Podcasts about asr

De Dekkingsgraad is de tweewekelijkse nieuwspodcast van Pensioen Pro. Daarin lichten redacteuren van Pensioen Pro in ongeveer een half uur het belangrijkste pensioennieuws van de afgelopen periode toe. In deze aflevering: De Nederlandsche Bank stelt dit jaar nog hogere eisen aan de netto-profijteffecten in de invaarplannen van pensioenfondsen dan vorig jaar, stelt een aantal consultants. Opmerkelijk, want actuarissen hadden begin dit jaar al kritiek, zegt hoofdredacteur Maarten van Wijk. DNB ontkent. Hoofdrapporteur Boeselager van het Europees Parlement wil dat pensioenfondsen met meer dan €1 mrd vermogen minstens 2% daarvan gaan beleggen in durfkapitaal. Dat moet in de herziene versie van de Europese richtlijn voor pensioenfondsen komen te staan, Iorp 2, vertelt redacteur Sameer van Alfen. En volgens verzekeraar ASR is het voor pensioenfondsen met een flexibele premieregeling en verzekeraars vaak niet interessant om een exclusieve overeenkomst te sluiten voor het uitvoeren van de vaste uitkering. Redacteur Rien Meijer legt uit hoe dit zit. Presentatie: Ilse AkkermansSee omnystudio.com/listener for privacy information.

https://novacut.ai/  https://genaimeetup.com/  Anthropic has officially closed a $65 billion Series H at a $965 billion valuation, nearly 2.5x its valuation from just 100 days ago. Meanwhile, funding is flowing across the ecosystem: Frameworks AI at $15B, Baseten at $11B, OpenRouter's $113M Series B, and Cognition AI's $1B Series D. NVIDIA went on an open-source super week with Nemotron 3 Ultra, Cosmos 3, and Nemotron 3.5 ASR. Microsoft dropped 5 new MAI models. Google released Gemma 4 12B, and Anthropic shipped Opus 4.8. On the benchmarks front, DeepSWE crowns GPT-5.5 as the leader in long-horizon coding tasks, while ITBench shows even frontier models struggle with real-world SRE incidents — Claude Opus 4.7 tops out at just 47%. Plus: Cloudflare acquires VoidZero to build the future of AI-native edge development, and Google is paying SpaceX $920M/month for compute. Topics covered: • Anthropic's $65B Series H and path to $1T • Fireworks AI, Baseten, OpenRouter & Cognition funding rounds • Microsoft's 5 new MAI models • NVIDIA's open-source super week (Nemotron, Cosmos 3) • MiniMax M3, Gemma 4 12B, JetBrains Mellum2, Opus 4.8 • DeepSWE benchmark: GPT-5.5 leads long-horizon coding • ITBench: Frontier models under 50% on real SRE tasks • Cloudflare + VoidZero for AI-native edge dev • Google's $920M/month SpaceX compute deal #AI #Anthropic #NVIDIA #OpenAI #AInews #TechNews #LLM     Funding rounds Anthropic formally confirmed the closure of its $65 billion Series H funding round at a post-money valuation of $965 billion. This represents a 2.5-fold increase over its $380 billion Series G valuation from February 2026, adding $585 billion in value in approximately 100 days https://www.anthropic.com/news/series-h  Frameworks AI raising at 15B valuation representing a near fourfold increase from its $4 billion Series C valuation recorded in October 2025 processing 15 trillion tokens daily for major production clients including Cursor, Notion, and Perplexity https://finance.yahoo.com/sectors/technology/articles/fireworks-ai-eyes-15-billion-174609357.html Baseten is raising 1B at 11B valuation annualized revenue, which skyrocketed from $200 million to $600 million over a single quarter https://techstartups.com/2026/05/26/ai-inference-startup-baseten-in-talks-to-raise-1-billion-at-11-billion-valuation/  OpenRouter has secured a $113 million Series B funding OpenRouter has experienced exponential traffic growth, with weekly production throughput expanding fivefold from 5 trillion to 25 trillion tokens over a six-month horizon https://www.businesswire.com/news/home/20260526953416/en/OpenRouter-Raises-%24113-Million-CapitalG-led-Series-B-as-Weekly-Volume-Explodes-to-25T-Tokens  Further up the stack: Cognition AI secured a $1 billion Series D round led by Lux Capital and 8VC https://cognition.ai/blog/series-d   Model Releases MAI models: MAI-Code-1-Flash: A 5-billion active parameter model optimized for ultra-low latency within GitHub Copilot and VS Code. MAI-Image-2.5: A high-fidelity image generation model ranking third on global image evaluation arenas, outperforming competing architectures like Nano Banana Pro. MAI-Transcribe-1.5: A multi-lingual speech processing engine offering fivefold speed improvements across 43 languages. MAI-Voice-2: Natural audio and voice generation across 15 languages, available at a highly competitive price point. Web IQ: A search-grounding API engineered to directly compete with Perplexity. https://microsoft.ai/models/    https://www.peoplematters.in/news/ai-and-emerging-tech/uber-imposes-dollar1500-monthly-ai-spending-limit-on-employees-amid-rising-costs-50073    Nvidia has executed an "Open-Source Super Week," positioning itself as a dominant software and model publisher: Nemotron 3 Ultra (best US open source open weights model but behind china): A massive 550-billion parameter MoE (55 billion active) designed with a 1-million token context window, optimized specifically for high-throughput, cyclical agent loops. It achieved peak throughput rates of 400 tokens per second on day-zero optimized clusters. Cosmos 3: A physical AI world-modeling framework comprising 16-billion Nano and 64-billion Super variants. Built on a Mixture-of-Transformers (MoT) architecture, Cosmos 3 natively binds textual, visual, auditory, and physical kinetic vectors. Nemotron 3.5 ASR: A highly compact 0.6-billion parameter streaming speech recognition model pushing sub-100 millisecond latencies across 40 language locales.   https://www.minimax.io/models/text/m3  MiniMax M3: A 1-million token context model hitting 59.0% on SWE-Bench Pro and 74.2% on MCP Atlas, though noted for high token consumption due to intensive internal self-validation loops.   https://blog.google/innovation-and-ai/technology/developers-tools/introducing-gemma-4-12b/  Gemma 4 12B: Google's Apache 2.0 on-device model, which utilizes an encoder-free architecture that projects vision and audio vectors directly into the text-token space, bypassing separate CLIP-style encoders to minimize local memory footprints. https://www.jetbrains.com/mellum/  JetBrains Mellum2: A compact 12-billion parameter MoE (2.5 billion active) engineered for ultra-low latency routing and retrieval-augmented generation (RAG) sub-agents within developer IDEs. Opus 4.8 https://www.anthropic.com/news/claude-opus-4-8    https://www.cnbc.com/2026/06/05/google-to-pay-spacex-920-million-a-month-for-xai-compute-capacity.html      Benchmarks: https://deepswe.d atacurve.ai/blog https://venturebeat.com/technology/deepswe-blows-up-the-ai-coding-leaderboard-crowns-gpt-5-5-and-finds-claude-opus-exploiting-a-benchmark-loophole (GPT 5.5 the winner in long horizon tasks) a highly complex software engineering benchmark focused on original, long-horizon tasks across five distinct programming languages. Comprising 113 chaotic tasks across 91 live, production-grade repositories, DeepSWE forces agents to generate 5.5 times more code and modify an average of 7 separate files per task compared to standard evaluations. On this challenging leaderboard, GPT-5.5 leads with a score of 70%, establishing a significant 16-percentage-point lead over contemporary alternatives I think older benchmarks where models reach ~90% accuracy can be considered saturated. Few percentage points don't give us any good signal.  https://research.ibm.com/publications/developing-ai-agents-for-it-automation-tasks-with-itbench  ITBench-AA, an evaluation framework focusing on live Kubernetes incident response and Site Reliability Engineering (SRE) operations. Comprising 59 live, containerized SRE incident snapshots, the results are remarkably sobering: every frontier model scored under 50% on successful incident resolution, with Claude Opus 4.7 leading at 47% and GPT-5.5 following closely at 46%.   Edge AI announcements: https://www.cloudflare.com/press/press-releases/2026/cloudflare-acquires-voidzero-to-build-the-future-of-the-ai-native-web/  The consolidation of the AI-native developer stack has reached the runtime virtualization layer. Cloudflare recently completed the acquisition of VoidZero, the development group responsible for Vite, Vitest, Rolldown, and Oxc, backing the transaction with a $1 million open-source ecosystem fund. This acquisition is highly strategic; as autonomous agents write an increasing proportion of production software, local development environments, compilation pipelines, and bundlers must be optimized for execution speeds that match agent speeds. Cloudflare's goal is to construct a localized, full-stack edge playground. In this sandbox, AI agents can generate, test, bundle (utilizing the highly parallelized, Rust-based Oxc and Rolldown engines), and deploy entire web applications end-to-end within milliseconds. This architecture completely bypasses traditional local machine container bottlenecks, enabling high-velocity agent loops to execute in a fully sandboxed, web-scale edge runtime.

Verzekeraars lijken saai. Tot je begrijpt hoe ze geld verdienen. In deze aflevering bespreken we hoe premies vandaag kunnen uitgroeien tot beleggingsrendement morgen, waarom float zo krachtig is en wat de belangrijkste valkuilen zijn waar je als belegger op moet letten. Met herkenbare voorbeelden als ASR, NN Group en natuurlijk Berkshire Hathaway laten we zien waarom verzekeraars voor lange termijn beleggers verrassend interessant kunnen zijn. Check snel de deep dive in de wereld van verzekeren.Beleggersbrieven | 2x per maand deel ik op zondag mijn beleggingsinzichten met 6000+ beleggers. Ook gratis ontvangen?Schrijf je hier gratis inMijn boek |  Benieuwd naar mijn strategie om zelf de beste aandelen te selecteren die de index kunnen verslaan?Koop hier mijn bestseller boek Lidmaatschap | Toegang tot mijn portefeuille, transacties, deep dive audio analyses van de beste bedrijven ter wereld & een community met 700+ lange termijn beleggers? Meld je hier aan voor het lidmaatschapTrainingen | Zelf een betere belegger worden en met een bewezen systeem aandelen leren selecteren? Klik hier om trainingen te bekijkenConnecten Volg me op Instagram en LinkedIn. Vragen & opmerkingenStuur een mail naar info@rowannijboer.nlDisclaimer: Beleggen brengt risico's met zich mee. Je kan (een deel van) je inleg verliezen. Ik geef geen financieel advies en de content op dit kanaal is expertise vrij. Denk altijd zelf na voordat je een beslissing maakt.

Hey folks, Alex here, let me catch you up! I've had a feeling that this week is going to be crazy, as it started on the weekend MiniMax M3, then with Jensen announcing new RTX Spark, NVIDIA's first PC chip packing 1 petaflop of local AI power into thin laptops.A few days later at Microsoft BUILD, Satya & Mustafa from MAI dropped 7 AI models, completely pre-trained from scratch, including a new MAI-thinking-1, MAI-code and MAI-image 2.5 that started topping the image gen charts. Then other image models started racing to the top of the Arena benchmarks, IdeoGram 4 hitting becoming SOTA open weights image-gen model, and Reve 2 beating Nano Banana just a few hours after that. And then today, NVIDIA dropped Nemotron 3 Ultra, their latest 550B open weights model, data and training and Arena published a new agentic eval leaderboard and we got a new Gemma 4 12B. I've had the great pleasure to host Chris (@llm_wizard) from Nvidia, Peter Gostev from Arena and Karan from Nous Research (who were featured prominently by Jensen!) all on the show. Def don't miss this one! Let's get into the details. ThursdAI - Join the flock of folks who know what is happening in AI before everyone else.Open Source LLMs

We're announcing AIEWF speakers this week! Take the AI Engineering Survey!Today's guest Ethan first joined us for the LS Paper Club as the lead on NVIDIA Cosmos World Model, but then joined xAI and built Grok Imagine in 3 months:He comes back on Latent Space with some nuclear hot takes: that Video Models primarily get their intelligence from LLMs, not from training on video data, and that the next frontier for truly interactive, realtime, long-horizon world models is to work on LLMs (perhaps Interaction Models as well…)Put it this way: In the near term, the next Sora won't be a better video model, but a video agent.Generative Media may more closely follow the evolution of AI coding which went from focusing on one-shot output performance and cost, to multiturn reasoning and planning models for agents and systems that can plan, edit, test, debug, and submit PRs.At a certain point, coding models got so good that the only significant next step to improve performance was handling the orchestration of these models.Now as the performance of video models increases significantly across realism, consistency, & prompt adherence while becoming more cost efficient, the next evolution of video generation may also be systems that can plan, generate, edit, critique, and iterate across an entire creative task. In this episode, Ethan joins swyx and Vibhu to unpack what it actually takes to build frontier image and video systems: data, VAEs, diffusion transformers, audio-video alignment, inference speedups, and the hidden cost of storing and moving massive video datasets. From building NVIDIA's Cosmos world model to joining xAI as Grok Imagine was being built from zero to one, Ethan He has been at the center of some of the most important work in video generation, multimodal models, and real-time world models.We go deep on Grok Imagine, how a small xAI team shipped its first multimodal video model in three months, why iteration speed matters more than almost anything in model development, and why many of the biggest gains come from fixing tiny bugs in data and training pipelines. Flipbook: The future of VideomaxxingVideo agents are almost a sure bet to be the trend in the coming year. We end with a glance at what's beyond video agents:Flipbook caused a minor sensation this year when it was released, but most treat it as a fun demo. Ethan takes it very seriously — with the speed and cost of inference coming down every year, the future of custom video JIT UI is closer than you think. We talked about why videogen models may become the front end of AI, how generative UI could replace traditional HTML/CSS, why world models need to be real-time, interactive, and long-horizon, and why the future of video generation may depend more on language models and agents than on diffusion alone.We discuss:* Why fast iteration mattered more than meetings* Why small training bugs can drive huge model quality gains* Why coding models may make compute the bottleneck again* How image and video models are trained with synthetic captions* The role of VAEs and latent space in frontier video models* Why image models are the foundation for video models* The tradeoff between temporal compression and real-time interactivity* Flipbook, Neural OS, and the future of generative UI* Why future interfaces may go from user intent to pixels* The hidden cost of training video models: storage, egress, and GPU hours* How step distillation and consistency models (like OpenAI sCM) makes video inference orders of magnitude faster* Grok Imagine 0.9 and large-scale audio-video generation* Why audio-video alignment is harder than text-video alignment* Ethan's definition of world models* Reference-to-video, video extension, and long-context video generation* Why xAI's research communication undersells Grok Imagine* How xAI culture shaped the speed of development* AI watermarking, SynthID, and detecting generated media* Why prompt rewriting matters for video models* Grok Imagine Agent and the rise of video agents* Why language models may unlock better video generation* Robotics, physical AI, and embodied world models* Why Ethan left xAI and shifted focus toward LLMs* Self-managed context, memory, and the next frontier for language modelsEthan He* LinkedIn: https://www.linkedin.com/in/ethanhe42* X: https://x.com/EthanHe_42Timestamps00:00:00 Introduction00:01:25 From NVIDIA Cosmos to xAI00:03:24 Building Grok Imagine from Zero to One00:10:07 How Image and Video Models Are Trained00:18:53 Video Compression, VAEs, and Real-Time Tradeoffs00:22:10 Generative UI, Flipbook, and Neural OS00:32:10 The Cost of Training Large Video Models00:37:04 Distillation, GANs, and Fast Video Inference00:41:21 Audio-Video Generation and Grok Imagine 0.900:48:34 What Makes a World Model?00:55:51 Reference Videos, Long Context, and Video Memory01:00:11 xAI Culture, Research, and First-Principles Building01:09:45 AI Safety, Watermarking, and Prompt Rewriting01:13:10 Video Agents and AI-Assisted Creation01:27:32 Why Language Models Unlock Better Video01:31:15 Robotics, Physical AI, and Embodied World Models01:32:38 Why Ethan Left xAI01:34:16 Self-Managed Context and the Future of LLMs01:38:43 Ethan's Career Path and Closing ThoughtsTranscriptIntroduction: Ethan He, Latent Space, and the Path to xAISwyx [00:00:00]: We're here in the studio with Ethan He, most recently of xAI. Welcome.Ethan [00:00:10]: Thank you. Glad being here.Swyx [00:00:11]: We're also here with Vibhu. you were first coming to us or joining the latent space world because you were working on Kosmos at NVIDIA, and you did a paper. We loved it. you presented it as well, so thank you for doing that.Ethan [00:00:23]: I've actually, I also presented the MoEs twice at latent space.Swyx [00:00:29]: How did you actually hear about us? Did we reach out to you? Is that how it worked?Ethan [00:00:33]: No, actually, I-- the community. Like I realized, oh, there is this online community that people talk about AI and also learn from each other through papers every week through the Paperclip. It's very nice.Ethan [00:00:49]: I learned a lot.Swyx [00:00:49]: I think three years stop. We haven't stopped even on Christmas and New Years. many weeks I want to stop but it keeps going.Vibhu [00:00:58]: No, that was good. I think you had posted that you worked on a paper, and I was “Oh, very cool. We have Paperclip. Present then.”Vibhu [00:01:04]: But I might have reached out to you after.Swyx [00:01:05]: you-- because it's an amateur club, right?Swyx [00:01:08]: so it's very unusual and but we have sometimes paper authors come by and actually explain the paper. Today we just did, the poolside paper, which was apparently very good.Vibhu [00:01:18]: Came out yesterday.Vibhu [00:01:19]: pretty interesting, right? Fully open. They talk about everything, systems. So it's a good one. We'll, we'll recommend people to read it.Swyx [00:01:25]: Bring us up to speed on your transition to xAI, ‘cause I actually don't even know when you joined. just like tell the, tell the story about the sort of transition.From NVIDIA Cosmos to xAI: Scaling Video and World ModelsEthan [00:01:34]: Before xAI, I was working on Kosmos world model as in-- at NVIDIA. So Kosmos is, it's a giant video foundation models that can-- that aims to simulate the world and for-- it serves as a foundation of-- for all of the roboticists to build on top of. There, once I built the Kosmos one, I realized as this thing also has a scaling law similar to language model, we need to scale up the video models further. that's, that's why I realized I need to move to somewhere with much more compute resources. That's how ISwyx [00:02:13]: Than NVIDIA?Vibhu [00:02:14]: The GPU rich came themselves.Vibhu [00:02:19]: And timeline-wise, when was Kosmo? It was pretty early, right? It was open world model, open paper, everything.Ethan [00:02:25]: It was end of twenty-four.Vibhu [00:02:28]: End of twenty-four.Ethan [00:02:30]: Then at mid twenty-five, I moved to xAI. At that time-- I joined about the time when xAI was about to build video models and in multi-model models. There were no infra, no data, and no model, and it just-- as a few engineers, we built it in three months and released the first model, Grok Imagine zero point nine.Ethan [00:02:55]: And since then, I keep working on video models and move more from training and to post-training of the video models. For example, like a reference to videos, kind of like the cameo feature and, video extensions. And, before I left, I worked on a world model, leading a small team to focus on the real-time long horizon video generation.Building Grok Imagine From Scratch in Three MonthsSwyx [00:03:24]: Can you give like a rough roadmap of okay, you're on a brand-new team. Grok previously was only text, or they partnered with BFL for their image gen stuff. What do you-- what are the building blocks, right? You have compute, data you can procure somewhere. Like just what are like the sequence of things that people should think about when you're setting up a new team?Vibhu [00:03:43]: actually even deeper, not just data you can procure. You guys had to go through getting the data too, right? So you shipped it pretty fast, but yeahSwyx [00:03:51]: three months is likeVibhu [00:03:52]: From everythingSwyx [00:03:52]: actually like very surprisingly fast.Ethan [00:03:55]: One thing I say like thanks to my experience at NVIDIA, ‘cause first time when we were building Kosmos together, we built it, for about a year. So this is like the second time I do it. Roughly have an idea, what to do. I say the most important thing is the talent. Everyone were very strong and clever, very close with each other towards a common goal. So that speed up things a lot. So you reduce the communication bandwidth among people, and everyone can work towards the same goal. It's, it's like every day there's not that much meetings on the calendar, like maybe like a, like a sync a day, and after that it's, it's just all building. It was pretty fun at that time.Ethan [00:04:47]: And another thing is that xAI has very strong foundations of like data inference, model inference, and the supporting there can help the model develop a lot. When I look at, training models, I don't so actually the top important thing is like how many, how many iterations can you do, per day? and the more iteration can you do, you can, you can train the model much faster. So if you have very strong infra and you have a lot of compute, you can, you can train these models in very short period of time. That can give you a much larger buffer to, for errors, and it also gives you the opportunity to spot more bugs.Iteration Speed, Compute, and Debugging Model PipelinesSwyx [00:05:46]: What is an iteration? Is it like a few hundred steps or what are youEthan [00:05:50]: Let's say just the train-training the model, like from acquire new data and maybe design new algorithms and train a new model, maybe at smaller scale orSwyx [00:06:01]: So cycle time for like any hyperparam that you're searching.Ethan [00:06:04]: Cycle time and tune to like eval this model. Is this model better than my previous iteration?Ethan [00:06:11]: SoSwyx [00:06:11]: So it's like before you, someone had already set this up that you can iterate very quickly.Ethan [00:06:15]: I think the foundation there is extremely good forDeveloping and research models.Ethan [00:06:23]: And often I find is it-- this is kind of boring, but like a lot of the improvements does not come from new algorithms. It comes from finding small bugs here and there in the data pipeline, in the, in the model training pipeline. Those give, those give the biggest boost to the model quality.Vibhu [00:06:46]: It's interesting, right? So you say it's like small team, less communication bandwidth, but also a lot of quality is like find little bugs. It seems counterintuitive, right? You have a lot of people, you can iron out more of those, but it's interesting to see the other side, right?Swyx [00:07:00]: I also wonder, have you-- do you try using LLMs to look for bugs? I don't know.Ethan [00:07:05]: I remember at that time it was mid two thousand and twenty-five, so it's the coding model wasn't quite there yet. I remem- I remember like December two thousand and twenty-five, it was extremely good. Yeah, I've been, I've been using it at that time. It's, it's helpful. sometimes it produce codes that are kind of difficult to maintain, even though like the first time it built something extremely fast. But it gave the, like a spaghetti code, thousands of lines that I couldn't maintain, and the LLM itself couldn't figure out what's, what's wrong and how to improve on top of it. But now I find it much better. Yeah, I want to bring up another point here is now coding models are much more efficient and can help us implement stuff much faster. Compute might become a bottleneck again because previously, like if you want to train a new model, say you want to generate new synthetic data and then or write a new algorithm, it might take a few weeks. And during that period of time, you don't-- you might not have experiments to run. But now you can build that thing within a few hours, then you can immediately train a model.Ethan [00:08:24]: Now you have to have enough compute to try all of the ideas. So compute might be the bottleneck of iterating speed again.Swyx [00:08:36]: yeah, I actually, honestly, I think it's like kind of a stressful job because you're “Well, I should be trying everything, and if I'm not, then I'm not doing my job well.”Vibhu [00:08:48]: there's also the stress of you're eating thousands of GPUs per hour, which is very expensive and, compute can go to other researchers.Swyx [00:08:56]: You got the daddy Elon toVibhu [00:08:57]: You got daddy Elon.Ethan [00:08:59]: It wasVibhu [00:09:00]: But there's still finite amount of compute, like you want to use it, you want to use it well, you want more of it.Ethan [00:09:06]: That was quite stressful indeed. Yeah, I think one thing is the-- with coding models now, like a lot of these jobs can be automated, which is much better. A second, it's a, it's a marathon, so you got to maintain good health and, a regular schedule.Vibhu [00:09:28]: It's, it's hard to hear that when you shift from zero to nothing in two months.Swyx [00:09:32]: and, I think obviously the culture at xAI is very famously, people work very hard. one thing I did want to dive into, in our-- in the notes that you, that you sent ahead of time, you had specific comments about the cost of Video Gen training. presumably this is on the Colossus-1, right? the two hundred megawatt cluster. Any whatever you want to just share on that.Vibhu [00:09:54]: I think there's, there's three things we're talking about, right? So there's Video Gen, there's also the Image Gen model that you put out. Do you want to like complete the, okay, so zero to one, you have a few months. Just what are the stages of create Image Gen model?Swyx [00:10:06]: Oh, yeah, maybe I got distracted.How Image and Video Models Are Trained: Synthetic Captions, Tokenizers, and VAEsVibhu [00:10:07]: Sorry. and then, from there's Video Gen, there's Audio Gen. Would love to get into those next. But what is that first few months like? So small team, a lot of bugs, iterations, but what does it look like? Do we take something off the shelf? Do we just get data compute? What's, what's the few months like? How do you go to state-art Image Gen model? How do you just start?Ethan [00:10:28]: I cannot comment specifically how xAI did, but it's, it's a quite standard process. I can draw some, examples from Cosmos. So mainly it's building a video model, you actually need to build a image model first. And building these two models, the data you need is a hundred percent synthetic pair of language and image or language to video. Because on the, on the internet, actually, the videos don't naturally associate with text. So you can say, oh, like on YouTube, you have the title and you have the description and the commentsSwyx [00:11:11]: TitleEthan [00:11:11]: of a video, but usually they're not relevant to the video itself. And say maybe like the video is a natural scene of mountains or something, and the title is, I'm so happy today.Ethan [00:11:26]: So they have they have no correlation at all. So the first step is to, you have to generate synthetic pair of language with the videos. So you gather videos from the internet, and you use a VLM to caption the videos. So that part, here's a question, like how do you, how do you gather VLM to begin with? So if there's noSwyx [00:11:55]: You, so you fuse the model, right? LikeEthan [00:11:57]: Say if there's no like VLM exists, like how do you generate the text to the beginning, right? It's, it's impossible.Swyx [00:12:04]: I see.Ethan [00:12:05]: In the beginning, it's like you ask human to describe the video as detailed as possible.For example, you ask them to describe everything, like all objects, all characters, and all interaction and dialogues in the, in the videos. So that's in the protocol of Cosmos labeling. We require the objective we give to the labelers was that you have to describe the video as detailed as possible, such that a blind person hears a blob of text can reconstruct what the video is like from their head.Swyx [00:12:43]: Video or image? You're talking about images.Ethan [00:12:44]: Video or image, either one of them.Vibhu [00:12:47]: This was pretty common when we went from clip and DALL-E, right?Vibhu [00:12:51]: It's all training on really detailed captioning of images. So same is applied to video, but insteadEthan [00:12:57]: same appliedVibhu [00:12:57]: of using multimodal model to pass in video images and write rich descriptions, you can alsoSwyx [00:13:04]: I think there's this traditional perspective of supervised, or, very highly human curated thing. I feel like there's a unlock with unsupervised, right? Where like you have enough to bootstrap that you can just throw common corpus on it or, whatever. like unsupervised vision and language pairing, right? Like where you just have, interspersed image and text and it just learns. To me, that is the VLM breakthrough that is different from the clip, different from the LM era.Ethan [00:13:36]: It's interesting to see that you kind of need both data.Ethan [00:13:41]: For example, for theSwyx [00:13:41]: You need it to bootstrap it up. YeahEthan [00:13:43]: for the generative model training, there's also usually like a small percentage of unlabeled data. So the model is instructed to generate a video without any text instruction. That can also help the model generalize. So after this stage of generative synthetic pair, so, one important common step is to train a compressor or a tokenizer of the image or videos. So because, if you train-- If you can technically, theoretically train image or video models on pure pixels, but the problem is that the, it's, it's a lot of tokens. So like one image, it's, a thousand by a thousand, it's like one million tokens, one million pixels. It's impossible to train transformer on that. So it's, you need to train a tokenizer, which can go from image to latent space and latent space back to image.Swyx [00:14:45]: That's why we named the podcast.Swyx [00:14:48]: But, basically, you're talking about vocabulary science.Ethan [00:14:50]: so vocab.Swyx [00:14:51]: And so, what is, what is imp-- like a million is impossible?Ethan [00:14:54]: In generative models, the vocab is continuous. It's a continuous space. We can think about like you map an image to a vector. It's a, it's a fixed length vector. It's sixteen or forty-eight, something like that. And then you map that vector back to the image space. And the mapping is, has-- The mapping is patch-based. So you say you haveEthan [00:15:22]: a sixteen by sixteen patch and you match, you map that patch of pixels into this latent space.Swyx [00:15:29]: We've covered thisVibhu [00:15:30]: This is like the vision transformersSwyx [00:15:32]: VAEs,Ethan [00:15:33]: VAEs.Vibhu [00:15:34]: You basically compress your input, you do your generation, you're reasoning all that generation in smaller dimension, and then you project back out.Swyx [00:15:43]: VAE is a form compression, but I think the for me, the patching thing is from VIT, right?Ethan [00:15:48]: You can make those.Swyx [00:15:49]: Literally the, yeah, the paper is titled like sixteen by sixteen is all you need. something like that. and then I think also, people make a lot of comparisons with this kind of patching with convolutions.Swyx [00:16:02]: Which is you're, you're kind of re- reconstructing the old paradigm with the new.Ethan [00:16:05]: Actually, in VAEs, there are, there are both convolution networks and transformers. You can actually do both.Ethan [00:16:14]: After this VAE, so what you've got is you've got latent space tokens and you've got the language tokens. So now the training of the diffusion transformer, usually generative models use diffusion transformers. It is actually quite standard. It's, it's very similar to how you train a language transformer models. It's not that much difference. It's just the tokens, the visual tokens in, visual tokens out. The only difference is there's a denoising process. So you train the model to unmask some of the noise. So you add, you add random noise to the visual tokens, and then you train the model to remove those noise to generate the clean tokens. Any inference, the model can iteratively remove noise from a hundred percent noise.Swyx [00:17:12]: And then there's also, to speed things along on the tech tree of diffusion, there's CFG, and then there's, there's also, latent diffusion that, there's, there's someone in there. I think, somewhere along the line, obviously, like stability and all these other guys, pioneered a lot of this, architecture. I don't know if you want to get into that or just, or do the video side up to you.Bootstrapping Video from Image Models and Temporal CompressionEthan [00:17:37]: After you train such model, such image model, the reason it's a, it's a foundation for video models is that image models are cheaper to train, and they have much denser connection between language and text. So, sorry, language and images. For example, you train a billion, you train on a billion images, and there's a mapping from the text to the image. And the cost to train the same, like the, a billion, a billion text to a billion videos, that's much more expensive because videosNaturally have more tokens than images. Because the diffusion models, their understanding of, language purely come from this mapping. So if you don't have enough mapping, so if you only train on like a ten million videos or something, there-- you might not see enough language tokens in your training, so your model does not understand human intention enough. So that's why you really-- you train-- you first train this image diffusion models, and then you bootstrap the video model from there.Swyx [00:18:53]: One thing I did want to ask, because I-- actually, I think you're, you're the first per-- video model person I've ever talked to, I think. we've, we've like talked to Luma and all those folks. There's all these tricks in video compression where basically frame by frame there's not that much difference, so actually you don't have to regenerate or save the whole frame, right? but I think MP4 compression or something else like that.Swyx [00:19:16]: is it tempting to use that? Or as far as I can tell, everyone just treats it as, “No, we would just generate every frame.” Is that roughly the state-art?Ethan [00:19:27]: There are a few different approaches. Let's say first, like you want to just directly use MP4 compression and use that as the tokens for the transformers to train, right? So people actually have tried that, but the main challenge is the latent space for the MP4 tokens were not, were not very comprehensible for the models. It's, it's extremely hard to train on that. And there's aEthan [00:20:01]: So that's why they created VAEs, which creates more continuous, latent space, so the models can understand that latent space and learn from it much easier. Even within the VAEs, there are different difficulties of the latent space. So you can imagine something the simplest, the most naive VAE is like you have an image, and you just shuffle all of the images into a, into a vector. So you don't need to train any VAEs, right? But that latent space is extremely hard for models to train on top of. That's why there are some debate on like how do you compress the tokens. So you mentioned like you can compress frame by frame. Also, you can compress, the temporal dimension.Ethan [00:20:52]: The difference is if you compress the temporal dimension, you get a much higher compression rate. Because there's temporal redundancy between frames, because, this frame and the last frame, likely they are mostly similar, so there's only some small difference. for example, I think in 12.1 VAE, they have like a eight by eight by four compression rate. So the four temporal tokens are compressed into one tokens. That can save a lot of, save a lot of the context length. If you do it frame by frame, you have to do maybe like eight by eight by one. Your context length will be four times larger. That being said, the benefit of the frame-- per frame compression, we might come back to this later, is, real-timeness and interactivity. ‘Cause if you, if you strain the output of the model, frame by frame, you can-- the model can respond to any user request immediately. So if you have like a temporal four compression, four times compression, thenSwyx [00:22:06]: It might be laggyEthan [00:22:07]: there's a lag there in nature.Swyx [00:22:10]: So you're very pilled on this. let's just go ahead and bring it up ‘cause we have the visual prepared anyway. There's some frontier applications of real-time video gen. So Flipbook is one of the examples that went viral recently, right? What is Flipbook?Real-Time Generative UI: Flipbook, Neural OS, and Diffusion Front EndsEthan [00:22:23]: Flipbook is kind of like a web brow- web browser. You can see like it has the web bro- browser UI on top. The difference is all of the UIs are generated by generative image model in real time, and anything here are fake. But you can, you can explore inside this wor- this imaginary world. Say like we-- here we have engineering the Great Pyramid. Like the model generates this for us to understand how it works, and if we want to navigate around and understand further, we can click on some of the, some of the description here, and the model will generate a new page, new subpage describing the details we want to know about.Swyx [00:23:14]: So it's basically kind of we're playing a video, but it's pausing for our next interaction, and then it just plays the next thing based on our interaction.Swyx [00:23:23]: Which is kind of cool.Vibhu [00:23:25]: and you kind of decide your story. So this was, how do you make a pyramid? levering technique seemed interesting, right? It shows how do you take Okay, I want to know what is thisSwyx [00:23:35]: The demo, the demo tweet had more animation between frames.Vibhu [00:23:38]: I think it's just skipping,Swyx [00:23:39]: Oh, it's just skipping a lot of frames.Ethan [00:23:40]: they also have a video modeVibhu [00:23:42]: It takes a lot. There's a lot of peopleEthan [00:23:42]: but, a lot of people are using it.Ethan [00:23:45]: So it's not available.Vibhu [00:23:46]: There's a live video stream. We can try,Swyx [00:23:50]: So this is an example of the kind of future that you see at the extreme. We don't-- we're obviously not in it today.Swyx [00:23:56]: But in a world where inference is completely free this is better than generating code and text?Ethan [00:24:02]: So this is, this is a final state of where Viva will be at for word model, I think. Imagine internet doesn't exist, and then you type in google.com. Like what should, what should, what should a model show you?the model can imagine something, and this is what the model imagine. And these web pages, they completely do not exist. So I think as the inference costs come down, we are going to have generative UI for everything. If you think about how the coding model works, so they write code for a web page, and they render the code might be con- converted into binary, and the binary render the pixels on the screen. So we in machine learning, every time we have some breakthrough, obviously it's, it's more intuit. So why don't we have like user instruction to the pixel directly? So the generative UI will be user intention to the pixels directly. And say like even if I want email, let's say everyone have the same interface, but I want, I want it slightly different. I want the email to show to me like a TikTok, so I can swipe left and right for the emails. And or maybe you want something else. We can have completely different things. Or like I have I'm looking at, Instagram stories, and I don't like the Like button. I always may click it. And, generative UI resolved it. So it's going to be a revolutionary replacement of the interface. So in the future, we might have much more powerfulEthan [00:25:50]: LLMs and coding models running behind the scene. And in the, in the front-end, the diffusion model will actually be the front-end to show stuff to you. That's how I imagine it.Swyx [00:26:02]: Diffusion front-end, deterministic back-end.Swyx [00:26:04]: Something like that. I find that very expensive, but,Vibhu [00:26:08]: I find it interesting you called LLMs writing code on the back end deterministic, but okay.Swyx [00:26:14]: you write it onceVibhu [00:26:15]: Compare it toSwyx [00:26:16]: And then you execute.Ethan [00:26:17]: If you think about the cost, say, let's say H100 costs $1 per hour, and if you use this eight hours a day and thirty days, so, every month you're paying this two forty, you'll actually not wanna pay for that. That's even more expensive than Cloud Code Max. But if you think about the compute costs come down like two times every year, and I think the future will likely arrive like within few years.Vibhu [00:26:49]: It's everything, right? compute cost comes down, compute gets faster, model gets smarterEthan [00:26:54]: More efficientVibhu [00:26:54]: model gets smaller.Swyx [00:26:55]: I don't know why you say two times, ‘cause I think it's like 100 times. In language models, it is roughly one hundred to a thousand times every twelve to eighteen months, for the same given level of LMSys, ELO.Vibhu [00:27:08]: That's a net of everything, right? That's model performance alongside compute. So different than just compute costs come down. But, a very interesting future.Swyx [00:27:19]: So the web designers will have to shout out that accessibility is an issue, right? how do you deal with screen readers or whatever. But yes, this is higher bandwidth storytelling than anything you can possibly generate with code, right? So I think that's the rough idea.Ethan [00:27:34]: And I'd like to add a little bit that so human naturally have the maximum bandwidth when we are looking at things, look at videos, and we also have maximum output bandwidth when we are talking. So in the future, it might be something like we talk to AI models, and the AI model responds back with a generative UI. So that would be the maximum input and output bandwidth to interact with AI models before neural link happens.Vibhu [00:28:06]: And it's also very custom, right? Some people are very visual, some people are not as visual, right? They prefer the text. But the best thing about generative UI, right, it can also be text.Swyx [00:28:17]: There's another project that we wanted to highlight, which is the Neural OS. Kinda similar idea, but here you're literally operating, simulating an operating system with a video model.Swyx [00:28:27]: and you can play Doom, you can do Firefox. I find this like mildly less impressive, obviously, because it's an OS that I can run.Swyx [00:28:37]: But here everything is imagined.Vibhu [00:28:40]: I was, used to the Command+W to close the Firefox tab. It didn't crash. That's why I saidSwyx [00:28:45]: It's too immersive.Vibhu [00:28:46]: It's, it's too immersive for me.Swyx [00:28:47]: Too immersive.Vibhu [00:28:48]: I wanted to close the tab.Vibhu [00:28:49]: But yes, I can play generated diffusion.Swyx [00:28:51]: this is shockingly fast.Swyx [00:28:54]: Because I remember there was a demo about like maybe one to two years ago. Someone tried to do the first-person shooter with a image model. There was no consistency. It was very slow. But here it looks like realistically it's-- this is Doom.Vibhu [00:29:07]: I think there's two sides to that, right? There's okay, what is running a game? The heavy part of it is actually the game engine, all the lighting, all that stuff, the graphics. This is just kind of video, right? Like we've solved consistency. This is still, it looks like a few years old image generation. There's some temporal consistency, but it's, it's kind of just images stitched together as frame video. But it's a good visual representation to pi- to picture the future you wanna see, right? that's, that's what I see in these more so.Ethan [00:29:38]: This reminds me of how the video models gets better and better. So Neural OS is kinda if you just look at it feels like it's just a crappy version of the, like the Windows we could have, right? And, but the difference is, so the model, this model is overfitted on the existing operating systems. It can generate nothing different than that. But it's actually also similar to video models. So when we are training these video model, image model, we train them on internet. There's no imaginary supernatural stuff on the internet. But once we train this model, you can prompt the model to generate something supernatural that have never existed in the data set. So if you train your Neural OS or neural computer on the standard screen recordings on the entire internet. The model can imagine completely new interface to interact with the computer.Swyx [00:30:43]: This is one of those things that is magical to me. usually generalizing out of distribution is bad, but somehow we have learned some kind of internal world model that you say, this plus, but it looks like rainbows and butterflies, it'll do it and it will kind of make sense.Swyx [00:31:03]: So yeah, that's kind of cool. Yeah, I don't know if there's any comment more on there. I do, I do wanted to, I did wanted to touch a little bit more on the model architecture stuff, which I think you were getting. It's, really fascinating. We don't get a chance to talk about this enough. So one of the papers that we covered, we've covered every annual, segment anything release. and I don't know if you follow-- you're a computer vision guy, so youEthan [00:31:26]: I knowSwyx [00:31:27]: . So they did memory attention, which is kind of interesting. And I always think, anything where you can, across the temporal dimension, keep some consistency, I think it's, very fascinating, and I don't know if Basically, does that-- the CV side bleeding into video gen side, I think is underexplored, right? we talk about it for labeling, but actually you can borrow the architecture itself.Ethan [00:31:50]: There's, there's also complete different approaches, right? you brought up the term world model, so we went from video model to world model. There is diffusion, but there's also other approaches that people are doing. So maybe we get into those after as well,?Swyx [00:32:03]: He has a whole definition of world models and stuff. I feel like we threw a lot at you. Whatever you want to comment on.Why Video Models Are Expensive: Storage, I/O, and Training ScaleEthan [00:32:10]: I think one thing that we should actually comment back on is okay, so we were talking about the steps to train image gen to video model. One thing we don't see as much of is okay, you brought up the delta in training data, right? SoEthan [00:32:24]: you won't have as much a video model might not generalize, but what is the cost of training a large video model? So we know for LLMs roughly, okay, even like the poolside thing that came out today, right? It's a Gemma level model trained on roughly forty trillion tokens at this many H200s over this much time, right? You can see what is the exact cost of that. So how many GPU hours over how much H200 costs? So how do we do the back-end math of, same thing for video models, image models. How do you, how do you kind of break that down? I can share some back-envelope calculation. So surprisingly, video models is-- the cost is very-- is comparable to language models and obviously the largest scale is language model, maybe like a medium scale to language models. I said just storing the videos alone, it costs a lot. You can, you can maybe look up on AWS or something.Ethan [00:33:20]: You really, say if you have a billion videos and let's say, let's just say like each video, like five megabyte, then you need five petabyte to just store those videos. And also remember we talk about you use a VAE to compress the videos, and you also need to store, typically you need to store those continuous feature, in-- also in your storage. That's also comparable size with the videos themselves. So just storing these videos and the features is tens of petabytes alone. And,Swyx [00:33:58]: I just, I just looked up the calculation. Five petabytes on S3 Standard is one hundred K per month.Ethan [00:34:05]: AndSwyx [00:34:05]: It's comparableEthan [00:34:05]: and you needSwyx [00:34:06]: AndEthan [00:34:06]: And then like tens of petabytes, two hundred K. And even more expensive is you have the ingress and egress.Swyx [00:34:13]: Oh, yeah.Ethan [00:34:14]: Like you-- through the internet. You have to just to download those videos, I believe it's, it's more expensive on AWS than just storing those videos.Swyx [00:34:25]: Storing, yeah.Ethan [00:34:25]: And each training runs, you probably need to pull them once. If you train multiple times, it's, it's even more than that. So it's like just storing the network, those costs is just, it would be a few, a few millions per month to just storing everything, not to mention the GPU cost.Ethan [00:34:45]: AndSwyx [00:34:45]: my side tangent, the compute rental, like GPU rental is very efficient. There's one side, okay, you can be XAI and build your data center. Should we not just build our, storage compute as well? LikeEthan [00:34:57]: Of courseSwyx [00:34:57]: cloud cost compared to just,Ethan [00:34:59]: You save so muchSwyx [00:35:00]: store. Yeah, exactly.Swyx [00:35:01]: Especially with like egress and stuff. So.Ethan [00:35:04]: That's a good idea, but it also comes to-- there are some of its own challenges.Swyx [00:35:09]: Of course, of course.Ethan [00:35:10]: like people who build the GPU data centers, they might not expect this much, storage. And yeah, people build storage, typically they just build it somewhere with just CPUs.Swyx [00:35:23]: I just looked it up. Five-- AWS only charges for egress, not ingress. Tier five for five petabytes is two hundred and thirty K.Ethan [00:35:32]: Even more expensive than the storage.Swyx [00:35:34]: But storing is per month, right? You check in, then you cannot check out. so it's so cool. It's okay. So there's that side.Ethan [00:35:41]: So the TLDR, my backhand mathSwyx [00:35:42]: Data is larger than you think. Yes.Ethan [00:35:44]: my backhand math of GPU hours times GPU cost is also very much, I'm missing some storage.Swyx [00:35:49]: You're also-- you're basically like also more IO bound than normal training.Swyx [00:35:55]: Yes. ‘Cause like data loading, so caching everything, it becomes super important.Ethan [00:36:00]: So in Cosmos, we did a lot of optimizations to make it not IO bound. So, speaking of the training, actually training the model, the GPU cost, if you look up like the open source model, how big these video models are, I think like LTX has nineteen B parameters. That's a dense model. And people are also exploring, MoEs, so it might be twenty B active and, like a hun- hundreds B, total. So that's, that's even-- that's similar size as medium-sized LLM models. And if you, if you look at number of tokens-Uh, we disclose that in Cosmos. It's also like tens of trillions of tokens on the visual tokens. So putting this together, the cost of, training these video models, it's actually comparable with LLMs. Not to mention, the infra is slightly different from LLM, so it might be less efficient to train these models.Inference Speedups: Step Distillation, Consistency Models, and GANsSwyx [00:37:04]: Do you get the benefits of traditional diffusion speed-up? So for, images, there's LCM, LoRAs for, fine-tuning. There's, there's a lot of stuff that's beenEthan [00:37:15]: Flow matching.Swyx [00:37:16]: there's flow matching. There's a lot of stuff that's been done. there's some overlap that applies to diffusion on the inference side and stuff or?Ethan [00:37:23]: so the difference-- the inference side is a completely different story.Ethan [00:37:28]: I think for the training side, it might be a little bit hard to reduce that cost. And for the inference side, the biggest gain is from the distillation of these models. You can-- It's called step distillation, slightly different from knowledge distillation in LLMs. So you-- Typically, for flow matching models, you need like 100 steps or something. Like a distortion model even need even more, like 1,000 steps to generate a good image or video. A step distillation is try to learn to generate fewer step from the model itself. It's kind of like now we-- you use the full model to generate in 100 steps, and then you take a model that only generate 10 steps and let that model to learn from the perfect one.Ethan [00:38:25]: why this workSwyx [00:38:27]: Strong to weak seemingly.Ethan [00:38:28]: It is. It's kind ofSwyx [00:38:29]: DistillationEthan [00:38:29]: kind of like strong to weak. the-- from the modeling perspective, the strong model, the teacher model is trying to model the image and videos of inter-internet, and that distribution is extremely complex. But the step distilled model is just trying to learn from the teacher. The teacher is a model, and the size is fixed, as the distribution is much simpler than the whole internet. That's the intuition I have why step distillation can work. So usually these models serve in productions, they only run in a few steps. In Cosmos, I believe we have, we have like four step and eight steps. If you do some simpler task, image-image translation, it can even run in fewer step, like one step in Cosmos Transfer.Swyx [00:39:22]: I think this is the same intuition that guides a lot of the consistency model work. I sent you a link for, SCM. I don't know if you covered that. To me, that was actually one of, the most impressive papers I've ever seen from OpenAI.Swyx [00:39:34]: That this is the unifying grand concept of consistency models. I don't know if you have any comments on this.Ethan [00:39:41]: So there are, there are a few different approaches,Swyx [00:39:46]: Oh, yeah. Here it is.Swyx [00:39:47]: Two steps versus twenty or 100 steps, whatever. It's already done.Ethan [00:39:52]: So there are, there are a few different approaches, for example, consistency model, and there are also Actually, we shouldn't forget GAN. So GAN, actually, that was, that was the OG ofSwyx [00:40:05]: OGEthan [00:40:05]: step distillation ‘cause it trained just one step to begin with. So actually, a lot of, uh-- For example, there's a distribution matching distillation which use, which uses GAN, as one of the laws for distillation. It-- GAN just tells you, “Hey, generate an image,” and thenEthan [00:40:31]: it has a discriminator to tell, is this image real or not? So the model, the model just need to learn one of the distribution, not the full distribution. Because in training, the model is asked to reconstruct the ground truth image from the internet, which is extremely hard. And in-- When you're training GAN, it's a step process. It's just a, “Hey, you generate image. Does this image look as real as the image from the internet?” Which is a much simpler task. And, yeah, combining a lot of these approaches together, people typically do that, like consistency model and distribution matching and GAN, and we can get these few step models.Audio-Video Generation and Time AlignmentSwyx [00:41:21]: Then there's one step I wanted to add, which is audio and video.Ethan [00:41:26]: So, Grok Imagine zero point nine, I believe it's, it's a first audio video transmodel deployed at a large scale. SoSwyx [00:41:39]: And that was your first model?Ethan [00:41:40]: that was, Grok Imagine's first model. It's, it's audio video, joint generation. I think the hard part is, the modality alignment, ‘cause before this transmodel, we have, we have text to video alignment. We have this, correspondence between text and video. Typically, most of the VLMs, they understand images and videos. Video's very rare, and they don't understand audio mostly. And if you look at the audio generation on the LLM side, you can talk to them perfectly fine, but if you ask them to sing a song or something, it typically is not very good. Also, they don't have, they don't have music either. The hard part is thatUh, actually audio has two component. It has like a discrete component, a continuous component. The discrete component is like the language.Ethan [00:42:44]: So when we speak, it's just, someSwyx [00:42:47]: It's an ASR issue, yeah.Ethan [00:42:49]: It's, it's text token with some characteristics, I would say.Ethan [00:42:54]: But musicSwyx [00:42:56]: I think the speech guys would disagree with this.Swyx [00:42:57]: Like disfluencies and then,Vibhu [00:43:00]: There's tones you can get angry.Ethan [00:43:01]: Well, I say largely.Ethan [00:43:03]: the mu- but the music is completely different. It's, it's very continuous, and you cannot model them like discrete tokens in language models. this is like the hard part for models is, not to mention we have to align text, video, and audio together.Ethan [00:43:26]: SoVibhu [00:43:26]: How?Ethan [00:43:28]: So significant-- some significant challenges are like-- So first, like we talk about as the VLMs, they cannot understand most of them cannot understand audio.Ethan [00:43:39]: So you have to have some way to do the synthetic data generation for audio. You have to caption the model, and that involve, that involve synthetic data and human data effort a lot. And not just surprisingly, most of the LLMs are very bad at recognizing, like the beat, tone, and the details of the of music. They can, they can give some general prediction of which song is this, but it's very hard to describe the details of the music. like we mentioned in image generation, like you have to describe image as detailed as possible so that someone blind can reconstruct that. So here is like someoneVibhu [00:44:32]: DeafEthan [00:44:32]: someone deaf can reconstruct how the music sounds like without actually listening to it. Maybe you can think of it need to have the-- or they call the script.Vibhu [00:44:49]: Subtitles, yeah.Ethan [00:44:49]: You gotta have all the details of the music, and the dialogue.Vibhu [00:44:55]: So is the challenge there typically stuff like music and audio, or is it just Like is there a baseline? Okay, there's enough data where we can understand, narration, conversation, but there's nuances in audio that's where you hit all the data issues or is it just from stage zero, you just do it all right?Ethan [00:45:15]: So one important thing is like the alignment. So the model, the model has to know like the video and audio, the, uh-- it has to have a time-based alignment, like at which time step the video and the audio token correspond to each other. But we actually don't have this kind of alignment for most of the other modalities. If you think about like text and image, text and video, they are loosely aligned. So you can, you can have a description of what's going on in the video, but you don't have to exactly, You typically don't have exact description, oh, at, time step one second like what happened?Vibhu [00:46:02]: It's veryEthan [00:46:03]: At time step two second what happenedVibhu [00:46:03]: coarse. Yeah.Swyx [00:46:05]: So what was the ideal time step? You have to oblate it, and then it's like four seconds or something.Ethan [00:46:09]: So that comes down to how you design the model to, for the model to be aware of as a time, as a time modality. So the model is like a time aware. And that's something pretty unique if you think about LLMs. So if you ask LLM to complete a task, say they, uh-- you ask them and they will say, “Oh, this task will probably take twelve hours to complete,” and they come back in one hour. Say “I've already spent two days on this and I've exhausted everything.”Ethan [00:46:47]: So the LLMs them-themselves, they don't have a sense of time there.Vibhu [00:46:53]: I actually don't think that's just them not having a sense of time. I think it's somewhat based, right?Vibhu [00:46:58]: Like you tell someone, “Okay, go work on this feature. Go implement this,” there's a general understanding you would have of how long that would take without LLMs working at LLM speed, right? So you think back like two years ago, if I tell you to like build me like a new front end for latent space, have a search bar, have all this, you'll estimate that it'll take a few days, right?Vibhu [00:47:19]: So you tell an LLM, “Go build this.” It'll take me a few days. But I think it's somewhat grounded as opposed to them not having the best-- Not saying that they have a great understanding, but I think that example is like you can see where it comes from, right? You're trained on all over the text.Swyx [00:47:35]: They're, they're trying to estimate what a human would say.Vibhu [00:47:37]: because that's what the, that's what the data kind of represents. It's not themEthan [00:47:41]: It came from the corpus on the internet. People have a estimate of how much time.Vibhu [00:47:45]: And not even just in direct like training samples, right? Just your world understanding of tokens of how long stuff takes, right? Go read a book. It'll take you a while, right?Vibhu [00:47:56]: Even if you do nothing but read a book, it takes a few days. So yeah, LLM, I read it took me a few hours.Vibhu [00:48:01]: It'll take me a few hours to go through this research. But this is a tangent.Swyx [00:48:05]: Somewhat, yeah.Swyx [00:48:06]: This is a train of thought I haven't really expressed until now is, which is basically like a full world model must also be recursive, meaning that the participant in the world model must also be aware that they have a world model. which is like this whole recursive thing down the, down the line. but yes, and that the world model can be wrong and that they need to update it and blah. Yeah. We've, argued this on the, newsletter as well, that there needs to be sort of recursive or adversarial world models.World Models: Real-Time, Long-Horizon, Interactive VideoVibhu [00:48:34]: just, to ask, how do you define world model?Swyx [00:48:38]: Oh, yeah, let's go there.Ethan [00:48:40]: SoVibhu [00:48:40]: So just for context, we talked about, video generation, and then there's a-- if you say there's a distinction between world models, what's your, what's your definition? How do you see the two?Ethan [00:48:53]: So disclaimer, I'm not going to debate, what is world model. Yeah. there are many definitions, so I'll just talk about my definition. Since I came from the multi-model, multi-model domain, so mainly talking from video. So world model is like real-time interactive long horizon videos. So there are three parts. so we-- let's talk about them one by one. So the so interaction, so we just, we just look at Facebook and neural computer. So the interaction part of it, so you, world model can allow you to interact with them through keyboard, mouse, and maybe also voice. So these all is-- all is a modality. You can, you can interact with the model, and the model should respond reasonably. Second part is real time. So once you, once, say, you move your mouse, if, say, the world model generate a game, how fast can the game respond? So if you're like professional CS: GO players- -my say, oh, you have to respond- He's beginner within sub ten milliseconds or- Yeah even less. So that's not most of the- No, sixty FPS. Let's go. Oh, three hundred FPS. Oh, five hundred FPS. Wait. okay, yeah. I didn't do the math, but yeah, okay. Uh- Yeah, three hundred FPS, that's a three millisecond. So you have to respond- Oh, s**t. Okay. YeahEthan [00:50:29]: within a millisecond. Most of the video models cannot do that. Yeah. And, but if you, say, if you have a video model that is, say, like a digital human, the response time might be more generous. Maybe typically, for real-time voice interaction, it's like two hundred millisecond. So that's, that's much more generous. But even two hundred millisecond is pretty, it is pretty tricky, ‘cause remember we mentionedEthan [00:51:01]: you have this, temporal compression coming from the VAE. So if you, if you don't compress the temporal dimension, your sequence length is going to explode. So if you want to have this real-time, real-timeness in your model, you have to do is one context problem. And the third part is long horizon, ‘cause we-- if you're not going to just play with, video games just, a few seconds, most video models only a few seconds. We're going to play with minutes, hours. The model have to be able to generate long-form content.Ethan [00:51:42]: So putting these three together, it's, real-time, long horizon interactive videos. I think the final state will be, for example, like a video, a video version of Playbook, where you can, you can interact with, a neural computer. You move your mouse, and you click on the generative interface, and it will reply to you through pixels- generating in real time. But getting there, it's, it's a very long way to get there. So one of the first step, at Grok Imagine, where I led a small world model team there, was to build video extension. So, video extension- it's the first step of interactivity. Yeah. It's, it's the first step. Yeah. So it's the first step- You have it here, video editing, yeah. Yeah. Yeah. So the first step is because, this unlocks long horizon videos. Typically, for most of the video generation models, you give it a prompt or an image as an initial frame. You generate video, that's it. That's just, one time, done. And some creators would try to, use the last frame as a first frame for the second video. It can-- sometimes it works, but if you do it a few times, it says the quality would decrease. And- It doesn't have that context- Yeah over the full video, so the temporal- Yeah, exactly. Yeah, ‘cause you only gave it the last frame, of course, right? Yeah. Exactly. And- it's actually a pretty fun hack. if you've seen like- Oh, no, he's saying something better. Yeah. And for example, like Vue, I remember Vue 3 has like a second context of the last video. It is slightly better than using the last frame, but it has the same problem-- similar problem that it, the quality would decrease. if you extend a few times to, one minute, the video quality would look much worse than the first video. Second, another problem is that the model doesn't have long-range knowledge of, what's happening before. Say, if they generate some dialogue, some, two people speaking, and their voice might change, over some time, especially if the second conditioning, it does not cover the previous context. So these are the core challenges. So the Grok Imagine video extension, it has historical context of all of the previous generated videos. It can, It has, it has the context of, who is speaking and what objects have appeared and everything, having that to generate the next video. So if we naively do this, you can imagine, just, put all of the previous history video tokens into the context. The context lens will easily explode. Especially for video models, that can be like a few, a few million context, I would imagine- context lens. Yes.Yeah.Swyx [00:54:58]: Let's run with that.Ethan [00:54:59]: for example, like in Cosmos, I think just five seconds of video is like a fifty K or sixty K number of tokens. So like if you do, if you do fifty second, that's a five hundred K tokens. If you do longer than that, easily explode. This long horizon, problem was the first step we're trying to solve world model. It turns out people, yeah, people love video extension. Like a lot, a lot of the creators love using video extension to create longer form videos. This is the part I liked that you have a, you have an intermediate step toward the final goal instead of just a straight shot to the final version very much.Swyx [00:55:48]: But I can see you have a strong vision of where we want to end up.Long Context, Redundancy, and Efficient Interactive VideoVibhu [00:55:51]: Does it seem like it's an efficiency issue? okay, we're at a few million tokens context,. If you draw the parallel to language models, we had very short context, two thousand, eight thousand, then, you scale it up one million, ten million. sure, there's effective context, but at the end of the day, it's just what's it worth? sure, there's a whole training data side. In video, it might be slightly easier ‘cause we have a hundred million token video, right? Just take a movie with the full context there. Like is this efficiency from an inference standpoint that like it's expensive, but we know how to solve it? Or like why is this not the approach? So like my broader point was on your second point of world models, you say it needs to be interactive and live, right? You should be able to play a game and see the interaction live. So one thing I see with research is a lot of what you actually serve is different than what you build, right? So we talked about distillation. You train big model, you distill it, you do quantization, speculative decoding. We do all this stuff to serve it efficiently. Should we not just have a solution, like a world model that can interact well, do inference optimization, serve it, distill it secondary, so make it real time after you solve it? So like a-- another parallel is say, continual learning, right? What we need is someone to solve it and show it works inefficiently. Give it a few years, people will make it efficient. Same thing with regular attention, right? It worked. Over a few years, people have different forms of attention, and we've scaled it to be efficient at log context,? So kind of two things there, right? One is it seems like it works. You've scaled it. Can we not just scale it a lot more efficiently over time? Do we need a separate approach if this works? And same thing with interaction, right? if we can get it done, like if we can solve some way that it works, we can solve making it more efficient from an inference standpoint later.Ethan [00:57:53]: that's actually a very good point. So in videos, there's actually a lot of redundancies. So we solve a lot of the pixel redundancy from VE, but there's more redundancy in long range and long horizon videos. Say, if a character appear in the first clip and then it disappeared, it only reappear at the end of the video, you probably don't need the-- the context, like in the middle of the generation. So you only need that character, where you need. So that's why, I helped build another feature. It's a reference video.Vibhu [00:58:36]: Is it here?Swyx [00:58:36]: is it the same model release or different one?Ethan [00:58:39]: It's a different one.Ethan [00:58:41]: You probably need to search onSwyx [00:58:43]: I'll find itEthan [00:58:43]: X reference to video.Ethan [00:58:46]: So reference video allow you to like upload up to seven images as condition and generate the video. Say, if like I want-- it can, it can be characters or objects or even scenes. Say like I want, I want condition on, Sean's selfie and holding a bladeSwyx [00:59:07]: We have a dogEthan [00:59:08]: or whatever.Swyx [00:59:08]: We put the dog in the thing.Ethan [00:59:09]: you can put them there and the video models will generate the video from and copies the context over. So that can solve a lot of the problems there, like the long context problem. It doesn't need to have a very long context, but it's-- I feel like it's an intermediate solution. The modelSwyx [00:59:29]: It's cheating.Ethan [00:59:30]: the model should be able to like selectively know, where should I draw the references. So say if I want to generate a movie, I generate it autoregressive, like a ten second at a time or something. And now this character appear, I can look back to where it first appear and, bring that back. Yeah, this one, I put the references. Yeah, that's, Optimus, Einstein myself, Annie.Vibhu [01:00:02]: Oddly enough, I used Grok Search to find it, and it pulled your LinkedIn post. But yeah we found it.Ethan [01:00:08]: Interesting.Vibhu [01:00:10]: ButxAI's Underrated Work, Culture, and WatermarkingSwyx [01:00:11]: this is a problem. This is not your fault, but like XAI doesn't communicate all this work that you do very well because they just have the model release and then that's it. But actually, these details are very good.Swyx [01:00:22]: As far as I understand, everything you just described is state-art, like no one else has done it.Vibhu [01:00:30]: A lot of-- yeah, I have a lot moreSwyx [01:00:32]: And then, and then you just put this blog post with the cookies. I'm this is not enough,?Swyx [01:00:37]: but I, obviously this is like the high level numbers that people want to know. But no, okay, soVibhu [01:00:42]: And I wonder, like part of that is also some labs don't share research into what happens. And ifSwyx [01:00:50]: No, but this is literally bragging about how good they are, right?Swyx [01:00:54]: Like, why would you not say that you are capable of extending with full context? this is not a secret sauce. This is like we did the work. yeah, I don't know.Ethan [01:01:02]: different labs have slightly different communication styles.Swyx [01:01:07]: Anyway, if anyone from XAI is listening we are always happy to help you tell your story. Yeah, okay, so you did references, and I think, I think kind of the point you're, you're making is it is sort of like a kludge, right? this is-- you can do seven, but what about 100?Swyx [01:01:23]: Right? Then you need a completely different thing.Ethan [01:01:26]: So I think it's-- this is, a mechanism to, select the context from the history, and you might not put the entire history into the context. for example, there's a paper called Frame Pack, which haveEthan [01:01:41]: a heuristic that the latest history, the last one second, I put the entire history, and the history before that, I would, compress it and makes the video smaller. So they follow this pattern, this build overall pattern that the maximum sequence length is fixed. So the further you are from the current frame, you have a smaller image. So this is just a heuristic. I think it can be more automatic. The model is aware like which history part of it can be select. So this part of the research is actually being actively, worked on by a lot of people. It's also quite interesting. I feel this is actually, this part of long context is a little bit ahead of the LLM part.Ethan [01:02:31]: So for example, like in LLMs, if you-- so contexts keep growing. Let's say if you call tool and the tool call history is extremely long, that's still in context, and keep growing, keep growing. Even if you switch the topic to something else, the whole context was there. There are some agentic harnesses that help you to, say, prune the tool results and, prune Like when you, when you query a file, only show like the top 200 lines or something. Those were very heuristic-driven.Swyx [01:03:08]: For listeners, we did a write-up on the cloud code, leak where there are eight different kinds of pruning, including like you prune the tool results and all that. So you can, you can read up on that kind of thing.Ethan [01:03:17]: I think, one breakthrough in continual learning might be like a way to automatically, manage its own context.Swyx [01:03:27]: These are all heuristics, and they will be replaced by machine learning.Ethan [01:03:30]: InterestinglyVibhu [01:03:32]: TheEthan [01:03:32]: the same thing is being researched in both LLMs and video models.Vibhu [01:03:36]: The interesting thing is also like in the paper you showed, it's actually happening at the model level, right? Compared to like language models, sure, we have base attention, but we'll do our own compression, we'll do our own pruning, which is separate from model error.Vibhu [01:03:49]: Eventually, it all just boils in, hopefully.Swyx [01:03:52]: I think this is a form of like attention, but like also know sort of reasoning attention. I feel like that's different than normal attention.Swyx [01:04:03]: Does that, does that make sense?Ethan [01:04:04]: It's, it's different in the sense that attention, not to mention, set sparse attention aside,

Siga o Missão Saber no Spotify No novo episódio do Missão Saber, Facundo Guerra e Murilo Garavello debatem livros que ajudam a compreender a história e as visões de mundo chinesas. Livros citados: -China em Dez Palavras — Yu Hua (2010) -Viver — Yu Hua (1993) -As Rãs — Mo Yan (2009) -1000 Anos de Alegrias e Tristezas — Ai Weiwei (2021) -A História da China — Michael Wood (2020) -A China Venceu? O Desafio Chinês à Supremacia Americana - Kishore Mahbubani (2020) -A Nova China: Para Além do Capitalismo e do Socialismo — Keyu Jin (2023) -Vessel — Cao Chongda (2024) -Apple in China: The Capture of the World's Greatest Company — Patrick McGee (2025) -Breakneck: China's Quest to Engineer the Future — Dan Wang (2023)

Siga o Missão Saber no Spotify  No novo episódio do Missão Saber, Facundo Guerra e Murilo Garavello debatem livros que ajudam a compreender a história e as visões de mundo chinesas. Livros citados: -China em Dez Palavras — Yu Hua (2010) -Viver — Yu Hua (1993) -As Rãs — Mo Yan (2009) -1000 Anos de Alegrias e Tristezas — Ai Weiwei (2021) -A História da China — Michael Wood (2020) -A China Venceu? O Desafio Chinês à Supremacia Americana - Kishore Mahbubani (2020) -A Nova China: Para Além do Capitalismo e do Socialismo — Keyu Jin (2023) -Vessel — Cao Chongda (2024) -Apple in China: The Capture of the World's Greatest Company — Patrick McGee (2025) -Breakneck: China's Quest to Engineer the Future — Dan Wang (2023)

"We don't fear spoilers because a good room has more to offer than just the set that you saw in a photo. It must be something that you feel also." When most people think of an escape room, they think of puzzles, padlocks, with maybe a light narrative slapped on top. When Rémy Strobbe and the team at Live Cinema created Zone 51, however, they thought: okay, but what if instead of solving puzzles, you were inside a movie? Rémy is the co-founder and creative director of Live Cinema, based in Paris, France. He joins us on REPOD to talk about Zone 51, a jaw-dropping 120-minute experience set across 550 square meters, 25 rooms, 3 live performers , and a cinematic globe-spanning story. David calls it "one of  the most remarkable experiences that I have seen in the immersive world." Zone 51 begins the moment you walk through the door. Indeed, for those unaware, they might be convinced that they're here to watch an actual movie. As you can see, the set is absolutely gorgeous. Rémy talks candidly about the difficult journey to creating this game. Amongst their troubles, they had a construction partner go bankrupt mid-build and take €80,000 with them, and a pandemic that halted production for over a year. He also shares the secrets behind the tight pipelining schedule that makes this project financially feasible. I especially loved hearing Rémy talk about their philosophy on marketing and allowing players to record their games. Live Cinema leans into sharing striking visuals. As Rémy put it, a good experience has more to offer than just what you see in a photo. And those photos are spectacular. Zone 51 is currently bookable in French, with limited English sessions available by email. English availability is expected to expand by fall 2026 — which happens to align perfectly with the Escape Immerse Explore Paris Tour. Zone 51 (review coming soon) will be on the Tour, along with a few other games from the same creators at Lock Academy. If you enjoyed this episode, consider joining us on the Tour, if it hasn't already sold out!     Episode Sponsors We are immensely grateful to our sponsors this season: REA Patreon Backers, PG's Playhouse, Buzzshot, and the Reality Escape Convention. We truly appreciate your support of our mission to promote and improve the immersive gaming community.   Buzzshot Buzzshot is Escape Room Software, Powering Business Growth, Player Marketing, and improving the Customer Experience. They offer an assortment of pre and post game features including robust waiver management, branded team photos, and streamlined review management for Yelp, TripAdvisor, Google Reviews, and Morty. Buzzshot now has integration with the other REPOD sponsors: Morty and COGS. Special Offer for REPOD Listeners: REPOD listeners get an extended 21-day free trial plus 20% off your first 3 months, with no set-up fees or hidden charges. Visit buzzshot.com/repod to learn more about this exclusive offer.   Support Us On Patreon Today Love escape rooms as much as we do? At Room Escape Artist, we've been analyzing, reviewing, and exploring the world of immersive games since 2014. We help players find the best experiences, and push the industry forward with well-researched, rational, and reasonably humorous escape room and immersive gaming content and events. By becoming a Patreon supporter, you're not just backing a blog — you're fueling a mission to make the escape room and immersive gaming community stronger, more thoughtful, and more connected. Access exclusive Patreon content such as: The Bonus Aftershow The Spoilers Club Early access to escape room Tour tickets and REA articles. Your Patreon support goes toward our mission: paying our contributors, funding our infrastructure, and supporting deep research and industry advocacy.   PG's Playhouse If you love wordplay, puzzles, and trivia, this is the podcast for you! PG's Playhouse recreates a fun game night, all in a short, 30-minute format. Of course, what's game night without making new friends? We bring on different guests for the different episodes. Each episode features a puzzle packed with wordplay and trivia, a short chat with the guest, and a segment exploring an interesting topic. I hope you'll take a listen and play along with us at PG's Playhouse.   Reality Escape Convention Our convention, RECON, will be in Laval, Quebec Canada on August 16th & 17th, 2026. RECON offers a curated collection of talks and experiences exploring the business and art of escape room and immersive game creation. All are welcome at this event that is crafted around professionals and aspiring professionals.   Production Credits Hosted by David Spira & Peih-Gee Law Produced by Theresa Piazza Supported by Lisa Spira Edited by Steve Ewing Music by Ryan Elder Logo by Janine Pracht

THIS EPISODE IS BROUGHT TO YOU BY: GPRSGPRS helps keep your jobsite safer by locating what is hidden before you cut, core, trench, or drill.Click the GPRS image on the Concrete Logic Podcast website or go here:https://www.concretelogicpodcast.com/gprsON THIS EPISODE OF THE CONCRETE LOGIC PODCASTSpring is when concrete starts telling the truth.After months of cold weather, snow, ice, rain, deicers, and freeze-thaw abuse, your existing concrete may start showing what it went through all winter.In this episode, Dr. Jon Belkowitz joins the show to talk about what to look for when the weather warms up. Scaling. Flaking. Blotchy spots. Exposed aggregate. White staining. ASR gel. Rust bleeding from cracks. All of it is concrete trying to tell you something.Some of it may be surface damage.Some of it may be a sign of a much bigger problem inside the concrete.And if you wait until the damage is obvious, you may already be late.WHAT YOU'LL LEARNWhy spring and early summer act like a “lie detector” for existing concreteWhy damaged concrete often looks darker or blotchy after rainWhat scaling, flaking, and surface loss can tell you about winter damageWhy broom finish disappearance may be a warning signHow ASR cracks hold water and reveal themselves after rainWhat white staining and gel coming out of cracks may meanWhy some concrete problems cannot simply be cleaned off or sealed overHow wetting, drying, deicing salts, and outside contaminants can keep feeding deteriorationWhy extending the life of existing concrete may be one of the most practical “green” moves in constructionCHAPTERS00:00 Introduction 02:52 What warm weather reveals about existing concrete 03:39 Concrete as a springtime lie detector 04:26 Why newer concrete may look white, blotchy, or damaged 05:22 Scaling, flaking, and lost broom finish 06:01 Why rain makes concrete damage easier to see 07:10 Why damaged concrete holds water 08:25 Rust, staining, and visible cracks 09:02 Spillways, white streaks, and concrete exudation 10:36 Alkali-carbonate reaction and internal concrete problems 11:27 What “oozing” gel from cracks means 12:43 Why cleaning the surface does not fix internal damage 13:00 Slowing deterioration versus fixing it 14:21 The practical side of reducing concrete's carbon footprint 15:11 How ASR cracks grow and spread 16:15 ASR research and gel morphology 17:17 Protecting concrete from outside contaminants 18:21 Concrete Logic Academy and PDH reminder 19:39 Closing thoughtsGUEST INFODr. Jon Belkowitz Intelligent Concrete https://www.concretelogicpodcast.com/guests/dr-jon-belkowitz/CONCRETE LOGIC ACADEMYMost concrete problems do not show up out of nowhere.They start with bad assumptions, missed warning signs, and people not knowing what they are looking at until the problem is already expensive.Concrete Logic Academy was built for the people who want to catch those problems earlier.Practical concrete training. PDH courses. Real-world education from people who actually understand the work.If you want to get better at reading concrete, asking better questions, and spotting issues before they turn into claims, start here:https://www.concretelogicpodcast.com/concreteschoolSUPPORT THE PODCASTIf the Concrete Logic Podcast has helped you think differently about concrete, consider supporting the show.You can make a one-time donation, become a monthly supporter, or share the podcast with someone in the industry who needs to hear it.https://www.concretelogicpodcast.com/support/You can also support the show through the KUIU affiliate link:https://www.concretelogicpodcast.com/kuiuCREDITSProducers: Jodi Tandett & Concrete Logic Media Music by Mike Dunton: https://www.mdunton.com/WHERE TO FIND SETHConcrete Logic Podcast: https://www.concretelogicpodcast.com/ YouTube: https://www.youtube.com/@concretelogicpodcast Concrete Logic Academy: https://www.concretelogicacademy.com/ Until next time, let's keep it concrete.

he world's largest producer of cane sugar is planning its largest-ever capital expenditure to overhaul a historic — and timeworn — refinery near New Orleans.American Sugar Refining this week broke ground on the first phase of what the company said would be a $785 million project to modernize the   — the largest sugar processing plant in the Western Hemisphere.Initial construction, which is already underway, will build a new $200 million process building by 2028; state and company officials said that modernizing the overall campus' refining capabilities would allow it to meet future demand, improve reliability, and reduce both water and energy use. Additional phases, officials said, would build on those process improvements, but the announcement did not outline the next steps of the overall project — or a timetable for starting them.ASR said that it anticipates adding 15 jobs to its current workforce of 500. NOLA.com reports that the state's incentive package for the project included a 80% property tax abatement over 10 years.The Chalmette Refinery began operations at the campus along the Mississippi River in 1909, and it wears the scars of a 117-year history that has included hurricanes, fires and dramatic changes in the sugar market. Its 32 production lines can reportedly produce some 6 million pounds of sugar per day across dozens of product platforms. #manufacturing, #foodsupply, #sugarindustry, #refinery, #infrastructure, #capitalinvestment, #industrialupgrade, #madeinusa, #supplychain, #foodmanufacturing, #energyefficiency, #sustainability, #industrialnews, #businessnews, #louisiana, #economicdevelopment, #manufacturingjobs, #plantmodernization, #operations, #industryupdates

Today, I welcome back pediatrician, Dr. Mary H Jones. Mary has been a guest on the show and contributed to Uniquely Us, chapter 11, discussing additional biases and stigma BIPOC individuals can face in getting an accurate diagnosis and accommodations. Dr. Jones shares her experience as a pediatrician and mom of neurodivergent kids and in a neurodiverse marriage.Today, Dr. Stephanie and Dr. Jones discuss early intervention and how diagnosis impacts accommodations and interventions. Dr. Mary Jones is licensed in over 20 states and specializes in ADHD and ASD diagnosis in children, teens, and adults.Dr. Stephanie asks Dr. Jones when she uses the three most well-known measures: the ADOS, CARS, and MIGDAS-2Today, with a focus on diagnosis, Patreon with Dan and Stephanie will discuss a different topic About Dr. Mary H. Jones & Bright StartsAt Bright Starts, we're dedicated to helping children, teens, and young adults reach their full potential through early, accurate ADHD evaluations and autism diagnostic services. We understand that every individual is unique — and so is their journey. That's why we provide personalized evaluations, compassionate guidance, and clear next steps for families navigating developmental concerns.https://brightstartshealth.com/home Dr. Mary "What is Autism?"https://brightstartshealth.com/about-us Mary H. Jones, MD, FAAPAssesses with ADOS-2, CARS and MIGDAS-2​Dr. Mary Jones believes there is greatness in each of us and is passionate about equipping and empowering women to reach their fullest potential. Throughout her life and career, she has had the privilege of working to empower and equip women and girls to be their best selves. Dr. Mary has also completed a study in autism as a speciality through PESI, and has become an Autism Spectrum Disorder Clinical Specialist. Her vision is to see women living out their purpose and affecting change in their worlds. She is an NT married to a neurodivergent spouse and the mother of a son on the autism spectrum.   

This episode is brought to you by the MLflow team. Check out more information at MLflow.org.What does it actually take to build voice AI at a billion-interaction scale? This episode features an ex-Amazon voice AI engineer who built customer support systems handling 2 billion+ interactions — now working on next-gen voice agent platforms. Anurag digs deep into the real engineering tradeoffs, design patterns, and use cases that separate production-grade voice agents from demos.Voice Agent Use Cases // MLOps Podcast #372 with Anurag Beniwal, Member of the Technical Staff at ElevenLabs

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 In this episode of Just the Guys, the conversation explores a simple but powerful relational reality: misalignment is inevitable — mismanagement is optional. Whether in marriage, friendships, or team dynamics, people regularly share the same overall goal while differing on expectations, timing, methods, or emotional responses. The group discusses how alignment is less about agreeing on every detail and more about moving in the same general direction while learning to define, notice, and communicate through the inevitable “bumps along the road.” As they note, “You can't manage something you can't define,” and many conflicts arise not from disagreement itself but from misalignment going unnoticed, unspoken, unmanaged, or unresolved. The episode uses humor and memorable metaphors to drive the point home — especially the idea that avoiding tension only makes it heavier to carry later. “Give me the extra stinky bag… it still has to go out.” Addressing relational drift early, when emotional weather is calm, prevents compounded frustration and protects the connection. Ultimately, the discussion reframes relational maturity as the ability to remain connected even when not fully aligned, recognizing that shared purpose can coexist with different approaches. Managing misalignment well may matter more than achieving perfect alignment at all.

We are so happy to have Tommy & Ginny with us on the show today, sharing their story. Tommy & Ginny share their neurodiverse journey from the diagnosis and freedom from shame in Tommy to Ginny realizing she needed self-care support and a community! They filled in their story with some blogs for us back in 2025.Check out their blogs:https://www.christianneurodiversemarriage.com/post/beauty-from-the-rapids-navigating-our-blended-neurodiverse-marriagehttps://www.christianneurodiversemarriage.com/post/beauty-from-the-rapids

Today, Dr. Stephanie & Barb discuss the four principles of expectations (outlined in Emotionally Healthy Relationships by the Scazzeros) and apply them to Neurodiverse marriages.Are your expectations:UnconsciousUnrealisticUnspokenUnagreed Upon

President Trump zou de bevolking van Iran uitwissen. Als de Straat van Hormuz niet werd geopend zou 'een beschaving sterven en nooit meer terugkeren'. Dat gebeurde niet. Er kwamen geen bombardementen, maar een ouderwetse TACO (Trump Always Chickens Out). Er ligt een tijdelijke wapenstilstand tussen de VS en Iran. Wat zorgde voor hysterisch hamstergedrag onder beleggers. Aandelen werden massaal ingeslagen. Daar hebben we het deze aflevering over en dan vooral over de duurzaamheid van dit akkoord. Ook hoor je over hét slachtoffer Shell en die andere olie- en gasbedrijven. Over Shell gesproken: dat kwam met een trading update en die zag er goed uit. Shell heeft niet alleen veel meer olie verkocht, de marges zijn ook nog eens gestegen. Ze verwachten een 'significant' goed eerste kwartaal. Ook opvallend: de divisie met hernieuwbare energie doet het heel erg goed. Of andere beursbedrijven het de komende weken goed doen, gaan we zien. Het cijferseizoen gaat weer van start! We blikken vooruit en brengen in kaart waar (en op wie) je moet letten. Verder deze aflevering ook aandacht voor Elon Musk (die Sam Altman weg wil hebben bij OpenAI), aandacht voor Jos Baeten (die 17 jaar ceo was van ASR) en we bespreken een defensiebedrijf dat onder druk van de eigen overheid afscheid moeten nemen van de ceo. Te gast: Jean-Paul van Oudheusden van Markets Are Everywhere BNR Beurs is een journalistiek onafhankelijke productie, mede mogelijk gemaakt door Saxo. Over de makers: Jelle Maasbach is presentator van BNR Beurs en freelance financieel journalist. Zijn favoriete aandeel om over te praten is Disney, maar daar lijkt hij de enige in te zijn. Sinds de eerste uitzending van BNR Beurs is 'ie er bij. Maxim van Mil is presentator van BNR Beurs en journalist bij BNR, waar hij zich focust op de financiële markten en ontwikkelingen in de tech-wereld. Je krijgt hem het meest enthousiast als hij kan praten over ASML, of oer-Hollandse bedrijven zoals Ahold of ABN Amro. Jorik Simonides is presentator van BNR Beurs, economieredacteur en verslaggever bij BNR. Hij wordt er vooral blij van als het een keer níet over AI gaat. Milou Brand is presentator van BNR Beurs, freelance podcastmaker en columnist bij het Financieele Dagblad. Jochem Visser is presentator van BNR Beurs, maakt Beursnerd XL en is redacteur bij de podcast Onder Curatoren. Vraag hem naar obscure zaken op financiële markten en hij vertelt je waarom het eigenlijk nóg leuker is dan je al dacht. Over de podcast: Met BNR Beurs ga je altijd voorbereid de nieuwe beursdag in. We praten je in een kleine 25 minuten bij over alle laatste ontwikkelingen op de handelsvloer. We blijven niet alleen bij de AEX of Wall Street, maar vertellen je ook waar nog meer kansen liggen. En we houden het niet bij de cijfers, maar zoeken ook iedere dag voor je naar duiding van scherpe gasten en experts. Of je nu een ervaren belegger bent of net begint met je eerste stappen op de beurs, de podcast biedt waardevolle inzichten voor je beleggingsstrategie. Door de focus op zowel de korte termijn als de lange termijn, helpt BNR Beurs luisteraars om de ruis van de markt te scheiden van de essentie. Van Musk tot Microsoft en van Ahold tot ASML. Wij vertellen je wat beleggers bezighoudt, wie de markten in beweging zet en wat dat betekent voor jouw beleggingsportefeuille.See omnystudio.com/listener for privacy information.

Sinds 2009 staat Jos Baeten aan het roer van ASR, een van de grootste verzekeringsmaatschappijen van Nederland. Baeten is onderdeel geweest van een aantal grote dossiers in het Nederlandse verzekeringslandschap, zoals de overname van Aegon en de afwikkeling van het woekerpolisdossier. Maar wat zal er bij Baeten zelf meteen bovenschieten, als hij terugblikt op de afgelopen twintig jaar? Jos Baeten, ceo en voorzitter van de Raad van Bestuur bij ASR is te gast in BNR Zakendoen. Macro met Mujagić Elke dag een intrigerende gedachtewisseling over de stand van de macro-economie. Op maandag en vrijdag gaat presentator Thomas van Zijl in gesprek met econoom Arnoud Boot, de rest van de week praat Van Zijl met econoom Edin Mujagić. Ook altijd terug te vinden als je een aflevering gemist hebt. Blik op de wereld Wat speelt zich vandaag af op het wereldtoneel? Het laatste nieuws uit bijvoorbeeld Oekraïne, het Midden-Oosten, de Verenigde Staten of Brussel hoor je iedere werkdag om 12.10 van onze vaste experts en eigen redacteuren en verslaggevers. Ook los te vinden als podcast. Lobbypanel Werkende vrouwen komen in de problemen bij aanvullend ouderschapsverlof. En: de Tweede Kamer debatteerde gisteren over de steun voor de vergroening van Tata Steel. Dat en meer bespreken we om 11.30 in het lobbypanel met: Nora van Elferen, partner bij EPPA en Tristan Bons, adjunct-directeur van Vastgoed Nederland. Luister l Lobbypanel Zakenlunch Elke dag, tijdens de lunch, geniet je mee van het laatste zakelijke nieuws, actuele informatie over de financiële markten en ander economische actualiteiten. Op een ontspannen manier word je als luisteraar bijgepraat over alles wat er speelt in de wereld van het bedrijfsleven en de beurs. En altijd terug te vinden als podcast, mocht je de lunch gemist hebben. Contact & Abonneren BNR Zakendoen zendt elke werkdag live uit van 11:00 tot 13:30 uur. Je kunt de redactie bereiken via e-mail. Abonneren op de podcast van BNR Zakendoen kan via bnr.nl/zakendoen, of via Apple Podcast en Spotify. See omnystudio.com/listener for privacy information.

In a discussion over a fast earlier this year, we were facing big decisions and in prayer and fasting together on misalignment and differing views. Dan came up with the idea to do a show on this- when you are in misalignment or competing needs- what do you do? Do you resort to the rules or roles - well, the man wins? Or do you ask God for more guidance? Relationships and slowing down are rarely efficient but always important.

We praise Allah for allowing us to experience and complete another Ramadan. And now that we've emerged from it, there's a question worth sitting with: what comes next?Imam Ibn Rajab al-Hanbali mentions that the pious predecessors would spend six months after Ramadan asking Allah to accept their deeds — and the remaining months begging Him to let them witness another one. That's the rhythm. Gratitude, then longing. Never stagnation.But the Qur'an gives us something even more precise than that rhythm. It gives us a transition.In Surah al-Baqarah, the discussion of Ramadan begins at ayah 183 — *kutiba alaykum al-siyam* — and runs through to ayah 187. Then, immediately, in ayah 189, Allah says:**يَسْأَلُونَكَ عَنِ الْأَهِلَّةِ***They ask you about the crescent moons.*The companions asked Rasulullah ﷺ about the significance of the moon's phases — crescent to full, waning and returning. Allah answered that the moon exists so that humanity can track time. So we know when a month begins and when it ends. (I understand this topic is sensitive in Perth. We'll leave that there.)But then, immediately, Allah connects this to Hajj. “Qul hiya mawaqitu li al-nas wa al-hajj.” The crescents are time-markers for people — and for Hajj.The transition is beautiful. One act of worship ends. The next one begins. No gap. No off-season. The life of a believer is simply moving from one ibadah to the next. The same Lord we worshipped in Ramadan is the same Lord who governs every moment outside of it. Ramadan ending doesn't mean the haram becomes negotiable again, or the wajib becomes optional. We have a new aim now.Grounded is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.-----Now, not everyone can perform Hajj. It's a mathematical impossibility. Two billion Muslims, roughly two million pilgrimage spots per year — the number has been reduced since COVID. Do the maths. It would take something like 700 years before every Muslim alive today gets a turn. That's why Hajj is the only pillar where Allah specifies man istata'a ilayhi sabila — for those who are able. Ability is a condition.But the mindset still applies. The transition from one ibadah to the next is for everyone.-----There are so many dimensions to Hajj worth unpacking. But I want to focus on one moment — a snapshot — from the stoning at the Jamarat.The backstory is Sayyidina Ibrahim عليه السلام. He was commanded by Allah, through a dream, to sacrifice his only son at that time, Isma'il. And when he told his son — and Allah recorded this exchange in the Qur'an — Isma'il responded with full submission: *ifʿal mā tu'mar* — do as you have been commanded. You will find me among the patient.But Isma'il set conditions. He said: don't do it in Makkah, because if I scream, my mother will hear and it will break her heart. And make sure the blade is sharp so it's quick.(Side note to the sons in the room: if your father knocks on your door and says he saw a dream about slaughtering you — dial 000. These days, the worst our fathers do is say, “Son, wake up for Fajr.” And even that's a struggle.)Father and son walked about five or six kilometres from Makkah to Mina. And at each of the three stations along the way, Iblis appeared. He whispered. He cast doubt. He said: *You've done enough. You built the Ka'bah. You migrated from Iraq to Jerusalem to Makkah. You've sacrificed so much already. Why this? Just say no.*At each station, Ibrahim took seven pebbles, threw them in the direction of Iblis — *Allahu Akbar* — and moved on.After the third station, Iblis left and never came back.Falamma aslama wa tallahu li al-jabin. When both of them submitted fully — the father resolute, the son's forehead on the stone — Allah called out. The test was fulfilled. A great sacrifice was sent in Isma'il's place.-----Thousands of years later, during the Hajj of the Prophet ﷺ — Hajjat al-Wada' — as he was riding his camel towards the Jamarat, he told Sayyidina Abdullah ibn Abbas: get me some pebbles.Ibn Abbas picked up pebbles about the size you could flick between your thumb and index finger. Our scholars later said: about the size of a chickpea.Rasulullah ﷺ took them and said: yes, get more of this size.And then he addressed the community. He said:**يَا أَيُّهَا النَّاسُ، إِيَّاكُمْ وَالْغُلُوَّ فِي الدِّينِ***O people, beware of extremism in religion. For nations before you were destroyed because of extremism in religion.*Think about that. This is a moment about picking up a rock. A small, mundane, physical act. But Rasulullah ﷺ saw the teaching opportunity and seized it.Because it's easy to go overboard here. You're reliving what Ibrahim went through. You're stoning Iblis. A chickpea-sized pebble? That's not going to cut it. You want to find the nearest cricket club, practice your bowling, and make sure Iblis doesn't come back next year.But no. The Prophet ﷺ said: this is the size. Not too big — you're not hurling rocks. Not too small — you're not flicking grains of rice. Just right. The balance.-----So where do we draw the line on extremism?I was speaking to some of the high school students at Qaswa about the practices of our predecessors in Ramadan. Imam al-Shafi'i would complete two full readings of the Qur'an every day during Ramadan — one in the day, one at night. That's sixty khatam in one month.The students said: that's extreme, isn't it?I said: well, how do you define extreme?Let's pull out our phones. Check the screen time. How many hours on TikTok? How many on Instagram? People are clocking seven, eight, ten hours a day staring at a screen.Now imagine we could transport Imam al-Shafi'i into 2026. We tell him: Muslims today stare at a glowing rectangle for ten hours a day, getting no benefit, and it's actually harming them.He would say: that's extremely stupid, isn't it?So who defines what's extreme? Rasulullah ﷺ does. Because he is the most balanced of humanity. The mark of this Ummah, as Allah describes it in the Qur'an: ummatan wasata — a balanced nation.When three companions each decided to push further — one would pray all night and never sleep, one would fast every day and never break it, one would worship and never marry — the Prophet ﷺ said: I am the one with the most taqwa among you. Yet I pray and I sleep. I fast and I break my fast. I worship and I marry. This is my sunnah. Whoever turns away from my sunnah is not from me.Everything has a right. Your body has a right — good nutrition, good rest. Your family has a right. Allah has a right over you in worship. Giving every aspect its due — that's balance.-----Let me sketch a few dimensions of this balance.Balance in belief. Islam respects both revelation and reason. We believe because Allah told us to believe — in Him, in the angels, in the books, in the prophets, in the Last Day, in qadar. These are revelatory matters.But our tradition also respects the intellect. Look at how Ibrahim عليه السلام argued with his people in Surah al-An'am. He didn't just say: stop worshipping your idols because Allah says so. He engaged their logic. Idols you carved with your own hands — you made them, and now you bow to them? They don't speak, don't benefit you, don't harm you. Why?And then the stars. He observed the kawkab — a beautiful star — and said sarcastically: this is my lord? But when it set, he said: I don't love things that disappear. God can't be present at some times and absent at others. I need God every moment.Then the moon appeared, full and bright. He said: this is my lord? But when it set, he said: *if my Lord had not guided me, I would certainly be among those who are astray.*Notice the shift. In the first argument, Ibrahim used pure logic — God can't appear and disappear. But in the second, he acknowledged that arriving at the worship of Allah requires revelation. Intellect can deny what is not God. But to know who God is, you need guidance.Imam al-Ghazali captured this beautifully. He said: revelation is like the sun, and reason is like eyesight. Without the sun, there's nothing to see. But without eyesight, you can't appreciate the light. Both together — that's how you see.If you rely only on revelation, your faith works fine within a Muslim bubble. The moment it's challenged from outside, it crumbles. If you rely only on reason, you can conclude that God must exist — but you'll never arrive at which God, or how to worship Him. Both, hand in hand. Ummatan wasata.Balance in practice. There are people so focused on the physicality of worship — how to raise the hands, where to place them, how to stand — that they forget the deeper purpose. Prayer isn't calisthenics. When Allah says aqim al-salah li dhikri — establish prayer to remember Me — He's pointing to something beyond movement.Every act of worship in Islam is meant to produce beautiful character. The Prophet ﷺ said: I was only sent to perfect noble character. If the more religious we become, the harsher our behaviour gets — something is broken. The balance is off.Allah tells us that prayer prevents shamelessness and evil. Yet we see people who pray, and in the same breath they double-park on someone without a care. The same tongue that recites Qur'an goes on to slander. The same hands that move in salah take what doesn't belong to them.How? Because the spiritual dimension was missing. If you truly stood before Allah in prayer — before the Creator of the heavens and the earth and everything in between — there has to be an after-effect. If you get called to the CEO's office and told off, you'll behave well for at least a few days. Now multiply that. You stood before the Lord of all worlds. You spoke to Him. Surely the effect lingers.And just as it starts to fade — Dhuhr arrives. Then before it fades again — Asr. Then Maghrib. Then Isha. Then sleep, then Fajr. The cycle continues. This is why prayer stops you from evil. You keep checking in with Allah. You keep reporting back.But strip away the spiritual dimension, focus only on the mechanics, and it loses its purpose.On the other hand, there are people who say: my heart is good, I don't need to pray. As long as I'm kind, the rituals are for other people. But then — who are you actually worshipping? If you abandon what Allah prescribed and follow only your own moral compass, you're worshipping your own nafs.-----This is the lesson of the chickpea.One nation before us fell into extremism through legalism — everything became so complicated that they abandoned practice altogether. Another fell through spiritualism — everything was about love, no boundaries, no halal or haram, just accept and you're saved. The religion dissolved. Nothing was left.Islam sits in the middle. As Imam al-Ghazali said: khayru al-umur awsatuha — the best of affairs is the middle path.The Prophet ﷺ reminded us, standing at the Jamarat, pebbles in hand: don't fall into extremism. The size of a chickpea. Not too much. Not too little. Just right.May Allah protect us from extremism in religion. May He grant us the strength to live by the Sunnah — balanced in every dimension, following our Prophet ﷺ externally and internally. Thanks for reading Grounded! This post is public so feel free to share it. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.grounded.day/subscribe

Continuing our Theme this month, looking at how the Church ministers to (or doesn't minister to) the entire body of Christ.We have on the panel today representatives from Together We Care, SOAR and KeyMinistry/Disability and the Church.Do you know the difference in Diversity- Inclusion & Belonging? The importance of community!About our panelistsJilliam Palmiotto/Together We CareJillian Palmiotto is the Founder and Executive Director of Together We Care, a Georgia-based nonprofit that equips and empowers families impacted by disabilities through strategic planning, advocacy, and practical support. With over a decade of experience in special needs ministry, Jillian also serves as the Special Needs Inclusion Coordinator at West Ridge Church and as the Executive Director of the Together Conference. Her passion for building inclusive communities is fueled by her personal journey as a special education teacher and disability ministry leader. Jillian brings a wealth of knowledge, compassion, and real-world expertise to every conversation—helping families, churches, and organizations navigate complex systems with hope and clarity. Doc Hunsley/SOARStephen “Doc” Hunsley, M.D. is the Executive Director and founder of SOAR Special Needs in Lenexa, Kansas. SOAR (Special Opportunities, Abilities, and Relationships) serves over 1500 individuals with special needs through regular respite events and the nation's largest Disability Day Camp. Doc is currently assisting over 725 churches locally, nationally, and globally in starting a Disability Ministry. Doc also organizes the Wonderfully Made Conference held annually every October in Kansas City. Doc is a USAF veteran and a retired disabled pediatrician while his wife, Kay, continues practicing pediatrics. They are proud parents to three beautiful children: Luke, Mark, and Sarah. The Hunsley's middle child, Mark, is presently running the halls of heaven. During Mark's five-year earthly stay, he gave his family the opportunity to learn from and love a child with autism. Dr. Steve Grcevich/Disability & the Church/Key MinistryDr. Steve Grcevich is a child and adolescent psychiatrist with 40 years of experience as a clinician, researcher, and professor who serves as President and Founder of Key Ministry. He plays a lead role in Key Ministry's work to support churches in evangelism and outreach to the mental health community. He is the author of Mental Health and the Church (Zondervan), the first comprehensive model to guide churches in their mental health outreach and inclusion efforts. In his role with Key Ministry, he has been invited to speak or create resources for the American Association of Christian Counselors, the Christian Medical Dental Society, the Colson Center, the Ethics and Religious Liberty Commission of the Southern Baptist Convention and the National Association of Evangelicals. He currently serves on Focus on the Family's Physician Resource Council and is a widely requested speaker at national ministry conferences.

Mistral has been on an absolute tear - with frequent successful model launches it is easy to forget that they raised the largest European AI round in history last year. We were long overdue for a Mistral episode, and we were very fortunate to work with Sophia and Howard to catch up with Pavan (Voxtral lead) and Guillaume (Chief Scientist, Co-founder) on the occasion of this week's Voxtral TTS launch:Mistral can't directly say it, but the benchmarks do imply, that this is basically an open-weights ElevenLabs-level TTS model (Technically, it is a 4B Ministral based multilingual low-latency TTS open weights model that has a 68.4% win rate vs ElevenLabs Flash v2.5). The contributions are not just in the open weights but also in open research: We also spend a decent amount of the pod talking about their architecture that combines auto-regressive generation of semantic speech tokens with flow-matching for acoustic tokens (typically only applied in the Image Generation space, as seen in the Flow Matching NeurIPS workshop from the principal authors that we reference in the pod).You can catch up on the paper here and the full episode is live on youtube!Timestamps00:00 Welcome and Guests00:22 Announcing Voxtral TTS01:41 Architecture and Codec02:53 Understanding vs Generation05:39 Flow Matching for Audio07:27 Real Time Voice Agents13:40 Efficiency and Model Strategy14:53 Voice Agents Vision17:56 Enterprise Deployment and Privacy23:39 Fine Tuning and Personalization25:22 Enterprise Voice Personalization26:09 Long-Form Speech Models26:58 Real-Time Encoder Advances27:45 Scaling Context for TTS28:53 What Makes Small Models30:37 Merging Modalities Tradeoffs33:05 Open Source Mission35:51 Lean and Formal Proofs38:40 Reasoning Transfer and Agents40:25 Next Frontiers in Training42:20 Hiring and AI for Science44:19 Forward Deployed Engineering46:22 Customer Feedback Loop48:29 Wrap Up and ThanksTranscriptswyx: Okay, welcome to Latent Space. We're here in the studio with our gues co-host Vibh u. Welcome. Thanks. Excited for this one as well as Guillaume and Pavan from Mistral. Welcome. Excited to be here.Guillaume: Thank you.swyx: Pavan, you are leading audio research at Mistral and Guillaume, you're Chief Scientist,Announcing Voxtral TTSswyxHost(00:05) Okay. (00:05) Welcome to Lean Space. (00:06) We're here in the studio with trustee co-hosts, Vibhu. (00:09) Welcome.VibhuHost(00:11) Very excited for this one.swyxHost(00:12) As well as Guillaume and Pavan from Mistral. (00:15) Welcome. (00:16) Excited to be here. (00:17) Thank you for having us.(00:18) Pavan, you are leading audio research at Mistral and Guillaume, you're a chief scientist. (00:23) What are we announcing today where we're coordinating this release with you guys?GuillaumeGuest(00:26) Yeah, so we are releasing Voxtral TTS. So it's our first audio model that generates speech. It's not our first audio model. We had a couple of releases before.(00:35) We had one in the summer that was Voxtral, our first audio model, but it was like a transcription model, ASR. Like a few months later, we released some update on top of this, supporting more languages. Also a lot of table stack features for our customers, context biasing, precision, timestamping and transcription. We also have some real-time model that can transcribe not just at the end of the level.(00:56) You don't need to fill your entire audio file, but that can also come in real-time. And here, this is a natural extension in the audio, so basically speech generation. So yeah, so we support nine languages, and this is a pretty small model, 3D model, so very fast, and also state of the art. Performed at the same level as the base model, but it's much more efficient in terms of cost, and also much, in terms of cost, it's also much cheaper, only a fraction of the cost of our competitors.(01:22) And we are also releasing the work that this model is running.swyx What's the decision factor?Guillaume It's a good question.swyxThere will be more. Yeah, Pavan, any sort of research notes to add on?Architecture and CodecPavan: But it's a novel architecture that we develop inhouse.We traded on several internal architectures and ended up with a auto aggressive flow matching architecture. And also have a new in-house neural audio codec. Which, converts this audio into all point by herds latent [00:02:00] tokens, semantic and acoustic tokens. And yeah, that's that's their new part about this model and we're pretty excited that it's, it came out with such good quality and Jim was mentioning. Yeah, it's a three B model. It's based off of the TAL model that we actually released just a few months back and insert trunk and mainly meant for like the TTS stuff, but they need text capabilities are also there. Yeah.swyx: So there's a lot to cover.I always I love any, anything to do with novel encodings and all those things because I think that's obviously I creates a lot of efficiency, but also maybe bugs that sometimes happen. You were previously a Gemini and you worked on post training for language models, and maybe a lot of people will have less experience with audio models just in general compared to pure language.What did you find that you have to revisit from scratch as you joined this trial and started doing this? At leastUnderstanding vs GenerationPavan: when it comes to, for, I think the, there are two buckets, I guess the audio understanding and audio [00:03:00] generation. The audio understanding, like the walkthrough models that Kim was mentioning that we released earlier.The walkthrough chat that we released I think July last year, and the follow up transcription only, models family that we released in January, that would be one bucket, and the generation is another bucket. I think. You can also treat them as a unified set of models, but currently the approaches are a little different between these two.To your question on how audio is fed to the model? In the understanding model, it's very similar to actually Pixar models that we also released,swyx: yes.Pavan: That'sswyx: amazing.Pavan: It was pretty, I, that was the first project I worked on after joined Misra. It was pretty, pretty nice. And Wtu was very similar in spirit.I guess So we feed audio through an audio encoder similar to images through a vision encoder, and it produces continuous embeddings and which are fed as tokens to the main transformer decoded transformer model. Yeah. On the model output is just text. So on the output side, there is nothing that needs to be done in these kinds of mode.I [00:04:00] guess the interesting part of what the generation stuff is, the output now has to produce audio and. The approach that we have is this neural audio codec, which converts audio into these latent tokens. There is a lot of existing attrition and a lot of models which are based off of this kind of approach.And we took a slightly. A different, design decisions around this. But at the end of the day, the neural audio product converts audio into a 12.5 herdz set of latents. And each latent is, has a semantic token and a set of acoustic tokens. And the idea is that you take these discrete tokens and then feed it on the input side.There's several ways to use this at each frame, but we just sum the embedding. So it's like having key different vocabularies. Combine all of them because they all correspond to one audio frame on the input side. The output side is the interesting part on the output side, the, it's not the, I don't know if it's the most popular, but one.Popular technique is to have a depth transformer [00:05:00] because you have K tokens at each time step, like with a text, you just have one token at each time step. So you just do predict the token from the vocabulary with, yeah, with just, you get probabilityswyx: This's a very straightforward text. VeryPavan: straightforward.swyx: Yeah.Pavan: But if you have K tokens, then the name thing would be to predict all of them in paddle. That doesn't work. At least that doesn't work that well because audio has more entropy. And the, one of the techniques people use is this depth transformer where you you almost have a small transformer, or it can be L-S-T-M-R in as well, but people use transformers and you predict the K tokens in auto aggressive fashion in that.So you have two auto reive things going on.Flow Matching for AudioPavan: So the thing we did differently is in, instead of having this auto aggressive K step prediction, we have a flow matching model. Instead of modeling this as a discrete token set we trained the codec to be both discrete and continuous to have this flexibility.So we did try the discrete stuff too, and which it works well, but the continuous stuff works just better. So yeah, we took this flow matching, so the, it's a flow [00:06:00] matching head, which takes the latent from the main transformer and like kind in fusion, it's denoising, but in this flow matching itself, velocity estimate.So you go from this noise t all the way to there. Audio latent, which corresponds to the 80 millisecond audio and then, which is sent through the work order to get back the 80 millisecond audio frame.swyx: Yeah. Is this the first application of flow matching in audio? Because usually I come across this in the image.Pavan: Yeah. Actually, in some sense there are models flow matching models in audio, but I think this specific combination I could be wrong. There could be somewhat. No. I haven't seen. I haven't seen much work in this, so I think it's novel and a lot of it's just a way bigger community, so they, I think they pioneer a lot of these diffusion flow matching work, and it's interesting to adopt some of the ideas there into audio and,swyx: yeah.Pavan: Yeah, I'm, personally that's the think part which is trying out about. One of more meta point is unlike text, even in vision, I think this is true, but in [00:07:00] audio step literature that there is no.Winner model, yet there is no, okay, this is the way you do things. It's it's still by, I think people are still iterating and figuring out like what's the best overall recipe. I guess the idea. Pretty sure there are models which are also completely end-to-end, like NATO audio. NATO audio, but it's still not come to a convergence point where this, the right way to think that.That also makes. A space pretty exciting to explore.Real Time Voice AgentsVibhu: What are some of the ways to look at it?Vibhu: There are ways where you can do diffusion for audio generation, but if you want like real time generation, that's a big thing with the approach I'm assuming that you took. Yeah. And also like how do you go about evaluating different axes of what you care about, yeah,Pavan: good point. I think we so you can do just flow matching diffusion for the whole audio. We didn't even go down that path because one of the main applications is voice agents and we want real time streaming, and that's the use case. That's not the only use case, but that's one of the primary use cases we want to get to.So we [00:08:00] picked the auto aggressive approach for that. And within the auto aggressive space, again, you can do chunk by chunk or you can do so we picked the. I think at least personally prefer the operations, which are the simplest, and so we try to see, can we just add audio as just another head to our regular transformer decode model because that kind of makes it easier for eventual end-to-end modeling of audio text native modeling.Yeah. And it works pretty well. So I guess we went with that and we tried a little bit, but the flow matching head itself, like we had a discreet. Diffusion kind of approach, which also works well, but the flow matching work better.swyx: I was just curious about how you also think about this overall direction of research.Do you basically, when you work with the audio team, do you set some high level parameters and then let them explore whatever, or how does it work between you guys?Guillaume: No I think the way it works is that we are the, we are prioritizing together, I think, what are the most important features because there are many things we can do [00:09:00] in audio.Yeah, I think we try to. These are like how we should do things, for instance. Ultimately what we want to do is to build this through duplex model, but we are not going to start this start there directly, I think is. Some of the project people are doing, butswyx: just to confirm, full effects means it can speak while I'm speaking or,Guillaume: yeah.Okay. Audio. Yeah. Yeah. So intimately we're going to get there, but for us it was, we decided to take it like a step by step. So we start with whatever is the most important. I think support customers, which is the transcription is the most popular use case. Then the speech generation, Soviet time, just a bit before that.And then actually to be like more, but try combining everything all together. But but yeah, we thought it was also important to like separate things and optimize each capability one by one before weswyx: measure of that together. And the super omni model. ButGuillaume: very interesting because as Par said, it's when you work on some other domains of this airline and everything, there are many areas where I think it's not as interesting.For instance. Many places, it's essentially just around data or like creating new environments on a lot of kind [00:10:00] of easy things. But things were, I think the research is maybe not as interesting. Were in audio. There are so many ways to actually build this model. So many ways to go around it. That's the sense I think is really interesting.And what we also tried for speed generation is that we tried multiple approaches. What was interesting that even though they were extremely different, they under the big know the particles but the for matching turned out to be quite more natural. So we are happy with this.swyx: Is there intuition why it maybe like flow matching is just models speech better in some natural fundamental, latent dimension?Pavan: No, I think the main thing is e even at a particular time step, there is a distribution of things.swyx: Yes.Pavan: To be predicted like the way you inflate. So you already know the word that you're speaking and Yeah. The intake space, let's say the word maps register a single token for simplicity.In most cases it does. So there is not a lot of so you just pick the word, but with within audio, even the same word could, even with your own voice, could be inflicted in so many different ways. And I think [00:11:00] any approach which like models this distribution and. And flow matching is one, one of the take.It's not the only one at all, but it's a one which works pretty reasonably well. I think that's better. So you have to pick across several different, the intuition I have is it's, there are some, several different clusters each corresponding to some specific way you would inflict, pronounce that thing.And you can't predict the mean of it because that corresponds to some blurred out speech or something like that. But you have to pick one. And then like sharpswyx: conditional inference.Pavan: Yeah, exactly.swyx: Is that all covered under disfluencies, which is I think the normal term of art. Pauses intonations. By the way, I have to thank Sophia for setting all this up, including like some of these really good notes becausePavan: Yeah.swyx: I'm less familiar with the audios for me.Pavan: No. I think dis dismisses are definitely one such Eno defenses is more likeswyx: which is arms are.Pavan: Yeah, arms. And also repeat like you like,swyx: yeah.Pavan: You do this full of words, your thinking, so you repeat the word.swyx: Okay. Whereas intonation is like a diff, it's up up [00:12:00] speak and all this.Okay.Pavan: Yeah. So I think there is a lot of like entropy. And modeling it as a distribution. And a, any technique which helps with it and the depth transformer is a conditional way of modeling this. And Transformers actually really good at it, even though that's a mini transformers. So I think that worked pretty well too for us too.It's just that the main concentration is when you have a depth transformer. If you have K tokens, you need to do K auto steps, right? Even though it's a small thing, it's K steps, which is very vacant, say heavy, but flow matching. We were able to cut it down significantly. So we are able to do the inference in quad steps or 16 steps and it works pretty well.And there are more normal techniques to bring it down even further to like, in extreme case, one step like we're not doing it yet, but it at least the framework, LEDs itself to more efficient and Yes.swyx: And the image guys have done.Pavan: Yeah.swyx: Incredible work guys. Yeah.Pavan: It now you just. Send a prompt and you get an image.swyx: Yeah. Surprisingly not enough. I think image model labs use those techniques in production. I think it's, I feel like it's a lot of research demos, but [00:13:00] nothing I can use on my phone today.Guillaume: The thing, there's a thing that would be interesting here is that since, indeed I've been so much sure that has been done in the vision community compared to radio dys, stomach, I think there are so many long infra Yeah.And there are so many things we can do to actually improve this further. So it's our first version, but we have so many ways to exist, much better and much more efficient, cost efficient, soswyx: yeah.Guillaume: So really it's not a new field at all, of course, but there are still so many things that can be done.Perfect. It'sswyx: nice. I should also mention for those who are newer to flow matching, I think the creator, this guy's name is Alex, he's done I think in Europe's maybe two Europes as ago. There was, there's a very good workshop. There's one hour on like this matching is I would recommend people look that up.That's the other thing, right?Efficiency and Model Strategyswyx: The efficiency wise, like I, I imagine like the reason is open weights the reason you pick 3.6 B backbone it you are 3.4 B you are, try to fit to some kinda hardware constraints. You kinda fits some kinda basic constraints. What are they?Guillaume: Not necessarily, I think something we care about in our model that they're efficient.So we have a [00:14:00] lot of separate model, for instance. So we have this that is very small, very efficient. We also have a small OCR model that is available. Good, highly efficient as well. And I think on a project maybe there, I think companies are going to take is to have a coverage general model that will do a bit of everything.But that is also going to be expensive. On here. What want say is if you care about this specific use case, if you can actually use this model, it just does that. It's extremely good at it. Survey, very efficient. That's why we can actually add. We do, but also OCR that are like really good at that.And that would be much more cost effective factors and the general model that will contain a lot of capabilities you don't really need. So yeah. So we're doing like general model, but also like more customized model. This,Open Weights and BenchmarksVibhu: how does it compare to other TTS models? It's, we are going follow open wave.We're just dropping it. I think it's pretty good.Pavan: Yeah, I think it's pretty good. Like it, it's definitely one of the best. For sure. It's probably I would say it's the best open source model, butVibhu: decipher themselves.swyx: Yeah.Voice Agents VisionVibhu: Why now? How does it fit into broader ral vision? How do you see voice agents?How do you see voice? I think every year I've heard, okay, you're a [00:15:00] voice. You're a voice. There's a lot of architectural stuff. There's a lot of end time that see it, your solving, but where do you see voice setting?Guillaume: We had so many customers asking for voice. That's also why we wanted to build it.What's interesting in this domain is that. In a sense, if you take something simple like transcription it doesn't seem like something that should be very hard to do for a model. It's essentially, it's pattern recognition. It's classification on this. Models are very good at classifying, right?Or nonetheless, when you talk to them it's not there yet, right? It's not, you don't talk to them the same way you talk to a person. On something, maybe people don't realize it. It's in English it's still much better than in any user language, even compared to French instance. If you talk to this million in French, when you see people talking to this they'll talk very slow.They'll articulate as much as they can. So it's not natural, right? We're not yet to this. And I think, yeah, maybe the next generation will not know this, but yeah, I think people that. But our edge will actually always keep this bias speaking very slowly when they talk to this model. Even if maybe, probably in a couple of years, maybe next year it'll not be necessary anymore.But yeah. But what's interesting is to see that yeah, even for like languages [00:16:00] like yeah, French and Spanish Germans that are not no, no resource on religion. You have a lot of audios there on still it's not as good. And I think a consequence. Because then for this, I suppose just is not as much energy, as much effort that has been put done in some other mod that for some vision or like coding.But but yeah, there's still a lot of progress to be done. I think it's just a question of doing the work and it's clear path I think to get there.Pavan: It's a little fascinating because I worked on Google Assistant I think while back at this point, but it's, I think it's, it like when you take a step back, it's fascinating.It's not that long ago. It was like four years ago or five years ago, and it's now it's completely audio in, audio out and the function calling and the whole thing happens completely end to end. And in a very natural,swyx: yeah,Pavan: natural way and still ways to go. Kim was telling, even despite all the previous, it's not like you're speaking to a person.When you talk to any of these agents, bots, or voice mode kind of situation, it's still like a gap. I think that's the great part and I feel like with even the existing [00:17:00] stack, we should be able to get to this very natural speech conversational abilities soon enough I guess.And we'll also hope. I get thatGuillaume: on this kind of the next step, right? Because when you talk to these agents, like usually people are just writing to them and sometimes they'll this very clear, for instance, you are, you want to write code, but you are, you have a very clear idea of how you want the model to implement what you in mind.But so here you are able to spend a lot of time writing. So it's not really efficient on audio is really like a natural interface that is just not there yet, but I think it's just gonna be the place.Vibhu: How's it like building, serving, inferencing, like we see a lot about, it's very easy to take LMS off the shelf, serve them.Fine tuning, deploying. I know you guys have a whole you have Ford, you have a whole stack of customizing, deploying. Is there a lag in getting that. Like distribution channel. Are you helping? There is. So like prompting, lms, you can have them be concise, verbose, all that.They're built on LM backbones, these models. How do you see all that?Enterprise Deployment and PrivacyGuillaume: Yeah, I think this is a lot of what we're doing with our own customers. Very [00:18:00] often they come to us, so it's for different reasons. I think one reason is sometimes they have this lot of privacy concerns.They have this data that it's very sensitive. They don't want data to leave. The companies, they wanted to stay. Inside the company. So we have them deploy model in-house. So either on a, either on premise or on private cloud. So they're not worried that it's given to a third party on the there some leakage.Sometimes they have this kind of many companies have this different, sensitivity of data they have like sometimes channel chat can send it to the cloud has to stay there. So then it creates some kind of heterogeneous workflows where it's annoying. You cannot send some data to the cloud.This one you can, so here, when we actually deploy the model for them, they don't have this consideration. They are like not worried that, this is going to leak. Everything is much easier. So we help them basically do this on the, so it's one of the very proposition. But but the other is very often, when customers use this off the shelf close model, but very sad is that they are not leveraging, these data that have been collecting for four years or something for decades.So much data. Sometimes it's trillions of tokens of [00:19:00] data in a very specific domain. Their domain, which is data that you'll not find in the public, on the public internet. So data on which, like close model, we actually not have access to one, which that's going to be really good. So if they're using like closed source models are basically not benefiting from all these insights.All these data they have collected three years, they can always give it into the context that in France, but is never as good as if you actually train the modern analysis. So yes, that's basically what we help them to do. We actually provide them some purchase, basically what we announced at GTC this week.So we provide them with this, it's basically like a platform with a lot of tools to actually help them process data. Trained on that. Yeah, it's actually the same thing that we're using in the science team. So it's actually very better tested infrastructure, like a lot of efficient training cut base.For a quality pre-training like a fine tuning, even doing S-F-T-I-L. So we help them do this using the same tools as what our science team is building is using. So since it's tools that we've been using for two years now, it's really better tested. It's really sophisticated.So it's the same thing. We are giving to them, giving the company the same thing [00:20:00] that what are same still using internally actually build their own ai and it makes a really big difference. I think sometimes customers. And many in general don't realize how much better the model becomes when you fine tune it on your own data.And you can have a, your model is here. You start from there. You have a cross source model, which is sort here, but if you actually fine tune it can actually really go much further than this. And then you have a very big advantage. The model is trained on your entire company knowledge, so it knows everything.You don't have to feed like 10 K tokens of contact at every query. So it's it's much easier. It's a bit, I think using a closed source model is really sad because it basically puts. You are not leveraging all this data and you are going to be using the same model as all your old competitors when you're actually using, everything you have been collected for years, which is really valuable.So yeah. So we help basically customers do this. We have a lot of solution I mean deployed for engineers that go in the company that basically look at the problem customers are facing to look at what they're struggling to do what we should do to solve it. So we help them solve them together.So it's I think our approach is a bit different, but here. [00:21:00] Some of their companies and competitors, it's, we don't just release an endpoint on sale, do some stuff on top of that, or we don't just give a checkpoint. We really look very closely with customers. We look at the issues they have, we had them solve them.We really make some tailored solution for the client are facing. Some example are also going to be, sometime we have some customers. They really wanted to have a really good model, really performance on some, like Asian languages on the, if you take some of the shelf models, they can speak it, they can write in this language, but it's not amazing.This language would be like maybe zero 1% of the mixture. So it has been included during training, but very little. So what we did here is upgrade. We trained a new model for them, but so this language was 50% of the mix, so it's much, much stronger. It knows of the dialects, it knows the, so it's yeah.So it's some example of things we can do and it's really arbitrary, custom. I think you had some of their customers, for instance, they wanted some. They wanted some 3D model that can do audio with a very good function cable. So something you wanted to put in the car in particular, they wanted this to be offline because in a car you don't necessarily have access to internet.So [00:22:00] yeah. So here we can actually build the solutions. There is no like model out of the box on this. In the internet you have this very, you have this very general model generalist, like he's strong model. But for things like this, they always want at specific solutions and on some other reasons.Sometimes they come to us is because, like they, they experiment with some closed source model. They get some prototype. They're happy with what they build. They, it works well. They're happy with the performance, and then they want to go to production and then they analyze. But it's extremely expensive.You cannot push this. It's so then they come back to us on this. They can help us build the same thing as this, but using something much cheaper on here. And here we can sometime be something 10 x cheaper by just functioning a model and it'll be better OnPrem on their old server and also much cheaper as well.So yeah,swyx: that's the drop pitch right there. Take all themoney.Vibhu: And outside of that you do, we do put open wave models so people can do this themselves. I feel like not enough people go outta their way.swyx: They're not going to, they're gonna ask them to do it as the expert. IGuillaume: think initially we didn't know, [00:23:00] we wanted completely short at the beginning of the company because, I think our study was not exactly the same as what it is today, but what we underestimated initially is the complexity of deploying this model and connecting them to everything to be sure it has access to the company knowledge on the, and it was, yeah, on, we were seeing customers struggling with this, but it was even, that was three years ago and no, things are much more complicated because now you don't just have, text on SFT on a simple instruction following.You have reasoning like your agents, you have like tools. You have a multimodal audio, so it's much more complicated than before. And even back then it was hard for customers. So they really need, have some support and this is why actually providing like always some four D position as well. The processFine Tuning and Personalizationswyx: I'm curious is there also voice fine tuning that people do?Pavan: So in this forge we also have a say unified framework. And the hope is like the er speech to text that we released earlier this year. And even the ER chart that we released last year. And I think a big people, I think there's a big, rich ecosystem [00:24:00] of people fine tuning whisper, and people want the same thing with w so it's much stronger than Whisper.And yeah, the the platform offers that kind of fine tuning yeah, which could be any kind of fine tuning. Like for instance, even sometimes people want to support new languages to this, which are tail languages, which we hope to cover. Certain natively, but if there is a language where you data and you want to frank you, I think this is a good use case.Or the other use cases, you, it's the same language, like even English but it's in a very domain specific way.swyx: Yeah. Terminology, jargon, medical stuff.Pavan: Exactly. And also there's specific acoustic conditions like there's a lot of noise or the, and. The model will do decently in most conditions, but you can always make it better.And that those are some of the use cases where you can improve it e even further. And that's one good use case for this and for text to speech. We're just releasing it so we'll have support for that soon too. I think it's similar use case.Voice Personalization Pavan: It's little different the kind of things that you want to extend a [00:25:00] text to speech model to, which could be like voice personalization, voice adaptation for enterprises.Many enterprises need very specific kind of tone, very specific kind of like personality for this kind of voice. And all of those are like good use cases for fine tuning.swyx: This one I was gonna ask you, we never talked about cloning voice clothing here. How important is it, right?Like I can clone a famous person's voice. Okay. ButPavan: the main use case would be like for enterprise personalization, like enterprises need like a lot of customization. You don't want the same. Voice for all the enterprises. Each enterprise want a customized, specialized something which is representative both their brand and also their, I guess safety considerations and the use case I think the kind of thing that you would deploy as a empathetic assistant in the context of a healthcare domain would be very different from the kind of thing that would be in a customer support bot and would be different from like more conversational aspects.I think those are the. [00:26:00] Customizations you would expect from enterprise. And that's the main use case, at least from our side.Vibhu: My, my basic example is you don't want to call to customer services and have the same exact voice. It's just, it's gonna be weird.Long-Form Speech ModelsLong-Form Speech ModelsVibhu: But also on the technical side of this, so there's like a few things in TRO that I thought were pretty interesting.He's a big fan of this paper. Oh, he said very good paper. He said this is the best SR paper he's ever read. Yeah. I've hyped up this voice paper enough. We covered it. Somewhere, but a big thing. So Whisper is known for 32nd generation a 32nd processing. You extended this to 40 minutes. There was a lot of good detail in the paper about how this was done.Even little niches of how the padding is. So it's very much needed. You need to have that padding in there, the synthetic data generation around this. I'm wondering if you can share the same about the new speech to text, right? Text to speech. So how do you. How do you generate long form, coherent?How do you generate, how do you do that? And then any gems? Is there gonna be a paper?Pavan: Yeah. Yeah. They would be a technical report. Okay. Yeah. I think I could have a lot of details.Real-Time Encoder AdvancesPavan: But me I think the [00:27:00] summary of it, actually, some of the considerations in this paper were, because we started with the wipa encoder as the starting point, and now we have in-house encoders, like the bigger time model, for instance, which we released in January.Also release a technical report for that real time model as well, which is this dual stream architecture. It's an interesting architecture. You should check it out. And there we have a causal encoder and I don't think there's any strong, multilingual causal encoder out in the community. So we thought it's a good contribution.So that's one nice encoder there. Other people want to adapt. That's a good end code. And we train it from scratch. I think her. Post stack is now mature enough that we are able to train super strong ENC codes. And some of these considerations, like spatting and stuff, is a function of the Whisper ENC code.And now that we train encoders, inhouse the design concentrations are different.Scaling Context for TTSPavan: And for the question on text to speech, I think that's also leans onto the original auto aggressive decoder backbone. I think, it says very, almost identical considerations. I think the long context in it's not even long con, [00:28:00] so the model processes audio at 12.5 herds, so one second maps to like 12.5 tokens.So I think one minute is like 7.8 tokens. You can get like up to 10 minutes in eight K context window and get half an hour and 30 K context window. So that's and 30 2K context is something that's we are very comfortable training on. We can extend it even much longer. 1 48 K. Okay. You can naturally see how it can extend to even our long generations.Yeah. We need the. Like data recipe and the whole algorithm to work coherently enough through such long context. But the techniques are some way very similar to the text, long context modeling. And the key differences, it's just doing flow matching order regressively instead of a text open prediction.swyx: Okay. I think that was most, most of the sort of voice questions that we had. ButWhat Makes a Model SmallVibhu: I have a big question on Mr. Al, Mr. Small. So what is small? How do we define [00:29:00] small? What is this? What is this? I remember the days of Misal seven B on my laptop. The snuff fitting on my laptop. I could run it on the big laptop, butGuillaume: it's just additional.Question of terminology, like here what we did, baseball is north active parameters, but it's true. Really not give it another name, but yeah, we could have called it medium, but only, I,I suppose it's a model that we released mixture of experts. It's a model that combines different model before which we were doing the same, is that we had one model, general model for Israel. Doing instruction following, were like a separate model that was Devrel trial. So qu coding specify specific to code with another model for Reason Maal.So this were separate artifacts built by different team at trial on what we're doing is basically merging all of this. It was, you had pixel trial was the first vision model. We was like a separate model on the way we do things internally is that we have one team focus on one capability, build one model.On the means mature, mature enough, we decide to merge this into the [00:30:00] matrix. But here it was the first time we basically match all of this into one. But there are some other things we did at first time to merge time, for instance, like more capabilities or function coding I think would be, are, it's going to be much, much better in this trial, small platform.But but yeah, so it's our latest model on the working is,Vibhu: and yeah, key things is it's very sparse. Six, be active pretty efficient to serve. 2 56 K context. Yeah,Merging Capabilities vs Specialistsswyx: I think what's interesting is just this general theory of developing individual capabilities in different teams and then merging them.Where is this going gonna end up?Vibhu: Like we've seen the five things put together in this. Yeah. What are the next five teams?swyx: I think actually OpenAI has gone away from the original four Oh. Vision of the Omni model. This was what they were selling. All modalities and all modalities out.But I feel like you might do it.Guillaume: I think there's some mod where it's not competitive use, for instance for audio. For audio here, if you want to do transcription, I think it makes no sense to use a model. If you just want to trans tech it, it'll be very inefficient. If you want to do audio, you probably just want to be the [00:31:00] one VR 3D model performance essentiallyswyx: the same.It's going to be incredibly cheaper. So here, that's why we wantGuillaume: to have a separate but just does this. Yeah, I think the question is just, yeah. If you are to, to your model. By speech and you asking like a very complex questions on how you do this on the, just to cascade things. Do you want to put a d in a model that has like a one key around it?It's like a, not a competitive discussion, I think unaware if you doing into the direction, but that's possible. Of course. But yeah. But I think for us, the next capabilities we want to try to integrate into these models when we are going to be yes, like marketing or no reasoning better, I think more capabilities that people don't talk too much about, but at high bottom, I think for our customers in our, on different industries, for instance, things are around like a legal computer.I design all these things that is this males out of the box are to put at that. Because people, if you don't prioritize this, there is not like too benchmark on that. Butswyx: this done how toGuillaume: make this good and this just start to do the work. Extracting some that processing it [00:32:00] expression. So yeah.But we are offering the imagine to this.swyx: I think for voice. Yeah. The key thing I think over maybe like the last year or so with VO and gr Imagine and all these things is joining voice with video, right? Which people don't understand spatial audio because like most TTS is just oh, I'm speaking to a microphone in perfect studio quality.But when you have video, like the voice moves around.Pavan: That's true. The constitution was a little different in the sense that there it's like a a standalone artifact where you get the whole thing and you consume it. But in a conversational setting, it's a, you need the extreme low latency.swyx: Yeah,Pavan: streaming would be one of the primary concentrations.swyx: You can build a giant company just doing that, right? So you don't need to do the voice, but I was just know on the theme of merging modalities, that is something I, I am like, wow. Like I didn't, everyone up till, let's say mid last year was just doing these like pipelines of okay, we'll stitch a TTS model with a voice thing and a lip sync [00:33:00] thing and what have you.Nope. Just giant model. Yeah.Open Source MissionVibhu: I have a two part question. So one is, it's still open. It seems like open source is still very core to what you guys do and I just have to plug your paper. Jan 2024. This is the one trial of experts like. Very fundamental research on how to do good.Moes paper comes out very good paper for anyone. That's just side tangent. No.swyx: This thing caused, we bring back, eight by 22 was like the nuclear bomb for open source. I think it takes Shouldn be more seven B more. Yeah. Yeah. But this is a bigger opposite than me.Yeah. Yeah I don't remember this. I remember, I don't think it was January, right? It was like new reps it was, it dropped during new reps and everyone in Europes was December of 25th, I think. Yeah. The model was did as well.Vibhu: It's just a little update probably.swyx: Yeah. No, but you have a point to make.Vibhu: No, you gotta check that. But then, I just want to hear more broadly on open source for you guys, and when you had asked earlier [00:34:00] about what's next, what are the other, side tapes working on you. You put out Lean straw. This,swyx: it's not necessarily surprise. I was like, I don't, this doesn't fit my mental model or Misra.Guillaume: Yeah. First for open source in general, I think it's really something which looks to the January of the company. I think we started it per once, is we so we have open sourcing with, since the beginning and even before this. So before this, so me and Tim were at Meta, we released LA and I think what was really nice.To see that before this, for most researchers like universities, it was impossible to work on elements. There was no alien outside. And if you look at many of the techniques that were developed after, for instance, was open source all this post-training approaches like even DPOD, like preference optimization, all of this were done by people that had access to this portal.And it'll have been impossible to do without this. So it's really making sense, move faster. So we really want to contribute to this ecosystem. I think like the deep and also like very lot of impact. All these papers that are I think in the open source community are really helping the science community as a whole to move faster.So [00:35:00] we want contribute to this ecosystem. That's why we're releasing very detailed technical reports. So ma trial and our first reason model, and ation, lot of results, things that work, things that did not work as well. Think helpful on the, yeah, so for the audio model also to share a lot of details, share of them for real time model.And the, yeah, so we really want to continue this, basically belong to this community of people who share science. I think we really don't want to be, leading in a world where the smartest model, the best models are only behind, close doors. Only accessible to a shoe companies that we, as a power to decide we can use them on it.I think it's a scary future. We don't want to live in, we really want this model to be accessible to anyone that want. Intelligence to be used unaccessible by anyone who can use it. So yeah, so that's why we are pushing this mission and source model. Yeah. So not, so yeah, no strategy. So it's open source, not the first model, so not the best on the Yeah.Lean and Formal ProofsGuillaume: LIN trial I think is also one step into this direction. So it's yeah, a bit different than what we are usually releasing. But we have a small team internally [00:36:00] working on them. Formal proofing, formal math. So I think a subject we care about in general and we were working on reasoning. I think we started too early before doing reasoning without LMD is very hard, especially when you work with formal systems because the amount of data you have is negligible.It's addressable community of people writing like formal proofs. But the reason why we like it is because I think there is if you look at what people are doing with reasoning, is there, the problems that you can use. Are usually going to be problems where you can verify the output. So for instance, all this ai ME problem where the solution is a number between 100, like a thousand.So you can verify, compare this with a reference or it's an expression. You can actually compare the output expression generic with the reference. But there are many, most of them have problem and most of the reason problem. There is no like way to easily verify the solution. If the question is show that F is continuous, cannot compare in the reference, right?If it's a probe that this is true or probes is properties, there is no way to. You cannot act, simply verify the correctness of your proof. So it's hard to apply the, there is no referable reward here. So [00:37:00] what you could provide is of course, like a judge and judge that will look at your proof. But it's very hard and it's very, you could do certain, some reward hacking happening there.So it's difficult. You could provide like a reference proof, but then there are also many ways to prove the same thing. So if the model says give negative reward because it's a different poop, maybe it was still digit proof, just different. So it's not going to work well. What's nice with lean and with formal probing is that you don't have to worry about this whatsoever.We just,swyx: they're all function is largely compiles in lean is functionally the same. Exactly.Guillaume: It's like a problem if it compiles it's correct. It's very easy. And you can apply this and then you can,swyx: it's just way too small. So no human will actually go and do it.Guillaume: Yeah, that's exactly.It's the only people can do it. It's like a very small committee of people doing a PhD on that. So it's super small. And it's sad because it's actually very useful on not just mat, but also in software verification. So for instance, software verification today. So tiny market. Very few industries work on this and we need that.It's usually going to be like companies like building airplanes, air robotics,swyx: likeGuillaume: things [00:38:00] where they absolutely want to be sure. Life depend on this, but it's very rare that people formally verify the correctness of their software. But I think one of the reasons for this is simply that it's just hard to do.swyx: Are you think of TLA plus? It's the language that some people do for software verification? No. That people use in a ference, but but yeah, it's the reason I think why people don't use it more and why this industry is not as big as could be is because it's very hard. But now with cutting edges that are there, it's going to be very different.Guillaume: We're going to see much more of this. So I think yes, industry there is going to be much larger in the future that we, these models. So yeah. Here also anticipating this a little bit, we wanted to work on that because it's proving like a math theory and like a, essentially the same tools.swyx: Yeah.Reasoning Transfer and Agentsswyx: One of my theories is that because the proofs takes so long, it's actually just a proxy for long horizon reasoning and coherence and planning. Maybe a lot of people will say okay, it's for people who like math. It's for being okay. It's like a niche math language. Who cares? But actually, and you use this as part of your data mixture for [00:39:00] post-training and reasoning, actually, it might spike everywhere else.Yeah. And I think that's un under explored or no one's like really put out a definitive paper on how this generalizes.Guillaume: Yeah, absolutely. AndPavan: I think evenGuillaume: that's what we're seeing already. For instance, you should do some reasoning on math as then the American should do reason even.Yeah. In the early stage. So we, the, there is some transfer, some sort of emergence that happens. And I think some, it's also interesting, it's not just I think the topic in general, but it's, there is a lot of connection with this on including agents because. Sometimes the model can see like a three that it has to prove it's very complex, but then it can take the initiative to say, I'm going to prove this three lr.I'm going to suggest three Rs, and I'm going to in parallel prove each R. So three of them in parallel with sub agents, but I'm also going to prove them in theory and the three tool so you can do this also. Pretty interesting. You can, even if you fail to put one of the LeMar, you can actually, maybe you succeed to put the normal lema too, so you get some possible reward here.So it's a bit less Spartan issue, just get to zero one for the entire thing. [00:40:00] So it's pretty interesting. I think we can actually,Vibhu: yeah, it's also an interesting case just for specialized models in general, right? Like the cost thing you show is pretty interesting yeah, similar score wise, you are, thirty, seventy, a hundred fifty, three hundred bucks.Smaller.swyx: I think cost is a bit unfair, right? ‘cause this one is at like inference cost. It's always there on top with their margins on top of it. But, we don't know anything else, so we gotta figure it out.Vibhu: Okay.Next Frontiers in TrainingVibhu: I did wanna actually push on that more. Not on cost, but you mentioned about, okay, it's a great way to have verifiable long context reasoning.What are other frontiers that, I'm sure you guys are working on internally, there's a lot of push of people pushing back on pre-training. Scaling, RL pushing, compute towards having more than half of your training budget. All on rl. Where are you guys seeing the frontier of research in that?Guillaume: You mean theVibhu: just in foundation model training in the next, one thing that you guys do actually is you do fundamental research from the ground up, right? So you probably have a really good look at where you can [00:41:00] forecast this out.Guillaume: Yeah. I think for us we're still working a lot on the pre-training side.I think we are very far from situational, the pre-training. I think ML four preprinting will be like big step compared to everything we have done before. So we are pretty excited about this. And I think on the other side, I think now we have more and more to think about this algorithm that will actually support this very long trajectories.I think when it was, for instance, GRPO for it doesn't really work this any bit of policy. Which was okay initially because you are solving math problem that can be solved in like a few thousand tokens. So the model can alize them pretty quickly. So when you do your update, the model is never too far off.It's never too far off. But now when you are moving towards this kind of problems where certain takes hours, like six hours to get a reward, then your model is co pick places. So you have bi new infrastructure that supports this, but also new A, so now everything we're doing internally, we're trying to. Build some infra that we actually anticipate is what we have in six months, one now, which is this extremely no scenarios on the, I think when we started Missal, part of me and [00:42:00] we wanted to, is very nice under element where people are there, they can do research, they like with a lot of resources.So it was nice. I think things changed a lot when I think when J Pity came out. I think after that I think was. This one is same again. But but yeah, but it was nice. And I think we also want to work part of this descrip beforeswyx: coming to the end.Hiring and Team Footprintswyx: We're just, obviously, I think you guys are doing incredible work.You've, they are a very impressive vision for open source and for voice. What are you hiring for? What's the what are you looking for that you are trying to join the company?Guillaume: Yeah, so we are hiring a lot of people in our sense team. We're hiring, in all our offices. So we have a, our H two is in France in Paris.We have a small team in London. We like a team in Pato as well. Co we open some offices in in SAU, in Poland. So one in Zurich. We also like some presence in New York as well on Sooner one in San Francisco. So we all bit either way also like hiring remotely. So we're going the team trying to hire like very strong people.I think we want to stay, so the team is not. Instead of fairly small team. [00:43:00] But I think we want to keep it that way. ‘Cause we we find it quite efficient. So like a small team they agile so yeah.swyx: Okay.AI for Science Partnershipsswyx: Let's focus on science and the forward deployed. We actually are strong believers in science.We started the our new science pod that focuses specifically on the air for science. What areas do you think are the most promis.Guillaume: What we're pretty excited about right now, and something we have already started doing or that we'd probably be able to share more about this in a couple of months, is that we are exploring AI for science.And there are a lot of areas where we think that you could get some extremely promising buzz. If you were to apply AI in these domains. There are a lot of long inputs. You just have to find these domains where actually AI has not been yet applied, and it's usually hard to do because the people working in those domains don't necessarily know the capability of these models.They don't know. How I would just have to pair them with Yeah, exactly. Your researcher slashing, which is actually hard to do. But this matching, we're doing it naturally with our customers. So we have some company we are very closely with. So for instance, ISM Andreesen are one of our partners, so we're doing some research with them on their other, like tons of extremely interesting problems.Columns in physics, in [00:44:00] science matter science that they're essentially the only ones to work on. ‘cause they're doing something No, no one else is doing on the, yeah. So there are many domains where AI can actually revolutionize things. Just you have to think about it on you familiar with what can do or to apply it.So yeah, it's something where more modeling with our partners, with our customers sort AI for s, but.swyx: Yeah. Okay.Forward Deployed Skillsswyx: And then for deployed what it makes a good four deployed engineer, what do they need? Where do people fail?Guillaume: I think it's usually you need people that are very familiar with the tech and not necessarily with a lot of research expertise, but that are actually pretty good at using this model that can actually like that know how to do functioning, that know how to like, start some error pipeline.And it's it's not easy. It's something that mucus. Majority of companies will not be able to do this on their own. So here I think we need people that are, that like to solve problems that are accept solving some complex, very concrete problem. It's applied science basically.And yeah, so I think it's not too different. I think from the case you need in research because it's essentially you are trying to find solutions to problems that in [00:45:00] customers have not yet. So sometimes it's easy. Sometimes you're here to do the work. You have to like create synthetic data.Find some edge case. So it can be, yeah. Depends on the problem. But but yeah, you have to, I think it also a bit of patience on the be creative. I think very similar skill is Asian,Pavan: the diversity of the work they do. It always surprises me. It's it's, it goes all the way from the kind of stuff they encounter in industries.It's just very interesting. I think.swyx: Any fun like success anecdotes.Guillaume: Yeah, it can be actually training this small model on edge that just we do one specific thing can be like training some very large model without some specific languages as well. Making models really good at some tube use, like for instance, computer ID design, these kind of things.Is that pairing with vision as well? Yeah,Pavan: and the fact detection for chips or like in, in factories identifying things like it, the. Diversity could be anything where you can deploy these foundation models. So yeah the work to make it work in that specific setting, basically whatever it takes to make it like add value in that, by the way, workflow.Vibhu: Yeah. [00:46:00] And it goes across the stack, right? Like even just pulling up the website like.swyx: It's so broad on compute. It is so broad.Vibhu: We didn't even touch on if you have a coding CLI tool. One thing you guys were actually like, I think the first tool was agents, ral agents. You had the agent builder, you can serve it via API and all that.And I'm guessing forward deploy people.Guillaume: Yeah.Vibhu: Help build that out and stuff.Customer Feedback LoopGuillaume: It is also why we are, so we're doing many things, but I think that's also part of the value proposition that sometime know customers. They're always very. Extremely careful about their data and they don't want to, they don't like, trusting so many partners, trusting one partner for code, giving the data to another third party for like audios and another one.So they don't like this here. What they really like with our approach that we can help them on anything so they don't have to send the data to so many clouds. So yeah,swyx: I think that there can be many orders of magnitude more. F Ds then research scientists and they don't need your full experience, but they're still super variable to customersGuillaume: in practice.These two teams [00:47:00] are still quite intertwine, very often. Yeah. So first of all, they're using the same tools, the same data pipeline and everything on the, it's it's very helpful for the science team to get the feedback and the solution team ‘cause they can. Look at these customers are trying to do this.This is not working. It can really be show in the next version. Yeah. But this is basically a real world eval. Yeah, it's real world eval and it's not something, for instance, if you're just working in the lab, it's just ships model. But you don't do this work of for customers. You have no idea for whether your model is good at this H case.For instance, you even in year found this, right? So yeah, there is a very gap, big gap between the public benchmarks that are very like academic. OnPavan: the rare cases are just very diverse and in the specific concept of a customer, you can fine tune and make it like first evaluate, create a solid eval, benchmark, and then measure in the context of their, the kind of audio.Like for instance, one use case is literally just, there's the word for kids and they have to just say it out. It's a very specific thing. You're just saying one word and then you have to you, you'll grade the kid whether they did it right or not. It's [00:48:00] like R for, but so there're very diverse use cases and the idea is that they, the.Applied scientist engineer will go and make it better. And then from the learnings we incorporate it into the base model itself. So it's it's just better out of the box.Vibhu: Yeah. It's a good full circle system. Like the foundation model evals are all just proxies of what you really, you're never gonna have one that says it, it doesn't make sense for there to be, a one word transcription like that.It's not something you wanna fit on. Perfect.Wrap Up and Thanksswyx: Everyone should go check out everything that Michelle has to offer and try the TTS model, which will link in the show notes. But thank you so much for coming tha thanks. Such a stretch. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe

March 2026 Sustainable Stock and ETF Picks. Includes an article with a terrific ranking of companies by their ethical standards. By Ron Robins, MBA Transcript & Links, Episode 165, March 27, 2026 Hello, Ron Robins here. Welcome to my podcast episode 165, published on March 27, 2026, titled "March 2026 Sustainable Stock and ETF Picks." This podcast is presented by Investing for the Soul. Investingforthesoul.com is your go-to site for vital global, ethical, and sustainable investing mentoring, news, commentary, information, and resources. Remember that you can find a full transcript and links to content, including stock symbols and bonus material, on this episode's podcast page at investingforthesoul.com/podcasts. Also, a reminder. I do not evaluate any of the stocks or funds mentioned in these podcasts, and I don't receive any compensation from anyone covered in these podcasts. Furthermore, I will reveal any investments I have in the investments mentioned herein. I have a great crop of 18 articles for you in this podcast! Note: Some companies are covered more than once. Now with so many articles to potentially cover, I've chosen 3 to quote from. The other 15 can be found on the webpage for this podcast edition, along with their titles and links. ------------------------------------------------------------- The Best 3 Renewable Energy Stocks to Buy and Hold for Decades from fool.com In this episode, I begin with an article on an investment sector at the core of most ethical and sustainable investors' portfolios: renewable energy. The article's title is The Best 3 Renewable Energy Stocks to Buy and Hold for Decades from fool.com. It's by Reuben Gregg Brewer. Here are some quotes on each of his picks. "1) Brookfield Renewable is 100% clean (BEP)(BEPC) Brookfield Renewable has exposure to hydroelectric, solar, wind, battery storage, and nuclear power… The assets… are spread across North America, South America, Europe, and Asia. If you are looking for a simple way to participate in the shift toward clean energy, Brookfield Renewable is a good choice… The one wrinkle is that you can buy Brookfield Renewable in two different ways. The partnership share class offers a distribution yield of 5.2% while the corporate share class has a dividend yield of 3.8%. They represent the same business and have the same dividend payment; the difference is that the corporate shares are in higher demand among institutional investors. Small investors should feel comfortable with the partnership. 2) NextEra Energy is a giant in two businesses (NEE) NextEra Energy operates one of the largest regulated electric utilities in the United States. That is the foundation on which it has built one of the largest solar and wind power businesses in the world. This combination has led to 11% annualized dividend growth over the past decade. Half that rate of dividend growth would be considered good for a utility. NextEra Energy's dividend yield is 2.7%, which is actually above the utility average of nearly 2.5%... That said, management is calling for dividend growth to slow to a still very healthy 6% in 2027 and 2028. 3) TotalEnergies mixes the old with the new (TTE) TotalEnergies will be the hardest sell as a clean energy investment because it is an integrated energy giant. That means that it is a vertically integrated oil and natural gas business. If you only want clean energy, you probably won't want to buy TotalEnergies." End quotes. ------------------------------------------------------------- 11 Best Ethical Companies to Invest In Now According to Reddit from insidermonkey.com My second article has some unusual investing options for many investors coming to my podcasts. It's titled 11 Best Ethical Companies to Invest In Now According to Reddit from insidermonkey.com and is by Noor Ul Ain Rehman. Here are some quotes from the article. "We first sifted through relevant threads on Reddit to compile a list of the best ethical companies and then selected the top 11 that were the most popular among elite hedge funds as of Q3 2025. We sourced the hedge fund data from Insider Monkey's database. The stocks are ranked in ascending order of hedge fund sentiment… All data was recorded on March 17… ​Our research has shown that we can outperform the market by imitating the top stock picks of the best hedge funds. 11. The Hershey Company (NYSE:HSY) Morgan Stanley lifted the price target on The Hershey Company to $247 from $238 on March 16, maintaining an Overweight rating on the shares. The firm told investors that it believes the market is overly focused on pricing rollback risk and is underestimating the earnings recovery from cocoa normalization beginning in the second half of 2026 and accelerating into 2027. 10. Cognizant Technology Solutions Corporation (NASDAQ:CTSH) On March 16, Cognizant Technology Solutions Corporation announced the launch of Cognizant AI Factory, which is a multi-tenant, enterprise-grade offering powered by Dell Technologies (DELL) and NVIDIA AI (NVDA) infrastructure and software platform. Management stated that the Cognizant AI Factory is designed to help organizations scale artificial intelligence more securely, efficiently, and responsibly, and aims at unifying the management of the AI lifecycle in a single environment. 9. Kimberly-Clark Corporation (NASDAQ:KMB) Piper Sandler cut the price target on Kimberly-Clark to $114 from $133 on March 13 and maintained an Overweight rating on the shares. The firm told investors that the company's Q1 top-line momentum is tracking in line with what it had expected, with costs hedged roughly nine months out, securing roughly the balance of 2026. However, Piper also stated that if increased oil costs persist, there could be a risk to the outlook for 2027… Kimberly-Clark sells its products under various brands, including Kleenex, Scott, Cottonelle, DryNites, Huggies, and others. 8. Ecolab Inc. (NYSE:ECL) March 16, Ecolab was upgraded to Buy from Hold by Berenberg, with the firm adjusting the price target on the stock to $326 from $300. Berenberg told investors in a research note that (Ecolab) will raise prices across all products, services, and geographies by 10%-14%, adding that it anticipates the price increases to be implemented swiftly and become a 'sticky component of Ecolab's structural pricing, rather than being rolled back'. The firm views Ecolab as a net inflation beneficiary… Ecolab offers hygiene, water, and infection prevention solutions and services. 7. FedEx Corporation (NYSE:FDX) On March 10, JPMorgan lifted the price target on FedEx to $424 from $294 while maintaining a Neutral rating on the shares. The firm told investors that it updated the company's model ahead of the earnings report. 6. Accenture plc (NYSE:ACN) TD Cowen cut the price target on Accenture to $275 from $282 on March 16, maintaining a Buy rating on the shares. The firm told investors that it updated its model ahead of the fiscal Q2 results, saying that, considering raised macro uncertainty and no clear end in sight with the Iran conflict, it believes that it is now reasonable that Accenture could just leave its FY26 growth guide as is… Accenture is a global professional services company that combines technology and leadership in data, cloud, and AI with functional expertise, industry experience, and global delivery capability. 5. Salesforce, Inc. (NYSE:CRM) March 16, Salesforce, announced the commencement of the prepayment and initial delivery of around 103 million shares under its previously announced $25 billion accelerated share repurchase (ASR) agreements. The company entered into these agreements on March 11, 2026, with certain financial institutions. Management stated that the transaction marks the largest accelerated share repurchase in history, and represents the immediate execution of half of the $50 billion aggregate Share Repurchase Program authorized by Salesforce's Board of Directors in February 2026. Salesforce, designs and develops cloud-based enterprise software for customer relationship management. 4. ServiceNow, Inc. (NYSE:NOW) ServiceNow, was upgraded to Outperform from Neutral by BNP Paribas on March 16, with the firm setting a $140 price target. The rating update came the same day ServiceNow, announced an expansion of its partnership with Carahsoft Technology Corp. to extend availability to the ServiceNow AI Platform across Carahsoft's full reseller ecosystem in the U.S. and Canada… ServiceNow, offers an AI platform for business transformation, boosting productivity and maximizing business outcomes. 3. Micron Technology, Inc. (NASDAQ:MU) On March 16, TD Cowen lifted the price target on Micron Technology, to $500 from $450 and maintained a Buy rating on the shares. The firm told investors that it updated its model ahead of the company's fiscal Q2 earnings, where a strong beat is expected. TD Cowen also said that while it continues to see upside to the buy-side view even after earnings, the majority of the long-term stock returns could be driven by re-rating… Micron Technology, provides innovative memory and storage solutions. 2. Eli Lilly and Company (NYSE:LLY) Eli Lilly was downgraded to Reduce from Hold by HSBC on March 17, with the firm bringing the price target on the stock down to $850 from $1,070. The firm told investors in a research note that it believes expectations for the total addressable market for obesity are elevated at over $150 billion, and the market is likely to be in the range of $80 billion -$120 billion by 2032, with price competition 'likely to be significant'. Eli Lilly develops, manufactures, discovers, and sells pharmaceutical products. 1. Mastercard Incorporated (NYSE:MA) Mastercard announced on March 17 a definitive agreement for the acquisition of BVNK for up to $1.8 billion, including $300 million in contingent payments. BVNK is a leader in stablecoin infrastructure. Management stated that the deal expands the company's end-to-end support of digital assets and value movement across currencies, rails, and regions… Mastercard is a technology company that provides payment solutions for developing and implementing debit, credit, prepaid, commercial, and payment programs via its brands." End quotes. ------------------------------------------------------------- Ethisphere Announces the 2026 World's Most Ethical Companies®; 2026 Ethics Premium™ Shows Honorees Outperformed Peers by 8.2 Percentage Points, from morningstar.com The last article I'm covering presents a highly respected ranking of companies according to a wide range of ethical criteria. The article's title is Ethisphere Announces the 2026 World's Most Ethical Companies®; 2026 Ethics Premium™ Shows Honorees Outperformed Peers by 8.2 Percentage Points, from morningstar.com. The article is from Business Wire. Here are some quotes from the introduction to the article. "Ethisphere today announced the 2026 World's Most Ethical Companies®, recognizing 138 companies for having best-in-class ethics and compliance programs, corporate governance practices, and cultures of integrity. Alongside the honoree announcement, Ethisphere released the Ethics Premium™, a five-year analysis comparing the stock performance of publicly traded 2026 World's Most Ethical Companies honorees with a broad global benchmark. From Jan. 1, 2021 through Dec. 31, 2025, this year's publicly traded honorees outperformed the benchmark by 8.2 percentage points. 2026 marks the 20th annual recognition of the World's Most Ethical Companies, which includes companies across 40 industries in 17 countries. This year's honorees are listed at www.worldsmostethicalcompanies.com. 2026 cohort highlights This year's cohort includes… six 20-time honorees, companies that have earned the designation every year since the recognition began. (The) six 20-time honorees: Aflac, Inc. (AFL) Ecolab (ECL) International Paper Company (IP) Kao Corporation (4452.T) Milliken & Company (Private company) PepsiCo Inc. (PEP) End quotes. ------------------------------------------------------------- More articles from around the world with Sustainable Investment Picks for March 2026. 1. Title: BlackRock Says Buy AI Energy Stocks Over Big Tech in 2026. Here Are 3 Top Picks from finance.yahoo.com. By James Brumley, The Motley Fool. 2. Title: How big business is rewriting the U.S. sustainability story from corporateknights.com. Introduction by Tristan Bronca and CK Staff. 3. Title: Best Renewable Energy Stocks To Watch Now from marketbeat.com. By MarketBeat. 4. Title: Billionaires David Tepper and Michael Platt Divested Nvidia Holdings and Acquired This AI Stock That Has Surged 40,000% Since Its Market Debut from bitget.com. By 101 finance. 5. Title: After 345% Run, This Solar Power Stock Heats Up As Oil Tops $102 from investors.com. By Juan Carlos Arancibia. 6. Title: socially responsible investment opportunities in renewable energy from financelipa.com. By Houssem Belhaj. 7. Title: British Land Company PLC (BLND.L) Stock Analysis: Evaluating A 20% Potential Upside Amid Robust Revenue Growth from directorstalkinterviews.com. By Olivia Thompson. 8. Title: Prediction: This Overlooked AI Infrastructure Stock Could Double in 2026. Here's Why from fool.com. By Harsh Chauhan. 9. Title: Asia Pacific's green champions step into the spotlight from corporateknights.com. By Gordon Feller. 10. Title: Three Green Energy Stocks to Buy from intellectia.ai. By Emily J. Thompson. 11. Title: Brookfield Asset Mgmt stock faces analyst scrutiny amid volatile asset management sector shifts from ad-hoc-news.de. By ad-hoc-news.de. 12. Title: The Best 3 Renewable Energy Stocks to Buy and Hold for Decades from fool.com. By Matt DiLallo. 13. Title: These top European firms are stepping up their sustainable growth from corporateknights.com. By Ashley Perl. 14. Title: The most innovative companies in corporate social responsibility in 2026 from fastcompany.com. By Anna-Louise Jackson. 15. Title: 5 Best Sustainable Investing Funds in 2026 from moneymagpie.com. By Ruby Layram. ------------------------------------------------------------- Ending Comment These are my top news stories with their stock and fund tips for this podcast, "March 2026 Sustainable Stock and ETF Picks." Please click the like and subscribe buttons wherever you download or listen to this podcast. That helps bring these podcasts to others like you. And please click the share buttons to share this podcast with your friends and family. Let's promote ethical and sustainable investing as a force for hope and prosperity in these tumultuous times! Contact me if you have any questions. Thank you for listening. My next podcast will be on April 24th. See you then. Bye for now.   © 2025 Ron Robins, Investing for the Soul

Today is a different feel for Just the Guys with guest panelists John Fela & Rev. Josh Davis.This month, we are putting the focus on disability, differences, and diversity, and the Church at large!Dan, John & Josh share personal and lived experiences, father, pastor, spiritual director, coach, and advocate experiences!What is grief? Is that wrong? Does Grief mean God didn't do something good for me?How is the CHURCH doing in supporting families with a member (or members) on the autism spectrum?The Guys talk about what went well, the guilt they carry for what they didn't know at the time, forcing assimilation/masking in the 'church environment' as well as what they know now, and how churches can better come along with children/teens/families. John Fela:John Fela (M.Ed) is a national disability advocate, working with both faith-based and non-faith-based disability organizations. He previously worked for Joni and Friends, a global disability ministry. Prior to that, he spent almost 20 years in education, serving in a variety of roles as a classroom teacher in both public and private school settings, as well as being a mentor teacher and school director. He holds certifications in both Montessori and traditional teaching methods and is trained in a variety of specializations, including ESL and Special Education.John is a public speaker, a blogger for a variety of disability advocacy platforms, and the author of Faith Like My Father, a memoir of his journey as a parent of a child with a disability. John lives in Lyons, IL, with his wife, Faith, and is father to his son, Christopher (ASD/NS).https://johnfela.com/contact/ Josh Davis:Josh Davis is not just a pastor—he is an advocate for neurodivergent individuals, using his platform to share hearts, build bridges, and encourage understanding in a world that often overlooks the unique experiences of those with autism and ADHD. He invites you to explore your own faith, engage in self-discovery, and discover the endless possibilities that lie ahead as you seek a deeper connection with yourself and God.Don't miss out on the ongoing conversation about faith and neurodivergence.To connect further, find Josh on Facebook, Instagram, or email at: justjosuedavis@gmail.com. Or, for personal encouragement on your own neurodivergence journey, consider booking a spiritual direction session with Josh on Patreon.Listen to NeuroDivergent Faith podcast at:https://www.buzzsprout.com/2441513/episodes/16504569-discovering-autism-adhd-through-faith-josh-s-journey-of-self-discovery

Dr. Ed Socci, recipient of the 2026 APR Recycling Leadership Award for Outstanding Leadership, joins host Kara Pochiro to reflect on more than three decades in packaging innovation and collaboration. As R&D Director for Packaging Technology Platforms at PepsiCo, Dr. Socci has played an important role in recyclable packaging design, contributing to APR's Design® Guide for Plastics Recyclability, and a wide variety of other significant achievements in the world of sustainability.  In this episode, he discusses how his career in packaging began, the collaborations that helped drive key design for recyclability improvements, and why innovation, collaboration, and optimism remain essential to the future of plastics recycling.

Continuing this month's theme of the CHURCH and how she supports the marginalized, hurting, and least of these. Today, Dr. Stephanie and Barb talk with Sarah McDugal about women and children in abusive situations, navigating the courts and the Church.About Sarah, in her own words:BIO:I'm Sarah McDugal, co-founder of Wilderness to WILD and the TraumaMAMAs mobile app. I'm an author, coach, survivor, and TraumaMAMA.As an autistic woman and survivor of both domestic violence and child sexual assault -- my hyper focus is developing gentle, proven resources for women who want to heal after toxic and traumatic stress.  I'm trained in:the Deceptive Sexual Trauma Model, andAPSATS (the Association of Partners of Sex Addicts Trauma Specialists)And I'm a Certified Assessor with the Johns Hopkins Danger Assessment.Some of my books include:He Chose Porn Over Me: Women Harmed by Men Who Use PornMyths We Believe: Predators We TrustOne Face: Shed the Mask, Own Your Values, and Lead WiselyMy goal is to provide accessible, affordable, authentic tools to guide you out of the wilderness of abuse, into the WILD thriving post-trauma life that waits ahead.How to find out more!Check out what I'm doing for (almost exclusively) ND protective parents these days: www.myfreedomnavigator.comthe SCOOP - Group Coaching Membershipwww.wildernesstowild.com/the-scoop  Righteous or Rotten? How to know if it is biblically bad enough to divorcehttps://www.wildernesstowild.com/unholy-fruit-your-wild-guide-to-discerning-toxic-character  Her two websites:https://www.wildernesstowild.com/https://www.myfreedomnavigator.com/

This month, we are focusing on autism/neurodiversity faith issues and the church!Starting out the month with permission from a former guest, Josh Davis, we are airing his "What Autistics Want the Church to Know."Dan and I introduced this topic for March, sharing a story from our daughter from our book Embracing the Autism Spectrum, and part 2 of our personal stories will be on Patreon. NeuroDiverse Christian Couples: Autism Spectrum Resources for Marriage & Family

February is the month of love- what is real love- real hesed- sacrificial love? How did Adam love Eve?Today is not our usual crew, but a discussion with guest Russell Grigsby about a book that radically changed his mindset about loving his wife well.In this episode of Just the Guys, Dan sits down with entrepreneur and executive coach Russell Grigsby to talk about late-in-life autism diagnosis, trauma, pride, and the hard work of rebuilding a marriage. Russell shares how childhood wounds, avoidant attachment, and unrecognized autism shaped his relationships — and how confronting shame, embracing humility, and rethinking biblical leadership transformed his life at home. Through books, prayer, coaching, and intentional growth, he learned to stop trying to fix his spouse and instead take responsibility for his own healing. The result is a marriage marked by safety, connection, and hope. This conversation is an honest look at what happens when a man chooses humility over defensiveness and growth over comfort.Books Mentioned:Mending the Soul Groups found at: MendingthesoulFind a GroupAdam loves EveEscaping Enemy ModeBrene Brown's books on Shame and Vulnerability  About Russell:Russell is passionate about encouraging others to fulfill their destiny. In one-on-one settings, Russell helps men and women discover what they are designed to do and then pursue their calling. After receiving an MBA from SMU in 1982, Russell began his career in commercial banking in Austin, Texas. After six years in banking, he joined a series of startups as CFO. He discovered he loved the startup process and began founding and running his own companies.Since 1993, he has run financial services, biotech, real estate, and mining companies. He loves building new businesses and creating a vision for their success. Russell is excited to work as a C12 Chair and call on his long entrepreneurial career to help others accomplish their God-given dreams. He loves meeting with people one-on-one to hear their stories and hear about their calling.Along the way, Russell has passionately pursued intimacy with God. As a follower of Jesus since 1967, Russell has had a profound relationship with God that continues to grow daily. He is a retreat speaker and loves to teach about living with greater power and authority as a believer in Jesus.Russell and his wife, Gina, live in Southwest Austin. They share five grown sons and a daughter.

Joseph Fournier presents “part two” on how Pacific Walker circulation controls Earth's largest greenhouse effect: cloud longwave radiative forcing. He explains cloud radiative forcing terminology, cites literature claiming cloud greenhouse warming dwarfs CO2 forcing, and shows satellite-era links between trade winds, cloud shifts during ENSO, outgoing longwave radiation, and global/tropical temperature anomalies. He contrasts absorbed solar radiation, OLR, and Earth energy imbalance, arguing global averages can be dominated by regional Pacific dynamics. He reviews multidecadal “dimming/brightening” sunshine trends in Europe, Japan and the U.S., discusses aerosols vs natural drivers, and briefly addresses future uncertainty, AMO/IPO impacts, and solar/cosmic-ray hypotheses. 00:00 Welcome Back: Joseph Fournier & Why This Is “Part Two”02:15 Cloud Basics 101: Shortwave vs Longwave, Net Cloud Radiative Forcing05:51 Albedo Matters: How Small Cloud Changes Rival CO₂ Forcing08:40 Evidence in the Literature: Trendberth and Early Satellite Cloud Forcing Maps14:28 Clouds vs CO₂ Since 2000: Step-Change in Cloudiness and OLR16:56 Geography Over Global Averages: The Western Pacific Warm Pool Hotspot20:12 Warm Pool Size, SST, and Real-World Impacts (Winters, ENSO Timescales)22:48 Walker Circulation Explained: Where Deep Convection Sits in La Niña vs El Niño25:34 Warm Pool “Thermal Capacitor”: Thermocline Slosh, Water Volume, and Cloud Shift30:32 Sea Level Pile-Up and the Gravity-Driven Discharge During El Niño32:36 Radiation Signatures of ENSO: DLW/OLR Links to Niño Indices36:13 Cloud Forcing Ratios & Decadal Patterns: What El Niño Does to Warm Pool Clouds40:34 Global Signals: OLR vs Global Air Temperature and the ENSO Lead–Lag45:14 Trade Winds as the Control Knob: Linking Pacific Easterlies to Global OLR47:44 Tropical temps, OLR & trade winds: Walker circulation link48:42 Clouds as the “other knob”: absorbed shortwave (ASR) vs temperature50:29 2023 El Niño cloud changes: low-cloud cover & shifting albedo53:49 ASR vs OLR since 2000: the hiatus ends and the energy budget shifts55:44 Earth Energy Imbalance (EEI) vs GAT: why the correlation breaks57:58 Seasonal cycle first: EEI swings, hemispheres, clouds & land–ocean contrast01:00:10 Wrap-up: two greenhouse effects & a call for academics to test it01:02:54 Sunshine hours & AMO: UK/Europe brightening over the 20th century01:07:26 Aerosols vs clouds: modern satellite trends and the “brightening” debate01:11:53 Global dimming/brightening goes global: Japan/China records & Pacific teleconnections01:12:56 Natural vs human drivers: when aerosols don't explain surface radiation01:18:13 Forecasting the next decade: sun, AMO/IPO, cooling claims & big uncertainties01:26:17 Closing remarks: slides, Substack, and the climate–energy–geopolitics linkMore information about Joseph Fournier: https://co2coalition.org/teammember/joseph-fournier/His 2024 presentation: https://youtu.be/P2hVW0R67CYJoseph's Substack: https://josephfournier.substack.com/X: https://x.com/JosephF55175005=========Slides, summaries, references, and transcripts of my podcasts: https://tomn.substack.com/p/podcast-summariesMy Linktree: https://linktr.ee/tomanelson1

Het lijkt deze week pas goed te zijn doorgedrongen tot de media: de belasting op vermogen in box 3 wordt drastisch verhoogd. Zelfs over niet gerealiseerde winsten, de 'papieren' koerswinsten, moet je een belasting van 36 procent afdragen. "Het ergste", zegt Ralph Wessels van ABN Amro, "is dat dit juist de kleine beleggers onevenredig hard raakt. De grote beleggers kunnen beleggen in hun BV en vallen dan buiten box 3. Deze forse belasting werkt niet bepaald nivellerend." Voor mensen die hebben belegd voor hun pensioen is dit een hard gelag. Ook Riaan Prinsloo van ING ziet er niets in. "De EU wil juist stimuleren dat mensen meer gaan beleggen, zodat Europa sterker wordt, meer kan investeren in bijvoorbeeld defensie." Riaan ziet niet hoe deze belasting Nederlandse beleggers nog enthousiast zal maken om meer in Europa te beleggen. De draconische belastingwet moet nog door de Eerste Kamer aangenomen worden. Is er niets positiefs te melden? Jawel, beide experts zijn, gegeven de geopolitieke onzekerheden, positief over de beurs. De groei van de wereldeconomie trek aan en ook de winstgroei van bedrijven, in alle regio's, is positief. Verder in de podcast aandacht voor JD.com, die de aanval opent op online winkels in Nederland en over de populariteit van Shell onder Nederlandse beleggers. We bespreken de cijfers van o.a. Besi, Aegon, ASR en Walmart. Natuurlijk bespreken we de luisteraarsvragen en geven de experts hun tips. Riaan tipt een ETF met de ISIN-code IE00B3WJKG14, Ralph tipt ook een ETF die de ISIN-code LU2504564761 heeft. Geniet van de podcast! Let op: alleen het eerste deel is vrij te beluisteren. Wil je de hele podcast (luisteraarsvragen en tips) horen, wordt dan Premium lid van BeursTalk. Dat kost slechts 9,95 per maand, 99 euro voor een heel jaar. Abonneren kan hier! VanEck ETF’s (advertorial) Deze week is ook weer het tweewekelijks gesprek te beluisteren met Martijn Rozemuller, ceo van VanEckETF’s, de partner van BeursTalk. In deze aflevering bespreken we de sterk toegenomen interesse van beleggers in dividendaandelen. Bij VanEck is de Morningstar Developed Markets Dividend Leaders ETF, na de VanEck Defense ETF, de grootste groeier. Opmerkelijk, want op het eerste gezicht is een breed gespreide ETF met degelijke bedrijven op het eerste gezicht nogal saai. Maar, zo legt Martijn uit, saai is het nieuwe sexy. En sexy met een reden, want naast een mooi dividendrendement laten de aandelen in de ETF vaak ook een nette koersstijging zien. Morningstar heeft een mooie index gemaakt met bedrijven die al 10 jaar een degelijke performance laten zien Een ander aantrekkelijk punt voor Nederlandse beleggers is dat de ETF in de Nederlandse fondsenparaplu zit. De ETF heeft de status van fiscale beleggingsinstelling. Dat betekent dat beleggers de ingehouden dividendbelasting terugkrijgen! Geniet van de podcast! De gepresenteerde informatie door VanEck Asset Management B.V. en de aan haar verbonden en gelieerde bedrijven (samen "VanEck") is enkel bedoeld voor informatie en advertentie doeleinden aan Nederlandse beleggers die Nederlands belastingplichtig zijn en vormt geen juridisch, fiscaal of beleggingsadvies. VanEck Asset Management B.V. is een UCITS-beheerder. Loop geen onnodig risico. Lees de Essentiële Beleggersinformatie of het Essentiële-informatiedocument. Meer informatie? https://www.vaneck.com/nl/nl/See omnystudio.com/listener for privacy information.

Verrassing! Ziggo komt naar de Amsterdamse beurs. Met een ingewikkelde constructie kun je vanaf 2027 beleggen in de Ziggo Group: een holding, bestaande uit VodafoneZiggo en het Belgische Telenet. Beide bedrijven zijn nu nog deels eigendom van een Amerikaans bedrijf. Maar dat wil er vanaf. Wil je er straks als eerste bij zijn, moet je dus aandelen in Liberty Global kopen. Maar waarom zou je dat doen? Gaan wij voor je uitzoeken. We vertellen je ook over Warren Buffett. Die heeft aan het einde van z'n carrière toch nog wat veranderingen doorgevoerd bij zijn Berkshire Hathaway, blijkt nu. Op het laatste moment bouwt Buffett het belang in Apple verder af, en hij neemt ook afscheid van een enorm deel van z'n Amazon-aandelen. Wat moeten we daarachter zoeken? Dan hoor je ook nog: Waarom de ECB misschien nu al op zoek moet naar een opvolger voor Christine Lagarde Waarom chemieconcern Bayer voor miljarden moet schikken Waarom Nvidia afscheid neemt, én juist nieuwe banden aanhaalt Te gast: Nico Inberg van De Aandeelhouder BNR Beurs is een journalistiek onafhankelijke productie, mede mogelijk gemaakt door Saxo. Over de makers: Jelle Maasbach is presentator van BNR Beurs en freelance financieel journalist. Zijn favoriete aandeel om over te praten is Disney, maar daar lijkt hij de enige in te zijn. Sinds de eerste uitzending van BNR Beurs is 'ie er bij. Maxim van Mil is presentator van BNR Beurs en journalist bij BNR, waar hij zich focust op de financiële markten en ontwikkelingen in de tech-wereld. Je krijgt hem het meest enthousiast als hij kan praten over ASML, of oer-Hollandse bedrijven zoals Ahold of ABN Amro. Jorik Simonides is presentator van BNR Beurs, economieredacteur en verslaggever bij BNR. Hij wordt er vooral blij van als het een keer níet over AI gaat. Milou Brand is presentator van BNR Beurs, freelance podcastmaker en columnist bij het Financieele Dagblad. Jochem Visser is presentator van BNR Beurs, maakt Beursnerd XL en is redacteur bij BNR Zakendoen en de podcast Onder Curatoren. Vraag hem naar obscure zaken op financiële markten en hij vertelt je waarom het eigenlijk nóg leuker is dan je al dacht. Over de podcast: Met BNR Beurs ga je altijd voorbereid de nieuwe beursdag in. We praten je in een kleine 25 minuten bij over alle laatste ontwikkelingen op de handelsvloer. We blijven niet alleen bij de AEX of Wall Street, maar vertellen je ook waar nog meer kansen liggen. En we houden het niet bij de cijfers, maar zoeken ook iedere dag voor je naar duiding van scherpe gasten en experts. Of je nu een ervaren belegger bent of net begint met je eerste stappen op de beurs, de podcast biedt waardevolle inzichten voor je beleggingsstrategie. Door de focus op zowel de korte termijn als de lange termijn, helpt BNR Beurs luisteraars om de ruis van de markt te scheiden van de essentie. Van Musk tot Microsoft en van Ahold tot ASML. Wij vertellen je wat beleggers bezighoudt, wie de markten in beweging zet en wat dat betekent voor jouw beleggingsportefeuille.See omnystudio.com/listener for privacy information.

Welcome back to part 2 of the Gold Standard of Care!If you did not hear part one, go back to January 19th to hear the panel introductions and what we believe is the Gold standard of care! We talk through some myths and stereotypes and share some truths about autism/neurodiversity and marriage.Jeremy tackles: Should you force a neurodivergent partner to undergo assessment?Barbara: Neurodiversity is not the ONLY issue in your marriage.Jenilee: Autism can express itself differently in girls/womenRobin: Emotional Regulation is part of Executive Function and is not a character issueShawna: It is a fallacy that ND people should be encouraged to watch porn to learn how to have sex or whattheir spouses would like in their intimate lifeDan: While you may never achieve the level of empath as an ND/AS husband, you can become more relationalStephanie: What is the cause of autism? How to read research critically.The study Dr. Stephanie mentions that holds a high standard of research credibility: Association of Genetic and Environmental Factors With Autism in a 5-Country Cohort

(2019)FULL study available: journals/jamapsychiatry/fullarticle/2737582

Hoera, het cijferseizoen is nog niet voorbij! In Nederland kun je rekenen op cijfers van verzekeraar ASR, IMCD, Besi, AirFrance-KLM, Aegon, bouwbedrijf BAM én Theon, de Griekse maker van nachtkijkers. In de Verenigde Staten komen Walmart én Ebay met cijfers. En de hele week wordt de AI Impact Summit georganiseerd in de Indiase hoofdstad New Delhi, met allerlei AI-kopstukken. Daar valt dus ook genoeg te beleven. Maar Han Dieperink richt zijn ogen op eigen land: op de fiscus, om precies te zijn. Want Box-3, is dat nog een belastinghervorming of kunnen we het beter een emigratieprogramma noemen? Han Dieperink van Auréus is niet onverdeeld enthousiast over de plannen van het kabinet om vanaf 2028 belasting te gaan heffen op papieren winst. Uiteindelijk kent de heffing zoals die er uit moet komen te zien alleen maar verliezers, zegt hij. Er ís een alternatief, dat alleen maar winnaars kent... Over de podcast: In Beurs in Zicht stomen we je klaar voor de beursweek die je tegemoet gaat. Want soms zie je door de beursbomen het beursbos niet meer. Dat is verleden tijd! Iedere week vertelt een vriend van de show waar jouw focus moet liggen. Over de makers: Jelle Maasbach is presentator van BNR Beurs en freelance financieel journalist. Zijn favoriete aandeel om over te praten is Disney, maar daar lijkt hij de enige in te zijn. Sinds de eerste uitzending van BNR Beurs is 'ie er bij. Maxim van Mil is presentator van BNR Beurs en journalist bij BNR, waar hij zich focust op de financiële markten en ontwikkelingen in de tech-wereld. Je krijgt hem het meest enthousiast als hij kan praten over ASML, of oer-Hollandse bedrijven zoals Ahold of ABN Amro. Jorik Simonides is presentator van BNR Beurs, economieredacteur en verslaggever bij BNR. Hij wordt er vooral blij van als het een keer níet over AI gaat. Milou Brand is presentator van BNR Beurs, freelance podcastmaker en columnist bij het Financieele Dagblad. Jochem Visser is presentator van BNR Beurs, maakt Beursnerd XL en is redacteur bij BNR Zakendoen en de podcast Onder Curatoren. Vraag hem naar obscure zaken op financiële markten en hij vertelt je waarom het eigenlijk nóg leuker is dan je al dacht. See omnystudio.com/listener for privacy information.

PRESENTED BY: CONCRETE LOGIC ACADEMY Practical education and ongoing development for concrete professionals at every stage of their career. Join here: https://www.concretelogicacademy.com/EPISODE SUMMARYConcrete cracks are often brushed off as shrinkage, restraint, or “just part of concrete.”That mindset gets structures in trouble.In this episode of the Concrete Logic Podcast, Seth Tandett is joined by Dr. Jon Belkowitz to break down alkali-silica reaction (ASR) and alkali-aggregate reaction (AAR)—what they are, how they develop, and why they continue to surprise engineers, contractors, and owners decades after first being documented.They walk through the history of ASR, how it shows up in real structures, why it's often misdiagnosed, and how modern testing and prevention strategies are improving—but still imperfect. The core message is simple: cracks are symptoms, not root causes, and ignoring them is how durability problems turn into long-term failures.This is a practical, field-informed conversation for anyone responsible for designing, specifying, building, or maintaining concrete structures.WHAT YOU'LL LEARNWhy ASR remains one of the most misunderstood concrete durability issuesThe difference between ASR and AAR—and why the distinction mattersHow reaction-driven cracking differs from typical shrinkage or restraint crackingWhen ASR damage can accelerate faster than expectedHow ASR is identified and diagnosed in real structuresWhere current ASR testing methods work—and where they fall shortWhy prevention is still more reliable than remediationHow concrete professionals should think about cracks before they spreadCHAPTERS00:00 – Why concrete cracks should never be ignored02:11 – ASR vs. AAR: definitions and mechanisms04:50 – How ASR damages concrete over time07:47 – Identifying and diagnosing ASR in the field10:08 – Testing methods and prevention strategies13:19 – The future of ASR management in concrete structuresGUEST INFODr. Jon BelkowitzIntelligent ConcreteJon@intelligent-concrete.comCONCRETE LOGIC PARTNERSINTELLIGENT CONCRETEConcrete not behaving the way it should?Intelligent Concrete combines lab-level testing with real-world field experience to identify the true root cause of concrete performance issues—not just treat the symptoms.Reach out for help: https://www.concretelogicpodcast.com/intelligent-concreteCONCRETE LOGIC ACADEMYEarn PDHs in the same straight-talk format as the podcast:Join now: https://www.concretelogicpodcast.com/academySUPPORT THE PODCASTDid you get value out of the show? Give some value back:https://www.concretelogicpodcast.com/donateBuy your KUIU work, workout & hunting gear and 10% goes to the show. No added cost to you:https://www.concretelogicpodcast.com/kuiuMedia, sponsorship, or content inquiries: seth@concretelogicpodcast.comCREDITSProducers: Jodi Tandett, Maya Richardson & Concrete Logic Media Music by Mike Dunton: https://www.mdunton.com/WHERE TO FIND SETHhttps://www.linkedin.com/in/seth-tandett/ https://www.youtube.com/@concretelogicpodcasthttps://www.concretelogicpodcast.comSeth@concretelogicpodcast.com

Today, our guest is Jeremy Rochford of NeuroFM and a fellow Neurodiverse couples' coach! Jeremy is a regular on Just the Guys, and today he talks about his coaching model, Only Chasing Safety (OCS). Why is safety important, and is it okay to rob someone else's safety for your safety?

Today, in the month of love, we talk about sacrificial love in your neurodiverse marriage. Many view this month of love and Valentine's Day as a day for big romantic gestures, but what about living out love every day? How is your love beneficial and sacrificial without giving up yourself? Dying to yourself does mean abandonment of self, but often there are competing needs and wants in an ND marriage.Part 2 will be on Patreon, and we will share more of what is going on in our personal lives, how, and what this means for us right now!Are you able to join hands or lock arms in hard times? Are you walking through life as friends, lovers, enemies, or strangers?

Double Tap Episode 444 This episode of Double Tap is brought to you by: Gideon Optics, Primary Arms, Medical Gear Outfitters, Bowers Group, Mitchell Defense, and Flatline Fiber Co   Welcome to Double Tap, episode 444! Your hosts tonight are Jeremy Pozderac, Aaron Krieger, Nick Lynch, and me Shawn Herrin, welcome to the show! Text Dear WLS or Reviews +1 743 500 2171 - Dear WLS Anonymous Coward from Nebraska - Dear wls, I have a Mitchell defense bought the green grips to go with the green gun. What would be a good green butt stock or just put on purple butt stock? Still deciding on a flow-through can to put on it. Justin T - This question is for Shawn. In the early parts of Covid, you were going to have a medical class in Canton, Ohio. Ohio shut down so you canceled the class. If you come teach a class at Rivers edge tactical. Can the people that paid for the Canton class come to that one? Ps. A couple shows ago that you guys were talking about a volcanic action pistol I'm fairly certain I seen Tippmann ordnance the same company that has the Glock mag Gatling gun was gonna make a volcanic pistol chambered in .380 and 22lr Trucker Matt - Do any of the cast members have experience with IWA civilian-legal flashbangs, smoke grenades or any of their other products? I've been looking at them and thinking about buying some for "airsoft/paintball", and definitely not for SHTF reasons. Would love your input, or just general thoughts on them even if you have never used them. Thanks. Full-grown Human - Shawn stated that you didn't like we the people holsters, and I had never had any issues with them but I also didn't have to deal with the customer service side of them. I now have had to deal with their half-ass customer service and their subpar duty belts. My question is what issue did you have with them? I don't buy nearly enough guns or gear so I do like to take the advice of people who do. Aside from blue alpha do you have any other companies that the cast would recommend for a duty belt? I don't carry too much on my belt at work but I don't want this cheap made product. Amanda Hungnkiss - I was thinking about getting a Marlin in .357, but then they announced the 10mm model. As a 10mm fan, I was stoked to hear that, but also a bit confused about which one would be the better choice for overall power?Ammo cost isn't a big deal since I already have a 10mm pistol and a 357 revolver. So, which caliber marlin would you pick and why? Robbie R - Ok, hear me out. Gun Fights, but poll the listeners. EXAMPLE, Mitchell Defense rifle, suppressor, optic, maybe accessories. Each cast member makes a package, and listeners vote. Maybe Mitchell Defense offers it as a package. The winner of this week's swag pack is Anonymous Coward from Nebraska! To win your own, go to welikeshooting.com/dashboard and submit a question!   Gun Industry News Derya Arms TM22 Flash Tactical .22LR Rifle Title: Derya Arms Launches TM22 Flash Link: The Firearm Blog Summary: Availability: Launched early January 2026; available through distributors like GunBroker. Cost: MSRP $249.00. Different/Special: A lightweight (4.85 lbs) semi-automatic .22LR rifle designed for speed ("Fast and Faster" motto). It features an 18-inch target barrel, integrated Picatinny top rail, M-LOK forend, and an adjustable stock. It uses a 10-round magazine (compatible with 15/25-round options) and is marketed as an affordable, tactical plinker. UK Police FN 15 ASR Title: UK Police Select FN 15 ASR for National Carbine Framework Link: The Firearm Blog Summary: Availability: Restricted to UK law enforcement agencies; not a commercial retail release. Cost: Undisclosed government contract pricing. Different/Special: The FN 15 ASR (Advanced Semi-Automatic Rifle) was selected for the UK's "Police Primary Carbine System" framework. It features a fully ambidextrous lower receiver, a hard chrome-lined barrel made from proprietary steel for extreme durability, and is optimized specifically for police patrol and response requirements. Taurus TX9 Title: New Taurus TX9 Modular Optics-Ready Handgun Series Link: Guns.com Availability: Launching January 2026. Cost: MSRP $499.99 (Street price likely ~$450). Different/Special: A striker-fired 9mm platform built around a serialized modular chassis (FCU), allowing users to swap grip frames and slide sizes (Full, Compact, Subcompact) similar to the SIG P320/P365. It comes standard with the TORO optics-ready system and boasts a high capacity (up to 17 rounds) at a budget-friendly price point. Glock GR-115 for UK Police Title: Glock GR-115 Selected as the Weapon of Choice for UK Firearms Units Link: Soldier Systems Availability: Restricted to selected law enforcement and military customers. Cost: Undisclosed government contract pricing. Different/Special: The GR-115 is an AR-15 style rifle (not a pistol) manufactured by Glock. It was selected after extensive UK police trials for its superior accuracy in both suppressed and unsuppressed configurations, beating out other major global manufacturers for the contract. Dark Storm Industries DS-25 Title: Dark Storm Industries Introduces the DS-25 Modern Fighting Rifle Link: Soldier Systems Availability: Currently available/on sale. Cost: MSRP ~$1,995.00. Different/Special: A "hybrid" platform that bridges the gap between AR-15 and AR-10 sizes. It offers the intermediate power of cartridges like 6.5 Creedmoor in a receiver set that is lighter and more compact than a traditional AR-10, designed as a "Modern Fighting Rifle" for patrol or long-range sport use. YHM Victra 20 Gauge Suppressor Title: YHM Announces 20 Gauge Victra Shotgun Suppressor Link: The Firearm Blog Availability: Production models shipping early 2026. Cost: Pricing not explicitly listed in announcement, but likely similar to the 12-gauge Victra (~$819 - $959). Different/Special: A dedicated 20-gauge version of the modular Victra line. It is user-configurable for length (can be shortened from 10.4" down to 4"), weighs just 22 oz in full config, and mounts via the host shotgun's choke threads (retaining choke functionality). EOTECH OGL-C Title: EOTECH Launches the OGL-C Commercial Laser System Link: Shooting Wire Availability: Now available (Commercial version). Cost: ~$2,799.00 - $2,999.00. Different/Special: The civilian-legal version of EOTECH's military "On-Gun Laser." It features a VCSEL infrared illuminator with variable beam divergence and a co-aligned visible green/IR aiming laser. It is extremely compact (deck-of-cards size) and features a unique, ergonomic lever for instant adjustment between spot and flood modes. Shield Sights OMSX Title: Shield Sights Announces the New OMSX Micro Red Dot Sight Link: Shooting Wire Availability: Debuting SHOT Show 2026; shipping to follow. Cost: MSRP $489.99. Different/Special: A hybrid design that combines the "translucent roof" architecture of the OMSsc (for maximum light gathering and visibility) with the wide, competition-style window of the RMSx. It is designed to offer the fastest possible sight picture acquisition in a micro-compact footprint. ATN 6th Generation Thermal Title: ATN Unveils Its Most Advanced Thermal Optics Platform Ever: The 6th Generation Line Link: Shooting Wire Availability: Unveiled January 2026; specific models (ThOR 6, Odin 6) entering market now. Cost: Varies by model; ThOR 6 ranges ~$1,995–$4,500; Odin 6 ~$5,000+. Different/Special: The new Gen 6 core features ultra-sensitive sensors (some

New year - new you- or at least a new perspective of yourself and your neurodiverse marriage! So many times, once the diagnosis is made, the sole focus can become the autism/neurodiversity, but Dr. Stephanie & Barbara talk about the many complexities that make up a neurodiverse Christian marriage!

Imagine healing cuts, rashes and irritated skin with one safe and powerful product that your body already knows how to use. In this fascinating episode, Active Skin Repair founder Justin Gardner explains the science behind hypochlorous acid, a healing molecule our bodies naturally produce. You'll learn why it works for everything from minor wounds to irritated skin, why it's safe even in eyes and how ASR products support rapid healing without harsh ingredients. Justin also shares insights on clean formulations, new product launches and how families can simplify their medicine cabinets with one powerful product line. Topics Covered In This Episode: How hypochlorous acid works Natural wound and skin healing Eczema and rash support for kids Clean and safe skincare ingredients Everyday uses for Active Skin Repair Show Notes: Learn more about Active Skin Repair  Follow @activeskinrepair on Instagram Click here to learn more about Dr. Elana Roumell's Doctor Mom Membership, a membership designed for moms who want to be their child's number one health advocate! Click here to learn more about Steph Greunke, RD's online nutrition program and community, Postpartum Reset, an intimate private community and online roadmap for any mama (or mama-to-be) who feels stuck, alone, and depleted and wants to learn how to thrive in motherhood. Listen to today's episode on our website Justin Gardner has been a leader in the health and wellness space for over 20 years. With a deep passion for regenerative medicine and holistic healing, he has worked with cutting-edge medical technologies, founded and sold multiple companies, and helped introduce over fifty innovative products into hospitals and doctor's offices. His journey took a transformative turn when he discovered Hypochlorous Acid—a naturally occurring molecule with powerful healing properties. Recognizing its potential, he shifted his focus entirely to making this medical-grade, non-toxic solution accessible to the public. This mission led to the creation of Active Skin Repair, a health-forward company dedicated to revolutionizing skin healing with clean, effective, and science-backed solutions. In an industry filled with outdated and chemical-laden products, Justin's vision is to empower people with more effective and cleaner alternatives  This Episode's Sponsors  Enjoy the health benefits of PaleoValley's products such as their supplements, superfood bars and meat sticks.  Receive 15% off your purchase by heading to paleovalley.com/doctormom  Discover for yourself why Needed is trusted by women's health practitioners and mamas alike to support optimal pregnancy outcomes. Try their 4 Part Complete Nutrition plan which includes a Prenatal Multi, Omega-3, Collagen Protein, and Pre/Probiotic. To get started, head to thisisneeded.com, and use code DOCTORMOM20 for 20% off Needed's Complete Plan! Active Skin Repair is a must-have for everyone to keep themselves and their families healthy and clean.  Keep a bottle in the car to spray your face after removing your mask, a bottle in your medicine cabinet to replace your toxic first aid products, and one in your outdoor pack for whatever life throws at you.  Use code DOCTORMOM to receive 20% off your order + free shipping (with $35 minimum purchase). Visit BLDGActive.com to order. INTRODUCE YOURSELF to Steph and Dr. Elana on Instagram. They can't wait to meet you! @stephgreunke @drelanaroumell Please remember that the views and ideas presented on this podcast are for informational purposes only.  All information presented on this podcast is for informational purposes and not intended to serve as a substitute for the consultation, diagnosis, and/or medical treatment of a healthcare provider. Consult with your healthcare provider before starting any diet, supplement regimen, or to determine the appropriateness of the information shared on this podcast, or if you have any questions regarding your treatment plan.

 It's 2026, and Dan and Stephanie start our podcast series this year on Atomic Habits. The month of January is all about NEW! Remember, Patreon is new! Part 2 of the discussion is on Patreon.In Atomic Habits, James Clear reminds us that real change doesn't come from dramatic overhauls but from small, consistent actions that add up over time. For neurodiverse marriages, this principle is especially powerful. Many couples feel stuck because change seems overwhelming or unpredictable. But Clear's 1% rule—tiny improvements repeated daily—offers a realistic, hopeful path forward for both partners.Clear also emphasizes identity formation, teaching that habits don't just shape what we do; they shape who we believe we are. “Every action is a vote for the type of person you wish to become.” This aligns beautifully with the work Dan and Stephanie bring in from Dr. Jim Wilder, who teaches that identity is formed through relational attachment, joy, and repeated experiences of being our best self with others. When neurodiverse couples practice small relational habits—brief check-ins, shared cues, predictable routines—they aren't only improving communication; they're also building trust. They are reinforcing a shared identity as a couple who grow, learns, and repair together.Starting small is essential for neurodiverse relationships. A five-minute conversation, one shared calendar habit, a single expression of appreciation, or one consistent environmental cue (like a reminder note or visual schedule) can be far more effective than trying to overhaul everything at once. Slow, steady repetition makes habits dependable, which builds trust and safety—core needs for both neurodiverse and neurotypical partners.The message is simple and deeply encouraging: meaningful change in a neurodiverse marriage doesn't require perfection or intensity. It requires small, steady steps and a shared commitment to becoming the couple God is forming you to be—one daily habit at a time.

Today, Dr. Holmes talks with neurodivergent psychiatrist, Dr. Stacy Greeter.Topics discussed:Dr. Greeter's diagnosis journey at the age of 40 as a practicing psychiatrist.Growing understanding of AutismMyths about AutismDifferent presentations of girls/women in AutismGender Fluidity & AutismMedications and How to be a psychiatric patient and advocate for yourself as an autistic patient About our Guest:Dr. Stacy Greeter is board-certified in both child/adolescent and adult psychiatry. She collaborates with children, adults, and their families to design a comprehensive individualized treatment plan. Dr. Greeter graduated summa cum laude from Duke University, where she was inducted into the Phi Beta Kappa Honor Society and received her Doctor of Medicine degree from the University of North Carolina at Chapel Hill under the Morehead Medical Scholarship. During her undergraduate and medical training, Dr. Greeter conducted extensive clinical research funded by the Howard Hughes Medical Institute on autism and on OCD. She completed both her adult studies and her child and adolescent subspecialty training at Northwestern University in Chicago, where she trained with nationally and internationally renowned psychiatrists. She is also certified in Internal Family Systems Therapy. More info: https://www.stacygreetermd.com/about-us Disclaimer:When we have guests on the ASR podcast, they are recognized for their expertise in autism as advocates, self-advocates, clinicians, parents, or other professionals in the field. They may or may not be part of the faith community; having a guest on the broader topic of autism does not reflect complete agreement with the guest, just as many guests may disagree with our faith perspective. Guests are chosen by topic for the selected podcast discussion and are not necessarily in complete agreement with all the beliefs of the selected guest(s).

In this powerful and deeply introspective episode of Just the Guys, host Dan Holmes sits down with pastor, musician, and spiritual director Josh Davis—also known as the “Autistic Pastor.” Josh shares his personal journey from a masked life of ministry and performance to one of authenticity, self-discovery, and spiritual transformation following his autism and ADHD diagnosis in adulthood. Together, they explore themes such as: The mental toll of lifelong masking and how dropping the mask opened up a more vivid, emotionally connected life.Discovering new ways to connect with God that honor neurodivergent wiring—including journaling, songwriting, and contemplative walking.Reimagining spiritual practices beyond traditional “quiet time” models and embracing embodied faith.The role of music, special interests, and authentic emotional expression in spiritual growth.What it means to show up to God—and others—with your own face, not someone else's version of what faith should look like.  This is a rich conversation for anyone exploring their identity, navigating neurodivergence, or longing to experience God in more personal, integrated ways. 

About:Today, Dan and Stephanie interview Jon Seidl, author of Confessions of a Christian Alcoholic. Neurodivergent men are 9 times more likely than their non-neurodivergent peers to develop an alcohol or substance use/abuse problem. The later in life diagnosed neurodivergent man (with ADHD or ASD) with a co-occurring issue of anxiety and or depression is the most vulnerable to alcohol or substance use, and the risk is higher for those who have suffered untreated childhood trauma. Hear our heart- this is not about shaming or blaming, this is about your healing and being set free! Jon talks about getting to the root of the problem: "Drinking is not the problem to solve- the unresolved issue that leads you to drinking is the problem to solve." As Leslie Vernick has said, "Drinking is not a marriage work issue. It is an individual issue that causes marriage problems."Dan and Stephanie feel passionately that marriage work should not be the focus of a couple if there is an active alcohol or substance issue.For the last 15 years, Jonathon M. Seidl (Jon) has been telling stories. In fact, he's written over 10,000 posts in his lifetime, first after helping start the top-50 news site TheBlaze in 2010, then as the editor-in-chief of the popular non-profit I Am Second. He writes, speaks, and consults on the power of storytelling, radical vulnerability, faith, mental health, and addiction.In 2024, he revealed his own struggle with alcohol, explaining how he was the Christian who became an alcoholic, not the other way around. His personal story—from spiraling into addiction to how he climbed out of it— is the focus of his next book, “Confessions of a Christian Alcoholic,” slated for release on October 7, 2025.His previous book on anxiety, “Finding Rest,” instantly became a #1 Amazon bestseller, topping the charts in several categories like anxieties and phobias, mood disorders, and obsessive compulsive disorder. In fact, it shot up to become the #17 new release on all of Amazon and became a top 100 bestseller on all of Amazon as well.Jon has seen how the power of storytelling and radical vulnerability can transform people, businesses, and culture, especially after sharing his own story of battling anxiety, OCD, and alcoholism. His passion is to help people with mental health struggles and addictions, while also sharing what he's learned, telling stories for—and working with—some of the media's biggest names and organizations, including Arthur Brooks, Glenn Beck, Kirk Cameron, and Chip and Joanna Gaines.In addition to his writing, he consults businesses, leaders, and non-profits on how to tell their stories through his digital media and content creation firm, The Veritas Network, and runs a daily devotional called The Veritas Daily. He's also currently finishing his master's in theological studies from Southwestern Seminary (SWBTS) and will graduate in December 2025.Originally from Wisconsin, he lives in Frisco, TX, with his wife, Brett, and his young children, Annie and Jack. 

Today on Coaches' Corner, we discuss burnout vs. autism burnout and the impact on neurodiverse marriage and family systems.What is burnout, and how is it different than autism burnout?When are neurodiverse couples most vulnerable?When the autistic spouse is in burnout, the impact on the non-autistic.Holidays are always a time that can bring on burnout- both positive and negative changes can bring about burnout. Resources mentioned:Burnout: The Secret to Unlocking the Stress Cycle by Emily and Amelia Nagoski Blog:https://embrace-autism.com/burnout-vs-autistic-burnout/https://embrace-autism.com/preventing-audhd-burnout/ Book:https://embrace-autism-store.myshopify.com/products/the-ultimate-guide-to-autistic-burnout-e-book About Dr. Mona Kay:Mona Kay, MSW, Ph.D., is the founder of the Neurodiverse Love community, the host of the Neurodiverse Love Podcast, and the creator of the Neurodiverse Love Conversation Cards. She was in a mixed-neurotype marriage for 30 years but didn't discover this until her 29th year of marriage. Mona has been divorced since 2018, and her mission is to increase understanding and acceptance of the strengths, differences, and challenges in mixed-neurotype relationships. She hosted the first virtual “Neurodiverse Love Conference” in February 2023, and more than 350 people from around the world attended. In addition, she facilitates online support groups for mixed-neurotype couples and neurotypical/non-autistic partners and shares lots of valuable resources on her website at: www.neurodiverselove.com.

As we enter the Advent season, we pause to remember the beautiful rhythms God gives us through the four candles of Advent—Hope, Peace, Joy, and Love—all pointing us toward the final candle: the Light of Christ. In the weeks leading up to Christmas, we are invited to not only celebrate these truths but to practice them in our daily life, our relationships, and our marriages.In this podcast, Dan and Stephanie will guide you through how each Advent theme can become a spiritual anchor for your heart and your neurodiverse marriage. Christmas50Use this code on special courses selected for 50% off!https://www.christianneurodiversemarriage.com/coursesCourses included:Struggling to ConnectAutism & NeurobiologyAutism Female PhenotypeMarriage & Family 22 sessions

 Today, Dr. Holmes talks to Heidi and Robin about AVA: Autistic Voiceover Artists.What if there were a place- a program- a community for autistic adults who want to use their voice talents in the voiceover industry? THERE IS! Have you thought about all the ways voiceover work is possible? About our Guests:Heidi S. Hackney MS, PCC, Co-FounderCertified Autism Coach, ICF Accredited Life Coach, Masters in Human Development   Robin Brooke SAG-AFTRA, AEA, Co-FounderProfessional Voiceover Talent of 20+ Years. Established VO Instructor and Coach. AVA Program Director. How to contact:https://autisticvoiceoverartists.org/About the ProgramHere at AVA, our mission is to empower autistic adults in the voiceover industry, redefine the creative community, and inspire positive change in the way society perceives and embraces neurodiversity. Please join us! Together, we can make voices heard, dreams realized, and a more inclusive world for all. Live classes led by voiceover professionalsElevate your artistry with specialty workshopsAccess voiceover video courses 24/7A subscription program designed to work for you The program is affordable!

Today on the podcast, the guys briefly discuss the demise of superboost as well as Jared's new ASR build and off season training regiments before jumping into a classic set of listener questions ranging from modern bike sizing to tire recommendations, weird tools and everything in between. Tune in! Our YouTube channel: www.youtube.com/channel/UCczlFdoHUMcFJuHUeZf9b_Q Worldwide Cyclery YouTube Channel: www.youtube.com/channel/UCxZoC1sIG-vVtLsJDSbeYyw Worldwide Cyclery Instagram: www.instagram.com/worldwidecyclery/ MTB Podcast Instagram: www.instagram.com/mtbpodcast/ Submit any and all questions to podcast@worldwidecyclery.com Join us on epic mountain bike trips that you will never forget in locations like Tasmania, Italy & Nepal. Grab $250 off any All Mountain Rides trip by just mentioning WWC: https://worldwidecyclery.com/blogs/worldwide-cyclery-blog/all-mountain-rides-all-inclusive-mountain-bike-guided-trips-w-worldwide-cyclery-crew Get your off season training program dialed with Train to Ride with Dee Tidwell: https://traintoride.com/programs/mtb-strong-worldwide-cyclery/