POPULARITY
355 episodes with GML
90 episodes with GML
4 episodes with GML
2 episodes with GML
2 episodes with GML
2 episodes with GML
2 episodes with GML
2 episodes with GML
2 episodes with GML
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
This episode has been an absolute NIGHTMARE to upload. This is literally my 5th attempt lol. Today, on the 234th episode of the As The Raven Dreams podcast, we have 6 True Chilling stories. These stories come from the shadowy corners of reality, where everyday life takes an eerie twist & ordinary people experience the extraordinary. Today we will be diving into The terrors of being a rideshare driver and other scary stories on the road. 2 Of the stories in his episode are True Crime Writeups written by me, Raven Adams. Would you like to participate in the postcard exchange? It comes with a free ATRD Sticker! Just Send a post card to the following... Lucas PO BOX 8198 Rochester, MN 55903 If you enjoyed this episode, be sure to like or rate the podcast, and leave me a comment with your thoughts if the platform your on supports it! I upload episodes every 3 days, so there are 2 days between new uploads. The podcast consists of new scary story collections, Glitch in the matrix collections, and also what I call the "Dark Dreams" collections (which are older stories, remastered and layered with rain sounds). If you have a story to submit, would like to find where to listen to the podcast, or want to find me on social media platforms, all of that info can be found at https://www.astheravendreams.com You can also send stories into my subreddit (r/theravensdream) or email them to me at AsTheRavenDreams@gmail.com Want to check out some ATRD Podcast Merch? ➤ https://teechip.com/stores/astheravendreams Or for signed merch ➤ https://ko-fi.com/AsTheRavenDreams I wrote a novel, "The Insomniac's Experiment" by Raven Adams! Check it out on amazon (Or you can email me for a signed copy!) Join Patreon to get early access and support the Podcast! ➤ https://www.patreon.com/AsTheRavenDreams Check out my gaming channel with my pal Ghost_Ink ➤ @superNefariousBros On YouTube Thank you to all of the authors that have stories in todays episode... Charlie, GML, Syd&Dani, GL As Well As Any Author That Has Requested Anonymity. TimeStamps… Ad breaks after Story 1 & Story 3 1 ➤ 1:42 2 ➤ 11:20 3 ➤ 19:34 4 ➤ 34:33 5 ➤ 46:04 6 ➤ 52:40 ----- Disclaimer ➤ Episodes include a content warning for language and sensitive/disturbing content. Listener discretion is always advised. ALL Audio and visuals on this podcast are copyright of AS THE RAVEN DREAMS / RAVEN ADAMS and may not be duplicated, in any format. Bless This Mess. None of my audio is AI Generated, I am a real person reading real stories into a real microphone. Note: The podcast nor the host endorses any advertisements played during the podcast, ads are not chosen by ATRD or Raven Adams, they are chosen automatically by the advertisement systems by the platforms that host the podcast. I do not endorse, support, or promote any opinions or statements made in any adverts played during the show. #ScaryStories #UnexplainedMysteries #UberStories Learn more about your ad choices. Visit megaphone.fm/adchoices
Campaign donation limits look strict. Super PACs can make those limits feel almost meaningless. James Harrigan and Antony Davies join Josh Martens to debate who really controls American elections: donors, politicians, parties, or voters. How much does it cost to win a House or Senate seat? The Words and Numbers hosts explain individual contribution limits, candidate committees, political action committees, independent expenditures, and the unlimited spending available through super PACs. The conversation examines campaign finance, political incentives, the Thomas Massie race, outside spending, foreign influence, publicly funded elections, mandatory voting, gerrymandering, the Republican and Democratic duopoly, and the direct election of senators. The uncomfortable conclusion might be that campaign money is only a symptom. When government has enough power to sell favors, people will spend enormous amounts trying to control it. https://x.com/antonydavies https://x.com/JamesRHarrigan https://wordsandnumbers.org/ Chapters 00:00 James Harrigan and Antony Davies Join GML 02:00 The Thomas Massie Race and Outside Spending 03:30 What American Elections Really Cost 05:45 Campaign Money, Speech, and Property Rights 07:15 Are Voters Responsible for Selling Their Votes? 10:15 Unlimited Government and the Rise of Oligarchy 13:00 Donation Limits and the Super PAC Loophole 18:15 Political Spoils, Corporations, and Union Money 22:45 Libertarians Debate Public Election Funding 27:30 The Massie Race and Foreign Influence 31:15 Stupid Voters, Mandatory Voting, and the Party Duopoly 42:00 One Change That Could Fix American Elections Links Watch All Episodes: https://www.youtube.com/playlist?list=PLi78svKlBr_8o0dDOX8DxO_Wwxu6WYhhA Watch Host Favorites: https://www.youtube.com/playlist?list=PLi78svKlBr__Zu40RL7mWxCuOOe54zgy2 Join the Fed Haters Club @ https://www.goodmorningliberty.us/fedhatersclub Join GML: https://joingml.com [Martens Minute]: https://martensminute.podbean.com/ All links @ gml.bio.link Subscribe to Good Morning Liberty, like the video, and tell us who deserves more blame: donors, politicians, or voters. Share this conversation with someone who thinks contribution limits solved campaign finance. Please also leave GML a rating and review on Apple Podcasts or Spotify.
The race for Minnesota House 44B just got interesting. No Republican on the ballot, a long-time Democrat incumbent, and TJ Hawthorne running as a liberty-minded independent. TJ Hawthorne joins Good Morning Liberty to talk about his campaign, why he is running independent, and how he wants to bring Defend the Guard, family court reform, emergency power limits, and occupational licensing reform to the Minnesota Legislature. This conversation covers Minnesota politics, independent candidates, libertarian ideas, small-government policy, Defend the Guard, National Guard deployments, COVID-era overreach, red tape, food shelves, local community action, and why state programs often create the incentives they claim to fix. TJ for Minnesota — Independent Candidate Chapters: 00:00 TJ Hawthorne returns to GML 03:30 Why the race opened up 06:00 Running independent without dropping liberty 09:15 A heads-up race in MN 44B 10:45 Defend the Guard and war powers 15:45 The biggest issues in TJ's platform 16:15 Family court and incentives 23:30 What Defend the Guard means 32:30 Emergency powers and COVID overreach 34:30 Red tape, state boards, and entrepreneurs 42:30 Licensing as the one liberty change 48:45 How to find TJ's campaign Links: Watch All Episodes: https://www.youtube.com/playlist?list=PLi78svKlBr_8o0dDOX8DxO_Wwxu6WYhhA Watch Host Favorites: https://www.youtube.com/playlist?list=PLi78svKlBr__Zu40RL7mWxCuOOe54zgy2 Join the Fed Haters Club @ https://www.goodmorningliberty.us/fedhatersclub [Martens Minute]: https://martensminute.podbean.com/ All links @ gml.bio.link Subscribe, like, comment, share, and leave a rating or review on your podcast app.
Congress just passed a massive bipartisan housing bill. That should make your wallet nervous. Nate and Chuck break down the Road to Housing Act, why Elizabeth Warren loves it, why most Republicans still voted for it, and why government "solutions" usually miss the incentives creating the problem in the first place. This Good Morning Liberty episode covers housing affordability, zoning laws, corporate landlords, institutional investors, mortgage rates, federal debt, modular homes, the SAVE Act, and whether the Founders would be pleased with America today. Chapters: 00:00 GML intro and birthday roast 03:45 What's in today's show 06:00 Would the Founders be pleased? 13:00 War powers and the Iran vote 15:30 The Road to Housing Act 20:00 Why housing is really expensive 24:00 Local zoning and federal grant carrots 27:30 Elizabeth Warren's housing pitch 29:30 Tim Burchett calls out the bill 32:15 The institutional investor scapegoat 45:00 The one good reform: modular homes 54:00 Trump, the SAVE Act, debt, and rates Links: Watch All Episodes: https://www.youtube.com/playlist?list=PLi78svKlBr_8o0dDOX8DxO_Wwxu6WYhhA Watch Host Favorites: https://www.youtube.com/playlist?list=PLi78svKlBr__Zu40RL7mWxCuOOe54zgy2 Join the Fed Haters Club @ https://www.goodmorningliberty.us/fedhatersclub [Martens Minute]: https://martensminute.podbean.com/ All links @ gml.bio.link Subscribe, like, comment, share, and leave a rating or review on the podcast app.
Trump says Iran is "finished," but the war pitch keeps changing. Now the Pentagon wants another $350 billion. In this Good Morning Liberty breakdown, Nate and Chuck react to Trump's latest Iran war comments, Fox News coverage, threats of more bombing, and the claim that America could walk in with a small group of soldiers and take over Iran. They also cover the new push for a $350 billion Pentagon add-on, the SAVE Act being attached to defense spending, and why Washington always finds more money for war while pretending to care about deficits. This one hits Iran, FISA, Trump, Pentagon spending, foreign policy, inflation, military budgets, and why both parties keep rewarding the same broken incentives. Chapters: 00:00 New background, GML intro, and Fed Haters Club 01:00 Charlie's politics update: Trump interview and FISA 03:00 Trump, Fox News, and the Iran war update 07:45 Bombing Iran until they sign the deal 09:30 Trump talks water, guns, and the Iranian people 12:15 "We could walk in there tomorrow" 17:45 Fake news, Iran, and the war narrative 24:45 Trump, Iraq, Venezuela, and "three months" 31:45 Trump wants another $350 billion for the Pentagon 38:45 Elon's debt warning meets the defense budget 43:15 Why Washington will not cut spending 45:00 How to actually persuade people Watch All Episodes: https://www.youtube.com/playlist?list=PLi78svKlBr_8o0dDOX8DxO_Wwxu6WYhhA Watch Host Favorites: https://www.youtube.com/playlist?list=PLi78svKlBr__Zu40RL7mWxCuOOe54zgy2 Join the Fed Haters Club @ https://www.goodmorningliberty.us/fedhatersclub [Martens Minute]: https://martensminute.podbean.com/ All links @ gml.bio.link Subscribe, like, comment, share, and leave a rating or review on your podcast app.
Send us Fan MailGoogle Marketing Live 2026 just happened - and the message wasn't about features. It was a warning. In this solo episode, Anya Chrisanthon - CCO at Anewgo - breaks down the biggest announcements from GML 2026 and translates them into plain language for home builder sales and marketing leaders.The headline nobody said out loud Google's Chief Business Officer opened with "I'm not exaggerating when I say we have made a decade's worth of innovation in the last year alone." That's not marketing speak. That's a warning for anyone still in wait-and-see mode.How your buyers are already searching differently AI Overviews now reaches 2.5 billion monthly users. AI Mode has passed 1 billion. Searches in AI Mode run three times as long as traditional searches. Your buyer is having a conversation with AI right now - and if your website doesn't give AI enough to work with, you're not in that conversation.The best ads must be answers Google's VP of Ads said it directly on stage: "The best ads must be answers." Google introduced a Business Agent for Leads - already being tested in real estate - where buyers can ask questions inside an ad and get answers pulled directly from your website. If your website doesn't have clear, specific answers to real buyer questions, AI has nothing to pull from.AI Brief: great news for smaller builder marketing teams AI Brief lets advertisers give Google's AI a creative brief in plain language and let it handle execution. The role of your marketing team is shifting from doing to directing - and that levels the playing field against national builders with big agencies.The Universal Cart - and why John Lee called this years ago Google's Universal Cart follows buyers across Search, Gemini, YouTube, and Gmail without losing their place. John Lee has been talking about this exact connected, agentic buyer journey for years. Anewgo's ChatGPT integration - where a buyer designs their home in ChatGPT and arrives on your website already identified and engaged - is the home building version of this. It exists today.Measurement finally grows up Google's Meridian marketing mix modeling tool is now inside Google Analytics 360. For the first time, builders have infrastructure to show leadership exactly which channels are driving sales - not just leads.
Meta has overtaken Google in ad budgets - and for ecommerce advertisers, that changes everything.It means Google is on the offensive. It means the pressure to split your budget between platforms is about to intensify. And it means that if you're not set up correctly on Google's AI surfaces right now, you're already losing ground to competitors who are.In this episode of Growing Ecommerce, Mike Ryan (smec's Head of Ecommerce Insights) and Chris share firsthand takeaways from GML 2026 — both the San Francisco and Dublin events — with unfiltered takes on what actually matters for your campaigns.What we cover:→ Google vs. Meta: The "War of the Titans" and why Google's messaging to advertisers is getting aggressive→ AI Max for Shopping: Why standard shopping campaigns may have limited eligibility in AI surfaces→ New AI-native ad formats: Conversational discovery ads, feed-based text ads, and why the line between shopping and search is collapsing→ Ask Advisor: A great idea — but oversold to an irresponsible degree (Mike's take)→ Universal Cart: Multi-retailer, cross-platform checkout — and Google's Amazon moment→ What the shift to agentic commerce means for how you monetize clicksCut through the hype. Know what to act on.
Recorded live at Google Marketing Live 2026, Phillip and eCommerce reporter Nicole Silberstein sit down with Ashish Gupta, VP & GM of Merchant Shopping at Google, who is behind the foundational commerce infrastructure powering the Shopping Graph and Universal Commerce Protocol. Gupta breaks down the GML announcements: UCP's expansion beyond shopping into hotels and food delivery, the multi-item Universal Cart that spans Search, Gemini, YouTube, and Gmail, and why the future of agentic commerce still depends on merchants nailing the fundamentals. A Shopper for Every Shopper Key takeaways: UCP is expanding beyond shopping into hotel bookings and local food delivery, giving every shopper their own personal shopper. The Universal Cart lets shoppers buy multiple items at once across Google surfaces, streamlining the buying experience as shoppers venture from inspiration to discovery and comparison. Merchants remains the seller of record no matter where the transaction is completed, tackling industry concerns about disintermediation. Conversational attributes enrich product feeds so AI can match nuanced shopper intent. Winning in agentic commerce starts with the fundamentals: feeds, first-party data, and UCP readiness. In-Show Mentions: Google Marketing Live 2026 and Google I/O 2026 Universal Cart & Universal Commerce Protocol (UCP) Further Reading: Google Imagines a Future Where Everyone Shops in Ads — A special edition of The Senses that distills the week's key announcements Episode 463: LIVE @ Google I/O: Universal Cart, Agentic Payments, and the Protocols Powering the Agent-Mediated Economy — Companion interview with Suresh Ganapathy Episode 464: LIVE @ Google Marketing Live: How Google Is Taking the Drudgery Out of Shopping— Companion interview with Nick Fox Google Solidifies Its Place in the AI Race — Insiders coverage of Google's UCP debut at NRF 2026, the foundation for this week's announcements [Member Brief] Agentic Commerce and the eCommerce Site's New Existential Crisis — How agentic platforms are reshaping the role of the branded eCommerce site Associated Links: Learn more about Check out Future Commerce on YouTube Check out Future Commerce Plus for exclusive content and save on merch and print Subscribe to Insiders and The Senses to read more about what we are witnessing in the commerce world Listen to our other episodes of Future Commerce Have any questions or comments about the show? Let us know on futurecommerce.com, or reach out to us on Twitter, Facebook, Instagram, or LinkedIn. We love hearing from our listeners! Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.
Spirit Airlines is gone. And Washington is already trying to blame everyone else. In this Good Morning Liberty breakdown, Nate and Chuck look at the collapse of Spirit Airlines, the blocked JetBlue merger, Elizabeth Warren's "Biden win for flyers," Robert Reich's airline consolidation argument, and the government's very normal habit of breaking things in the name of helping consumers. Was it fuel prices? Was it the Biden DOJ blocking the JetBlue Spirit merger? Was it bad business? The answer from GML: there is blame to go around, but the deeper problem is politicians and regulators having the power to jack with markets in the first place. Spirit Airlines, JetBlue, airline antitrust, Elizabeth Warren, Robert Reich, airline consolidation, consumer prices, and the myth of government-managed competition all collide in this one. Chapters: 00:00 Good Morning Liberty intro 00:01 Spirit Airlines is done 00:03 The left and right fight over blame 00:06 Both sides are a little bit right 00:07 The DOJ blocking the merger 00:09 Spirit CEO on the JetBlue offer 00:11 Was the merger actually "illegal"? 00:14 Sean Duffy, Biden DOJ, and competition 00:16 Government got involved, travelers lost 00:20 Robert Reich's airline argument 00:27 Airline deregulation and cheaper airfare 00:40 Elizabeth Warren's "win for flyers" ages badly Watch All Episodes: https://www.youtube.com/playlist?list=PLi78svKlBr_8o0dDOX8DxO_Wwxu6WYhhA Watch Host Favorites: https://www.youtube.com/playlist?list=PLi78svKlBr__Zu40RL7mWxCuOOe54zgy2 Join the Fed Haters Club @ joingml.com All links @ gml.bio.link Subscribe, like, comment, and share. Then leave a rating and review on the podcast app because unlike Spirit, we are still accepting baggage.
Spirit Airlines is gone.And Washington is already trying to blame everyone else. In this Good Morning Liberty breakdown, Nate and Chuck look at the collapse of Spirit Airlines, the blocked JetBlue merger, Elizabeth Warren's "Biden win for flyers," Robert Reich's airline consolidation argument, and the government's very normal habit of breaking things in the name of helping consumers. Was it fuel prices? Was it the Biden DOJ blocking the JetBlue Spirit merger? Was it bad business? The answer from GML: there is blame to go around, but the deeper problem is politicians and regulators having the power to jack with markets in the first place. Spirit Airlines, JetBlue, airline antitrust, Elizabeth Warren, Robert Reich, airline consolidation, consumer prices, and the myth of government-managed competition all collide in this one. Chapters:00:00 Good Morning Liberty intro00:01 Spirit Airlines is done00:03 The left and right fight over blame00:06 Both sides are a little bit right00:07 The DOJ blocking the merger00:09 Spirit CEO on the JetBlue offer00:11 Was the merger actually "illegal"?00:14 Sean Duffy, Biden DOJ, and competition00:16 Government got involved, travelers lost00:20 Robert Reich's airline argument00:27 Airline deregulation and cheaper airfare00:40 Elizabeth Warren's "win for flyers" ages badly Watch All Episodes: https://www.youtube.com/playlist?list=PLi78svKlBr_8o0dDOX8DxO_Wwxu6WYhhAWatch Host Favorites: https://www.youtube.com/playlist?list=PLi78svKlBr__Zu40RL7mWxCuOOe54zgy2Join the Fed Haters Club @ joingml.comAll links @ gml.bio.link Subscribe, like, comment, and share. Then leave a rating and review on the podcast app because unlike Spirit, we are still accepting baggage. Become a supporter of this podcast: https://www.spreaker.com/podcast/heartland-newsfeed-radio-network--2904397/support.
Thomas Massie's money bomb is taking off, and for Nate, that race matters more than most presidential drama. Why? Because if constitutional conservatives and liberty-minded reps can be crushed for refusing to rubber stamp spending, war, and party-line garbage, that sends a brutal message to anyone thinking about standing on principle. Then the conversation turns hard toward Iran. Nate and Chuck break down the messaging shift from "no war" to "military operation," the open talk about dodging congressional approval, the media war hype, and the very real fear that "no ground troops" could turn into the exact opposite. They also hit the hypocrisy around "No Kings" protests, why the propaganda language feels so familiar, and how quickly the legal and moral lines get blurred when power wants what it wants. It's blunt, skeptical, and very GML. Follow and subscribe wherever you listen. Leave a rating and review, join the Fed Haters Club at joingml.com, and grab everything else at gml.bio.link. 00:00 Intro + Massie money bomb starts 02:00 Why the Massie race matters more than people think 06:30 No Kings protests and anti-Trump reaction 09:30 Ground troop rumors and "weeks not months" war talk 12:30 Mixed signals on Iran as damage continues 15:15 Trump says "military operation" for legal reasons 21:15 Trump pushes Mark Levin and the ground troops case 24:30 Mark Levin clip on troops, uranium, and escalation 31:00 "Greatest military campaign" argument gets shredded 38:15 Polymarket bet on US ground troops entering Iran 41:15 Pentagon ground-ops report and congressional authorization 43:00 War Powers Act, imminent hostilities, and gaslighting 47:15 Outro, comments, Fed Haters Club, and Massie update
Thomas Massie's money bomb is taking off, and for Nate, that race matters more than most presidential drama. Why? Because if constitutional conservatives and liberty-minded reps can be crushed for refusing to rubber stamp spending, war, and party-line garbage, that sends a brutal message to anyone thinking about standing on principle. Then the conversation turns hard toward Iran. Nate and Chuck break down the messaging shift from "no war" to "military operation," the open talk about dodging congressional approval, the media war hype, and the very real fear that "no ground troops" could turn into the exact opposite. They also hit the hypocrisy around "No Kings" protests, why the propaganda language feels so familiar, and how quickly the legal and moral lines get blurred when power wants what it wants. It's blunt, skeptical, and very GML. Follow and subscribe wherever you listen. Leave a rating and review, join the Fed Haters Club at joingml.com, and grab everything else at gml.bio.link. 00:00 Intro + Massie money bomb starts02:00 Why the Massie race matters more than people think06:30 No Kings protests and anti-Trump reaction09:30 Ground troop rumors and "weeks not months" war talk12:30 Mixed signals on Iran as damage continues15:15 Trump says "military operation" for legal reasons21:15 Trump pushes Mark Levin and the ground troops case24:30 Mark Levin clip on troops, uranium, and escalation31:00 "Greatest military campaign" argument gets shredded38:15 Polymarket bet on US ground troops entering Iran41:15 Pentagon ground-ops report and congressional authorization43:00 War Powers Act, imminent hostilities, and gaslighting47:15 Outro, comments, Fed Haters Club, and Massie updateBecome a supporter of this podcast: https://www.spreaker.com/podcast/heartland-newsfeed-radio-network--2904397/support.
1-27-26 Tonight we're talking with Ryan Carlson again. He had a lot of information, and we just ran out of time last time! Ryan is a Colorado mead maker who has been making mead for quite a while. Those of you who have listened to the early GML episodes may remember him. He has been quiet publicly for a few years, dealing with personal things, but during that quiet time, he's also been doing a lot of learning and experimentation to deepen his mead making expertise. I'll just let Ryan tell you himself: Hi, my name is Ryan. Some of you will know me from the mead world going back a long way. Some of you won't know me at all — and that's just fine. I was very active in mead for many years, right up until around 2020. Then life hit hard. Multiple things at once. Survival mode. For about five years I was mostly just circling the drain and trying to stay upright. Things are finally closer to normal now, and I guess you could say I'm back in the saddle. I've been a successful meadmaker for a long time. I'm a certified judge. I've taught hundreds of people — maybe more — how to make good mead. I love teaching, but I'm picky about it. I only teach what I've lived. I only teach what I can prove. If someone wants receipts, I can bury Mount Rushmore with them. I also teach mead in a way most people have never experienced. From here forward, I'll be approaching things a little differently. We're going to “get small.” We're going to pretend we're a single-celled yeast and jump down inside Mead City. We'll look at fermentation from the inside of the vessel outward and change our perspective completely. Mead is going to do what mead does. Yeast is going to do what yeast does. When we actually understand the science and biology, our job isn't control — it's assistance. We learn when the window opens, and when it does, we can hand the yeast exactly what they need so they can do the job they were already built to do. Most of the time, when people don't understand what's happening, they get in the way. They step on the yeast without realizing it. They try to force outcomes that biology simply doesn't allow, and they miss the chance to co-create. My goal is to help raise everyone's bar by changing perspective first, then layering in real science and real biology — not folklore, not wives' tales, and not parroted bad science. If you want to understand mead well enough that you don't have to beg for recipes anymore — recipes that often produce mediocre results at best — then this is a good place to sit down with us and learn the craft the way it's actually lived. To listen live, you can find us on Youtube, Twitch, X (Twitter), and Facebook on the Gotmead Page. On our new platform, chat is part of the podcast! Just comment from wherever you are watching, and we'll see it!! If you'd like to call in, we can get you a link to come on! Twitch: https://www.twitch.tv/meadwench YouTube: YouTube: https://m.youtube.com/@Gotmead X(Twitter): https://x.com/RealGotMead Facebook: https://www.facebook.com/GotMead Instagram: https://www.instagram.com/GotMead JOIN CHAT ON DISCORD: https://discord.gg/zEKNujQTtM Listen in! This player will show the latest episode: Sponsor: Look no further than Honnibrook Craft Meadery in Castle Rock, Colorado, for your go-to destination for wonderful, light, and refreshing mead! We have 20 meads on tap and four seasonal mead slushees. Go to honnibrook.com for review our tap list, upcoming events and to order online! If you want to ask your mead making questions, you can send us a question via email, join to ask a question on the show, or via X @realGotMead and we'll tackle it online! The show runs from 9PM EDT/6PM PDT (United States) for about 2 hours every other Tuesday starting Jan 13, 2026. To join live, you can use this link, and here are instructions on how to join in. Once you enter the waiting room, we get a notification and will bring you in! Upcoming Shows Feb 10 - Roger Wanner - W A Meadwerks - New York Show links and notes Let There Be Melomels by Rob Ratliff The Big Book of Mead Recipes by Rob Ratliff Let There Be Session Meads by Rob Ratliff Upcoming Events Jan 29 - Valhalla Meadery, Bozeman, MT - Live Music by Bozeman Area String Jams Jan 29 - Bardic Wells Medery, Montague, MI - Henna Party by Bohemian Awakenings Jan 29 - Dancing Skeleton Meadery, Sepulpa, OK - Karaoke Night Jan 30 - Manic Meadery, Crown Point, IN - Paint and Drink: First Snowflake Jan 31 - St. Ambrose Meadery, Beulah, MI - Barefoot Music party Feb 5 - Dancing Skeleton Meadery, Sapulpa, OK - Trivia Night Feb 6 - Red's on 7th, Delavan, WI - Meads and Masterpiece - mead tasting and wine glass painting Feb 6 - Manic Meadery, Crown Point, IN - Grown Up Book Fair Feb 6 - Chubby Cheeks Meadery, Temecula, CA - Halo Mega Bowl LAN party Feb 8 - Clear Skies Meadery, Rockville, MD - Acoustic Jam with Mike Rocke Feb 8 - Drinking Horn Meadery, Flagstaff, AZ - Beeswa Candle Making Valentine Workshop Feb 13 - Starrlight Meadery at the Honeysuckle Tea House, Chapel Hill, NC - Valentines Mead Tasting Feb 13 - Clear Skies Meadery, Rockville, MD - Make Your Own Mosaic Heart Feb 13 - Hex Meadery, Kaukauna, WI - Anniversary Party Feb 13 - Manic Meadery, Crown Point, IN - Paint and Drink: Valentine Animal Feb 14 - Bee Immortal Mead, Round Rock, TX - Valentines Mead Workshop Feb 14 - Southern Origin Meadery, Canon, GA - Couples Painting Each Other (Tiktok trend) Feb 18 - Lancashire Mead Company at the Jorvik Viking Centre, York, UK - Mead Tasting event Feb 19 - Dancing Skeleton Meadery, Sepulpa, OK - Fiber Crafting Feb 21 - Monks Meadery, Atlanta, GA - Exhibition Armored Combat Feb 28 - Grimsby Hollow Meadery, Middleville, MI - Drink Mead, Learn Things: Anatomy of a Killer: When Doctors Become Predators Mar 20-21 Valkyrie's Horn Mead Competition, Minneapolis, MN - entries open! Mar 28 - Zymarium Meadery, Orlando, FL - Bonsai and Cheers with L&J Nursery Mar 28 - Folklore Brewing and Meadery, Dothan, AL - Dothan Songwriters Festival April 11 - Mershon's Artisan Cider, Stoughton, WI - Wisconsin Cider and Mead Festival You can buy mead online at https://shopmeads.com
1-27-26 Tonight we're talking with Ryan Carlson again. He had a lot of information, and we just ran out of time last time! Ryan is a Colorado mead maker who has been making mead for quite a while. Those of you who have listened to the early GML episodes may remember him. He has been quiet publicly for a few years, dealing with personal things, but during that quiet time, he's also been doing a lot of learning and experimentation to deepen his mead making expertise. I'll just let Ryan tell you himself: Hi, my name is Ryan. Some of you will know me from the mead world going back a long way. Some of you won't know me at all — and that's just fine. I was very active in mead for many years, right up until around 2020. Then life hit hard. Multiple things at once. Survival mode. For about five years I was mostly just circling the drain and trying to stay upright. Things are finally closer to normal now, and I guess you could say I'm back in the saddle. I've been a successful meadmaker for a long time. I'm a certified judge. I've taught hundreds of people — maybe more — how to make good mead. I love teaching, but I'm picky about it. I only teach what I've lived. I only teach what I can prove. If someone wants receipts, I can bury Mount Rushmore with them. I also teach mead in a way most people have never experienced. From here forward, I'll be approaching things a little differently. We're going to “get small.” We're going to pretend we're a single-celled yeast and jump down inside Mead City. We'll look at fermentation from the inside of the vessel outward and change our perspective completely. Mead is going to do what mead does. Yeast is going to do what yeast does. When we actually understand the science and biology, our job isn't control — it's assistance. We learn when the window opens, and when it does, we can hand the yeast exactly what they need so they can do the job they were already built to do. Most of the time, when people don't understand what's happening, they get in the way. They step on the yeast without realizing it. They try to force outcomes that biology simply doesn't allow, and they miss the chance to co-create. My goal is to help raise everyone's bar by changing perspective first, then layering in real science and real biology — not folklore, not wives' tales, and not parroted bad science. If you want to understand mead well enough that you don't have to beg for recipes anymore — recipes that often produce mediocre results at best — then this is a good place to sit down with us and learn the craft the way it's actually lived. To listen live, you can find us on Youtube, Twitch, X (Twitter), and Facebook on the Gotmead Page. On our new platform, chat is part of the podcast! Just comment from wherever you are watching, and we'll see it!! If you'd like to call in, we can get you a link to come on! Twitch: https://www.twitch.tv/meadwench YouTube: YouTube: https://m.youtube.com/@Gotmead X(Twitter): https://x.com/RealGotMead Facebook: https://www.facebook.com/GotMead Instagram: https://www.instagram.com/GotMead JOIN CHAT ON DISCORD: https://discord.gg/zEKNujQTtM Listen in! This player will show the latest episode: Sponsor: Look no further than Honnibrook Craft Meadery in Castle Rock, Colorado, for your go-to destination for wonderful, light, and refreshing mead! We have 20 meads on tap and four seasonal mead slushees. Go to honnibrook.com for review our tap list, upcoming events and to order online! If you want to ask your mead making questions, you can send us a question via email, join to ask a question on the show, or via X @realGotMead and we'll tackle it online! The show runs from 9PM EDT/6PM PDT (United States) for about 2 hours every other Tuesday starting Jan 13, 2026. To join live, you can use this link, and here are instructions on how to join in. Once you enter the waiting room, we get a notification and will bring you in! Upcoming Shows Feb 10 - Roger Wanner - W A Meadwerks - New York Show links and notes Let There Be Melomels by Rob Ratliff The Big Book of Mead Recipes by Rob Ratliff Let There Be Session Meads by Rob Ratliff Upcoming Events Jan 29 - Valhalla Meadery, Bozeman, MT - Live Music by Bozeman Area String Jams Jan 29 - Bardic Wells Medery, Montague, MI - Henna Party by Bohemian Awakenings Jan 29 - Dancing Skeleton Meadery, Sepulpa, OK - Karaoke Night Jan 30 - Manic Meadery, Crown Point, IN - Paint and Drink: First Snowflake Jan 31 - St. Ambrose Meadery, Beulah, MI - Barefoot Music party Feb 5 - Dancing Skeleton Meadery, Sapulpa, OK - Trivia Night Feb 6 - Red's on 7th, Delavan, WI - Meads and Masterpiece - mead tasting and wine glass painting Feb 6 - Manic Meadery, Crown Point, IN - Grown Up Book Fair Feb 6 - Chubby Cheeks Meadery, Temecula, CA - Halo Mega Bowl LAN party Feb 8 - Clear Skies Meadery, Rockville, MD - Acoustic Jam with Mike Rocke Feb 8 - Drinking Horn Meadery, Flagstaff, AZ - Beeswa Candle Making Valentine Workshop Feb 13 - Starrlight Meadery at the Honeysuckle Tea House, Chapel Hill, NC - Valentines Mead Tasting Feb 13 - Clear Skies Meadery, Rockville, MD - Make Your Own Mosaic Heart Feb 13 - Hex Meadery, Kaukauna, WI - Anniversary Party Feb 13 - Manic Meadery, Crown Point, IN - Paint and Drink: Valentine Animal Feb 14 - Bee Immortal Mead, Round Rock, TX - Valentines Mead Workshop Feb 14 - Southern Origin Meadery, Canon, GA - Couples Painting Each Other (Tiktok trend) Feb 18 - Lancashire Mead Company at the Jorvik Viking Centre, York, UK - Mead Tasting event Feb 19 - Dancing Skeleton Meadery, Sepulpa, OK - Fiber Crafting Feb 21 - Monks Meadery, Atlanta, GA - Exhibition Armored Combat Feb 28 - Grimsby Hollow Meadery, Middleville, MI - Drink Mead, Learn Things: Anatomy of a Killer: When Doctors Become Predators Mar 20-21 Valkyrie's Horn Mead Competition, Minneapolis, MN - entries open! Mar 28 - Zymarium Meadery, Orlando, FL - Bonsai and Cheers with L&J Nursery Mar 28 - Folklore Brewing and Meadery, Dothan, AL - Dothan Songwriters Festival April 11 - Mershon's Artisan Cider, Stoughton, WI - Wisconsin Cider and Mead Festival You can buy mead online at https://shopmeads.com
Send us a textjoin us for an other amazing episode of Church. We are joined by Edgar from Masonic Seeds, as well as Kevin Jodfrey for a conversation on Breeding and seed making. At Hash Church, we talk a lot about ritual, respect for the plant, and elevating the experience. That's exactly why we're proud to be supported by Puffco.Puffco continues to set the standard for modern consumption with tools built for people who truly care about flavor, temperature, and intentional use.From the Puffco Peak Pro with the 3D XL Bowl — delivering consistent heat, bigger hits, and unmatched terp expression —to the Proxy, redefining modular, ritual-based consumption,and the Pivot, bringing true Puffco performance into a compact, everyday format…These aren't gadgets.They're purpose-built tools for hash and solventless.We're genuinely grateful for Puffco's continued support of Hash Church, our guests, and our community. Their belief in education, culture, and quality helps us keep these conversations alive.
Good Morning Liberty breaks down Trump's Davos/W.E.F. moment, the "quiet part out loud" politics, Congress' latest spending tricks, and why immigration arguments keep turning into excuses for bigger government. We also cover Thomas Massie's attack ad drama, Turning Point-style "fund ICE at any cost" logic, and the uncomfortable libertarian reality: the welfare state is the root problem, and mass deportation has no clean moral path under the system we've built. Then we revisit COVID-era gaslighting, natural immunity, and the Paul Offit admissions that make "we didn't know" sound like a cover story.
1-13-26 Tonight we're talking with Ryan Carlson. Ryan is a Colorado mead maker who has been making mead for quite a while. Those of you who have listened to the early GML episodes may remember him. He has been quiet publicly for a few years, dealing with personal things, but during that quiet time, he's also been doing a lot of learning and experimentation to deepen his mead making expertise. I'll just let Ryan tell you himself: Hi, my name is Ryan. Some of you will know me from the mead world going back a long way. Some of you won't know me at all — and that's just fine. I was very active in mead for many years, right up until around 2020. Then life hit hard. Multiple things at once. Survival mode. For about five years I was mostly just circling the drain and trying to stay upright. Things are finally closer to normal now, and I guess you could say I'm back in the saddle. I've been a successful meadmaker for a long time. I'm a certified judge. I've taught hundreds of people — maybe more — how to make good mead. I love teaching, but I'm picky about it. I only teach what I've lived. I only teach what I can prove. If someone wants receipts, I can bury Mount Rushmore with them. I also teach mead in a way most people have never experienced. From here forward, I'll be approaching things a little differently. We're going to “get small.” We're going to pretend we're a single-celled yeast and jump down inside Mead City. We'll look at fermentation from the inside of the vessel outward and change our perspective completely. Mead is going to do what mead does. Yeast is going to do what yeast does. When we actually understand the science and biology, our job isn't control — it's assistance. We learn when the window opens, and when it does, we can hand the yeast exactly what they need so they can do the job they were already built to do. Most of the time, when people don't understand what's happening, they get in the way. They step on the yeast without realizing it. They try to force outcomes that biology simply doesn't allow, and they miss the chance to co-create. My goal is to help raise everyone's bar by changing perspective first, then layering in real science and real biology — not folklore, not wives' tales, and not parroted bad science. If you want to understand mead well enough that you don't have to beg for recipes anymore — recipes that often produce mediocre results at best — then this is a good place to sit down with us and learn the craft the way it's actually lived. OPEN-TOP FERMENTATION — HISTORICAL RECEIPTS & DEEP DIVES JIAHU — Neolithic China (~7000 BCE) https://www.smithsonianmag.com/science-nature/ancient-chinese-used-fermented-beverages-180964191/ https://www.pnas.org/doi/10.1073/pnas.0407921102 https://en.wikipedia.org/wiki/Jiahu GODIN TEPE — Ancient Near East (~3500–3000 BCE) https://www.pnas.org/doi/10.1073/pnas.0507742102 https://en.wikipedia.org/wiki/Godin_Tepe ROMAN FERMENTATION — POMPEII & DOLIA https://penelope.uchicago.edu/~grout/encyclopaedia_romana/wine/winemaking.html https://www.britannica.com/topic/dolium https://www.pompeii-sites.org/en/ MEDIEVAL & MONASTIC FERMENTATION https://www.medievalists.net/2014/10/medieval-brewing-ale/ https://www.britannica.com/topic/monasticism/Brewing-and-winemaking GENERAL FERMENTATION ARCHAEOLOGY https://www.penn.museum/sites/biomoleculararchaeology/ https://www.nationalgeographic.com/history/article/ancient-alcohol-drinking-history To listen live, you can find us on Youtube, Twitch, X (Twitter), and Facebook on the Gotmead Page. On our new platform, chat is part of the podcast! Just comment from wherever you are watching, and we'll see it!! If you'd like to call in, we can get you a link to come on! Twitch: https://www.twitch.tv/meadwench YouTube: YouTube: https://m.youtube.com/@Gotmead X(Twitter): https://x.com/RealGotMead Facebook: https://www.facebook.com/GotMead Instagram: https://www.instagram.com/GotMead JOIN CHAT ON DISCORD: https://discord.gg/zEKNujQTtM Listen in! This player will show the latest episode: Sponsor: Look no further than Honnibrook Craft Meadery in Castle Rock, Colorado, for your go-to destination for wonderful, light, and refreshing mead! We have 20 meads on tap and four seasonal mead slushees. Go to honnibrook.com for review our tap list, upcoming events and to order online! If you want to ask your mead making questions, you can send us a question via email, join to ask a question on the show, or via X @realGotMead and we'll tackle it online! The show runs from 9PM EDT/6PM PDT (United States) for about 2 hours every other Tuesday starting Jan 13, 2026. To join live, you can use this link, and here are instructions on how to join in. Once you enter the waiting room, we get a notification and will bring you in! Upcoming Shows Feb 10 - Roger Wanner - W A Meadwerks - New York Show links and notes Let There Be Melomels by Rob Ratliff The Big Book of Mead Recipes by Rob Ratliff Let There Be Session Meads by Rob Ratliff Upcoming Events Jan 15 - Pinesmoke Bee Company, Eustis, FL - Monthly Mead Up - gathering for home mead makers Jan 17 - Slash-O-Meadery, Nacodoches, TX - Bonfire and Mead Jan 18 - Michigan Mead Coalition at Cadillac Straits Brewing Company, Madison Heights, MI - Beginning Mead Making Class Jan 22- Nucleus Mead, Linesville, PA - Mead and Read Jan 23 - MeadKrieger Meadery, Loveland, CO - 3 Year Anniversary Party Jan 24 - Four Brothers Mead, Festus, MO - Zoe Vox live music Jan 24 - Hive Five Meadery, Kingman, AZ - Music and Mead with The Park Rangers Jan 26 - Batch Mead, Temecula, CA - Yoga, Mead and Pancakes Jan 31 - St. Ambrose Meadery, Beulah, MI - Barefoot Music party Feb 6 - Red's on 7th, Delavan, WI - Meads and Masterpiece - mead tasting and wine glass painting Feb 13 - Starrlight Meadery at the Honeysuckle Tea House, Chapel Hill, NC - Valentines Mead Tasting Feb 14 - Bee Immortal Mead, Round Rock, TX - Valentines Mead Workshop Feb 18 - Lancashire Mead Company at the Jorvik Viking Centre, York, UK - Mead Tasting event Feb 28 - Grimsby Hollow Meadery, Middleville, MI - Drink Mead, Learn Things: Anatomy of a Killer: When Doctors Become Predators Mar 20-21 Valkyrie's Horn Mead Competition, Minneapolis, MN - entries open! April 11 - Mershon's Artisan Cider, Stoughton, WI - Wisconsin Cider and Mead Festival You can buy mead online at https://shopmeads.com
1-13-26 Tonight we're talking with Ryan Carlson. Ryan is a Colorado mead maker who has been making mead for quite a while. Those of you who have listened to the early GML episodes may remember him. He has been quiet publicly for a few years, dealing with personal things, but during that quiet time, he's also been doing a lot of learning and experimentation to deepen his mead making expertise. I'll just let Ryan tell you himself: Hi, my name is Ryan. Some of you will know me from the mead world going back a long way. Some of you won't know me at all — and that's just fine. I was very active in mead for many years, right up until around 2020. Then life hit hard. Multiple things at once. Survival mode. For about five years I was mostly just circling the drain and trying to stay upright. Things are finally closer to normal now, and I guess you could say I'm back in the saddle. I've been a successful meadmaker for a long time. I'm a certified judge. I've taught hundreds of people — maybe more — how to make good mead. I love teaching, but I'm picky about it. I only teach what I've lived. I only teach what I can prove. If someone wants receipts, I can bury Mount Rushmore with them. I also teach mead in a way most people have never experienced. From here forward, I'll be approaching things a little differently. We're going to “get small.” We're going to pretend we're a single-celled yeast and jump down inside Mead City. We'll look at fermentation from the inside of the vessel outward and change our perspective completely. Mead is going to do what mead does. Yeast is going to do what yeast does. When we actually understand the science and biology, our job isn't control — it's assistance. We learn when the window opens, and when it does, we can hand the yeast exactly what they need so they can do the job they were already built to do. Most of the time, when people don't understand what's happening, they get in the way. They step on the yeast without realizing it. They try to force outcomes that biology simply doesn't allow, and they miss the chance to co-create. My goal is to help raise everyone's bar by changing perspective first, then layering in real science and real biology — not folklore, not wives' tales, and not parroted bad science. If you want to understand mead well enough that you don't have to beg for recipes anymore — recipes that often produce mediocre results at best — then this is a good place to sit down with us and learn the craft the way it's actually lived. OPEN-TOP FERMENTATION — HISTORICAL RECEIPTS & DEEP DIVES JIAHU — Neolithic China (~7000 BCE) https://www.smithsonianmag.com/science-nature/ancient-chinese-used-fermented-beverages-180964191/ https://www.pnas.org/doi/10.1073/pnas.0407921102 https://en.wikipedia.org/wiki/Jiahu GODIN TEPE — Ancient Near East (~3500–3000 BCE) https://www.pnas.org/doi/10.1073/pnas.0507742102 https://en.wikipedia.org/wiki/Godin_Tepe ROMAN FERMENTATION — POMPEII & DOLIA https://penelope.uchicago.edu/~grout/encyclopaedia_romana/wine/winemaking.html https://www.britannica.com/topic/dolium https://www.pompeii-sites.org/en/ MEDIEVAL & MONASTIC FERMENTATION https://www.medievalists.net/2014/10/medieval-brewing-ale/ https://www.britannica.com/topic/monasticism/Brewing-and-winemaking GENERAL FERMENTATION ARCHAEOLOGY https://www.penn.museum/sites/biomoleculararchaeology/ https://www.nationalgeographic.com/history/article/ancient-alcohol-drinking-history To listen live, you can find us on Youtube, Twitch, X (Twitter), and Facebook on the Gotmead Page. On our new platform, chat is part of the podcast! Just comment from wherever you are watching, and we'll see it!! If you'd like to call in, we can get you a link to come on! Twitch: https://www.twitch.tv/meadwench YouTube: YouTube: https://m.youtube.com/@Gotmead X(Twitter): https://x.com/RealGotMead Facebook: https://www.facebook.com/GotMead Instagram: https://www.instagram.com/GotMead JOIN CHAT ON DISCORD: https://discord.gg/zEKNujQTtM Listen in! This player will show the latest episode: Sponsor: Look no further than Honnibrook Craft Meadery in Castle Rock, Colorado, for your go-to destination for wonderful, light, and refreshing mead! We have 20 meads on tap and four seasonal mead slushees. Go to honnibrook.com for review our tap list, upcoming events and to order online! If you want to ask your mead making questions, you can send us a question via email, join to ask a question on the show, or via X @realGotMead and we'll tackle it online! The show runs from 9PM EDT/6PM PDT (United States) for about 2 hours every other Tuesday starting Jan 13, 2026. To join live, you can use this link, and here are instructions on how to join in. Once you enter the waiting room, we get a notification and will bring you in! Upcoming Shows Feb 10 - Roger Wanner - W A Meadwerks - New York Show links and notes Let There Be Melomels by Rob Ratliff The Big Book of Mead Recipes by Rob Ratliff Let There Be Session Meads by Rob Ratliff Upcoming Events Jan 15 - Pinesmoke Bee Company, Eustis, FL - Monthly Mead Up - gathering for home mead makers Jan 17 - Slash-O-Meadery, Nacodoches, TX - Bonfire and Mead Jan 18 - Michigan Mead Coalition at Cadillac Straits Brewing Company, Madison Heights, MI - Beginning Mead Making Class Jan 22- Nucleus Mead, Linesville, PA - Mead and Read Jan 23 - MeadKrieger Meadery, Loveland, CO - 3 Year Anniversary Party Jan 24 - Four Brothers Mead, Festus, MO - Zoe Vox live music Jan 24 - Hive Five Meadery, Kingman, AZ - Music and Mead with The Park Rangers Jan 26 - Batch Mead, Temecula, CA - Yoga, Mead and Pancakes Jan 31 - St. Ambrose Meadery, Beulah, MI - Barefoot Music party Feb 6 - Red's on 7th, Delavan, WI - Meads and Masterpiece - mead tasting and wine glass painting Feb 13 - Starrlight Meadery at the Honeysuckle Tea House, Chapel Hill, NC - Valentines Mead Tasting Feb 14 - Bee Immortal Mead, Round Rock, TX - Valentines Mead Workshop Feb 18 - Lancashire Mead Company at the Jorvik Viking Centre, York, UK - Mead Tasting event Feb 28 - Grimsby Hollow Meadery, Middleville, MI - Drink Mead, Learn Things: Anatomy of a Killer: When Doctors Become Predators Mar 20-21 Valkyrie's Horn Mead Competition, Minneapolis, MN - entries open! April 11 - Mershon's Artisan Cider, Stoughton, WI - Wisconsin Cider and Mead Festival You can buy mead online at https://shopmeads.com
Send us a textAt Hash Church, we talk a lot about ritual, respect for the plant, and elevating the experience. That's exactly why we're proud to be supported by Puffco.Puffco continues to set the standard for modern consumption with tools built for people who truly care about flavor, temperature, and intentional use.From the Puffco Peak Pro with the 3D XL Bowl — delivering consistent heat, bigger hits, and unmatched terp expression —to the Proxy, redefining modular, ritual-based consumption,and the Pivot, bringing true Puffco performance into a compact, everyday format…These aren't gadgets.They're purpose-built tools for hash and solventless.We're genuinely grateful for Puffco's continued support of Hash Church, our guests, and our community. Their belief in education, culture, and quality helps us keep these conversations alive.
n this episode of Good Morning Liberty, host Nate Ths discusses the recent spike in violent incidents around the world, including a mass shooting at Bondi Beach in Sydney, Australia, the tragic killing of Rob Reiner and his wife, and the deaths of three Americans in Syria. Nate delves into the implications of gun control laws, the political conversations they ignite, and the complexities tied to racial demographics. He also highlights the inefficiencies in the current system, the importance of maintaining individual rights, and the challenges of National Guard deployment in foreign lands. Tune in for an in-depth analysis and raw commentary on these critical issues. 00:00 Introduction and Weekend Overview 01:37 Australia Mass Shooting Analysis 05:04 Gun Control Debate 13:55 ProLon Fasting Mimicking Diet 27:08 Brown University Shooting 33:28 Americans Killed in Syria 41:09 Conclusion and Call to Action
In this episode of Good Morning Liberty, Nate Thurston and Charles Chuck Thompson discuss a range of topics from Nashville, TN, including Trump's announcement of a $12 billion bailout for farmers, deregulation efforts aiming to help farmers and the automotive industry, and the potential introduction of tiny Japanese cars in the American market. They delve into the complexities of tariffs, trade deficits, the economic impacts on farmers, and the challenges surrounding vehicle safety and emission standards. The episode also highlights historical tariff practices, such as the chicken tax, and the barriers they create to importing affordable vehicles like the Toyota Helix. Join Nate and Chuck as they explore the implications of these political and economic decisions on everyday American life. 00:00 Intro 02:54 Farmer Bailout Discussion 05:29 Impact of Tariffs and Trade Wars 07:49 Economic Challenges for Farmers 11:34 Deregulation and Its Benefits 20:48 Automotive Industry and Emission Standards 32:44 Tiny Cars and International Influence 33:18 Affordable Cars: A Mixed Blessing 33:52 The Briggs and Stratton Engine Anecdote 34:20 Small Cars in America: A Policy Shift 35:28 Regulatory Hurdles and Manufacturing Challenges 39:56 The Chicken Tax Explained 45:26 Workarounds and Loopholes 49:03 The Future of Tiny Cars in the US
Enjoy Nate's interview on Martens Minute! GML will return this week. Nate Thurston, co-host of the Good Morning Liberty podcast and former guitarist for the band Darling Parade, joins me to share his powerful journey through alcoholism and recovery. He opens up about his struggles with alcohol and addiction as a touring musician, and how his alcoholism spiraled out of control as his music career came to an end, nearly costing him his life. With the support of his wife, Nate confronted his addiction, quit drinking, and rebuilt his life. Now seven years sober, he enjoys a happy marriage, hosts a successful podcast, and is a respected figure in the liberty community. His advice to those struggling with addiction: You don't have to commit to quitting forever today—that can feel overwhelming. Instead, choose each morning to stay sober for that day. Find meaning in your life, channel it into a passion, and pursue it with purpose. 00:00 Intro 03:08 The Start of Nate's Music Journey 06:26 Alcoholism and the Music Industry 12:01 Struggles with Success and Addiction 24:40 Hitting Rock Bottom 42:02 Reflecting on Past Mistakes and Luck 44:26 Finding Meaning in Everyday Work 47:28 The Turning Point: Admitting the Problem 49:13 Support Systems and Personal Responsibility 54:22 Overcoming Social Anxiety and Alcohol Triggers 01:03:26 Rebuilding Life with New Goals 01:14:06 Advice for Those Hitting Rock Bottom
Nate talks election results in New York, New Jersey, and Virginia, focusing on candidates like Zohran Mamdani. He expresses concern over the rise of democratic socialism and emphasizes the need for a stronger advocacy for free-market capitalism. Nate criticizes the current political rhetoric that fails to provide a clear alternative to socialism and advocates for the importance of educating the public on the benefits of a free-market economy. Throughout the episode, Nate stresses the necessity for conservatives and Republicans to promote real free-market solutions rather than presenting socialism light. 00:00 Intro 01:08 Election Recap: Democrat Victories 04:44 The Rise of Socialism 06:18 The Free Market vs. Socialism 12:25 Republican Strategies and Critiques 16:29 The Importance of Free Market Advocacy 27:30 Trump's Funding Threats and Socialism's Excuses
Nate & Chuck bring you the dumbest things in politics this week! We dive deep into recent dramatic moments surrounding the SNAP benefits program, including controversial opinions and responses from recipients. Kamala Harris deflects questions about Joe Biden's frailties, and there's a heated debate on lowering the voting age to 16. We also explore the ongoing discourse around Israel and the Republican party, responses to potential political prosecutions, and polling results on Americans' views of federal government power. 00:00 Intro 01:43 SNAP 09:38 Economic Impact of SNAP 15:58 Misconceptions and Statistics 43:22 SNAP Recipients' Perspectives 55:07 Kamala Harris 01:01:05 Protests and Political Prosecutions 01:05:11 AOC vs. Riley Gaines Debate 01:11:47 Israel and Anti-Semitism Debate 01:34:57 Government Power and Political Dynamics
In this episode of 'Good Morning Liberty,' Charlie and Nate dive into the week's most absurd events and statements in part one of our 'Dumb Bleep of the Week!' From the strange tattoos of the Maine Senate candidate Graham Plattner and Elizabeth Warren's No Kings protest to the truth about Amazon's job market and Trump's inaccurate claims about inflation and gas prices, we break it all down. Stay tuned for an even crazier part two coming tomorrow! 00:00 Intro 00:40 No Kings Protest and Elizabeth Warren 10:14 Amazon's Automation and Job Replacement 26:11 Graham Platner's Controversial Tattoo 32:24 Controversial Tattoo Discussion 33:00 Political Symbolism and Reactions 35:10 Economic Policies and Public Perception 37:08 Inflation and Government Spending 42:29 Tariffs and Taxes Debate 48:40 Political Loyalty and Strategy
In today's episode of Good Morning Liberty, hosts Nate Thurston and Charles 'Chuck' Thompson discuss various current events and political happenings. Topics include the contentious Israel-Gaza ceasefire and the alleged violations by Hamas, the ongoing feud between Thomas Massie and Donald Trump with a focus on recent internal polling and political endorsements, and the 'No Kings' protests which sparked controversy due to its timing and message. The hosts also delve into the recent social media uproar, providing a balanced perspective amid the widespread misinformation and political rhetoric. 00:00 Intro 00:37 Today's Topics Overview 03:00 Israel-Gaza Ceasefire Discussion 05:07 Made-Up News Stories 16:21 ProLon Fasting Mimicking Diet 18:23 No Kings Protests 24:18 Trump vs. Massie 32:45 Political Integrity and Polling Insights 33:25 Trump's Influence and Endorsements 35:29 MAGA's Campaign Tactics 37:06 Social Media Reactions and Criticisms 39:25 Misinformation and Political Manipulation 46:20 Constitutional Principles and Government Actions 56:25 Hypocrisy in Political Allegiances
Lora Karch, Young Voices Middle East History and Peace Fellow, joins Josh to explore how stronger diplomatic relations with Russia could promote global stability, particularly in the Middle East. They also discuss recent developments in Syria and the Israeli-Gaza peace plan. Lora's article in Real Clear World: Stronger Ties with Russia Could Help Stabilize the Middle East | RealClearWorld Follow Lora on X and at Young Voices: https://x.com/lorakarch?s=21&t=S8JoQpY3m4n6bFrTo8tLrg Lora Karch
In this episode of Good Morning Liberty, Nate Thurston and Chuck Thompson dive into the U.S. government acquiring a 10% stake in Intel and discuss the broader implications for free-market capitalism. Nate shares his recent jury duty experience, and the conversation shifts to the potential dangers of government ownership in private companies. The episode also explores the contentious topic of flag burning, spotlighting a new executive order from Trump that could see violators imprisoned for a year. Nate and Charlie scrutinize these developments, their principles, and what it means for American values and freedom. (00:00) Introduction and Greetings (00:38) Jury Duty Experience (04:03) Discussion on Jury Nullification (05:42) US Government's Stake in Intel (18:52) Debate on Government Investments (26:02) Debating Political Hypocrisy (28:59) Trump's Business Deals and Legacy (29:26) Government Control and Fascism (29:54) Gavin Newsom's Trump Trolling (30:35) Government Ownership and Economic Concerns (31:28) Historical Parallels and Future Risks (39:54) Trump's Executive Order on Flag Burning (50:04) Closing Remarks and Call to Action Links: https://gml.bio.link/ YOUTUBE: https://bit.ly/3UwsRiv Check out Martens Minute! https://martensminute.podbean.com/ Follow Josh Martens on X: https://twitter.com/joshmartens13 CB Distillery 25% off with promo code GML cbdistillery.com Join the Fed Haters Club! joingml.com
Join Nate Thurston and Charles 'Chuck' Thompson on another episode of Good Morning Liberty as they discuss a range of topics including the controversial Cracker Barrel rebrand, Trump's flag burning executive order, misconceptions about 55 million U.S. visas, Hitler comparisons, and much more. From political blunders to corporate decisions, this episode has it all! Don't miss the Dumb Bleep of the Week! (00:00) Intro (02:51) Gavin Newsom and Bed Bath & Beyond (10:00) Kroger Store Closures (18:20) Nina Turner and Gerrymandering (23:27) Joy Reed's Controversial Comments (30:51) SNAP Benefits and Health (34:38) ADL and America First (39:21) Conservative Reactions to Gun Laws (39:40) Debate on Gun Possession and Crime (40:42) Thomas Massey's Stance on Gun Rights (41:37) Libertarian Views on Gun Ownership (42:17) Critique of Government Policies (45:45) Trump and Putin Assassination Comments (49:32) Trump's Executive Order on Flag Burning (53:13) Andrew Yang's Mobile Voting Proposal (56:07) Cracker Barrel Rebranding Controversy (01:06:35) Misconceptions About Visa Holders (01:13:17) ESPN's Barry Sanders Mix-Up (01:18:02) Concluding Remarks and Voting Links: https://gml.bio.link/ YOUTUBE: https://bit.ly/3UwsRiv Check out Martens Minute! https://martensminute.podbean.com/ Follow Josh Martens on X: https://twitter.com/joshmartens13 CB Distillery 25% off with promo code GML cbdistillery.com Join the Fed Haters Club! joingml.com secure.thomasmassie.com/donate
In this episode of Good Morning Liberty, hosts Nate Thurston and Charles 'Chuck' Thompson delve into the recent split between the American Academy of Pediatrics (AAP) and the CDC over COVID-19 vaccination recommendations for children under two years old. They explore the controversial research and policies related to the pandemic, cover recent foreign policy developments including possible US military actions against Venezuelan cartels, and discuss the latest findings on the origins of COVID-19. The hosts emphasize the need for truth and transparency to rebuild trust in institutions. Tune in for an insightful discussion filled with debate, data analysis, and some unexpected humor. (00:00) Introduction and Banter (03:31) COVID-19 Vaccine Debate (05:53)Foreign Policy and Trump-Putin Relations (11:40) Military Actions and Cartel Plans (18:06) Venezuela and Cartel Accusations (23:22) COVID-19 Lab Leak and Fauci Controversy (30:39) Government's Controversial Virus Research (31:42) The Role of mRNA in Vaccine Development (34:31) Historical Context and Policy Changes (37:40) COVID-19 Origins and Conspiracies (38:41) Impact of COVID-19 on Society and Economy (42:39) Accountability and Consequences (47:52) Debate on Vaccination for Children (57:20) Trust in Health Institutions Links: https://gml.bio.link/ YOUTUBE: https://bit.ly/3UwsRiv Check out Martens Minute! https://martensminute.podbean.com/ Follow Josh Martens on X: https://twitter.com/joshmartens13 CB Distillery 25% off with promo code GML cbdistillery.com Join the Fed Haters Club! joingml.com
In this episode of Good Morning Liberty, hosts Nate Thurston and Charles Thompson discuss various topics with their usual dose of humor and deep dives into the world of liberty and economics. They celebrate Ron Paul's birthday and his lasting impact on libertarianism, especially his influence on free-market economics. The duo also touches upon the role of government in economics, specifically in the context of President Trump's stance on wind and solar energy projects. Finally, they tackle the issues surrounding Spirit Airlines and their blocked merger with JetBlue by the DOJ, discussing its potential impact on the airline industry and competition. (01:32) Celebrating Ron Paul's Influence (05:37) Economic Concerns and Government Policies (11:18) Trump's Stance on Wind and Solar Energy (22:05) Energy Market Competition (22:28) Amazon and Quality vs. Price (23:14) Spirit Airlines and the DOJ Block (24:42) JetBlue and Spirit Merger Details (25:50) DOJ's Antitrust Concerns (27:19) Spirit Airlines' Financial Struggles (35:11) Economic Definitions and Government Control Links: https://gml.bio.link/ YOUTUBE: https://bit.ly/3UwsRiv Check out Martens Minute! https://martensminute.podbean.com/ Follow Josh Martens on X: https://twitter.com/joshmartens13 CB Distillery 25% off with promo code GML cbdistillery.com Join the Fed Haters Club! joingml.com
In this episode of Good Morning Liberty, Nate Thurston and Charles 'Chuck' Thompson discuss a variety of topics, starting with their new studio setup and a humorous exchange about a convenience store chain. They delve into Nate's recent trip to Yellowstone as well as the challenges of staying connected in today's world. The duo addresses the ongoing war in Ukraine, the European leaders' meetings in Washington, and the potential for peace talks led by Trump. They also tackle the controversy surrounding an Israeli government official caught in a child solicitation sting in Nevada. Finally, they discuss the potential dangers of radioactive shrimp sold at Walmart, highlighting how government budget cuts can affect food safety. The episode combines light-hearted banter with in-depth discussions on current events, making it an engaging listen. (06:12) European Leaders and Zelensky's Visit (09:55) Trump's Efforts to End the War (18:58) Press Secretary's Response and Media Tactics (26:19) Casualties of War (26:36) Possible Outcomes of the Ukraine-Russia Conflict (27:32) Diplomacy and Virtue Signaling (29:04) Public Opinion Polls on Ukraine (31:21) Trump's Controversial Comments (35:26) Israeli Official's Scandal in Vegas (51:51) Radioactive Shrimp Warning Links: https://gml.bio.link/ YOUTUBE: https://bit.ly/3UwsRiv Check out Martens Minute! https://martensminute.podbean.com/ Follow Josh Martens on X: https://twitter.com/joshmartens13 CB Distillery 25% off with promo code GML cbdistillery.com Join the Fed Haters Club! joingml.com secure.thomasmassie.com/donate
In this episode of Good Morning Liberty, hosts Nate Thurston and Charles Chuck Thompson kick off with some playful banter about their recent move and the hilarious saga of Nate's lost and found wallet. They discuss their new studio setup and the lack of preparation for the show. The episode heats up as they delve into a controversial debate between Dave Smith and Alex Berenson, tackling the accusations and awkward moments that ensued. The hosts dissect Berenson's claims, the dynamics of the debate, and its implications. They also touch on topics like Israel-Gaza conflicts, disaster aid tied to political stances, and Trump's response to job numbers. The episode rounds out with a fun Q&A session with the live audience, covering diverse topics from personal experiences to political insights. (00:00) Intro (03:58) Debate Recap: Dave Smith vs. Alex Berenson (16:48) Trump Administration's New Policy on Israel (20:54) Trump's Reaction to Jobs Numbers (28:08) Trump Fires US Labor Statistics Commissioner (28:32) Trust Issues with Government Data (29:27) Potential Solutions and Conspiracy Theories (31:11) Revisions and Survey Methodology (33:15) Critique of Centralized Control (35:05) Q&A Session Begins (40:51) Discussion on Federal Taxes Links: https://gml.bio.link/ YOUTUBE: https://bit.ly/3UwsRiv Check out Martens Minute! https://martensminute.podbean.com/ Follow Josh Martens on X: https://twitter.com/joshmartens13 CB Distillery 25% off with promo code GML cbdistillery.com Join the Fed Haters Club! joingml.com
Join Nate Thurston and Charles Thompson in this episode of Good Morning Liberty, recorded in Nashville, Tennessee. The hosts discuss the backlash surrounding actress Sydnee Sweeney's controversial American Eagle ad and the ensuing social media uproar. They also dive into the debate over privatizing Social Security, prompted by recent comments from Treasury Secretary Scott Besson. The episode covers Mayor Pete Buttigieg's stance on trans athletes and the mixed reactions it received from the LGBTQ+ community. Lastly, they address the economic struggles faced by Gen Z and compare them to past generations, focusing on the housing market and inflation. This episode features Dumb Bleep of the Week and much more! (00:00) Introduction and Greetings (00:43) Studio Move and Upcoming Plans (01:47) Dumb Bleep of the Week: Sydney Sweeney Controversy (04:29) Public Reactions and Media Coverage (07:10) Psychological Analysis and Feminine Toxicity (11:34) Further Reactions and Commentary (27:45) Joey Swoll and Hulk Hogan Controversy (32:12) Addressing Past Mistakes and Apologies (33:18) Critique of Apology Culture (34:39) Discussion on Hulk Hogan's Influence (36:30) Debate on Social Media Reactions (40:46) Privatizing Social Security Controversy (54:51) Economic Comparisons Across Generations Links: https://gml.bio.link/ YOUTUBE: https://bit.ly/3UwsRiv Check out Martens Minute! https://martensminute.podbean.com/ Follow Josh Martens on X: https://twitter.com/joshmartens13 CB Distillery 25% off with promo code GML cbdistillery.com Join the Fed Haters Club! joingml.com
Welcome to another exciting episode of Good Morning Liberty! Hosts Nate Thurston and Charlie Thompson kick off the show by celebrating Milton Friedman's 113th birthday, sharing their favorite Friedman videos, and discussing the limited social media engagement on Thursdays. The episode features Part 1 of the popular 'Dumb Bleep of the Week' segment, where they critique various absurdities, including the sentencing for the Canadian Freedom Convoy leaders, California's minimum wage hikes, Elon Musk being termed a 'money hoarder,' and controversial gun control debates. They also touch on the Epstein files, human trafficking, and a new voting system for 'Dumb Bleep of the Week'. Tune in for an engaging conversation filled with humor and critical analysis! (00:00) Intro (00:43) Milton Friedman's Birthday (01:28) Dumb Bleep of the Week: Part 1 (02:56) Canadian Freedom Convoy (05:26) California's Minimum Wage Impact (10:10) Democrats' Outreach Strategy (14:41) Elon Musk and Wealth Distribution (19:39) Stock Market Dynamics and Wealth Valuation (20:48) Elon Musk's Wealth and Investments (22:10) Financial Literacy and Misconceptions (22:49) Debate on AR-15 Assault Rifles Ban (23:48) Gun Laws and Public Safety (31:12) Epstein Files and Political Controversies (36:53) Trump's Trade Policy and Palestine Links: https://gml.bio.link/ YOUTUBE: https://bit.ly/3UwsRiv Check out Martens Minute! https://martensminute.podbean.com/ Follow Josh Martens on X: https://twitter.com/joshmartens13 CB Distillery 25% off with promo code GML cbdistillery.com Join the Fed Haters Club! joingml.com
In this episode of Good Morning Liberty, hosts Nate Thurston and Charles 'Chuck Hoodie' Thompson dive into two hot topics. They discuss the Canadian Freedom Convoy's protest against vaccine mandates, their broader implications, and the harsh prison sentences sought for the organizers. The episode also covers the latest GDP numbers, questioning the legitimacy of the reported growth and exploring the real state of the economy amidst political propaganda. Tune in for in-depth insights on life, liberty, and the pursuit of meaning. (00:00) Welcome to Good Morning Liberty (01:27) Economic Update: GDP Numbers (02:02) Canadian Freedom Convoy: A Deep Dive (04:25) Trudeau's Response to the Protests (08:18) Comparing Sentences: Freedom Convoy vs. Serious Crimes (12:27) Government Overreach and Public Reaction (22:36) GDP Numbers: More Lies from the Government? (23:27) Market Reactions to GDP Report (23:57) Trump's Reaction on Social Media (24:38) Debating Inflation and GDP Numbers (27:47) Conflicting Media Narratives (37:36) Economic Lessons and Final Thoughts Links: https://gml.bio.link/ YOUTUBE: https://bit.ly/3UwsRiv Check out Martens Minute! https://martensminute.podbean.com/ Follow Josh Martens on X: https://twitter.com/joshmartens13 CB Distillery 25% off with promo code GML cbdistillery.com Join the Fed Haters Club! joingml.com
In this episode of Good Morning Liberty, hosts Nate Thurston and Charles Chuck Thompson discuss a range of hot-button issues. Topics include the latest news about a New York City gunman, the EPA's proposed repeal of significant climate change regulations, and Senator Josh Hawley's introduction of a $600 tariff rebate bill. The hosts also touch on the heated debate around CTE (Chronic Traumatic Encephalopathy) and its impact on mental health. Tune in for a lively discussion full of important news updates and critical analysis. (00:00) Intro (00:50) Gunman Incident in New York (03:12) CTE and NFL Controversy (10:12) Trump EPA's Deregulatory Moves (17:05) AI Tool to Slash Federal Regulations (18:53) Virginia's AI Initiative on Regulations (19:36) Josh Hawley's Tariff Rebate Proposal (21:12) Understanding Tariffs and Their Impact (26:04) Tariff Revenues and Economic Implications (31:24) The Real Cost of Tariffs on American Goods Links: https://gml.bio.link/ YOUTUBE: https://bit.ly/3UwsRiv Check out Martens Minute! https://martensminute.podbean.com/ Follow Josh Martens on X: https://twitter.com/joshmartens13 CB Distillery 25% off with promo code GML cbdistillery.com Join the Fed Haters Club!
In this episode of Good Morning Liberty, Nate and Charles discuss the sweltering Nashville weather and chat about a libertarian wedding they attended. They delve into the latest developments in the Ghislaine Maxwell case, including her Supreme Court appeal and new information she has potentially provided to the DOJ. The hosts also highlight the importance of concealed carry as they discuss a stabbing incident at a Walmart in Traverse City, Michigan which was stopped by a concealed carry permit holder. Tune in for discussions on liberty, transparency in government, and a touch of humor about current events. (00:00) Intro (03:11) Epstein and Ghislaine Maxwell Updates (05:32) Media Coverage and Analysis (10:20) Trump's Potential Pardon for Ghislaine Maxwell (17:37) Conspiracy Theories and Government Trust Issues (23:18) Shocking Revelations from the FBI Deputy Director (24:18)The Impact of Corruption on the Republic (28:48) The Power Structure and Its Consequences (32:57) Trump's Tariff Deal and Weekend News (33:31) Controversy Over Sidney Sweeney's Ad (38:56) Traverse City Walmart Stabbing Incident (42:12) Concealed Carry Hero Stops Attacker Links: https://gml.bio.link/ YOUTUBE: https://bit.ly/3UwsRiv Check out Martens Minute! https://martensminute.podbean.com/ Follow Josh Martens on X: https://twitter.com/joshmartens13 CB Distillery 25% off with promo code GML cbdistillery.com Join the Fed Haters Club! joingml.com secure.thomasmassie.com/donate
Meet Fed Hater, Kosco, one of the Kings of Dumb Bleep submissions, as he shares his liberty story. Hear his experience as a Good Morning Liberty listener and member of the Fed Haters Club. Links: https://gml.bio.link/ YOUTUBE: https://bit.ly/3UwsRiv Check out Martens Minute! https://martensminute.podbean.com/ Follow Josh Martens on X: https://twitter.com/joshmartens13 CB Distillery 25% off with promo code GML cbdistillery.com Join the Fed Haters Club! joingml.com secure.thomasmassie.com/donate
Join Nate Thurston and Charles Chuck Thompson in another fantastic episode of Good Morning Liberty. This week, they dive into the 'Dumb Bleep of the Week' segment, presenting some of the dumbest happenings, as submitted by the live group, and letting the Fed Haters Club vote on the winner. Topics include WNBA players demanding higher pay, the controversy surrounding Stephen Colbert's show cancellation, the US postal service's expensive all-electric fleet, the razor-thin credibility of a noose found at the Titans stadium construction site, and the ongoing saga of the Washington Redskins name change. Additional discussions highlight Trump's battles with Jerome Powell and the Federal Reserve's budget issues, Mike Johnson's comments on Thomas Massey, and a potential 'tariff rebate' for Americans. Tune in for lively debates, sharp commentary, and plenty of laughs. (00:00) Introduction and Weekend Plans (01:44) WNBA Pay Dispute (11:00) Stephen Colbert Show Cancellation (30:36) Hilarious Democrat Inflation Post Fail (44:14) Redskins Name Debate (47:21) Trump vs. Jerome Powell (55:57) Epstein Files and Political Maneuvering (01:04:20) Distractions and Media Manipulation (01:19:20) Tariff Rebate Proposal Links: https://gml.bio.link/ YOUTUBE: https://bit.ly/3UwsRiv Check out Martens Minute! https://martensminute.podbean.com/ Follow Josh Martens on X: https://twitter.com/joshmartens13 CB Distillery 25% off with promo code GML cbdistillery.com Join the Fed Haters Club! joingml.com secure.thomasmassie.com/donate
In this episode of Good Morning Liberty, Nate Thurston and Charles Chuck Thompson delve into newly declassified information released by Tulsi Gabbard, which counters the long-standing Russia-Trump collusion narrative. The hosts examine the key findings of a 46-page report that reveals how the Obama administration allegedly manipulated intelligence to claim that Russia favored Donald Trump in the 2016 election. Discover the depths of the alleged cover-up, the flawed intelligence reports cited, and how this disinformation campaign could have influenced significant geopolitical events and domestic politics. Don't miss this shocking breakdown that challenges everything you thought you knew about the Russia investigation. (00:00) Intro (01:04) Tulsi Gabbard's Revelations (01:54) Debunking the Russia-Trump Collusion Narrative (14:06) The Intelligence Community Assessment (ICA) (19:07) CIA's Role and Missteps (32:29) Putin's True Intentions (44:28) Kremlin's Secret Material on Clinton's Health (45:13) Clinton's Alleged Health Issues and Campaign Strategies (46:51) Bribery and Secret Meetings Exposed (47:52) Government Corruption and Public Distrust (48:44) FBI Director Comey's Testimony (50:49) Tulsi Gabbard's Bombshell at the White House (51:33) Declassified Report on Obama's Intelligence Community Assessment (59:01) The Steele Dossier and Its Implications (01:06:08) Putin's Alleged Preference for Trump (01:11:04) Post-Election Russian Influence Operations (01:11:48) Rushed Intelligence Community Assessment Links: https://gml.bio.link/ YOUTUBE: https://bit.ly/3UwsRiv Check out Martens Minute! https://martensminute.podbean.com/ Follow Josh Martens on X: https://twitter.com/joshmartens13 CB Distillery 25% off with promo code GML cbdistillery.com Join the Fed Haters Club! joingml.com secure.thomasmassie.com/donate
In this episode of Good Morning Liberty, Nate Thurston and Charles Chuck Thompson discuss the latest developments on Representative Thomas Massey's efforts to release the Epstein files. They delve into the political controversy surrounding the Russiagate hoax and Obama's alleged involvement. Nate shares his personal struggles with back pain, leading to a heartfelt thank you to his doctor. The conversation also covers their hiatus due to health issues and dehydration from a recent golf tournament. They analyze the potential outcomes of the Epstein case, the ongoing efforts to unseal crucial information, and speculate on future political maneuvers. The duo concludes with a hopeful message about uncovering truth and holding accountable those who misuse power. Join them for a comprehensive analysis of current events and political strategies. (00:00) Intro (02:37) Thomas Massey and Epstein Files (04:04) Congressional Maneuvering (06:20) Public Opinion and Government Transparency (15:36) Russiagate and Political Distractions (22:29) Russia Hacked the Election: Media Consensus (23:32) Intelligence Agencies' Conclusions (25:06) Tulsi Gabbard's Revelations (27:35) The Creation of the Russia Hoax (28:17) Obama Administration's Role (28:54) Media Leaks and Public Perception (35:26) The Need for More Evidence Links: https://gml.bio.link/ YOUTUBE: https://bit.ly/3UwsRiv Check out Martens Minute! https://martensminute.podbean.com/ Follow Josh Martens on X: https://twitter.com/joshmartens13 CB Distillery 25% off with promo code GML cbdistillery.com Join the Fed Haters Club! joingml.com secure.thomasmassie.com/donate
New York native, Daniel Idfresne, joins Josh to discuss Zohran Mamdani"s victory in the Democratic primary for New York Mayor. Daniel breaks down the demographics in the primary that lead to Mamdani's victory. They also discuss how the general election looks now that both Mayor Adams and former Governor Cuomo have decided to stay in the race, running as independents. Daniel also analyzes Mamdani's plans and policies. He shows how they would likely affect New Yorkers if they are implemented. Daniel's article in the Real Clear Politics: https://www.realclearpolitics.com/articles/2025/06/25/zohran_mamdani_working-class_mayor_for_upper_class_voters.html Follow Daniel on X and at Young Voices: https://x.com/danielidfresne?s=21&t=S8JoQpY3m4n6bFrTo8tLrg https://www.joinyv.org/talent/daniel-idfresne (00:00) Introduction and Guest Welcome (00:26) Discussing Zan Ani's Primary Victory (04:36) Impact of Ani's Policies on New York (11:05) General Election Dynamics (16:54) Demographic Breakdown of Ani's Support (29:01) Advice for New Yorkers and Final Thoughts Links: https://gml.bio.link/ YOUTUBE: https://bit.ly/3UwsRiv Check out Martens Minute! https://martensminute.podbean.com/ Follow Josh Martens on X: https://twitter.com/joshmartens13 CB Distillery 25% off with promo code GML cbdistillery.com Join the Fed Haters Club! joingml.com secure.thomasmassie.com/donate
Welcome to another thought-provoking episode of Good Morning Liberty, where Nate Thurston and Charles Chuck Thompson break down the most ridiculous events and statements of the week. Today, we dive deep into various reactions and controversies surrounding the Epstein files, including Trump's controversial stance and Ben Shapiro's defense. We also discuss Marjorie Taylor Greene's betrayal accusations, Laura Loomer's reactions to TPUSA, and Cat Turd's misunderstanding of inflation data. Additionally, we critique Gavin Newsom's appearance on the Sean Ryan show, the UK's decision to lower the voting age to 16, and more idiotic comments from pundits like Destiny. Don't miss out on this jam-packed episode filled with sharp insights and plenty of humor! (00:00) Introduction and Greetings (00:31) Fed Haters Club and YouTube Shoutouts (01:08) Charlie's Golf Tournament (02:50) Dumb Leap of the Week Introduction (03:28) Criticizing Political Parties (04:20) Alex Jones and Cults (06:07) Trump and Epstein Speculations (28:13) Ben Shapiro's Trust in Government (35:36) Marjorie Taylor Greene and Betrayal (43:07) Hank Johnson's Epstein Song (46:39) Rediscovered Song and Guitar Mishap (47:27) Tucker Carlson's Controversial Claims (49:02) TPUSA Event Highlights (51:46) Laura Loomer's Reaction to TPUSA (54:13) Inflation and Tariffs Debate (01:01:58) Gavin Newsom on Gun Control and California's Population (01:08:39) UK's Voting Age and Meat Allergy Engineering (01:15:37) Destiny's Controversial Statement on Children (01:18:25) Nina Turner's Minimum Wage Argument Links: https://gml.bio.link/ YOUTUBE: https://bit.ly/3UwsRiv Check out Martens Minute! https://martensminute.podbean.com/ Follow Josh Martens on X: https://twitter.com/joshmartens13 CB Distillery 25% off with promo code GML cbdistillery.com Join the Fed Haters Club! joingml.com secure.thomasmassie.com/donate
In this episode, the hosts dive deep into the ongoing debate surrounding the release of the Epstein files. The conversation starts with Trump's ambiguous and evolving stance on the matter, highlighting his various excuses and deflections. The hosts discuss the broader implications of this issue, especially in relation to trust in government and the fight against child sex trafficking. Additionally, they examine recent legislative moves to force the release of these files, including efforts by Democrats and a new resolution introduced by Representatives Thomas Massie and Ro Khanna designed to bypass leadership and compel transparency. Throughout the episode, the hosts also critique the procedural complexities of Congress and the political maneuvering around these sensitive files, pressing the importance of exposing the truth behind Jeffrey Epstein's criminal activities and the potential involvement of powerful figures. (00:00) Intro (00:30) Discussion on Transparency and Accountability (01:15) Public Opinion and Polls (03:06) The Importance of Continued Discussion (05:04) Potential Conspiracies and Government Power (08:32) Trump's Response and Public Perception (12:43) Shifting Narratives and Credibility Issues (21:37) Trump's Social Media Statements (25:53) The Outrage Over Child Sex Trafficking (26:41) Trump's Administration and the Deep State (27:49) The Epstein Files Controversy (30:12) Political Maneuvering and Hypocrisy (34:05) Procedural Votes and Misunderstandings (36:57) The Push for Transparency (44:33) Conspiracy Theories and Metadata (48:55) Final Thoughts and Announcements Links: https://gml.bio.link/ YOUTUBE: https://bit.ly/3UwsRiv Check out Martens Minute! https://martensminute.podbean.com/ Follow Josh Martens on X: https://twitter.com/joshmartens13 CB Distillery 25% off with promo code GML cbdistillery.com Join the Fed Haters Club! joingml.com secure.thomasmassie.com/donate
In this episode of Good Morning Liberty, Nate and Chuck dive into recent economic news, discussing the unexpected surplus posted by the Treasury in June due to a surge in tariff collections. They analyze the impact of these tariffs on the economy, particularly inflation rates. Additionally, they touch on Trump's novel plan to send weapons to Ukraine, with the unique twist that other countries will be footing the bill. The episode also hints at discussions on Jeffrey Epstein and the proposals to release related files, providing a comprehensive overview of current political and economic happenings. (00:00) Intro (00:43) Economic News and Tariff Surplus (02:13) Tariff Exemptions and Government Surplus (03:22) Fiscal Year Deficit and Calendar Adjustments (08:33) Tariff Impact on Inflation (21:18) Interest Rates and Federal Reserve (25:01) The Ethics of Posting Misinformation (25:27) Debunking Inflation Myths (27:00) The Real Cost of Tariffs (34:21) Trump's Novel Plan for Ukraine (44:23) The Epstein Files Controversy (48:17) Closing Remarks and Call to Action Links: https://gml.bio.link/ YOUTUBE: https://bit.ly/3UwsRiv RUMBLE: https://rumble.com/c/GML Check out Martens Minute! https://martensminute.podbean.com/ Follow Josh Martens on X: https://twitter.com/joshmartens13 CB Distillery 25% off with promo code GML cbdistillery.com Join the Fed Haters Club! joingml.com secure.thomasmassie.com/donate
Join Nate Thurston and Charles Thompson in another exciting episode of Good Morning Liberty, where they dive into a weekend of reunions, high school memories, and political discussions. Nate shares his unexpected fun at his 20-year high school reunion while Chuck manages the podcast banter with humor. The episode takes a deep dive into recent controversies at the TP USA event, including Tucker Carlson's controversial speech, the debate between Josh Hammer and Dave Smith on Trump's legacy, and the ongoing discourse surrounding Jeffrey Epstein. As always, the hosts provide a mix of sarcasm, truth, and witty commentary on the current political landscape. Perfect for liberty-lovers, this episode is packed with insights, laughter, and the pursuit of truth. (00:00) Intro (00:51) High School Reunion Reflections (02:18) Libertarian Podcast Overview (03:43) Debate on Israel and Trump (12:49) Dave Smith's Debate Tactics (16:49) Critique of Political Movements (23:41) Strange Noises and Speculations (24:05) Tucker Carlson's Controversial Speech (24:19) Epstein's Mysterious Wealth (25:14) Criticizing Government Agencies (26:32) Foreign Government Involvement (27:58) The Woke Debate (28:31) Tucker Carlson and the CIA (29:51) Deep State and Shadow Governments (31:25) Israeli Prime Minister's Response (32:56) Roof Repairs and Distractions (34:18) Laura Loomer's Criticisms (43:52) Ghislaine Maxwell's Potential Revelations Links: https://gml.bio.link/ YOUTUBE: https://bit.ly/3UwsRiv RUMBLE: https://rumble.com/c/GML Check out Martens Minute! https://martensminute.podbean.com/ Follow Josh Martens on X: https://twitter.com/joshmartens13 CB Distillery 25% off with promo code GML cbdistillery.com Join the Fed Haters Club! joingml.com secure.thomasmassie.com/donate
Lora Karch joins Josh to discuss the Middle Eastern nation's reaction to the 12-day war. They discuss how the war has affected not only Israel and Iran, but also the new government in Syria, the nation of Lebanon and other nations in the region. Follow Lora on X and at Young Voices: https://x.com/lorakarch?s=21&t=S8JoQpY3m4n6bFrTo8tLrg https://www.joinyv.org/talent/lora-karch Links: https://gml.bio.link/ YOUTUBE: https://bit.ly/3UwsRiv RUMBLE: https://rumble.com/c/GML Check out Martens Minute! https://martensminute.podbean.com/ Follow Josh Martens on X: https://twitter.com/joshmartens13 CB Distillery 25% off with promo code GML cbdistillery.com Join the Fed Haters Club! joingml.com secure.thomasmassie.com/donate
In today's episode of Good Morning Liberty, Nate Thurston and Charles 'Chuck' Thompson dive into this week's 'Dumb Bleep of the Week.' The duo discusses a plethora of topics including a baffling text exchange that could've been an email, the controversy surrounding Epstein's death and alleged intelligence ties, and the biggest political blunders of the week. They also touch on accusations against FEMA funding, clown activism, and a bizarre 'BBB Bump It Up' campaign by Trump. Tune in to hear their rapid-fire critique of the week's most ridiculous moments! (00:00) Introduction and Banter (00:58) Dumb Bleep of the Week: Flood in Texas (10:22) Dumb Bleep of the Week: Measles Outbreak (16:41) Dumb Bleep of the Week: Julia's Date Drama (22:29) Dumb Bleep of the Week: Elon Musk's America Party (28:19) Dumb Bleep of the Week: Rashida Tlaib's Bill Critique (38:07) Firearm Suppressors and the Hearing Protection Act (38:52) Taxpayer Spending on July 4th Celebrations (39:37) Trump's Stance on Ukraine Weapons (40:35) Supreme Court Ruling on Federal Workforce Reduction (44:00) Target Boycott and Living Wage Debate (46:53) ICE Raids and Religious Freedom (50:44) Clowns Offended by Trump Comparisons (55:29) Epstein Conspiracy Theories and DOJ Findings (01:11:49) Dumbest Comments of the Week Links: https://gml.bio.link/ YOUTUBE: https://bit.ly/3UwsRiv RUMBLE: https://rumble.com/c/GML Check out Martens Minute! https://martensminute.podbean.com/ Follow Josh Martens on X: https://twitter.com/joshmartens13 CB Distillery 25% off with promo code GML cbdistillery.com Join the Fed Haters Club! joingml.com secure.thomasmassie.com/donate
© 2020-2026 Ivy Podcast Discovery LLC
