Podcasts about sft

If your pension is approaching or has passed €2 million, the question is no longer just how to grow it, it's how to generate income without handing a significant portion to Revenue unnecessarily. The Standard Fund Threshold in Ireland rose to €2.2 million in January 2026, and the decisions you make in the next few years will determine how much of that headroom you actually use. In this episode, Paddy covers the income strategies that matter most at the SFT level. If your pension is approaching €1.5 million or more, this episode is for you. • Why timing your Benefit Crystallisation Events can shelter up to €500,000 from Chargeable Excess Tax • How the lump sum offset mechanism reduces your CET exposure — and what the effective SFT really is in 2026 • What the imputed distribution rules mean when your ARF exceeds €2 million • How to manage income through the standard rate tax band efficiently • Why spousal pension planning is one of the most underused strategies at this level  

You've built a €2 million pension. Now here's the question nobody asked you: how much of it will you actually keep? In this episode, Paddy runs the real numbers on what a €2 million ARF looks like in Ireland in 2026: mandatory drawdowns, income tax, USC, PRSI, and the phased strategy that could save you tens of thousands every year in the early stages of retirement. What this Episode covers: Why a €2M ARF triggers a mandatory €120,000 income. Whether you need it or not The real net income after tax: €72,614 at a 39.5% effective rate How phasing your drawdown across two crystallisation events drops your annual tax bill from €47,386 to €8,088 What happens to the deferred pot if it grows at 6% for 8 years, and how that interacts with the Standard Fund Threshold The couple scenario: why joint assessment changes everything SFT mechanics at each Benefit Crystallisation Event and where the margin gets tight The numbers are stark. The structure matters. And getting this wrong (or not thinking about it at all) is one of the most expensive planning gaps we see. Discover the full blog post and show notes on informeddecisions.ie  

Today, we check in a year after the first Unsupervised Learning x Latent Space Crossover special to discuss everything that has changed (there is a lot) in the world of AI. This episode was recorded just after AIE Europe, but before the Cursor-xAI deal.Unsupervised Learning is a podcast that interviews the sharpest minds in AI about what's real today, what will be real in the future and what it means for businesses and the world - helping builders, researchers and founders deconstruct and understand the biggest breakthroughs.Thanks to Jacob and the UL production team for hosting and editing this!Jacob Effron* LinkedIn: https://www.linkedin.com/in/jacobeffron/* X: https://x.com/jacobeffronFull Episode on Their YouTubeWe discuss:* swyx's view from the center of the AI engineering zeitgeist: OpenClaw, harness engineering, context engineering, evals, observability, GPUs, multimodality, and why conference tracks now reveal what matters most in AI* Whether AI infrastructure has finally stabilized: why “skills” may be the minimal viable packaging format for agents, why infra companies have had to reinvent themselves every year, and why application companies have had an easier time surviving model volatility* The vertical vs. horizontal AI startup debate: why application companies can act as the outsourced AI team for enterprises, why some horizontal companies still matter, and why sandboxes may be the clearest reinvention of classic cloud infrastructure for the AI era* The “agent lab” playbook: starting with frontier models, specializing for your domain, then training your own models once you have enough data, workload, and user behavior to justify the cost and latency savings* Why domain-specific model training is real, not just marketing: how companies like Cursor and Cognition can get users to choose their in-house models, and why search, domain specialization, and distillation are becoming more important* Open models, custom chips, and alternative inference infrastructure: why swyx has turned more bullish on open source, why non-NVIDIA hardware is suddenly getting real attention, and why every 10x speedup can unlock new product experiences* What it means to sell to agents instead of humans: why agent experience may mostly just be good developer experience by another name, why APIs and docs matter more than ever, and how pretraining-data incumbents are compounding advantages in an agent-first world* Why memory and personalization may become the next big wedge: today's models mostly reward frequency of mentions, but in the future, swyx expects product choice to be shaped much more by personalized memory systems* The state of the AI coding wars: why coding has become one of the largest and fastest-growing categories in AI, how Anthropic, OpenAI, Cursor, and Cognition have all ridden the wave, and why the category may still have more room to run* Capability exploration vs. efficiency: why the industry is still in a token-maxing, experiment-heavy phase where people are rewarded for spending more rather than less* Claude Code vs. Codex and the strange stickiness of coding products: why first magical product experiences may matter more than expected, and why the bigger mystery may be why only a few names have emerged as real winners so far* What the end state of the coding market might look like: two major players, a longer tail of niche products, and possible disruption if Microsoft, Mistral, xAI, or the Chinese labs push harder into coding* Where application companies still have room against the labs: why frontier labs are trying to expand into verticals like finance and healthcare, but still leave space for focused companies that own the workflow and the last mile* Why coding may be a preview of every other AI market: the first category to truly go parabolic, the clearest example of foundation model companies colliding with application companies, and a template for how future vertical AI markets may develop* Why AI valuations now feel unbounded: from billion-dollar ARR products built in a year to trillion-dollar market caps, swyx and Jacob unpack how the AI market has broken traditional startup intuitions about scale and durability* Consumer AI vs. coding AI: why ChatGPT's consumer category may have plateaued on frequency and product design, while coding continues to feel like a daily-use category with real momentum* The next product frontier beyond coding: consumer agents, computer use, and “coding agents breaking containment,” with swyx's thesis that 2025 was the year of coding agents and 2026 may be the year they begin to do everything else* Whether foundation models are really killing startup categories: why swyx is less worried for early founders, more worried for mid-size startups and traditional SaaS, and why building something ambitious may now be the best job interview for a frontier lab* AI vs. SaaS and the internal culture war around adoption: the tension between AI-native employees who want to rip out expensive software and skeptics who think quick AI-built replacements create fragile systems* Why traditional SaaS may be under real pressure: swyx's own experience spending six figures on event and sponsor management software, the temptation to rebuild it cheaply with AI, and the broader question of whether teams will trust custom AI-native replacements* Biosafety, security, and frontier model access: why swyx raised biosafety at a dinner with Anthropic's Mike Krieger, why Krieger argued security is the bigger issue, and what restricted model releases reveal about Anthropic vs. OpenAI* The era of giant models: why 10T+ parameter systems may only be a temporary rationing phase before bigger clusters arrive, why labs may increasingly keep their most powerful models private for distillation, and why scale alone no longer feels like a complete answer* Memory as the slowest scaling factor in AI: why context windows have improved far more slowly than people hoped, why million-token context still has not changed most real workflows, and why memory may be the key bottleneck for the next generation of systems* What swyx changed his mind on in the past year: becoming more bullish on open models, more convinced that the top tier of agent startups behaves very differently from the median AI company, and more optimistic about fine-tuning and specialized model adaptation* “Dark factories” and zero-human-review coding: the next frontier after zero human-written code, where models not only write the code but ship it without human review, forcing companies to rethink testing and verification from first principles* Why RL and post-training may matter more than people assumed: even if the resulting models get thrown out every few months, the data, workflows, and domain-specific improvements persist* Synthetic rubrics, Doctor GRPO, and multi-turn RL: why reinforcement learning is becoming much more domain-specific and multi-step than many people realize, opening the door to much deeper customization* The next frontier after coding: memory, personalization, and world models, including why swyx thinks world models matter not just for robotics or gaming, but for giving AI something closer to lived understanding* Fei-Fei Li, spatial intelligence, and the Good Will Hunting analogy: the idea that today's LLMs may know everything by reading it all, but still lack the lived experience that turns knowledge into a deeper kind of intelligenceTimestamps* 00:00:00 Intro preview: AI coding wars, startup pressure, and market structure* 00:00:28 Welcome to the Latent Space × Unsupervised Learning crossover* 00:01:17 What AI builders are focused on now: OpenClaw, harnesses, and infra* 00:04:33 Why AI infra is harder than apps, and where startups can still win* 00:06:39 Should companies train their own models?* 00:09:28 Open models, custom chips, and the new inference race* 00:11:25 Designing products for agents, not just humans* 00:16:49 The state of the AI coding wars in 2026* 00:19:27 Capability exploration, token-maxing, and why coding is going parabolic* 00:21:41 What the end state of the coding market could look like* 00:23:50 Where app companies still have room against the labs* 00:27:02 Why AI valuations and market swings feel unprecedented* 00:28:56 Consumer AI vs. coding AI, and why sticky products still matter* 00:32:28 What the next breakthrough product experience might be* 00:32:53 2026 thesis: coding agents break containment and eat the world* 00:35:27 Are foundation models wiping out startup categories?* 00:37:33 AI vs. SaaS, vibe coding, and internal team tensions* 00:40:01 Biosafety, security, and the politics of restricted model releases* 00:42:19 Giant models, compute constraints, and the limits of scale* 00:44:30 Memory as the real bottleneck in AI* 00:44:57 Why swyx changed his mind on open models* 00:47:44 Dark factories and the future of zero-human-review coding* 00:49:36 Why post-training and RL may matter more than people think* 00:51:50 Memory, world models, and the next frontier of intelligence* 00:53:54 The Good Will Hunting analogy for LLMs* 00:54:21 OutroTranscript[00:00:00] swyx: Isn't that crazy? That number is just mind boggling.[00:00:03] Jacob Effron: What is the state of the AI coding wars today?[00:00:05] swyx: We're in a phase of sort of like capability exploration. The general thesis that I have been pursuing now is that the same way that 2025 was a year coding agents 2026 is coding agents breaking containments to do everything else.[00:00:16] Jacob Effron: Do you worry about the foundation models just getting into a bunch of these startup categories?[00:00:21] swyx: Mid-size startups. Yes.[00:00:23] Jacob Effron: What do you think the end state of this market is[00:00:25] swyx: for the market structure to, to significantly change? There would be[00:00:28] Jacob Effron: today on unsupervised learning. We had a, a fun episode and what's really become an annual tradition, a crossover episode with our friends at Latent space.Swix and I sat down and we talked about everything happening in the AI ecosystem today. What we thought of the various changes at the model layer, what's happening in the infra world, the coding wars, and a bunch of other things. It's a ton of fun to do this with someone I really respect and another great podcaster in the game.Without further ado, here's our episode. Well switch. This is, uh, super fun to be back with another unsupervised learning, uh, latent space crossover episode.[00:01:02] swyx: Yeah,[00:01:02] Jacob Effron: I feel like a lot of places we could start, but you know, one thing I always find fascinating, uh, about the way you spend your time is you obviously are like at the epicenter of this engineering movement and community, and you run these events and conferences and put on these.Awesome talks and, and I think just have a great pulse on the zeitgeist of what's going on.[00:01:16] swyx: Yeah.[00:01:17] Jacob Effron: Maybe to, to start just what are the biggest topics people are thinking about right now?[00:01:21] swyx: Yeah, so I just came back from London, uh, where we did a IE Europe and we're doing roughly one per quarter now, which Yeah, you've[00:01:27] Jacob Effron: really up[00:01:27] swyx: the, hopefully[00:01:28] Jacob Effron: up the, up the pace.[00:01:29] swyx: It's trying. We're trying to match AI speed, youknow?[00:01:30] Jacob Effron: Yeah, exactly. The tops would be completely different, I imagine. Uh,[00:01:33] swyx: yeah. You know, I definitely curate the tracks, like you can see what I think. When you see the track list and the, the speakers that I invite, obviously Open Claw is like the story of the last four or five months, and then be, be just below that.I would consider harness engineering, context engineering to be two related topics in agents and rag. And then there's a long tail of Evergreen stuff like evals, observability, GPUs, uh, and uh, LM infra and just general, just in general. We also have other updates on like multimodality and, uh, generative media, let's call it.Um, but I definitely, the, the first three that I mentioned are top of mind people. Yeah.[00:02:13] Jacob Effron: I think harness is particular like, so interesting. Um, you know, there was this tweet from Harrison Chase, the, the lane chain, CEO, that, that caught my eye recently where he said, you know, it finally feels like we have stability, uh, around the infrastructure for, uh, you know, around ai.And I think what. He basically was implying his like, look over the past two, three years as a company at the epicenter of AI infrastructure, it was a bit like playing whack-a-mole, right? You were constantly moving around with, however, the building patterns were evolving[00:02:36] swyx: for Harrison for sure. Right? Like he's basically had to reinvent the company every year since he started Lang Chain.Right? It was Lang chain, Ang graph and LP agents and like, uh, I think he's like one of the most nimble, adept sharp people about this. Yeah. Yeah.[00:02:49] Jacob Effron: Saying now, now is finally the time stability[00:02:51] swyx: this. Yeah.[00:02:52] Jacob Effron: Yeah. Um, do you buy that or what have you kind of make of that take?[00:02:56] swyx: I think that. It, it's very expensive to say this Time is different sometimes, but when you're just writing code, like it's actually okay to just like try to make a call and I think it may not even matter if this call is right or not.Like I just don't even care that much because you can be right on a thesis, but if you don't, you don't figure out how to monetize the thesis, then who cares if you said something first that said, um, it does feel like, for example. Uh, we went through a lot of different ways of passion packaging integrations up with, uh, with agents.And it feels like we've landed at skills, which is like the minimal viable format. Yeah. Which is just a markdown file, uh, with some scripts attached to it, and I don't see how it can be more simple than that. And so there is some justification for. The stability around harnesses. I feel like there may be more adaptation with regards to maybe like the real time elements or subagents or memory or any of those like agent disciplines, let's call it in, in agent engineering.Uh, but if, if the thesis is that, okay, you just want agents are LMS with tools in the loop with a file system, what they can do. Retrieval with, with skills and all these like standard tooling that now seems to be relatively consensus then probably. That makes sense. Um, I just think like there's no point trying to stake your reputation on this thesis that we're there because if it changes again, just change with it.It's fine.[00:04:33] Jacob Effron: Yeah. It's always, you know, I've always been struck by how that is. Much more challenging for infrastructure companies and application companies. Like obviously I think, yeah. You know, on the application side you've seen, you know, Brett Taylor from Sierra Max, from Lara. Like, they're like, look, we build, you know, what's ahead of the models and we're willing to throw everything out every three months, you know, as the models get better and better.Exactly. Yeah. But the thing you at least have there is you have. Uh, you have an end customer, right? That's like decently sticky. Um, you know, they will mostly stick, you know, they'll, they'll give you a shot at least of, of building these things. What I've always found more challenging, uh, at, at the kind of like, you know, reinvent yourself every three months of the infrastructure layer, it's like, you know, developers are definitely a, a pickier audience maybe than an accounting firm or, uh, you know, a bank.Yeah. And so it's definitely a, a, a more challenging position to be in to, to have to constantly reinvent yourself.[00:05:17] swyx: Yeah. Yeah. Yeah. And, and like when they turn, it's like. Very complete. Like, they'll leave to like the, the hot new thing, uh, because there's like no defensibility, I guess. Like e even, even if you are a database, like, uh, people can migrate workloads off databases.Like it's, it's a, it's a known thing. Uh, so I think like basically what we're talking about is the vertical versus horizontal, uh, debate in, in AI startups. And uh, the way I think about it also is just that like when you are. Um, Lara, when you are a bridge, like you are the outsource AI team, right? You, you are, your job is to apply whatever state ofthe art AI methods.[00:05:55] Jacob Effron: Yeah. Like this translation layer between model capabilities and your[00:05:57] swyx: own customers. Yeah. To, to the end customers and like, well, if they didn't have you, they would've to hire in house and they're not gonna hire in house so they have you. And like, I think that's like a reasonable, like very robust to any whatever trends and, and discoveries that people make in, in the engineering layer.I do think like there is, um. It like sort of useful horizontal companies being built, but they're all. Very much like, sort of like the reinventions of classic cloud in the AI era and the, the primary one being sandboxes. Yeah. Um, which like, it's another form of compute guys, like, let's not get too excited about it.But I mean, like the, the workloads are enormous.[00:06:38] Jacob Effron: Right.[00:06:38] swyx: Yeah.[00:06:39] Jacob Effron: It's interesting, and I feel like as, as part of this, you know, the questions that folks are asking around infrastructure, there's a lot around, you know, the extent to which companies should have their own AI teams and what they should be doing in-house.And, you know, uh, I think there's questions around should people be training their own models? Should people be doing, you know, rl, uh, in-house based on the data they have? I feel like, you know, one has to evolve their takes on this every, every three months with paces. But where, where are you at on this today?[00:07:00] swyx: I think, well, I mean actually all models have gone up. Um, and obviously I'm involved in cognition and also cursors doing, doing, uh, a lot of own model training. And I think that that is some part of the, what I've been calling the agent lab playbook, where you start off with the state of the art models from, uh, from the big labs and you, uh, specialize for your domain.But once you have enough workload and enough high quality data from your users, then you can obviously train your own models and like save a lot on cost and latency and all that, all that good stuff. Um, you also get like a marketing bonus of like calling it some fancy name and putting out some research[00:07:38] Jacob Effron: from my seat.I can't tell how much of it is like actual, you know, value that's provided to the end user. And how much of it is that marketing bonus? Right. It seems some combination of the[00:07:45] swyx: I think it's both.[00:07:46] Jacob Effron: Yeah.[00:07:46] swyx: Um, no, no. There, there actually is real value. Um, and you, you know that for a number of reasons. Like one, even when it's not subsidized, people do choose it as like one of the top four or five.This is both composer two and, uh, suite 1.6 I one of the top five models. Like in a, in a fair market? In a free market, yeah. In a, in a, in a model switch. Or people do choose it and like, it's not subsidized. Like, so that's as good as it gets. Uh, but beyond that, like domain specific models, for example. For search with, with both, which both companies have absolutely makes, makes a ton of sense.Everyone says like, yeah, we should always, always do this. And honestly like, I think the infrastructure for that is becoming easier with, um, like thinking machines tinker thing as well as primary like, uh, lab stuff. Yeah, I mean like, this is one of those like reversal of the, the bitter lesson where you first bootstrap on the large models and the general purpose models to get big.And as you get very well-defined workloads that are just high quantity but not high variance, um, then you just distill down to a smaller model and run that on your own. Right. Which like totally makes sense.[00:08:50] Jacob Effron: What I'm less clear on is the kind of DIY RL use case, which I think is really mostly around, you know, improved, uh, quality for, for different things.Obviously there's probably like more efficient ways to, you know, get a smaller model that's that's faster and cheaper. And it'll be interesting to see whether. You know, obviously you had, you know, uh, two, three years ago this whole case of companies that were, you know, pre-training and claiming better outcomes in, in their domains than getting kind of cooked as each model iteration improved.You know, I wonder whether that's a, a similar story plays out in the, uh, in, in the, our all space. Yeah, for the focus on, on on pure outcomes and quality, not the cost side, which clearly your own models for cost at scale makes a ton of sense.[00:09:28] swyx: I think there are this, there are two sides of the same coin.Like you basically always want to hold, uh, quality constant or trade off a little bit of quality for a drastic decreasing cost. And that's true for everyone. Uh, one element I wanted to bring out, which is very much in favor of open models, is custom chips. So this would be cereus, but also talu. And then there's a huge range of stuff in between.This has been a huge story this past year on just like everything non Nvidia is getting bid up, including like freaking MatX is working for, which is very, which is very rewarding for me, but I think one of those things where like, oh, like the suddenly, because the number of alternative. Hard, uh, hardware is increasing and the inference that you can get is insanely high.Like, um, we're talking thousands of tokens per second instead of less than a hundred. So the trade off for qua quality doesn't hold as much anymore because the speed is so high.[00:10:24] Jacob Effron: Have you seen a lot of companies go all in on the alternative chip?[00:10:26] swyx: So cognition has Yeah. On Cerebras, uh, and, and so has OpenAIUm, uh, and so no, I don't think so beyond that, uh, and that, do you think that's like a, that's mostly, that's foreshadowing of, that's, yeah. I used to be kind of a skeptic in terms of like, okay, so what if I get my inference at a hundred to a hundred tokens per second sped up to 200 tokens per second. It's only two X faster.It's not that big a deal. Um, but when you, uh, I think every 10 x does unlock a different usage pattern. Um, and you, we have proof in Talas and, and some of the others. That you can actually, um, drastically imp improve inference speed and what happens from there? I don't even really know, like it's, it's so hard to predict when entire applications just appear at once.Yeah. Uh, and it also isn't that expensive, right? So like, um, this is one of those things where like, I, I think the, the investment cycle is gonna be multi-year. Um, and I. Would caution people to not dismiss it too, too quickly.[00:11:25] Jacob Effron: Yeah. I mean, one other like infra question I was curious to get your thoughts on is obviously it seems increasingly a lot of the cutting edge infra companies are building for agents as the buyers of their product or users of their product, right?[00:11:35] swyx: Ooh,[00:11:36] Jacob Effron: and[00:11:37] swyx: another huge theme. Yeah. Yeah.[00:11:38] Jacob Effron: And I'm trying to figure out like what. What, what do you have to do differently about selling into agents? Um, are they just the ultimate rational developers? Uh, or is there, you know,[00:11:46] swyx: no, absolutely not. Um, I think they are easily prompt, injected and, uh, very tuned towards like, basically com compounding existing winners.[00:11:57] Jacob Effron: Yeah,[00:11:57] swyx: so like if, like, congrats if you won the lottery for getting into the training data right before 2023, because now you're like installed in there for the foreseeable future. But yeah. Uh, you know, one stat that Versal, uh, CTO Malta dropped at my conference was that there are now, uh, 60% of traffic to Elle's, um, like app arch, like admin app architecture for like configuring versal applications, uh, is bought.It's not, it's not human. Uh, so like your primary customer is agents now. Um, and it's mostly co like mostly coding agents, mostly people using CLI on CP or whatever. But yeah, I mean, I think. More. I, I think step one, if it doesn't exist as an API that agents can use, it doesn't exist. Right, right. Which I think is like, uh, it's a good hygiene thing anyway, to, to make everything API available, but not as like an extra, um.Push on like products, people to not only work on the ui, um, you should probably work on the on SCLI stuff. Beyond that, I think honestly there is like, so I, I come from the sensibility of, I think everything that you are trying to do for agents experience now, which is the term that Matt Bowman and Nullify is trying to coin, is the same thing that you should have been doing for developer experience.That you should have had good docs, you should have had a consistent API, uh, that is. Mostly stateless. Um, you should have, I guess, discoverable or progressive disclosure or like search or like whatever. And so now that people have energy in like finding these customers to do that, that's great. Um, do I believe in.Extending beyond that into something like a EO, um, for gaming The chatbots? Not necessarily, but obviously there's gonna be huge advantages when people who figure out the short term wins. Yeah. And short term wins can compound.[00:13:43] Jacob Effron: Do you think these compounding advantages to like the, the pre-training data cutoff companies, like, you know, obviously over some period of time, I imagine that doesn't persist.And so as you think about like. I dunno, three, four years from now what the, you know, selection criteria end up being. Do you think it still mirrors exactly what you were saying before? Like it's exactly what you should have been doing all along to sell a good product to developers?[00:14:01] swyx: It could be, except that I think in three, four years we'll probably have much better memory and personalization.So then general a EO or GEO doesn't really matter as much. So I think whatever memory or personalization system we end up with will probably d determine what you end up choosing much more. Than, than what is currently the case, which is just frequency of mentions, let's call it. Yeah,[00:14:26] Jacob Effron: yeah.[00:14:26] swyx: Uh, so you just spa quantity and I think that's, I mean, that's something I'm looking forward to.I do think, like, like, you know, I, I think that the fundamental exercise to work through for yourself is if you start a new, um, sort of. Uh, disruptor company. Now there's a, there's a big incumbent that everyone knows, like, like superb base. Super base is like, kind of like the Postgres, like database, uh, incumbent.If you wanna start like new superb base, how would you compete with them? And I don't necessarily have the answer, but I, I, I do think like people, like resend like relatively new. I think they would start like 20, 23 and still there was, there was a recent survey where like, people. Checked what Claude recommends by default.If you just don't prompt it with anything, just say, gimme an email provider and says, resent as in like 70, 70% of each cases. Like the fact that you can get in there with like such a relatively short existence, I think is, is encouraging.[00:15:14] Jacob Effron: Yeah.[00:15:14] swyx: I do think like. Um, you do want to do whatever it is to, to like to, to get in that Very short mentions this because, um, it's not gonna be 20 of them, it's gonna be like three.[00:15:26] Jacob Effron: No, definitely. It feels like, uh, you know, probably more, more consolidation than ever. Uh, or, or kind of like, you know, uh, a winner take most market than maybe the, the, the physics of go-to market in the past. Yeah. Might have, uh, enabled.[00:15:38] swyx: The other thing also is like, semantic association is gonna be very important, uh, in the sense that like, you want to do like the combo articles where you're like, use my thing with for sale, with blah, blah.And like that all gets picked up in a, in a corpus. And so that's. Probably one thing that you, you wanna do? Well, I don't know what else. Uh, it's, it's, it's, it's one of those things where like, I think I feel, I feel I'm behind, uh, I don't know how you feel about this, but like,[00:16:04] Jacob Effron: I think AI is just everyone constantly feeling like they're behind some, uh,[00:16:08] swyx: yeah.With,[00:16:09] Jacob Effron: I wanna meet the person that doesn't feel behind,[00:16:11] swyx: but like with, with ax, right? Like, so, so like, my, my stance was that exactly what I said before, like everything that you, that you should do for agents is something that you should have done for humans anyway. Yeah. And so. To the extent that you're just getting it more energy to, to do things for agents, great.But like, uh, it's hard to articulate what new thing apart from just like more spam, um, that you should be doing. Anyway, that would be my take right now. Um, I I, I do think like there, there will be more turns at this. I think the personalization turn that is coming, um, will be big. And I don't know what that looks like because like basically we're kind of, we feel kind of tapped out on the memory side of things.[00:16:49] Jacob Effron: Yeah. I, I guess since we last chatted, you know, you, you took this role over at cognition, um, and you've obviously have a, have a front row seat to the AI coding space today. You know, I feel like coding in many ways. You know, people view it as this, like, I mean, besides being like the, the mother of all markets and this massive opportunity, I think it's kinda a preview of like, what's to come for many other spaces.Both. Yeah. You know, I feel like agents are most advanced in coding. I also feel like the, you know, competition between foundation models and application companies, you know, and, uh, mirrors what we may see in other spaces. And so maybe for our listeners, can you just lay out like what is the state of the AI coding wars today?[00:17:25] swyx: Um, it is massive, right? Like, uh, and I don't think necessarily, last time we talked about this, we appreciated the size of what[00:17:32] Jacob Effron: No, I wish we did.[00:17:33] swyx: I state of AI coding wars today, um, both opening eye philanthropic have made it their p serials to competing coding. Um, and. Tropic is like 2.5 billion in a RR just from Cloud Code.The way they recognize a RR is. Opt for debate, uh, open ai. I don't think the, a public number is known, but let's call it 2 billion as well. And then cursor is like, rumored to be 2 billion, you know? And, and those, those are like the public numbers that are known? Yeah. Um, so like huge markets that have just been created in the past one year.Like, like anthropic, just like Claude Code just recently celebrated their one year anniversary, which is, yeah, pretty nice. Um, so, and then I think, like the other thing that I see is there's, there's some other people who are like, oh, here's like the, the sort of relative penetration of, uh, Claude use cases, right?Like, and it's like coding 50% and then legal, whatever. Health, uh, it's like the, the remaining ones. And there was a very popular tweet that was like, okay, I'll look at the, the empty space and all these other use cases. If you are a new founder today, you should be betting on the other stuff because on, on a sort of catch up Yeah.Theory and my. Consider my, my pushback is the same pushback that, uh, I had on app over Google, which is like, well, well why is this time different? Like, why, if it went from let's say 10 to 50% in the past year, why can't I keep going? Uh, and like getting that wrong is actually a very painful one because you could have just did, did the momentum bet.Instead of the mean reversion bed. So I, I, I think that that is the, the state of things now that people are very, very much into psychosis. Um, they're are getting rewarded for spending more rather than spending less. And I think we're not in that phase of efficiency. We're in a phase of sort of like capability exploration.So I think people who are more crazy, who are more. Uh, creative, um, get rewarded comparatively. Yeah.[00:19:27] Jacob Effron: Well, it's interesting. I mean, it feels like behind these like token maxing, leaderboards and whatnot is this, it's like the first phase of this transition from a workforce perspective is you just gotta show your employer like, Hey, I, I use these tools.[00:19:37] swyx: Here's my nu number of tokens I cost, and that's it. They don't care about the quality. Right. It is, uh, maybe distasteful to someone who cares about the craft and, and all that. Um, but directionally everyone just wants you to go up regardless. And so, um, there it is not very discerning. It's, and it's probably very sloppy, but I think it's net fine because we're still probably underusing ai just in generally.Yeah. Um, and so I think that's like very interesting. Like we had on the podcast, uh, Ryan La Poplar from OBI, who spends a billion tokens a day. Yeah. Um, and that's for those county home, it's like something like 10,000 worth, $10,000 worth a day of API tokens. If they, they did market rates, um, and like most of us can't afford that.Yeah. But like. And, and, and probably a lot of what he does is slop.[00:20:25] Jacob Effron: Right.[00:20:25] swyx: But like, he's going to dis, he's like, if there were a new capability, he would discover it first before you because he was, he was trying and you were not trying. Right. And like, you only do things that work like, well, good for you.But like the, the people who are going to discover the next hot thing are living at the edge.[00:20:42] Jacob Effron: Right and increase in living at the edge of just having the compute budget to like run these experiments. I mean, kind of similar to what living at the edge on the research side has always been. You know, it was constrained in many ways by the amount of compute you had to run these experiments.It feels similarly on the, almost on the builder or like actualizing these tools now.[00:20:56] swyx: Yeah. The other thing that's, I mean, very obvious is philanthropic is kind of like the high price premium player. Um, that where, you know. Restricting limits or restricting model releases even is like the name of the game.Whereas Codex is like, come on in guys, use our SDK, use our login and we don't care. We're gonna reset limits. Whatever you do want to try to exploit the subsidies where you can get it. And definitely Codex is super subsidized right now. Gemini also very subsidized. Um, and. Comparatively, like, I think you should make, Hey, I guess while, while that's going on, it's not that bad to be a capabilities explorer on just the $200 a month plan from Cloud Code or from OpenAI.Um, and, uh, I I, I, my sense is that people aren't even there yet.[00:21:41] Jacob Effron: How do you think this, like, market ultimately plays? I mean, it's obviously such a big market that, you know, any slice of that market is interesting for, for anyone going after it. But I think what, what makes people so interesting in the coding market particularly is it feels like it's kind of this.Foreshadowing of what will happen in other, you know, any other kind of application market that the foundation models eventually turn to and are all their models against and gather data around. And so how do you think, you know, like does there end up being room for lots of different kinds of players or like, what do you think the end state of this market is and is that, do you think that's applicable to other markets?[00:22:10] swyx: I feel like there will be, I mean. Status quo is probably the most likely outcome, which is there are two big players and there's a small range of longer tail people that, um, fit other use cases that the, the two big players don't. That feels right to me. I think that, um, for it to, for the market structure to, to significantly change there would be, there needs to be significant change in like the economics or like the, the brand building or like the, the, the, the value propositions of the, of the companies involved and I.Haven't seen any in the last six months that, that have really changed the stories materially. So I feel like they would just keep going until something, something else happens. Something else happens, meaning like Microsoft wakes up and like goes like. Guys, we have GitHub, we have, uh, you know, we, we, we'll, we'll do something much bigger here than other, other than just copilot.Um, and, uh, that would be a big change. Um, MSL has put out a model now, and I was in a breakfast with, uh, Alex Wang, where they were like, yeah, like, we, we really, really want to go after the coding use case. We haven't done anything yet, but like, don't underestimate them. Right. Um, and, and similarly for the Chinese labs.Um, I think they're trying to go after it. Like ZAI is doing stuff. GLM uh, ZI and GLM is same thing. Um, uh, and, and so it's, so like everyone's trying to get a piece of that pie. I, I feel like the, the status quo has been pretty stable for the past, like almost a year I'll say.[00:23:39] Jacob Effron: Yeah. And is the room for the, not like, you know, for, for the application companies more on like the enterprise side or like where do the, where do the, like what surface area do the model companies leave for application companies?[00:23:50] swyx: Yeah, that's a good one. Um. It's very much evolving. Um, it, I, I, I will say because opening I did not have this, the, this level of attention on coding. Yeah. Uh, a year ago. We just don't have that much history. Right. Um, and it seems like, for example, so the big push at Open I now is the Super app. Um, is that a consumer thing?Is that like a products like. Portfolio rationalization thing, how much is that gonna take away attention from coding at the time when they actually do want to put more coding? I think it's, it's very unclear. So I do think like there's, there's all these, like in both big labs, there's. Uh, sorry. Both of the, and, and drop and, and deep minus and XAI are are separate cases.Um, they are trying to see the other time expansion areas. So cloud code for finance. Yeah. Um, uh, cloud cowork, all those, all those things. Whereas I think cursor and cognition are like comparatively just focused on coding and so I, I do think they leave space and I do think for the other verticals that also means the same thing.Right. That, uh, that they're not gonna be that. Um, intensely focused on, on, on that domain. Except for, I, I think I would mark out finance and healthcare as like the next ones, um, that they're clearly going after. Uh, I, I would say comparatively, healthcare seems more thorny. There, there, there've been some announcements about it, but like, I would respect the, the finance work a lot more just because like the, the path to money is a lot clearer.[00:25:12] Jacob Effron: Yeah, no, I mean, obviously like, I, I think, you know, maybe similar to, to the space that's being left in these other domains, you know, there's obviously. Uh, a lot that's required to actually implement these tools in enterprises, uh, versus, you know, maybe just giving them, uh, giving model access to, to folks outta the box.[00:25:27] swyx: Yeah, yeah. Yeah. So the, the agent lab thing is like, we'll do the last mile for you. Whereas I think the model labs tend to just trust the model and, and be minimalist about it. Both of them work.[00:25:38] Jacob Effron: Yeah.[00:25:38] swyx: I, I don't, I don't necessarily think one, uh, beats the other, uh, for every, for every use case. Um, all I, all I do know is that it does seem like.Uh, the large enterprises do want a dedicated partner that isn't just the model labs, which is kind of interesting.[00:25:55] Jacob Effron: We, we've been in this phase of, of pure capability exploration. And so I think nothing has been, you know, better for the large labs, right? I mean, they're always gonna be, uh, uh, the frontier of, of capability exploration.And so I think have a very good relationship with a lot of these enterprises. But ultimately over time, like. The, uh, the incentive structure of these labs is always gonna be maximal, you know, token consumption for, uh, for the end customers they work with. And there's just, I think, so few companies that have actually gotten to massive scale.Maybe coding again is the most interesting. So it's the first space that really is just completely gone, you know? Yeah. You must love it every day. Like absolutely insane. And. I think it[00:26:32] swyx: gets even. Okay. I mean, like, I think we, we say good things about crystal cognition, but the sheer liftoff of like both end UPIC and open ai.‘cause they, they, they have independent valuations. I mean, let's throw an XEI in there because it's now I ping at 1.2 trillion. That number is just mind boggling. Like I, I feel like in normal investing or normal startups, there's kind of like a ceiling market cap or valuation. Totally. That, that like you, you reach and you go like, all right, let's, it's gonna be chiller from now on.And these guys are not slow down. No.[00:27:02] Jacob Effron: Well, I also think the dynamic is fascinating about some of these later stage companies is, is, you know, in the past, I feel like in, in venture world, if you got to a certain level of scale, the question around you was really more a valuation question. And this is like why there was different phase, like, you know, types of venture people did and like the late stage growth people were just incredible at like, you know, a little bit of what's the ultimate market opportunity of this company, but also what's the right way to, to value it.Like we know it's, it's in some bands of an outcome that is like. Sure there's some variance to it, but it's like relatively understood what that bands is and then maybe you get over time surprised to the upside. Whereas any kind of like later, even the labs themselves, any later stage company, the bands of which that company might be worth right now, even in a year or two years are so massive because of how fast the ecosystem changes that it's like.Even for later stage companies, every three months could be an existential level event to the upside to the downside. Yeah. Um, and I think that, like, you are obviously seeing it in the, in the positive with code, which, you know, if you think about a company like philanthropic, you know, that. For a while, it was like unclear if they were going to have access to enough capital, um, to really stay in the, in the race, right?And then coding hit at the exact right time. They had the perfect model for it. They executed brilliantly. Um, and you know, now are, are, you know, uh, you know, one of the most valuable companies in the world.[00:28:13] swyx: Uh, at the same time, I, I don't find, I, I have zero sympathy for opening eye because they're crushing it and they're all rich.You know, this is like a high class champagne problem to have to, uh, to be number two at coding or whatever. Like, who cares? Like, you're, you're doing great.[00:28:27] Jacob Effron: Yeah. It's funny though. I can't even, I mean, you would be closer to this, uh, you know, even that you're in the AI coding space, but it's like a lot of people I talk to think Codex is just as good, if not better than Claude Code.Right. I think one thing that I've been really surprised by, and maybe, maybe Cloud Code is a better product in some ways, I'm curious your thoughts is just in consumer AI with chat GBT. You saw this big first mover advantage, right? Where admittedly today, like, I don't know, Claude Gemini. Great products.Not sure, not abundantly clear chat GBTs any better, but like. People stick with chat, GBT, it's the first thing to introduce them.[00:28:56] swyx: They stay, but they're not growing anymore. I don't know if you've seen[00:28:59] Jacob Effron: Right. But that to me is more of like a, a, a product problem than it is. They're not like, it's not like they've like lost share to someone else.My understanding is the overall problem with consumer AI today is much more of a how do you take this tool and, you know, for, for folks like us, like knowledge workers, it's like this incredible magic tool, but it's not necessarily a daily active use tool for a lot of people around the world today. And what are the like products?It's, it's kind of a category wide problem. Like in coding, for example, like. The entire space has gone parabolic. There may be some relative growth in, uh, in other consumer AI players, but it's not like consumer AI as a category is like going parabolic and they're not capturing most of that thing. I think it's actually the larger problem is much more, hey, the category has kind of hit a bit of a plateau of people haven't figured out how to bring, you know, tons more users on board.Yeah, yeah. Or increase the frequency of those users. And so it seems more of a category wide problem than it is, you know, a massive market share of change. I was gonna draw the comparison to, to the coding space where Claude Co is the first product, obviously, to introduce people to this magical experience.You know, by all accounts, codex is, is pretty damn close to as good, if not better. Um, but like still that first product, you, you would've thought that would not be a super sticky, uh, you know, product surface area. And it actually has, it turns out, I, it feels like the first lab to introduce you and experience really does, uh, keep a lot of, uh, a lot of the focus.[00:30:12] swyx: I, I think. M maybe it's like still, still early days. You know, Chad, BT is like three plus years old and Yeah. Cloud code is only one. Just turned a year. Yeah. So give it time, you know? Yeah. Like, yeah. I mean, definitely sometimes a lot of people have switched from to Codex. Maybe that will keep going. I, it's like really hard to tell.Uh, yeah. I, I, I do, I do think that. Because we are in this like, high volatility, high temperature phase. Um, the loyalty and stickiness to first movers and category creators, I don't think is as high as it might be in some other, uh, areas in our careers that we've looked at.[00:30:47] Jacob Effron: Yeah. Though, I mean, I've been surprised by the cloud code thing.I, I would've thought that, like, in many ways I always worried about the[00:30:52] swyx: enterprise. You think you would've been gone by now?[00:30:53] Jacob Effron: Not gone. But I would've, I I always worried that the, that the consumer business of these companies would be quite sticky. And then the enterprise API business. Uh, was actually like, you know, in some ways like your least loyal buyers, like they would, they would move to,[00:31:05] swyx: right, right.But, but they worked out that it wasn't the enterprise API it was enterprise product.[00:31:09] Jacob Effron: Totally. And maybe that was the, that was the secret that like, but the amount of lock-in or just default behavior that has happened in that space, uh, is, is more than I might've imagined with two products that by all accounts are pretty damn similar.Yeah.[00:31:22] swyx: No fight there. Uh, I will say I do think that Codex is still in like a catch up. Like in terms of personal experience. Um, the only thing I like out of, out of Codex is the, is like Spark and like yeah. Uh, the, I, I feel like the skills integration is a little bit better. I feel like, uh, the, the speed is a bit better.Maybe ‘cause it's in, is written in rust or whatever. Um, very minor things that you like. Almost like telling yourself rather than like objectively assessing between two, two of them. I, I, I do think, like vibes wise, I think that's going on. Um, the, the, you know, I, I feel like the, the missing questions, uh, in, in this whole debate is like, why is this so concentrated in only two names, right?Yeah. Like, um, how, where, like, where is the Gemini? You know, presence, where's the Xai presence? Um, and like they are trying, it's just they haven't made that much progress yet.[00:32:12] Jacob Effron: But what the, what the Claude Co moment does show, and it actually in some ways makes you a little more bullish on the potential for someone else to catch up because it does feel like if you're the first person to introduce some magical net new product experience, that that actually might be stickier than one might have imagined.[00:32:27] swyx: Right, right, right. Okay. Yeah.[00:32:28] Jacob Effron: And so it's, everyone can believe they have shot[00:32:29] swyx: that. What do you think that new product experience might be like? I, I, it's, it's like, and this is a failure of imagination on my part. Like, I always wonder, like, people always say this like, well, the, the thing that will save us is like being first to the next new thing.Like what is it?[00:32:41] Jacob Effron: Yeah.[00:32:42] swyx: It's like,[00:32:45] Jacob Effron: I dunno, something around like, uh, consumer agent, computer use, like hybrid. I think, obviously, I think we're like scratching the surface on the consumer side.[00:32:53] swyx: So my, my current theory is like the. Open claw is like a vision of things to come.[00:32:58] Jacob Effron: Totally.[00:32:58] swyx: Um, and uh, it's good that O open I has like the association with open claw, but by no means do they have the rights to win it.The general thesis that I have been pursuing now is that the year the same way that 2025 was the year of coding agents, 2026 is coding agents breaking containment to do everything else. Um, and so coding agents continue to still win, but because they generate software and software eats the world, so like, it's kind of like the trans.Associated property of like software, eat the world, coding agents, eat software, therefore coding agents eat the world. Um, which is like an interesting,[00:33:30] Jacob Effron: yeah, and breaking containment always an easier phase phrase in the consumer context than the enterprise one. You've seen people run these really cool, uh, experiments in their own personal lives.I think like,[00:33:37] swyx: yes.[00:33:38] Jacob Effron: Figuring out, you know, how you, obviously everyone's focused, you know, on the enterprise side now around how you create these experiences. I feel like the vibes, you know, people love to have these narratives of like, everything is completely shifted. It's like I actually, you know, open AI.Organizationally, uh, you know, volatility aside is, you know, great products, great team, great models like everyone else in the world is incentivized for there to be. Two, three more. Everyone would love more like great model companies. And so I feel like the, the natural forces of the world revolt when any one company, you know, is too much the star of the show, right?There's so many people in the ecosystem that are incentivized for that not to happen. And so I think I'd be shocked if we don't have. Uh, uh, reversion of vibes, not maybe completely the other way, but at least a little bit more equal at some point over the next six, 12 months.[00:34:24] swyx: I, I think there's just a kind of different stages when, when you talk about the world, one wanting more model companies, I talked think about like the neo labs.[00:34:30] Jacob Effron: Yeah.[00:34:31] swyx: And I mean, I don't know, is it fair to say none of them have really broken through in the past year?[00:34:35] Jacob Effron: I think that's totally fair,[00:34:37] swyx: which is rough. Um, and well, how are we gonna, how are we gonna grow that diversity in, in, in choice, like. Um, that's, this is it.[00:34:46] Jacob Effron: Yeah. It'll be really interesting to see what, what, what ends up happening with that.And you've seen, you know, folks like Nvidia, you know, very incentivized to make sure there's, there's a broader platform of, of other model providers.[00:34:57] swyx: I think, uh, I don't know people say this, but I, I, I don't think they try it hard. Nvidia tries harder to build neo clouds[00:35:05] Jacob Effron: Yeah.[00:35:06] swyx: Than neo labs.[00:35:07] Jacob Effron: Well, they try pretty damn hard to build neo Cloud, so[00:35:09] swyx: that's,[00:35:09] Jacob Effron: yeah.[00:35:10] swyx: But like, you know, let's call it like the, the core weaves of the world, much happier place in the, you know, than any neo lab built on top of them.[00:35:18] Jacob Effron: Yeah. That one might argue it's, it's easier to, to enable a neo cloud to be successful than it is. Uh, you can't will a neo lab into existence the same way you, soNvidia[00:35:25] swyx: has more direct control over it.Uh, for sure.[00:35:27] Jacob Effron: What else is kind of catching your eye today on the startup side? I mean, you worry, there's obviously this whole narrative of like, you know, the foundation models, you know, they announced a product and every stock goes down 15%. Like[00:35:36] swyx: Yeah.[00:35:37] Jacob Effron: Do you, do you worry about the foundation models just kind of eating into to a bunch of these startup categories?[00:35:43] swyx: Not really. I, I think actually like. As, uh, there's, there's, okay, there's, there's, there's the, there's the point of view of like being an investor in startups, and there's a point of view of like, do you wanna start something? And I think honestly, like the, the downside for all these is so. Minimal in, in a sense of like, the worst you do is you just get hired into one of these labs anyway.So I, I think the, the market for people who just do things and try things and try to execute in like a competent way, even if like it doesn't work out commercially, even if it just wasn't that great anyway. Like, but like that's your job interview to go into, into one of these things anyway, so, um, I don't feel that.From a, from a very, very small startup perspective, mid-size startups. Yes. Uh, I will say there's been a lot of dead, um, LM Infra, a lot of LM infra consolidation like the, the, uh, lang fuses of the world getting absorbed into, into click house. And I, I think. Like people have maybe worked out the domain specific playbook, uh, and like, I think that's okay.Um, and, and yeah, I'm not that, not that worried about, uh, okay. So, um, I, I would say I'd be more worried about traditional SaaS, like low NPSS. This is the whole AI versus SaaS debate that has, that's been going on. Uh, and, and like literally I'm going through that exact thing in my company where, so I like kind of.Thinking through this on a very visceral, visceral level, right? On one hand you have the people who say you vibe coders don't appreciate the amount of work that goes into A-A-C-R-M and like, yeah, you think you can rip out Salesforce? So did the 30 entrepreneurs before you, right? Like, like, you know, you classically underestimate the things that you don't.Deeply, no. And, and, and target audience is not you. Uh, at the same time, like we have never been able to build software so easily and customize software so easily and like Yeah, you're not gonna use 90% of the things in Salesforce. So like, yeah. What's the typical, so what have you, what[00:37:33] Jacob Effron: have you done internally?[00:37:34] swyx: So we have there the main SaaS that we do for event management and sponsor management. That's, and we paid 200 KA year for that. Not, not huge, but like chunky for, for, for my, my scale. Um, and like, yeah, I could probably spend 2000 and, and build like a custom version of that. Um, the, the, the trick has been dealing with my, the rest of my team and getting them on board.Yeah. ‘cause I'm the most ethical person on my team, but like, I can't make that decision myself. And I think in the same way I've been telling with other CEOs team leaders as well, it's like, well you can be super cloud pilled. You can be super LM psychosis and that you think that's okay, but you like you have to bring your team with you.And I think like there, the sort of widening disparity in LM psychosis in companies is causing real s real riffs because. And on one hand, on one hand, the people who are less AI native are not getting with the picture. They're not, they're actually like behind, they're actually not waking up to the fact that like you, everything you think is necessary is not actually that necessary.And in fact, exactly would be better of you if you just like held your nose and went in and when came out the other side. Yeah, only talking to agents in natural language and like your life would actually be better and you just, you're just like close-minded. There's that perspective. The other perspective is, oh, you vibe coder.You, you did this in a weekend and you got the 80% solution and now the rest of your employees. Have to pick up the rest of your s**t, right, that you, that you thought you were, you were such hot, amazing, uh, uh, at, but like, actually you didn't figure it out. And like, actually LMS are still useless at this and blah, blah, blah.So like, I think there's this huge debate going on in every company right now. Um, and like, um, you know, I have a small microcosm of it, but like, yeah, it, it's making me hesitate to, to pull the trigger. But like I will at some point, it's like maybe I've put it off for one year, but not like five. Yeah, but like, so, so like SaaS is definitely getting squeezed.Um, it does make me wonder, like, I, I do think that there's an opportunity for a more AI native, um, system of record thing that is not just Postgres. Um, or not just MongoDB, although both are very good. Maybe it's like a convex or like people Yeah. Bring up convex a lot. I don't know, like, like, I, I just feel like the sort of quote unquote firebase of, of AI apps isn't really a thing yet.Um, beyond what we have. Uh, which, which is fine. It's, it's, it's just. We could probably start in a more sort of rapid iteration cycle first before scaling up to like a Postgres or MongoDB, which are more sort of old tech. I was at a dinner with, uh, Mike Krieger, the CPO of en philanthropic, and, and he, we were just kind of going around the room going like, what are people most worried about?Yeah. And, uh, for me, uh, I, instead of security, I brought up biosafety. Yeah,[00:40:21] Jacob Effron: classic.[00:40:22] swyx: Um, actually, like I said, it was. Cliche and classic, and the rest of the table were, were like, what do you mean? Someone sitting at home can manufacture a virus that wipes out half of humanity,[00:40:32] Jacob Effron: almost like the OG Jeffrey Hinton.Like, this is why you should be scared.[00:40:35] swyx: I'm like, yeah, like the read the, you know, risk reports. Like this is like the thing. Um, I think, and Mike was just sitting there knowing he was sitting on Mythos and going like, actually it's security. Um, and I think like, um, I think the, there's, there's, part of it is.A very good marketing. Like too good. Yeah, like I would actually advise and topic to tune down the marketing because also it's, it is just a very good model and you don't have to make so many marketing claims around it. At the same time, it is not really a private model. If you give it to 40 companies.Each of whom have like 10,000 employees or whatever. Right. It's not, it's not private, it's, it's like there's bad actors in there.[00:41:18] Jacob Effron: Yeah. Hopefully, hopefully not as, uh, as bad as releasing it widely, but, uh, no, I mean, it's an interesting. You know, it's an interesting case study for how all, I mean, many model releases might, I mean, you know, this might be the first model release that looks like the rest of ‘em from from now on, right?[00:41:31] swyx: It, it, so it's, it's the, there's an overall product strategy, uh, for anthropic of like bundle, uh, you know, restrict access bundle, uh, product with model maybe.Whereas, uh, OpenAI has definitely been a lot more sort of. Philosophically aligned on like, we will just enable access everywhere and we don't know what you, what will come out of it. Right.[00:41:51] Jacob Effron: Right. Though, I mean, this current moment, uh, obviously the cynical take is also just ties to the amount of compute that both companies[00:41:56] swyx: Yeah.Right, right, right. Yeah, I think, I think that's true. I I do think like the, the, this is the, the, the scale, the dawn of like larger than 10 trillion parameter models is very interesting. I don't think it, I think it's a temporary phenomenon because we have much larger compute clusters coming online for everyone over the next like three, five years.It's, and this is like already written in, in the cards.[00:42:18] Jacob Effron: Yeah.[00:42:19] swyx: So to the extent that like, you know, will we have rationing of models, uh, above 10 trillion, uh, in like two years? I don't think so. I think everyone will have no, we'll just[00:42:29] Jacob Effron: have rationing of the next phase.[00:42:30] swyx: Right. Right. But like, that's as it should be almost like, um.My, my classic example, which I, this is just me theorizing, not anything confirmed by Google. When Google announced Gemini, they actually announced three sizes, which was Flash Pro Ultra. They never released Ultra. They only have Pro and Flash. Um, so my theory is they have ultra sitting in a basement and they just could distilling from it for, for flashing pro.Um, which like, yeah, I mean, I, I actually think that's. As it should be for any lab that they, that they do that.[00:43:02] Jacob Effron: Yeah. Just because those are the models that people actually wanna end up using. And it's just like cost prohibit.[00:43:06] swyx: It is more, yeah, it's cost. Yeah. It's, it's not the want, it's just, just, just the cost.Um, I do think, like, uh, it is interesting that, uh, for a while I was, I was considering the theory that models capped out at two, 2 trillion, and I think that's proving to be wrong. And well then if I'm wrong, how wrong? How wrong am I? Do we do 200 trillion? Do we do two quarter trillion, whatever? Um, and I don't think we have the straight answer to that, but like, uh, it's interesting that we are continuing to scale number of pers when everyone kind of assu like can see that we're not going to get like the next thousand or 1 million x from this paradigm.So like the others, like the alias of the world are working on other. Um, model architecture improvements. We need a different scaling law, I guess, because like, we're, I, I feel like people already already feel like we're tapped out on this. Like the, the end, the end state of this is we turn most of the world into data centers and like, I don't know.I don't know if we want that.[00:44:08] Jacob Effron: Yeah, I mean, uh, if the, if, if, if the return of intelligence are there, maybe, uh, maybe not so bad.[00:44:13] swyx: I, I, I think there, there's just a sheer amount of like, like un scalability that like is wrangling people's sensibilities right now. Um, especially in terms of like context lengths.Um, my classic quote is that context length is like the slowest scaling factor in, in lms.[00:44:30] Jacob Effron: Yeah.[00:44:30] swyx: Um, we, like, we took maybe. Three years to go from like 4,000 context length to a million and that's about it. Yeah. Like Gemini has had a million token context length for two years now. Um, and no one's using it.Like, so like yeah, it's memory. Memory is probably gonna be the, the biggest limiting constraint on all these things.[00:44:50] Jacob Effron: Yeah. Certainly seems that way. I guess I'm curious over the last year since you recorded last, like what's one thing you've changed your mind on?[00:44:57] swyx: I feel like I was kind of bearish on open models like last year.Um, in a sense of, like, I, I had just done the podcast with an Al[00:45:07] Jacob Effron: Yeah.[00:45:08] swyx: Of Braintrust where he, and he, I mean, you know, he has a good cross section of all the top AI companies and he says market share of open source is 5% and going down. Um, I think that's changed. I think it's going up. Um, and even if,[00:45:22] Jacob Effron: even though the capability gap does seem to be increasing.Spending on the[00:45:26] swyx: time. It's hard to tell. Yeah, it's, it's really hard to tell. ‘cause like, okay, for, for listeners, capability gap increasing is like on public benchmarks. And let's say you're comparing mythos versus like, I don't know, G-T-O-S-S or like GLM 5.1. And, um, it's, it is really hard to tell. ‘cause even if they were closing, you will also not believe that they were closing that much because it's very easy to gain the benchmarks.Yeah. So you just don't really, really know. Um, all you know is like. Uh, there's somewhat objective open router stats on like what people choose in a free market. And people do choose some of these open models in significant volume, except that a lot of them are heavily discounted. So you need to kind of like price adjust, uh, these things.So even if, even if that were true, which I, I'm not sure, like I, I, I feel like the numbers just up now instead of down. Uh, I think the. Separation between what the top tier agent labs

Na primeira edição deste boletim você confere:- Relator da CPI do Crime Organizado solicita indiciamento do procurador-geral da PGR e de ministros do SFT; - Luis Felipe Salomão é eleito como novo presidente do STJ; - Trump crítica a ação da premiê da Itália na guerra contra o Irã.   O Boletim Rádio Gazeta Online é um conteúdo produzido diariamente com as principais notícias do Brasil e do mundo. Esta edição contou com a apresentação das monitores Fábio Barreto e Maria Eduarda Palermo, do curso de Jornalismo.Escute agora!

In this episode, I'm joined by Kym Tolson, LCSW for an eye-opening conversation about what AI really means for the future of mental health practice. Together, we unpack some of the biggest myths and misconceptions therapists have about AI, including concerns about privacy, ethics, environmental impact, burnout, and whether technology could ever replace the therapeutic relationship. Kym shares practical ways therapists can begin using AI right now to reduce documentation burden, streamline practice operations, support marketing efforts, and create more sustainable workflows, all while staying clinically grounded and ethically informed. We also talk about why the future of therapy may require clinicians to lean even more into relational skills, clinical judgment, and authentic human connection. Plus, we highlight the free 1.25 NBCC-approved CE training available through Berries Academy, where I'm serving as the course administrator and Kym is the presenter. If AI has felt overwhelming, confusing, or even a little scary, this episode will help you feel informed, empowered, and ready to take the first step.

For this episode of the SFT Podcast, we hear from Owen Shiers – Welsh folk singer, researcher, grain grower and cultural historian. In his late 20s, Owen was rewarded a scholarship from the Finzi Trust to research folk music in Ceredigion, where he grew up – a moment which he describes as the 'beginning of his journey'. What was originally intended to be an exploration of culture, became an exploration of agriculture as Owen came to learn about the significance of black oats in Wales. During his research, Owen crossed paths with Gerald Miles, an organic farmer from Pembrokeshire, and Iwan Evans Coedfadre, a Welsh folk singer and farmer. Whilst Gerald had been searching for black oats for 20 years, having all but disappeared and been replaced by more modern varieties of oats, Iwan was the last farmer in Wales to be growing them. Through this research, Owen was able to connect Gerald and Iwan, which led to the creation of the Llafur Ni Network ('Our Cereals') – a project they co-founded with the Gaia Foundation, bringing together farmers and growers across Wales to revive black oats and other rare welsh grains. Owen's music, (Cynefin – Owen's 'musical brainchild') is firmly rooted in the customs and cultural vernacular of Ceredigion. His most recent album Shimli, explores the intersection between music, poetry, food and the natural world. Stick around until the end of this episode to hear 'Y Medelwr' (The Reaper Man) from Owen's latest album, and for an explanation about the origins of the song. Elsewhere in the episode, Patrick and Owen also talk about the impact that religion has had on Welsh folk music; how the cultivation and preservation of seeds compares to that of the cultivation of wool and other animal by-products; and they ask, how can we create economic opportunities for people in a food and farming system which is often working against the principles of sustainability? You can listen to Cynefin's music here and and follow him on Instagram. To find out more about the Llafur Ni Network and how Owen first came to meet Gerald and Iwan, watch this brilliant film from The Gaia Foundation and Andy Pilsbury. To listen to more SFT podcasts, featuring some of the biggest names in regenerative food and farming, head to our main podcast page. And to keep up to date with our news, you can subscribe to our monthly newsletter or follow us on Instagram, Facebook, LinkedIn and Bluesky. This conversation was recorded in January 2026.   Timestamps: 0:00: Welcome to the SFT Podcast! 0:49: Who is Owen Shiers? 3:17: Exploring the roots of Welsh folk music 5:03: The Methodist Church's impact on folk music in Wales 7:55: Black Oats in Wales 11:06: What can poetry tell us about farming culture? 13:40: Stuck between a rock and a hard place: valuing culture versus making a profit in farming 17:53: Opportunities in agricultural education 23:40: How do we create economic opportunities for people in a system set against the principles of sustainability? 28:18: The Llafur Ni Network 42:06: How does the cultivation and valuing of seeds compare to wool? 45:28: How can we make sustainable food and clothing more affordable? 49:11: Owen reads some Welsh poetry 51:52: Goodbye! 52:31: 'Y Medelwr' (The Reaper Man)

Na segunda edição deste boletim você confere:- Presidente da Fifa afirma que seleção do Irã estará na Copa do Mundo;    - Senado recebe a indicação de Lula para Jorge Messias ingressar no SFT; - Convocação de Cláudio Castro e Ibaneis Rocha é aprovada na CPI do Crime Organizado. O Boletim Rádio Gazeta Online é um conteúdo produzido diariamente com as principais notícias do Brasil e do mundo. Esta edição contou com a apresentação dos monitores Fábio Barreto e Maria Eduarda Palermo, do curso de Jornalismo.Escute agora!

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

Everyone wants to be AI-first. But no one wants to fund it. In this HFS Unfiltered Stories conversation, Saurabh Gupta, President at HFS Research, sits down with Kailash Attal, Chief Solutions Officer at UST, to tackle one of the toughest questions facing enterprises today: How do you fund AI when budgets are tight, and legacy costs are high? With CFO scrutiny increasing and capital discipline at an all-time high, enterprises can't simply “wait for the next budget cycle.” Instead, a new operating model is emerging, a self-funded transformation, where savings generated from optimization are reinvested directly into AI and innovation.They together explored:Why 8 out of 10 enterprises say they cannot wait to transformHow to combine “run” and “transform” into one operating modelWhy AI is blurring the lines between IT and businessHow services-as-software becomes realistic through structured self-fundingWhat must change culturally and operationally to make this workThis isn't about cost-cutting. It's about creating a flywheel of savings, reinvestment, and growth, and if your organization is struggling with the how of AI, this conversation is for you.For more insights, read our POV, Stop waiting for budget: How enterprises are funding transformation from within: https://www.hfsresearch.com/research/stop-waiting-for-budget-how-enterprises-are-funding-transformation-from-within/Learn more about UST's approach to self-funded transformation here: https://www.ust.com/en/sft If you're interested in discussing SFT with UST, you can connect with their team here: https://www.ust.com/en/contact-us

What's really going on in food and farming? Two farmers – Patrick Holden, founder of the Sustainable Food Trust, and Stuart Oates, founder of the Fossil Free Farm project – get behind the headlines to unpack the biggest stories shaping what we eat, how we farm and the future of the planet. Expect lively debate, real-world experience, and unique insights from some of food and farming's top voices. In this episode of the SFT Podcast we're talking about food security – as the war in Iran shows no sign of easing, with thousands of casualties and many people displaced from their homes, we discuss the impact of the conflict in terms of food security in the Middle East, as well as how agriculture is often weaponised in times of conflict.  We also reflect on how the conflict has impacted food security in the UK, and what steps could be taken to ensure the country is more food secure and our farming sector is more resilient in the face of local and global shocks. This week, Patrick and Stuart are also joined by Megan Perry, the SFT's Head of Policy & Campaigns. Megan has a degree in international politics and has been working with the SFT for 12 years, heading up our work the UK's small abattoir sector. Alongside her experience at the SFT, Megan has also spent time visiting and supporting organisations and individuals working in the agriculture sector in places like Palestine and Lebanon who have been significantly affected by the ongoing conflict. To join in the conversation, get in touch with us at info@sustainablefoodtrust.org or send us a message via any of our social media channels. Resources mentioned in the episode: Feeding Britain report: https://sustainablefoodtrust.org/our-work/feeding-britain/ Food sovereignty report: https://www.arab-reform.net/publication/pathways-towards-food-sovereignty-in-lebanon/?tztc=1 Tim Lang: UK must stockpile food in readiness for climate shocks or war: https://www.theguardian.com/global-development/2026/mar/07/uk-stockpile-food-climate-shocks-war

Hey, Alex here

Hey folks, Alex here, let me catch you up! Most important news about this week came today, mid-show, OpenAI dropped GPT 5.4 Thinking (and 5.4 Pro), their latest flagship general model, less autistic than Codex 5.3, with 1M context, /fast mode and the ability to steet it mid-reasoning. We tested it live on the show, it's really a beast. Also, since last week, Anthropic said no to Department of War's ultimatum and it looks like they are being designated as supply chain risk, OpenAI swooped in to sign a deal with DoW and the internet went ballistic (Dario also had some .. choice words in a leaked memo!) On the Open Source front, the internet lost it's damn mind when a friend of the pod Junyang Lin, announced his departure from Qwen in a tweet, causing an uproar, and the CEO of Alibaba to intervene. Wolfram presented our new in-house wolfbench.ai and a lot more! P.S - We acknowledge the war in Iran, and wish a quick resolution, the safety of civilians on both sides. Yam had to run to the shelter multiple times during the show. ThursdAI - Highest signal weekly AI news show is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.OpenAI drops GPT 5.4 Thinking and 5.4 Pro - heavy weight frontier models with 1M context, /fast mode, SOTA on many evalsOpenAI actually opened this week with another model drop, GPT 5.3-instant, which... we can honestly skip, it was fairly insignificant besides noting that this is the model that most free users use. It is supposedly “less cringe” (actual words OpenAI used). We all wondered when 5.4 will, and OpenAI once again proved that we named the show after the right day. Of course it drops on a ThursdAI. GPT 5.4 Thinking is OpenAI latest “General” model, which can still code, yes (they folded most of the Codex 5.3 coding breakthroughs in here) but it also shows an incredible 83% on GDPVal (12% over Codex), 47% on Frontier Math and an incredible ability to use computers and browsers with 82% on BrowseComp beating Claude 4.6 at lower prices than Sonnet! GPT 5.4 is also ... quite significantly improved at Frontend design? This landing page was created by GPT 5.4 (inside the Codex app, newly available on Windows) in a few minutes, clearly showing significant improvements in style. I built it also to compare prices, all the 3 flagship models are trying to catch up to Gemini in 1M context window, and it's important to note, that GPT 5.4 even at double the price after the 272K tokens cutoff is still.... cheaper than Opus 4.6. OpenAI is really going for broke here, specifically as many enterprises are adopting Anthropic at a faster and faster pace (it was reported that Anthropic is approaching 19B ARR this month, doubling from 8B just a few months ago!) Frontier math wizThe highlight from the 5.4 feedback came from a Polish mathematician Bartosz Naskręcki (@nasqret on X), who said GPT-5.4 solved a research-level FrontierMath problem he had been working on for roughly 20 years. He called it his “personal singularity,” and as overused as that word has become, I get why he said it. I've told you about this last week, we're on the cusp. Coding efficiencyThere's tons of metrics in this release, but I wanted to highlight this one, where it may seem on first glance that on SWE-bench Pro, this model is on par with the previous SOTA GPT 5.3 codex, but these dots here are thinking efforts. And a medium thinking effort, GPT 5.4 matches 5.3 on hard thinking! This is quite remarkable, as lower thinking efforts have less tokens, which means they are cheaper and faster ultimately! Fast mode arrives at OpenAI as wellI think this one is a direct “this worked for Anthropic, lets steal this”, OpenAI enabled /fast mode that.. burns the tokens at 2x the rate, and prioritizes your tokens at 1.5x the speed. So, essentially getting you responses faster (which was one of the main complains about GPT 5.3 Codex). I can't wait to bring the fast mode to OpenClaw with 5.4, which will absolutely come as OpenClaw is part of OpenAI now. There's also a really under-appreciated feature here that I think other labs are going to copy quickly: mid-thought steering. OpenAI now lets you interrupt the model while it's thinking and redirect it in real time in ChatGPT and iOS. This is a godsend if you're like me, sent a prompt, seeing the model go down the wrong path in thinking... and want to just.. steer it without stopping! Anthropic is now designated as supply-chain risk by DoWLast week I left you with a cliffhanger: Anthropic had received an ultimatum from the Department of War (previously the Department of Defense) to remove their two remaining restrictions on Claude — no autonomous kill chain without human intervention, and no surveillance of US citizens. Anthropic's response? “we cannot in good conscience acceede to their request” So much has happened since then; US President Trump said “I fired Anthropic” referring to his Truth Social post demanding intelligence agencies drop the use of Claude (which apparently was used in the war with Iran regardless); Sam Altman announced that OpenAI has agreed to DoW and will provide OpenAI models, causing a lot of people to cancel their OpenAI subscriptions, and later apologizing for the “rushed rollout”; Dario Amodei posted a very contentious internal memo that leaked, in which he name-called the presidency, Sam Altman and his motives, Palantir and their “safety theater”, for which he later apologizedHonestly this whole thing is giving me whiplash trying to follow, but here's the facts. Anthropic is now the first US company in history, being designated “supply chain risk” which means no government agency can use Claude, and neither can any company that does contracts with DoW. Anthropic says it's illegal and will challenge this in court , while reporting $19B in annual recurring revenue, nearly doubling since last 3 months, and very closely approaching OpenAI at $25B. Look, did I want to report on this stuff when I decided to cover AI? no... I wanted to tell you about cool models and capabilities, but the world is changing, and it's important to know that the US Government understands now that AI is inevitable, and I think this is just the first of many clashes between tech and government we'll see. We'll keep reporting on both. (but let me know in the comments if you'd prefer just model releases) OpenAI's GPT-5.3 Instant Gets Less Cringe, Google's Flash-Lite Gets Faster (X, Announcement)We also got two fast-model updates this week that are worth calling out because these are the models that often end up powering real product flows behind the scenes. As I wrote before, OpenAI's instant model is nothing to really mention, but it's worth mentioning that OpenAI seems to have an answer for every Gemini release. Gemini released Gemini Flash-lite this week, which boasts an incredible 363 tokens/s speed, which doing math at a very good level, 1M context and great scores compared to the instant/fast models like Haiku from Anthropic. Folks called out that this model is more expensive than the previous 2.5 Flash-lite. But with 86.9% on GPQA Diamond beating GPT-5 mini, and 76.8% MMMU-pro multimodal reasoning, this is definitely worth taking a look at for many agentic, super fast responses! For example, the heartbeat response in OpenClaw. Qwen 3.5 Small Models & The Departure of Junyang Lin (X, HF, HF, HF)Alibaba's Qwen team continued releasing their Qwen 3.5 family, this time with Qwen 3.5 Small, a series of models at 0.8B, 2B, 4B, and 9B parameters with native multimodal capabilities. The flagship 9B model is beating GPT-OSS-120B on multiple benchmarks, scoring 82.5 on MMLU-Pro and 81.7 on GPQA Diamond. These models can handle video, documents, and images natively, support up to 201 languages, and can process up to 262K tokens of context. And.. they are great! They are trending on HF right now. What's also trending is, tech lead for Qwen, a friend of the pod Junyang Lin, has posted a cryptic tweet that went viral with over 6M views. There was a lot of discussions on why he and other Qwen leads are stepping away, what's goig to happen with the future of OpenSource. The full picture seems to be, there are a lot of internal tensions and politics, with Junyang being one of the youngest P10 leaders in the Alibaba org.A Chinese website 36KR ( Kind of like a chinese techcrunch) reported that this matter went all the way up to Alibaba CEO, who is no co-leading the qwen team, and that this resignation was related to an internal dispute over resource allocation and team consolidation, not a firing. I'm sure Junyang is going to land somewhere incredible and just wanted to highlight just how much he did for the open source community, pushing Qwen relentlessly, supporting and working with a lot of inference providers (and almost becoming a co-host for ThursdAI with 9! appearances!) StepFun releases Step 3.5 Flash Base (X, HF, HF, Announcement, Arxiv)Speaking of Open Source, StepFun just broke through the noise with a new model, a 196B parameter sparse Mixture of Experts model activating just 11B parameters when ran. It has some great benchmarks, but the main thing is this: they are releasing the pretrained base weights, a midtrain checkpoint optimized for code and agents, the complete SteptronOSS training framework, AND promising to release their SFT data soon - all under Apache 2.0! Technically the model looks strong too, with multi-token prediction, 74.4% on SWE-bench verified bench (though, as we told you last week, it's.. no longer trusted) and full apache 2! This Week's Buzz: presenting Wolfbench.ai I'm so excited about this weeks “this weeks buzz”, Wolfram has been hard at work preparing and presenting a new framework to test out these models, and named it wolfbench.ai Wolfbench is our attempt to compare how the same model performs via different agentic harnesses like ClaudeCode, OpenClaw and Terminalbench's own Terminus. You can check out the website on wolfbench.com but the short of it is, a single number is not telling the full story. Wolf Bench breaks it into a four-metric framework: the average score across runs, the best single run, the ceiling (how many tasks can the model solve at least once across all runs), and the floor (how many tasks does it solve consistently across every single run). That last one is what I find most illuminating. Opus 4.6 might be able to solve 88% of Terminal Bench tasks on average, but only about 55% of tasks it solves every single time. Reliability matters enormously for agents, and benchmarks almost never surface this. If you want to run your own evals with the same config, reach out to Wolfram—he's open to community contributions. Wolfram has also already kicked off a Wolf Bench run on GPT-5.4 since we tested it live today, so stay tuned for those results.There's quite a few more releases we didn't have time to get into on the show given the GPT 5.4 drop, you'll find all those links in the show notes! Next week will mark 3 years since I've started talking about AI on the internet and created ThursdAI (It was March 14th, 2023, same day as GPT4 launched) and we'll have a little celebration, I do hope you join us live

On this episode, we catch up with Julia Castro (@fuertejulia), the versatile Spanish waterwoman from Fuerteventura, during her time repping Foil Drive at BOOT Düsseldorf 2026. Fresh from the massive indoor pool demos in "the surf hole" at the world's leading water sports trade show, Julia shares the electric vibe of the event, her journey into foil assist tech, and candid thoughts on the evolving state of professional watersports.Episode Highlights:Life inside BOOT Düsseldorf: 5 days deep in the massive yachting halls, repping Foil Drive in the dedicated surf/foil area with a huge indoor pool setup—constant crowds, endless questions, and non-stop demos showing why foil assist is exploding in popularityHow Julia discovered Foil Drive by chance at her local spot, went from skeptic ("this cannot be true, this is sorcery") to instant addict after binge-watching videos, and now handles marketing, content, and pro team duties for Foil Drive Europe after nearly two yearsThe groundbreaking side of Foil Drive: first brand to make a truly universal mast-mount motor, plus their pioneering collaborative spirit—openly partnering with Slingshot, Armstrong, F-One, Axis, and more to grow the whole foil ecosystem instead of gatekeepingOvercoming major personal hurdles: shoulder destruction, a cancer scare right after recovery, COVID lockdowns in strict Spain, sponsor cuts, and shifting from full-time competition to event filming/social media work (now 60-70% of her income) while staying positive and healthyThe tough realities of modern watersports sponsorship: the shift from skill-based deals to needing massive social media fame, pressure on guys to go ultra-extreme (with scary injuries), and the unfortunate bikini-heavy expectations for women—Julia fights for real choice, authenticity, and legacy beyond "only fans" style contentWhy brands hold huge responsibility: pushing real people as role models instead of curated illusions that harm mental health (especially kids idolizing non-real personas), prioritizing quality/value over raw views, and supporting diverse paths for women in the sportFoil Drive fun and future: addictive for water lovers, game-changing for wave sessions (catching 30-50 waves vs. friends' 5), altered dynamics (more rear-foot weight), potential SFT foil-assist/wave divisions, and her dream to compete again—plus a cheeky plan to "borrow" a battery for a van road trip back to Fuerteventura with snowboarding and foil stopsGrowing up in paradise: Fuerteventura childhood on the beach, late start to kiting/watersports (tourist-priced), realizing after world travel that home really is one of the best spots, now packed with visitorsIf you're into foil assist tech like Foil Drive, the intersection of e-foiling/wave foiling/pump foiling, honest talks on gender dynamics and mental health in action sports, athlete resilience stories, or just pure stoke from someone living the waterwoman life—this episode delivers real talk, inspiration, and plenty of foil obsession!Catch the full conversation and follow Julia Castro on Instagram @fuertejulia for her road trip adventures, Foil Drive sessions, event coverage, and more. Big thanks to Foil Drive for the ongoing innovation, and stay tuned for more from the growing foil world in 2026. Listen now!

If you listened to my last episode about creating income by teaching without becoming an influencer and thought, "That sounds great… but when would I actually find the time?",this episode is for you. In this conversation, I talk honestly about time management for therapists who want to create continuing education, without hustle culture, productivity pressure, or burnout. I break down why "I don't have time" makes complete sense for clinicians, and why the real issue usually isn't time at all, it's structure, energy, and permission. I explore the difference between clinical energy and creative energy, why waiting for long blocks of free time keeps therapists stuck, and how continuing education can be built in small, realistic containers that actually fit therapist life. I also share why most therapists already have far more teachable material than they realize and why starting smaller than you think is often the key to getting unstuck. This episode is especially for therapists who feel curious about teaching or creating CE, but overwhelmed by where it would fit, or whether they're "ready" to begin. Special Promotion: Berries + Free CE Podcourse Bundle (New Annual Customers) At the beginning of this episode, I shared a limited-time promotion in partnership with Berries. New customers who purchase the Berries annual subscription plan using my referral link will receive free access to my CE Podcourse Bundle, which includes over 30 hours of NBCC-approved continuing education contact hours, with new podcourses added throughout the year. A Podcourse is a podcast and an audio course in one - designed for busy clinicians. You can listen on a walk, between sessions, or whenever it fits into your schedule. When you're ready, you log in, complete a short self-study quiz, and download your certificate of completion.

Many therapists feel a pull to create income outside of the one-to-one therapy model, but feel conflicted about influencer culture, constant posting, or anything that feels misaligned with their professional values. In this episode, I explore how therapists can diversify their income by teaching, without becoming influencers or leaving the field. I break down why teaching is a natural extension of clinical work, how continuing education fits ethically within scope, and why so many therapists already have teachable expertise without realizing it. I discuss practical, realistic options for therapists, including continuing education, consultation, digital educational products, and podcast-based learning, and reframe income diversification as an issue of sustainability, not commitment. This episode is especially relevant for seasoned clinicians who want longevity in the profession without hype or hustle culture. Special Promotion: Berries + Free CE Podcourse Bundle (New Annual Customers) At the beginning of this episode, I shared a limited-time promotion in partnership with Berries. New customers who purchase the Berries annual subscription plan using my referral link will receive free access to my CE Podcourse Bundle, which includes over 30 hours of NBCC-approved continuing education contact hours, with new podcourses added throughout the year. A Podcourse is a podcast and an audio course in one - designed for busy clinicians. You can listen on a walk, between sessions, or whenever it fits into your schedule. When you're ready, you log in, complete a short self-study quiz, and download your certificate of completion.

On this episode we sit down with Edan Fiander, the young Swiss powerhouse and reigning SFT Pump Foil World Champion. Fresh off his dominant win at the BOOT Düsseldorf event to kick off the 2026 season, Edan opens up about his rapid rise in the emerging world of competitive pump foiling—from his roots on Lake Geneva to claiming multiple Swiss titles and the global crown.Episode Highlights:- Edan's journey from a skateboarding background (with plenty of injuries) to discovering pump foiling as the perfect no-wind, no-wave solution for Switzerland's Lake Geneva—turning a casual try into daily obsession and a full community at Tropical Corner with around 250 yearly pass holders- Why pump foiling stands out as one of the toughest foiling disciplines to start (needing pure self-generated power and balance) yet offers endless accessibility—no wind or waves required—and cross-training benefits for wing foiling, surf foiling, and beyond- Training secrets behind his success: consistent dock starts, explosive full-range strength work, strapless sessions for feel, high-level coaching (physio, massages, and prep), altitude mask breathing drills for respiratory power, and handling turbulence from other riders in tight races- Competing in the SFT: from a rushed debut in Düsseldorf (finishing 5th after a foil issue) to winning the event this year; key factors like lightning-fast starts, energy management, risk calculation in heats, mindset, and avoiding common falls (toe slips in straps, speed wobbles, buoy touches)- Gear insights: riding the Lift HA 120 (775 cm², ~10.2 aspect ratio) for its perfect balance of glide, speed, playfulness, and tight turns—ideal for technical SFT courses—plus how modern high-aspect foils have evolved for racing vs. all-around use- Life as a 19-year-old multi-time champion: balancing a gap year of intense training with upcoming university studies in business management, staying motivated to defend his title, and the joy of growing a small but passionate sport- Bonus vibes: Swiss community spirit, why pump foiling opens doors for flat-water riders everywhere, quick tips for aspiring competitors, and even a taste of Swiss desserts (meringue with double cream from Gruyère—rich, not healthy!)If you're into pump foiling, competitive foiling scenes, training deep dives, gear geekery, or stories of young athletes pushing limits in non-wind-powered sports—this episode is packed with motivation, technique breakdowns, and pure stoke!Catch the full conversation and stay tuned to the Surf Foil World Tour (SFT) for more pump foil action in 2026.Follow Edan Fiander on Instagram @edan.fndr for clips, training, and updates, and check out the Foil Life Podcast channels for the episode drop. Listen now!

Improve your foiling skills in paradise! Join us in Montanita Ecuador May 23-30, 2026 for a foil drive / tow / prone foil camp with Ecuador Foil, KT Foiling & Julia Castro. Learn MoreOn this episode we sit down with Edan Fiander, the young Swiss powerhouse and reigning SFT Pump Foil World Champion. Fresh off his dominant win at the BOOT Düsseldorf event to kick off the 2026 season, Edan opens up about his rapid rise in the emerging world of competitive pump foiling—from his roots on Lake Geneva to claiming multiple Swiss titles and the global crown.Episode Highlights:- Edan's journey from a skateboarding background (with plenty of injuries) to discovering pump foiling as the perfect no-wind, no-wave solution for Switzerland's Lake Geneva—turning a casual try into daily obsession and a full community at Tropical Corner with around 250 yearly pass holders- Why pump foiling stands out as one of the toughest foiling disciplines to start (needing pure self-generated power and balance) yet offers endless accessibility—no wind or waves required—and cross-training benefits for wing foiling, surf foiling, and beyond- Training secrets behind his success: consistent dock starts, explosive full-range strength work, strapless sessions for feel, high-level coaching (physio, massages, and prep), altitude mask breathing drills for respiratory power, and handling turbulence from other riders in tight races- Competing in the SFT: from a rushed debut in Düsseldorf (finishing 5th after a foil issue) to winning the event this year; key factors like lightning-fast starts, energy management, risk calculation in heats, mindset, and avoiding common falls (toe slips in straps, speed wobbles, buoy touches)- Gear insights: riding the Lift HA 120 (775 cm², ~10.2 aspect ratio) for its perfect balance of glide, speed, playfulness, and tight turns—ideal for technical SFT courses—plus how modern high-aspect foils have evolved for racing vs. all-around use- Life as a 19-year-old multi-time champion: balancing a gap year of intense training with upcoming university studies in business management, staying motivated to defend his title, and the joy of growing a small but passionate sport- Bonus vibes: Swiss community spirit, why pump foiling opens doors for flat-water riders everywhere, quick tips for aspiring competitors, and even a taste of Swiss desserts (meringue with double cream from Gruyère—rich, not healthy!)If you're into pump foiling, competitive foiling scenes, training deep dives, gear geekery, or stories of young athletes pushing limits in non-wind-powered sports—this episode is packed with motivation, technique breakdowns, and pure stoke!Catch the full conversation and stay tuned to the Surf Foil World Tour (SFT) for more pump foil action in 2026.Follow Edan Fiander on Instagram @edan.fndr for clips, training, and updates, and check out the Foil Life Podcast channels for the episode drop. Listen now!

In this episode of The Therapy Show, I chat with Ann Mac Prevost, a licensed professional counselor who specializes in teen anxiety. Ann Mac dives into how anxiety shows up in today's teens, why it's more prevalent than ever, and how parents and therapists can help teens manage it effectively. We also explore the role of family systems, the impact of social media, and practical tools that support emotional growth in adolescents. In this episode, we cover: The most common anxiety presentations in teens today Why COVID and smartphones have intensified anxiety in adolescents How to know when anxiety crosses from "normal" to "problematic" Tips for therapists working with teens and their families The importance of involving parents in the therapeutic process Helpful frameworks for validating emotions while promoting behavior change Practical CBT and exposure strategies that teens can actually use Whether you're a therapist, parent, or just curious about teen mental health, this conversation is packed with relatable insights and tools you can use right away.  Connect with Ann Mac. Links mentioned:

At some point in your career as a therapist, the work begins to shift. You may feel a quiet pull to share what you've learned beyond the therapy room but feel unsure what that means or whether you're "qualified" to teach. In this episode, I explore the transition from clinician to educator and why this identity shift can feel both exciting and uncomfortable. I talk about common myths therapists hold about teaching, ethical considerations, visibility, and how stepping into an educator role doesn't require a new certification or a major career change. If you're a mid-career or seasoned clinician curious about teaching, mentoring, or continuing education, this episode is an invitation to notice the pull and explore it with clarity and confidence. Links mentioned:

This episode is brought to you by Armstrong Foils. Armstrong Foils are a founding member and proud supporter of the all new Global Foil Board Sports Association (GFA) To learn more visit: www.gfafoilworld.com / www.armstrongfoils.comOn this episode, we're diving into the thrilling world of SFT e-foiling with Agnes Wicander — the Swedish powerhouse, Waydoo ambassador, sales & marketing rep, and standout women's SFT e-foil champion who dominated the scene in her debut racing season!Episode Highlights:- Agnes's journey from a sporty Swedish family (kite foiling since 2015, yachting background) to becoming a top e-foil racer — spotting the Waydoo Kickstarter, starting with her dad in Sweden, touring Europe in a 6-meter camper van to build the brand, and shifting from chill cruising to high-speed, high-adrenaline racing- Why e-foiling is the perfect complement to wind sports (wing foiling, kiting, paragliding) — ultimate accessibility in low-wind Sweden, tech appeal, silent exploration, and that addictive “foil throttle” rush that feels more like driving a fast car than anything else on the water- Behind-the-scenes of competitive e-foiling: inaugural SFT & E-Foil Racing League seasons, technical courses rewarding skill over pure speed, prop wash battles, nerve management in sensitive-trigger racing, common falls (sharp turns & wing breaches), evolving rules, and how racing pushes brands like Waydoo to innovate (lighter batteries, better wings, upcoming Foil Boost for swell & wave riding)- Gear talk & setup tweaks: Shimming madness (2 degrees too much = no lift!), custom wing choices for different riding styles, reverse mode for board recovery, GPS tracking, challenges in chop/swell without motor glide, and the dream of wing-breach capable foils- Epic e-foiling destinations unlocked by wind independence: ultra-remote Papua New Guinea sugar-top islands, serene Norwegian fjords, canal cruising in Australia (coffee run on the board!), backcountry lakes, camping adventures, and exploring places a boat or jet ski could never reach- The supportive women's division vibe, growing female participation, ideas for making racing more accessible (battery rentals, event support), and why e-foiling delivers a unique adrenaline hit that's hard, technical, and seriously addictiveIf you're curious about motorized foiling, the future of e-foil racing, silent exploration in any conditions, the massive progression from cruiser to competitor, or just want to feel that pure throttle rush — this episode is packed with inspiration and stoke!Listen to the full conversation with Agnes on the Foil Life Podcast channels. Follow Agnes on instagram @agneswicander

Improve your foiling skills in paradise! Join us in Montanita Ecuador May 23-30, 2026 for a foil drive / tow / prone foil camp with Ecuador Foil, KT Foiling & Julia Castro. Learn MoreOn this episode, we're diving into the thrilling world of SFT e-foiling with Agnes Wicander — the Swedish powerhouse, Waydoo ambassador, sales & marketing rep, and standout women's SFT e-foil champion who dominated the scene in her debut racing season!Episode Highlights:- Agnes's journey from a sporty Swedish family (kite foiling since 2015, yachting background) to becoming a top e-foil racer — spotting the Waydoo Kickstarter, starting with her dad in Sweden, touring Europe in a 6-meter camper van to build the brand, and shifting from chill cruising to high-speed, high-adrenaline racing- Why e-foiling is the perfect complement to wind sports (wing foiling, kiting, paragliding) — ultimate accessibility in low-wind Sweden, tech appeal, silent exploration, and that addictive “foil throttle” rush that feels more like driving a fast car than anything else on the water- Behind-the-scenes of competitive e-foiling: inaugural SFT & E-Foil Racing League seasons, technical courses rewarding skill over pure speed, prop wash battles, nerve management in sensitive-trigger racing, common falls (sharp turns & wing breaches), evolving rules, and how racing pushes brands like Waydoo to innovate (lighter batteries, better wings, upcoming Foil Boost for swell & wave riding)- Gear talk & setup tweaks: Shimming madness (2 degrees too much = no lift!), custom wing choices for different riding styles, reverse mode for board recovery, GPS tracking, challenges in chop/swell without motor glide, and the dream of wing-breach capable foils- Epic e-foiling destinations unlocked by wind independence: ultra-remote Papua New Guinea sugar-top islands, serene Norwegian fjords, canal cruising in Australia (coffee run on the board!), backcountry lakes, camping adventures, and exploring places a boat or jet ski could never reach- The supportive women's division vibe, growing female participation, ideas for making racing more accessible (battery rentals, event support), and why e-foiling delivers a unique adrenaline hit that's hard, technical, and seriously addictiveIf you're curious about motorized foiling, the future of e-foil racing, silent exploration in any conditions, the massive progression from cruiser to competitor, or just want to feel that pure throttle rush — this episode is packed with inspiration and stoke!Listen to the full conversation with Agnes on the Foil Life Podcast channels. Follow Agnes on instagram @agneswicander

In this episode I had a powerful conversation with Dr. Andrew Hartz, a clinical psychologist and founder of the Open Therapy Institute. We explored how politics, values, and therapy are increasingly showing up together in the therapy room and why that matters for both clients and clinicians. What's New with Berries: Berries now lets you generate a complete, personalized treatment plan in seconds - built from your diagnoses, session notes, and clinical preferences, using customizable templates that match your voice and style. Its powerful "golden thread" ensures your treatment plan and notes stay clinically aligned, continuously informing each other as care evolves. With the new Magic Update feature, your plan updates effortlessly without the need to rewrite from scratch. The result? A streamlined workflow where every session builds on the last, and documentation becomes part of your clinical process - not just another admin task.  Use code TherapyShow50 for $50 off your first month - CLICK HERE.  Key takeaways: Therapist bias is a real and growing concern. Many clients feel alienated due to perceived political or ideological leanings of their therapists, often unintentionally communicated through things like pronoun usage or assumptions about worldview. Most therapists lean left politically, which can lead to clients self-censoring, feeling misunderstood, or avoiding therapy altogether. The Open Therapy Institute (OTI) was created to support therapists who want to offer politically neutral, values-attuned therapy and serve populations that feel underserved, especially those with conservative or centrist views. Therapists can grow their practice by learning to work effectively with clients from across the political spectrum. There is high demand and low supply of therapists trained to do this well. We discussed the importance of dialectical thinking. This means helping clients (and ourselves) hold multiple perspectives and tolerate ambiguity, especially around politics, religion, and identity. If you're a therapist who wants to grow in this area or reach more clients who feel left out by traditional therapy, check out Open Therapy Institute, https://opentherapyinstitute.org. Browse all my NBCC approved Podcourses - just $5 each. Get one CE contact hour. Build your first CE course (free) Get my Coping with Political Stress Ebook and Peaceful Politics AI Guide  Therapist Conversation Framework: Politics in Session A printable PDF with 97 questions to navigate political talk in therapy - without taking sides. Solution-Focused Therapy Guide72 questions + prompts to help adult clients clarify goals and move forward using SFT. Check out all my Counselor Resources.   

In this episode of The Effortless Podcast, Amit Prakash and Dheeraj Pandey are joined by Alex Dimakis for a wide-ranging, systems-first discussion on the future of long-horizon AI agents that can operate over time, learn from feedback, adapt to users, and function reliably inside real-world environments.The conversation spans research and industry, unpacking why prompt engineering alone collapses at scale; how advisor models, reward-driven learning, and environment-based evaluation enable continual improvement without retraining frontier models; and why memory in AI systems is as much about forgetting as it is about recall. Drawing from distributed systems, reinforcement learning, and cognitive science, the trio explores how personalization, benchmarks, and context engineering are becoming the foundation of AI-native software.Alex, Dheeraj, and Amit also examine the evolution from SFT to RL to JEPA-style world models, the role of harnesses and benchmarks in measuring real progress, and why enterprise AI has moved decisively from research into engineering. The result is a candid, deeply technical conversation about what it will actually take to move beyond demos and build agents that work over long horizons.Key Topics & Timestamps 00:00 – Introduction, context, and holiday catch-up04:00 – Teaching in the age of AI and why cognitive “exercise” still matters08:00 – Industry sentiment: fear, trust, and skepticism around LLMs12:00  – Memory in AI systems: documents, transcripts, and limits of recall17:00  – Why forgetting is a feature, not a bug22:00 – Advisor models and dynamic prompt augmentation27:00 – Data vs metadata: control planes vs data planes in AI systems32:00 – Personalization, rewards, and learning user preferences implicitly37:00 – Why prompt-only workflows break down at scale41:00 – RAG, advice, and moving beyond retrieval-centric systems46:00 – Long-horizon agents and the limits of reflection-based prompting51:00 – Environments, rewards, and agent-centric evaluation56:00 – From Q&A benchmarks to agents that act in the world1:01:00 – Terminal Bench, harnesses, and measuring real agent progress1:06:00 – Frontier labs, open source, and the pace of change1:11:00 – Context engineering as infrastructure (“the train tracks” analogy)1:16:00 – Organizing agents: permissions, visibility, and enterprise structure1:20:00 – SFT vs RL: imitation first, reinforcement last1:25:00 – Anti-fragility, trial-and-error, and unsolved problems in continual learning1:28:00 – Closing reflections on the future of long-horizon AI agentsHosts:Amit PrakashCEO & Founder at AmpUp, Former engineer at Google AdSense and Microsoft Bing, with deep expertise in distributed systems, data platforms, and machine learning.Dheeraj PandeyCo-founder & CEO at DevRev, Former Co-founder & CEO of Nutanix. A systems thinker and product visionary focused on AI, software architecture, and the future of work.Guest:Alex DimakisAlex Dimakis is a Professor in UC Berkeley in the EECS department. He received his Ph.D. from UC Berkeley and the Diploma degree from NTU in Athens, Greece. He has published more than 150 papers and received several awards including the James Massey Award, NSF Career, a Google research award, the UC Berkeley Eli Jury dissertation award, and several best paper awards. He is an IEEE Fellow for contributions to distributed coding and learning. His research interests include Generative AI, Information Theory and Machine Learning. He co-founded Bespoke Labs, a startup focusing on data curation for specialized agents.Follow the Hosts and the Guest: Dheeraj Pandey:LinkedIn - https://www.linkedin.com/in/dpandeyTwitter - https://x.com/dheerajAmit Prakash:LinkedIn - https://www.linkedin.com/in/amit-prak...Twitter - https://x.com/amitp42Alex Dimakis:LinkedIn - https://www.linkedin.com/in/alex-dima...Twitter - https://x.com/AlexGDimakis           Share Your Thoughts                                                                                          Have questions, comments, or ideas for future episodes?

I'm breaking down what to do when the Facebook group grind stops working. I share real, practical strategies that I've used myself to market my continuing education Podcourses, without burning out or relying solely on social media. I talk about: Why Facebook posts lose momentum How to build a simple email marketing funnel that actually works Why partnerships and podcast guesting can open new doors The power of searchable, evergreen content And how to make it ridiculously easy for someone to buy your course If you're tired of spinning your wheels, this episode will help you pivot, not panic. Plus, I give you a behind-the-scenes look at how I market my Podcourse bundle, including what didn't work and what finally clicked. Read the blog here. Links mentioned in this episode: Browse all the Podcourses Build your first CE course (free) Save time with Berries AI:  get $50 off your first month with code THERAPYSHOW50 Get my Coping with Political Stress Ebook and Peaceful Politics AI Guide  Therapist Conversation Framework: Politics in Session A printable PDF with 97 questions to navigate political talk in therapy - without taking sides. Solution-Focused Therapy Guide72 questions + prompts to help adult clients clarify goals and move forward using SFT. Check out all my Counselor Resources.   

I'm helping you plan your 2026 growth goals in this episode, whether that's continuing education courses, certifications, clinical skills, or supervision. If you're feeling a little unsure about where to focus next year, this is your chance to reflect, regroup, and set a clear, simple direction for your professional development. I'll walk you through how to identify what actually worked in 2025, choose what's worth your time in 2026, and share a few of my favorite tools that save time and reduce overwhelm - including a note-writing AI I'm loving, and a free CE Course Builder I created just for therapists like you. In this episode, I cover: How to plan CEUs you'll actually enjoy Choosing certifications that align with your goals Building your skills and setting one focus per quarter Tools to streamline your work and free up space to grow Links mentioned in this episode: Browse all the Podcourses Build your first CE course (free)Save time with Berries AI:  get $50 off your first month with code THERAPYSHOW50 Get my Coping with Political Stress Ebook and Peaceful Politics AI Guide  Therapist Conversation Framework: Politics in Session A printable PDF with 97 questions to navigate political talk in therapy - without taking sides. Solution-Focused Therapy Guide72 questions + prompts to help adult clients clarify goals and move forward using SFT. Check out all my Counselor Resources.   

Sponsored by Berries AI: Use code TherapyShow50 for $50 off your first month - CLICK HERE.    If you are a therapist or counselor looking for continuing education, check out my NBCC Approved $5 Podcourses and other continuing education offerings. Plus, get your first Podcourse half off. In this episode of The Therapy Show, I share something I've been working on behind the scenes - a free tool I created just for mental health clinicians: the CE Course Builder, a custom GPT designed to help you create and launch your own continuing education courses. If you've ever thought about teaching but felt overwhelmed by the tech, compliance, or where to even start, this tool walks you through it all - step-by-step.  I also talk about group discounts available for practice owners (email me to discuss offering my CE Podcourses to your clinicians) and invite you to fill out a short survey to help shape future CE content. If you're ready to move beyond the therapy room and share your expertise, this episode is for you. Get my Coping with Political Stress Ebook and Peaceful Politics AI Guide  Therapist Conversation Framework: Politics in Session A printable PDF with 97 questions to navigate political talk in therapy - without taking sides. Solution-Focused Therapy Guide72 questions + prompts to help adult clients clarify goals and move forward using SFT. Check out all my Counselor Resources. 

This episode is brought to you by Villa Carina Apartments in beautiful Bonaire. In this episode, we sit down with the newly crowned 2025 E-Foil Surf Foil World Tour (SFT) World Champion – the undefeated e-foil racer who took the title in the season finale in Abu Dhabi.Fresh off dominating the inaugural SFT season, the Florida-based ripper (and Flightboard early adopter) joins us to break down what it actually feels like to turn a five-year hunch into a world championship, how e-foil racing went from “nice idea” to a full-blown global tour in record time, and why this sport is exploding faster than anyone predicted.We go deep on:- From kite-smash accidents to building one of the first e-foil schools in South Florida  - The wild Atlanta Foil Fest Enduro with Brian Grubb, Nick Leeson, and 20 riders dodging submerged trees at full throttle  - Unsanctioned full-send dawn patrols through Amsterdam's canals (don't try this at home)  - Gear geek-out: custom shims, chopped tails, 900 Flow vs 707 Flux wings, aftermarket race props, and why everything is still basically stock… for now  - Why full-face helmets and downhill MTB armor are becoming mandatory at 33–35 mph  - Mental warfare on the beach, prop-wash tactics, hot launches, and pulling 3+G turns  - Traveling the world with boards but no batteries (and how the Flightboard rental network saves the day)  - The massive progression from the first dealer races in 2022 to riders now training full-time and closing the gap second by second  - Where e-foil racing is headed: open-ocean courses, city canal sprints, Everglades gator-chasing, and boards that will eventually hit 50 mph  Year one of the Surf Foil World Tour is in the books, prize money is real, brands are paying attention, and the level is skyrocketing. The champ gives us the unfiltered look at what it took to stay on top — and why 2026 is about to get even crazier.If you've ever wondered what the cutting edge of foiling actually looks, sounds, and feels like… this is it.Follow the Surf Foil Tour → https://www.surffoilworldtour.com Justin Chait → https://www.instagram.com/_justinchait_/

Improve your foiling skills in paradise! Join us in Montanita Ecuador May 23-30, 2026 for a foil drive / tow / prone foil camp with Ecuador Foil, KT Foiling & Julia Castro. Learn MoreIn this episode, we sit down with the newly crowned 2025 E-Foil Surf Foil World Tour (SFT) World Champion – the undefeated e-foil racer who took the title in the season finale in Abu Dhabi.Fresh off dominating the inaugural SFT season, the Florida-based ripper (and Flightboard early adopter) joins us to break down what it actually feels like to turn a five-year hunch into a world championship, how e-foil racing went from “nice idea” to a full-blown global tour in record time, and why this sport is exploding faster than anyone predicted.We go deep on:- From kite-smash accidents to building one of the first e-foil schools in South Florida  - The wild Atlanta Foil Fest Enduro with Brian Grubb, Nick Leeson, and 20 riders dodging submerged trees at full throttle  - Unsanctioned full-send dawn patrols through Amsterdam's canals (don't try this at home)  - Gear geek-out: custom shims, chopped tails, 900 Flow vs 707 Flux wings, aftermarket race props, and why everything is still basically stock… for now  - Why full-face helmets and downhill MTB armor are becoming mandatory at 33–35 mph  - Mental warfare on the beach, prop-wash tactics, hot launches, and pulling 3+G turns  - Traveling the world with boards but no batteries (and how the Flightboard rental network saves the day)  - The massive progression from the first dealer races in 2022 to riders now training full-time and closing the gap second by second  - Where e-foil racing is headed: open-ocean courses, city canal sprints, Everglades gator-chasing, and boards that will eventually hit 50 mph  Year one of the Surf Foil World Tour is in the books, prize money is real, brands are paying attention, and the level is skyrocketing. The champ gives us the unfiltered look at what it took to stay on top — and why 2026 is about to get even crazier.If you've ever wondered what the cutting edge of foiling actually looks, sounds, and feels like… this is it.Follow the Surf Foil Tour → https://www.surffoilworldtour.com Justin Chait → https://www.instagram.com/_justinchait_/

Improve your foiling skills in paradise! Join us in Montanita Ecuador May 23-30, 2026 for a foil drive / tow / prone foil camp with Ecuador Foil, KT Foiling & Julia Castro. Learn MoreIn this episode, we catch up with Tom Hartmann – tour manager of the GKA Kite World Tour, Wingfoil World Tour, and founder of the brand-new Surf Foil World Tour (SFT) – fresh from the biggest water sports spectacle the Middle East has ever seen in Abu Dhabi and now chasing the final Kite World Tour stops in Brazil.Tom takes us behind the scenes of the massive nine-day Abu Dhabi event on the soon-to-be “Miami Beach of the Gulf” (Fahid Island), where kite big air, wingfoil racing, e-foil, and wakefoil all shared the spotlight, 2,500+ spectators showed up on weekends, and the whole thing was broadcast live on TV across the region. With €10,000 prize money per SFT discipline, perfect glassy morning conditions, and a level of organization that left athletes speechless, this was the perfect season finale for the inaugural Surf Foil World Tour.- Abu Dhabi deep dive – why foiling (e-foil, wakefoil, wing, kite, surf, pump) is exploding in the Gulf and how the event showcased every flavor of the sport.- E-foil racing at the highest level yet – Justin Chait remains undefeated in 2025, Agnes takes the women's division, and we talk 3G corners, wingtip-out carving, and why technical skill still beats raw speed.- Wakefoil's breakout moment – first fully independent SFT wakefoil comp, drone + boat broadcasting magic, and why wakefoiling could be the most spectator-friendly foiling discipline out there.- The massive growth nobody saw coming – from a hopeful start to nine events worldwide in year one, with a 2026 calendar dropping in the next couple weeks.- What's next for SFT in 2026 – more surf foil, downwind, wakefoil, the return of the epic indoor Düsseldorf pump & wing event, and a brand-new Foil Assist discipline that mixes propulsion take-offs with pure pumping sections.- Plus Tom's love for açaí bowls at Brazilian sunset and maybe sneaking in some surf trips to Nicaragua or Costa Rica before heading home.Year one of the Surf Foil World Tour is officially in the books and it's safe to say foiling just went global – big budgets, big crowds, and bigger stoke. Here's to 2026 being even wilder.Follow the Surf Foil Tour → https://www.surffoiltour.com 

This episode is brought to you by Villa Carina Apartments in beautiful Bonaire. In this episode, we catch up with Tom Hartmann – tour manager of the GKA Kite World Tour, Wingfoil World Tour, and founder of the brand-new Surf Foil World Tour (SFT) – fresh from the biggest water sports spectacle the Middle East has ever seen in Abu Dhabi and now chasing the final Kite World Tour stops in Brazil.Tom takes us behind the scenes of the massive nine-day Abu Dhabi event on the soon-to-be “Miami Beach of the Gulf” (Fahid Island), where kite big air, wingfoil racing, e-foil, and wakefoil all shared the spotlight, 2,500+ spectators showed up on weekends, and the whole thing was broadcast live on TV across the region. With €10,000 prize money per SFT discipline, perfect glassy morning conditions, and a level of organization that left athletes speechless, this was the perfect season finale for the inaugural Surf Foil World Tour.- Abu Dhabi deep dive – why foiling (e-foil, wakefoil, wing, kite, surf, pump) is exploding in the Gulf and how the event showcased every flavor of the sport.- E-foil racing at the highest level yet – Justin Chait remains undefeated in 2025, Agnes takes the women's division, and we talk 3G corners, wingtip-out carving, and why technical skill still beats raw speed.- Wakefoil's breakout moment – first fully independent SFT wakefoil comp, drone + boat broadcasting magic, and why wakefoiling could be the most spectator-friendly foiling discipline out there.- The massive growth nobody saw coming – from a hopeful start to nine events worldwide in year one, with a 2026 calendar dropping in the next couple weeks.- What's next for SFT in 2026 – more surf foil, downwind, wakefoil, the return of the epic indoor Düsseldorf pump & wing event, and a brand-new Foil Assist discipline that mixes propulsion take-offs with pure pumping sections.- Plus Tom's love for açaí bowls at Brazilian sunset and maybe sneaking in some surf trips to Nicaragua or Costa Rica before heading home.Year one of the Surf Foil World Tour is officially in the books and it's safe to say foiling just went global – big budgets, big crowds, and bigger stoke. Here's to 2026 being even wilder.Follow the Surf Foil Tour → https://www.surffoiltour.com 

In this special release episode, Matt sits down with Nathan Lambert and Luca Soldaini from Ai2 (the Allen Institute for AI) to break down one of the biggest open-source AI drops of the year: OLMo 3. At a moment when most labs are offering “open weights” and calling it a day, AI2 is doing the opposite — publishing the models, the data, the recipes, and every intermediate checkpoint that shows how the system was built. It's an unusually transparent look into the inner machinery of a modern frontier-class model.Nathan and Luca walk us through the full pipeline — from pre-training and mid-training to long-context extension, SFT, preference tuning, and RLVR. They also explain what a thinking model actually is, why reasoning models have exploded in 2025, and how distillation from DeepSeek and Qwen reasoning models works in practice. If you've been trying to truly understand the “RL + reasoning” era of LLMs, this is the clearest explanation you'll hear.We widen the lens to the global picture: why Meta's retreat from open source created a “vacuum of influence,” how Chinese labs like Qwen, DeepSeek, Kimi, and Moonshot surged into that gap, and why so many U.S. companies are quietly building on Chinese open models today. Nathan and Luca offer a grounded, insider view of whether America can mount an effective open-source response — and what that response needs to look like.Finally, we talk about where AI is actually heading. Not the hype, not the doom — but the messy engineering reality behind modern model training, the complexity tax that slows progress, and why the transformation between now and 2030 may be dramatic without ever delivering a single “AGI moment.” If you care about the future of open models and the global AI landscape, this is an essential conversation.Allen Institute for AI (AI2)Website - https://allenai.orgX/Twitter - https://x.com/allen_aiNathan LambertBlog - https://www.interconnects.aiLinkedIn - https://www.linkedin.com/in/natolambert/X/Twitter - https://x.com/natolambertLuca SoldainiBlog - https://soldaini.netLinkedIn - https://www.linkedin.com/in/soldni/X/Twitter - https://x.com/soldniFIRSTMARKWebsite - https://firstmark.comX/Twitter - https://twitter.com/FirstMarkCapMatt Turck (Managing Director)Blog - https://mattturck.comLinkedIn - https://www.linkedin.com/in/turck/X/Twitter - https://twitter.com/mattturck(00:00) – Cold Open(00:39) – Welcome & today's big announcement(01:18) – Introducing the Olmo 3 model family(02:07) – What “base models” really are (and why they matter)(05:51) – Dolma 3: the data behind Olmo 3(08:06) – Performance vs Qwen, Gemma, DeepSeek(10:28) – What true open source means (and why it's rare)(12:51) – Intermediate checkpoints, transparency, and why AI2 publishes everything(16:37) – Why Qwen is everywhere (including U.S. startups)(18:31) – Why Chinese labs go open source (and why U.S. labs don't)(20:28) – Inside ATOM: the U.S. response to China's model surge(22:13) – The rise of “thinking models” and inference-time scaling(35:58) – The full Olmo pipeline, explained simply(46:52) – Pre-training: data, scale, and avoiding catastrophic spikes(50:27) – Mid-training (tail patching) and avoiding test leakage(52:06) – Why long-context training matters(55:28) – SFT: building the foundation for reasoning(1:04:53) – Preference tuning & why DPO still works(1:10:51) – The hard part: RLVR, long reasoning chains, and infrastructure pain(1:13:59) – Why RL is so technically brutal(1:18:17) – Complexity tax vs AGI hype(1:21:58) – How everyone can contribute to the future of AI(1:27:26) – Closing thoughts

Sponsored by Berries: Use code TherapyShow50 for $50 off your first month - CLICK HERE.   If you are a therapist or counselor looking for continuing education, check out my NBCC Approved $5 Podcourses and other continuing education offerings. Plus, get your first Podcourse half off. In this episode of The Therapy Show, I'm thrilled to chat with Dr. Tobin Richardson, a fellow continuing education creator and the founder of Save the Therapist. We dive into how Tobin combined his passion for narrative podcasting and his background in counselor education to create a unique, story-driven CE platform for therapists. He shares the behind-the-scenes of launching CE courses that actually engage and inspire, why he believes continuing education needs a serious upgrade, and how he's offering these courses completely free thanks to his creative business model. We also get real about the challenges of building something meaningful, how to market CE content without burning out, and what it's like to cover nuanced, sometimes controversial topics with honesty and integrity. If you've ever thought about creating your own CE content or just want to hear how another therapist is innovating in the field, this episode is for you. Tune in and get inspired by Tobin's journey and maybe even find your next favorite CE podcast. Tobin Richardson, EdD, NCC, is a counselor educator with a decade of experience building and delivering innovative educational resources to therapists in both community mental health and large VC-backed provider organizations. Since launching in early 2025, his NPR-style CE platform Save the Therapist has garnered over 4,000 therapists registrants with over 7,000 course completions. And don't forget! If you're ready to spend less time on notes and more time doing what you love, check out heyberries.com. Use code THERAPYSHOW50 for $50 off your first month with Berries.  Get my Coping with Political Stress Ebook and Peaceful Politics AI Guide  Therapist Conversation Framework: Politics in Session A printable PDF with 97 questions to navigate political talk in therapy - without taking sides. Solution-Focused Therapy Guide72 questions + prompts to help adult clients clarify goals and move forward using SFT. Check out all my Counselor Resources. 

Sponsored by Berries: Use code TherapyShow50 for $50 off your first month - CLICK HERE.  If you are a therapist or counselor looking for continuing education, check out my NBCC Approved $5 Podcourses and other continuing education offerings. Plus, get your first Podcourse half off. In this episode, I'm sharing my recent conversation from Between Sessions with Berries, a podcast created for mental health professionals who want to simplify documentation, fight burnout, and reconnect with their purpose. Kym Tolson and I dive into my journey from therapist to continuing-education creator, how burnout inspired me to reimagine CE for busy clinicians, and what it takes to blend creativity, courage, and aligned action into your career. We also discuss lessons learned from losing a major podcast sponsor, building confidence through reinvention, and the power of staying curious in the face of change. If you've ever felt stuck, uninspired, or ready for something new in your professional life, this conversation will encourage you to take the next step - one aligned action at a time. And don't forget! If you're ready to spend less time on notes and more time doing what you love, check out heyberries.com. Use code THERAPYSHOW50 for $50 off your first month with Berries.  Get my Coping with Political Stress Ebook and Peaceful Politics AI Guide  Therapist Conversation Framework: Politics in Session A printable PDF with 97 questions to navigate political talk in therapy - without taking sides. Solution-Focused Therapy Guide72 questions + prompts to help adult clients clarify goals and move forward using SFT. Check out all my Counselor Resources.   

Alex Westphal, Senior Director, Market Practice and Regulatory Policy, talks about the latest milestones in Europe's journey to T+1, also looking at SFT specific impacts and discussions which are central to the success of T+1.

Improve your foiling skills in paradise! Join us in Montanita Ecuador May 23-30, 2026 for a foil drive / tow / prone foil camp with Ecuador Foil, KT Foiling & Julia Castro. Learn MoreIn this episode, we sit down with Tom Hartmann and Nico Hopp of Hoppline to dive into the exhilarating world of pump foiling at Lake Traunsee, Upper Austria. Broadcasting from their respective homes, Tom and Nico share their passion for this rapidly growing sport, the vibrant community, and the unique vibe of the SFT.- Lake Traunsee Triumph: Tom and Nico recap the SFT event at Lake Traunsee, a stunning venue surrounded by mountains with a top-notch setup. With four starting docks and a professional organization running alongside the Austrian Wing Foil Championships, the event offered a perfect mix of competition and community, capped off with exciting wake foiling sessions behind a boat.- Pump Foiling's Appeal: Tom and Nico discuss the sport's accessibility, thriving in flatwater lakes and ideal for urban and inland locations. They highlight how pump foiling draws in everyone from pros to beginners. - Community-Driven Competition: Nico emphasizes the inclusive nature of the SFT, where pros like Eden Fiander and Robert von Roll race alongside amateurs, creating a social and competitive atmosphere. Tom explains the division structure—pro, open, masters, youth, and women's categories—ensuring everyone, from seasoned athletes to first-timers, feels motivated to join. - Gear and Technique Evolution: The duo dives into the latest gear trends, with Nico noting the pros' use of tiny, high-performance wings and unique dock-start techniques. From Eden's strap-based approach to Rob's hands-on style, the diversity in equipment and skills keeps the sport dynamic and exciting. - A Family Affair: Tom highlights the family-friendly vibe, with free dinners for competitors and their families, fostering a welcoming environment. Nico shares a heartwarming story of a young competitor and his mother camping out to participate, showcasing the sport's appeal across generations.- The Future of SFT: Tom reveals plans for the final 2025 event in Abu Dhabi, featuring e-foiling and wake foiling, and a 2026 season kicking off in Düsseldorf. With ambitions to expand prize money and bring events to urban centers like Venice's Grand Canal, the SFT aims to grow pump foiling's global reach.Join us for a lively discussion packed with insights into pump foiling's rise, the thrill of close-knit competition, and the community spirit driving this niche sport forward. From stunning venues to innovative gear, this episode captures the excitement of foiling without wind.Visit: https://www.instagram.com/supfoiltour & https://www.instagram.com/hoppline/

This episode is brought to you by Villa Carina Apartments in beautiful Bonaire. In this episode, we sit down with Tom Hartmann and Nico Hopp of Hoppline to dive into the exhilarating world of pump foiling at Lake Traunsee, Upper Austria. Broadcasting from their respective homes, Tom and Nico share their passion for this rapidly growing sport, the vibrant community, and the unique vibe of the SFT.- Lake Traunsee Triumph: Tom and Nico recap the SFT event at Lake Traunsee, a stunning venue surrounded by mountains with a top-notch setup. With four starting docks and a professional organization running alongside the Austrian Wing Foil Championships, the event offered a perfect mix of competition and community, capped off with exciting wake foiling sessions behind a boat.- Pump Foiling's Appeal: Tom and Nico discuss the sport's accessibility, thriving in flatwater lakes and ideal for urban and inland locations. They highlight how pump foiling draws in everyone from pros to beginners. - Community-Driven Competition: Nico emphasizes the inclusive nature of the SFT, where pros like Eden Fiander and Robert von Roll race alongside amateurs, creating a social and competitive atmosphere. Tom explains the division structure—pro, open, masters, youth, and women's categories—ensuring everyone, from seasoned athletes to first-timers, feels motivated to join. - Gear and Technique Evolution: The duo dives into the latest gear trends, with Nico noting the pros' use of tiny, high-performance wings and unique dock-start techniques. From Eden's strap-based approach to Rob's hands-on style, the diversity in equipment and skills keeps the sport dynamic and exciting. - A Family Affair: Tom highlights the family-friendly vibe, with free dinners for competitors and their families, fostering a welcoming environment. Nico shares a heartwarming story of a young competitor and his mother camping out to participate, showcasing the sport's appeal across generations.- The Future of SFT: Tom reveals plans for the final 2025 event in Abu Dhabi, featuring e-foiling and wake foiling, and a 2026 season kicking off in Düsseldorf. With ambitions to expand prize money and bring events to urban centers like Venice's Grand Canal, the SFT aims to grow pump foiling's global reach.Join us for a lively discussion packed with insights into pump foiling's rise, the thrill of close-knit competition, and the community spirit driving this niche sport forward. From stunning venues to innovative gear, this episode captures the excitement of foiling without wind.Visit: https://www.instagram.com/supfoiltour & https://www.instagram.com/hoppline/

We're pleased to welcome the seasoned driver, Geoffrey 'JB' Njoroge, to the show for this episode of SFT! Today's conversation features a lot of practical wisdom regarding the various opportunities in trucking as well as the daily execution of the craft. JB also recounts some tense and amusing moments from his life behind the wheel as a driver and trainer. You'll enjoy the wit and wisdom of this guest as we once again dive deep into the world of driveaway. If you're not already subscribed to the show, please do so in order to see and hear our weekly content and so you don't miss guys like JB. In fact, he's going to rejoin the show next week to talk about his home country and foundational passions.Show Notes:John and “JB” share a laugh about the exercise challenges over the road (0:44)JB navigates COVID and other experiences in his trucking journey (4:00)Finding Norton and getting “spoiled” in Driveaway (6:08)Crazy stories and sage advice from the Road (9:57)The importance of planning, logs, and dispatch relations (16:12)JB's stats, certifications, and future plans (21:15)Keep Trucking, JB!. The Six-Figure Trucker is a weekly podcast about driveaway trucking brought to you by Norton Transport. For more information or to subscribe, please visit Six-FigureTrucker.com. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

If you are a therapist or counselor looking for continuing education, check out my NBCC Approved $5 Podcourses and other continuing education offerings. Plus, get your first Podcourse half off.

If you are a therapist or counselor looking for continuing education, check out my NBCC Approved $5 Podcourses and other continuing education offerings. Plus, get your first Podcourse half off. Check out all my Counselor Resources. 

Improve your foiling skills in paradise! Join us in Montanita Ecuador May 23-30, 2026 for a foil drive / tow / prone foil camp with Ecuador Foil, KT Foiling & Julia Castro. Learn MoreIn this episode we catch up with Tom Hartmann about the Atlanta Foil Fest at Lake Lanier's Olympic Park. Tom dives into the three-day event, featuring E-Foil, Pump Foil, Wake Foil, and Airchair foiling competitions, alongside demos and clinics. From Justin Chait dominant performance to Nick Leason's E-Foil legacy and the innovative Betafoil Enduro, the event united foilers from the US, Europe, and beyond. Tom also teases upcoming Surf World Tour events at Lake Garda and Abu Dhabi, promising bigger competitions and live streams.Episode Highlights:Atlanta Foil Fest's debut as a foiling mecca with a custom-built start dockHigh-level E-Foil and Pump Foil races, plus the return of AirchairMeeting foiling pioneer Nick Leeson and testing Betafoil's massive wingsLake Garda's Foiling Week and Abu Dhabi's massive prize purse on the horizonGrowing the global foiling community through passion and connectionFollow Tom & SFT: @surffoilworldtour on Instagram, Facebook, YouTubeWebsite: surffoilworldtour.com

In this episode we catch up with Tom Hartmann about the Atlanta Foil Fest at Lake Lanier's Olympic Park. Tom dives into the three-day event, featuring E-Foil, Pump Foil, Wake Foil, and Airchair foiling competitions, alongside demos and clinics. From Justin Chait dominant performance to Nick Leason's E-Foil legacy and the innovative Betafoil Enduro, the event united foilers from the US, Europe, and beyond. Tom also teases upcoming Surf World Tour events at Lake Garda and Abu Dhabi, promising bigger competitions and live streams.Episode Highlights:Atlanta Foil Fest's debut as a foiling mecca with a custom-built start dockHigh-level E-Foil and Pump Foil races, plus the return of AirchairMeeting foiling pioneer Nick Leeson and testing Betafoil's massive wingsLake Garda's Foiling Week and Abu Dhabi's massive prize purse on the horizonGrowing the global foiling community through passion and connectionFollow Tom & SFT: @surffoilworldtour on Instagram, Facebook, YouTubeWebsite: surffoilworldtour.com

Today, we're joined by Mahesh Sathiamoorthy, co-founder and CEO of Bespoke Labs, to discuss how reinforcement learning (RL) is reshaping the way we build custom agents on top of foundation models. Mahesh highlights the crucial role of data curation, evaluation, and error analysis in model performance, and explains why RL offers a more robust alternative to prompting, and how it can improve multi-step tool use capabilities. We also explore the limitations of supervised fine-tuning (SFT) for tool-augmented reasoning tasks, the reward-shaping strategies they've used, and Bespoke Labs' open-source libraries like Curator. We also touch on the models MiniCheck for hallucination detection and MiniChart for chart-based QA. The complete show notes for this episode can be found at https://twimlai.com/go/731.