Podcasts about Orpheus

Nieuwe games in oude stijl, dat zien we tegenwoordig volop. Joe van Burik houdt er, zoals de vaste luisteraar weet, een groeiende verzameling retrogames op na en is ook gecharmeerd van nieuwe releases in een klassiek jasje. Dus heeft hij een reeks recente titels gespeeld om te bespreken met Donner Bakker in deze Mini-Game Round-Up van All in the Game. Orpheus: To Hell and Back 70s-style Robot Anime Geppy-X Agent 64: Spies Never Die Duskfade Vragen? Mail ons! Op allinthegame@bnr.nl Over All in the GameAll in the Game is de podcast over games voor iedereen. Wanneer er iets speelt in de wereld van games, hoor je dat hier: spannende ontwikkelingen, boeiende onderzoeken en natuurlijk de nieuwste releases om te spelen op je PlayStation, Xbox, pc of welk platform dan ook. Onder leiding van BNR's techredacteur Joe van Burik hoor je gesprekken met andere gamekenners, zoals beursnerd Jochem Visser, techredacteurs Niels Kooloos en Daniël Mol én popcultuurkenners Donner Bakker en Sam van Zuilen. Ook hoogleraar computerwetenschappen Felienne Hermans en universiteit docent Laura van der Lubbe schuiven geregeld aan, en je hoort bijdragen van audioproducers André Dortmont, Ivo Klokman en Jeanne Heeremans. Elke week zijn er minimaal twee afleveringen van All in the Game. Of nog meer, wanneer er veel speelt in de wereld van games. Soms met impressies en analyses over actuele ontwikkelingen en nieuwe games. Andere keren kun je luisteren naar interviews met makers van bijzondere games, van Grand Theft Auto (GTA) tot Baldur's Gate 3 - zowel Nederlandse als internationale ontwikkelaars. Of we praten met e-sport-atleten, onderzoekers en andere experts in de wereld van videogames. In deze podcast kijken we verder dan alleen wat een game leuk maakt: we bespreken juist ook in de culturele, maatschappelijke, economische en technologische impact ervan. Jaarlijks gaat er immers zo'n 200 miljard euro om in de wereldwijde game-industrie, dat is al (vele jaren zelfs) daadwerkelijk meer dan de muziek- en filmindustrie bij elkaar opgeteld. Zo hoor je bij All in the Game niet alleen wat je moet spelen - en op welk nieuwe (game)platform - maar kun je daar nog bewuster mee bezig zijn, over praten en natuurlijk van genieten. Of het nou gaat om Super Mario of Sonic the Hedgehog, Fortnite of Roblox, voetbalgames van EA Sports FC of de FIFA, Call of Duty of Battlefield, League of Legends of Dota,of goude oude titels zoals Tetris, Rollercoaster Tycoon, The Sims of zelfs Snake. En we hebben ook aandacht voor liefhebberijen die dicht op games zitten, zoals Dungeons & Dragons, Lego en de films, series en strips rond reeksen zoals Star Wars en Marvel. Het komt allemaal aan bod in All in the Game. All in the Game werd als podcast al in 2022 opgenomen in het archief van Het Nederlands instituut voor Beeld & Geluid in Hilversum - als eerste podcast van BNR Nieuwsradio en één van de eerste gamepodcasts van allemaal. Gezamenlijk met talloze Nederlandse televisieprogramma’s, radioshows, games, websites, webvideo’s en podcast vormt dit materiaal de Nederlandse mediageschiedenis. Over Joe van BurikJoe van Burik is presentator, podcastmaker en techredacteur bij BNR Nieuwsradio. Je hoort hem bijna dagelijks in de Tech Update met het laatste nieuws over digitale technologie, en gaat daar in De Grote Tech Show (samen met Ben van der Burg) elke woensdag dieper op in met gasten uit de techwereld. Daarnaast maakt hij onder meer de podcast All in the Game, voor iedereen die meer wil horen over videogames.See omnystudio.com/listener for privacy information.

KPFA Theatre Critic Richard Wolinsky reviews “Orpheus Descending” by Tennessee Williams, at Oakland Theatre Project through August 23, 2026.         Text of Review: Over the course of his career, Tennessee Williams wrote thirty full length plays and a host of one-acts. Yet looking at the list of plays produced by local and regional theaters, you'd think he only wrote The Glass Menagerie and A Streetcar Named Desire. While it's true there was a fall off in quality after 1965, and some of the early works were sketchy, there's still quite a roster of plays that hardly ever get revived outside of New York. So it's a kind of gift that Orpheus Descending is now playing at Oakland Theatre Project through August 23rd. 

The play has a curious and confusing history. A rewrite of one of Williams' early plays, Battle of Angels, Orpheus Descending opened on Broadway in 1957 in the midst of Williams' most creative period. It received mixed to negative reviews, and closed quickly, later becoming a Brando film titled The Fugitive Kind, which had no relationship to a same-titled early Williams play, and was later revived on Broadway in 1989 with Vanessa Redgrave, which was filmed but isn't available streaming. Orpheus Descending opens in a dry goods store in a small white town in the South in the 1950s when Val, a drifter whose car has broken down, comes into the emporium and throws everyone in the town into turmoil, particularly Lady, wife of the store's ill-tempered invalid owner. A romance, doomed of course, ensues. We do have to wait to meet them after a long expository duet from two lesser characters that Williams should have cut seventy years ago. 

O rpheus Descending is, as one critic noted for a recent revival, pure over the top Southern Gothic, verging on fantasy and brain rattling hysteria. It's both Williams at his best, and at his most histrionic. This production, directed by Will Detlefsen, shows the play at its best through some of the actors, particularly Natalie Pasquinelli as Carol and Lisa Ramirez in the role of Lady. But we never understand Val's attraction to Lady and the play suffers from the theatre's configuration, forcing actors to shout from across the horizontal space, turning the play often into a one-note yelling spree. And some directorial choices that sounded good in theory just don't work. But in a world of half-baked world premieres and undeserved Pulitzers, any Tennessee Williams is a treat, and when this production works, and it does work here and there, and when Williams is on a roll, it's theatre as its meant to be seen. Orpheus Descending plays at Oakland Theatre Project through August 23rd. For more information go to oakland theatreproject. org. I'm Richard Wolinsky on Bay Area Theatre for KPFA. The post Review: “Orpheus Descending” at Oakland Theatre Project appeared first on KPFA.

Silvana, Eric, and Tegan watch Season 7 Episode 4  "Orpheus," which seems to have nothing to do with the Greek myth. Teal'c is injured and has a slow recovery ahead of him and he's having a  hard time with it. Jack and Daniel are, in fairness, doing a pretty good job of supporting him. Sam gets to explains a sci fi film to the team in the weight room while Teal'c is doing Physical Therapy and Daniel gets to show off his new muscles. Daniel is hearing things and seeing visions and it turns out Ry'ac and Bra'tac are on a labor planet so the team comes up with a plan to set them free. They have to move quickly because Bra'tac has run out of tretonin! Bra'tac gives a great pep talk and Daniel convinces Teal'c that kelno'reem isn't just for Jaffa with symbiotes, actually. They escape, saving the day and Teal'c gets his mojo back!  Peter DeLuise unsurprisingly directed this episode and a side character shared the name of his 21 Jump Street character. The hosts praise the emotional depth of the episode and think it was a long time coming. This episode also raises a lot of questions about how the Jaffa have been written up to this point and why the Jaffa rebellion wasn't prioritized by the SGC and Teal'c far earlier? Episode Ratings: Comedic Effect - 5/7 Chevrons Emotional Impact - 7/7 Chevrons Enjoyability - 6/7 Chevrons Culture/history/lore - 5/7 Chevrons Novelty - 4/7 Chevrons Technical Quality  - 6/7 Chevrons Plot - 5/7 Chevrons Relevance to the overall story? Yes relevant, don't skip Join the conversation on our socials.  

THIS IS A PREVIEW PODCAST. NOT THE FULL REVIEW. Please check out the full podcast review on our Patreon Page by subscribing over at - https://www.patreon.com/NextBestPicture "Hadestown: The Musical" is a 2026 musical film presenting a live stage recording of the Tony Award-winning musical of the same name. It tells two versions of the ancient Greek myths, Orpheus and Eurydice and Hades and Persephone, to explore enduring and contemporary themes such as poverty. Brett Sullivan directed the film, which features five principal cast members of the original Broadway production: Reeve Carney, André De Shields, Amber Gray, Eva Noblezada, and Patrick Page. Recorded at the Lyric Theatre, London, the remainder of the cast are performers in the West End production. Bleecker Street division Crosswalk launched a limited release for North American cinemas after the film premiered at the Tribeca Festival to rave reviews. What did we think of it? Please tune in as Cody Dericks, Lauren LaMagna, Josh Parham, Dan Bayer, and Tom O'Brien talk about the pro-shot version compared to the stage version and other pro-shot films such as "Hamilton," their favorite musical numbers, the performances, and more in our SPOILER-FILLED review. We appreciate your support and hope you enjoy our review! https://youtu.be/VXWbrMKn51E Check out more on NextBestPicture.com Please subscribe on... Apple Podcasts - https://itunes.apple.com/us/podcast/negs-best-film-podcast/id1087678387?mt=2 Spotify - https://open.spotify.com/show/7IMIzpYehTqeUa1d9EC4jT YouTube - https://www.youtube.com/channel/UCWA7KiotcWmHiYYy6wJqwOw And be sure to help support us on Patreon for as little as $1 a month at https://www.patreon.com/NextBestPicture and listen to this podcast ad-free Learn more about your ad choices. Visit megaphone.fm/adchoices

Es ist ein schicksalhaftes Wiedersehen: 1920, die Malerin Baladine Klossowska begegnet dem Dichter Rainer Maria Rilke, sie verliebt sich leidenschaftlich, Rilke hingegen entfernt sich von ihr, sucht und braucht Rückzug und Ruhe zum Schreiben. Im Jahr von Rilkes 100. Todestag erzählt die Schriftstellerin Julia Franck die Geschichte der vergessenen Malerin Klossowska, von ihren Gefühlen und einer starken Liebe. "Ich ertrinke in den Wassern meiner eigenen Liebe" schreibt die liebestrunkene Baladine Klossowska an Rilke. Ihrer ungleichen Liebe ist die Malerin sich sehr bewusst. Julia Franck hat ihr Buch, frisch erschienen im Verlag Pfaueninsel, nach diesem Satz benannt.Für ihr schriftstellerisches Werk wurde Julia Franck vielfach mit Literaturpreisen ausgezeichnet, erhielt 2007 den Deutschen Buchpreis für ihren Roman "Die Mittagsfrau", der in Bühnenbearbeitungen aufgeführt oder, wie auch ihr Roman "Lagerfeuer", verfilmt wurde.Über eine außergewöhnliche Liebesgeschichte, über das Schreiben und die dazugehörige Recherche spricht Julia Franck bei NDR Kultur à la carte mit Andrea Schwyzer.(00:00:00) Intro(04:37) La vie en rose(16:05) Tosca (Oper) - "E lucevan le stelle"(24:45) Orpheus singt. 3 Sonette von Rainer Maria Rilke für gemischten Chor a cappella -(29:41) Sonate für Violine und Violoncello a-Moll - Allegro (1. Satz)(39:29) La bohème(47:39) Avalon

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

What Happens When the Story You're Reporting Is the Story You're Not Allowed to Tell?  Gabriel Harrison was 30, working as a journalist in Tunisia, and had been handed the story that would end his career. It described a specific mechanism operating across North Africa in the years after Gaddafi's fall, one whose implications did not align with the version of that region most Western audiences had been given. His editor said the story was "not relevant to Libya."  He understood immediately what that meant. Three months later he quit.  Two years of a dark night of the soul followed. Then he moved to South America to join what he was told was an autonomous community, which turned out to be a sophisticated scam whose leaders are now being searched for by Interpol. Gabriel is known online as Orpheus. He crossed 65,000 Instagram followers in four weeks by saying, publicly, the things he had spent years being told he could not. He is the founder of Aquari, a bioresonance jewelry brand backed by peer-reviewed cell research.  He is a student of Rupert Sheldrake's work on morphic resonance and formative causation. He thinks seriously about what he calls the fork between transhumanism and ultrahumanism, and whether the next chapter of human civilization merges us with machine systems or takes us in the opposite direction entirely.Topics include:  What two years after Gaddafi actually looked like from the ground The story his editor would not let him publish Belonging to yourself at 33 The autonomous community in Paraguay and the four-year legal battle it produced Transhumanism vs ultrahumanism Morphic resonance and the hundredth monkey Why attention is currency, and most people are spending it without knowing.CONNECT WITH GABRIEL HARRISON  Instagram: @disco_orpheus  Bioresonance jewelry: aquari.storeWORK WITH DOV Website: https://dovbaron.com Email: dov@dovbaron.com    Please rate, review, and subscribe wherever you listen. It helps the show reach the leaders it was built for. Connect with Dov Baron:https://DovBaron.comdov@dovbaron.comRate, review, and send this episode to the most thoughtful builder you know. That is how the algorithm finds the people who still ask why. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Grab your Comic-Con lanyards and punch your train tickets, because the studio is taking a trip to the underworld! This week on The Filmlosophers, Hosts Eddie and Spencer are joined by the ever-glorious Intern Manager/Managing Intern Amy for a massively packed episode discussing the latest in pop culture and a legendary Broadway hit hitting the big screen. The San Diego Comic-Con Aftermath Before the crew boards the train to the underworld, they unpack the massive, earth-shattering news that rocked their pop-culture hearts at last week's San Diego Comic-Con. With the dust settling from the ultimate nerd Mecca, Eddie, Spencer, and Amy unveil what hit them the hardest from the convention floor. The team geeks out over Marvel's historic Hall H presentation, buzzing over the massive announcements of Ryan Gosling joining the MCU as Ghost Rider and the highly anticipated confirmation of Black Panther 3. The trio also dives into the jaw-dropping Avengers: Doomsday trailer, dissecting Robert Downey Jr.'s terrifying debut as Victor Von Doom and what it means for the future of the cinematic universe. A Cinematic Journey to Hadestown Once the convention hype is unpacked, the team ventures far below to review their cinematic experience of the absolute box-office juggernaut, Hadestown: The Musical. The crew discusses the historic achievement of the professional live stage capture, which was filmed with the original Broadway cast in London and just shattered the theatrical record previously held by Hamilton. Eddie and Amy are Team Hadestown 100%, wholly celebrating the impeccable artistry, the breathtaking musicality of Anaïs Mitchell's score, and the phenomenal performances of stars like Reeve Carney and Eva Noblezada. However, things get a little heated as they try to understand Spencer's deep frustrations with the narrative execution of the century-old myth of Orpheus and Eurydice. While the entire table admires the sheer talent in every scene, Spencer reveals a few major reservations before he books his next ticket to ride this train again. Is this pro-shot a masterpiece of modern theater, or does the myth get lost in the music? It's time to raise a cup and find out! You can catch The Filmlosophers podcast now streaming on all major platforms!

In this episode, Julie and I sit down to talk about our experience watching Hadestown: The Musical, the filmed live stage production that brings Anaïs Mitchell's Tony Award-winning musical to the big screen. (Rotten Tomatoes⁠) We discuss what makes this story of Orpheus and Eurydice, along with Hades and Persephone, such a powerful blend of mythology, music, love, loss, and hope. From the incredible performances to the way the cameras capture the energy of the stage, we share our thoughts on what worked, what surprised us, and why this production feels like more than just a recording of a Broadway show.

This episode of The Other Side of the Bell, featuring trumpet performer, ensemble leader and recording artist CJ Camerieri, is brought to you by Bob Reeves Brass. This episode also appears as a video episode on our YouTube channel, you can find it here: "CJ Camerieri Trumpet Interview" And, find the expanded show notes, transcript and more photos here --- "If you want to have a career in this, you have to know why you're the right person to call, and why you have a skill set that's exactly right for this particular thing. It's hard to make a living at it if they can just hire a different person and pay 'em less." - CJ Camerieri Julliard grad, Grammy winner, collaborator with Bon Iver, Paul Simon, Rufus Wainwright, Sufjan Stevens, Ben Folds, Bruce Hornsby and so many more, CJ Camerieri is the personification of passion: starting piano lessons at 4 years old and never looking back. But it's one thing to be music-obsessed from childhood, it's another to forge a successful, sustainable career in a cutthroat industry. Our conversation today is a fascinating and introspective look into the the business side of music: the back end, the royalties, the songwriting credits, the mindset and the versatility required to succeed and thrive. For CJ, this has meant being adaptable, embracing different styles of music, being open to change and doing things that others may be less interested in. But also, always, doing it for joy, not just a paycheque. You'll rarely meet someone more passionate about trying new things and taking on new challenges. Hence the formation in 2008 of yMusic, "A chamber ensemble with a unique mission: to work on both sides of the classical/popular music divide, without sacrificing rigor, virtuosity, charisma or style." The threads of yMusic are woven throughout our conversation today. Transitioning to a new music scene can reignite your passion, while touring teaches practical lessons about maintaining your craft. CJ will inspire you with his insight and innovation! About CJ Camerieri: Since graduating from The Juilliard School in 2004 with a degree in Classical Trumpet Performance, CJ Camerieri has become an esteemed artist, songwriter, producer, and an indispensable collaborator for some of the most important artists of our time. Camerieri is a two-time Grammy award winner (Best New Artist, 2011 and Best Alternative Album, 2011) and a co-founder of the acclaimed contemporary classical sextet yMusic (who the New Yorker has called "six contemporary classical polymaths who playfully overstep the boundaries of musical genres"). Camerieri has toured the world as a core member of Paul Simon's band since 2013 and with artists such as Bon Iver, Sufjan Stevens, Ben Folds, Plastic Ono Band, and Sting, among many others. He has contributed to over 300 recordings and has commissioned over 100 new works of chamber music featuring the trumpet, including pieces by Andrew Norman, Gabriella Smith, Nico Muhly, Marcos Balter, Ryan Lott, and Missy Mazzoli. In 2021 Camerieri debuted his solo project CARM, which was highlighted by performances on The Colbert Show and Tiny Desk Concert. For Paul Simon's "Farewell Tour" in 2018, Camerieri brought yMusic into Simon's touring band. This resulted in contemporary classical arrangements of the artist's iconic songs being performed in arenas around the world, as well as in featured performances on Saturday Night Live and The Colbert Show. His trio project, Heavy MakeUp, featuring himself, Edie Brickell, and Trever Hagen just released their second full length record on Sony Records. Camerieri is also an accomplished French Horn player, keyboardist, arranger, composer, and improviser—comfortable in all styles and genres of music. In addition, Camerieri has held numerous chairs on Broadway; played principal trumpet with orchestras such as Orpheus, The Knights, and Orchestra of St. Lukes; and has written arrangements for many types of ensembles on countless recordings and performances.   Episode Links: yMusic: https://www.ymusicensemble.com Linktree: linktr.ee/CJ_Camerieri Instagram: @carm_band Bob Reeves Brass Events and Appearances: Bob Reeves Brass pre-owned instruments, find your next treasure! https://trumpetmouthpiece.com/collections/used-instruments   Podcast Credits: "A Room with a View" - composed and performed by Howie Shear Podcast Host - John Snell Cover Photo Credit - Courtesy CJ Camerieri Audio Engineer - Ted Cragg

Can't make it to Broadway? Hadestown: The Musical has you covered. The film adaptation of the Tony-winning hit captures the musical retelling of the Greek tragedy of Orpheus and Eurydice. Fans can see the principal original Broadway cast including Reeve Carney, Eva Noblezada, and André De Shields reunite on the big screen for five nights only. Need more musicals? Check out these episodes:'Wicked: For Good' will bring you to tearsWe debate the best movie musical numbersConnect with Pop Culture Happy Hour:Letterboxd / FacebookOur weekly newsletterSupport Pop Culture Happy Hour+See pcm.adswizz.com for information about our collection and use of personal data for sponsorship and to manage your podcast sponsorship preferences.NPR Privacy Policy

David discusses the movies he's been watching, including Gail Daughtry and the Celebrity Sex Pass, Macho Dancer, Black Chariot, Barrio Triste, Beauty and the Beast and Orpheus.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Sube al podio, levanta la batuta y, sin hacer un solo sonido, logra que noventa músicos produzcan un muro de sonido que te atraviesa. Un director de orquesta es una delas imágenes más extrañas del poder, porque consigue el resultado más impresionante quedándose casi quieto. ¿Qué hace en realidad esa persona, y qué tiene que ver con dirigir una empresa?En este episodio, Sebastián e Ibo traducen el oficio del director al de cualquiera que lidere. A partir de Carlos Kleiber, obsesivo en el ensayo y mínimo en el concierto, y de la Orpheus, una orquesta que toca sin director, desarman unaconfusión cara: intervenir y controlar no son lo mismo, y muchas veces son lo contrario. El que se mete en cada nota suele estar confesando que perdió el control donde de verdad se decidía, que era antes.Hablan de por qué el control verdadero es silencioso e invisible, de lo que le pasa a un equipo cuyo líder corrige todo, y de la soledad de hacer un trabajo que nadie aplaude porque ocurrió en el ensayo que nadie vio. Cierran, sin conclusión, con una pregunta para responderte a solas. Para quien dirige y siente el tirón de estar en todo

First, Faith recovers from her smackdown at Angelus's hands. The team tries to regroup and shore up their defenses. Angelus lives it up in a demon bar in LA until he starts hearing a mysterious British man inside his head. The voice demands his obedience, and Angelus is not amenable.   Then, Faith's desperate gambit to get inside Angelus's head leaves her comatose but on a mystical joyride through flashbacks of Angel's life. Wesley calls in a ringer to help pluck Angel's soul out of the ether and restore it, but unbeknownst to the team, the opposition is coming from inside the hotel.   Hear us discuss… Petty Angelus gives us LIFE OK Fred, we forgive you Cordelia, could you possibly make this any creepier—oh, you can? Ok then Wesley, why wasn't Willow on your speed dial from day 1??? Angel vs. Angelus: the fight of the century!   Trigger warning Drug use  

The Orpheus Crew Was Rewritten by the Relay's Haunted SignalBecome a supporter of this podcast: https://www.spreaker.com/podcast/dark-mysteries-unsolved-mysteries-forgotten-secrets-unanswered-questions--5684156/support.Darkest Mysteries Online

That's IT! I've had it with these motherf*ckin' SNAKES on this motherf*ckin' PODCAST! Our ongoing surprise series JetBag: Emergency Landing continues with another instalment, and this one is a real first class episode if I do say so myself. Featuring yet another Evil Flight Attendant theory, reflections on the band Cobra Starship, and a pitch for a brand new Obra Dinn-alike video game (TM TM TM). Over in the Pop Corner we talked about so many things I've divided them into categories: New Release Movies The Invite, Toy Story 5, Couture Old Movies Come Back to the Five and Dime, Jimmy Dean, Jimmy Dean (1982), Orpheus (1950), The Doom Generation (1995), Crooked House (2017), O Brother Where Art Thou (2000), Kiki's Delivery Service (1989), A Woman Under the Influence (1974) TV The Traitors NZ Season 3, Widow's Bay Video Games Alice: Madness Returns (2011), The Seance of Blake Manor (2025), Ratline (2026), Utter a Name (2025) Music CMAT's Euro-Country (2025) RIP Bonnie Tyler, your rasp will live on forever.

A typology of human erotic psychology in Ovid's Metamorphoses. The feminine male, including the feminine gay male: Adonis, Orpheus (after Eurydice), Ganymede, Hyacinthus. Self-love: Narcissus. Feminine psychology: the mother-daughter relationship. Demeter and Persephone.

Mit Christoph vom Sneakpod schaue ich mich weiter durch die Filmographie von Baz Luhrmann. Angelangt sind wir in der ästhetische Überwältigung seines postmodernen Jukebox Musicals. Wird es Orpheus diesmal gelingen, Eurydike aus dem Hades zu befreien? Und muss eine Sexworkering überhaupt "befreit" werden? Wie problematisch sind "Love conquers all" und kulturelle Aneignung in diesem Fantasy-Paris des Fin de Siecle? Wir sollen am Ende mehr fühlen als über Plotmechaniken nachdenken. That's the beauty of it!

Join in the conversation!Welcome back to another episode of the Hella Chisme Podcast!Best-selling YA author Julian Winters on writing joyful queer romances, the mess of creative beginnings & why LGBTQ+ characters deserve stories that aren't defined by trauma.This week on Hella Chisme, we sit down with award-winning and USA Today bestselling author Julian Winters — the voice behind Running With Lions, Right Where I Left You, Prince of the Palisades, and the upcoming Find My Way Down to You. Julian opens up about his journey from Thundercats fan fiction to publishing queer romances that center joy, community, and hope for young readers.We unpack his creative process (spoiler: it starts messy), how rom-coms shaped his worldview before he ever saw himself in books, and why he refuses to write the trauma narrative — choosing instead to show queer kids that they deserve happily-ever-afters without having to earn them first.This one's for writers, readers, and anyone who believes stories can save lives.Topics to include: Julian Winters author interview, queer YA fiction, queer romance books, LGBTQ authors, Black queer authors, diverse queer representation, joyful queer storytelling, queer joy vs queer trauma narrative, writing queer characters, fan fiction to published author, Thundercats fan fiction origin, rewriting endings creative practice, alternate universe fan fiction, marginalized voices in publishing, creative writing process, storyboarding and mood boards, fighting perfectionism as a writer, starting a book messy first draft, rom-coms as inspiration, Prince of the Palisades book, royal romance YA, Black Panther inspiration T'Challa Nakia, Red White and Royal Blue influence, Young Royals Netflix inspiration, The Princess Diaries influence, Tokyo Ever After book, social media scrutiny marginalized individuals, young adult perspective queer youth, healthy intimacy in YA fiction, consent comfort protection in romance, hypersexualization of young characters critique, I Think They Love You book, second chances in love trope, fake dating romance trope, emotional vulnerability in romance, multi-layered queer characters, authenticity in romance novels, Fire Island rom-com, Always Be My Maybe underrated rom-com, rom-com pet peeves wedding objection amnesia trope, rom-com casting Justice Smith Tessa Thompson, Find My Way Down to You book, Orpheus and Eurydice retelling, Greek mythology YA romance, speculative YA fiction, Charon ferryman of souls character, queer book recommendations, YA romance novels 2026, reading preferences ebooks audiobooks physical books, creative process anxiety new releases, journaling shadow work therapy, prosperity mantras Queen of Pentacles, Hella Chisme Podcast, LGBTQ+ podcast 2026, queer podcast crossover, author interview podcast, book podcast, writing community podcast

Judge Dredd meets Biker Mice from Mars, Midnight Island channels mystery-box island energy, Rocketeer: Infiltrator punches Nazis, and then we dive into Catwoman: Lonely City — one of the smartest future-Gotham heist comics around. RSS Feed Show your thanks to Major Spoilers for this episode by becoming a Major Spoilers Patron at http://patreon.com/MajorSpoilers. It will help ensure the Major Spoilers Podcast continues far into the future! Join our Discord server and chat with fellow Spoilerites! (https://discord.gg/jWF9BbF) Thanks for listening to the Major Spoilers Podcast! Subscribe for free to receive new posts and support our work. REVIEWS ROCKETEER: INFILTRATOR #1 Writer: Gabriel Hardman Artist: Dean Kotz Publisher: IDW Publishing Cover Price: $4.99 Release Date: July 08, 2026 It's the height of World War II, and Betty is a Nazi collaborator! Or so we're led to believe. As Allied planes mysteriously explode in midair across Europe, rumors of a Nazi superweapon compel the U.S. to send its fastest asset undercover: the Rocketeer! With Betty posing as a defecting American actress in a German motion picture, Cliff Secord, a.k.a. the Rocketeer, accompanies as her brother and manager. And since neither has experience in deep-cover work, the Allies send a handler to support them—a debonair MI6 agent who assumes the role of Betty's co-star. The Rocketeer will need to maintain his cover, destroy a superweapon no Allied agent has actually seen, and keep Betty away from the handsome British secret agent with a penchant for beautiful women. It's a lot for a reckless stunt pilot to handle! MIDNIGHT ISLAND #1 Writer: Lylian Artist: Nicolas Grebil Publisher: Papercutz Cover Price: $9.99 (digital $5.99, Hardcover $14.99) Release Date: July 28, 2026 Mysteries unfold in this adventurous series set on an enigmatic island full of secrets and wonders—it's Lost for a younger generation. Four children wake up on a deserted island. After taking refuge in an abandoned boarding school, they find an automaton who, every night at midnight, gives them a mission. If they fail or refuse to take up the challenge, the consequences could be terrible. From mission to mission, the children explore their surroundings, encounter other participants, and try to discover the reasons behind their presence on this island… TRADE DISCUSSION CATWOMAN: LONELY CITY Writer: Cliff Chiang Artist: Cliff Chiang Publisher: DC Comics Cover Price: $29.99 Ten years ago, the massacre known as Fools' Night claimed the lives of Batman, the Joker, Nightwing, and Commissioner Gordon…and sent Selina Kyle, the Catwoman, to prison. A decade later, Gotham has grown up—it's put away costumed heroism and villainy as childish things. The new Gotham is cleaner, safer…and a lot less free, under the watchful eye of Mayor Harvey Dent and his Batcops. It's into this new city that Selina Kyle returns, a changed woman…with her mind on that one last big score: the secrets hidden inside the Batcave! She doesn't need the money—she just needs to know…who is "Orpheus"? Looking for more? This week's pre-show IS FREE as part of our anniversary celebration, so if you want to hear the full conversation about Supergirl, movie theaters, and more, head over to Patreon.com/MajorSpoilers. Join the conversation We'd love to hear your thoughts on any of this week's topics. At Major Spoilers, we strive to create original content that you find interesting and entertaining. Producing, writing, recording, editing, and researching require significant resources. We pay writers, podcast hosts, and other staff members who work tirelessly to provide you with insights into the comic book, gaming, and pop culture industries. Help us keep Major Spoilers strong. Become a Patron (and our superhero) today. If you know someone who loves comics, share this post and episode with them!

“Silent friend[s] of many distances,” I write to you from the mountains where Rilke wrote his final elegies and the whole of the Sonnets to Orpheus. I am thankful to the Dartmouth Leslie Center Faculty Research Fellowship funds for their support. Someone in Berlin said to me, You’re going to where there are cow bells. […]

Robert McGinley (Shredder Orpheus, Devi Danger) comes to $2 Late Fee & Podcasting After Dark to discuss his films, Greek mythology, and more! Robert McGinley is the true definition of “artist!” Director, writer, actor, musician, skater! We discuss Robert's films like Shredder Orpheus, “backrooms”, and so much more in this thought provoking conversation. We go deep. So prepare to have your mind expanded! Enjoy! Links to buy Robert's films, music, the Shredder Orpheus novelization by David Irons, and more can be found here…Boom Cult Get Devi Danger on BluRay at Enjoy the Ride Records Dig the show? Please consider supporting $2 Late Fee & Podcasting After Dark on Patreon for tons of bonus content (like Tales From The Video Store)! Links are below: Two Dollar Late Fee: ⁠www.patreon.com/twodollarlatefee⁠ Podcasting After Dark: www.patreon.com/podcastingafterdark Please follow/subscribe and rate us on Spotify and Apple Podcasts! Apple Podcasts: ⁠podcasts.apple.com/us/podcast/two-dollar-late-fee⁠ Spotify: ⁠open.spotify.com/show/⁠ Instagram: ⁠@twodollarlatefee⁠ Subscribe to our ⁠YouTube⁠ Check out Jim Walker's intro/outro music on Bandcamp: ⁠jvamusic1.bandcamp.com⁠ Facebook: ⁠facebook.com/Two-Dollar-Late-Fee-Podcast⁠ Merch:⁠ https://www.teepublic.com/user/two-dollar-late-fee⁠ IMDB: ⁠https://www.imdb.com⁠ Two Dollar Late Fee is a part of the nutritious ⁠Geekscape Network⁠ Every episode is produced, edited, and coddled by Zak Shaffer (⁠@zakshaffer⁠) & Dustin Rubin (⁠@dustinrubinvo⁠) You can watch the entire interview on our YouTube channel here. Don't forget to like & subscribe!You can listen & NOW watch on Spotify here. Don't forget to like & subscribe! Learn more about your ad choices. Visit megaphone.fm/adchoices

“Silent friend[s] of many distances,” I write to you from the mountains where Rilke wrote in final elegies and the whole of the Sonnets to Orpheus. I am thankful to the Dartmouth Leslie Center Faculty Research Fellowship funds for their support. Listen to the promo and follow link to follow on Patreon and hear it […]

Enzo Coco"Premio Merano Europa"www.plime.euPremio Letterario Internazionale Merano Europa XVI edizioneIl premio ponte tra la cultura italiana e quella tedesca I Vincitori:Sezione italianaPIERA VENTRE – STELLA RANDAGIA – NN editoreSezione tedescaVERNESA BERBO – Der Sohn und das Schneeflöckchen – Frankfurter VerlangsanstaltSezione Poesia tradottaRICCARDO HELD “I sonetti di Orfeo” – MondadoriSabato 6 giugno al Pavillon des Fleurs di Merano si è tenuta la cerimonia conclusiva della XVI edizione del Premio Letterario Internazionale Merano-Europa.Durante la serata i lettori della giuria popolare hanno posto le proprie schede nelle apposite urne e successivamente si è proceduto allo spoglio e alla proclamazione dei vincitori.Lo scrutinio pubblico dei voti dei 100 lettori (50 di lingua italiana e 50 di lingua tedesca) ha decretato la vittoria delle autriciPIERA VENTRE – STELLA RANDAGIA – NN editoreVERNESA BERBO – Der Sohn und das Schneeflöckchen – Frankfurter VerlangsanstaltDopo i saluti delle conduttrici Valentina Berengo, giornalista culturale e di Roxana Höchsmann, Ufficio stampa dell'Agenzia di Comunicazione Wolkenlos di Vienna, e di Enzo Coco, Presidente dell'Associazione culturale Passirio Club organizzatore del Premio, sono intervenuti i rappresentanti delle istituzioni locali: Antonella Costanzo e Barbara Hölzl, Assessore Comune di Merano, Angelo Giannaccaro, Presidente del Consiglio Provinciale e Assessore regionale alle iniziative per la promozione dell'integrazione europea; Philipp Achammer, Assessore provinciale alla cultura e istruzione lingua tedesca; Marisa Giurdanella, ufficio cultura italiana della Provincia, e in chiusura l'intervento di Aldo Mazza, Presidente della Giuria di selezione.Per la sezione “Poesia tradotta” è stato premiato Riccardo Held per l'opera I sonetti di Orfeo [Die Sonette an Orpheus] di Rainer Maria Rilke (Mondadori). Le motivazioni sono state lette da Valentina Di Rosa – docente Dipartimento studi letterari, linguistici comparati Università Orientale/Napoli, membro del Comitato scientifico di selezione, coordinato da Stefano Zangrando.Ai vincitori delle sezioni narrativa è stato riconosciuto un premio di 5.000 Euro, per i finalisti un premio di 1.000 Euro ciascuno. Al vincitore della sezione Poesia tradotta è stato consegnato un premio di 4.000 euro.Gli altri finalisti in gara:Sezione italianaSaverio Gangemi, “Calura”, RubbettinoPippo Russo, “L'estate di Totò Schillaci”, Derive ApprodiSezione tedesca:Dimitre Dinev “Zeit der Mutigen“ – Kein & AberDidi Drobna “Ostblockherz” – PiperLe terzine finaliste per la narrativa sono state selezionate dalle giurie tecniche:Per la sezione italiana:Alessandro Gazzoli – dottore di ricerca in letteratura italiana – esperto di narrativa del NovecentoGiuliano Geri – editor e traduttore – coordinatore della giuriaMariagrazia Mazzitelli – direttrice editoriale Salani Anna Vallerugo – giornalista e critica letterariaPer la sezione tedesca:Ferruccio Delle Cave – critico letterario e storico – coordinatore della giuriaSepp Mall – scrittore e vincitore dell'edizione 2024 del Premio Merano-Europa – Romanzo in lingua tedescaIngrid Runggaldier – pubblicista e traduttriceMichael Scholz – direttore artistico del festival di letteratura “Poetische Quellen”Le interviste ai finalisti sono state condotte da Valentina Berengo per la lingua italiana, e da Roxana Hôchsmann per la lingua tedesca.L'evento è stato allietato dagli interventi musicali dell'Orchestra d'archi della Merano Pop Symphony Orchestra diretta dal Maestro Roberto Federico.L'Associazione culturale Passirio Club Merano ODV, in collaborazione con l'associazione Südtiroler Künstlerbund, la Biblioteca civica di Merano e il patrocinio e contributo di Regione Autonoma Trentino Alto Adige, Provincia Autonoma Bolzano Alto Adige e il Comune di Merano, ha promosso la XVI edizione Premio Letterario Internazionale Merano Europa riservato ai Romanzi editi, in lingua italiana e tedesca pubblicati nel 2025, e alla Poesia tradotta dall'italiano o il tedesco e pubblicata nel biennio 2024/2025.Il Premio letterario Merano Europa è unico nel suo genere per la particolarità di rivolgersi contemporaneamente alle culture italiana e tedesca. Si connota particolarmente perché si svolge a Merano, una città dalle caratteristiche uniche poiché registra la presenza paritaria di abitanti di lingua italiana e tedesca ed è quindi punto ideale di incontro per le rispettive culture, anche in campo internazionale. Merano in particolare e l'Alto Adige, rappresentano infatti da sempre l'ideale crocevia tra mondo tedesco e italiano.Diventa un supporter di questo podcast: https://www.spreaker.com/podcast/il-posto-delle-parole--1487855/support.IL POSTO DELLE PAROLEascoltare fa pensarehttps://ilpostodelleparole.it/

Pippa Hudson speaks to Veronica Paeper, who first created and choreographed Orpheus in the Underworld more than four decades ago, and former CAPAB and Cape Town City Ballet principal Janet Lindup, who returns to the stage for a special guest appearance in this revival and plays a mentoring role. Lunch with Pippa Hudson is CapeTalk’s mid-afternoon show. This 2-hour respite from hard news encourages the audience to take the time to explore, taste, read, and reflect. The show - presented by former journalist, baker and water sports enthusiast Pippa Hudson - is unashamedly lifestyle driven. Popular features include a daily profile interview #OnTheCouch at 1:10 pm. Consumer issues are in the spotlight every Wednesday while the team also unpacks all things related to health, wealth & the environment. Thank you for listening to a podcast from Lunch with Pippa Hudson Listen live on Primedia+ weekdays between 13:00 and 15:00 (SA Time) to Lunch with Pippa Hudson broadcast on CapeTalk https://buff.ly/NnFM3Nk For more from the show, go to https://buff.ly/MdSlWEs or find all the catch-up podcasts here https://buff.ly/fDJWe69 Subscribe to the CapeTalk Daily and Weekly Newsletters https://buff.ly/sbvVZD5 Follow us on social media: CapeTalk on Facebook: https://www.facebook.com/CapeTalk CapeTalk on TikTok: https://www.tiktok.com/@capetalk CapeTalk on Instagram: https://www.instagram.com/ CapeTalk on X: https://x.com/CapeTalk CapeTalk on YouTube: https://www.youtube.com/@CapeTalk567 See omnystudio.com/listener for privacy information.

In 1957, Tennessee Williams returned to the Martin Beck Theatre with Orpheus Descending, a play he had spent nearly two decades trying to get right. Originally produced in 1940 as Battle of Angels, the drama had collapsed amid censorship battles, technical problems, and public outrage. Yet Williams could never leave it behind. In this episode, we trace the remarkable seventeen-year journey of the play, from its disastrous Boston tryout to its rebirth on Broadway under a new title. Along the way, we explore Williams's complicated relationship with success and failure, the creative partnership that shaped his work, and the cast and collaborators who helped bring Orpheus Descending to life, including Maureen Stapleton, Harold Clurman, Boris Aronson, Robert Loggia, Cliff Robertson, and Lois Smith. Featuring archival interviews and firsthand accounts, this is the story of one of Broadway's most ambitious productions, why audiences rejected it, and how its failure marked a turning point in the life of America's greatest playwright. -- Click ⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠⁠ for a transcript with photos, videos, and a list of all resources used. Produced by Patrick Oliver Jones and WINMI Media with Dan Delgado as co-producer. Theme music created by Blake Stadnik. Learn more about your ad choices. Visit megaphone.fm/adchoices

Orpheus Heyward joins the Real Life Theology Podcast to discuss how Christians should engage and resist culture. Drawing on Romans 12, he explains being a transformed, non‑conformist driven by God's mercy, the ‘‘New Testament sacrificial system,'' and living differently so others see Christ in us.   Download the free RENEW.org App - https://renew.org/renew-app/    Visit RENEW: https://renew.org/   Check out the following from RENEW.org: Events: https://renew.org/resources/events/ Videos: https://renew.org/media/videos/ Podcasts: https://renew.org/media/podcasts/ Articles: https://renew.org/articles/ Free eBooks: https://renew.org/resources/free-ebooks/ Books: https://renew.org/resources/books/ Audiobooks: https://renew.org/resources/audiobooks/ Sermon Tools: https://renew.org/resources/sermon-tools/ Job Board: https://jobs.renew.org/ Renew University: https://renewuniversity.org/ Real Life Theology Conversations: https://renew.org/rltc/ Sign up for our newsletter: https://renew.org/resources/newsletter-sign-up/

Cape Town City Ballet is presenting "Orpheus in the Underworld", based on Offenbach's music and choreographed by Veronica Paeper. On People of Note this week, Rodney Trudgeon spoke to Tracy Li, the Artistic Manager at CTCB, about the production and about her career in ballet

While the show is on hiatus, we're sharing some episodes from our Bunker 9 Bonus Feed! We'll be back on Tuesday, June 2, 2026!Bunker 9 - Episode 74: We Love Hadestown (w/ Dylan Hunter)Nearly three years ago, we recorded an episode about the myth of Orpheus and Eurydice. Our guest was the fabulous Dylan Hunter, who came on the podcast to discuss the Tony Award-winning Broadway musical, Hadestown. Unfortunately, at the time neither co-host had seen the musical, so the topic was largely tabled. Two years and nine months later, we finally have that conversation. Major Spoilers for HadestownContent Warning: This episode contains mention of death, starvation, and climate change. Support the showNo portion of this episode may be used for AI training purposes or to create derivative works without express written permission from the creators and co-hosts Darien Smartt or Davis Smartt. 

A podcast introducing the exciting new Spanish-language opera with music by Gabriela Lena Frank to a libretto by Nilo Cruz: what would happen if the myth of Orpheus and Euridice were reenacted by the famous Mexican painters Diego Rivera and Frida Kahlo? Some Seattle Opera personnel attended the Chicago performances of this magic realist opera, coming to Seattle Opera January 2027. Michaela Calzaretta, chorus master and head of music staff, discussed FRIDA & DIEGO with Alex Minami, co-director of programs and partnerships, and Alicia Moriarty, director of production.

I know. You're sick of hearing about yet another huge Hollywood blockbuster about a skateboarding rock star who reenacts the legend of Orpheus in a possibly post-apocalyptic Seattle made of shipping containers. But I promise, this one is different. Matt and Tristan talk, Shredder Orpheus. 

Visit Renew.org to sign up for our email newsletter and be the first to know about new content, books and resources.  https://renew.org/ Join RENEW.org at an upcoming event: https://renew.org/resources/events/ Join RENEW.org's Newsletter: https://renew.org/resources/newsletter-sign-up/ Orpheus Hayward explains the authority and purpose of Scripture through 2 Timothy 3:16, showing how God-breathed Scripture teaches, reproves, corrects, and trains believers for every good work. He applies this to church leadership, the preacher's role as God's herald, and uses a pilot-and-tower analogy to encourage trusting Scripture in life's uncertain moments.

In the Greco-Roman world, gods were known to tame soundscapes, or acoustic landscapes. Zeus, Apollo, Orpheus, and other Classical deities demonstrated their power by bringing order to chaotic sound worlds, replacing cacophony with harmony. In late antiquity, Christians took up this archetype and applied it to Jesus. For many early Christians, the advent of Christ resembled the modern phenomenon of a musical key change, but on a grand scale: Jesus initiated a recalibration of the cosmic soundscape, ushering in a new world. However, according to many Christians in late antiquity, this universal key change was not yet complete. Late ancient Christians believed that they could participate in the ongoing sonic work of Christ by Christianizing the acoustic landscapes of the world.In Sounds for a New World: The Christianizing Soundscapes of Late Antiquity (Oxford UP, 2026), Dr. Philip Abbott explores how late ancient Christians envisioned themselves as participants in the worldwide retuning effort, harmonizing the Classical world to the new Christian reality. Rejecting the sounds of traditional Greco-Roman and Persian cultures, Christians advocated a variety of sonic practices to realize their grand retuning endeavor, including shouting, singing, silent meditation, chanting, and even belching. From the Latin West to the Syriac East, late ancient Christians formed a polyphonous chorus of diverse voices all joining in the great harmonizing work of Jesus as they Christianized the soundscapes of the world.For years, scholars have noted the monumental changes that took place in early Christianity during the so-called Constantinian Revolution. But Dr. Abbott turns our attention to an unexplored aspect of this transitional moment, arguing that it was not simply a political or religious revolution - it was a revolution of the senses. Central to this sensorial transformation was sound. As Christianity gained imperial power in the fourth century, Christians began the process of re-tuning the world for Christ. This interview was conducted by Dr. Miranda Melcher whose book focuses on post-conflict military integration, understanding treaty negotiation and implementation in civil war contexts, with qualitative analysis of the Angolan and Mozambican civil wars. You can find Miranda's interviews on New Books with Miranda Melcher, wherever you get your podcasts. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/new-books-network

In the Greco-Roman world, gods were known to tame soundscapes, or acoustic landscapes. Zeus, Apollo, Orpheus, and other Classical deities demonstrated their power by bringing order to chaotic sound worlds, replacing cacophony with harmony. In late antiquity, Christians took up this archetype and applied it to Jesus. For many early Christians, the advent of Christ resembled the modern phenomenon of a musical key change, but on a grand scale: Jesus initiated a recalibration of the cosmic soundscape, ushering in a new world. However, according to many Christians in late antiquity, this universal key change was not yet complete. Late ancient Christians believed that they could participate in the ongoing sonic work of Christ by Christianizing the acoustic landscapes of the world.In Sounds for a New World: The Christianizing Soundscapes of Late Antiquity (Oxford UP, 2026), Dr. Philip Abbott explores how late ancient Christians envisioned themselves as participants in the worldwide retuning effort, harmonizing the Classical world to the new Christian reality. Rejecting the sounds of traditional Greco-Roman and Persian cultures, Christians advocated a variety of sonic practices to realize their grand retuning endeavor, including shouting, singing, silent meditation, chanting, and even belching. From the Latin West to the Syriac East, late ancient Christians formed a polyphonous chorus of diverse voices all joining in the great harmonizing work of Jesus as they Christianized the soundscapes of the world.For years, scholars have noted the monumental changes that took place in early Christianity during the so-called Constantinian Revolution. But Dr. Abbott turns our attention to an unexplored aspect of this transitional moment, arguing that it was not simply a political or religious revolution - it was a revolution of the senses. Central to this sensorial transformation was sound. As Christianity gained imperial power in the fourth century, Christians began the process of re-tuning the world for Christ. This interview was conducted by Dr. Miranda Melcher whose book focuses on post-conflict military integration, understanding treaty negotiation and implementation in civil war contexts, with qualitative analysis of the Angolan and Mozambican civil wars. You can find Miranda's interviews on New Books with Miranda Melcher, wherever you get your podcasts. Learn more about your ad choices. Visit megaphone.fm/adchoices

In the Greco-Roman world, gods were known to tame soundscapes, or acoustic landscapes. Zeus, Apollo, Orpheus, and other Classical deities demonstrated their power by bringing order to chaotic sound worlds, replacing cacophony with harmony. In late antiquity, Christians took up this archetype and applied it to Jesus. For many early Christians, the advent of Christ resembled the modern phenomenon of a musical key change, but on a grand scale: Jesus initiated a recalibration of the cosmic soundscape, ushering in a new world. However, according to many Christians in late antiquity, this universal key change was not yet complete. Late ancient Christians believed that they could participate in the ongoing sonic work of Christ by Christianizing the acoustic landscapes of the world.In Sounds for a New World: The Christianizing Soundscapes of Late Antiquity (Oxford UP, 2026), Dr. Philip Abbott explores how late ancient Christians envisioned themselves as participants in the worldwide retuning effort, harmonizing the Classical world to the new Christian reality. Rejecting the sounds of traditional Greco-Roman and Persian cultures, Christians advocated a variety of sonic practices to realize their grand retuning endeavor, including shouting, singing, silent meditation, chanting, and even belching. From the Latin West to the Syriac East, late ancient Christians formed a polyphonous chorus of diverse voices all joining in the great harmonizing work of Jesus as they Christianized the soundscapes of the world.For years, scholars have noted the monumental changes that took place in early Christianity during the so-called Constantinian Revolution. But Dr. Abbott turns our attention to an unexplored aspect of this transitional moment, arguing that it was not simply a political or religious revolution - it was a revolution of the senses. Central to this sensorial transformation was sound. As Christianity gained imperial power in the fourth century, Christians began the process of re-tuning the world for Christ. This interview was conducted by Dr. Miranda Melcher whose book focuses on post-conflict military integration, understanding treaty negotiation and implementation in civil war contexts, with qualitative analysis of the Angolan and Mozambican civil wars. You can find Miranda's interviews on New Books with Miranda Melcher, wherever you get your podcasts. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/sound-studies

In the Greco-Roman world, gods were known to tame soundscapes, or acoustic landscapes. Zeus, Apollo, Orpheus, and other Classical deities demonstrated their power by bringing order to chaotic sound worlds, replacing cacophony with harmony. In late antiquity, Christians took up this archetype and applied it to Jesus. For many early Christians, the advent of Christ resembled the modern phenomenon of a musical key change, but on a grand scale: Jesus initiated a recalibration of the cosmic soundscape, ushering in a new world. However, according to many Christians in late antiquity, this universal key change was not yet complete. Late ancient Christians believed that they could participate in the ongoing sonic work of Christ by Christianizing the acoustic landscapes of the world.In Sounds for a New World: The Christianizing Soundscapes of Late Antiquity (Oxford UP, 2026), Dr. Philip Abbott explores how late ancient Christians envisioned themselves as participants in the worldwide retuning effort, harmonizing the Classical world to the new Christian reality. Rejecting the sounds of traditional Greco-Roman and Persian cultures, Christians advocated a variety of sonic practices to realize their grand retuning endeavor, including shouting, singing, silent meditation, chanting, and even belching. From the Latin West to the Syriac East, late ancient Christians formed a polyphonous chorus of diverse voices all joining in the great harmonizing work of Jesus as they Christianized the soundscapes of the world.For years, scholars have noted the monumental changes that took place in early Christianity during the so-called Constantinian Revolution. But Dr. Abbott turns our attention to an unexplored aspect of this transitional moment, arguing that it was not simply a political or religious revolution - it was a revolution of the senses. Central to this sensorial transformation was sound. As Christianity gained imperial power in the fourth century, Christians began the process of re-tuning the world for Christ. This interview was conducted by Dr. Miranda Melcher whose book focuses on post-conflict military integration, understanding treaty negotiation and implementation in civil war contexts, with qualitative analysis of the Angolan and Mozambican civil wars. You can find Miranda's interviews on New Books with Miranda Melcher, wherever you get your podcasts. Learn more about your ad choices. Visit megaphone.fm/adchoices

In the Greco-Roman world, gods were known to tame soundscapes, or acoustic landscapes. Zeus, Apollo, Orpheus, and other Classical deities demonstrated their power by bringing order to chaotic sound worlds, replacing cacophony with harmony. In late antiquity, Christians took up this archetype and applied it to Jesus. For many early Christians, the advent of Christ resembled the modern phenomenon of a musical key change, but on a grand scale: Jesus initiated a recalibration of the cosmic soundscape, ushering in a new world. However, according to many Christians in late antiquity, this universal key change was not yet complete. Late ancient Christians believed that they could participate in the ongoing sonic work of Christ by Christianizing the acoustic landscapes of the world.In Sounds for a New World: The Christianizing Soundscapes of Late Antiquity (Oxford UP, 2026), Dr. Philip Abbott explores how late ancient Christians envisioned themselves as participants in the worldwide retuning effort, harmonizing the Classical world to the new Christian reality. Rejecting the sounds of traditional Greco-Roman and Persian cultures, Christians advocated a variety of sonic practices to realize their grand retuning endeavor, including shouting, singing, silent meditation, chanting, and even belching. From the Latin West to the Syriac East, late ancient Christians formed a polyphonous chorus of diverse voices all joining in the great harmonizing work of Jesus as they Christianized the soundscapes of the world.For years, scholars have noted the monumental changes that took place in early Christianity during the so-called Constantinian Revolution. But Dr. Abbott turns our attention to an unexplored aspect of this transitional moment, arguing that it was not simply a political or religious revolution - it was a revolution of the senses. Central to this sensorial transformation was sound. As Christianity gained imperial power in the fourth century, Christians began the process of re-tuning the world for Christ. This interview was conducted by Dr. Miranda Melcher whose book focuses on post-conflict military integration, understanding treaty negotiation and implementation in civil war contexts, with qualitative analysis of the Angolan and Mozambican civil wars. You can find Miranda's interviews on New Books with Miranda Melcher, wherever you get your podcasts. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/christian-studies

The boys discuss the anti-trust case against Live Nation, MP obsesses about the LPT map, and Ben brings the love. Front and center is a playlist of brand new indie rock tracks that will sync your cycles and beat you at Scrabble.EPISODE PLAYLIST:Cashier, "A Curse I Know So Well"Snarls, "No Lock, No Prayer"Tanzana, "Pulse Pose Position"Palette Knife, "Honors English"Lily Seabird, "Demon In Me"Ain't, "Grazer"Hater, "Mosquito"Latchkey Kids, "Orpheus (ft. Tom May)"Beck Zegans, "I Want You"Chroma, "Coalminer's Granddaughter"Heartworms, "Just to ask a Dance"cruush, "Great Dane"BONUS TRACK: Palette Knife, "Phoenix Down"Cities added to the map this episode: 3

Written by Mere Smith and directed by Terrence O'Hara, this episode originally aired March 19, 2003.  EXTRA BITS Trevor is currently working on creating an art piece for every single episode of Buffy and you can find everything that's been released so far here. You can also find many of his fun Lego animations all over the internet! Kristin co-hosts another show called "So...I'm Watching This Show" with Wil Brooks. You can check them out here. Please rate, review and subscribe! Help us get those fancy numbers :)

What makes a Faust story? Michael, Ethan, and special guests Josiah & Jacob discuss this ontological question and all things Faust in this special episode.In this episode:Michael begins confrontationally.Is this episode Faust? Is this podcast Faust?Ethan is NOT in Law School.Is capitalism Faust?The Bible is inevitable.Josiah brought it up. Totally. It was Josiah. He's very smart. The Josian Anti-Faust idea (C) 2026.Demon, Helen, Margaret - Where are the lines of demarcation between them?Two questions: 1) What are you willing to sacrifice [to get what you want; and is it your very soul]? 2) What do you worship?(Josiah came up with the smart thing again. (C) 2026.)What you worship you will sacrifice.Ethan cheats with Wikipedia.Vein.American Faust: do we win?(Sorry for the Beetlejuice summoning.)If it looks like an exchange but is a gift, it's grace. If it looks like a gift but is an exchange, it's vampi-- Faust.When you're having night terrors, fart in the Devil's face and confess the Apostles' Creed.Shout-out to Reading Revisited!Every time he comes on this podcast, Jacob makes it a different podcast and/or a deal we need to wiggle out of.There are other things in Detroit other than Eminem.Let it be known, we can be charitable to a garbage fire.The answer is a tautology. Or cake.Here's each work we discuss: Is it Faust?The Age of Innocence, by Edith Wharton: Is it Faust?The book of Job: Is it Faust?It's A Wonderful Life: Is it Anti-Faust? Is it Faust?Jesus tempted in the desert: Is it Faust? Is it Anti-Faust? Is Faust a fan-fiction of Jesus?Genesis 3: Is it the OG Faust?A Christmas Carol, by Charles Dickens: Is it Faust?Trust, by Hernan Diaz: Is it Faust?The Secret History, by Donna Tartt: Is it Faust?The Picture of Dorian Gray, by Oscar Wilde: Is it Faust?"The Ballad of Reading Gaol," by Oscar Wilde: Is it Faust?Of One Blood, by Pauline Hopkins: Is it Faust?Hadestown, by Anaïs Mitchell: Is it Faust? Orpheus & Euridice: Is it [proto-]Faust?Dracula, by Bram Stoker: Is it Faust?Hades & Persephone: Is it Faust?Interview with the Vampire: Is it Faust?Underworld: Is it Faust?"The Devil Went Down to Georgia," by Charlie Daniels: Is it Faust?Robert Johnson: Is he Faust?O Brother, Where Art Thou?: Is it Faust?Oedipus: Is he Faust?Piranesi, by Susanna Clarke: Is it Faust?Devil's Advocate: Is it Faust?Beetlejuice: Is it Faust?Macbeth, by William Shakespeare: Is it Faust?The Waterboy: Is it Faust?Rick & Morty: Is it Faust?Doctor Who: Is it Faust?Peter Pan, by J.M. Barrie: Is it Faust?Ghosts, S4E22-S5E1: Is it Faust?The Passenger, and Stella Maris, by Cormac McCarthy: Are they Faust?Previously featured on Michael & Ethan in a Room with ScotchNo Country for Old Men, by Cormac McCarthy: Is it Faust?Grimm's Fairy Tales, e.g. "The Devil's Sooty Brother," "Bearskin," "The Devil and His Grandmother," "The Gravemound," "The Peasant and the Devil," "Doctor Know-all," "The Spirit in the Bottle": Are they Faust?Irish Fairy Tales, and Russian Stories: Are they Faust?Luther throwing his inkwell at the Devil: Is it Faust?"The Little Mermaid," by Hans Christian Andersen: Is it Faust - WAIT, we'll talk about it later!"The Magic Thread": Is it Faust?Click: Is it Faust?The Merchant of Venice, by William Shakespeare: Is it Faust?Breaking Bad: Is it Faust?Rapid(-ish) Fire:Fullmetal Alchemist"Ain't No Rest for the Wicked," by Cage the ElephantBetter Call SaulMelmoth the Wanderer, by Charles Maturin - WAIT, we might also talk about that later(?)It FollowsThe Imaginarium of Doctor ParnassusDeath Note"Button, Button," by Richard Matheson"The Monkey's Paw," by W.W. JacobsThe Third Man[, by Graham Greene]The Screwtape Letters, by C.S. LewisThe Lion, the Witch and the Wardrobe, by C.S. LewisThat Hideous Strength, by C.S. LewisThe Magician's Nephew, by C.S. LewisOut of the Silent Planet, by C.S. LewisPerelandra, by C.S. LewisThe Book of the New Sun, by Gene WolfeHamlet, by William ShakespeareLove's Labours Lost, by William Shakespeare"Goblin Market," By Christina RosettiHowl's Moving Castle, by Diana Wynne JonesParadise Lost, by John MiltonThe Tempest, by William Shakespeare"Calliope," by Neil GaimanRavelstein, by Saul BellowPreviously featured on Michael & Ethan in a Room with ScotchKPop Demon HuntersThe Life and Opinions of Tristram Shandy, Gentleman, by Laurence SternePreviously featured on Michael & Ethan in a Room with ScotchDon Quixote, by Miguel de CervantesPreviously featured on Michael & Ethan in a Room with ScotchThe Brothers Karamazov, by Fyodor DostoevskyCrime and Punishment, by Fyodor DostoevskyLolita, by Vladimir NabokovFrankenstein, by Mary ShelleyLiar, LiarBruce AlmightyBigDarby O'Gill and the Little PeoplePhantom of the Opera[, by Gaston Leroux]"Alastor," by Percy Bysshe ShelleyLes Miserables, by Victor Hugo"Young Goodman Brown," by Nathanael HawthorneWittenberg, by David Davalos - DON'T BOTHERStranger Things (especially Season 5)Next time Michael and Ethan will discuss “Johannes Cabal and the Blustery Day,” by Jonathan L. Howard! Join the discussion! Go to the Contact page and put "Scotch Talk" in the Subject line. We'd love to hear from you! And submit your homework at the Michael & Ethan in a Room with Scotch page. Join us on GoodReads!Get on our Substack!Donate to our Patreon! MUSIC & SFX: "Kessy Swings Endless - (ID 349)" by Lobo Loco. Used by permission. "The Grim Reaper - II Presto" by Aitua. Used under an Attribution-NonCommercial-ShareAlike License. "Thinking It Over" by Lee Rosevere. Used under an Attribution License.(Links to books & products are affiliate links.)

While our last episode discussed Orpheus through the lens of archetypal creativity, this episode focuses on the grief elements of the story as depicted in Ovid’s version of the story. We share our own grief stories and explore the hazards experienced when people do not allow themselves to grieve. This episode we will be reading from: Metamorphoses – by Ovid⁠⁠⁠ Parables and Portraits – by Stephen Mitchell Orpheus. Euridice. Hermes. – by Rainer Maria Rilke You can listen to El Maleh Rachamim prayer on My Jewish Learning here. This prayer asks God to grant rest to departed souls and is often recited at funerals. Our intro/outro music a sample of Seikilos Epitaph with the Lyre of Apollo, by Lina Palera, under an Attribution-NonCommercial-ShareAlike 3.0 International License. You can find the full version at ⁠⁠⁠⁠⁠FreeMusicArchive.org⁠⁠⁠⁠⁠. Banner Image: Kratzenstein orpheus.jpg – Wikipedia Email: jungianeverafter@gmail.com Twitter: @JEA_Podcast Discord: https://discord.gg/GEdn4TPgHR Ko-fi: https://ko-fi.com/jungianeverafter

While our last episode discussed Orpheus through the lens of archetypal creativity, this episode focuses on the grief elements of the story as depicted in Ovid’s version of the story. We share our own grief stories and explore the hazards experienced when people do not allow themselves to grieve. This episode we will be reading from: Metamorphoses – by Ovid⁠⁠⁠ Parables and Portraits – by Stephen Mitchell Orpheus. Euridice. Hermes. – by Rainer Maria Rilke You can listen to El Maleh Rachamim prayer on My Jewish Learning here. This prayer asks God to grant rest to departed souls and is often recited at funerals. Our intro/outro music a sample of Seikilos Epitaph with the Lyre of Apollo, by Lina Palera, under an Attribution-NonCommercial-ShareAlike 3.0 International License. You can find the full version at ⁠⁠⁠⁠⁠FreeMusicArchive.org⁠⁠⁠⁠⁠. Banner Image: Kratzenstein orpheus.jpg – Wikipedia Email: jungianeverafter@gmail.com Twitter: @JEA_Podcast Discord: https://discord.gg/GEdn4TPgHR Ko-fi: https://ko-fi.com/jungianeverafter

“Black Orpheus” and the Globalization of Afro-Brazilian Culture (Rutgers UP, 2026) is the first historical study in English to examine the development, production, and reception of the 1958 film Black Orpheus and its legacy in the 1960s and 1970s. It focuses on the making of the film and the trajectories of the major actors and musicians who helped construct an image of Black Brazil and provides an analysis of the globalization of Afro-Brazilian images and music in France and the United States in the wake of the movie's success. Using archival sources, interviews, and the secondary literature from France, Brazil, and the United States, this book reveals information about the cultural histories of all three countries and gives readers new insight into the trajectories of diverse actors such as Breno Mello, Marpessa Dawn, and Léa Garcia and performers such as Agostinho dos Santos, Baden Powell, and Maria D'Apparecida. Darién J. Davis is a professor and the chair of Africana studies at Rutgers University–Newark. He is the author of four books, three edited volumes, and more than forty essays and articles in English, Spanish, and Portuguese. Reighan Gillam is Associate Professor in the Department of Latin American, Latino, and Caribbean Studies at Dartmouth College. Her research examines the ways in which Afro-Brazilian media producers foment anti-racist visual politics through their image creation. She is the author of Visualizing Black Lives: Ownership and Control in Afro-Brazilian Media (University of Illinois Press). Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/new-books-network

“Black Orpheus” and the Globalization of Afro-Brazilian Culture (Rutgers UP, 2026) is the first historical study in English to examine the development, production, and reception of the 1958 film Black Orpheus and its legacy in the 1960s and 1970s. It focuses on the making of the film and the trajectories of the major actors and musicians who helped construct an image of Black Brazil and provides an analysis of the globalization of Afro-Brazilian images and music in France and the United States in the wake of the movie's success. Using archival sources, interviews, and the secondary literature from France, Brazil, and the United States, this book reveals information about the cultural histories of all three countries and gives readers new insight into the trajectories of diverse actors such as Breno Mello, Marpessa Dawn, and Léa Garcia and performers such as Agostinho dos Santos, Baden Powell, and Maria D'Apparecida. Darién J. Davis is a professor and the chair of Africana studies at Rutgers University–Newark. He is the author of four books, three edited volumes, and more than forty essays and articles in English, Spanish, and Portuguese. Reighan Gillam is Associate Professor in the Department of Latin American, Latino, and Caribbean Studies at Dartmouth College. Her research examines the ways in which Afro-Brazilian media producers foment anti-racist visual politics through their image creation. She is the author of Visualizing Black Lives: Ownership and Control in Afro-Brazilian Media (University of Illinois Press). Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/latin-american-studies

Dante has now crossed Lethe and is ready to face Beatrice head on. She has moved to get ready for this eye-to-eye conversation. She's positioned nearer the griffin, a complicated symbol that may have more than one interpretation.Join me, Mark Scarbrough, as we explore both Beatrice (particularly her emerald eyes) and this dual-natured beast that seems to become more difficult to interpret with its every move in the poem.To support this podcast, consider a one-time donation or a small monthly stipend through this PayPal link right here.Here are the segments for this episode of WALKING WITH DANTE:[01:19] My English translation of PURGATORIO, Canto XXXI, Lines 112 - 126. If you'd like to read along or continue the conversation with me, please find the entry for this episode on my website, markscarbrough.com.[02:44] Beatrice has moved . . . but where?[05:09] With her emerald eyes, Beatrice and Dante finally escape the Francesca episode.[09:15] Dante is the Orpheus who can look into the eyes of his Eurydice.[10:49] Here are at least two additional interpretations for the griffin.[13:58] Beatrice's eyes are the methodology of revelation (and mystery).[16:41] The passage drops the first hint about Jesus' transfiguration.[18:50] Reflection is transfiguring, as in the craft of poetry.[19:34] Rereading the passage: PURGATORIO, Canto XXXI, lines 112 - 126.

"I think you need a witch."   Ian Carlos Crawford, Zachary Patton Garcia, Jamie Jirak, and Philip Ellis talk Angel season 4's "Orpheus"   CONTACT:  slayerfestx98@gmail.com Support us on Patreon: www.patreon.com/slayerfest98 Buy our stuff on etsy: https://www.etsy.com/shop/Slayerfestx98 Like us on Facebook: www.facebook.com/Slayerfestx98 Follow us on Bluesky: https://bsky.app/profile/slayerfestx98.bsky.social Follow us on TikTok: https://www.tiktok.com/@slayerfestx98 Follow us on insta: https://www.instagram.com/slayerfestx98/ Follow us on Twitter: https://x.com/slayerfestx98 Follow us on YouTube: https://www.youtube.com/@Slayerfestx98  

A new week means new questions! Hope you have fun with these!The cookbook "Mastering the Art of French Cooking" was written, in part, by which American?What first lady founded a clinic for substance abuse recovery in Rancho Mirage, California?How many points does snowflake have?Which musical, that won the best musical Tony, tells a version of the ancient Greek myth of Orpheus and Eurydice?Who won the first Golden Globe Award for Best Podcast?What prequel to the Hunger Games, was one of the best selling books of 2025?Merv Griffin adapted his song ‘A Time for Tony' into the iconic theme for what TV show?What is the name of the multiplayer-focused game that was a spinoff to Elden Ring?Wild dogs found almost exclusively in Australia are known by what name?According to legend, Roman Emperor Caligula once declared war on which god?Magic The Gathering wants to engage with a whole new crew of nerds by pairing with what Franchise that has 60 years of nerdery to draw from?In Greek Mythology, the story of Lycaon is one of the earlier stories where a man is turned into what animal?The Greenhouse Effect is caused by the buildup of what gas compound in the atmosphere?Which British panel game show that is going into its 21st season was created by little Alex Horne?MusicHot Swing, Fast Talkin, Bass Walker, Dances and Dames, Ambush by Kevin MacLeod (incompetech.com)Licensed under Creative Commons: By Attribution 3.0 http://creativecommons.org/licenses/by/3.0/Don't forget to follow us on social media:Patreon – patreon.com/quizbang – Please consider supporting us on Patreon. Check out our fun extras for patrons and help us keep this podcast going. We appreciate any level of support!Website – quizbangpod.com Check out our website, it will have all the links for social media that you need and while you're there, why not go to the contact us page and submit a question!Facebook – @quizbangpodcast – we post episode links and silly lego pictures to go with our trivia questions. Enjoy the silly picture and give your best guess, we will respond to your answer the next day to give everyone a chance to guess.Instagram – Quiz Quiz Bang Bang (quizquizbangbang), we post silly lego pictures to go with our trivia questions. Enjoy the silly picture and give your best guess, we will respond to your answer the next day to give everyone a chance to guess.Twitter – @quizbangpod We want to start a fun community for our fellow trivia lovers. If you hear/think of a fun or challenging trivia question, post it to our twitter feed and we will repost it so everyone can take a stab it. Come for the trivia – stay for the trivia.Ko-Fi – ko-fi.com/quizbangpod – Keep that sweet caffeine running through our body with a Ko-Fi, power us through a late night of fact checking and editing!

Beyond the Taboo: Uncovering the Secrets of Cuckolding and Hot Wifing Join us on this episode of Shameless Sex as we dive into the fascinating world of cuckolding and hot wifing with the charismatic Orpheus Black. With his unique blend of wisdom, insight, and storytelling, Orpheus will guide us through the intricacies of these often-misunderstood practices and reveal the surprising benefits they can bring to your relationships. Here's what you'll learn from this episode: • The real deal on cuckolding and hot wifing: what they are, how they work, and why they're not just for the adventurous few • How these practices can actually strengthen your relationship and bring you closer to your partner (yes, you read that right!) • The female perspective: exploring the equivalent practices and how they can be just as empowering for women • The secret to navigating cuckolding and hot wifing safely and lovingly, without sacrificing your emotional well-being • How to tap into your own desires and ignite a deeper passion in your relationships As a healer, scholar, and storyteller, Orpheus Black has spent years helping people break free from societal limitations and tap into their true power and freedom. With his passion, precision, and charisma, he'll inspire you to think differently about your desires and relationships. You can learn more about his work at https://orpheusblack.com By tuning in to this episode, you'll gain a deeper understanding of the complexities of human desire and the surprising ways that cuckolding and hot wifing can bring people closer together. So, are you ready to push beyond the taboo and explore the secrets of cuckolding and hot wifing? Get ready to have your mind blown and your desires ignited. Orpheus Black is about to take you on a journey that will leave you breathless, curious, and ready for more... Learn more about the Intimacy Rewired program on episode #458 or click here: ⁠⁠https://www.intimacyrewired.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. And mention Shameless Sex to get $100 off! Do you love us? Do you REALLY love us? Then order ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠our book⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ now! Go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠shamelesssex.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ to snag your copy Support Shameless Sex by sending us gifts via our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Amazon Wish List⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Other links: Get up to 50% off any annual membership at ⁠⁠⁠⁠⁠⁠http://Masterclass.com/shameless⁠⁠⁠⁠ Get 10% off + free shipping with code SHAMELESS on Uberlube AKA our favorite lubricant at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠http://uberlube.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Get 10% off while learning the art of pleasure at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠http://OMGyes.com/shameless⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Get 15% off all of your sex toys with code SHAMELESSSEX at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠http://purepleasureshop.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠