Podcasts about Stephen Hawking

Black holes, as Stephen Hawking discovered, do grow old: they emit radiation, lose mass, and eventually evaporate away. But our fascination with black holes never grows old. This is especially true today, as we are seeing a flood of new data and intriguing theoretical ideas, which both tests the limits of Einstein's general relativity and teach us new things about the astrophysical universe. At the Center of Gravity at the University of Copenhagen, they are currently celebrating Black Hole Week, which provides an excellent opportunity to talk with Center director Vitor Cardoso about what we've been learning about these singular cosmic objects. Use code MINDSCAPE at https://monarch.com/ to get your first year of Monarch Core half off at just $50. #ad Upgrade your everyday and get free shipping and 365-day returns at https://quince.com/MINDSCAPE. #ad See what ElevenAgents can do for your specific workflows at https://elevenlabs.io/MINDSCAPE. #ad Blog post with transcript: https://preposterousuniverse.com/podcast/2026/08/24/365-vitor-cardoso-on-why-black-holes-are-special/ Support Mindscape on Patreon. Vitor Cardoso received his Ph.D. in physics from the Instituto Superior Técnico in Portugal. He is currently a Villum Investigator and Director of the Center of Gravity at the Niels Bohr Institute in Copenhagen, and a Distinguished Professor at Técnico. Web site University of Copenhagen web page Simons Collaboration page Google Scholar publications

Has the earth been visited by extraterrestrials? Are we alone? Despite no formal evidence for conscious life outside of earth, the belief that we are not alone has become more common among both scientists and everyday people. Listen as author Adam Kirsch talks to EconTalk host Russ Roberts about the urge to believe that there are sentient beings elsewhere in the universe and that these aliens want to connect with human beings. Topics discussed include why belief in extraterrestrials has become more mainstream, existential loneliness, religion, extinction, and Elon Musk's desire to get to Mars.

A three-year-old girl's imaginary friend turned out to be a real man – one who had been dead for fifteen years before she was born.BOOK: “They Told Me My Newborn Died – They Lied: A Global History of Stolen Babies” by Darren Marlar: https://amzn.to/45fNXptEPISODE BLOG PAGE (includes sources): https://weirddarkness.com/heidiwyrickREAD or DOWNLOAD the full transcript of this episode: https://weirddarkness.tiny.us/5a4fwj7tFEATURED STORIES IN THIS EPISODE: Deep in the Balsam Mountains of North Carolina, a shaggy eight-foot creature has been stealing gems and watching women bathe since the early 1900s — and the word used to name him came from a Lewis Carroll poem written the moment after a sleepless night at a dying man's bedside. (The Boojum) *** In 1980, a group of artists in Baltimore took out an ad in a major international magazine — not to sell anything, and not to promote a show. They were sending an invitation. The guests they were hoping to attract just hadn't been born yet. (Krononauts Party) *** A three-year-old girl in rural Georgia started playing with an imaginary friend — and then her family found out he was real, and had been dead for fifteen years. The question that nobody has been able to answer isn't whether she knew him. It's how. (The Heidi Wyrick Haunting)CHAPTERS & TIME STAMPS (All Times Approximate)…00:00:00.000 = The Foreboding00:01:23.333 = Show Open00:03:14.569 = The Heidi Wyrick Haunting, Part 100:13:30.069 = The Heidi Wyrick Haunting, Part 2 ***00:32:01.091 = The Boojum ***00:53:27.330 = Krononauts Party ***01:01:57.222 = SONG: “Krononauts Party” by Dark Weirdness (https://weirddarkness.com/music)01:06:24.212 = Show Close*** = Begins immediately after inserted ad breakLISTEN ON PODCAST APPS: Look for this podcast on Apple Podcasts, Spotify, iHeart Radio, Amazon Music, Pandora, TuneIn Radio, and other podcast apps. Get a list of free listening apps here: https://weirddarkness.com/wdapps*No AI Voices Are Used In The Narration Of This Podcast*SOURCES and RESOURCES:The Heidi Wyrick Haunting: https://weirddarkness.com/heidi-wyrick/The Boojum: https://weirddarkness.com/boojum/Krononauts Party: https://weirddarkness.com/krononauts-party/(Over time links may become invalid, disappear, or have different content. I always make sure to give authors credit for the material I use whenever possible. If I somehow overlooked doing so for a story, or if a credit is incorrect, please let me know and I will rectify it in these show notes immediately. Some links included above may benefit me financially through qualifying purchases.)WeirdDarkness® is a registered trademark. Copyright ©2026, Weird Darkness.Originally aired: August 11, 2026Weird Darkness moves through three cases this episode: a decade-long haunting in rural Georgia that a university parapsychologist wired with scientific instruments, an eight-foot gem-hoarding creature that Appalachian locals have hunted for over a century, and a 1980 Baltimore art stunt that advertised a party for time travelers.It opens with the Wyrick haunting, which began in March of 1989 when three-year-old Heidi Wyrick walked in from the backyard of her family's new home on Swint Loop in Ellerslie, Georgia, and asked to keep swinging with an old man her mother couldn't see. The friend Heidi called Mr. Gordy matched James Gordy, a former caretaker of the property who had died in 1974, down to his silver hair, dark suit, and top hat; a second, wounded visitor she named Con matched Lon Batchelor, a man who lost his hand in a cotton gin accident and died in 1957. Before she could read, Heidi ran straight to Gordy's headstone in a cemetery of hundreds and later picked his face from a blind photo lineup. By 1993 the activity had turned violent, with a faceless dark figure, claw marks appearing on Heidi and her father Andy across consecutive nights, and a ribbon found wound around the throat of infant daughter Jordan, and the family brought in Dr. William Roll, a parapsychologist known from Unsolved Mysteries, who recorded an electromagnetic spike past 40 milligauss, elevated positive ions, and nearby seismic activity, then admitted he could not explain the scratches. Medium Amy Allan, later of The Dead Files, and a second psychic working separately placed the same entities in the same rooms, and the Wyricks finally left the house after Andy and Lisa found Heidi suspended upside down above her bed. The case appears in the Discovery Channel's 2002 film A Haunting in Georgia and the 2013 movie The Haunting in Connecticut 2: Ghosts of Georgia, and in The Veil: The Heidi Wyrick Story, the firsthand account written by Heidi's aunt Joyce.From there the episode climbs into the Balsam Mountains of western North Carolina for the legend of the Boojum, a creature said to stand six to eight feet tall, covered in shaggy gray hair, with a disturbingly human face. According to Haywood County lore, the Boojum hoards the region's real gemstones – North Carolina is the only state that holds all four major precious gems, and the 1,869-carat uncut emerald pulled from the North American Emerald Mine in Hiddenite in 2003 is a matter of record – stashing them in moonshine jugs buried in hidden caves. The first written accounts cluster around the Eagle Nest Hotel, a forty-room hay-fever resort that opened on Eagle Nest Mountain in 1900 and burned in 1918, where guests were warned that the Boojum hid in the mountain laurel to watch women bathing in the forest pools. At the center of the legend is a woman named Annie, who chose to live with him as his wife and whose calls across the ridges are said, in local folk etymology, to have given the word "hootenanny" its name. The segment traces the creature's name back to Lewis Carroll's 1876 nonsense poem The Hunting of the Snark, sets the story against the Cherokee removal along the Trail of Tears and the eviction of Appalachian families to create Great Smoky Mountains National Park, and lands in present-day Waynesville, where Boojum Brewing carries the legend's name.The episode closes with the Krononauts, a group from Baltimore's avant-garde art scene who, in January of 1980, ran an advertisement on page 90 of Artforum magazine inviting time travelers to a party in Baltimore on March 9th, 1982, a date chosen because all nine planets would line up more closely than they had in nearly two hundred years, an alignment popularized by the 1974 book The Jupiter Effect. Hundreds of people came, the New York Times covered it and described the scene as an epidemic of temporary lunacy, and no one produced any evidence of time travel, since the organizers' real aim was the permanent paper trail, built on the logic that a documented invitation could be found and answered from any point in the future. The segment follows the same experiment forward through MIT graduate student Amal Dorai's 2005 Time Traveler Convention and physicist Stephen Hawking's 2009 reception at Gonville and Caius College in Cambridge, where Hawking mailed the invitations only after the party had ended and took the empty room as evidence for his chronology protection conjecture, and it ends with an original song, "Krononauts Party," by Dark Weirdness.LIKE WHAT YOU'RE HEARING? Get the daily WEIRD DARKNESS podcast (seven days per week) at WeirdDarkness.com • Paranormal, true crime, UFO, cryptid, ghost, and unexplained stories every day • Listen FREE wherever you get podcasts • My “OFFICIAL WEIRDOS” at https://WeirdDarkness.com/OFFICIAL get the commercial-free version, along with at least two bonus episodes each week, live chats every weeknight, free audiobooks I narrate, and more!

Shake Shack up charging if customers don't leave a tip, a Stephen Hawking impression goes viral, and a Wendy's manager earns his way to Yale. See omnystudio.com/listener for privacy information.

The servers were not behaving last week, sorry for the lack of pods on Thursday and Friday. But Dave (and our servers) were back today, and treating listeners to a rather incredible Stephen Hawking impression, a possible fast one that Shake Shack is trying to pull, some 92-year-old grandma isn't letting anything stand in the way of her Taco Bell, and Dave shared another "Desperate Wife Text From Summer!" And since it's back-to-school season, people shared advice on how to handle a bully... See omnystudio.com/listener for privacy information.

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

Lincoln Stoller is a psychotherapist, physicist, and author of books including The Learning Project, Becoming Lucid, and Operating Manual for Enlightenment.In this episode, we discuss living with honesty, courage, and commitment, and why meaningful growth often means moving beyond conformity and reward-driven paths. Lincoln shares perspectives on taking action without guarantees, embracing exploration for its own sake, and giving yourself space to get lost while remaining calm. We explore creativity, lucid awareness in dreams and daily life, doing things you're not good at, finding mentors, breaking the rules, exploring beyond the traditional boundaries of education and career, and not focusing on profit or reward. The conversation centers on expanding awareness, stepping out of limiting boxes and choosing a path guided by curiosity, authenticity, and adventure.Connect and Learn MoreWebsite: mindstrengthbalance.comBooks: The Learning Project | Becoming Lucid | The Path to Sleep, Exercises for an Ancient Skill | Operating Manual for Enlightenment | COVID-19 | Sensations Thoughts and Emotions Podcast: Stream of SubconsciousnessFacebook: @lincolnstollerLinkedIn: linkedin.com/in/lincolnstollerResourcesBooks: Fight Club by Chuck Palahniuk, The Hitchhiker's Guide to the Galaxy by Douglas AdamsTV Series: Dirk Gently's Holistic Detective AgencyPeople: Albert Einstein, Douglas Adams, Franz Kafka, Isaac Newton, Joey Krebs, John Cage, Louis Pasteur, Martin Luther King Jr., Stephen Hawking, Sydney Coleman, Theodore Maiman, Thomas Edison, Vincent van Gogh

Dr. David Jackson of Cold Spring Harbor Lab joins Heart of The East End Gianna Volpe on WLIW-FM after being inducted as a Fellow into the Royal Society—putting him in league with likes of Albert Einstein, Benjamin Franklin, Charles Darwin, and Stephen Hawking—for his lab's discovery of several genes controlling plant architecture by exerting influence on stem cells, which could be used to boost plant yield.Listen to the playlist on Apple Music

The US hit Iran's most important naval base - then satellite images revealed the damage, "The harbor was burning" Are we seeing the first evidence of alien life? The Tiny Colorado Company Building America's Rare Earth Empire NASA-backed doomsday warning from Stephen Hawking could happen soon 'Experimental explosion' sparks panic off US coast as experts admit they've 'never seen this before' Why the collapse of Iran's Chabahar port tower could signal a major shift in Trump's war strategy Aliens are already here, says Age of Discovery director Dan Farah Earth's ‘next-door neighbour' could be home to aliens Humiliation for Putin as true extent of Russian battlefield casualties finally revealed 9 million Americans have already left - here's where they're going The CIA just put a number on how long a Russian recruit survives in Ukraine — and it's measured in minutes, not days or weeks We may be about to find thousands of alien life forms at once The only thing that will make me come back: Fox's Jesse Watters says he'll leave US if Kamala Harris wins in 2028 Declassified: Every UAP sighting released by the US government 26 odd pictures that have people convinced time travelers walk among us Watch how Elon Musk plans to build houses on Mars The Dark Eagle LRHW full breakdown, how America's 300-missile hypersonic arsenal creates an unstoppable strike force China and Russia cannot counter Finally released! The James Webb Telescope image we've all been waiting for The sealed Ice Age cave hiding impossible ancient tools Lt Gen Keith Kellogg: If you control Iran's economy, it starts to break Underwater ruins off Louisiana have revived the theory of a 12,000-year-old lost city Scientists say they figured out what causes ghosts UAP Gerb reacts to Vance's UFO and alien comments to Joe Rogan Mars life evidence grows: The discovery scientists can't ignore United Public Radio & UFO Paranormal Radio www.uprntalkradio.com

Every documentary the Criterion Collection shows us leads to a discussion on what documentaries are, what they can do, and what their relationship to truth can and should be. Inevitably as well the supplements to a Criterion Collection release of a documentary provide context that can reshape how we view the original piece's relationship to truth. Errol Morris's A Brief History of Time (1991) is no different. In part a presentation on the ideas featured in Stephen Hawking's book of the same name and a biography of Hawking himself, the film distorts reality like an event horizon. In the supplemental material Morris states that one shouldn't judge a documentary on whether it presents the truth, but if it attempts to find the truth and whether it makes the viewer think about what the text's relationship to the truth is. The movie itself may not provide enough information to do that.

Well guess who's back on Rick Flynn Presents again this week? PAM LEWIS returns to show us a different side of herself as she speaks on re-inventing herself.She was honored in 2023 by becoming a distinguished fellow of the Royal Society of Arts (FRSA) during Queen Elizabeth II's Platinum Jubilee Celebration year. Founded in 1754, The Royal Society of Arts fellows include Stephen Hawking, Benjamin Franklin, Charles Dickens, Helen Keller, and Margaret Thatcher. Lewis is also a U.S. Ambassador for The Unity of Faiths Foundation (TUFF) as well as a member of the Churchill Society. In June of the same year, she was the guest keynote speaker with TUFF co-founder Dr. Shamender Talwar at the annual Professional Learning Conference held at MTSU.Lewis' charitable board work is extensive and has included The Tennessee State Museum, Tennessee First Lady Andrea Conte's “You Have The Power,” BRIDGES Domestic Violence Center, Franklin's Charge, Sister Cities of Franklin, Friend of Franklin Parks, Belmont Mansion, Nashville City Cemetery Board, Franklin's Historic Battlefield Commission, the ARC Board, the Tennessee Preservation Trust, the mayor-appointed Franklin Housing Commission, and the Franklin Civil War Historical Commission.Her other community outreach efforts include historic preservation and global green space causes, women and children's advocacy, educational scholarships, fair housing and environmental, and animal rights protection. Her foundation has given away hundreds of thousands of dollars to numerous charities over the last 10 years.Contact: www.plamedia.com

https://www.youtube.com/watch?v=zXW7uXo04aE Hosted by Tony Darnell. Streamed live on Mar 14, 2018. Join Tony Darnell, Dr. Jeff Kuhn, Dr. Svetlana Berdugina and Kevin Lewis as they discuss the latest development in the field of astrobiology.  Want to learn the latest in Exoplanet research? The cutting edge of finding related to our search for life elsewhere?  Then this is the hangout for you!   This week we'll talk about Pi Day, Stephen Hawking's death, the GAO report on the JWST mission and much, much more.   We've added a new way to donate to 365 Days of Astronomy to support editing, hosting, and production costs.  Just visit: https://www.patreon.com/365DaysOfAstronomy and donate as much as you can! Share the podcast with your friends and send the Patreon link to them too!  Every bit helps! Thank you! ------------------------------------ Do go visit http://www.redbubble.com/people/CosmoQuestX/shop for cool Astronomy Cast and CosmoQuest t-shirts, coffee mugs and other awesomeness! http://cosmoquest.org/Donate This show is made possible through your donations.  Thank you! (Haven't donated? It's not too late! Just click!) ------------------------------------ The 365 Days of Astronomy Podcast is produced by the Planetary Science Institute. http://www.psi.edu Visit us on the web at 365DaysOfAstronomy.org or email us at info@365DaysOfAstronomy.org.

Send us Fan MailDeath of the Week is officially back—and somehow the rest of the episode gets even crazier.On this episode of The Days Grimm Podcast, Brian, Thomas, and Cory dive headfirst into some of the strangest, funniest, and most unbelievable news stories making headlines. The guys break down the return of Death of the Week, Joey Chestnut eating an absurd 66 hot dogs in 10 minutes, a suspected drunk driver who escaped police only to get attacked by an alligator, and Gracie the giraffe going on the run for two weeks in Texas.Then things get even weirder. A live elephant creates chaos at the Texas GOP convention, a Yu-Gi-Oh tournament gets suspended after complaints about players' hygiene, and the crew reacts to the almost unbelievable case of a quadruple-amputee professional cornhole player facing first-degree murder charges.This is The Days Grimm Podcast at its best: dark comedy, bizarre current events, brutally honest reactions, completely unpredictable tangents, and three guys asking the questions nobody else thought—or wanted—to ask.Subscribe, turn on notifications, and join us every week for more comedy, strange news, dark humor, and absolutely unfiltered conversations.Subscribe to The Days Grimm Podcast for more dark comedy, bizarre news, insane true stories, and completely unfiltered conversations every week.Drop a comment and tell us: Which story from this episode was the most unbelievable?And most importantly—what story deserves to be featured on the next Death of the Week?Visit thedaysgrimm.com for the latest TDG updates, episodes, and more.Timestamps00:00 – Intro 00:00 – Stephen Hawking and The Theory of Everything 00:00 – The Stephen Hawking impression 00:00 – Death of the Week returns 00:00 – Pastor charged after fatal baptism 00:00 – Joey Chestnut and the Nathan's Hot Dog Eating Contest 00:00 – Fugitive attacked by alligator during police chase 00:00 – Gracie the giraffe escapes in Texas 00:00 – Elephant incident at the Texas GOP convention 00:00 – Yu-Gi-Oh tournament suspended over player hygiene 00:00 – Quadruple-amputee cornhole player facing murder charges 00:00 – The TDG archive and the worst Death of the Week ever 00:00 – Outro[The Days Grimm Podcast Links]- YouTube: https://www.youtube.com/c/TheDaysGrimm- Our link tree: linktr.ee/Thedaysgrimm- GoFundMe account for The Days Grimm: https://gofund.me/02527e7c [The Days Grimm is brought to you by]Sadness & ADHD (non-medicated)

Thomas Hertog deed jarenlang onderzoek naar het ontstaan van het heelal met Stephen Hawking. Het groeide uit tot een hechte vriendschap, waarin ze samen „de oude grote filosofische vragen vastpakten en herkneedden op een wiskundige manier”. Hun kosmologisch onderzoek draaide niet alleen om natuurwetten maar juist om existentiële vragen: „Wie zijn wij? En waar komen wij vandaan?”De aantasting van onze eigen planeet roept intussen de vraag op of we ooit de Melkweg zullen koloniseren. Elon Musk en Jeff Bezos spelen daarop in, maar hun ruimtevisie baadt volgens Hertog in escapisme. „Het heelal lijkt bijzonder levensvatbaar”, zegt hij, „en toch lijken we alleen. Er is dus blijkbaar geen enkele beschaving op die miljarden exoplaneten in de Melkweg, die erin geslaagd is om die Melkweg te exploreren. ” Hertog vraagt zich af: waar zit die bottleneck?Heeft u vragen, suggesties of ideeën over onze journalistiek? Mail dan naar podcast@nrc.nl.Presentatie: Pieter van der WielenRedactie: Merel van Waalwijk van DoornProductie: Rhea StroinkMixage: AudiochefMuziek: Rufus van BaardwijkFoto: NRCZie het privacybeleid op https://art19.com/privacy en de privacyverklaring van Californië op https://art19.com/privacy#do-not-sell-my-info.

1938 Cadılar Bayramında neden insanlar aniden sokağa döküldü? Voyager 1 Altın Plağına koymadığımız fotoğraflar neyi ifade ediyor? Stephen Hawking ile H. G. Wells arasında nasıl bir bağlantı var?  Barış Özcan'la 111 Hz'in bu bölümünde Orson Welles'in ABD'de yarattığı panikten, Azteklerin, Hernán Cortés'i tanrı sanmasına, evrene “biz buyuz” dediğimiz ifadeleri konuşacağız.  Bu bölümde:  (00:00) 1938 Cadılar Bayramı Paniği (05:13) Münih Krizi (06:26) 1519 Aztekler (08:42) H. G. Wells (11:12) Ozma Projesi  (16:05) Voyager 1 Altın Plağı  (18:00) Stephen Hawking  Yaratıcı: Barış Özcan  Yapımcı: Podbee Media  Bu bölüm reklam ve sponsorluk içerebilir. Tüm bölümler, videolar ve daha fazlasına Podbee App ve podbee.com'dan ücretsiz olarak ulaşabilirsiniz.

Saludos, tripulación de Historias para ser leídas. Bienvenidos a bordo. 🚀💫 Hace tres años iniciamos una misión que quedó fragmentada en el tiempo. Hoy, he unificado las transmisiones. He recopilado todas las bitácoras pasadas para ofreceros el viaje completo, mejor calidad y sin interrupciones rumbo al corazón mismo de la gravedad. 🚀👨‍🚀 Imagina que tú eres el propietario y capitán de una gran nave espacial, con ordenadores, robots y una tripulación de cientos de personas a tus órdenes. La Sociedad Geográfica Mundial te ha asignado la misión de explorar los agujeros negros en regiones lejanas del espacio interestelar y transmitir por radio a la Tierra una descripción de sus experiencias. Tras seis años de viaje, tu nave está decelerando en la vecindad del agujero negro más próximo a la Tierra: un agujero llamado «Hades» cercano a la estrella Vega. En la video pantalla de tu nave, la tripulación y tú observáis manifestaciones de la presencia del agujero: los escasísimos átomos de gas en el espacio interestelar, aproximadamente uno por centímetro cúbico, son atraídos por la gravedad del agujero negro. Las únicas singularidades representadas en las cartas de viaje de su nave son las que están dentro de los agujeros negros, y usted se niega a pagar el precio de la muerte para explorarlas. Pero atención, capitán. El espacio es impredecible y el destino de esta tripulación no está escrito. Al final de este trayecto, la realidad se bifurcará. Os enfrentaréis a una decisión crucial en los límites del horizonte de sucesos: Tendréis que elegir entre dos transmisiones finales que se incluyen en este mismo audio. 🔴OPCION 1 🚀 ✅OPCION 2 🚀 Dos caminos. Dos desenlaces posibles. Dos destinos para un mismo misterio cósmico. Encended los motores de curvatura ¡Comenzamos el viaje! 🚀💫 Thorne comienza llevándonos a un viaje por los agujeros negros y, desde allí, nos hace seguir el descubrimiento de las nuevas concepciones, desde Einstein hasta nuestros días, en una especie de relato histórico sazonado de anécdotas vividas, a lo largo del cual vamos aprendiendo los conceptos básicos, hasta llegar al punto en que agujeros de gusano y máquinas del tiempo nos parecen posibilidades lógicas y comprensibles. Stephen Hawking calificó esta historia como «un relato fascinante», y dijo: todos cuantos aman los misterios científicos disfrutarán con él. Comenzamos el viaje....! Este relato ha sido escrito por Kip Stephen Thorne (Logan, Utah, 1940), físico teórico estadounidense, conocido por sus contribuciones prolíficas en física, astrofísica y gravitación. Gran amigo y colega de Stephen Hawking y Carl Sagan, ocupó la cátedra «Profesor Feynman» de Física Teórica en el Instituto de Tecnología de California hasta el año 2009, y es uno de los mayores expertos sobre las implicaciones astrofísicas de la teoría general de la relatividad de Einstein. Ha escrito y editado libros sobre temas de teoría de la gravedad y astrofísica de alta energía. En 1973, fue coautor del libro de texto clásico Gravitation , con Charles Misner y John Wheeler, del que la mayor parte de la actual generación de científicos han aprendido la teoría de la relatividad general. En 1994, publicó Agujeros negros y tiempo curvo: el escandaloso legado de Einstein , un libro de referencia para los no científicos por el que recibió numerosos premios y que ha sido publicado en seis idiomas. Su trabajo ha aparecido en revistas y enciclopedias, tales como Scientific American , McGraw-Hill Yearbook of Science and Technology y la Collier's Encyclopedia , entre muchos otros, y ha publicado más de 150 artículos en revistas especializadas. Ha presentado diversos programas de la PBS estadounidense (televisión pública) y la BBC inglesa sobre temas como los agujeros negros, las ondas gravitatorias, la relatividad, el viaje en el tiempo y los agujeros de gusano. La lista de premios, reconocimientos y honores recibidos es larga y variada: Science Writing Award in Physics and Astronomy del American Institute of Physics; Science Writing Award de la Phi Beta Kappa Society; Karl Schwarzschild Medal por la German Astronomical Society ; Robinson Prize in Cosmology por la Universidad de Newcastle; California Scientist of the Year Award por el California Science Center; Medalla Albert Einstein (2009) por la Sociedad de Albert Einstein (Berna, Suiza), etc. Una producción de Historias para ser Leídas, Voz: Olga Paraíso, música y efectos Epidemic Sound, gracias al artista Lotus (Licencia autorizada para este Podcast). Muchísimas gracias a los taberneros galácticos que apoyan este podcast, vamos rumbo a las estrellas,🌌🚀 ¿nos acompañas? Puedes apoyar mi trabajo desde el botón azul APOYAR por tan solo 1,99 € al mes. Credit Imagen Shutterstock Escucha el episodio completo en la app de iVoox, o descubre todo el catálogo de iVoox Originals

Sponsor Link:This episode of Space Nuts is brought to you by NordVPN, your reliable partner for online security. To take advantage of our exclusive offer, including four extra months for free, visit www.nordvpn.com/spacenuts.Space Exploration: Blue Origin's Explosive Test and the Mysteries of the Universe In this thrilling episode of Space Nuts, hosts Andrew Dunkley and Professor Fred Watson reunite to discuss a range of captivating topics, including the recent explosive test of Blue Origin's New Glenn rocket, primordial black holes, and the ongoing debate around dark energy. Buckle up as we delve into the cosmos and explore these fascinating themes.Episode Highlights:- Blue Origin's Test Launch: The episode kicks off with an analysis of the dramatic Blue Origin test that resulted in an explosive incident at Cape Canaveral, raising questions about the future of the Artemis programme and the implications for upcoming lunar missions.- Primordial Black Holes: Andrew and Fred Watson discuss a recent microlensing event observed in the Large Magellanic Cloud, exploring the possibility that the mysterious object, dubbed Phoebe, could be a primordial black hole, a concept first proposed by Stephen Hawking.- Gravitational Microlensing Explained: The hosts break down the phenomenon of gravitational microlensing, illustrating how invisible objects can magnify the light of distant stars and what this means for our understanding of dark matter and the universe.- Dark Energy: A Possible Furphy? A thought-provoking discussion ensues about the nature of dark energy, with insights from a recent paper suggesting that our current model of the universe may be oversimplified, raising the possibility that dark energy may not be necessary at all.For more Space Nuts, including our continuously updating newsfeed and to listen to all our episodes, visit our website. Follow us on social media at SpaceNutsPod on Facebook, Instagram, and more. We love engaging with our community, so be sure to drop us a message or comment on your favourite platform.If you'd like to help support Space Nuts and join our growing family of insiders for commercial-free episodes and more, visit spacenutspodcast.com/about.Stay curious, keep looking up, and join us next time for more stellar insights and cosmic wonders. Until then, clear skies and happy stargazing.Become a supporter of this podcast: https://www.spreaker.com/podcast/space-nuts-astronomy-insights-cosmic-discoveries--2631155/support.- Blue Origin's Explosive Test- Understanding Primordial Black Holes- Gravitational Microlensing Phenomenon- The Debate Around Dark Energy- Implications for Future Space Exploration

This was one of our most listened-to conversations of the past year. If you missed it the first time, here's your second chance. She moderated the fly debate. She interviewed Stephen Hawking. She covered 12 presidential campaigns and sat down with the last 10 presidents. And she spent years inside Queen Elizabeth's extraordinary vantage point on American democracy — one that no American journalist could ever fully replicate. Two minutes. Real impact. Leave a review: lovethepodcast.com/politicsandreligion Susan Page, Washington Bureau Chief of USA TODAY, joins Corey to discuss her latest book, The Queen and Her Presidents: a sweeping account of Queen Elizabeth II's relationships with every American president from Truman to Biden. But this conversation goes well beyond the book. Susan reflects on a career that began in a converted car dealership on Long Island, the lessons she learned covering her first president (and how badly she blew it), what it really takes to develop sources across decades of political reporting, and why — from a Kansas girl's perspective — the people on both sides of our divide love America more than we give them credit for. Calls to Action ✅ If this conversation resonates, consider sharing it with someone who believes connection across difference still matters. ✅ Subscribe to Corey's Substack: coreysnathan.substack.com ✅ Leave a review on Apple Podcasts, Spotify, or wherever you listen:  lovethepodcast.com/politicsandreligion ✅ Subscribe to Talkin' Politics & Religion Without Killin' Each Other on your favorite podcast platform. ✅ Watch the full conversation and subscribe on YouTube: youtube.com/@politicsandreligion Key Takeaways Preparation is a framework, not a script. Susan goes into every major interview with a plan — what she wants to get, how to get it, what to do if the answer goes sideways. But the goal is to inform the conversation, not control it. The worst thing an interviewer can do, she says, is fail to listen to the answer. Great sourcing is built on respect and fairness, not on pulling punches. Rich Bond, the young Long Island operative she profiled in 1979, became a top Republican official and a reliable source for decades — not because she went easy on him, but because he trusted her to be fair. She would not have softened a story about him, and he knew it. Books and daily journalism use the same muscle, differently. The skills transfer directly — the sourcing, the curiosity, the nose for a good detail — but the bar is higher and the time horizon is longer. Writing a book means people are paying thirty dollars and spending real time. You owe them something they couldn't get from clicking a link. The best research rewards patience. Sifting through archival files at eight presidential libraries and the National Archives in Britain yielded moments that almost nobody else has read. The sarcastic cables British ambassadors sent back about LBJ as vice president confirmed everything LBJ already suspected they thought of him. They love America. Whether she's at a No Kings rally or a MAGA rally, Susan hears the same thing: people who care deeply, who revere the Constitution, who think they're fighting for the country. The polarization isn't about love of country — it's about a failure to extend basic respect across the divide. Queen Elizabeth perfected the art of getting people to talk. Her small talk strategy — chatter briefly, then turn the question back — was especially effective with men, who, as Susan notes diplomatically, tend to enjoy talking about themselves. Susan has consciously adopted the technique and credits it with making her better at navigating rooms full of strangers. About Our Guest Susan Page is the Washington Bureau Chief of USA TODAY and one of the most respected political journalists in America. She has covered 12 presidential campaigns and interviewed the last 10 presidents. She moderated the 2020 vice presidential debate between Kamala Harris and Mike Pence — yes, the one with the fly — and is the bestselling author of biographies of Barbara Bush, Nancy Pelosi, and Barbara Walters. Her latest book, The Queen and Her Presidents, chronicles Queen Elizabeth II's relationships with every American president from Truman through Biden. Links and Resources The Queen and Her Presidents by Susan Page — susanpagedc.com Grateful to our friends at The Democracy Group: www.democracygroup.org Connect on Social Media Corey is @coreysnathan on all the socials… Substack LinkedIn Facebook Instagram Twitter Threads Bluesky TikTok “Clarity, charity, and conviction can live in the same room.” Yes, really.

“I wanna hear THAT at my grocery store, when I'm going for the meat section…”   Whether it's the song we all rise for at sports ball events,  the first song on Rush's “Fly By Night” album (not to mention the first song on Anthrax's EP of classic rock covers), or the sing-along, encore at the end of a rock show, ANTHEMS represent a clarion call, a rallying cry that unites fans in unspeakable joy, unity, and celebration. It's almost always the song that EVERYONE knows (even your grandmother) and is very much NOT a deep cut.    “AC/DC's kind of an anthem band…they have at least one on every album…”   In fact, ANTHEMS are those kinds of songs where, if you hear one in a public place: at a bar, a restaurant, the grocery store, or a friend's party, you're not going to NOT like it. Sure, it's probably the most well-known, popular, played into the ground, mainstream “hit” that EVERYONE (again, probably even your grandmother) is familiar with, but no matter what song it is or where you might hear it, ANTHEMS are  guaranteed to get you fired up, lift your spirit, and bring a smile to your face.   “I wanna do a little birthday squig…”   Find out what's happening “THIS FRIDAY”, realize that birthdays are the anniversaries of life, and JOIN US for a Bunkerpoon birthday celebration full of cake, refreshments, and good cheer as we cuss and discuss the unifying power behind rock and metal ANTHEMS. Visit www.metalnerdery.com/podcast for more on this episode Help Support Metal Nerdery https://www.patreon.com/metalnerderypodcast Leave us a Voicemail to be played on a future episode: 980-666-8182 Metal Nerdery Tees and Hoodies – metalnerdery.com/merch and kindly leave us a review and/or rating on your favorite Podcast app Follow us on the Socials: Facebook - Instagram - TikTok Email: metalnerdery@gmail.com Can't be LOUD Enough Playlist on Spotify Metal Nerdery Munchies on YouTube @metalnerderypodcast   Show Notes: (00:01): “I think I just turned it up…”/ “I'm upright and taking nourishment…”/  “I'll give myself the bean…”/ “I'll put it wherever you want…you want it on the inside or the outside?” / ***WARNING: #listenerdiscretionisadvised *** / #keepitthrashy / “Maybe he just unlocked another dimension with his weed…”/ “Dr. Pepper, Captain Morgan, and PB&J's…”/ #yeah / #McDonalds #DirtyDrPepper #Rim / “Was the rim the best part?” / “If you have foam down there, you should really go see your doctor…to have him finish you to completion…”/ ***WELCOME BACK TO THE METAL NERDERY PODCAST AND THE BUNKERPOON CENTER FOR METAL EXCELLENCE!!!*** / #happybirthday / #birthdayepisode / “I eat once a day…I'm the lightest I've been in 3 years…”/ “A handful of food, 5 times a day…”/ #cavemandiet / “Fasting is the way…”/ “Now they're saying that sugar is WORSE than heroin…”/ “It's all mental…” (07:37): ***PATREON US at patreon.com/metalnerderypodcast *** / #SpiritInTheSky /  “I was looking at the name: I thought it said ‘My Boss'…”/ ***SOCIAL MEDIA US at #MetalNerderyPodcast on #YouTube #Facebook #InstaGram and #TikTok *** / #drysockets and #wisdomteeth #viralaf / #Pyscroptic GATHERING A VENOMOUS HERD / (The Pulse of Annihilation – 2026) / #technicaldeathmetal / #Tasmania #Australia #AussieMetal / “They got a Wiki page, that's saying something…”/ Tasmania, Australia (“Yeah…”) / “How any albums do they have?” / “Let's go through the album titles…”/  “You wanna watch a video?”/ #youwatchamovie / ARCHITECTS OF EXTINCTION /  “Those guys are re-tah-diculous!” / The reaction of laughter to unbelievable riffs and technical prowess   (18:18): “So here's what we've got to contend with…I'm gonna ask you, right here in front of everybody…” / “You think God listens to this podcast, dude?”/ “I wanna do a little birthday squig in the beginning…”/ “This year marks the 25th anniversary of Pig Destroyer's Prowler In The Yard, which is like the Reign In Blood of grindcore…”/ “I've got a kink now, where I can't get off unless Stephen Hawking is watching…”/ “I hate this so bad…”/ “This is beautiful…this is art.” /  #PigDestroyer JENNIFER/CHEERLEADER CORPSES (Prowler In The Yard – 2001) /  #TwentyFifthAnniversary #ProwlerInTheYard / “Oh, I farted…”/ “A little bit of WHAT?” / PISS ANGEL / “Is that better or worse?”/ “Should we start calling birthdays anniversaries instead of birthdays?”   (24:00): #TheDocket METAL NERDERY PODCAST PRESENTS: ANTHEMS / “It's gotta be bolth…” / #rockanthems #heavymetalanthems / “What is an anthem? What do you think of when you hear the word ‘anthem' (if it's metal or rock)?”/ “Everybody knows it…it gets people fired up…”/ “When the band plays the song where you all become a part of the show…that's the sing along…”/ “What is the first anthem you can think of?” /  “Everybody knows it…it's the one that your grandmother has heard…it's the non-deep cut…”/ “What would Van Halen's anthem be?”/ #BetterOffDead /  “What would AC/DC's anthem be?”/ “AC/DC's kind of an anthem band…they have at least one on every album…”/ “Did we just unlock a new dimension here?” (30:06): “What's a Zeppelin anthem?” / “We're all humans, who cares?”/ #markthetime /  “Anthems are one of those things…if you hear it at a public place like in a bar, a restaurant, or even at the grocery store…”/ “Cart…buggy…”/ “You can't NOT like it…even if you're a #Rush super deep cut fan…”/ #BlackSabbath WAR PIGS (Paranoid – 1970) /  “Now we're smiling, all of a sudden…”/ “I don't know if you've ever heard this before…about that song…those first few words…it almost has a country vibe to it…the vocal…”/  “They put the Eventide voice machines on him…”/ “All the guitar nerds will get that…”/  “What's an Eventide? Is that like a dildo or something? What do you do with that?”/  “What about Ozzy anthems?”   (38:48): #Motorhead ACE OF SPADES (Ace Of Spades – 1980) / “This is one of the most metal parts, evah!”/ “If your doctor doesn't recommend metal to you as a lifestyle improvement and a medical treatment, find a different doctor.”/ “Was the Van Hagar more anthem-y than the Roth era?” / “Yeah…you've got to, with the hairdryer and the whole thing…you don't remember the video?” / #hairdryerASMR / “Soooo…for metal bands…”/  #IronMaiden THE NUMBER OF THE BEAST (The Number Of The Beast – 1982) / “Everybody in America is like: ‘You went to the pub and got pissed off? What's wrong with you?' / “I've got one…”/ #QuietRiot METAL HEALTH (Metal Health – 1983) / “Assgrinder?”/ “That's a sing-along…”/ “Which do you think was bigger?” / “I think that's what possibly killed Twisted Sister…”/ #TwistedSister I WANNA ROCK (Stay Hungry – 1984) / #No / “How much fun was that?” / “Every band probably has their own anthem…”/ “Testament, what would be theirs?” / “For the mainstream customer…but for their fans, it would be…”/  “It is a weird anthem…that's probably one of their encores…” (51:55): “Everybody knows the Pantera one…”/ “At a bar, at a party, at a friend's house…if I hear it in a public place…I'm not complaining…I'm cool with that.”/ “It still feels like it belongs to you…”/ “Do you feel like they (Metallica) are almost in the AC/DC bucket?” / “Does your grandmother know Dyer's Eve?” / “What's Creed's anthem?”/  #Pantera COWBOYS FROM HELL (Cowboys From Hell – 1990) / “I wanna hear THAT at my grocery store, when I'm going for the meat section…”/ #markthetime / “Fun and anthems kinda go together…”/ “I don't NOT like ‘Walk'…” (58:58): “I got another one for us metal heads…”/ #StormtroopersOfDeath MARCH OF THE S.O.D. (Speak English Or Die – 1985) / “Does #Anthrax have an anthem?” / “Okay, so how about Priest?” / #abigone / “Let me know if you think this is THEIR anthem…it's gotta be…”/  “THIS FRIDAY!” / #keepitin / “That was awesome…”/ #KoRn BLIND (KoRn – 1994) / “That's gotta be, right?” / “Are you ready!?” / “This Friday…are you ready?”  (1:04:00): “What is the most recent rock or metal anthem you can think of?” /  “Has everybody lost their ability to do anthems?”/ “The anthem is the call to arms…”/  “Here's my answer to that question…in my opinion…”/ “See, I thought that was Cake…” / #TheWhiteStripes SEVEN NATION ARMY (Elephant – 2003) / “I'll take the bottom off?” /  “What's the band that everybody fucks with…everybody hates on them…?”/ “Nickelback has some anthems, for sure…now that you mention it, maybe not…”/ “That's how I clap, by the way…”/ THANK YOU FOR JOINING US!!! / #untilthenext #outroreel

At age 13, Dr. John Demartini left home after being told by the “experts” around him that his life would never amount to much. People claimed his physical and learning challenges were too much to overcome…Nearly six decades later, John is living the life none of those “experts” could've ever imagined, as a world-renown teacher who helps his students come to a greater understanding of human behavior and maximizing their potential.John shares his amazing journey from his early days as teenage surfer without a home, then explains why he gave up the self-help movement — it's a moral trap — and reveals the importance of asking the right questions of ourselves and others this week on Spirit Gym.Find out more about John and his work on his website and on social media via Facebook, Instagram, Linkedin and YouTube. Listen to his podcast, The Demartini Show, on Apple Podcasts, Spotify or wherever you listen to them.Also, John invites Spirit Gym listeners to take advantage of some free gifts:· Download his 7 Steps to Expand to the Next Level of Empowerment workbook.· Watch John's inspiring presentation, Awakening Your Astronomical Vision.· Complete the Demartini Value Determination Process, a 13-step process that helps you determine your highest values or priorities in life.· Take his master classes, Increase Your Deserve Level and Finally Get What You Want and Discover The Hidden Order and Its Power to Transform Your Life.Timestamps5:34 John leaves home at age 13 to hitchhike cross-country and surf.8:31 John's life-changing encounter with Paul Bragg.19:59 From learning-challenged to speaking to University of Houston students daily.35:20 “Everything that's going on our life is on the way to help us be our most magnificent self.”46:00 Lean into your uniqueness and authenticity with help from your teachers.53:29 The Demartini method.1:01:45 Holy curiosity.1:13:32 “I really believe all of the randomness that we have is missing information.”1:22:29 “The Master lives in a world of transformation, not the illusion of gain, loss, pleasure, pain or polarities.”1:27:39 John's research leads him to give up the self-help movement.1:39:32 “Quality questions are ones that make us aware of what we're unconscious of when we're interpreting things in this polarized way.”1:41:47 What is depression?1:54:56 True science and true religion mirror each other, but with different languages.2:03:25 John's definition of evil: An incomplete awareness of a mental construct.2:12:59 Good and evil: The only labels we impose on things that are exaggerations rather than synthesis.2:15:41 The soulmate question.2:31:05 “The first thing I tell my students: Whatever you perceive in me, let's find it in you.”ResourcesEssentials of Emotional Intelligence by Dr. John DemartiniSpecial Relativity and Classical Field Theory: The Theoretical Minimum by Leonard Susskind and Art FriedmanThe Principles of Quantum Mechanics by P.A.M. DuracThe C.T. Bauer College of Business at the University of HoustonDr. John Demartini's heated conversation with Aubrey Marcus on YouTubeThe Science of Mind: The Complete 1926 edition by Ernest HolmesThe Law of Eristic Escalation explained by Dr. John DemartiniThe Power of Positive Thinking by Norman Vincent PealeThe work of Jack LaLanne, Buddy Miles, Gottfried Wilhelm Leibniz, Claude Shannon, Frank Tipler, Stephen Wolfram, Vera Rubin, Sri Aurobindo, Stephen Hawking, Edward Edinger and EpictetusThe Schrödinger equationRudolf Clausius and the second law of thermodynamicsThe Boltzmann equationHeraclitis and the LogosWhat is Life? by Erwin SchrodingerGeocentrismThe RigvedaCosmic Consciousness: A Study in the Evolution of the Human Mind by Richard BuckeGate control theory by Ronald Melzack and Patrick WallThe Secret (film) on YouTubeThe Trans-Planckian problemPaul's podcast conversation with Sean O'LaoireFind more resources for this episode on our website.Music Credit: Meet Your Heroes (444Hz), Composed, mixed, mastered and produced by Michael RB Schwartz of Brave Bear MusicThanks to our awesome sponsors:PaleovalleyBIOptimizers US and BIOptimizers UK PAUL15Organifi CHEK20Wild PasturesPique LifeSpirit GymCHEK InstituteWe may earn commissions from qualifying purchases using affiliate links.

Frank Ruda and Agon Hamza sit down with the Belgian cosmologist Thomas to discuss his current work, his collaboration with his PhD advisor and collaborator Stephen Hawking, cosmology, the nature of the Big Bang, the relation between physics and philosophy, Hawking's “Darwinian revolution in cosmology”, observation, history, the problem of origin, and many other (non)related things.You can listen to our podcast here: https://anchor.fm/crisisandcritiqueIf you like this and other episodes, please consider subscribing and supporting us at our Patreon page: https://www.patreon.com/user?u=71723553You can find our Substack here: https://crisiscritique.substack.com/Crisis and Critique Journal: https://www.crisiscritique.org/

Another fun episode. In this episode, we talk about the amazing adventure going to the set of Beyond Belief with Jimmy Church talking about Amelia Earhart.  Amelia shows up to give her two cents about the show , then Prince shows up to remind us that June 7th is "Prince Day" - and reiterates we should dance, sing, play some of his tunes... why not?  I ask a question to Stephen Hawking about his journey - he makes a joke about speeding away from the Epstein files... and I ask him about that as well.  Mind bending stuff. (not going down that path, but did ask him if he feels embarrassment or any reaction) - then "Five" steps in to rescue him and talk about the akashic library. All in all another mind bending session. enjoy!

Stephen Hawking lo advirtió: si los extraterrestres llegan algún día, lo más probable es que la cosa no acabe bien para nosotros. Pero ¿cuántos vecinos hostiles podríamos tener ahí fuera? Un investigador de la Universidad de Vigo, Alberto Caballero, ha intentado ponerle número a la pregunta. Su respuesta: alrededor de cuatro civilizaciones potencialmente maliciosas en la Vía Láctea. Pocas, sí, pero suficientes para hacernos pensar dos veces antes de seguir enviando tecnofirmas al cosmos. En este episodio repasamos el estudio, sus cálculos, sus limitaciones y la pregunta de fondo: ¿estamos paseándonos por el universo con un Rolex en la muñeca sin saber en qué barrio vivimos? Escucha el episodio completo en la app de iVoox, o descubre todo el catálogo de iVoox Originals

text us if..."She gets her GED and now she's Stephen Hawking?" - RubyIn this episode: cousin/brother debate continues..., Beth is a DEI hire, & full circle referencesIn other news... Why does Beth bother locking her door?Original episode air date 05/09/2021please visit https://www.patreon.com/Sasspod/redeem/1785B to get a free 3 month membership!Please note our modified summer schedule Support the showTake our listener survey The views expressed by our guests may not reflect the views of Sass n Sips.Check out Spreadshop!http://arthemisclothing.ca - Use SASSPOD for 15% off https://www.muzmm.com- Code SASSPOD for 20% offhttps://www.podpage.com/?via=sasspod to create your own webpagehttps://www.buzzsprout.com/?referrer_id=682706 to start your own podhttps://www.lyft.com/i/LISA594490?utm_medium=p2pi_iacc For a LyftGet in touch:(732) 595-2922sass.n.sips@gmail.com / sassnsips.comIG @sassnsipsFB @Sass N SipsYouTube @Sass N SipsPodchaser podchaser.com/sassnsipsClips used in this podcast were used in accordance with the US Copyrights act FAIR USE Exemption for criticism and commentary....

I sat down with filmmaker and producer Sophie Power following her trip to the Cannes Film Festival. We discussed her 11-minute experimental documentary short, Whatever A Son Will Always Sing. Bypassing the heavy-handed exposition of traditional documentaries, the film uses professional actors and a dreamscape visual approach to blend fiction with real-life interviews. Sophie spoke with 50 different women about the exact, pivotal moment they were forced to grow up, ultimately weaving together four universal narratives that explore everything from the discomfort of being under a microscope to the sudden awareness of danger that shatters childhood bliss. Her goal was to dismantle the shame that often shrouds these collective female experiences, offering a platform where they can be spoken loudly and frankly. Sophie's background spans from a lifelong obsession with human stories and visual art to working on high-profile archival projects. Before directing her own work, she lent her production and research talents to major documentary teams—including assisting Motto Pictures on a Sundance film about the iconic singer Selena Quintanilla-Perez, and serving as a production coordinator for the Stephen Hawking documentary Hawking: Can You Hear Me? Check out the full conversation, and stay updated on her upcoming festival circuit by following her on Instagram at @sophiefrpower. Thanks for listening.Kyler---Episode Links:SophiePower.infoInstagram: @sophiefrpowerMore interview at SaltLakeDirt.com

The sermon centers on the destructive power of unbiblical expectations, using Naaman's reaction to Elisha's simple healing instruction as a pivotal illustration. It argues that when individuals impose unrealistic or self-centered expectations on God, others, or life circumstances, they become blind to divine provision, lose joy, and foster resentment, frustration, and relational damage. The message emphasizes that true gratitude and spiritual freedom come not from demanding life conform to our desires, but from surrendering expectations and embracing God's sovereign, often unexpected, ways of blessing. Through personal anecdotes, cultural critiques, and biblical examples—from the Emmaus disciples to Stephen Hawking—the preacher calls believers to humility, gratitude, and a radical release of expectations in order to experience God's abundant life and restore joy in relationships, worship, and daily living.

Welcome to Science Quest!

Episode 810 is loaded with massive Nintendo drops and landscape-shifting news! ? Star Fox Direct Shadow Drop! A brand new Star Fox 64 remake is coming with modern multiplayer, online play, and Switch N64 controller support. ? Switch 2 Price Increase: Nintendo is bumping the console price to $499 this September, but they are softening the blow with early June bundles featuring Mario Kart World, DK Bananza, or Pokopia. ? End of an Era: Legendary director Takashi Tezuka is retiring from Nintendo after 40 incredible years of shaping Super Mario and The Legend of Zelda. ? Plus, a look at the upcoming Mineru's Construct amiibo from Tears of the Kingdom! ? In Change the System: Justin ventures into Xenoblade Chronicles X squad missions and updates his chaotic Tomodachi Life island featuring Walt Disney and Stephen Hawking. Brandon samples Dead as Disco, tinkers with his 3DS, and hits Level 50+ on his island. Eugene goes retro with emulatorJS, blasting through NES Super Spike V'Ball, World Cup 98 on N64, and classic arcade staples like Tapper and Rampart.

News On The Flipside WOW China 3 days lots to cover Iran war on standby ? more good news for the economy and some good news for gop on midterms.Plus some good news on poles for Trump Cuba descends into violent riots as it runs out of fuel - after rejecting $100million in US aid 30 vessels including Chinese ships transited the Strait of Hormuz with Iran's permission — while the US blockade redirected 70 others JetBlue announces first-ever route to Europe at a Spirit Airlines price point Scientists tried to contact alien life - then Stephen Hawking sounded the alarm Ukraine advances AI drone swarms and robotic ground units US destroyers just fought through the Hormuz trap Something weird and worrying is happening with rain, study finds What's at the center of a black hole? Scientists have a sobering answer Ukraine strikes Russian airbase and major oil refinery Underwater bomb discovered at base of dam holding entire city's drinking water supply Nuclear-Powered Trump Class Battleships Will Reverse One Of The Navy's “Largest Mistakes”: Navy Boss Democrats discover 'rigged' elections Prediction markets cut Democrats' House flip odds after court ruling The Milky Way ate a galaxy called Loki, and scientists think they found its bones Putin says another country 'requires special consideration' — Russia warns of war SpaceX finally named a date for flight 12 — and Starship will fly with deliberate damage "They would already be dead": NATO pauses drill 3 times as troops get crushed This is very rare': The US Navy ‘surfaced' an Ohio-class missile submarine as a warning to Russia and Iran As Britain and France try prying Hormuz open with their own crowbars, Uncle Sam forms new coalition Paranoid Putin makes first indication he will pull out of Ukraine after humiliation At 13,000 mph, DARPA's Falcon HTV-2 could fly from NYC to LA in under 12 minutes at Mach 20, nobody has built anything faster

A live in-person performance of Sleeping with Celebrities? At a real theater, the Bayfront Theater,  with lights and guests and an audience? At 5pm in the afternoon? Believe it because it happened. We ventured to the home of Rice-a-Roni, San Francisco to join comedy geniuses from all over at the SF Sketchfest. Hear actor John DiMaggio reveal the secrets of voiceover in a mellow voice that he's not used to using. Catch a rundown of warm springs with actor, comic, and memoirist Moshe Kasher. Tune into beautiful tunes from musical guest Meredith Edgar, who even covers Metallica in a duet with our host John Moe. John's daughter Margaret joins us onstage to try to fall asleep during the show - see if she succeeds. The show was taped in January and if you couldn't be there, well, you're here now. Meredith Edgar's album Melancholy Baby. You can find her music on BandCamp @MeredithEdgarMusic. Instagram @MosheKasher @TheJohnDimaggio @MeredithEdgarMusic Hey Sleepy Heads, is there anyone whose voice you'd like to drift off to, or do you have suggestions on things we could do to aid your slumber?  Email us at:  sleepwithcelebs@maximumfun.org. Follow the Show on: Instagram @sleepwcelebs Twitter @SleepWithCelebs TikTok @SleepWithCelebs John is on Twitter @johnmoe. John's acclaimed, best-selling memoir, The Hilarious World of Depression, is now available in paperback. Join | Maximum Fun If you like one or more shows on MaxFun, and you value independent artists being able to do their thing, you're the perfect person to become a MaxFun monthly member.  Thanks to everyone who participated in this year's MaxFunDrive! Still want to get in on the action? Follow this link to support this show (and get in on our limited-time keychain sale to benefit the Center for Constitutional Rights): https://maximumfun.org/joinsleeping

BEST OF HACKING THE AFTERLIFE These are excerpts from a number of podcasts - with conversations with people offstage about how things work.  Charles Grodin, Robert Towne, Phil Hartman, Kurt Cobain, Jimi Hendrix stop by briefly Prince stops by, Abraham Lincoln, Stephen Hawking, Robin Williams, and two animals stop by - Hira, the dog and companion of screenwriter Robert Towne, and Mr. Bailey, the companion and pet of Luana Anders. Both have insight into how animals incarnate, how consciousness functions.  It's a free wheeling discuss with lots of different concepts flying around.  Someone requested us doing a conversation with animals - and out of the many podcasts, conversations where animals showed, up, these were a few with a dog I knew (Hira) and walked for 3 years for my boss Robert Towne.. Hira showed up during a session we did with Robert. He was a skeptic until I asked Hira to tell Robert something I didn't know, that only he could remember. Jennifer said he was showing her challenging a buffalo on Catalina. Something only Robert could have known. Luana is our moderator on the flipside, and is responsible for teaming me and Jennifer up - and also was close friends with any number of people who show up (Charles Grodin, etc).   It's hard to characterize this as anything but an extended conversation about how consciousness functions or incarnation works. Enjoy. 

Get ready for a deep dive into the unknown as Walter M. Sterling welcomes Canadian radio host and UFO expert Dave Scott to discuss the highly anticipated presidential release of UFO files. They hilariously break down what the public should actually expect from the government—mostly heavily blacked-out documents that will make Sharpie investors rich, and grainy UFO videos that look like they are playing on a 1968 black-and-white TV. This out-of-this-world conversation covers the Pentagon's reluctance to share secrets, the absurd threat of "space pirates" stealing your candy, and Stephen Hawking's grim warning that hostile aliens are probably just visiting Earth to steal our pearls. Finally, they debate whether the newly formed Space Force is a vital defense system for hunting UFOs, a budget-grabbing threat narrative, or simply a well-funded sitcom with excellent logos and really nice uniforms. Learn more about your ad choices. Visit megaphone.fm/adchoices

We're back in Melbourne, this week with HOT DEPARTMENT! This week we get into holistic gay house shares, chugging 4 pints of milk, Stephen Hawking, gabagool and cult parents. Follow Honor and Patrick @hotdepartment. WE NOW HAVE MERCH! Get your Glue t-shirts, mugs and totes in time for Christmas here (discount code for Patrons is on the Patreon): https://visualanticsapparel.com/collections/glue-factory Olga's tour dates can be found here: https://www.rocknrolga.com/ Milo's tour dates can be found here: https://www.miloedwards.co.uk/liveshows Follow us online to get Glue-related clips and updates: https://linktr.ee/gluefactorypod Learn more about your ad choices. Visit podcastchoices.com/adchoices

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Peter Russell has spent over 50 years exploring one of the most quietly radical ideas of our time: that the mind doesn't need to be forced, pushed, or perfected — it just needs to be allowed to rest. In this episode of The Spiritual Perspective Conversations, Light Watkins and Peter Russell take us from his childhood on the English coast, through his time studying under Stephen Hawking at Cambridge, to sitting with Maharishi in India, and eventually getting banned by the very movement he valued the most. Along the way, he shares what decades of meditation teaching — and a lifetime of curious, honest inquiry have revealed about the nature of the mind, presence, and what it actually means to just sit quietly with yourself.Key Insights:Meditation is effortless by design. The moment you try to control your mind, you've already gone off track. Peter learned this directly from Maharishi: the whole point is to allow the mind to settle naturally, not force it into stillness.Monkey mind isn't a problem. Thoughts showing up during meditation isn't a sign you're doing it wrong. It's completely natural. The skill is simply noticing when you've followed a thought, and gently choosing not to follow it anymore.Stress needs a release valve. Pressure in life isn't inherently bad but without a way to decompress, you become a walking pressure cooker. Meditation is that release valve, and even 15 minutes can feel like a vacation.Micro-meditations count. You don't need a cushion or a scheduled sit. Peter keeps sticky notes around his home that just say "pause." A few conscious breaths between tasks done 20 or 50 times a day can quietly transform how you move through life.Letting go means letting in first. When something uncomfortable surfaces in meditation, the instinct is to push it away. Peter's discovery: go to the body, get curious about the sensation, and let it in. That's when it starts to soften and dissolve on its own.The deepest part of you never changes. Beneath the ego, the persona, and the noise of daily life is a quiet sense of "I am" that has always been there. Learning to recognize and rest in that what Peter calls being is the heart of the whole practice.Great teachers speak from experience, not just knowledge. What struck Peter most about both Maharishi and Eckhart Tolle wasn't their intellect but it was that they were teaching from something they had genuinely lived. That's what made their words land differently.More about Peter's work: https://www.peterrussell.com/Get Peter's new book, How to Meditate Without Even Trying: https://www.peterrussell.com/HMWETbook/index.phpSend us a text message. We'd love to hear from you!

Sponsor Link:To check out our special NordVPN deal with big savings and 4 extra months free, visit nordvpn.com/spacenutsPrimordial Black Holes, Ultra Hot Jupiters, and a New Moon Crater In this captivating episode of Space Nuts, hosts Andrew Dunkley and Professor Fred Watson delve into some of the most exciting developments in astronomy. From the intriguing possibility of primordial black holes being linked to dark matter to groundbreaking discoveries about the chemical composition of an ultra hot Jupiter, and the recent formation of a massive crater on the Moon, this episode is packed with cosmic revelations.Episode Highlights:- Primordial Black Holes: Andrew and Fred Watson discuss the recent findings from LIGO that suggest the existence of black holes with masses less than that of the Sun. They explore how these primordial black holes, predicted by Stephen Hawking, could provide new insights into the nature of dark matter and the formation of the universe.- Chemical Analysis of WASP 189B: The hosts examine the exciting discovery that the chemical makeup of the ultra hot Jupiter WASP 189B matches that of its parent star, challenging long-held assumptions about planetary formation and composition. This finding reinforces the connection between stars and their planets, providing vital clues for understanding exoplanetary systems.- New Moon Crater: A recent impact on the Moon has created a stunning new crater measuring 225 metres across. Andrew and Fred Watson discuss the implications of this discovery, including the significance of ongoing lunar observations and the potential for future research into the Moon's geological history.For more Space Nuts, including our continuously updating newsfeed and to listen to all our episodes, visit our website. Follow us on social media at SpaceNutsPod on Facebook, Instagram, and more. We love engaging with our community, so be sure to drop us a message or comment on your favourite platform.If you'd like to help support Space Nuts and join our growing family of insiders for commercial-free episodes and more, visit spacenutspodcast.com/about.Stay curious, keep looking up, and join us next time for more stellar insights and cosmic wonders. Until then, clear skies and happy stargazing.Become a supporter of this podcast: https://www.spreaker.com/podcast/space-nuts-astronomy-insights-cosmic-discoveries--2631155/support.

SPONSORS: - Sign up for Claude today at https://Claude.ai/theoriesofeverything and checkout Claude Pro — which includes access to all of the features mentioned in today's episode - Go to https://shortform.com/toe for a free trial and an exclusive $50 OFF on your annual subscription. - I subscribe to The Economist for their science and tech coverage. As a TOE listener, get 35% off! No other podcast has this: https://economist.com/TOE George Ellis is one of those guests who makes you rethink what you thought you understood. Co-author with Stephen Hawking of the singularity theorems, he's spent decades insisting on something most physicists won't touch: that reductionism is simply — patently — false. Physics doesn't decide outcomes. Context does. The thermostat sets the temperature. The algorithm tells the electrons what to do. The physics is the servant, not the master. FOLLOW: - Spotify: https://open.spotify.com/show/4gL14b92xAErofYQA7bU4e - Substack: https://curtjaimungal.substack.com/subscribe - Twitter: https://twitter.com/TOEwithCurt - Discord Invite: https://discord.com/invite/kBcnfNVwqs - Crypto: https://commerce.coinbase.com/checkout/de803625-87d3-4300-ab6d-85d4258834a9 - PayPal: https://www.paypal.com/donate?hosted_button_id=XUBHNMFXUX5S4 TIMESTAMPS: - 00:00:00 - Reductionism is Patently False - 00:07:37 - Top-Down Causation Mechanics - 00:13:46 - Modular Hierarchical Structures - 00:21:00 - Causation at Emergent Levels - 00:26:56 - Universal Biological Principles - 00:36:07 - Critiquing Penrose and CCC - 00:42:41 - The Physics of Infinity - 00:48:50 - Agency and Physical Constraints - 00:56:08 - The Open Future - 01:07:23 - Bioelectricity and Goal-Directedness - 01:12:28 - Evolving Block Universe - 01:19:32 - Multiverse as Metaphysics - 01:24:49 - Moral Realism as Data LINKS MENTIONED: - George's Papers: https://inspirehep.net/authors/1010821 - George's Books: https://amazon.com/stores/George-Francis-Rayner-Ellis/author/B00287T2PW - Arrow of Time [Paper]: https://arxiv.org/abs/1302.7291 - Why Reductionism Does Not Work [Paper]: https://link.springer.com/chapter/10.1007/978-3-662-63187-4_6 - Recognizing Top-Down Causation [Paper]: https://arxiv.org/abs/1212.2275 - Top-Down Causation by Information Control [Paper]: https://pmc.ncbi.nlm.nih.gov/articles/PMC3226993/ - Causal Closure of Physics [Paper]: https://arxiv.org/abs/2006.00972 - Issues in Philosophy of Cosmology [Paper]: https://arxiv.org/abs/astro-ph/0602280 - The Music of Life [Book]: https://amazon.com/dp/0199228361?tag=toe08-20 - How Can Physics Underlie the Mind? [Book]: https://amazon.com/dp/3662498073?tag=toe08-20 - Large Scale Structure of Space-Time [Book]: https://amazon.com/dp/0521099064?tag=toe08-20 - The Selfish Gene [Book]: https://amazon.com/dp/0199291152?tag=toe08-20 - World Beyond Physics [Book]: https://amazon.com/dp/0190871334?tag=toe08-20 - Contextual Wavefunction Collapse [Paper]: https://arxiv.org/abs/1807.08171 - A Theory of Biological Relativity [Paper]: https://pubmed.ncbi.nlm.nih.gov/23386960/ - Facing Up to the Problem of Consciousness [Paper]: https://consc.net/papers/facing.pdf - Denis Noble [TOE]: https://youtu.be/K-U-ZB3yHK4 - Michael Levin [TOE]: https://youtu.be/c8iFtaltX-s - Sean Carroll [TOE]: https://youtu.be/9AoRxtYZrZo - Quantum Physics, Digital Computers, and Life [Paper]: https://arxiv.org/abs/2403.06306 - Dynamical Emergence of Biology from Physics [Paper]: https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2018.01966/full - The Whole Truth [Book]: https://amazon.com/dp/0691231354?tag=toe08-20 - Endless Forms Most Beautiful [Book]: https://amazon.com/dp/0393327795?tag=toe08-20 - Topology and Cosmology [Paper]: https://link.springer.com/article/10.1007/BF02450512 More links at https://curtjaimungal.substack.com Guests do not pay to appear. #science Learn more about your ad choices. Visit megaphone.fm/adchoices

Christophe Galfard is a theoretical physicist and science communicator, renowned for his ability to explain the great concepts of the universe in an accessible and engaging way. He earned his PhD at the University of Cambridge, where he worked alongside Stephen Hawking, one of the most influential figures in contemporary physics. His work focuses on bringing science closer to the general public, transforming complex theories into clear narratives that spark curiosity and wonder. Through his books and lectures, he aims to help anyone understand how the universe works. Convinced that science is a tool to broaden our perspective, Galfard argues that understanding the cosmos not only provides answers, but also helps us live more comfortably with uncertainty and the unknown.

The views of my guest do not reflect that if my own. That being said, this is on of our attempts to bring levity to conspiracy world. Try not to get offended, Eric is a comedian after all.Eric's website https://www.erichollerbach.com/Forbidden Knowledge Network https://forbiddenknowledge.news/ FKN Link Treehttps://linktr.ee/FKNlinksMake a Donation to Forbidden Knowledge News https://www.paypal.me/forbiddenknowledgenehttps://buymeacoffee.com/forbiddenWe are back on YouTube! https://youtube.com/@forbiddenknowledgenews?si=XQhXCjteMKYNUJSjBackup channelhttps://youtube.com/@fknshow1?si=tIoIjpUGeSoRNaEsDoors of Perception is available now on Amazon Prime!https://watch.amazon.com/detail?gti=amzn1.dv.gti.8a60e6c7-678d-4502-b335-adfbb30697b8&ref_=atv_lp_share_mv&r=webDoors of Perception official trailerhttps://youtu.be/F-VJ01kMSII?si=Ee6xwtUONA18HNLZListen to Forbidden Knowledge News on clearair.fm every Tuesday, Thursday, and Saturday 12:15pm CSThttps://clearair.fm/Pick up Independent Media Token herehttps://www.independentmediatoken.com/Be prepared for any emergency with Prep Starts Now!https://prepstartsnow.com/discount/FKNStart your microdosing journey with BrainsupremeGet 15% off your order here!!https://brainsupreme.co/FKN15Book a free consultation with Jennifer Halcame Emailjenniferhalcame@gmail.comFacebook pagehttps://www.facebook.com/profile.php?id=61561665957079&mibextid=ZbWKwLWatch The Forbidden Documentary: Occult Louisiana on Tubi: https://link.tubi.tv/pGXW6chxCJbC60 PurplePowerhttps://go.shopc60.com/FORBIDDEN10/or use coupon code knowledge10Johnny Larson's artworkhttps://www.patreon.com/JohnnyLarsonSign up on Rokfin!https://rokfin.com/fknplusPodcastshttps://www.spreaker.com/show/forbiddenAvailable on all platforms Support FKN on Spreaker https://spreaker.page.link/KoPgfbEq8kcsR5oj9FKN ON Rumblehttps://rumble.com/c/FKNpGet Cory Hughes books!Lee Harvey Oswald In Black and White https://www.amazon.com/dp/B0FJ2PQJRMA Warning From History Audio bookhttps://buymeacoffee.com/jfkbook/e/392579https://www.buymeacoffee.com/jfkbookhttps://www.amazon.com/Warning-History-Cory-Hughes/dp/B0CL14VQY6/ref=mp_s_a_1_1?crid=72HEFZQA7TAP&keywords=a+warning+from+history+cory+hughes&qid=1698861279&sprefix=a+warning+fro%2Caps%2C121&sr=8-1https://coryhughes.org/Our Facebook pageshttps://www.facebook.com/forbiddenknowledgenewsconspiracy/https://www.facebook.com/FKNNetwork/Instagram @forbiddenknowledgenews1@forbiddenknowledgenetworkXhttps://x.com/ForbiddenKnow10?t=uO5AqEtDuHdF9fXYtCUtfw&s=09Email Forbidden Knowledge News forbiddenknowledgenews@gmail.comsome music thanks to:https://www.bensound.com/ULFAPO3OJSCGN8LDDGLBEYNSIXA6EMZJ5FUXWYNC6WJNJKRS8DH27IXE3D73E97DC6JMAFZLSZDGTWFIBecome a supporter of this podcast: https://www.spreaker.com/podcast/forbidden-knowledge-news--3589233/support.

She moderated the fly debate. She interviewed Stephen Hawking. She covered 12 presidential campaigns and sat down with the last 10 presidents. And she spent years inside Queen Elizabeth's extraordinary vantage point on American democracy — one that no American journalist could ever fully replicate. Susan Page, Washington Bureau Chief of USA TODAY, joins Corey to discuss her latest book, The Queen and Her Presidents: a sweeping account of Queen Elizabeth II's relationships with every American president from Truman to Biden. But this conversation goes well beyond the book. Susan reflects on a career that began in a converted car dealership on Long Island, the lessons she learned covering her first president (and how badly she blew it), what it really takes to develop sources across decades of political reporting, and why — from a Kansas girl's perspective — the people on both sides of our divide love America more than we give them credit for. Calls to Action ✅ If this conversation resonates, consider sharing it with someone who believes connection across difference still matters. ✅ Subscribe to Corey's Substack: coreysnathan.substack.com ✅ Leave a review on Apple Podcasts, Spotify, or wherever you listen: ratethispodcast.com/goodfaithpolitics ✅ Subscribe to Talkin' Politics & Religion Without Killin' Each Other on your favorite podcast platform. ✅ Watch the full conversation and subscribe on YouTube: youtube.com/@politicsandreligion Key Takeaways Preparation is a framework, not a script. Susan goes into every major interview with a plan — what she wants to get, how to get it, what to do if the answer goes sideways. But the goal is to inform the conversation, not control it. The worst thing an interviewer can do, she says, is fail to listen to the answer. Great sourcing is built on respect and fairness, not on pulling punches. Rich Bond, the young Long Island operative she profiled in 1979, became a top Republican official and a reliable source for decades — not because she went easy on him, but because he trusted her to be fair. She would not have softened a story about him, and he knew it. Books and daily journalism use the same muscle, differently. The skills transfer directly — the sourcing, the curiosity, the nose for a good detail — but the bar is higher and the time horizon is longer. Writing a book means people are paying thirty dollars and spending real time. You owe them something they couldn't get from clicking a link. The best research rewards patience. Sifting through archival files at eight presidential libraries and the National Archives in Britain yielded moments that almost nobody else has read. The sarcastic cables British ambassadors sent back about LBJ as vice president confirmed everything LBJ already suspected they thought of him. They love America. Whether she's at a No Kings rally or a MAGA rally, Susan hears the same thing: people who care deeply, who revere the Constitution, who think they're fighting for the country. The polarization isn't about love of country — it's about a failure to extend basic respect across the divide. Queen Elizabeth perfected the art of getting people to talk. Her small talk strategy — chatter briefly, then turn the question back — was especially effective with men, who, as Susan notes diplomatically, tend to enjoy talking about themselves. Susan has consciously adopted the technique and credits it with making her better at navigating rooms full of strangers. About Our Guest Susan Page is the Washington Bureau Chief of USA TODAY and one of the most respected political journalists in America. She has covered 12 presidential campaigns and interviewed the last 10 presidents. She moderated the 2020 vice presidential debate between Kamala Harris and Mike Pence — yes, the one with the fly — and is the bestselling author of biographies of Barbara Bush, Nancy Pelosi, and Barbara Walters. Her latest book, The Queen and Her Presidents, chronicles Queen Elizabeth II's relationships with every American president from Truman through Biden. Links and Resources The Queen and Her Presidents by Susan Page — susanpagedc.com Connect on Social Media Corey is @coreysnathan on all the socials… Substack LinkedIn Facebook Instagram Twitter Threads Bluesky TikTok Thanks to our Sponsors and Partners Thanks to Pew Research Center (pewresearch.org) for making today's conversation possible. Links and additional resources: The Village Square: villagesquare.us Meza Wealth Management: mezawealth.com Proud members of The Democracy Group “Clarity, charity, and conviction can live in the same room.” Yes, really.

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TV Royalty Scarlett Moffatt joins Grace this week, she's also back on our screens for I'm A Celebrity All Stars and we catch up on all the chaos of the season! We also take it back as Scarlett gives us a behind the scenes look at Goggle Box and meeting her finance. The pair also chat about their dreams of having gay children, wedding planning and conspiracy theories!

At 64, marathon swimming champion Diana Nyad inspired the world by becoming the first person to swim 110 miles from Cuba to Florida without a shark cage. Proving the human spirit is capable of triumphing over extreme adversity, Diana explains why she decided to take on the quest and shares why the swim was about far more than breaking records. She also reveals her empowering three-word mantra, and shares how “The Wizard of Oz,” Stephen Hawking and the Taj Mahal helped her through the toughest times in the ocean. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

The Boys are BACK!In this episode of High Society Radio, Stanley shows off his new "Super Bowl" hat, the crew dives deep into the bizarre world of Canadian healthcare, and we explore the "Red Heifer" prophecy.Then, Bronx "Boston" Johnny joins the show to share his "expertise" on South American politics, the chaos in Ecuador, and his personal run-ins with the member of the cabinet. Also, revels in Stephen Hawking related vindication!Air Date 3/12/26DON'T FORGET TO WATCH FAGA'S NEW SPECIAL "BURN AFTER SAYING" ON THE HSR YOUTUBE PAGE!⁠⁠https://www.youtube.com/watch?v=TxIHJU2LotU⁠⁠Support Our Sponsors!Body Brain Coffee: https://bodybraincoffee.com/ - Grab A Bag of Body Brain Coffee with Promo Code HSR20 to get 20% off!YoKratom: https://yokratom.com/3rd Mic Harrington: https://3rdmicharrington.com/High Society Radio is 2 native New Yorkers who started from the bottom and didn't raise up much. That's not the point, if you enjoy a sideways view on technology, current events, or just an in depth analysis of action movies from 2006 this is the show for you.Chris Stanley is the on air producer for Bennington on Sirius XM.Chris Faga is a lifelong street urchin, a former head chef, county comitteman and supposed comedian. Twitter: https://twitter.com/ChrisFromBklynInstagram: https://www.instagram.com/chrisfrombklynEngineer: DomExecutive Producer: JorgeSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Watch every episode ad-free & uncensored on Patreon: https://patreon.com/dannyjones Jim Gates is a theoretical physicist who works on supersymmetry, supergravity, and superstring theory. Jim led the creation of a new NASA-funded research center, called the Center for the Study of Terrestrial and Extraterrestrial Atmospheres (CSTEA) & was it's first director. SPONSORS https://liquid-iv.com - Use code DANNY for 20% off your first order. http://amentara.com/go/dj - Use code DJ22 for 22% off. https://shopify.com/dannyjones - Sign up for your one-dollar-per-month trial & start selling today. https://whiterabbitenergy.com/?ref=DJP - Use code DJP for 20% off EPISODE LINKS Jim's latest book "Proving Einstein Right" - https://a.co/d/0cg0Dqjz FOLLOW DANNY JONES https://www.instagram.com/dannyjones https://twitter.com/jonesdanny OUTLINE 00:00 - Jim's dream to become a scientist at 4 years old 06:25 - The importance of imagination 12:05 - Growing up during the Space Race 17:55 - Jim's journey to MIT 21:41 - Meeting Stephen Hawking at MIT 25:11 - Jim's dinner with Richard Feynman 30:49 - 2 habits that create a genius 36:15 - Meeting Ed Witten 41:12 - When gravity research went dark 45:28 - Why Jim is worried about the future 50:54 - China's rockets are more advanced than SpaceX 53:02 - Why populating Mars is not possible 54:53 - Radiation belts 56:30 - NASA engineers said we can't go back to the moon 59:07 - Technology that could replace rockets for space travel 01:02:07 - The new, larger hadron collider 01:05:28 - Supersymmetry 01:14:05 - Supersymmetry could lead to antigravity "transporter" 01:17:30 - Adinkra symbols 01:21:56 - Evidence the universe is actually evolving 01:29:11 - How data has mass & entropy 01:35:46 - The reality of quantum computing 01:39:53 - How consciousness could be "built" 01:43:56 - Jim downloads physics knowledge in his dreams 01:49:50 - AI will become indistinguishable from consciousness 01:52:52 - Why we will never have time travel 01:58:09 - Why shadow government is interested in physics 02:00:18 - Jim isn't surprised by the Epstein files 02:03:49 - Jim's case for hopeless optimism 02:08:04 - Alien life in Jupiter's atmosphere 02:13:38 - Working on Stephen Hawking's documentary Learn more about your ad choices. Visit podcastchoices.com/adchoices

Jim Carrey & Kelly Osbourne spark concern, Chet Hanks stranded, Megan Rapinoe v. US Men's Hockey Team, Stuttering John bombs in NYC, Eli Zaret joins us, and content creator Think Beautiful joins us to tear apart Meghan Markle. Eli Zaret joins the show to break down the David Montgomery trade to the Houston Texans, the upcoming NFL Draft, Detroit Lions CB Terrion Arnold's trouble, Emmanuel Clase's perfect plan, Eli vs Gambling Part 745, Detroit Tigers Javier Báez's marijuana problem, the Tigers in Spring Training, the tale of Chris Pittaro, USA Hockey controversy, Jewish athletes, another Michigan scandal, Floyd Mayweather Jr. vs. Manny Pacquiao II and more. Iran is taking a pounding by the USA and allies. They haven't given up yet. Some turd decided to shoot up a bar in Austin, Texas in response. Think Beautiful joins us to rip apart Meghan Markle. Follow her on YouTube for all your Markleverse needs. Dan Leach held court at Lady Jane's while Marc got his hair cut. Stuttering John Melendez BOMBED at a Manhattan comedy club on Friday night. Anthony Cumia had an interesting night as well. Beast Games wrapped up another phenomenal season. Influencers are using Nancy Guthrie's house for clout. Bhad Bhabie is still battling cancer. Rolling Stone dove into the recent celebrity GoFundMe's. Chet Hanks is stuck in Colombia. Poor Chet. Why You Look Different? Jim Carrey? Kelly Osbourne? David Caruso? Michael Jackson's estate is being sued for child trafficking. Mark Geragos is a turncoat. The USA Men's Hockey Team is still feeling the heat from laughing at a Donald Trump joke. Megan Rapinoe and Sue Bird are NOT happy about it. Stephen Hawking has been vindicated… but he's still a creep. Bill and Hillary Clinton had to talk about their Jeffrey Epstein ties and were none too pleased. Shia LaBeouf did an interview with Channel 5 and Andrew Callaghan. Mikerophone has a good breakdown of Stefon Diggs latest news. Rashee Rice is not a good person. Receiver? He's pretty good. Merch is still available. Buy it before it's gone. If you'd like to help support the show… consider subscribing to our YouTube Channel, Facebook, Instagram and Twitter (Drew Lane, Marc Fellhauer, Trudi Daniels, Jim Bentley and BranDon)

Disturbing pics of Stephen Hawking on Epstein Island released, USA Men's Hockey visits Trump, Nancy Guthrie reward raised, Bonnie Blue knocked up, and Trudi fights her toilet. Programming Note: Marcie Hume (Corey Feldman vs. The World) and Lita Ford will join us tomorrow. The State of the Union is going down tonight. The US Men's Hockey Team is getting some heat following their recent communication with Donald Trump. Savannah Guthrie is now offering a $1M reward for her mother Nancy. Some turds are threatening to boycott the Met Gala due to Jeff Bezos' sponsorship. Stephen Hawking photos have emerged of him living it up on Epstein Island. Drew confirms John Lenon's weiner is uncirc'd. AI confirms they all were uncircumcised. Legacy Partner's drops a new $50 gift card winner. Congrats to _____________! Darren McCarty dropped by the studio today for ML's Soul of Detroit. TJ Miller is in town. Check him out in Royal Oak this weekend. Jim Breuer is popping off at American Airlines. Mickey Redmond's grandson, Teddy, has a rare form of leukemia and could use financial help. A BAFTAs judge has quit following the n-word incident. Eric Dane's family is still fundraising. Rebecca Gayheart has broken her silence. Hey Taylor Swift... why you look different? Cruz Beckham and the Breakers are the hot new rock act. Andy Dick remains in physical shambles. Lisa Rinna has been drugged... in front of everyone. Some people are saying she might have been over served. The Olympic Men's Hockey Final is the most watched pre-9am sports event in history. Evan Dando of The Lemonheads can't catch a break. Trudi destroyed her toilet.Drew's hot water heater took a dump. Drew was nearly bamboozled by credit card thieves again. It's tax season. Hooray. Steven Spielberg is bailing on California for New York. Congressman Tony Gonzales has himself quite the scandal. Is Bonnie Blue really pregnant or is this all a stunt? Maury Povich wants nothing to do with the situation. Drew reeducated himself on the crimes of D.B. Cooper. The trial has resumed for the Alexander Brothers. Merch is still available. Buy it before it's gone. If you'd like to help support the show… consider subscribing to our YouTube Channel, Facebook, Instagram and Twitter (Drew Lane, Marc Fellhauer, Trudi Daniels, Jim Bentley and BranDon)

02-20-26 - Stephen Hawking Super Genius - 2009 - BOSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

We were graced by the Deep Dive gods this week with Brooklyn Beckham's very public break up with his parents (including that BIZARRE wedding dance mention? We NEED to see the video!). Plus, Stephen Hawking carnival, Aaron Rodgers and his AI wife, and Jeffrey Epstein's weird ass masks. And P.S., we recorded this ep right before the Blake / Taylor texts dropped, so we'll be deep diving all of that next week!

Tommy and Ben talk about the growing and very real threat of President Trump attempting to take Greenland by force, the spectrum of reactions from world leaders from feckless NATO Secretary General Mark Rutte to scorning French President Emmanuel Macron, and how this would lead to the end of NATO if Europeans don't stand up to the US. They also discuss how Trump's walking back of military threats to Iran led to the death of thousands of protesters, the confusing and corrupt intentions behind the “Board of Peace”, a rocky ceasefire between the Syrian government and Kurdish forces, Grok's sexualized image crisis, and a bizarre tribute to Stephen Hawking. Then, Ben speaks to Sky News Africa Correspondent, Yousra Elbagir, about Uganda's contested election. Preorder Ben's new book, All We Say: The Battle for American Identity: A History in 15 Speeches, out on May 26. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.