Podcasts about feynman

Episode: 2631 The Double Slit Experiment.  Today, a mysterious experiment.

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

A Nobel laureate's surprising secret to genius wasn't just intellect, but a playful defiance of norms. This audiobook summary reveals his unconventional path.

A Wall Street Journal story about a 17-year-old helping his family with financial decisions kicks off a much bigger Stacking Benjamins question: who should you actually trust with your money? Joe, Doug, Paula Pant, Jesse Cramer, and special guest Roger Whitney dig into where great advice comes from, why bad advice often comes from people who love you, and how to build a better filter before you act. Along the way, they talk books, podcasts, family advice, AI, confirmation bias, homebuying myths, index funds, retirement plans, and why "smart" isn't enough.What You'll Walk Away WithWhy Roger says "advice" has a high bar: real advice should apply to your specific life, not just sound smart in publicThe difference between information and advice -- and why confusing the two can lead you into troubleWhy books often beat random internet advice: they usually have more vetting, structure, and accountabilityHow well-meaning friends and family can still give terrible money advice when they speak confidently about things they don't really understandPaula's advice pyramid: avoid people who profit from outrage, be skeptical of people with no accountability, and seek sources with both expertise and vettingWhy AI can be useful as a sparring partner, but not as a substitute for your own thinking or fact-checkingThe danger of "always" and "never" advice: always buy a house, always max your 401(k), never finance a car, always buy index fundsWhy renting isn't automatically throwing money away -- and how the price-to-rent ratio can help you think more clearlyWhy maxing out your workplace retirement plan may not always be the right move, especially when tax flexibility, business investment, or other goals matter moreHow confirmation bias, present bias, and absolute certainty can fool you into believing your plan is stronger than it isWhat to look for in your personal board of directors: people you respect, people with a high signal-to-noise ratio, and people who are kind enough to tell you the truthWhy Roger says a kind person is better than a merely nice one when you need real feedbackWhy This Matters NowFinancial advice is everywhere: podcasts, books, TikTok, AI, coworkers, relatives, advisors, and confident strangers with strong opinions. The hard part isn't finding advice. It's knowing which advice deserves your attention. This episode gives Stackers a filter for separating useful guidance from noise before the wrong voice gets too close to their money.From the BasementJoe uses a Wall Street Journal piece about a teenage family financial advisor to launch a bigger card-table debate with Paula Pant, Jesse Cramer, and Roger Whitney. The crew builds a money-advice pyramid, debates which financial rules should be ignored, and explores when to trust yourself versus when to bring in your board of directors. Doug celebrates Art Linkletter with Game of Life trivia, Paula admits she's never played it, and OG's trivia lead might get a little more uncomfortable.Resources MentionedThe Wall Street Journal piece by Oyin Adedoyin about a 17-year-old helping his family with financial decisionsRoger Whitney -- The Retirement Answer Man podcastPaula Pant -- Afford Anything podcastJesse Cramer -- Personal Finance for Long-Term Investors podcastSeth Godin -- LinchpinThomas Stanley and William Danko -- The Millionaire Next DoorRobert Kiyosaki -- Rich Dad Poor DadRobert Cialdini -- InfluenceRichard Feynman -- Surely You're Joking, Mr. Feynman!Beth Kobliner -- referenced as an upcoming Afford Anything guestStacking Benjamins Newsletter, The 201 -- stackingbenjamins.com/201Stacking Benjamins YouTube channel -- youtube.com/stackingbenjaminsSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

durée : 00:05:10 - Les Matins de France Culture - par : Alexandra Delbot - Ce problème, imaginé par Richard Feynman il y a près de 50 ans, est au cœur de nombreuses décisions humaines. À partir des notes du physicien, une nouvelle étude dévoile une solution optimale et montre que des volontaires s'en approchent naturellement, sans résoudre aucune équation. - invités : Stefano Palminteri Chercheur en neurosciences cognitives. Chargé de recherche inserm dans l'équipe “Apprentissage par renforcement” du laboratoire de Neurosciences cognitives et computationnelles de l'Ecole normale supérieure de Paris. Vous aimez ce podcast ? Pour écouter tous les épisodes sans limite, rendez-vous sur Radio France

Struggling to scale your insurance agency without adding more chaos? Discover how embracing fun and authenticity can transform your business and life.In this fun episode, we sit down with Nicholas Sakha, a dynamic insurance agent from Las Vegas. We dive into the evolution of Nicholas's career, exploring how he leveraged social media to grow his agency and connect with clients. The conversation touches on the importance of balancing work and personal life, the power of authenticity in content creation, and the unexpected benefits of stepping back to gain perspective. Nicholas shares his journey of creating engaging content that resonates with audiences beyond the insurance world, emphasizing the value of humor and relatability. We also discuss the challenges and rewards of scaling an agency, the significance of maintaining a strong social presence, and the impact of personal stories in building connections.Key Takeaways:✔ Discover why your sales pipeline is leaking opportunities✔ Learn how top-performing agencies create predictable growth✔ Find out how better processes improve producer accountability✔ Understand what separates average agencies from elite performersJoin the elite ranks of P&C agents. Sign up for Agent Elite today and get exclusive resources to grow your agency!

I think this was one of my most enjoyable dialogues in our What's new series. Maybe Sabine and I are getting more used to each other's cadence and interests or maybe it was the subject matter. Either way, I think you will find this to be a fascinating and provocative discussion of science at the forefront, and at the not-so-forefront, because that science is interesting too!We began our discussion describing a new finding of a Giant Ring of galaxies billions of light years across in the sky. The key questions are: Is it real? And is it surprising? We both have slightly different takes on this.Next we described a new measurement of the strength of gravity on scales from 80 to 800 million light years in distance. And guess what? Gravity falls off just like Newton predicted! This may seem like a big yawn, but one of the most popular models that claims to do away with dark matter would imply that Gravity would fall off differently on these scales. Does this new result kill that idea? Stay tuned.Microsoft, which has cried wolf a number of times so far when it comes to something called Majorana qubits as the basis of a new viable quantum computer just published a new paper claiming they finally have it. Sabine and I discuss why we are both still skeptical, but why the effort is worth it.Next, CERN, the large European particle physics laboratory, and the world particle physics community seem to have converged on plans for building a huge new accelerator in the current CERN site.. this time involving an underground ring 91 km in circumference, in which electrons and positrons would collide to explore the detailed properties of the Higgs particle. Is the effort worth it? Again, Sabine and I have slightly different takes on this.Fusion power, which we have talked about in a number of earlier episodes, continues to tempt humanity with the promise of unlimited energy. Many people, myself included, have tended to argue that fusion seems to be 25 years in the future, and may always be 25 years in the future. But many new efforts are underway, so who knows. Unfortunately, a group of economists has analyzed fusion in the context of other large energy programs and have argued that even if we can achieve it, it may not be as economically viable as many claim. Finally, one day Richard Feynman went to a Thai restaurant with his young companion Ralph Leighton, and wondered what he should order. Should it be the same old dish he loved or something new. An equation filled napkin later, and he had the answer. Fifty years later some cognitive scientists resurrected Feynman's napkin and explained it, and argued it might have important implications in other social situations. Such is the power of science.As always, an ad-free video version of this podcast is also available to paid Critical Mass subscribers. Your subscriptions support the non-profit Origins Project Foundation, which produces the podcast. The audio version is available free on the Critical Mass site and on all podcast sites, and the video version will also be available on the Origins Project YouTube. Get full access to Critical Mass at lawrencekrauss.substack.com/subscribe

This episode of the New Books Network's Entrepreneurship and Leadership channel features Richard Lucas in conversation with entrepreneur and community builder Ben Brabyn about Walkabout, a global movement that brings people together for monthly walks and open conversations. Walkabout began in Green Park, London, in June 2023 as a low‑friction alternative to venue‑based events and now runs in about 37 locations worldwide, welcoming anyone who wants to join a friendly, curiosity‑driven walking group. Ben explains how Walkabout's simplicity—free, open, lightly structured—attracts a high proportion of multidisciplinary participants, many with PhDs, and how emergent collaborations have led to startups, investment, hiring, and pro bono work on “thorny” challenges like non‑compressible haemorrhage and electric vehicle battery fires. Inspired by Richard Feynman's habit of carrying a dozen long‑term problems in his back pocket, Walkabout offers participants an evolving set of shared challenges they can keep in mind and revisit whenever they learn something new, effectively serving as a living, collective version of “Feynman's 12 problems. A recurring theme is serendipity: Richard and Ben discuss how Walkabout exemplifies the kind of designed chance encounters that David Cleevely describes in his book “Serendipity: It Doesn't Happen By Accident,” and how Cleevely himself both influenced and later joined Walkabout events. Lessons learned include the power of radical welcome, the importance of not over‑optimizing for scale or vanity metrics, and the value of formats where multidisciplinary dialogue and unexpected connections can flourish. Ben and Richard also touch on Walkabout's business structure within Amitypath Limited, its use of platforms like Mighty Networks and LinkedIn, and Ben's broader journey from the Royal Marines and JP Morgan to founding crowdfunding platform BmyCharity and leading Level39.Links Ben Brabyn Linkedin Amitypath Interview with David Cleevely on the NBN about his book Serendipity About Richard Feynman's 12 problems Walkabout Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/new-books-network

This episode of the New Books Network's Entrepreneurship and Leadership channel features Richard Lucas in conversation with entrepreneur and community builder Ben Brabyn about Walkabout, a global movement that brings people together for monthly walks and open conversations. Walkabout began in Green Park, London, in June 2023 as a low‑friction alternative to venue‑based events and now runs in about 37 locations worldwide, welcoming anyone who wants to join a friendly, curiosity‑driven walking group. Ben explains how Walkabout's simplicity—free, open, lightly structured—attracts a high proportion of multidisciplinary participants, many with PhDs, and how emergent collaborations have led to startups, investment, hiring, and pro bono work on “thorny” challenges like non‑compressible haemorrhage and electric vehicle battery fires. Inspired by Richard Feynman's habit of carrying a dozen long‑term problems in his back pocket, Walkabout offers participants an evolving set of shared challenges they can keep in mind and revisit whenever they learn something new, effectively serving as a living, collective version of “Feynman's 12 problems. A recurring theme is serendipity: Richard and Ben discuss how Walkabout exemplifies the kind of designed chance encounters that David Cleevely describes in his book “Serendipity: It Doesn't Happen By Accident,” and how Cleevely himself both influenced and later joined Walkabout events. Lessons learned include the power of radical welcome, the importance of not over‑optimizing for scale or vanity metrics, and the value of formats where multidisciplinary dialogue and unexpected connections can flourish. Ben and Richard also touch on Walkabout's business structure within Amitypath Limited, its use of platforms like Mighty Networks and LinkedIn, and Ben's broader journey from the Royal Marines and JP Morgan to founding crowdfunding platform BmyCharity and leading Level39.Links Ben Brabyn Linkedin Amitypath Interview with David Cleevely on the NBN about his book Serendipity About Richard Feynman's 12 problems Walkabout Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode, we dive into the seismic shifts happening in the insurance world, led by industry veteran Justin Brock. From skyrocketing agency growth to the transformative power of AI, we uncover how agents can capitalize on these trends to scale faster and smarter.Justin Brock runs Bobby Brock Insurance and GoGuru, a high-growth agency specializing in Medicare, with over 500 agents and 50 employees. Justin's focus on innovation, content, and scaling strategies has made him a leader in the space.Join the elite ranks of P&C agents. Sign up for Agent Elite today and get exclusive resources to grow your agency!

-Crecimiento de agujeros negros supermasivos en el universo temprano (00:00)-Magnetocepción en palomas (10:30)-Agujeros negros de los Little Red Dots (34:30)-Propuesta de test experimental de ER=EPR usando la estructura fina del hidrógeno (47:30)-Los axiones crean singularidades en agujeros negros extremales (1:03:30)-Señales de los oyentes (1:10:30) Hosted on Acast. See acast.com/privacy for more information.

-Bacterias en la niebla (11:40)-Metales que flotan (32:30)-Meteoritos y el Infierno de Dante (58:30)Contertulios: Luisa Achaerandio, Ignacio Crespo, Borja Tosar, Gastón Giribet, Alberto Aparici, Francis Villatoro, Héctor Socas Hosted on Acast. See acast.com/privacy for more information.

-Ya a la venta Horizontes de la civilización (12:00)-Dimite el nuevo director científico del CNIO antes de tomar posesión (28:00)-El “problema del restaurante” de Feynman (52:00)Contertulios: Silvana Tapia, Luisa Achaerandio, Juan Carlos Gil, Ignacio Crespo, Borja Tosar, Francis Villatoro, Héctor Socas Hosted on Acast. See acast.com/privacy for more information.

In his final years, Richard Feynman's curiosity took him to some surprising places. We hear from his companions on the trips he took — and one he wasn't able to. (Part three of a three-part series originally published in 2024.)    SOURCES:  Alan Alda, actor and screenwriter. Barbara Berg, friend of Richard Feynman. Helen Czerski, physicist and oceanographer at University College London. Michelle Feynman, photographer and daughter of Richard Feynman. Cheryl Haley, friend of Richard Feynman. Debby Harlow, friend of Richard Feynman. Ralph Leighton, biographer and film producer. Charles Mann, science journalist and author. John Preskill, professor of theoretical physics at the California Institute of Technology. Lisa Randall, professor of theoretical particle physics and cosmology at Harvard University. Christopher Sykes, documentary filmmaker. Stephen Wolfram, founder and C.E.O. of Wolfram Research; creator of Mathematica, Wolfram|Alpha, and the Wolfram Language.   RESOURCES:  I Love My Wife..., directed by Ian Tierney (2020). Quantum Man: Richard Feynman's Life in Science, by Lawrence M. Krauss (2011). Perfectly Reasonable Deviations From the Beaten Track: Selected Letters of Richard P. Feynman, edited by Michelle Feynman (2005). The Pleasure of Finding Things Out, by Richard Feynman (1999). The Demon-Haunted World: Science as a Candle in the Dark, by Carl Sagan and Ann Druyan (1995). Genius: The Life and Science of Richard Feynman, by James Gleick (1992). The Quest for Tannu Tuva, by Christopher Sykes (1988) “What Do You Care What Other People Think?” by Richard Feynman and Ralph Leighton (1988). The Second Creation: Makers of the Revolution in Twentieth-century Physics, by Robert Crease and Charles Mann (1986). Surely You're Joking, Mr. Feynman!, by Richard Feynman and Ralph Leighton (1985). Fun to Imagine, BBC docuseries (1983).   EXTRAS:  “The Curious, Brilliant, Vanishing Mr. Feynman,” series by Freakonomics Radio (2024). Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

What happens when an existentially depressed and recently widowed young physicist from Queens gets a fresh start in California? We follow Richard Feynman out west, to explore his long and extremely fruitful second act. (Part two of a three-part series originally published in 2024.)   SOURCES: Seamus Blackley, video game designer and creator of the Xbox. Carl Feynman, computer scientist and son of Richard Feynman. Michelle Feynman, photographer and daughter of Richard Feynman. Ralph Leighton, biographer and film producer. Charles Mann, science journalist and author. John Preskill, professor of theoretical physics at the California Institute of Technology. Lisa Randall, professor of theoretical particle physics and cosmology at Harvard University. Christopher Sykes, documentary filmmaker. Stephen Wolfram, founder and C.E.O. of Wolfram Research; creator of Mathematica, Wolfram|Alpha, and the Wolfram Language. Alan Zorthian, architect.   RESOURCES: "Love After Life: Nobel-Winning Physicist Richard Feynman's Extraordinary Letter to His Departed Wife," by Maria Popova (The Marginalian, 2017). Quantum Man: Richard Feynman's Life in Science, by Lawrence M. Krauss (2011). The Pleasure of Finding Things Out, by Richard Feynman (1999). Genius: The Life and Science of Richard Feynman, by James Gleick (1992). "G. Feynman; Landscape Expert, Physicist's Widow," (Los Angeles Times, 1990). "Nobel Physicist R. P. Feynman of Caltech Dies," by Lee Dye (Los Angeles Times, 1988). The Second Creation: Makers of the Revolution in Twentieth-century Physics, by Robert Crease and Charles Mann (1986). Surely You're Joking, Mr. Feynman!, by Richard Feynman and Ralph Leighton (1985). Fun to Imagine, BBC docuseries (1983). "Richard P. Feynman: Nobel Prize Winner," by Tim Hendrickson, Stuart Galley, and Fred Lamb (Engineering and Science, 1965). F.B.I. files on Richard Feynman.   EXTRAS: "The Curious Mr. Feynman," by Freakonomics Radio (2024). Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Ready to eliminate the hidden problems slowing down your agency growth? In this episode, we delve into the challenges of lead generation, contact rates, and the impact of recent phone system changes on insurance sales. We explore practical solutions like managing phone numbers, leveraging AI, and optimizing dialing strategies to boost sales efficiency.Learn why clear expectations, measurable standards, and consistent coaching are the keys to building a high-performance insurance agency without the chaos.Join the elite ranks of P&C agents. Sign up for Agent Elite today and get exclusive resources to grow your agency!

From the Manhattan Project to the Challenger investigation, the physicist Richard Feynman loved to shoot down what he called “lousy ideas.” Today, the world is awash in lousy ideas — so maybe it's time to get some more Feynman in our lives? (Part one of a three-part series originally published in 2024.)   SOURCES: Helen Czerski, physicist and oceanographer at University College London. Michelle Feynman, photographer and daughter of Richard Feynman. Ralph Leighton, biographer and film producer. Charles Mann, science journalist and author. John Preskill, professor of theoretical physics at the California Institute of Technology. Stephen Wolfram, founder and C.E.O. of Wolfram Research; creator of Mathematica, Wolfram|Alpha, and the Wolfram Language.   RESOURCES: "How Legendary Physicist Richard Feynman Helped Crack the Case on the Challenger Disaster," by Kevin Cook (Literary Hub, 2021). Challenger: The Final Flight, docuseries (2020). Perfectly Reasonable Deviations From the Beaten Track: Selected Letters of Richard P. Feynman, edited by Michelle Feynman (2005). The Pleasure of Finding Things Out, by Richard Feynman (1999). Genius: The Life and Science of Richard Feynman, by James Gleick (1992). “What Do You Care What Other People Think?” by Richard Feynman and Ralph Leighton (1988). "Mr. Feynman Goes to Washington," by Richard Feynman and Ralph Leighton (Engineering & Science, 1987). The Second Creation: Makers of the Revolution in Twentieth-century Physics, by Robert Crease and Charles Mann (1986). Surely You're Joking, Mr. Feynman!, by Richard Feynman and Ralph Leighton (1985). "The Pleasure of Finding Things Out," (Horizon S18.E9, 1981). "Los Alamos From Below," by Richard Feynman (UC Santa Barbara lecture, 1975).   EXTRAS: "Exploring Physics, from Eggshells to Oceans," by People I (Mostly) Admire (2023). Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this episode, we cut through the BS around lead generation and show you how elite agencies win regardless of lead quality by having the right systems, follow-up, and discipline. If you're tired of chasing bad leads or wasting money, this is the no-fluff strategy guide you've been waiting for.In this episode:Why lead quality is less important than process and environmentThe critical role of response time, persistence, and follow-up cyclesHow elite agencies standardize scripts and roles for consistency and resultsThe economics of lead conversion and creating predictable revenue streamsThe discipline and operational discipline that separates winners from quittersJoin the elite ranks of P&C agents. Sign up for Agent Elite today and get exclusive resources to grow your agency!

Are high turnover and ineffective leadership draining your agency's culture and momentum? In this no-fluff episode, we break down how emotional burnout, unclear expectations, and toxic environments are killing your growth and reveal proven strategies to fix it fast. You'll discover how to build loyalty, create stability, and keep your team engaged with practical tactics like celebrating activity, defining clear career paths, and maintaining consistent leadership, so your agency can thrive without the chaos.Join the elite ranks of P&C agents. Sign up for Agent Elite today and get exclusive resources to grow your agency!

Most people use AI like a chatbot: one short prompt, a back-and-forth, and a mediocre output that gets worse the longer the thread runs. In this Content to Close episode, Richmond Taylor breaks down a smarter way to think about AI across the whole go-to-market motion. Richmond uses the Feynman technique to simplify go-to-market into three connected functions, sales is how you speak, marketing is how you look, and customer success is how you get the second date, and explains where AI can take over 80 percent of the work in each. He digs into why prompt engineering is the single skill that determines whether AI helps you or hallucinates on you, walks through the four prompt categories (system, user, developer, assistant), and explains why one big detailed prompt beats twenty short follow-ups every time. If you want a practical view of where AI fits inside a real business cycle, and how to stop wasting tokens on prompts that contradict themselves, this episode is worth your time.About RichmondRichmond Taylor played professional soccer until he was 26, then channeled that discipline into building skills across sales, marketing, and customer success. He now runs his own business in the AI automation and education space, working with clients from enterprise down to SMB, and is the founder of a startup built to make prompt engineering easier for non-technical users. Richmond's perspective is that AI is not a replacement for creativity, it is a force multiplier for anyone willing to learn how to communicate with it.Show Notes- Connect with Richmond on LinkedIn: https://www.linkedin.com/in/richmondbtaylor/- promptanything.ioText us what you think about this episode!

Rhiannon Ward, owner of Phipps Insurance Agency, is building more than just a business, she's creating impact by giving back to her community through the Allstate Foundation Helping Hands grant.In this episode, we break down why most teams don't fail because of people, they fail because of systems. From fixing messy onboarding to setting clear expectations and using data instead of emotion, Rhiannon shares what actually moves the needle in sales performance. If you've ever felt stuck blaming leads, team members, or results, this conversation will shift your perspective and show you what really needs to change.Join the elite ranks of P&C agents. Sign up for Agent Elite today and get exclusive resources to grow your agency!

We had the pleasure of sitting down with Michelle O'Connor, a seasoned insurance professional with over 25 years of experience, to talk about the ins and outs of growing a successful business. From mastering follow-ups and building consistent sales processes to leveraging technology and AI to better serve clients, Michelle shares practical insights that any entrepreneur or agent can apply.In this episode, she opens up about managing teams, implementing structure without losing flexibility, and creating systems that make business easier and more efficient.Join the elite ranks of P&C agents. Sign up for Agent Elite today and get exclusive resources to grow your agency!

Laurie Moroco is a leadership coach and speaker who helps women build confidence, communicate powerfully, and make bold decisions, because extraordinary lives begin with the courage to take action.In this episode, we unpack the real skills behind successful communication, emotional intelligence, leadership, and execution. From miscommunication in everyday conversations to leading teams, handling conflict, and creating a strong culture, this discussion highlights what truly makes the difference.We also explore why overthinking holds people back, how leaders can model behavior instead of just talking about it, and why mastering people, not just strategy, is the key to long-term growth.If you're looking to communicate better, lead stronger, and take action with more clarity, this episode will shift how you approach both business and life.Join the elite ranks of P&C agents. Sign up for Agent Elite today and get exclusive resources to grow your agency!

Laurie Moroco is a leadership coach and speaker who helps women build confidence, communicate powerfully, and make bold decisions, because extraordinary lives begin with the courage to take action.In this episode, we unpack the real skills behind successful communication, emotional intelligence, leadership, and execution. From miscommunication in everyday conversations to leading teams, handling conflict, and creating a strong culture, this discussion highlights what truly makes the difference.We also explore why overthinking holds people back, how leaders can model behavior instead of just talking about it, and why mastering people, not just strategy, is the key to long-term growth.If you're looking to communicate better, lead stronger, and take action with more clarity, this episode will shift how you approach both business and life.Join the elite ranks of P&C agents. Sign up for Agent Elite today and get exclusive resources to grow your agency!

By The Seat of Our Pants WFMU Playlist & Interactive Live Chat Fresh off of a few weeks away from home, we present a “catch-up” episode of our program, as we fly by the seat of our pants, and grab “whatever was lying around” to offer a “Feynman vs. Holmes” mash-up that is both fun … Continue reading By The Seat of Our Pants

Frank sits down with Aaron Cohen and Alex Schumacher to talk about Sticks and Stones: Holiday Special, a new comic centered on the Feynman family and their holiday misadventures. The conversation gets into how Passover, Hanukkah, and Purim shaped the book, why Ben makes such a strong lead, and how the team blends Jewish specificity with themes that feel familiar to anyone who grew up in a loud, loving family. Aaron and Alex also break down the book's comic strip DNA, the warmth behind its humor, and why this world has room to grow far beyond holiday stories. Timestamps and Topics 00:00 Introduction and welcoming Aaron Cohen and Alex Shoemaker 00:37 What Sticks and Stones: Holiday Special is about, the Feynman family, and why releasing during Passover feels right 02:12 Representation, family traditions, and how the story connects Jewish identity with universal family experiences 03:29 Visual inspiration behind the book, including Sunday newspaper strips, classic cartooning, and old-school comic energy 05:41 Family memories, Passover traditions, and how real holiday experiences shaped the tone of the story 07:37 Why Passover felt like the perfect setup for a mystery story 08:09 Comic strip storytelling, all-ages humor, and the influence of books the whole family can enjoy together 09:45 Ben's deeper motivation and why wanting a seat at the adult table gives the story emotional weight 11:09 Ben's oversized suit, kid noir energy, and how his design reflects a child trying to act bigger than he is 12:18 Why Ben became the center of the story and how his imagination drives the whole book 15:03 Expanding the Feynman family, new characters, and future story possibilities 16:26 Protecting family, immigrant community parallels, and why the book feels relatable beyond one specific culture 22:38 Ralph's coloring, visual warmth, and how the page design supports the story's emotional center 28:47 Future plans for Sticks and Stones beyond holiday stories 32:30 Final thoughts on Ben and the larger story potential of this world 32:54 Rapid fire questions, favorite characters, and what holiday the family should tackle next Key Takeaways Sticks and Stones: Holiday Special uses Passover, Hanukkah, and Purim to tell one connected family story. Ben works as a lead because he sees himself as the star of his own detective movie, which gives the book a playful inner voice. The creative team wanted the comic to feel deeply Jewish while still speaking to anyone who has experienced a big, chaotic family gathering. The visual style pulls from classic newspaper strips, animation, and old all-ages comics. Humor is treated as an act of love, not just a punchline, which gives the book its warmth. The team clearly sees this family as a long-term storytelling world, not just a one-off holiday concept. Future stories could move beyond holidays and into everyday family life, school stories, and more character-focused adventures. Memorable Quotes "There's something universal in specificity." "Ten people around a long table for two hours is a great place for a murder mystery." "What he really wants ultimately is to be respected and be a part of the family and to be at the adult table and be taken seriously." "You don't joke with someone and you don't gag with someone who you don't care about." Call to Action If you enjoyed this episode, make sure to subscribe, leave us a review, and share the episode on social media using #GeekFreaksPodcast. That support helps more listeners find the show and keeps these conversations going. Links and Resources GeekFreaksPodcast.com This is the source of all news discussed during our podcast. Check out Sticks and Stones: Holiday Special Follow Us Follow Geek Freaks for more interviews, reviews, and geek culture coverage. Instagram: @geekfreakspodcast Twitter: @geekfreakspod Threads: @geekfreakspodcast Facebook: Geek Freaks Podcast Patreon: Geek Freaks Podcast Listener Questions Have a question, guest suggestion, or topic you want us to cover in a future episode? Send it our way and let us know what you want to hear next. Sticks and Stones, Holiday Special, Aaron Cohen, Alex Shoemaker, comic books, indie comics, Jewish comics, Passover, Hanukkah, Purim, family comics, all ages comics, Geek Freaks Podcast

Bill Snow, mergers & acquisitions professional and author of Mergers & Acquisitions For Dummies, shares practical insights on business, marketing, and deal-making.In this episode, we break down what really matters, why execution beats overthinking, how marketing is simply creating opportunities to sell, what valuation actually means in real-world deals, and why building value attracts opportunities instead of chasing them. A no-fluff conversation on business, growth, and making smarter moves.Join the elite ranks of P&C agents. Sign up for Agent Elite today and get exclusive resources to grow your agency!

Continuing from quantum electrodynamics part 1, here we explore the mathematical machinery used to compute interactions between particles, including propagators, Feynman diagrams, cross sections. We then walk through a simple example calculation to illustrate how these tools are applied. I conclude with an introduction to the problem of divergent loop integrals and how these can be resolved using renormalisation. Recommended pre-listening is Episode 158: Quantum Electrodynamics Part 1.  If you enjoyed the podcast please consider supporting the show by making a PayPal donation or becoming a Patreon supporter. https://www.patreon.com/jamesfodor https://www.paypal.me/ScienceofEverything

We're honored to sit down with Kirk Chester, Principal Broker at Grit Insurance Group. He shares practical insights on building and scaling a business. Specializing in blue-collar industries across multiple states, Kirk focuses on tailored solutions that help businesses manage risk and grow sustainably.In this episode, the conversation explores the importance of execution over overthinking, the power of going niche to stand out, and how AI is changing the way businesses operate. It also touches on leadership, hiring, and building systems that support long-term growth.Join the elite ranks of P&C agents. Sign up for Agent Elite today and get exclusive resources to grow your agency!

Beau Vincent, an insurance agency owner, leadership coach, speaker, and host of ‘The Conviction of a Leader' podcast, joins the conversation to share the leadership mindset behind building a high-performing business. After growing Vincent Family Insurance from 300 to nearly 13,000 policies, Beau now helps business owners scale with a stronger culture, clearer leadership, and better systems.In this episode, the discussion explores what it really takes to build great teams, set higher standards, develop people, and create a culture where performance and accountability thrive. It's a practical conversation on leadership, growth, and building businesses that last.Join the elite ranks of P&C agents. Sign up for Agent Elite today and get exclusive resources to grow your agency!

In this episode, we sit down with Dean Bowen from Patriotic Insurance Group. He shares how he transitioned from blue-collar work into insurance and built a modern prospecting system using cold email, automation, and data. In this episode, he discusses producer development, why selling to friends and family isn't a sustainable strategy, and how younger agents can build credibility and win commercial clients in today's insurance market.Join the elite ranks of P&C agents. Sign up for Agent Elite today and get exclusive resources to grow your agency!

In this episode, Chris Cochrane dives into Apple’s $599 MacBook Neo – the cheapest Mac laptop ever made – and whether it spells trouble for Chromebook makers. He also covers Samsung’s CEO blaming AI for rising phone prices, Framework raising RAM prices for the third time in three months, Meta unveiling four custom AI chips, NVIDIA’s GTC 2026 conference preview, a billion-dollar bet against large language models, Microsoft’s game-changing Project Helix Xbox with native Steam support, Windows 11’s new Xbox Mode, and SpaceX gearing up for a critical Starship Flight 12 test. – Want to start a podcast? Its easy to get started! Sign-up at Blubrry – Thinking of buying a Starlink? Use my link to support the show. Subscribe to the Newsletter. Email Chris if you want to get in touch! Like and Follow Geek News Central’s Facebook Page. Support my Show Sponsor: Best Godaddy Promo Codes Get 1Password Apple MacBook Neo The lead story covers Apple’s MacBook Neo. It launched at $599 and marks the cheapest Mac laptop ever made. The device runs on the A18 Pro chip from the iPhone 16 Pro. Cochrane notes a solid market for students, casual users, and anyone who needs a reliable home laptop. However, he advises photographers and videographers to invest in a MacBook Air or Pro instead. The real question remains whether this kills Chromebook sales in education. Samsung CEO Blames AI for Price Hikes Cochrane tackles Samsung’s Galaxy S26 price increases. CEO TM Roh blamed AI infrastructure demand for the hikes. Meanwhile, DDR4 DRAM prices surged sevenfold in a single year. Cochrane points out the irony. Samsung manufactures memory chips, shifted production toward AI data centers, and now cites that same shortage to justify higher consumer prices. He calls the situation “a little shady” but appreciates the transparency. Framework RAM Prices Up Again The RAM crisis extends beyond phones. Framework raised RAM prices for the third consecutive time in three months. Cochrane reinforces advice from a recent episode. He urges listeners to buy now before prices climb further. Analysts project peak prices by mid-2026. The shortage could last through late 2027. Sponsor: GoDaddy Economy hosting $6.99/month, WordPress hosting $12.99/month, domains $11.99. Website builder trial available. Use codes at geeknewscentral.com/godaddy to support the show. Meta Unveils Four Custom AI Chips Cochrane reports on Meta’s four new MTIA chip generations. The company aims to reduce its dependence on NVIDIA by building custom silicon. The MTIA 300 is already in production. New generations will ship every six months through 2027. The chips are built on open-source RISC-V architecture and manufactured by TSMC. NVIDIA GTC 2026 Preview NVIDIA’s GTC conference starts Monday in San Jose. Jensen Huang promises “chips the world has never seen.” Rumored architectures include Rubin Ultra and Feynman. The keynote streams free at nvidia.com on Monday at 11am Pacific. Cochrane notes that while companies like Meta are building chips to escape NVIDIA, competition will eventually catch up. Yann LeCun’s AMI Labs Raises $1.03 Billion Former Meta AI chief Yann LeCun raised $1.03 billion for AMI Labs at a $3.5 billion valuation. It marks the largest European seed round in history for a company just four months old. LeCun is building “world models” that learn from physical reality rather than text. Backers include Jeff Bezos, NVIDIA, and Samsung. Cochrane notes both approaches to AI can coexist. Microsoft Project Helix Microsoft revealed Project Helix at GDC 2026. For the first time, an Xbox will natively support Steam and GOG. Cochrane sees it as both desperate and inevitable. The only reason to buy from the Xbox store would be exclusives. He notes this is a breath of fresh air after months of talk that the Xbox era was ending. Dev kits ship in 2027 with a consumer launch likely late 2027 or 2028. Windows 11 Xbox Mode Microsoft is rolling out Xbox Mode to all Windows 11 PCs in April. The full-screen controller-optimized interface works with Steam, Epic, and Battle.net. Cochrane sees it as the first half of Microsoft’s two-phase gaming strategy. Xbox Mode trains users now. Project Helix delivers dedicated hardware later. He asks whether Sony and Nintendo will follow in Xbox’s footsteps. SpaceX Starship Flight 12 SpaceX announced stacking complete for the next Super Heavy booster at Starbase. Flight 12 targets April and debuts V3 hardware with Raptor 3 engines. Orbital refueling remains the critical unknown for NASA’s Artemis III moon landing. SpaceX has a track record of delivering eventually, just never on Elon’s original timeline. The post Is the MacBook Neo a Chromebook Killer? #1860 appeared first on Geek News Central.

Host Dave Schlom is joined by California Institute of Technology professor Sarah Reisman for an in-depth conversation about teaching one of the most difficult subjects taught on any college campus: organic chemistry.

Brendan Keegan, a 6-time CEO and bestselling author known for scaling companies through transformational growth, shares insights on leadership, career development, and building resilient teams in a changing workplace.The conversation explores how mindset, presence, and mentorship influence professional success, why embracing failure fuels innovation, and how simplifying complexity creates room for better thinking and progress. From early leadership experiences to guiding large-scale organizations, the discussion highlights practical perspectives on overcoming self-doubt, supporting emerging talent, and fostering environments where growth can thrive.Join the elite ranks of P&C agents. Sign up for Agent Elite today and get exclusive resources to grow your agency!

We walk through the Four Streams of Leadership—reservoir, downstream, upstream, and side stream—and show how leadership is a continuous flow. Reservoir is self-management: values, habits, and the reflection that keeps you steady under pressure. Downstream is team and operations: assembling roles, setting standards, and maintaining momentum. Upstream is partnering with your boss and senior leaders: aligning priorities and preventing strategic drift. Side stream is collaborating with peers: building shared commitments and removing cross-team friction. When each stream runs clean, you move faster with fewer surprises.• reframing the Peter Principle as unpreparedness• replacing stories with explanatory frameworks and exercises• defining the four streams: reservoir, downstream, upstream, side stream• building a culture that holds when we are absent• habits to fill the reservoir: reading, audiobooks, feedback loops• composing teams with visionaries, implementers, and closers• interviewing for role fit through consistent depth• timing process for discovery versus reliability• making disagreement and commitment possible with a clear why• further reading influences: Popper, Feynman, Deutsch• where to learn more and get the bookHiring and team design get specific through three vital roles: visionaries who define the problem and direction, implementers who build the thing, and closers who ship it. Too many visionaries means swirl; too few closers means value never lands. We share interviewing tactics that probe for consistent depth across envisioning, building, and finishing, so you can place people where they thrive. Then we tackle the third rail—process. Early on, heavy process kills discovery; after product-market fit, light process kills reliability. We map the why, when, what, and how of process so your team can innovate without chaos and deliver without drift. Along the way, we unpack “disagree and commit” the right way: explain the why, or you'll get “disagree and resent.”If you're ready to trade fables for frameworks and build a culture that acts the right way when you're not in the room, this conversation is your field guide. Subscribe, share with a manager who just took the leap, and leave a quick review to tell us which framework you'll try first.

How do you really know if someone will succeed as a producer before you hire them? In this Mailbag episode, we discuss the real traits that separate average producers from the ones who consistently perform, stay longer, and fit the culture.We dive into how DISC profiles, values, and sales strength attributes reveal far more than interviews ever could, why the classic “high D, high I” assumption can backfire, and how stability and alignment often matter more than raw personality.You'll hear lessons learned from years of hiring, scaling agencies, and dealing with turnover plus how understanding how people think, communicate, and stay motivated can completely change the way you build a team.If you want fewer hiring mistakes, stronger culture, and producers who actually stick around, this episode gives you the framework to think differently about talent.Join the elite ranks of P&C agents. Sign up for Agent Elite today and get exclusive resources to grow your agency!

Is test prep a lifeline or a scam? Jessica Wynn reveals who's really cashing in on your SAT anxiety here on Skeptical Sunday!Welcome to Skeptical Sunday, a special edition of The Jordan Harbinger Show where Jordan and a guest break down a topic that you may have never thought about, open things up, and debunk common misconceptions. This time around, we're joined by writer and researcher Jessica Wynn!Full show notes and resources can be found here: jordanharbinger.com/1288On This Week's Skeptical Sunday:The test prep industry is a multi-billion-dollar machine built on manufactured anxiety — not better education. Companies exploit the fear that a single test determines your entire future, turning parental stress and student panic into a lucrative marketplace where confusion plus fear equals profit.The same corporations that create standardized tests often sell the prep materials to pass them — a staggering conflict of interest. It's vertical integration at its most cynical: they've engineered both the problem and the solution, and students pay on both ends.Standardized tests like the SAT don't predict college success as well as high school GPA does, and access to expensive prep widens inequality rather than leveling the playing field. Kids in the top 1% of income have a 1 in 4 shot at elite schools — kids in the bottom 20% have a 1 in 300 chance.Social media has supercharged test prep anxiety, turning studying into a performative competition. Students spiral comparing their materials and scores to strangers online, and prep companies profit without even advertising — the students do it for them through posts and affiliate links.You don't need to spend a fortune to prepare well. Start with official practice tests and free resources like Khan Academy, use proven techniques like spaced repetition and the Feynman method, and remember — one good resource used properly beats five expensive ones you never open.Connect with Jordan on Twitter, Instagram, and YouTube. If you have something you'd like us to tackle here on Skeptical Sunday, drop Jordan a line at jordan@jordanharbinger.com and let him know!Connect with Jessica Wynn at Instagram and Threads, and subscribe to her newsletters: Between the Lines and Where the Shadows Linger!And if you're still game to support us, please leave a review here — even one sentence helps! Sign up for Six-Minute Networking — our free networking and relationship development mini course — at jordanharbinger.com/course!Subscribe to our once-a-week Wee Bit Wiser newsletter today and start filling your Wednesdays with wisdom!Do you even Reddit, bro? Join us at r/JordanHarbinger!This Episode Is Brought To You By Our Fine Sponsors: HexClad: 10% off: hexclad.com/jordanBombas: Go to bombas.com/jordan to get 20% off your first orderWayfair: Start renovating: wayfair.comHomes.com: Find your home: homes.comThe President's Daily Brief: Listen here or wherever you find fine podcasts!See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

 Hiring doesn't have to feel like a gamble. In this Mailbag episode, we break down a simple 5-step hiring framework designed to remove guesswork, reduce unknowns, and help you consistently find the right people faster. We walk through why paid traffic creates a predictable candidate pipeline, how assessments help you see past interview “A-game,” why group interviews reveal real behavior, and how structured one-on-ones help you choose the best fit. The conversation also dives into onboarding with clarity, defining success paths, setting activity standards, and focusing on behaviors instead of just results, so new hires know exactly what winning looks like from day one. If you've ever hired out of desperation, struggled with turnover, or felt like your hiring process is reactive instead of intentional, this episode gives you a practical roadmap to build a repeatable hiring system that actually works.Join the elite ranks of P&C agents. Sign up for Agent Elite today and get exclusive resources to grow your agency!

In this episode, we explore the real mechanics behind catastrophe insurance, how underwriting discipline, reinsurance strategy, pricing cycles, and regulatory constraints shape the market more than hype or headlines. Featuring insights from SageSure Co-Founder, President & CEO Terrence McLean, who shares lessons from scaling a catastrophe-focused underwriting platform, maintaining carrier profitability, and navigating risk across volatile markets. A practical, operator-level discussion for agents, founders, and insurance leaders.The conversation breaks down where AI is genuinely useful in insurance operations, where it's overrated, and why agent relationships and trust-based distribution still win.Join the elite ranks of P&C agents. Sign up for Agent Elite today and get exclusive resources to grow your agency!

Most people “learn” by hoarding facts, which doesn't mean you understand them. Then you forget them all anyway... In this episode, learning stops being about cramming and starts being about curiousity. Using Richard Feynman's four-step technique, you'll find a mindset that will add joy into your learning whilst also making it effective. We unpack the learning pyramid, why lectures and highlights barely stick, and why embarrassment, curiosity and mild panic are actually your brain's upgrade buttons. By the end, you'll have a simple, repeatable system to turn any topic—from maths to marketing—into something you genuinely own, not just recognize. Turn any topic into a one-page “Feynman sheet” Use emotions (not more hours) to cement ideas Build a teaching habit that secretly becomes your study habit Hit play and turn your next confusing idea into something you could explain in your sleep. SPONSORS

In this episode, we sit down with Jack Wingate, an experienced small business owner and strategic leader, to unpack what truly drives long-term success in today's competitive business world. From outworking the competition and building disciplined systems to using automation without losing the human touch, Jack shares real-world insights that every entrepreneur and leader needs to hear.This conversation dives deep into leadership mindset, smart resource allocation, team productivity, business scalability, and why consistency and adaptability matter more than shortcuts. Whether you're growing an agency, launching a startup, or leading a team, this episode will leave you with practical strategies and a renewed drive to build something meaningful.Join the elite ranks of P&C agents. Sign up for Agent Elite today and get exclusive resources to grow your agency!

From Ancient Egypt to Leibniz... Brand‑new interview out with Robert Spekkens of the Perimeter Institute, one of the sharpest minds working on quantum foundations. In 2004, he constructed a classical toy theory where your maximum knowledge is always incomplete—and out popped the no-cloning theorem, teleportation, and interference effects Feynman deemed impossible to reproduce classically. Spekkens compares our situation to Egyptian hieroglyphs before Champollion: a category mistake where we treat quantum states as descriptions of reality when they actually describe knowledge of reality. If you're interested in the topics above, you'll love this podcast. As a listener of TOE you can get a special 20% off discount to The Economist and all it has to offer! Visit https://www.economist.com/toe SUPPORT: - Support me on Substack: https://curtjaimungal.substack.com/subscribe - Support me on Crypto: https://commerce.coinbase.com/checkout/de803625-87d3-4300-ab6d-85d4258834a9 - Support me on PayPal: https://www.paypal.com/donate?hosted_button_id=XUBHNMFXUX5S4 JOIN MY SUBSTACK (Personal Writings): https://curtjaimungal.substack.com LISTEN ON SPOTIFY: https://open.spotify.com/show/4gL14b92xAErofYQA7bU4e TIMESTAMPS: - 00:00:00 - Defining Quantum Innovation - 00:06:40 - Realism vs. Empiricism - 00:12:12 - Leibnizian Methodological Principle - 00:23:40 - Causal Explanations of Correlations - 00:30:24 - Epistemic Quantum States - 00:41:00 - Foil Theory Methodology - 00:54:00 - Causal Influence vs. Signaling - 01:07:27 - Thermodynamics and Ignorance - 01:15:00 - Conceptual Understanding in Physics - 01:21:00 - Philosophy of Physics Utility - 01:30:00 - Speckins' Toy Theory Origins - 01:40:13 - Perimeter Institute's Ambitious DNA - 01:52:00 - PBR Theorem Implications - 02:05:40 - Ontic Separability Assumptions - 02:17:40 - Hieroglyphs and Category Mistakes - 02:29:00 - Revolutionizing Modern Physics - 02:37:20 - Unscrambling Causation and Inference LINKS MENTIONED: Journals, papers, books: - https://www.rwspekkens.com - https://pirsa.org/speaker/Robert-Spekkens - https://arxiv.org/pdf/2507.01122 - https://arxiv.org/pdf/quant-ph/0401052 - https://arxiv.org/abs/0706.2661 - https://arxiv.org/abs/quant-ph/0406166 - https://arxiv.org/pdf/2207.11779 - https://amazon.com/dp/1108066488?tag=toe08-20 - https://www.jstor.org/stable/687269 - https://plato.stanford.edu/entries/qm-copenhagen/ - https://plato.stanford.edu/entries/identity-indiscernible/ - https://www.fourmilab.ch/etexts/einstein/specrel/specrel.pdf - https://plato.stanford.edu/entries/spacetime-holearg/ - https://www.sciencedirect.com/topics/mathematics/hidden-variable-theory - https://www.nature.com/articles/299802a0 - https://arxiv.org/pdf/2011.01286 - https://link.springer.com/article/10.1007/BF02058098 - https://arxiv.org/abs/2005.07161 - https://www.sciencedirect.com/topics/engineering/maxwells-equation - https://www.einstein-online.info/en/spotlight/equivalence_principle/ - https://perimeterinstitute.ca/ - https://amazon.com/dp/9810241054?tag=toe08-20 - https://journals.aps.org/pr/pdf/10.1103/PhysRev.47.777 - https://arxiv.org/abs/1111.3328 - https://www.smithsonianmag.com/history/rosetta-stone-hieroglyphs-champollion-decipherment-egypt-180980834/ - https://www.sciencedirect.com/science/article/abs/pii/S0160932707000282 Videos: - https://youtu.be/gEK4-XtMwro - https://youtu.be/YWbjI-QsH2E - https://youtu.be/fU1bs5o3nss - https://youtu.be/NKOd8imBa2s - https://youtu.be/6I2OhmVWLMs - https://youtu.be/Tghl6aS5A3M - https://youtu.be/HIoviZe14pY - https://youtu.be/bprxrGaf0Os - https://youtu.be/4MjNuJK5RzM - https://youtu.be/c8iFtaltX-s - https://youtu.be/9AoRxtYZrZo - https://youtu.be/uOKOodQXjhc - https://youtu.be/3mhctWlXyV8 - https://youtu.be/gsSJPLX-BTA - https://youtu.be/FFW14zSYiFY - https://youtu.be/HhWWlJFwTqs Learn more about your ad choices. Visit megaphone.fm/adchoices

In this insightful episode, we sit down with an entrepreneur, sales strategist, and creator of Racing Snail and Leaderboard Legends, Seth Preus. We explore leadership, motivation, sales productivity, accountability, and performance psychology. His work has transformed the approach of thousands of professionals to productivity and team engagement.From understanding intrinsic motivation and building accountability-driven cultures to using data intelligently, improving ROI, and creating systems that help teams stay consistent, focused, and inspired, this conversation unpacks what it truly takes to build high-performing teams that grow sustainably, lead with purpose, and deliver long-term results in business and life.Join the elite ranks of P&C agents. Sign up for Agent Elite today and get exclusive resources to grow your agency!

TWiV explains emergence of a neurovirulent double recombinant from the 'improved' nOPV2 in Uganda, and efficiency of viral entry determined whether cells are latently or lytically infected with cytomegalovirus. Hosts: Vincent Racaniello, Alan Dove, Rich Condit, and Brianne Barker Subscribe (free): Apple Podcasts, RSS, email Become a patron of TWiV! Links for this episode Support science education at MicrobeTV ASV 2026 Positions in Rosenfeld Lab (email) nOPV2 neurovirulent recombinant in Uganda (Nat Micro) 2023 IMB report on polio eradication Viral entry shapes HCMV latency establishment (Nat Comm) Letters read on TWiV 1291 Timestamps by Jolene Ramsey. Thanks! Weekly Picks Brianne – Dark Matter by Blake Crouch Rich – Sequoiadendron giganteum; Sequoia & Kings Canyon National Park Alan – The Murderbot Diaries book series, by Martha Wells Vincent – Surely You're Joking, Mr. Feynman! Listener Picks Rocky – Cheetah mummies found in cave (Nature, National Geographic) Intro music is by Ronald Jenkees Send your virology questions and comments to twiv@microbe.tv Content in this podcast should not be construed as medical advice.

In this episode, we are honored to sit with Sheppard Bowen, co-founder of EVER.PARTY and a former Farmers Insurance agency owner to talk about what actually drives long-term success in the insurance business. From consistency and lead generation to retention, tracking, and team motivation, this conversation breaks down the real levers behind sustainable growth. This episode is a must-listen for agents and entrepreneurs who want clarity, structure, and real growth strategies that work in the real world.Join the elite ranks of P&C agents. Sign up for Agent Elite today and get exclusive resources to grow your agency!

In this episode, we talk with Justin Thomas about how insurance agents can use digital advertising and funnels to create consistent, high-quality conversations without chasing cold leads. Justin shares his journey from insurance to building Insurance Advertising Masters, where he helps agents bring marketing in-house using Facebook and YouTube ads. We cover the impact of iOS changes, why traditional lead models fall short, the difference between P&C and life and health marketing, and why owning your traffic is critical for long-term success.Join the elite ranks of P&C agents. Sign up for Agent Elite today and get exclusive resources to grow your agency!

In this episode, we talk with Justin Thomas about how insurance agents can use digital advertising and funnels to create consistent, high-quality conversations without chasing cold leads. Justin shares his journey from insurance to building Insurance Advertising Masters, where he helps agents bring marketing in-house using Facebook and YouTube ads. We cover the impact of iOS changes, why traditional lead models fall short, the difference between P&C and life and health marketing, and why owning your traffic is critical for long-term success.Join the elite ranks of P&C agents. Sign up for Agent Elite today and get exclusive resources to grow your agency!

In this episode, we're joined by Jason Levine for a candid conversation about building, growing, and sustaining an insurance agency in an ever-changing market. Jason shares how his path into insurance wasn't planned, from early ambitions in aviation to stepping into the family business and discovering the strategic depth of the industry. With experience in enterprise-level agency planning, human resources, and VIP account management, he brings a risk-aware mindset shaped by years of evaluating financial exposure and maximizing outcomes for clients and businesses alike.Jason dives into the realities of agency growth, including the shift from captive to independent, the importance of understanding underwriting and carrier operations, and why serving existing clients well often drives stronger, more sustainable growth than constantly chasing new leads. He also speaks openly about hiring challenges, market volatility, and the importance of staying adaptable, building the right team, and leading with clarity in uncertain times. This episode offers practical insight, real talk, and perspective for anyone navigating leadership and growth in today's insurance landscape.Join the elite ranks of P&C agents. Sign up for Agent Elite today and get exclusive resources to grow your agency!

In this episode, we sit down with Eric, Accredited Accident Reconstructionist, Ohio Peace Officer, Marine Corps veteran, and founder of Crash Tech Reconstruction Services, to discuss the real-world risks of car accidents and the staggering costs of being underinsured. Eric shares insights from his nationally recognized reconstruction work, illustrating how one accident can lead to $600,000 in liability, why young adults and seniors are often at higher risk, and the importance of proper property damage and liability coverage.We also dive into the misconceptions around price versus value in insurance, explaining how professional guidance and thoughtful conversations can protect not just your assets, but your livelihood. Packed with real-life examples, expert advice, and actionable tips, this episode is essential for anyone looking to make informed insurance decisions and understand the true stakes on the road.Join the elite ranks of P&C agents. Sign up for Agent Elite today and get exclusive resources to grow your agency!

Kelly Donahue, a public speaker and consultant with over a decade of experience in the local insurance agency space, joins Insurance Dudes to break down what it really takes to build a profitable, future-ready insurance agency. The conversation explores leadership at the agency-owner level, creating a strong internal culture, and using systems instead of shortcuts to drive sustainable growth. From sales processes and account rounding to marketing, technology, and accountability, this episode highlights how agency owners can align their mission with smart business strategy, adapt to industry changes, and build teams that perform consistently while delivering long-term value.Join the elite ranks of P&C agents. Sign up for Agent Elite today and get exclusive resources to grow your agency!