Podcasts about speculators

The corporatization of the hobby has its positives, for sure: better products, higher values, etc. However, if big names and bigger money keep coming into the hobby, people will be forced out. Mike and Jesse dive into the latest round of big names with deep pockets entering the space. 0:00 - Intro 4:30 - Big money in the hobby forces more and more people out. 6:00 - Everything is more expensive now 9:10 - This is not a doom-and-gloom situation, but we are reaching a breaking point 12:10 - Is now the time to sell and get money where you can? Can everything keep going up? 24:00 - Cooper Flagg debut patch auto-pulled, and the reactions were priceless.  32:00 - Prison vs. Jail 44:00 - Collectors vs. Speculators  52:00 - Emotional ties to a card Follow Sports Cards Nonsense: https://www.tiktok.com/@sportscardsnonsense https://www.instagram.com/sports_cards_nonsense/ https://x.com/SCN_Pod https://www.facebook.com/groups/sportscardsnonsense https://collectibleslife.beehiiv.com/subscribe Learn more about your ad choices. Visit megaphone.fm/adchoices

been a while.. almost like a WTBS

Today, we look at the latest market moves as we position the importance of Friday's US jobs report, noting the reaction to key incoming earnings from Western Digital and Sandisk in particular and the impressive rally move in gold and silver. Considerable coverage as well of Victor Niederhoffer, a larger than life Wall Street figure who died this Tuesday and whose life inspired and touched countless speculators and professionals over his career, including this podcast host, Saxo Global Head of Macro Strategy John J. Hardy. Links The all-time must read article for want-to-be speculators: The New Yorker article by Malcom Gladwell: Blowing Up, comparing and contrasting Victor Niederhoffer and Nassim Taleb. The Henry Clews book Fifty Years in Wall Street. This book inspired Niederhoffer's giving out canes to people who offered valuable inspiration while writing his column for MSN's MoneyCentral. Education of a Speculator - the legendary book. Recent (less recent than I said on podcast - actually latest I can find is from this February) Mark Spitznagel prediction on one final meltup followed by 80% crash in stocks. About twice per week (in normal times, hopefully soon to resume), you will find links discussed on the podcast and a chart-of-the-day over at the John J. Hardy substack. Read daily in-depth market updates from the Saxo Market Call and the Saxo Strategy Team here. Please reach out to us at marketcall@saxobank.com for feedback and questions. Click here to open an account with Saxo. Intro music by AShamaluevMusic DISCLAIMER This content is marketing material. Trading financial instruments carries risks. Always ensure that you understand these risks before trading. This material does not contain investment advice or an encouragement to invest in a particular manner. Historic performance is not a guarantee of future results. The instrument(s) referenced in this content may be issued by a partner, from whom Saxo Bank A/S receives promotional fees, payment or retrocessions. While Saxo may receive compensation from these partnerships, all content is created with the aim of providing clients with valuable information and options.

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

Don't Stop When Tears Start Don't stop when tears start, because tears, sorrow, disappointments, and difficulties are part of the Christian life and part of the Christian journey. A lot of people start into this journey thinking that the presence of God means the absence of trouble, and that isn't true. Jesus Christ himself said, In this world you will have tribulation. You'll have difficulty, there will be trouble, but be of good cheer, for I have overcome this world. In other words, I have overcome this world's power to overcome you. When you opened your heart to Jesus Christ, the Holy Spirit of God took up residence inside your physical body, and the Christ who won that victory on the cross — defeated the power of hell and darkness — lives inside of you now and will carry you to the finish line in this journey. You will be opposed, there will be difficulties along the way. For those who are in the middle of the journey, I remember those days so clearly in my life. I told my wife Teresa one time, I said, "I feel like if I wasn't sighing, I wouldn't be breathing." There was a season — a long season — where it was difficult. There are individual moments, but then there's a long season where long-suffering has got to come into your life, and God gives you the power. The Joy of First Salvation — Psalm 126:1–3 Psalm 126, we're going to start there, verses 1 to 3: When the Lord brought back the captivity of Zion, we were like those who dream. Then our mouth was filled with laughter and our tongue with singing. Then they said among the nations, "The Lord has done great things for them." The Lord has done great things for us, and we are glad. I remember those first days of salvation. I remember when I sat in church the first time and the person who led me to Christ shared the verse of Scripture that if anyone be in Christ, he is a new creation. The old things are passed away, and behold, all things have become new. I remember sitting and wondering, is that reality, or is that just a pipe dream? Can it actually happen? God, can you come and give me a future that I could never find in my own strength? No matter how much I would try, it would always escape me because I wasn't capable of fulfilling it. And then one day you begin to realize that you've been forgiven your sin, and the Holy Spirit has come and indwelt your human body. It's like a dream. Life begins to change line upon line, precept upon precept. From place to place, God begins to work a work that only the Holy Spirit can do, and you become aware of it. I remember the first time I ever went to a Christian police officer's conference in Speculator, upstate New York. For the first time ever in my life, I saw grown men weep before God for joy — Texas Rangers, people from all over the country, tough guys too, a lot of them, tenderized by the presence of God. I cried for three days. I couldn't stop crying. I don't know how many boxes of tissue I went through. I actually felt foolish at one point because I simply couldn't stop crying — it was like a dream. I know many of you remember those days, especially if you're in the middle of your journey now. Your mouth is filled with laughter. Your tongue is filled with singing. When I first started going to church, I'd open a hymn book — I wasn't raised on the hymns, so they were brand new to me. "It Is Well with My Soul." "Blessed Assurance, Jesus Is Mine." I would stand there and the tears would come down my face. I remember one time my wife said to me, "You have to stop laughing when the preacher is preaching. He's going to think that you're mocking him." I said, "I'm not. I can't stop." I was just so filled with joy that I had this new life that God had promised. I had a hope. I had a future. I had now laid hold of the possibility of becoming the person that God intended my life to be. It would bubble out of me like a river. And then people among the nations — as Psalm 126 says — began to say, "The Lord has done great things for them." People around you started to say, "Wow, you are changing." Now, some things change quickly, don't they? Some things, the very day you come to Christ — maybe your vocabulary changes. You stop using the words you used to use, or if one slips, you're quick to apologize for it. And then there are certain things that change, and other things don't change quite as fast. I speak from experience. There were some things I was able to put away almost day one, and other things that took forever. Have you ever watched one of those old Western movies on television? They get in a gunfight in a saloon, and one guy gets shot and falls over the table. Then he climbs the stairway, falls over the balcony, stumbles behind the bar, gets up, falls over the bar, stumbles out through the swinging doors, and falls over the hitching post — and you're watching the movie saying, "Is this guy ever going to die?" Well, I don't know about you, but some things in my life went that way. They were shot, in a sense, by the blood of Jesus Christ. They lost their power, but they took time. Don't get discouraged when things in your life take time. They are dead. They might be still standing like the fig tree when Jesus and the disciples went back into Jerusalem, but they've lost their power to dictate your future. Praise be to God. A Season of Sorrow — Bring Back Our Captivity Now the psalmist goes on and he says, Bring back our captivity, O Lord, as streams in the south. Those who sow in tears shall reap in joy, and he who continually goes forth weeping, bearing seed for sowing, shall doubtless come again with rejoicing, bringing his sheaves with him. So this psalm starts out with such victory, and then it has a transitional point where the psalmist is saying, "God, do it again for me. Do it again for us. Bring back that joy that I once knew of my captivity being released. God, I'm in a season of sorrow now. I'm in a season where tears are taking over for what used to be joy. Would you help us in this?" To understand this, I want to go back to the actual journey that most agree this psalm speaks about. It began with the people of Israel being taken into captivity into a land called Babylon. They were taken there because they played fast and loose with the things of God. We go into captivity when we don't acknowledge the presence of God or the reason for which we were created — when we choose, as Adam and Eve once did, to follow our own reasoning and go our own way. Because of it, they lost the protection of God and were taken captive. For 70 years they were in captivity — more than a generation. Many, if not most, of these people were actually born in captivity. They knew the story of what God had done in the past, but they themselves had been the victims of captivity, and their lives were short of what they should have been. The Decree of Cyrus — A Picture of the Cross Now, the interesting thing is that in the book of Ezra, it gives us the history. In Ezra 1:1 it says, In the first year of Cyrus, king of Persia. This is where it really gets interesting, because about 150 years before Cyrus became king, it was prophesied that a king would be born whose name would be Cyrus. He is actually called "my servant" in the Word of God, and he would issue a decree letting the people go home to rebuild. It's phenomenal. God knows everything. There's nothing that happens that he doesn't know. Everything is in his control. He knows where you are today. He knows where you'll be tomorrow. As a matter of fact, he's already in your tomorrow before you even get there. He foretold through Jeremiah that his own people would go into captivity for 70 years. But through Isaiah, he declared that a king called Cyrus would be born who would issue a decree and let the people go back home to rebuild again. That's the context, really, of Psalm 126. Can you imagine being there? All you've ever kno...

Kaitlyn Tiffany notes that by 1966, the media labeled these researchers as "housewives" to dismiss their work, describing them as a "keening pack of speculators." Central to their dissent was the "magic bullet" theory, which claimed a single pristine projectile caused multiple wounds in both JFK and Governor Connally. Dissenters highlighted the bullet's lack of deformation and the FBI's initial reports of a non-exiting back wound. They also investigated the Grassy Knoll for additional shooters. (9)1963

Where Boyle Heights residents can get masks, air purifiers and other services following the warehouse fire. A proposed state bill could protect burned properties in Altadena from real estate speculators. And we'll tell you about an eco-challenge that wants you to ditch the plastic in July. Support The L.A. Report by donating at LAist.com/join and by visiting https://laist.comSupport the show: https://laist.com

Stijn Schmitz welcomes Doug Casey to the show. Doug Casey is a Bestselling Author, Speculator, Founder of Casey Research, and Voluntarist Philosopher. The conversation opens with an analysis of the disconnect between geopolitical turmoil, specifically the disruption of oil flows through the Strait of Hormuz, and equity markets trading near all-time highs. Casey argues the recent de-escalation between the U.S. and Iran is likely temporary, as the core dispute between Israel and Iran remains unresolved. Despite this volatility, he remains bullish on oil, favoring oil and gas stocks due to their low representation in the market and high dividend yields, a sentiment he backs with his own investment strategy. Casey introduces his thesis of a “Greater Depression,” a period of declining real standards of living masked by a debt-fueled financial economy. He contrasts the struggling real economy, burdened by consumer and government debt, with the booming stock market, suggesting the current stability is unsustainable. Looking at long-term trends, he posits that all commodities historically trend toward zero in real terms as technology advances. However, he notes that commodities are currently the cheapest asset class compared to grossly overvalued stocks, bonds, and real estate, making them especially attractive. The discussion shifts to gold and silver, which Casey treats primarily as savings vehicles, noting the 55-year bull market is still intact. While he believes gold is no longer a great speculation at current prices, he finds mining stocks to be exceptionally undervalued, driven by industry-wide unpopularity and neglect from institutional investors. He extends this bullishness to agricultural commodities and fertilizers, deeming corn ultra-cheap and noting natural gas, a key input for urea, is also priced at a bargain in North America. For speculation, he expresses a strong preference for private placements and warrants in smaller, entrepreneur-led companies. The conversation concludes with a grim outlook for U.S. fiscal health, predicting rising interest rates driven by unsustainable deficits and a bond market that will eventually slip the Federal Reserve's control. Timestamps: 00:00:00 – Introduction00:01:02 – Oil Market Geopolitics and Prices00:06:57 – Oil Inventories and Demand Outlook00:09:06 – Debt Economy and Greater Depression00:11:03 – Electrification and Nuclear Future00:14:27 – Long-term Commodity Price Trends00:16:21 – Agricultural Commodities Discussion00:21:34 – Fertilizers and Natural Gas00:25:30 – Potash, Phosphate, & Sulphur00:28:21 – Gold and Silver as Savings00:33:20 – Mining Stocks and Value00:40:00 – Mining Sector Companies00:44:00 – Investment Strategies and Placements00:47:26 – Other Commodities Opportunities00:49:48 – Guest Projects and Resources Guest Links: YouTube: https://www.youtube.com/channel/UCEJR3OAeHBNz7aGtFRZXArQ Doug Casey’s Take: https://internationalman.com Amazon Novels: https://tinyurl.com/an3uxhc Book ‘The Preparation’: https://tinyurl.com/theprepa Best-selling author, world-renowned speculator, and libertarian philosopher Doug Casey has garnered a well-earned reputation for his erudite (and often controversial) insights into politics, economics, and investment markets. Doug is widely respected as one of the preeminent authorities on “rational speculation,” especially in the high-potential natural resource sector. Doug’s most recent book, “Assassin,” can be found on Amazon. He has been a featured guest on hundreds of radio and TV shows, including David Letterman, Merv Griffin, Charlie Rose, Phil Donahue, Regis Philbin, Maury Povich, NBC News, and CNN; has been the topic of numerous features in periodicals such as Time, Forbes, People, and the Washington Post. Doug has lived in 10 countries and visited over 175. Today you’re most likely to find him at La Estancia de Cafayate (Casey’s Gulch), an oasis tucked away in the high red mountains outside Salta, Argentina.

Commodities trading has ancient roots including the commodity price index that help hedge for all parties involved in a trade. Our Summer School series kicks off with Kurt Nelson and a course in how commodity futures markets work, why speculators and farmers genuinely need each other, and why the cattle price rally may not peak until 2027 or 2028.

Joe's Premium Subscription: www.standardgrain.comGrain Markets and Other Stuff Links —Apple PodcastsSpotifyTikTokYouTubeFutures and options trading involves risk of loss and is not suitable for everyone.

Listen to the SF Daily podcast for today, May 11, 2026, with host Lorrie Boyer. These quick and informative episodes cover the commodity markets, weather, and the big things happening in agriculture each morning. A big focus is on the upcoming USDA reports coming out tomorrow. There are expected reductions in US grain stocks, and steady soybean supplies. Speculators increased their net long positions on corn and beans, with 344,641 futures contracts held as of May 5. Livestock markets saw a decline in cattle futures, while box beef prices rose. Weather warnings were issued for the northern plains and central states, affecting crop conditions and market outlooks. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Veteran junior mining speculator John Kaiser of KaiserResearch.com, believes we are “in the third inning” and on the threshold of a three to five year “mother of all resource bull markets.” John explains how he is playing this bull market and offers his current forecast in this MSE episode. 00:00 Bull Market Setup 00:54 Gold Pause Outlook 02:46 Rotating Winners 04:24 Portfolio Framework 07:19 Small Cap Focus 08:39 AI Research Tools 10:39 Critical Metals Reality 17:02 Prospect Generator Strategy 20:40 LIFE Financing Debate 22:29 Management Versus Project 26:59 Third Inning Timeline John's subscription services: https://www.kaiserresearch.com/g11/Home.asp https://kaiserresearch.substack.com/ Sign up for our free newsletter and receive interview transcripts, stock profiles and investment ideas: http://eepurl.com/cHxJ39 Mining Stock Education offers informational content based on available data but it does not constitute investment, tax, or legal advice. It may not be appropriate for all situations or objectives. Readers and listeners should seek professional advice, make independent investigations and assessments before investing. MSE does not guarantee the accuracy or completeness of its content and should not be solely relied upon for investment decisions. MSE and its owner may hold financial interests in the companies discussed and can trade such securities without notice. MSE is biased towards its advertising sponsors which make this platform possible. MSE is not liable for representations, warranties, or omissions in its content. By accessing MSE content, users agree that MSE and its affiliates bear no liability related to the information provided or the investment decisions you make. Full disclaimer: https://www.miningstockeducation.com/disclaimer/

In this episode of The Sunday Roast, Phil Carroll and Kevin Hornsby are joined by Charles Archer to discuss the major market themes shaping the week, including rising geopolitical tensions around Iran and the Strait of Hormuz and what that could mean for global energy supply, commodities, and investor sentiment.The show features interviews with Rick Rule of Rule Investment Media, who shares his macro outlook on gold, silver and precious metals, the future of fiat currencies, and why he believes the long-term bull market in resources is still underway. The episode also includes company interviews with Arkle Resources and Ajax Resources, covering uranium exploration in Namibia, copper and silver development in Argentina, and the strategic decisions driving growth across the junior mining sector.00:00 - 00:51:45 Rick Rule Interview00:51:45 #ARK Interview01:22:23 #AJAX  Interview01:45:15 #GAL 01:46:33 #UJO #DELT 01:48:49 #SCE 01:51:33 #HEX 01:51:54 #SVML Disclaimer & Declaration of InterestThis podcast may contain paid promotions, including but not limited to sponsorships, endorsements, or affiliate partnerships. The information, investment views, and recommendations provided are for general informational purposes only and should not be construed as a solicitation to buy or sell any financial products related to the companies discussed. Any opinions or comments are made to the best of the knowledge and belief of the commentators; however, no responsibility is accepted for actions based on such opinions or comments. The commentators may or may not hold investments in the companies under discussion. Listeners are encouraged to perform their own research and consult with a licensed professional before making any financial decisions based on the content of this podcast. 

Bible Prophecy Under the Microscope-Episode 82 Gary continues his overview of biblical eschatology by refuting current speculation about the "prophecy of Elam." The OT reference to Elam does not begin (or end) in Jeremiah 49 and there is much to say about how this is fulfilled in the New Testament, not in modern-day "wars and rumors of wars."

What happens to your collection when your player goes down?In this Collector Conversation, I sit down with Rodney (@enjoycards_ig) to talk about what it's really like to collect Tyrese Haliburton during a lost season. We revisit the Game 7 injury. The shock. The numbness. The reality of a gap year.Then we dig into the cards.Does an injury change your conviction?Do you slow down or lean in?Are lower prices an opportunity or a warning sign?We talk about market data. We talk about emotion. We talk about why we collect in the first place.Haliburton's index is down while the broader basketball market is up. Sales volume is shifting. Speculators fade. Core collectors stay.This conversation is about risk, loyalty, patience, and perspective.If you've ever collected an active player through an injury, this one will hit home.Get your free copy of Collecting For Keeps: Finding Meaning In A Hobby Built On HypeStart your 7 day free trial of Stacking Slabs Patreon Today[Distributed on Sunday] Sign up for the Stacking Slabs Weekly Rip Newsletter using this linkFollow Stacking Slabs: | Twitter | Instagram | Facebook | Tiktok ★ Support this podcast on Patreon ★

Gary introduces a two-part discussion about all the recent speculation and finding biblical "proof" that the current Iran invasion is fulfilling Bible prophecy. Gary gives an overview about how Jesus viewed the end times, the events He said to watch for, and who would be involved.

Listen to the SF Daily podcast for today, March 2, 2026, with host Lorrie Boyer. These quick and informative episodes cover the commodity markets, weather, and the big things happening in agriculture each morning. Geopolitical tensions are affecting markets, with crude oil leading a rally due to potential supply disruptions from the Middle East. OPEC is prepared to increase production if needed. Speculators cut net short positions in corn and raised bullish positions in soybeans, with net long positions in soybeans reaching the highest since December 9. In wheat, bearish positions decreased. Livestock markets saw losses in live and feeder cattle futures, and a drought is impacting 49% of U.S. pasture land, up from 36% last year. Winter weather advisories were issued for several regions. Learn more about your ad choices. Visit podcastchoices.com/adchoices

For most of their lives, Stephen and Zack have kept their eyes to the stars and wondering if NASA or anybody else will ever again get serious about launching ships up there.[1. Image credit: SpaceX on X.com.] Now it seems that moment is upon us. Lord willing, next month's launch of Artemis II will drive new great leaps back to the Moon, not only to orbit or put down boots, but to put down roots. Meanwhile, private firms build reusable rockets and plan satellite networks while setting their sights on Mars. So what other science fictions will come true in reality? Join us to discern and celebrate the God-exalting glories of human spaceflight to faraway lands for this landmark 300th episode of Lorehaven's Fantastical Truth. Episode sponsors The Restitching of Camille DuLaine by Lindsay A. Franklin Realm Makers 2026 Conference & Expo Interregnum by J. A. Webb Above the Circle of Earth by E. Stephen Burnett Mission update New at Lorehaven: reviews on break this very busy week. Last week brought a bot swarm and other technical nonsense. Subscribe free to get updates and join the Lorehaven Guild. Authors, want to talk real sci-fi and beyond? Join the Authorship. Quotes and notes 72. When Our World Groans Under Sin, Should Christians Support Space Flight? 121. Will Humans Colonize the Cosmos Before Jesus Returns? 157. Will We Get Superpowers After the Resurrection? 252. What if Space Missionaries Fought the Secular State? | Above the Circle of Earth with E. Stephen Burnett 253. How Do Classic Sci-Fi Novels Explore the Planet Mars? 255. What Are Space Westerns? | After Moses with Michael F. Kane 256. When Have Newer Christian Authors Explored Mars? 1. Today, every space mission starts on Earth A brief summary of spaceflight: Sputnik 1 satellite (Oct. 1957), Yuri Gagarin (April 1961 aboard Vostok 1), Alan Shephard first American (May 1961), John Glenn first to orbit (Feb. 1962 aboard Friendship 1), 1960s moon race, moon landing (July 1969), six moon landings 1980s to early 2000s: Space Shuttle program, ISS, many others Alas, disasters: 1986 Challenger explosion, 2003 Columbia disaster Late 2000s to present: private companies brings new energy Elon Musk: classic humanist, entrepreneur, controversial, mess But a genius billionaire, anyway, and pioneer in new rocketry Same with Amazon's Jeff Bezos, whatever else you think of him These and more are winning goals to make ships less expensive SpaceX rockets can now reverse themselves to land on platforms 2024: Space X “mechazilla” arms caught a returning rocket This month, NASA postponed the Artemis II launch until March. Last week, SpaceX routinely launched a new crew to the ISS. And finally, Elon Musk revealed he's prioritizing lunar missions: For those unaware, SpaceX has already shifted focus to building a self-growing city on the Moon, as we can potentially achieve that in less than 10 years, whereas Mars would take 20+ years. The mission of SpaceX remains the same: extend consciousness and life as we know it to the stars. It is only possible to travel to Mars when the planets align every 26 months (six month trip time), whereas we can launch to the Moon every 10 days (2 day trip time). This means we can iterate much faster to complete a Moon city than a Mars city. That said, SpaceX will also strive to build a Mars city and begin doing so in about 5 to 7 years, but the overriding priority is securing the future of civilization and the Moon is faster. 2. In years, new rockets will reach the Moon Artemis I (Nov. 2022) tested the Space Launch System. Notably, this system is developed separately from reusable rockets. Artemis II (March 2026?) will launch astronauts around the Moon. The mission will last four days and orbit the Moon's far side. The names of these absolutely real, nonfictional astronauts are: Commander Reid Wiseman Pilot Victor Glover Mission specialist Christina Koch Mission specialist astronaut Jeremy Hansen (CSA) As memes foretold, we hope they come back with superpowers. Artemis III will be a real moon landing, first since Apollo 17 in 1972. That mission may launch as early as 2028. No crew announced yet. Axiom Space developed new super-upgraded spacesuits for this. NASA identified possible nine landing sites, all near the South Pole. That region has stable daylight/temperatures plus crater water ice. All said, the first lunar bases could be south polar settlements. Many speculators suggest future lunar manufacturing in this area. NASA, Department of Energy to Develop Lunar Surface Reactor by 2030 Materials include water ice, lunar regolith, and other metals. Musk wants to make AI satellites there and launch them into space. Elon Musk Wants to Build an A.I. Satellite Factory on the Moon Risks: extra radiation could drive habitats under protective layers. You could shield with thick ceilings or else use lunar lava tubes. Listen to our March 2025 podcast series: Martian Month. 3. In decades, mankind may land on Mars In the recent past, Musk and others thought the Moon was jejune. After all, we've already landed there. Where's the fun in returning? But now the Moon seems more accessible. Walk before you run. Last year for ACE's launch, we shared a series: Martian Month. Unlike the Moon, Mars has atmosphere and daylight cycles. It's a little “warmer,” with slightly more radiation protection. Also, Mars has less known surface ice but more carbon dioxide. How to get there? You need to wait about once every two years. Possible transport: nuclear-powered rockets, now in development. NASA administrator Jared Isaacman: nuclear-electric propulsion? 6 Things You Should Know About Nuclear Thermal Propulsion That may reduce travel time by 25 percent (from 6 to 4 months?). Timing: a matter of decades, perhaps the 2030s at the earliest. So yes, you may live to see this happen, yet likely not travel there. Speculators/rocketeers see philosophical, humanitarian motives. For the Christian, our motives for spaceflight are a bit different. After all, God made humans to steward the Earth and maybe more. Alas, sin interferes with our purpose and our very human nature. We're mortal. Space couldn't have killed us before. Now it does. Personally, I see humanity's future with limited spaceflight at best. Yet after Jesus returns and we get New everything, who knows? Either way, with cautious optimism, Christians can rejoice at this. It's healthy to stop navel-gazing and look upward and onward. And someday, yes, missionaries may come to the Moon and Mars. Com station Top question for listeners What big spaceflight news, past or future, is your favorite? Will you watch the Artemis II launch, currently set for early March? Jeremiah Friedli remarked about episode 298: Excellent podcast episode, Stephen! Thanks for tackling these issues from a sound and biblical perspective. I'm looking forward to part 2! Next on Fantastical Truth Three hundred episodes down. Who knows how many to go? Whether you've just found the podcast or have been listening since January 2020, we're grateful for your support of this journey to escape bad books and find the best Christian-made fantasy for Christ's glory. Let's continue to seek and find His fantastical truth!

Listen to the SF Daily podcast for today, February 9, 2026, with host Lorrie Boyer. These quick and informative episodes cover the commodity markets, weather, and the big things happening in agriculture each morning. Futures markets are seeing little movement, with minor adjustments expected in the upcoming WASDE report. South American production, particularly Brazilian soybean sales, is slow, affecting export potential. Speculators increased bullish bets on soybeans and reduced net short positions in corn and wheat. The JBS plant strike and tariff reduction on Argentine beef may impact the market psychologically. Dry conditions in the U.S. southern plains and Nebraska pose fire risks. The podcast also highlighted cattle futures volatility and weather warnings. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Send Us A Message! Let us know what you think.The start of 2026 hasn't been as quiet as expected. From new tax proposals that have investors on edge to economic data forcing banks to tear up their interest rate forecasts, there is a lot to unpack.In this episode of New Zealand Property Insights, Paul and Debbie Roberts deep dive into the proposed 28% Capital Gains Tax (CGT), the reality of sticky inflation, and why "resilience" is the new keyword for due diligence.In this episode, Paul and Debbie cover:The 2026 Election Proposal: A breakdown of Labour's proposed flat 28% tax on investment property profits. Paul and Debbie analyse what this means for portfolios and compare it to Australia's system (highlighting the lack of an inflation adjustment as a major sticking point).The "Speculator" Myth: Why the narrative around property investors often misses the mark regarding long-term holds versus flipping.The Economic Shift: With annual inflation hitting 3.1% and breaching the Reserve Bank's target band, the hosts discuss why some economists are now predicting OCR hikes as early as late 2026 and the implications for mortgage strategies.Climate Resilience: With insurance premiums rising and flood zones under scrutiny, the episode explains the extra due diligence checks investors need to perform before buying in today's market.Whether listeners are worried about future taxes or navigating the current interest rate environment, this episode cuts through the noise to provide the facts.Resource Links:

Hello Interactors,Minnesota has seen federal incursion and overreach before. And not just in 2020. These removal tests we're witnessing are rooted in the premise of US ‘manifest destiny' and how quickly the notion of ‘home' can be made fungible by a violent state. But likeminded bodies always resist being bullied.SCAFFOLD, SOVEREIGNTY, AND SEIZUREOn December 26, 1862, during the Civil War, President Abraham Lincoln authorized the hanging of 38 Dakota men in Mankato, Minnesota. The execution, staged as public theater, was not a solemn judicial act. A special scaffold was built, martial law was declared, and an estimated 4,000 spectators witnessed the largest mass execution in U.S. history. The spectacle mattered because it carried meaning beyond Mankato. The hanging marked the end of the six-week U.S.–Dakota War of 1862. This brutal conflict devastated the Minnesota River Valley and left deep trauma in Dakota communities. It also conveyed that the state could swiftly and effectively attempt control of contested land by violent force.Mankato was the visible climax, but Fort Snelling was the quieter cruelty that continued. After the war, Dakota families — women, children, elders — were confined in harsh conditions near the fort during the winter of 1862–63. Disease and exposure killed between 130 and 300 Dakota people. Execution and exile worked together. One provided public power, the other attempted to ensure territorial outcomes.Here's what Dakota Chief Wabasha's son-in-law, Hdainyanka, wrote to him shortly before his execution:“You have deceived me. You told me that if we followed the advice of General Sibley, and gave ourselves up to the whites, all would be well; no innocent man would be injured. I have not killed, wounded or injured a white man, or any white persons. I have not participated in the plunder of their property; and yet to-day I am set apart for execution, and must die in a few days, while men who are guilty will remain in prison. My wife is your daughter, my children are your grandchildren. I leave them all in your care and under your protection. Do not let them suffer; and when my children are grown up, let them know that their father died because he followed the advice of his chief, and without having the blood of a white man to answer for to the Great Spirit.”This moral failing was part of a larger burgeoning political economy. In 1862, the Twin Cities were still emerging, with mills, river commerce, and infrastructure. Yet the region's future as an urban, financial, and political center depended on converting Dakota and Ojibwe homelands into transferable property. The spring prior to the massacre, in May 1862, Lincoln signed the Homestead Act, handing out 160-acre chunks of stolen land labeled now as “public.” Colonizers and immigrants could occupy this land, and be defended by the US government, if they showed they could “improve” it through five years of occupation.This act negated all Dakota treaties, seized 24 million acres of Minnesota lands, and mandated removal of what were now called Dakota “outlaws.” This converted communal Indigenous homelands into surveyed “public domain” eligible for homesteading, auctions, and rail grants, directly feeding wheat production for Minneapolis mills. Speculators and railroads exploited the act via proxy filings, reselling “cleared” parcels at profit to European immigrants.By 1870, non-Native population surged from 172,000 to over 439,000. The “clearing” of land was not metaphorical. It was the prerequisite for surveying, fencing, settlement, rail corridors, and the wider commodity circuits that would bind the Upper Midwest to national and global markets.That is what Harvard historian Sven Beckert calls war capitalism. He argues that global capitalism's ascent was not a clean evolution toward free exchange. It relied on coercion, conquest, and violence. As his book on the history of Capitalism lays out, state funded war capitalism fundamentally relied on slavery, the dispossession of Indigenous peoples, imperial expansion, armed commerce, and the imposition of sovereignty over both people and territory. In this framing, the Dakota and Ojibwe were obstacles to industrialization and commodification. The frontier needed to be safe for settlement and investment of Germans, Irish, and Scandinavians, as well as railroads and industry. This included these two flour mills, the world's largest by 1880: General Mills and Pillsbury.The gallows in Mankato were the blunt instrument that made the state-capital alliance credible. The point was not only to punish alleged crimes, but to demonstrate a capacity and will to kill. The American state needed to show it could override Indigenous sovereignty and reorder space. The subsequent removals and confinement at Fort Snelling completed the transformation. “Home” was recoded from relationship into asset. This land was no longer lived geography but extractable territory, from stewarding real soil to the selling of real estate.TOPHOPHILIA, TIES, AND TENSIONSWar capitalism is not merely to punish resistance, but to convert a lived place into a fungible asset. But violence plays a deeper role than just legal rearrangement. It has to break this constant of human life: our attachment to place.Behavioral geographer Yi-Fu Tuan borrowed the term topophilia to describe this attachment — the “affective bond between people and place or setting.” The phrase can sound soft and sentimental but it can also cause friction in projects of political economy.The state may be able to abolish or rewrite a treaty, redraw a border, rename a river, and issue new deeds, but it still confronts bodies that have been oriented by firm ground. It's on these grounds that paths are walked, food gathered, relatives buried, stories anchored to landmarks, and seasonal rhythms internalized as a habit of life. The obstacle is embedded and embodied in the physiology, including cognitive, and grounds to location.Modern neuroscience gives a concrete account of how place becomes part of a person. The hippocampus plays a central role in spatial memory and navigation, and research on place cells shows that hippocampal neurons fire in relation to specific locations in an environment. Familiar surroundings are not only around us they are within us. The brain builds spatial scaffolding that links location to memory, routine, prediction, and emotional regulation.When cognition is tied to the specificity of place, it becomes hard for a parcel to be made equivalent to another. Commodification demands interchangeability. A home cannot easily be made equivalent to another home when it's part of the nervous system — not quickly, not cleanly, and often not at all. When the state-capital alliance imagines territory as a grid of extractable value, it is implicitly trying to override how humans experience territory. That is why “simple” displacement so often produces disproportionate harm. Psychiatrist Mindy Fullilove coined the term root shock to describe the traumatic stress that follows the destruction of one's “emotional ecosystem.” Root shock is not only grief or nostalgia. It is a stress response to the sudden loss of the social and spatial cues that stabilize daily life. The shredding of a mesh of relationships, routines, and meanings embedded in a neighborhood or homeland.The root shock of the state violence of 1862 was not just incidental to the project of transformation. It was structurally necessary. If topophilia is a biological and psychological anchor, then a purely legal or economic strategy (bureaucratic coercion) will often be insufficient because the anchor of topophilia holds. To clear land at speed and scale, the state reaches for tools that can sever attachment abruptly. Public executions, mass incarceration, forced marches, and exile doesn't just relocate people. They're violent attempts to scramble the conditions under which people can remain attached at all. It transforms topophilia into vulnerability.Work on social exclusion and “social pain” helps explain why. In a widely cited fMRI study, Naomi Eisenberger and colleagues found increased activity in the anterior cingulate cortex during experiences of exclusion. This parallels patterns seen in physical pain studies where distress is tracked with painful activities. The point is not that social threat is “just like” physical injury, but that the brain treats social severing as a serious alarm condition. It's something that demands attention, vigilance, and behavioral change to overcome.ROOTS, RESISTANCE, AND REPAIRTopophilia doesn't end with the so-called frontier or attempts at ‘removing' its inhabitants. It reappears wherever people form durable bonds. That includes the streets and schools, churches and parks, language, kin, and the local economies and cultures war capitalism eventually built. The Dakota and Ojibwe were never “removed” in any final sense. Many live and organize in and around the Twin Cities today.In South Minneapolis, the Indigenous Protector Movement, a biproduct of the American Indian Movement, works out of the American Indian Cultural Corridor along Franklin Avenue — an immediate target for ICE. The protectors made their presence known as a form of ongoing place-based care and defense. It is a living archive of tactics for defending attachment under pressure through direct action, community building, patrols, and the mundane discipline of showing up. What it offers is not merely a critique of state violence, but vigilance without spectacle, care without permission, and solidarity as a daily habit rather than a momentary sentiment.Other areas of Minneapolis show how when federal enforcement turns public space into a zone of uncertainty, topophilic neighbors often respond by adopting exactly those same “weapons” of persistence — care, documentation, rapid communication, mutual aid — that have long characterized Indigenous resistance and slavery abolitionist networks.Standing Rock, where the Standing Rock Sioux Tribe and allies gathered in 2016 to oppose the Dakota Access Pipeline, demonstrated how quickly infrastructure can scale when a place becomes a shared object of defense.The #NoDAPL movement assembled a broad coalition of Indigenous nations and allies, over 200 tribes, alongside legal support, medical care, and communications systems designed to withstand state patience. The 2020 George Floyd uprising in Minneapolis also revealed how love of place can become a platform for organized care rather than retreat. Alongside protest, residents built mutual-aid channels, street-medic networks, food distribution, and neighborhood defense efforts that treated the city as an emotional ecosystem worth repairing. What looked to outsiders like spontaneous eruption was, on the ground, a rapid layering of roles that included medics, legal observers, supply runners, translators, and de-escalators. This ecology of participation made it possible for large numbers of people to act without centralized command.Social psychology helps explain why these movements generate allies rather than only sympathizers. One key concept is collective efficacy — the combination of social cohesion and a shared willingness to intervene for the common good. It blossoms when people repeatedly see each other act, learn local norms of mutual obligation, and build trust that intervention will be supported rather than punished. All rooted in topophilia.Place attachment can bridge boundaries that would otherwise keep people separate. Work in community psychology and planning shows that place attachment and meaning can support participation and collective engagement, especially when development or coercion threatens everyday life. In other words, topophilia is not just private feeling. When it's under threat it can become public motive and an engine for coalition.The coalition in Minneapolis is being characterized by the federal government as terrorists. This borrows from a long history of resistance to violence because war capitalism has never been only domestic. The United States and its allies refined coercive governance overseas through night raids and “capture-or-kill” operations in Afghanistan, midnight house raids in Iraq, and broader militarized campaigns that treat homes as “searchable terrain” and communities as “intelligence environments.”Many of the officials, contractors, and voters who authorized or normalized these methods rarely imagined the same atmosphere of violent seizure in their neighborhood. As unimaginable as it may be watching unmarked vehicles, sudden detentions, and public uncertainty coming to American streets — used against the very citizens and taxpayers who fund such operations — it's not to those victims overseas in places like Afghanistan, Iraq, Palestine, or even inner city America.That return is what the poet and politician Aimé Césaire called the “imperial boomerang” effect, the idea that techniques tolerated in peripheral countries can come home to roost. In the U.S., the boomerang has long “landed” first on people of color. It emerges through surveillance and disruption campaigns like the two decades of the covert and illegal COINTELPRO program where the FBI targeted counterculture groups of the so-called New Left.Or the “Palmer Raids” of 1919 and 1920 targeting largely Italian and Eastern European Jewish immigrants and their left-leaning politics. These led to riots in 30 US cities and culminated in the bombing of the home of A. Mitchell Palmer, the US attorney general. These programs all reflect the notion that war can come home — just look at the increased militarizing of policing complete with SWAT tactics. And the same history that produced the scaffold of war capitalism of the past also produced reservoirs of resistance we see here and now. When neighbors anywhere respond to incursions not only with fear but with organized vigilance and material support, they are adapting older strategies of care found in Indigenous, abolitionist, and other movement-based defenses of people and places against infiltration, intimidation, and attempted violent removal.We can see how war capitalism endures. Mankato's 1862 gallows aimed to clear Dakota homelands of their people for homesteading, rails, and mills. Meanwhile, today's Operation Metro Surge includes thousands of federal agents raiding Minneapolis homes and streets, attempting to sever immigrant attachments to allegedly enforce labor control and national security. These militarized spectacles of warrantless entries, tear gas, and shootings echo what Beckert has uncovered. They treat people and place as obstacles to commodification rather than roots of stewardship.Yet topophilia also persists. These cross cultural rapid-response networks are not new to these lands, even though the US government tried to erase them centuries ago. The inspiring actions we see in Minneapolis reflect the values of compassion, positiveness, and respect for all relatives with neighborly solidarity that the first occupants of that land embraced. They're now woven with their allied 21st century neighbors in common and shared resistance. As best expressed here by Indigenous studies and political ecology scholar Melanie Yazzie. (and the longer version here) Minneapolis, like those acts of resistance in the nearby Dakotas, enacts and rehearses an alternative form of civil governance that centers mutual obligation over coercion and extraction. It shows how cities can survive the strain and stay alive — not through fear and gain, but through care that grounds and sustains. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit interplace.io

Stijn Schmitz welcomes back the legendary Rick Rule to the show. Rick Rule is Investor, Speculator, Founder & CEO of Rule Investment Media. In this wide-ranging interview, Rule provides insights into various commodity markets and economic trends, highlighting key perspectives on precious metals, oil, and global economic dynamics. Reflecting on the remarkable performance of commodities in 2025, Rule notes that gold has actually been steadily growing at 9% compounded annually since 2000. While he doesn’t expect the same parabolic moves to continue, he believes gold will continue to appreciate over the next decade. For silver, Rule discusses significant market disruptions, including changes in trading patterns in Dubai and China, and notes that industrial demand remains structurally inelastic. Regarding the broader economic landscape, Rule offers a stark assessment of the US dollar’s purchasing power, which has lost 97% of its value since 1913. He predicts a potential further 75% loss in purchasing power, suggesting that governments will likely continue to inflate away debt obligations. This perspective underpins his strategy of saving in gold and maintaining liquidity in US dollars. In the commodity sector, Rule sees significant opportunities in copper, oil, and select mining stocks. He emphasizes the long-term supply constraints in copper, driven by decades of underinvestment and lengthy permitting processes. For oil, he recommends companies like Exxon and Canadian producers, noting the sector’s current undervaluation. Timestamps: 00:00:00 – Introduction 00:00:40 – 2025 Commodity Rally Drivers 00:01:25 – Gold Bull Market History 00:02:52 – Silver Shortage Fundamentals 00:05:49 – Silver Market Disruptions 00:08:00 – Silver Demand Inelasticity 00:12:40 – US Dollar Purchasing Power Loss 00:17:13 – Fiscal Challenges and Inflation 00:19:17 – Precious Metals Miners Value 00:23:20 – Private Placements 00:25:25 – Oil and Gas Opportunities 00:32:25 – Hated Commodities Overview 00:36:00 – Coal & Copper 00:44:20 – Concluding Thoughts Guest Links: X: https://x.com/@realrickrule Website: https://ruleinvestmentmedia.com YouTube: https://www.youtube.com/@RuleInvestmentMedia Classroom: https://ruleclassroom.com Rick Rule has dedicated his entire adult life to many aspects of natural resources securities investing. Besides the knowledge and experience gained in a long and focused career, he has a global network of contacts in the natural resources and finance sectors. Mr. Rule is a frequent speaker at industry conferences and is regularly interviewed for radio, television, print, and online media outlets concerning natural resources investment and industry topics. Prominent natural resources-oriented newsletters and advisories frequently quote him. Mr. Rule and his team have expertise in many resource sectors, including agriculture, alternative energy, forestry, oil and gas, mining, and water.

Listen to the SF Daily podcast for today, December 15, 2025, with host Lorrie Boyer. These quick and informative episodes cover the commodity markets, weather, and the big things happening in agriculture each morning. Corn and soybeans showed mixed trading, while wheat faced selling pressure. The November NOPA crush report is expected, and US export forecasts remain high, including a record 3.2 billion bushels of corn. Speculators turned bullish on corn, with net long positions increasing. Soybean bullish bets also rose. Wheat saw reduced bearish bets. Livestock markets saw higher cash cattle prices, with narrower price ranges for futures. Extremely cold weather advisories were issued for parts of the Eastern US, potentially causing frostbite. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Listen to the SF Daily podcast for today, December 8, 2025, with host Lorrie Boyer. These quick and informative episodes cover the commodity markets, weather, and the big things happening in agriculture each morning. Soybeans faced pressure due to weekend rains in South America and weak energy complexes, with doubts about China's import intentions. US corn and wheat markets were supported by low-quality grain in China and Black Sea export disruptions. Speculators raised net long positions in soybeans and reduced bearish stances on corn. The global food cost index declined to 125.1 points in November. Livestock markets saw higher cash cattle prices, but were affected by President Trump's price-fixing investigation. Severe weather is forecasted for the Northern US. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Looking for B2B advertising on our podcast for the coffee industry: support@mapitforward.org or DM us here https://www.instagram.com/mapitforward.coffee/••••••••••••••••••••••••••••••••This is episode four of a 5-part podcast series on The Daily Coffee Pro Podcast by Map It Forward, hosted by Lee Safar and featuring returning guest Carley Garner.Carley is a commodity broker and the founder of US-based commodity brokerage firm, DeCarley Trading.In this series, Lee and Carley discuss the coffee futures market in 2025 and 2026.No information in this series is financial advice and trading comes at the risk of losing money.The five episodes of this series are:1. 2025 Has Been An Unusual Year in Coffee Futures - https://youtu.be/fuyIL1PJjN82. The Forces That Moved Coffee Futures in 2025 - https://youtu.be/7-I7iduViAQ3. Taking Advantages of High Coffee Prices Outside the Cash Market - https://youtu.be/djwdbraAi2w4. Speculators Are Important To The Coffee Futures Market - https://youtu.be/K_Z6lny-wsI5. Coffee Futures Markets in 2026 - https://youtu.be/TG_TUCwi7eAIn this episode of the podcast series, Lee and Carley discuss the crucial role of speculators in the coffee futures market. Discover how speculators provide liquidity, affect pricing, and why their participation can prevent market volatility. Learn about the significant challenges coffee farmers face, such as high interest rates on agricultural loans and the generational knowledge gap in hedging tools. Understand the vital connection between speculators and traders, and how the industry's dynamics may impact coffee production in the years ahead.Connect with Carley and DeCarley Trading at:https://www.decarleytrading.comhttps://www.linkedin.com/in/carleygarner/https://www.instagram.com/decarleytrading/https://decarleytrading.substack.com/https://www.decarleytrading.com/learn-to-trade-commodities ‍••••••••••••••••••••••••••••••••Connect with Map It Forward here: Website | Instagram | Mailing list

Looking for B2B advertising on our podcast for the coffee industry: support@mapitforward.org or DM us here https://www.instagram.com/mapitforward.coffee/••••••••••••••••••••••••••••••••This is episode four of a 5-part podcast series on The Daily Coffee Pro Podcast by Map It Forward, hosted by Lee Safar and featuring returning guest Carley Garner.Carley is a commodity broker and the founder of US-based commodity brokerage firm, DeCarley Trading.In this series, Lee and Carley discuss the coffee futures market in 2025 and 2026.No information in this series is financial advice and trading comes at the risk of losing money.The five episodes of this series are:1. 2025 Has Been An Unusual Year in Coffee Futures - https://youtu.be/fuyIL1PJjN82. The Forces That Moved Coffee Futures in 2025 - https://youtu.be/7-I7iduViAQ3. Taking Advantages of High Coffee Prices Outside the Cash Market - https://youtu.be/djwdbraAi2w4. Speculators Are Important To The Coffee Futures Market - https://youtu.be/K_Z6lny-wsI5. Coffee Futures Markets in 2026 - https://youtu.be/TG_TUCwi7eAIn this episode of the podcast series, Lee and Carley discuss the crucial role of speculators in the coffee futures market. Discover how speculators provide liquidity, affect pricing, and why their participation can prevent market volatility. Learn about the significant challenges coffee farmers face, such as high interest rates on agricultural loans and the generational knowledge gap in hedging tools. Understand the vital connection between speculators and traders, and how the industry's dynamics may impact coffee production in the years ahead.Connect with Carley and DeCarley Trading at:https://www.decarleytrading.comhttps://www.linkedin.com/in/carleygarner/https://www.instagram.com/decarleytrading/https://decarleytrading.substack.com/https://www.decarleytrading.com/learn-to-trade-commodities••••••••••••••••••••••••••••••••Connect with Map It Forward here: Website | Instagram | Mailing list

Listen to the SF Daily podcast for today, November 26, 2025, with host Lorrie Boyer. These quick and informative episodes cover the commodity markets, weather, and the big things happening in agriculture each morning. Agricultural markets are in holiday mode, with December grain registrations at 80 corn contracts, 34 lots of soft red wheat, and 176 lots of hard red wheat. US soybean export pace is low, with 29 million bushels loaded this week, the lowest in 19 years but still within weekly needs. Speculators increased their net short positions in corn and wheat. Cattle futures showed substantial weakness, with northern dress cattle trading $15 lower. A major winter storm is forecast for the Midwest, expected to bring significant snow and high winds. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Gianni Kovacevic reveals battery metals opportunities with a “20-bagger future” in this MSE episode. Gianni is a copper and lithium speculator with deep insights into battery metals. Gianni shares his perspectives on the future of electric metals, focusing on the importance of lithium, phosphoric acid in LFP batteries, and emerging technologies like direct lithium extraction (DLE). He discusses his portfolio's heavy weighting in battery metals and provides a detailed analysis of why lithium and phosphate are poised for significant growth. Gianni also touches on his approach to speculation, the importance of thorough research, and learning from past investment mistakes. He concludes by offering his thoughts on the timeline for these emerging technologies and the potential for substantial returns. 00:00 Intro 00:26 Deep Dive into Battery Metals 01:51 The Future of Phosphate in Batteries 04:46 Speculating on First Phosphate 05:53 Macro Trends and Micro Opportunities 08:05 Lithium Market Insights 11:46 Direct Lithium Extraction (DLE) Technology 18:12 Cobalt and Other Battery Metals 22:05 China's Energy Market and Future Projections 23:55 China's Energy Transformation 24:53 The Role of Copper and Aluminum 25:35 Battery Storage and Lithium Demand 27:03 Traceability of Electric Metals 31:09 Speculation in the Mining Industry 40:22 Lessons from Past Mistakes 45:20 Final Thoughts and Advice https://twitter.com/GianniKov https://kovacevic.com/ Sign up for our free newsletter and receive interview transcripts, stock profiles and investment ideas: http://eepurl.com/cHxJ39 This interview was not sponsored. Mining Stock Education (MSE) offers informational content based on available data but it does not constitute investment, tax, or legal advice. It may not be appropriate for all situations or objectives. Readers and listeners should seek professional advice, make independent investigations and assessments before investing. MSE does not guarantee the accuracy or completeness of its content and should not be solely relied upon for investment decisions. MSE and its owner may hold financial interests in the companies discussed and can trade such securities without notice. If you buy stock in a company featured on MSE, for your own protection, you should assume that it is MSE's owner personally selling you that stock. MSE is biased towards its advertising sponsors which make this platform possible. MSE is not liable for representations, warranties, or omissions in its content. By accessing MSE content, users agree that MSE and its affiliates bear no liability related to the information provided or the investment decisions you make. Full disclaimer: https://www.miningstockeducation.com/disclaimer/

Stijn Schmitz welcomes Doug Casey to the show. Doug Casey is Bestselling Author, Speculator, Founder of Casey Research, & Voluntarist Philosopher. In this wide-ranging discussion, Casey provides a comprehensive perspective on the global economic landscape, focusing on precious metals, commodities, and potential monetary shifts. Casey argues that the world is entering the "greatest monetary crisis in world history," with gold and Bitcoin positioned as potential alternative monetary assets. He believes the current financial system is fundamentally broken, with governments printing money and eroding currency value. While bullish on gold, he suggests it's no longer underpriced as it historically was, but remains a critical savings vehicle, especially when stored offshore. Regarding investment strategies, Casey recommends focusing on gold and silver mining stocks, particularly smaller companies with entrepreneurial management. He emphasizes evaluating mining investments through his "nine peas" approach, with people and management quality being the most critical factor. He sees significant potential in junior mining companies, noting they remain dramatically undervalued. Casey is equally enthusiastic about broader commodity opportunities, especially in energy sectors like coal, oil, natural gas, and uranium. He views these commodities as critically undervalued and essential for global economic development. He's particularly optimistic about emerging markets in the Orient, suggesting they represent better economic potential than Western economies. On silver, Casey sees it as a "poor man's gold" with significant upside potential, particularly given its industrial applications and relatively small market capitalization. He believes silver could potentially reach $200-$250 per ounce in real terms. Throughout the discussion, Casey maintains a provocative, libertarian perspective, critiquing government institutions and advocating for decentralized monetary systems. He remains fundamentally optimistic about human potential, believing that technological innovation and entrepreneurial spirit will ultimately drive economic progress. Casey concludes by directing listeners to his various platforms, including internationalman.com, his Crisis Investing newsletter, and his podcast with Matt Smith, encouraging further exploration of his economic perspectives.

In this week's Live from the Vault, Andrew Maguire explains how the mid-October gold and silver correction forced short-term traders to sell on COMEX, transferring physical metal to long-term holders and creating a solid base for the next rally.The precious metals expert highlights tight COMEX liquidity, steady sovereign accumulation, and the shift from paper to physical markets, noting that both gold and silver are set for a strong rebound that could leave speculative traders behind.Timestamps: 00:00 Start02:09 Fed battles gold volatility amid BRICS-driven market pressure11:20 Speculators flushed as gold resets for next bullish rally20:03 China leads de-dollarisation as COMEX loses global gold control29:28 China's physical gold secures global price, challenging dollar dominance38:01 China controls gold and silver markets. Tight supply drives rallySend your questions to Andy here: https://www.speakpipe.com/LFTVSign up for Kinesis on desktop:https://kinesis.money/kinesis-precious-metals/?utm_source=youtube&utm_medium=video&utm_campaign=lftv_245Download the Kinesis Mobile app - available App Store and Google Play:Apple: https://kms.kinesis.money/signupGoogle: https://play.google.com/store/apps/details?id=com.kinesis.kinesisappAlso, don't forget to check out our social channels where you can stay up to date with all the latest news and developments from the team.X: https://twitter.com/KinesisMonetaryFacebook: https://www.facebook.com/kinesismoney/Instagram: https://www.instagram.com/kinesismoney/Telegram: https://t.me/kinesismoneyTikTok: https://www.tiktok.com/@kinesismoneyThe opinions expressed in this video by Andrew Maguire and any guest are solely their own and do not reflect the official policy, position, or views of Kinesis. The information provided is for general informational purposes only and does not constitute investment advice, financial advice, or any other type of professional advice.Viewers are encouraged to seek independent financial advice tailored to their individual circumstances before making any decisions related to the gold market or other investments. Kinesis does not accept any responsibility or liability for actions taken based on the content of this video.

On this week's Stansberry Investor Hour, Dan and Corey are joined by Eric Fry. Eric is the editor of multiple newsletters at our corporate affiliate InvestorPlace, including Fry's Investment Report and The Speculator.  Eric kicks off the show by discussing his time working alongside legendary financial publisher Jim Grant and his top-down approach to investing. His strategy involves finding industry leaders that have fallen on hard times but still have favorable underlying dynamics. Eric says that with this method, he has collected 100%-plus gains in the past few years in companies like Amazon and Corning. He also talks about investing in foreign stocks, the unbalanced risk in microcaps that many investors don't consider, and three industries he stays away from. (0:00) Next, Eric shares his time horizon for investing, whether he recommends adding to existing winners, his past experience with bitcoin, and the advice he gives his subscribers on position sizing and risk management. He notes that investors will often overstate their risk tolerance and understate their investment goals, which can cause problems. This leads to a conversation about the advantages of long-dated options versus short-term options. (20:39) Finally, Eric breaks the world of AI investment down into four groups: builders, enablers, appliers, and survivors. He says most of his current investment ideas are focused on the survivor category – and he names three such stocks he likes today. This includes a for-profit thrift-store chain, an English beverage company with rising U.S. sales, and an international food-delivery company that just became profitable. (40:03)

On this week's Stansberry Investor Hour, Dan and Corey are joined by Eric Fry. Eric is the editor of multiple newsletters at our corporate affiliate InvestorPlace, including Fry's Investment Report and The Speculator.  Eric kicks off the show by discussing his time working alongside legendary financial publisher Jim Grant and his top-down approach to investing. His strategy involves finding industry leaders that have fallen on hard times but still have favorable underlying dynamics. Eric says that with this method, he has collected 100%-plus gains in the past few years in companies like Amazon and Corning. He also talks about investing in foreign stocks, the unbalanced risk in microcaps that many investors don't consider, and three industries he stays away from. (0:00) Next, Eric shares his time horizon for investing, whether he recommends adding to existing winners, his past experience with bitcoin, and the advice he gives his subscribers on position sizing and risk management. He notes that investors will often overstate their risk tolerance and understate their investment goals, which can cause problems. This leads to a conversation about the advantages of long-dated options versus short-term options. (20:39) Finally, Eric breaks the world of AI investment down into four groups: builders, enablers, appliers, and survivors. He says most of his current investment ideas are focused on the survivor category – and he names three such stocks he likes today. This includes a for-profit thrift-store chain, an English beverage company with rising U.S. sales, and an international food-delivery company that just became profitable. (40:03)

Stijn Schmitz welcomes Rick Rule to the show. Rick Rule is Investor, Speculator, Founder & CEO of Rule Investment Media. In this comprehensive discussion, Rule provides deep insights into commodity markets, focusing on gold, oil, and various other resources. Regarding gold, Rule believes the precious metal is positioned for significant growth over the next five to ten years. He anticipates a potential 75% decline in the US dollar's purchasing power, which could translate to a three-fold increase in gold's nominal price. Rule emphasizes that while gold's trajectory won't be a smooth ascent, investors should be prepared for volatility and cyclical movements. In the energy sector, Rule is particularly bullish on oil and gas. He argues that despite narratives about alternative energy, fossil fuels will remain the dominant global energy source for decades. He sees tremendous value in companies like Exxon, which he believes is trading at a 50% discount to its net present value. Rule suggests that the industry's ongoing infrastructure investments and technological advancements make oil and gas an attractive investment opportunity. Rule also shares perspectives on various commodities, including nickel, copper, zinc, and uranium. He highlights the significant underinvestment in these sectors over the past decades, which creates potential long-term investment opportunities. For instance, he sees a substantial copper supply deficit emerging in the next five years due to decades of underinvestment. Beyond commodities, Rule discusses his involvement with Rule Investment Media and Battle Bank, offering investors resources to analyze natural resource stocks and providing innovative banking services. He encourages investors to conduct thorough research, be patient, and look for opportunities in sectors experiencing market disfavor. Throughout the conversation, Rule's investment philosophy emphasizes understanding long-term trends, focusing on high-quality producers, and being willing to take calculated risks in undervalued sectors. His approach combines deep industry knowledge with a pragmatic, patient investment strategy.

In this episode, Phil, Kevin, and Charles are joined by legendary investor Rick Rule of Rule Investment Media. Rick shares why he saves in gold, the difference between bullion and mining equities, and the arithmetic behind a potential long decline in fiat purchasing power. He also explores historical volatility in precious metals bull markets, U.S. policy theater, tariffs, permitting gridlock, and political risk—from California to Congo—before discussing his upcoming Battle Bank, designed for multi-currency savers and gold-backed credit lines. We then sit down with Sapan Ghai, COO of Sovereign Metals, for a deep dive into Kasiya in Malawi—set to be the world's largest natural rutile deposit and a key source of low-cost graphite. Sapan covers ESG operations on the ground, the Nacala export corridor, Japanese partnerships, and the project's strong economics. Finally, we round off with the movers and shakers of the week, covering Genflow, Alba, Alert Defence, Cloudbreak, African Pioneer, and more. 00:00 -00:04:26  Weekly News Roundup  00:04:26 Rick Rule Interview 01:09:28 #SVML Interview 01:33:08 #GENF  01:35:25 #ALBA  01:36:23 #GROC  01:37:10 #BZT  01:38:48 #ALRT  01:39:08 #CDL  01:40:16 #XTR  01:40:30 #GLR  01:40:49 #JLP  01:41:18 #INC  Disclaimer & Declaration of Interest This podcast may contain paid promotions, including but not limited to sponsorships, endorsements, or affiliate partnerships. The information, investment views, and recommendations provided are for general informational purposes only and should not be construed as a solicitation to buy or sell any financial products related to the companies discussed. Any opinions or comments are made to the best of the knowledge and belief of the commentators; however, no responsibility is accepted for actions based on such opinions or comments. The commentators may or may not hold investments in the companies under discussion. Listeners are encouraged to perform their own research and consult with a licensed professional before making any financial decisions based on the content of this podcast. 

Listen to the SF Daily podcast for today, September 08, 2025, with host Lorrie Boyer. These quick and informative episodes cover the commodity markets, weather, and the big things happening in agriculture each morning. Frost was noted on crop quality in the northern Midwest, and drought conditions have led to draft restrictions on the Mississippi River, affecting harvest and fertilizer transportation. The U.S. and Japan agreed on a trade deal, with Japan increasing corn, soybean, and bioethanol purchases to $8 billion annually. Speculators reduced net short positions in corn and soybeans, while hedge funds increased short positions in wheat. Cattle futures showed weakness, with cash prices fluctuating, and box beef prices declined. The September WASDE report will be issued on Friday. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Listen to the SF Daily podcast for today, August 18, 2025, with host Lorrie Boyer. These quick and informative episodes cover the commodity markets, weather, and the big things happening in agriculture each morning. Soybean prices are affected by a correction from last week's gains, with the NOPA crush report showing a six-month high. The Pro Farmer crop tour will provide insights into crop potential. Futures demand remains high, especially in grains, with concerns over Chinese trade. The USDA plans to invest $750 million in a sterile fly production facility in Texas. Speculators raised their net short positions on corn and reduced bearish bets on soybeans. Cattle market volatility is high, influenced by the potential Mexican border reopening. Extreme weather alerts were issued for parts of the Midwest and South. Learn more about your ad choices. Visit podcastchoices.com/adchoices

In this episode of Mining Stock Education, Bill Powers and Brian Leni each share one of their ten-bagger losses. The duo delves into the intricacies of investing in junior mining stocks through their monthly MSE chat. They tackle a listener's question about missed investment opportunities and discuss the importance of establishing and adhering to a sound investment process. Brian shares his reasons for passing on Filo Mining due to jurisdictional risks and contrasts it with his success in investing in AbraSilver. The episode also covers investment strategies, such as balancing risk and return and the differences between conservative and speculative approaches. They analyze market dynamics, including the challenges of opaque markets like rare earth elements, and discuss the significance of due diligence, valuation, corporate transparency, and management credibility. Additionally, the episode explores the importance of learning from investment losses to refine one's process and the contrasting approaches between “spray and pray” and targeted, strategic investing. Listen to these two full-time mining speculators share insights and reflect upon your own approach to junior mining speculation! 0:00 Introduction 0:31 Ten-bagger losses 7:39 Process modification 11:02 “Spray & Pray” vs Rifle approach 19:19 Conservative vs Speculative 22:36 Corp decks 28:10 New geological model speculation 31:43 Investing with failed management 36:50 Lower-tier management with cash 39:40 Director's overcompensation 44:14 Pump-n-dump case study 49:22 Honorable founder's shares escrow 52:13 Rare earths Brian's website: https://www.juniorstockreview.com/ Bill's Twitter: https://x.com/MiningStockEdu Sign up for our free newsletter and receive interview transcripts, stock profiles and investment ideas: http://eepurl.com/cHxJ39 Mining Stock Education offers informational content based on available data but it does not constitute investment, tax, or legal advice. It may not be appropriate for all situations or objectives. Readers and listeners should seek professional advice, make independent investigations and assessments before investing. MSE does not guarantee the accuracy or completeness of its content and should not be solely relied upon for investment decisions. MSE and its owner may hold financial interests in the companies discussed and can trade such securities without notice. MSE is biased towards its advertising sponsors which make this platform possible. MSE is not liable for representations, warranties, or omissions in its content. By accessing MSE content, users agree that MSE and its affiliates bear no liability related to the information provided or the investment decisions you make. Full disclaimer: https://www.miningstockeducation.com/disclaimer/

The finale of our trilogy centered on Technofeudalism begins with the intersection of political economy with aesthetics and literary forms, followed by a synthesis of financial and fictive definitions of speculation [18:00], what can be done with the technofeudal thesis [27:30], the role of solidarity in the age of insecurity [33:00], the Panic of 1873 as a model [41:30], panics to come [53:00], and some final words from Yanis Varoufakis [72:00] Cast (in order of appearance): Matt Seybold, Yanis Varoufakis, Jordan S. Carroll, James Livingston, Astra Taylor, Leigh Claire La Berge Soundtrack: DownRiver Collective Narration: Nathan Osgood & SNR Audio For more about this episode, including a complete bibliography, please visit MarkTwainStudies.com/Speculators, or subscribe to Matt Seybold's newsletter at TheAmericanVandal.substack.com

Market speculation into commodities like Bitcoin and Crude oil pose dangerous risks during the best of times. Heading into a recession, the stakes are even higher. The temptation to participate during volatile periods is a psychological phenomenon that takes hold among casual investors who rarely realize when they’re part of an institutional con. When leveraged institutions are in desperate need of liquidity they’ll stop at nothing to game the system. In this episode, Max offers a cautionary tale of speculative behavior in the crude oil markets during the financial crisis that very few people even know about. He then connects it to the dangers posed to investors who dabble in digital currencies like Bitcoin, especially under a pro-crypto Trump administration. Access the episode resources. Resources Forbes: ‘Worse Than 2008’—Bitcoin Price Braced As Billionaire Ray Dalio Warns Of ‘Monetary Order Breakdown’ Forbes: ‘Big Catalyst’—Serious Fed Warning Spurs Huge BlackRock Bitcoin Price Prediction NPR: What to know about Trump's 'crypto strategic reserve' plan Yahoo Finance: Donald Trump’s 29-year-old crypto guru lays out the president’s plans for regulating crypto and rolling back a Biden-era crackdown Pillsbury Law: Trump 2.0: A New Era for the Regulation of Cryptocurrency and Digital Assets SDLC Corp: Stablecoins: Characteristics, How They Work, Functionality, and Use Cases Bitpanda Academy: Meme Coins: Definition & Characteristics St. Edward’s University: What Are the Exchange Coins The Digital Chamber: Why is Bitcoin a Commodity? Investopedia: Is Crypto a Commodity? What It Means, Examples Book Love Dan Dicker: Oil's Endless Bid: Taming the Unreliable Price of Oil to Secure Our Economy Emily Lambert: The Futures: The Rise of the Speculator and the Origins of the World's Biggest Markets Peter Maass: Crude World Leah McGrath Goodman: The Asylum: Inside the Rise and Ruin of the Global Oil Market UNFTR Episode Resources Peak Oil: It’s a Crude, Crude World. The U.S. Dollar and 10 Year Treasury: Why Economists Are Freaking the F*ck Out. -- If you like #UNFTR, please leave us a rating and review on Apple Podcasts and Spotify: unftr.com/rate and follow us on Facebook, Bluesky, TikTok and Instagram at @UNFTRpod. Visit us online at unftr.com. Join our Discord at unftr.com/discord. Become a member at unftr.com/memberships. Buy yourself some Unf*cking Coffee at shop.unftr.com. Visit our bookshop.org page at bookshop.org/shop/UNFTRpod to find the full UNFTR book list, and find book recommendations from our Unf*ckers at bookshop.org/lists/unf-cker-book-recommendations. Access the UNFTR Musicless feed by following the instructions at unftr.com/accessibility. Unf*cking the Republic is produced by 99 and engineered by Manny Faces Media (mannyfacesmedia.com). Original music is by Tom McGovern (tommcgovern.com). The show is hosted by Max and distributed by 99.Support the show: https://www.buymeacoffee.com/unftrSee omnystudio.com/listener for privacy information.

Is George Soros a philanthropic visionary… or the shadow architect of a globalist empire? “Shadow Money: The Soros Conspiracy Files” plunges into the labyrinth of myths, money, and manipulation surrounding one of the most polarizing figures on Earth. Hosted by an investigative journalist and a geopolitical strategist, this explosive documentary-style podcast dissects Soros's meteoric rise from Holocaust survivor to billionaire “Great Speculator,” unraveling the tangled web of his hedge fund dominance, political kingmaking, and the conspiracy theories that paint him as a modern-day puppet master.Was Soros the invisible hand behind regime changes, economic collapses, and cultural revolutions? We'll expose never-before-seen financial records, clandestine meetings with world leaders, and whistleblower testimonies that blur the line between philanthropy and power plays. Dive into the dark heart of far-right fever dreams—the “Soros-funded migrant caravans,” “Antifa bankrolling,” and his alleged “Great Reset” agenda—while forensic economists and intelligence insiders separate fact from fiction. But this isn't just a hit job: Hear Soros in his own words through rare archival audio, and discover how his Open Society Foundations became both a beacon of hope and a lightning rod for rage.From his $10 billion “bet against the British pound” to whispers of a “deep state” alliance, Shadow Money doesn't just ask questions—it follows the money. Whether he's saint or saboteur, one thing's clear: Soros's influence is a ticking time bomb in the war for democracy's soul. Subscribe now—before the truth gets shorted.

Noah Healy is a market designer and game theorist working on better economic systems. #financialsystem #blockchain #crypto ================ All Episodes can be found at www.thecryptopodcast.org   All about Roy / Brain Gym & Virtual Assistants at https://roycoughlan.com/ ------------------    About my Guest Noah Healy: Noah Healy is a market designer and game theorist working on better economic systems. After training in nuclear engineering he worked for tech startups at the peak of the dot com boom. Becoming fascinated by the mathematics of information and computation led to patent work on a better commodity market design. What we Discussed:   00:00 Who is Noah Healy 03:30 His Blockchain Journey 08:00 The Bitcoin failure issues 11:50 Coordisc 16:05 Stopping the Corruption 22:30 The Basic Economic Assumptions 24:00 Mathematical Foundations 26:05 The Solution 28:50 The Chaos of commodities from Wars 32:05 Qualitative arguments 34:30 What Happens to Speculators with a disaster 37:30 Regulations for the Commodities Markets 40:40 His Patent and his experience with the Patent Office 46:10 Time & money Suck from Government 49:15 The Difficulty of getting International Patents 55:10 What he need to make this work How to Contact Noah Healy : https://coordisc.com/ linkedin https://www.linkedin.com/in/noah-healy/ YouTube https://www.youtube.com/watch?v=v8aOEcDV7MA   ------------------ All about Roy / Brain Gym & Virtual Assistants at ⁠https://roycoughlan.com/⁠  ___________________

Noah Healy is a market designer and game theorist working on better economic systems.#financialsystem #blockchain #crypto================All Episodes can be found at www.thecryptopodcast.org All about Roy / Brain Gym & Virtual Assistants athttps://roycoughlan.com/------------------   About my Guest Noah Healy:Noah Healy is a market designer and game theorist working on better economic systems. After training in nuclear engineering he worked for tech startups at the peak of the dot com boom. Becoming fascinated by the mathematics of information and computation led to patent work on a better commodity market design.What we Discussed: 00:00 Who is Noah Healy03:30 His Blockchain Journey08:00 The Bitcoin failure issues11:50 Coordisc16:05 Stopping the Corruption22:30 The Basic Economic Assumptions24:00 Mathematical Foundations26:05 The Solution28:50 The Chaos of commodities from Wars32:05 Qualitative arguments34:30 What Happens to Speculators with a disaster37:30 Regulations for the Commodities Markets40:40 His Patent and his experience with the Patent Office46:10 Time & money Suck from Government49:15 The Difficulty of getting International Patents55:10 What he need to make this workHow to Contact Noah Healy :https://coordisc.com/linkedin https://www.linkedin.com/in/noah-healy/YouTube https://www.youtube.com/watch?v=v8aOEcDV7MA ------------------All about Roy / Brain Gym & Virtual Assistants at ⁠https://roycoughlan.com/⁠ ___________________

Noah Healy is a market designer and game theorist working on better economic systems.#financialsystem #blockchain #crypto================All Episodes can be found at www.thecryptopodcast.org All about Roy / Brain Gym & Virtual Assistants athttps://roycoughlan.com/------------------   About my Guest Noah Healy:Noah Healy is a market designer and game theorist working on better economic systems. After training in nuclear engineering he worked for tech startups at the peak of the dot com boom. Becoming fascinated by the mathematics of information and computation led to patent work on a better commodity market design.What we Discussed: 00:00 Who is Noah Healy03:30 His Blockchain Journey08:00 The Bitcoin failure issues11:50 Coordisc16:05 Stopping the Corruption22:30 The Basic Economic Assumptions24:00 Mathematical Foundations26:05 The Solution28:50 The Chaos of commodities from Wars32:05 Qualitative arguments34:30 What Happens to Speculators with a disaster37:30 Regulations for the Commodities Markets40:40 His Patent and his experience with the Patent Office46:10 Time & money Suck from Government49:15 The Difficulty of getting International Patents55:10 What he need to make this workHow to Contact Noah Healy :https://coordisc.com/linkedin https://www.linkedin.com/in/noah-healy/YouTube https://www.youtube.com/watch?v=v8aOEcDV7MA ------------------All about Roy / Brain Gym & Virtual Assistants at ⁠https://roycoughlan.com/⁠ ___________________

Noah Healy is a market designer and game theorist working on better economic systems.#financialsystem #blockchain #crypto================All Episodes can be found at www.thecryptopodcast.org All about Roy / Brain Gym & Virtual Assistants athttps://roycoughlan.com/------------------   About my Guest Noah Healy:Noah Healy is a market designer and game theorist working on better economic systems. After training in nuclear engineering he worked for tech startups at the peak of the dot com boom. Becoming fascinated by the mathematics of information and computation led to patent work on a better commodity market design.What we Discussed: 00:00 Who is Noah Healy03:30 His Blockchain Journey08:00 The Bitcoin failure issues11:50 Coordisc 16:05 Stopping the Corruption22:30 The Basic Economic Assumptions24:00 Mathematical Foundations26:05 The Solution28:50 The Chaos of commodities from Wars32:05 Qualitative arguments34:30 What Happens to Speculators with a disaster37:30 Regulations for the Commodities Markets40:40 His Patent and his experience with the Patent Office46:10 Time & money Suck from Government49:15 The Difficulty of getting International Patents55:10 What he need to make this workHow to Contact Noah Healy :https://coordisc.com/linkedin https://www.linkedin.com/in/noah-healy/YouTube https://www.youtube.com/watch?v=v8aOEcDV7MA ------------------All about Roy / Brain Gym & Virtual Assistants at ⁠https://roycoughlan.com/⁠ ___________________

Joe's Premium Subscription: www.standardgrain.comGrain Markets and Other Stuff Links-Apple PodcastsSpotifyTikTokYouTubeFutures and options trading involves risk of loss and is not suitable for everyone.0:00 Massie Hates Ethanol4:34 Corn Holds Support6:15 Russian Wheat Exports8:58 Mex/US Negotiations10:00 Gold Selloff11:00 Stock Market SelloffMassie's Controversial Comment on Ethanol

Surprise Inflation numbers, yet markets shrug it off. Axing government spending - shuddering entire agencies. Trend following and the "dumb/smart" money. Commodities on the move - great time to bring on our guest - Carley Garner of DeCarley Trading NEW! DOWNLOAD THIS EPISODE'S AI GENERATED SHOW NOTES (Guest Segment) Carley Garner is a futures and options broker with DeCarley Trading, a division of Zaner Financial Services in Las Vegas, Nevada. With nearly two decades of experience, her commodity market analysis is often referenced on Jim Cramer's Mad Money on CNBC, and she is a regular guest on Bloomberg Television's Options Insight segment with Abigail Doolittle. You might also see her on the Cow Guy Close hosted by Scott Shellady on RFD-TV and "Futures" aired on Schwab Network. Garner is a regular contributor to TheStreet.com and its Pro service and is also a regular on the speaking circuit. She can be found at TradersEXPOs and MoneyShows throughout the country. Garner is also an award-winning author of commodity futures and options trading books. In addition to Trading Commodity Options with Creativity, Garner has authored Higher Probability Commodity Trading; A Trader's First Book on Commodities (three editions); Currency Trading in the Forex and Futures Markets; and Commodity Options. She pens a monthly column for the long-running Technical Analysis of Stocks & Commodities Magazine. Her e-newsletters, The DeCarley Perspective and The Financial Futures Report have garnered a loyal following; she is also proactive in providing free trading education at www.DeCarleyTrading.com Follow @andrewhorowitz More information available on Horowitz & Company's TDI Managed Growth Strategy Check this out and find out more at: http://www.interactivebrokers.com/ Stocks mentioned in this episode: (SLV), (GLD), (USO), (UNG), (SPY), (NVDA), (TSLA)

Joe's Premium Subscription: www.standardgrain.comGrain Markets and Other Stuff Links-Apple PodcastsSpotifyTikTokYouTubeFutures and options trading involves risk of loss and is not suitable for everyone.Trump's Reciprocal Tariffs Proposal

Outlier Media's Aaron Mondry joins us to talk about his in-depth reporting about foreigners investing in Detroit through cryptocurrency. More, as part of his series on the "Speculators of Detroit": https://outliermedia.org/crypto-real-estate-realt-cryptocurrency-detroit/ Daily Detroit shares what to know and where to go in Detroit every day.  Find us on Apple Podcasts: https://podcasts.apple.com/us/podcast/daily-detroit/id1220563942  Or sign up for our newsletter: https://www.dailydetroit.com/newsletter/  

Tom welcomes back Alasdair Macleod, Head of Research at GoldMoney to discuss his insights into the silver market and its relationship with gold prices. He suggests that despite a seemingly undersupplied market, the price disparity between gold and silver does not reflect this reality. Macleod anticipates a significant shift in investor behavior once patience runs thin among those who have already bought into gold but yet to enter the silver market. The role of foreign investors, particularly central banks, in driving gold prices is highlighted. Macleod also emphasizes the importance of understanding the impact of the ongoing credit bubble on financial markets and encourages listeners to consider reducing their exposure to credit. Alasdair expresses his views on Donald Trump's impact on gold prices, citing increased foreign demand due to Trump's status as an inflationist and his executive orders. However, concerns over tariffs and potential economic repercussions remain. Macleod also touches upon historical examples of tariffs and interest rates and their relationship with an economy's purchasing power. He emphasizes the importance of understanding this connection for investors during the upcoming credit bubble. Throughout the conversation, Alasdair highlights the importance of considering global economic trends and various factors influencing gold and silver prices. He also discusses the role of speculators versus central banks in driving these markets and the potential for a significant shift once investor sentiment changes. Time Stamp References:0:00 - Introduction0:39 - Trump & Macro Picture10:30 - Trump Inflationist15:26 - Strong Dollar Impact19:24 - Debt, Yields, & Economy26:18 - Global Bubbles & Dollar33:04 - Gold Industry & ETFs36:47 - Speculators & Price39:52 - Tariffs & C.B. Buying?41:49 - Silvers Underperformance49:05 - Tariffs & Consequences50:16 - Silver Supply Outcomes?56:06 - Biggest Bubble & Wrap Up Talking Points From This Episode Alasdair Macleod predicts a shift in investor behavior towards silver due to gold price disparity. Foreign investors, particularly central banks, influence gold prices significantly. Macleod emphasizes understanding the impact of credit bubble and reducing exposure to it. Guest Links:Twitter: https://twitter.com/MacleodFinanceSubstack: https://substack.com/@macleodfinanceWebsite: https://goldmoney.comResearch: https://www.goldmoney.com/research/ Alasdair Macleod is Head of Research for GoldMoney. He is an educator and advocates for sound money thru demystifying finance and economics. His background includes being a stockbroker, banker, and economist. Alasdair started his career as a stockbroker in 1970 on the London Stock Exchange. Within nine years, he had risen to become senior partner of his firm. Subsequently, he held positions at the director level in investment management and worked as a mutual fund manager. Mr. Macleod also worked at a bank in Guernsey as an executive director. For most of his 40 years in the finance industry, he has been demystifying macro-economic events for his investing clients. The accumulation of this experience has convinced him that unsound monetary policies are the most destructive weapon governments use against the common man. Accordingly, his mission is to educate and inform the public in layman's terms what governments do with money and how to protect themselves from the consequences.