a late start and even their guest was later than expected. Add to that the camera still wasn't working and you get a hot mess of Tuesday. But the show must go on! Theoretically speaking, of course. And it did.Leah Zainc, NVL Champion Cross Country runner, was the guest of the hour.Girls Soccer won NVL tournament and the Dodgers finally finished an 18-inning marathon, about two minutes ago.

Amato smells. Well, he smelled a skunk on the drive in to work, then began reminiscing about missing skunks (I don't know, I just write the cutlines for the episode). Tomlin finds a bunch of stories about ancient artifacts and dead bodies and Decker notes that post season play is here. The girls cross country team landed 3rd overall in the NVL with Leah Zainc finishing in 1st place, and the boys came in at 7th. Boys soccer missed post season by one match with their loss to Oxford.

Decker realizes that Columbus Day is still a week away, experts reassess how quickly AI will take over the world and Woodland sports is showing strength in the NVL.Sports team mascot count: 15:10 (wins/losses)

In today's episode, we had the pleasure of speaking with Alexander Drilon, MD, about the phase 1/2 ARROS-1 trial (NCT05118789) investigating zidesamtinib (NVL-520) in TKI-pretreated patients with advanced ROS1-positive non–small cell lung cancer (NSCLC). Dr Drilon is chief of the Early Drug Development Service at Memorial Sloan Kettering Cancer Center in New York, New York. In our exclusive interview, Dr Drilon discussed the efficacy data and implications of the ARROS-1 trial, highlighted the unique mechanism of action of zidesamtinib, noted the high central nervous system (CNS) response rates and favorable safety profile associated with the agent, and emphasized the potential for zidesamtinib to become a standard first-line therapy in the NSCLC treatment paradigm, especially for patients with prior TKI resistance or CNS disease.

Deutschlands größter Rüstungskonzern Rheinmetall will die Militärsparte Naval Vessels Lürssen (NVL) der Bremer Lürssen Werft kaufen. Die Kartellbehörde muss das noch absegnen - wir wagen einen Blick auf die Strategie von Rheinmetall, gucken insgesamt auf die boomende Rüstungsindustrie und auf die Auswirkungen für Mitarbeitende. Unsere Folge zur neuen Rheinmetall-Fabrik in Unterlüß: https://www.ardaudiothek.de/episode/urn:ard:episode:04826543e4dbbbf4/ Podcast "Streitkräfte und Strategien": https://www.ardaudiothek.de/sendung/streitkraefte-und-strategien/urn:ard:show:794d51bd58e1747c/

Dave Palm is an American beach volleyball coach, professional player, and indoor coach. His accomplishments on the AVP and NVL scene are as well-documented as they are commendable. And it seems that the work he is doing as coach only showed he was just getting started. He resides in Florida and is the founder of "Palm Coast Volleyball Club." 01:31 - The transition from player to coach, and how seamless it was by appearance 12:11 - paid to coach, caring is free, tips for indoor tryouts, on the surface, and behind the scenes, regional strategies on how they hold onto a power dynamic 19:21 - finding out where you are one the indoor depth chart and what it means to your playing time, playing on a "2" team for more in-game reps 27:19 - concerns that parent air coming in, most likely generating from their experience from previous clubs or having it be their kids' first club 34:10 - skills and advantages that indoor players playing beach for the summer during he indoor off-season 40:16 - what percentage of the game is mental, and how much do you implement it in practice? Plus, the recruiting process of coaches 53:05 - what coach inspired you to a point where you will never forget them? About "Manny," plus, books to inspire the athlete #nyvarsitysports #optionpodcast

In this JCO Article Insights episode, host Peter Li summarizes "Taletrectinib in ROS1-Mutated Non–Small Cell Lung Cancer: TRUST" by Pérol et al, published April 03, 2025, followed by an interview with first author, Dr Maurice Pérol. TRANSCRIPT The disclosures for guests on this podcast can be found in the show notes. Dr. Peter Li: Welcome to this episode of JCO Article Insights. I am Dr. Peter Li, JCO's editorial fellow, and today I am joined by Dr. Maurice Pérol on “Taletrectinib in ROS1-Mutated Non–Small Cell Lung Cancer: TRUST,” by Pérol et al. At the time of this recording, our guest has disclosures that will be linked in the transcript. Before we start our interview, I want to give our listeners a quick summary of the TRUST study. For those tuning in, the TRUST study is a phase II, single-arm, open-label, nonrandomized, multicenter trial looking at the efficacy and safety of a novel, next-generation ROS1 TKI, taletrectinib, in advanced ROS1-mutated non–small cell lung cancer. While a relatively rare mutation, the prevalence of ROS1 mutations ranges from 0.9% to 2.6% of patients, with a third of patients presenting with brain mets at diagnosis.Current FDA-approved therapies include crizotinib, entrectinib, and repotrectinib, which have varying degrees of efficacy, in-coming with trade-offs in CNS penetrance and safety with newer generations, particularly in the realm of neurological side effects, highlighting an unmet need in this arena. A total of 273 patients with advanced non–small cell lung cancer with confirmed ROS1 mutation were recruited for this study. 160 patients were TKI-naive, while 113 were TKI-experienced with either crizotinib or entrectinib. Patients with asymptomatic brain mets were also allowed to enroll. In the TKI-naive arm, the median age was 57, with 91% of patients having stage IV disease, 20% having no more than one cycle of chemo, and 23% having brain mets at baseline. In the TKI-experienced arm, the median age was 53, with 97% having stage IV disease, 37% having received prior chemo, and about 50% having brain mets. Furthermore, about 10% of the study population had received entrectinib, while more than 90% had received crizotinib. About 10% had a known G2032R acquired resistance mutation. Taletrectinib was dosed at 600 mg daily until disease progression or unacceptable toxicities. The primary endpoint was overall response rate, with secondary endpoints being disease control rate, duration of response, time to response, and progression-free survival. For those with brain mets, intracranial overall response rate and disease control rate were also assessed. Median follow-up time was about 21 months in both cohorts. In the TKI-naive cohort, the overall response rate was 89%, with 8 patients achieving a complete response. Disease control rate was 95%, with a median duration of response of 44.2 months. Time to treatment response was about 1.5 months. Median progression-free survival was 45.6 months, with 52.6% not having progressed at 3 years. While overall survival data were immature, 66% of patients were still alive at 3 years. In the pretreated cohort, overall response rate was 56%, with 5 patients achieving a complete response. Overall response rate was 53% for those who were crizotinib-pretreated and 80% for the entrectinib-pretreated patients. Disease control rate was 88%, and median duration of response was about 16.5 months. Time to treatment response was also 1.5 months, and median progression-free survival was 9.7 months. Median overall survival was not reached, but 77.5% of patients were still alive at 1 year. Responses were consistently seen across subgroup analyses. 17 TKI-naive and 32 TKI-pretreated patients had measurable brain mets. In the TKI-naive arm, intracranial overall response rate was 77%. Disease control rate was 88%, and duration of response was 15 months. In the TKI-pretreated arm, intracranial overall response was 66%, with one patient achieving complete response. The disease control rate was 94%, and duration of response was about a year. For the 13 patients who had a known G2032R mutation, a 62% response rate was noted. Most common treatment-related side effects were AST/ALT elevation, nausea, and vomiting, with most being grade 1 or 2. Most common neurological side effects were dizziness, dysgeusia, and headache. Again, most were grade 1. QTc prolongation is another important adverse event to note, occurring in about 18% of all patients. Discontinuation rate from treatment was only 7%. There were three treatment-related deaths in this study: one from hepatic failure, one from pneumonia in the naive arm, and one from liver dysfunction in the pretreated arm. Dr. Peter Li: Maurice, thank you so much for joining us today to talk about your paper. Would you mind just giving yourself a brief introduction to the listeners out there of who you are? Dr. Maurice Pérol: So, my name is Maurice Perol. I'm a thoracic oncologist working in the Cancer Center of Lyon in France. And I'm involved in clinical research in thoracic oncology. I've been involved for many years now. Dr. Peter Li: Okay. And for listeners out there, don't forget, he's also the primary author of the paper that we just talked about. So, Maurice, let's begin. Can you tell our listeners what is the significance of your study? Dr. Maurice Pérol: Well, the results of these two large phase II studies - TRUST-I, which has been conducted in China, and TRUST-II, which was a global, worldwide phase II study - so, the results place taletrectinib as the TKI with the most favorable efficacy-tolerability ratio of the available ROS1-targeting TKIs, especially in frontline therapy. And this is based on the response rate, which was very impressive, the CNS penetration with a great CNS activity, the duration of response with a compelling 45 months median PFS in frontline setting. The level of activity in pretreated patients after crizotinib or entrectinib was also impressive and similar to that of repotrectinib, for example, but with a more favorable neurological tolerance profile. The toxicity is mainly represented with grade 1 or 2 transaminase elevation, but without clinical symptoms, and GI toxicity, but mainly grade 1 and 2. The neurological toxicity is low, especially for dizziness, showing that taletrectinib spares TrKB in a large part. And finally, there is also a decrease in toxicity over time, especially for GI toxicity and liver toxicities, which allows a very long and a prolonged administration, which is very important in this setting. Dr. Peter Li: These are all excellent points. Can you tell the listeners if there are any limitations that we should be concerned about, about this study? Dr. Maurice Pérol: Sure. This data comes from single-arm phase II studies. So, this is not comparative data. And a phase III trial, which compares taletrectinib to crizotinib, is ongoing to evaluate the superiority of taletrectinib over the standard of care. Another limitation comes from the lack of systematic brain imaging at each tumor evaluation in patients without brain metastases at baseline, not allowing to assess the intracranial PFS in all patients, and which did not allow us to assess the CNS protective issue from taletrectinib, especially in patients without brain metastases at baseline. Dr. Peter Li: Another question that I have is, with this novel TKI now available, how would you recommend the sequencing of these drugs? Would you start with someone on an alternate TKI and then reserve taletrectinib second line or later? Or would you use it upfront? Or does it depend? Dr. Maurice Pérol: Well, it is a very important question, as we have now different available TKIs. Looking at the efficacy-toxicity balance, I would strongly favor the use of taletrectinib in frontline setting, in first line. The response rate, the CNS activity, the duration of response with a very compelling 45 months median PFS, and moreover, the good tolerance profile over time are strong arguments in favor of giving taletrectinib in frontline. Generally speaking, the use of the most active agent as frontline treatment in lung cancer depending on an oncogenic addiction is probably the best way to improve the patient's outcome. This is true for patients with EGFR mutation, for patients with ALK fusions, and this is probably also true for patients with ROS1 fusion. So, I would probably argue in favor of a frontline use of taletrectinib. Dr. Peter Li: Listeners are going to ask, well, if you use taletrectinib upfront, then what are you going to use second line once they progress? Dr. Maurice Pérol: Well, we have some new compounds which are under development today. For example, the NVL-520, which is a very interesting compound, which seems also to be active in case of resistance mutation. But I do think that we have to use the best-in-class TKI in frontline because, you know, the extension of PFS after acquired resistance you can obtain with a second-line TKI is always shorter than the benefit you can obtain by using the most active agent in frontline. And this is true for the majority of oncogenic addiction in lung cancer. Dr. Peter Li: That makes sense. I also noticed that cognitive impairment wasn't listed in the safety table. Is that not an issue that you've observed at all with taletrectinib, or is it still an issue but less so because, like you mentioned earlier, because of its higher selectivity? Dr. Maurice Pérol: Well, this is a good question because we have some ROS1-targeting TKIs like repotrectinib, entrectinib, and even lorlatinib, with some neurological adverse events and some cognitive issues. Taletrectinib is a very selective ROS1-targeting TKI, and it spares very well the TrKB, for example, explaining that we did not observe any cognitive impairment with taletrectinib in the TRUST study, showing also with the low level of other neurological adverse events, dizziness, dysgeusia, for example, the high selectivity of the compound and the preservation of TrKB. So, this is very important when you consider the long duration of treatment in those patients with ROS1 fusion. If you have to take a drug for more than 2, 3, or 4 years, of course, the neurological adverse events are very important, and they can clearly impair the quality of life. So, this is a very important point, the very low level of neurological toxicity of taletrectinib. Dr. Peter Li: And I think that goes to say why you would favor using it frontline as well compared to entrectinib or repotrectinib. Last question that we have for you is: well, what's next? You mentioned there's a phase III trial comparing it to crizotinib. I think one of the questions that a lot of us would have is: why not compare it to one of the newer agents as a comparator arm? Dr. Maurice Pérol: Well, this is a good question. Crizotinib remains the standard of care in many countries for ROS1-positive advanced non–small cell lung cancer outside of the US, especially in Europe, and in particular in patients who do not have brain metastases at diagnosis. Entrectinib has a better CNS penetration, but it did not achieve a better PFS than crizotinib in phase I/II trials, and clearly, it has a less favorable tolerance profile with weight gain, edema, and neurological adverse events. Repotrectinib has overall a level of activity which seems close to that of taletrectinib. So, it makes it difficult to consider a comparative trial that would, for example, test taletrectinib in comparison with repotrectinib because this kind of study would need a very large number of patients and a very late readout. Considering if you have a median PFS of more than 3 or 4 years, it would be very difficult to have results in before 4-5 years. So, from a pragmatic point of view, the comparison of taletrectinib to crizotinib is probably the best way to evaluate in a phase III setting the level of activity of taletrectinib, especially in the CNS, because this study will probably allow us to assess the CNS protective effect of the compound for patients without brain metastates at baseline. So, I think probably it's a pragmatic study that will allow us to confirm the high level of activity and the good tolerance profile of taletrectinib. Dr. Peter Li: Well, thank you, Maurice, so much for speaking about the JCO article, “Taletrectinib in ROS1-Mutated Non–Small Cell Lung Cancer: TRUST,” and for all your valuable input today. Thank you for listening to JCO Article Insights. Please come back for more interviews and article summaries, and be sure to leave us a rating and review so others can find our show. For more podcasts and episodes from ASCO, please visit asco.org/podcasts. The purpose of this podcast is to educate and to inform. This is not a substitute for professional medical care and is not intended for use in the diagnosis or treatment of individual conditions. Guests on this podcast express their own opinions, experience, and conclusions. Guest statements on the podcast do not express the opinions of ASCO. The mention of any product, service, organization, activity, or therapy should not be construed as an ASCO endorsement.  

Jeff Samuels is an American Professional Beach Volleyball Player, Entrepreneur, and Model. He is also a spokesperson for Hyundai. The prosperity he is enjoying in his sport is well-documented. From the AVP, FIVB, NVL, to regional tournament wins, this versatile player is fun to watch, wherever the venue may be. 01:50 - Jon Jones retires, charged, the evolution of how we choose our role models, "Being like Mike," what it meant before and what it means now 12:14 - People who are not "big's" who play big at the net, finding out what defines you and to what end, working with different partners and what they bring 30:11 - Should Jon Jones not be on the GOAT list, who are your top 5 NBA GOATS, how good is Demetrious Johnson? Plus, loving Muhammad Ali, athletes who were activists, 42:55 - Volleyball is life, and at the same time, it is not. Playing multiple sports and the compare and contrast of that "feeling." Plus, does success change players, who they remember and associated with, and when is this a good thing or a bad thing, what is "conversational memory." 55:33 - When your escapism becomes your career, about the HBO series "The Wire," coming back from the roller coaster ride in 2019, re-shaping himself for the next evolution 1:06:22 - different versions of ourselves, the Motherlode, inside beach volleyball - what are we fighting over? Plus, is there a "hunger games" happening in the volleyball climate? About Chase Frishman 1:20:41 - The league, calculated risks, things changing vs things staying the same, Eric Cam - the one-man team - raised the bar for all, the disc gold promotion, thoughts from the comment section, the creation of city vs city, introducing 4's, 1:58:04 - What does it take to be and stay marketable 2:06:50 - How did he transition to coaching so seamlessly? #nyvarsitysports #optionpodcast #avp #beachvolleyballworld Available on iTunes and Spotify:

Ende März werfen drei Fachgesellschaften bei der NVL KHK hin. Sie üben deutliche Kritik an der Leitlinie. Was steckt hinter der Kritik? Wir schauen uns die Argumente und Quellen hinter den 8 Punkten im EvidenzUpdate-Podcast genauer an.

This is the second episode of a two-part series on the HER2 diagnostic and treatment landscape in non-small cell lung cancer (NSCLC), hosted by the Oncology Brothers, Drs Rohit and Rahul Gosain.      In this episode, Dr Isabel Preeshagul and Dr Eric Singhi provide the benefit of their experience when discussing how to approach different treatment scenarios in HER2-mutant NSCLC.   The conversation unfolds to cover: • Ways to distinguish HER2 alterations from other alterations on biomarker reports  • The latest efficacy and safety data of currently approved and emerging treatments for HER2-altered NSCLC   • The potential CNS activity of these treatments in patients with HER2-mutated NSCLC  • How the treatment pathway may look in the near future      Clinical takeaways • In NSCLC, HER2-positivity includes mutations, amplifications and overexpression. It's important to distinguish HER2 alterations from EGFR mutations, particularly exon 20 insertions, when interpreting next-generation sequencing (NGS) results  • Trastuzumab Deruxtecan (T-DXd) is currently the only approved targeted agent for HER2-altered NSCLC in the 2nd-line setting. It shows promising efficacy, especially in HER2-mutant cases, but has limited brain penetration and is associated with notable side effects, including pneumonitis, which requires close monitoring  • Emerging TKIs, such as zongertinib, BAY 2927088 (sevabertinib), and NVL-330, target HER2-mutations and have shown high response rates and CNS activity in early studies, without ILD/pneumonitis. These treatments come with unique side effects like diarrhoea and rash, which can be managed with supportive care  • CNS metastases are common, with up to 30% of HER2-altered NSCLC patients presenting with or quickly developing CNS metastases. Current large molecule therapies (like T-DXd) have limited brain penetration, making small-molecule TKIs, like zongertinib, BAY 2927088 (sevabertinib), and NVL-330, promising for their potential CNS activity • Current standard 1st-line care for HER2-mutant NSCLC remains platinum-based chemotherapy ± immunotherapy. Targeted agents (like T-DXd) are generally reserved for 2nd-line use, but ongoing trials are evaluating the move toward frontline therapy Follow us on social media: •⁠  ⁠X/Twitter: https://twitter.com/oncbrothers •⁠  ⁠Instagram: https://www.instagram.com/oncbrothers •⁠  Website: https://oncbrothers.com/ Don't forget to subscribe for the next episode

In this episode, listen to Alice T. Shaw, MD, PhD, and Jessica J. Lin, MD, share their thoughts on the available and emerging clinical data for second-line and beyond treatments in patients with recurrent ROS1-altered advanced NSCLC including:Assessing ROS1-TKI resistance mutations with tumor and liquid biopsies in patients with ROS1-altered advanced NSCLCPrevious TKI-treated cohort from the TRIDENT-1 study: efficacy of repotrectinib in patients with recurrent ROS1-altered NSCLC and measurable baseline brain metastases   Phase II TRUST-1 trial of taletrectinib: activity in patients with known ROS1 G2032R resistance mutation ROS1-altered advanced NSCLCThe global phase I/II ARROS-1 study of zidesamtinib (NVL-520): safety summary in patients with ROS1-altered advanced NSCLC  Program faculty:Jessica J. Lin, MDAttending PhysicianMassachusetts General HospitalAssociate Professor of MedicineHarvard Medical SchoolBoston, MassachusettsAlice T. Shaw, MD, PhDChief, Strategic PartnershipsAttending Physician, Thoracic OncologyDana-Farber Cancer InstituteHarvard Medical SchoolBoston, MassachusettsResources:To download the slides associated with this podcast discussion, please visit the program page.

In this podcast, we'll be talking and learning about Nuvola, a project focused on democratizing Decentralized Physical Infrastructure (DePIN). Today I'm join by Raul De Benedittis, Nuvola's co-founder & CEO. In this interview, we cover the Nuvola Drive, partnerships, catalyst proposals, business models, NVL token staking, revenue share models, and so much more! Stay tuned to learn more! -- Socials-- Website: https://crypto-loops.com/cardanoconvo/ Twitter: https://twitter.com/CardanoConvo Discord: https://discord.gg/atNKP2TUam  StakePool Site: https://www.crypto-loops.com/loops-pool/ StakePool Pool.pm: https://pool.pm/c9bc139d73045ba6c5b002ff88ea2a5854b3e783c468adb9250df917 -- Nuvola -- Nuvola Website: https://www.nuvoladigital.io/ Nuvola Twitter: https://x.com/NuvolaDigital Nuvola YouTube: https://www.youtube.com/@nuvoladigital Nuvola Medium: https://nuvoladigital.medium.com/ Nuvola Staking: https://app.nuvoladigital.io/ Nuvola Catalyst Proposal: https://cardano.ideascale.com/c/idea/120650 Nuvola Catalyst Tweet: https://x.com/NuvolaDigital/status/1803501900177793504 --Want to Help the Channel Out?-- Share on Twitter, Facebook, and other Socials! Delegate to LOOPS Stake Pool! ADA address: addr1qxjwn9huhcjurnztn8qp92y3j39xxjr224k78wlehashfg793m7fx6usftwtfhg9g53ddshhhssvpr859llf3w987gjqvpy37j

EP773 - Qual a diferença entre NVL, NULLIF e COALESCE | Podcast Oracle SQL   Entre no nosso canal do Telegram para receber conteúdos Exclusivos sobre Banco de dados Oracle:   https://t.me/joinchat/AAAAAEb7ufK-90djaVuR4Q

Jeff Samuels is an American professional beach volleyball player, entrepreneur and coach. A former lacrosse and hockey player, he fell in love with the beach and has been doing it full-time, ever since. Starting his career with the NVL, he won newcomer of the year. He has since won a slew of open tournaments, regionally and on big stages, and remains a top player on the tour. A spokesperson for Hyundai for various branches, he spends a ton of time doing clinics for juniors and sometimes adults. 02:30 - Huntington Beach, and Marcus Carvalhaes, winner's mentality, a path to tournament wins, 11:01 - Taking care to the mental, and how the mental affects the physical, taking care of things you can control 19:54 - Nutrition, good food habits, myth-busting the carnivore vs vegan, "Wagu" in his life, 34:29 - How Jeff hit the ground running as a competent juniors coach, speaking at "eye level," taking beats, the camps the clinics, Pre-Dino 43:18 - What do "be coachable" mean to you? Kloth and Nuss getting over the Brazilian "hump," plus, who do you trust with your juniors, collectively? 1:01:30 - Do people really WANT to put in the work in detail? To people overanalyze their approach? Are there really volleyball "secrets?" 1:12:56 - What in Volleyball should be 2 points, what rule are you glad is gone, plus, lightning rounds

John Moran was an American professional beach volleyball player. He has played with a slew of partners, and it seems that he has hit every possible stop the regionals and National scene has to offer. From the AVP to the NVL, to NORCECA, to the Motherlode, his prosperity at every level has been well-documented, which pales in comparison to how much he is well-loved by his contemporaries, fans and pundits alike. One can only speculate how he did this and a full-time job logistics and a start-up in a 24-hour day. 02:00 - The late-summer conditions of the last NVL tournament in Hermosa Beach 11:39 - Take advantage of opportunities with great players, good left-handed players, making your own greatness 18:55 - Hard work vs talent: do not be fooled by players that make it look easy, Rafu Rodriguez and Kevin McColloch 21:21 - The talent during the time he played in Arizona and the ridiculous perks 27:45 - Who is a good player flying under the radar a lot of people do not know (yet) 33:05 - Did Miles Partain force the domestic men's scene to get better? Will he take a break after 2024? 43:21 - Which US men's or women's team has the best shot at the Gold in Paris? 47:50 - The future of the AVP, and the "how," about training groups 1:01:44 - Professional beach volleyball: investment vs trust in the product, and the Dinosaur tournament, is traveling to stops worth it? 1:15:22 - Content creation vs viewership, enlisting the best people for the positions 1:32:13 - About coach Placek 1:34:52 - Lightning Round

We've reached the penultimate week of the 2023 regular season, the week where we set the Thanksgiving table and learn, definitively, who is in and who needs what to qualify for the CIAC playoffs.  And, yes, your crack staff at GameTimeCT hunkered down with our slide rules and abacuses this weekend so that we may give you our best estimates on the latest scenarios heading into the final week and a half.  We also run down the latest top 10 poll and recap some of last week's big games, beginning with No. 9 Ansonia's 35-0 takedown of Oxford in an unbeaten NVL battle at Jarvis Stadium last week in, perhaps, a harbinger of playoff matchups to come.  We also recap the return of North Haven coach Tony Sagnella from his weekend suspension and sift through the ongoing soap opera in Monroe, where Masuk's seniors have risen about the controversy surrounding their coaching staff, to deliver an emphatic, playoff-qualifying victory over New Milford.  Don't miss our weekly shoutouts and a look at the upcoming NEPSAC Bowl championship games.  So join us, won't you? RUNDOWN 0:00 — Introduction 3:34 — Ansonia knocks off unbeaten Oxford, heads into Thanksgiving 9-0 9:42 — Week 11 GameTimeCT Top 10 Football Poll (Nos. 10-7) 11:38 — Sagnella returns from suspension for No. 6 North Haven 12:49 — Week 11 GameTimeCT Top 10 Football Poll (Nos. 5-1) 14:53 — 2023 Playoff Scenarios: Explaining CIAC's playoff formula and website issues 19:45 — Class S Playoff Scenarios (North Branford's huge win)  22:38 — Class SS Playoff Scenarios  25:16 — Class M Playoff Scenarios 26:35 — Class MM Playoff Scenarios (Cheshire in trouble) 32:22 — Class L Playoff Scenarios 34:22 — Class LL Playoff Scenarios (Can Fairfield Prep pull this off??) 43:20 — Seniors rise above the mess at Masuk 54:42 — SHOUTOUTS 1:03:43 — Suffield Academy upsets Cheshire Academy, NEPSAC Bowl matchups announced Learn more about your ad choices. Visit megaphone.fm/adchoices

We knew it was coming and here it is. The Alliance brass has interrupted the season with their latest call to remove Thanksgiving Day as Connecticut's crown jewel of sporting events. The idea: Dump a century's worth tradition in favor of a balanaced schedule based on class, 50% more (whyyy??) playoff teams, the ending the season before the wrestling mats get rolled out. A win for 12 schools' winter programs and 24 middling football teams, perhaps. Naturally, our intrepid podcast hosts have their own opinions on this latest move to remove Thanksgiving Day football, and they lay out their pros and cons of this budding tradition of yearly CT high school football playoff ideas. This week's special guest star is none other than JOE STOCHMAL, the only coach Oxford's ever known. His program is 8-0 for the first time and on pace to reach the state playoffs for the first time, even as they head to Jarvis Stadium to face unbeaten NVL rival Ansonia on Thursday night.  Stox gives us the lowdown on his program and, as a member of the CIAC football committee, shows us the roadmap on how playoff proposals become CIAC law. He also gives us his personal opinion on the Alliance proposal.  In the meantime, check out highights from a handful of Week 9's biggest games, including: No. 3 Maloney rallying past Southington; North Haven, sans coach Tony Sagnella, knocking off Matt Jeffery-less No. 2 Cheshire; and, finally, Choate's 17-14 victory over Avon Old Farms in the most anticipated Prep school game of the year, since last year. It's a jam-packed and long episode. But, with this last full week of non-Thanksgiving football, why not spread our wings a bit? So join us, won't you? RUNDOWN  0:00 — Introduction. 2:00 — Turkey Day games in September?? No, Thanks: The Alliance playoff proposal. 20:07 — The Week 10 GameTimeCT Top 10 Poll (Nos. 10-8) 21:30 — Sagnella-less North Haven takes out Jeffery-less No. 2 Cheshire 28:33 — GTCT Top 10 Poll (Nos. 5-1) 29:55 — No. 3 Maloney rallies, holds off Southington 37:09 — Bunnell knocks off Notre Dame-Fairfield 38:27 — CLASS S playoff update:  39:40 — CLASS SS playoff update:  41:44 — CLASS M playoff update:  44:19 — CLASS MM playoff update:  46:36 — CLASS L playoff update 48:28 — CLASS LL playoff update 54:34 — Interview with OXFORD coach JOE STOCHMAL 1:31:24 — Shoutouts! 1:38:40 — Choate beats Avon Old Farms in anticipated NEPSAC unbeaten showdown 1:46:07 — Wrapping up Learn more about your ad choices. Visit megaphone.fm/adchoices

Week 5 is upon us and to celebrate we have not one but two great guests this week as Benny from the block & fan favourite Chris Milner from the NVL joins Nat to preview the games & also chat a bit of daily fantasy. The guys start though with the slate on Sunday & each pick the game they are most excited about inc. Texans/Falcons, Bills/Jags & Cowboys/49ers before focusing on the teams that everyone should be concerned about for the rest of the season & you won't want to miss this as Nat goes on a 4 minute monologue about the Pats. If this wasn't enough Chris picks a fantastic show team for the Draft Kings listener league & of course we have the 57 seconds challenge as well! Check out the brilliant National Vintage League website for some sensational vintage items: https://nationalvintageleague.com/ You can enter your own team for free by signing up to DraftKings (dkng.co/NatCoombsShow) and using the promo code NC Show. Each week we have free to enter contests with prizes courtesy of DraftKings and USA Sports! 18+ Be Gamble Aware. The highest score from the first 3 weeks gets the tickets so make sure you enter your team!  Play against your show faves in our Draft Kings listener league where you can win merchandise & tickets - https://www.draftkings.co.uk/leagues/nfrs2hi8/contests/upcoming - Eligibility Restrictions Apply. See website for details. 18+ only. www.BegambleAware.org® The Nat Coombs Show is proudly partnered by USA Sports: the best place to buy all of your American sports merch this side of the pond. From football, hockey, basketball, baseball and much more - there's something for everyone! Rep your favourite teams and get 20% off your order using discount code ‘NCSHOW' today. USA Sports merch for the fans, by the fans. Check out USA Sports: https://www.usasports.co.uk/ Subscribe to our Brand New YouTube channel for loads of video and bonus footage from the show - https://www.youtube.com/@TheNCShow Other show socials: Twitter: https://twitter.com/thencshow Facebook: https://www.facebook.com/thencshow Instagram: https://www.instagram.com/thencshow/ Tik Tok: https://www.tiktok.com/@thencshow?lang=en Threads: https://www.threads.net/@thencshow

Nat & Ben get into all latest from across the NFL including the sale of the Commanders, the Saquon dilemma and the Lions new WR before Ben looks ahead at the 2024 Draft and names his Top 3 Receivers that he predicts will go first round. The boys look ahead to the Notre Dame/Navy game in Dublin - Road Trip!- and are joined by friend of the show Chris Milner of NVL fame who nominates his fave football movie into The NC Show HOF. In association with our great friends at College Football Ireland - https://collegefootballireland.com/ Join Nat, Ben and lots of your show faves and NC Show fam in our listener league. Win prizes each week, including merch and NFL UK tickets. Eligibility Restrictions Apply. See website for details. 18+ only. www.BegambleAware.org®

Die Diagnose "Diabetes Typ 2" wird vor allem in der Hausarztpraxis gestellt. Die Aktualisierung der Nationalen Versorgungsleitlinie (NVL) trägt dem Rechnung. Dr. med. Kai Florian Mehrländer ist Koautor der NVL und selbst Hausarzt. Im Gespräch erklärt er, welche Änderungen sich durch das Update im Praxisalltag ergeben und wie eine wertschätzende Kommunikation mit Betroffenen gelingt.

NVL CÓ ĐỔ NỢ? RỦI RO VỚI TRÁI PHIẾU BĐS? LIỆU CÓ CON ĐƯỜNG SÁNG? Nhiều bạn hữu cho rằng NVL đã “lột xác” từ blue-chip thành penny, vậy áp lực từ NVL lên thị trường bất động sản và hệ thống các ngân hàng là gì? Chúng ta sẽ phân tích chi tiết đến từng con số dựa trên báo cáo tài chính gốc của NVL, các báo cáo phân tích NVL cùng phần mềm Kungfu Stocks Pro,... và xem liệu có con đường sáng nào dành cho NVL không nhé bạn hữu.

We have show debut for the brilliant comedian, NFL expert, entrepreneur & Channel 5 broadcaster Chris Milner! Nat & Chris discuss Chris' route into the NFL & how he set up the brilliant NVL (2:30), Baker's move to LA (25:30), his love for Washington and how it came about (31:30), the NFC East race (38:40), Detroit's playoff chances (43:30), the Mike White v Zach Wilson debate (47:00) + much, much more.  National Vintage League Website: https://nationalvintageleague.com/ Think you know your football? Join our NC Show listener league on Draft Kings and play daily fantasy each week! Go up against Nat, Prop O, The Guru Sandrini & more of your show faves, pick a brand new team every week, win prizes....and the respect of your peers! Free to enter contest will feature throughout the season too! League Contests: http://dkng.co/NatCoombsShow DraftKings Contests: https://dkng.co/DKContests Follow the fellas on twitter:  Nat Coombs: https://twitter.com/NatCoombs National Vintage League: https://twitter.com/NVLTweets

Last week marked FIVE STRAIGHT YEARS OF SANDCAST! In five years, somehow, Tri Bourne and Travis Mewhirter haven't missed a single episode. So much has happened: - Travis made his first AVP main draw (then a bunch of international medals) - Tri battled an autoimmune disease...then became an Olympian...then a two-time Manhattan Beach Open champion - Tri had a kid, Naia Bourne, who makes many appearances on this episode - Travis got married to Delaney Knudsen, who has since won a bunch of big time matches and tournaments, including the USA Volleyball Queen of the Beach - Gabby Bourne produced her own movie, and is soon appearing in one with Ben Affleck and Matt Damon (!!!) - Our numbers have exploded, growing, on average, 30 percent per year - The NVL came and went - p1440 came and went - Volleyball World became a thing - The AVP was bought by Bally's - And a whole, whole lot more. We just got the full family together on this one and reflected on five years of podcasting, and the wild and surreal life changes that have come with it. Thank you all SO MUCH for being part of this wonderful journey. Cheers to five years, and cheers to five more SHOOTS! *** NEW BOOK ALERT!!! Travis Mewhirter and Kent Steffes just published a seminal work on the history of beach volleyball in their new book, Kings of Summer: The Rise of Beach Volleyball. Check it out on Amazon!! https://www.amazon.com/Kings-Summer-rise-beach-volleyball/dp/B0B3JHFKM7/ref=sr_1_1?crid=1WGJFWHPBGPQ2&keywords=kings+of+summer+book&qid=1658922972&sprefix=kings+of+summer+book%2Caps%2C1328&sr=8-1 We are FIRED UP to announce that we've signed on for another year with Athletic Greens! Stay healthy with Tri Bourne and Travis Mewhirter and get your greens today! https://athleticgreens.com/partner/d35ctoffer-strength/en?utm_source=podcast&utm_medium=influencer&utm_campaign=sandcast_d35ct__a3172__o27&utm_term=cac__a3172__o27&utm_content=sport__a3172__o27 We now have SANDCAST MERCHANDISE!! Rock the gear of your favorite podcast today! https://www.sandcastmerch.com/ If you want to receive our SANDCAST weekly newsletter, the Beach Volleyball Digest, which dishes all the biggest news in beach volleyball in one quick newsletter, head over to our website and subscribe! We'd love to have ya! https://www.sandcastvolleyball.com/ This episode, as always, is brought to you by Wilson Volleyball, makers of the absolute best balls in the game, hands down. You can get a 20-percent discount using our code, SANDCAST-20! https://www.wilson.com/en-us/volleyball Check out our book, Volleyball for Milkshakes, written by SANDCAST hosts Tri Bourne and Travis Mewhirter: https://www.amazon.com/Volleyball-Milkshakes-Travis-Mewhirter/dp/B089781SHB  

Welcome back to another edition of The Meat Grinder, your weekly inside look at high school football in Connecticut with GameTimeCT's Pete Paguaga and Sean Patrick Bowley. It was the Weekend of the Living Dead, in which a bunch of teams across the state got an early Halloween scare. Pete and SPB talk all about the great NVL showcase game won by Holy Cross rallying past Naugatuck on Thursday, plus East Hartford and Newington scares vs. No. 1 Southington and No. 3 Maloney, respectively. SPB also breaks down his trip to Granby Memorial for Cromwell/Portland's 14-7 victory and, his hectic Prep school Saturday, capped by the insane Avon Old Farms comeback that fell one agonizing point short against Choate in Saturday's NEPSAC showdown. Joining us for a chat this week is Killingly's 18-year head coach Chad Neal, who enjoyed a nice weekend off, but now gets back to business as he and his players get ready for a showdown with unbeaten Windham on Friday night. Coach Neal discusses this year's No. 4-ranked squad, and what it took to get his program playing at a championship level after a good 10-year struggle.

Welcome back to another edition of the Meat Grinder, your weekly inside look at high school football in Connecticut with Pete Paguaga and Sean Patrick Bowley. If Week 4 was the Alliance Love Fest, Week 5 was the return of the jilted lover, our forgotten friend, the league game. We start in the SCC where Hamden took out Fairfield Prep for the first time since 2009 and North Haven dropped No. 3 Shelton for its first win over the Gaels since 1994. In the NVL, Gilbert/Northwestern/Housy put a scare into Ansonia; Staples outlasted Trumbull in an FCIAC thriller; Berlin rallied to beat Bloomfield in the CCC and so much more. Though the SCC, FCIAC and SWC have bye weeks, league play continues this week. Chris Anderson, a two-time championship coach at Woodland and now the head coach at Naugatuck, joins us this week to discuss his league's big game against Tom Ligi and Holy Cross at Municipal Stadium on Thursday night. Anderson, now in his second year in command of the Valley powerhouse, gives us his take on the matchup and a look at his team as it attempts to repeat last year's successful run to the NVL championship and state playoffs. So join us, won't you? Listen to the Meat Grinder now on your favorite streaming service, but sure to check out the Live Action version of the show on Tuesday evenings.

Jeff Samuels is an American professional beach volleyball player, entrepreneur and coach. A former lacrosse and hockey player, he fell in love with the beach and has been doing it full-time, ever since. Starting his career with the NVL, he won newcomer of the year. He has since won a slew of open tournaments, regionally and on big stages, and remains a top player on the tour. A spokesperson for Hyundai for various branches, he spends a ton of time doing clinics for juniors and sometimes adults. David "Doc" Vander Meer is an American beach volleyball player. A defensive standout at Ball State university on the indoor scene, he quickly took his talents to the beach, where he remains a top player on the beach scene. His fifth-place finish in Atlanta a year ago was highlighted by upsetting the number one seed Dahlhauser and Lucena. Tune in, as we chat up "hands," chatting up with special guest, ref Dave Carson, the NCAA 16 team format, what we miss/not miss about the freeze, venues we would like the AVP to come back to, lighting rounds, taking care of your body, Phil's greatness, Doc's Atlanta upset, the best players on the world and domestic scene, advice to the next generation of male players, and MORE!