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Hannah Simone and Lamorne Morris are back for more! First, they work with Jake to discourage the advances of a local security guard. Then, they confront a catfishing Mother-in-Law.See images from the episode here: https://www.heretohelppod.com/post/episode-289Get an exclusive 15% discount on Saily data plans! Use code heretohelp at checkout. Download Saily app or go to https://saily.com/heretohelpWant to call in? Email your question to helpfulpod@gmail.com.PATREON: https://patreon.com/heretohelppodMERCH: heretohelppod.comINSTAGRAM: @HereToHelpPodIf you're enjoying the show, make sure to rate We're Here to Help 5-Stars on Apple Podcasts.Advertise on We're Here to Help via Gumball.fmSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Early bird discounts for the San Francisco World's Fair, the biggest AIE gathering of the year, end today - prices will go up by ~$500 tonight so do please lock in ASAP!From near-universal AI tool adoption inside Shopify to internal systems for ML experimentation, auto-research, customer simulation, and ultra-low-latency search, Mikhail Parakhin joins us for a deep dive into what it actually looks like when a 20-year-old, $200B software company goes all-in on AI. We cover why Shopify has become much more vocal about its internal stack, what changed after the December model-quality inflection, and why the real bottleneck in AI coding is no longer generation, but review, CI/CD, and deployment stability.We also go inside Tangle, Tangent, SimGym, which are three major AI initiatives that Shopify is doing to make experimentation reproducible, optimization automatic, customer behavior simulatable, and search and catalog intelligence faster and cheaper at scale. Along the way, Mikhail explains UCP, Liquid AI, and why token budgets are directionally right but often measured badly, why AI-written code can still increase bugs in production, what makes Shopify's customer simulation defensible, and what he learned from the Sydney era at Bing.We discuss:* Mikhail's path from running a major Microsoft business unit spanning Windows, Edge, Bing, and ads to becoming CTO of Shopify* Why Shopify is talking more publicly about AI now, and why staying at the frontier has become necessary for the company* Shopify's internal AI adoption curve, the December inflection, and why CLI-style tools are rising faster than traditional IDE-based tools* Why Jensen Huang is directionally right on token budgets, but raw token count is still the wrong way to evaluate engineering output* Why the real unlock is not more agents in parallel, but better critique loops, stronger models, and spending more on review than generation* Why AI coding can still lead to more bugs in production even if models write cleaner code on average than humans* Why Shopify built its own PR review flow, and why Mikhail thinks most off-the-shelf review tools miss the point* How PR volume, test failures, and deployment rollback are becoming the real bottlenecks in the agent era* Why Git, pull requests, and CI/CD may need a new metaphor once code is written at machine speed* What Tangle is, and how Shopify uses it to make ML and data workflows reproducible, collaborative, and production-ready from the start* Why Tangle is different from Airflow, and why content-addressed caching creates network effects across teams* What Tangent is, and how Shopify is using auto-research loops to optimize search, themes, prompt compression, storage, and more* Why Tangent is becoming a democratizing tool for PMs and domain experts, not just ML engineers* Why AutoML finally feels real in the LLM era, and where auto-research still falls short today* Why Tangle, Tangent, and SimGym become much more powerful when combined into one system* What SimGym is, why simulated customers only work if you have real historical behavior, and why Shopify's data gives it a moat* How SimGym evolved from comparing A/B variants to telling merchants what to change on a single live storefront to raise conversions* Why customer simulation is so expensive, from multimodal models to browser farms to serving and distillation costs* How Shopify models merchant and buyer trajectories, runs counterfactuals, and thinks about interventions like discounts, campaigns, and notifications* Why category-level behavior is so different across commerce, and why ideas like Chinese Restaurant Processes are showing up again in practice* Shopify's new UCP and catalog work, including runtime product search, bulk lookups, and identity linking* Why Shopify is using Liquid AI, and why Mikhail sees it as the first genuinely competitive non-transformer architecture he has used in practice* Where Liquid already works inside Shopify today, from low-latency query understanding to large-scale catalog and Sidekick Pulse workloads* Whether Liquid could become frontier-scale with enough compute, and why Shopify remains pragmatic and merit-based about model choice* Who Shopify is hiring right now across ML, data science, and distributed databases* The Sydney story at Bing, why its personality was not an accident, and what Mikhail learned from deliberately shaping AI character early onMikhail Parakhin* LinkedIn: https://www.linkedin.com/in/mikhail-parakhin/* X: https://x.com/MParakhinTimestamps00:00:00 Introduction: Mikhail Parakhin, Microsoft, and Shopify00:01:16 Why Shopify Is Talking More About AI00:02:29 Internal AI Adoption at Shopify and the December Inflection00:06:54 Token Budgets, Jensen Huang, and Why Usage Metrics Can Mislead00:10:55 Why Shopify Built Its Own AI PR Review System00:12:38 AI Coding, More Bugs, and the Real Deployment Bottleneck00:14:11 Why Git, PRs, and CI/CD May Need to Change for Agents00:18:24 Tangle: Shopify's Reproducible ML and Data Workflow Engine00:21:19 Why Tangle Is Different from Airflow00:26:14 Tangent: Auto Research for Optimization and Experimentation00:30:07 How Tangent Democratizes Experimentation Beyond ML Engineers00:33:06 The Limits of Auto Research00:36:36 Why Tangle, Tangent, and SimGym Compound Together00:37:20 SimGym: Simulating Customers with Shopify's Historical Data00:42:47 The Infra Behind SimGym00:46:00 Why SimGym Gets Better with Real Customer History00:47:30 Counterfactuals, HSTU, and Modeling Merchant Trajectories00:51:55 CRPs, Clustering, and Category-Level Customer Behavior00:53:30 UCP, Shopify Catalog, and Identity Linking00:55:07 Liquid AI: Why Shopify Uses Non-Transformer Models00:59:13 Real Shopify Use Cases for Liquid01:03:00 Can Liquid Scale into a Frontier Model?01:09:49 Hiring at Shopify: ML, Data Science, and Databases01:10:43 Sydney at Bing: Personality Shaping and AI Character01:13:32 Closing ThoughtsTranscript[00:00:00] swyx: Okay. We're here in the studio, a remote studio, with Mikhail Parakhin, CTO of Shopify. Welcome.[00:00:08] Mikhail Parakhin: Thank you. Welcome.[00:00:10] swyx: I don't even know if I should introduce you as CTO of Shopify. I feel like you have many identities. Uh, you led sort of the, the Bing ML team, I guess, uh, uh, or ads team. I, I don't know, I don't know, uh, you know, it's, uh, people va-variously refer you as like CEO or, or, uh, I don't know what that, that, that said previous role at Microsoft was.[00:00:29] Mikhail Parakhin: Uh, that was... Yeah, my previous role w- at Microsoft was the-- I actually was the CEO of one of Microsoft's business units, which included, as I, you know, as we discussed, all the things that people like to laugh about, uh, including Windows and Edge and Bing and ads and everything.[00:00:47] swyx: Yeah, yeah. What a, what a, what a wild time.You've obviously, uh, done a lot since you landed at Shopify. Uh, one of the reasons I reached out was because you started promoting more sort of internal tooling, uh, primarily Tangle, but also a lot of people have seen and adopted Tobi's QMD, uh, and obviously, I think, uh, Shopify has always been sort of leading in terms of, uh, engineering.I think more-- it's just more recent that you guys have been more vocal about your sort of AI adoption. Is that, is that true?[00:01:16] Mikhail Parakhin: Well, I think AI tools in general are fairly recent development, uh, and we've-- Shopify, you know, at this stage of its development, we're developing AI in-in-house and other, uh, building tools that use AI and, you know, interfacing with the wider AI community, uh, you know, are on the sort of the, uh, runaway trajectory.So it just did by sort of natural byproduct. We, we talk about it more also. We just, uh, just even yesterday, Andrej Karpathy was famous in tweeting about, oh, are there some, uh, ways, uh, that, that you can organize your agents to store the data and then, uh, look up the data so that you don't have to research or, or lose context every- Yestime. And a little bit tongue in cheek, I tweeted that, “Hey, we've, we've done it much earlier, and we even have different approaches, Tobi and I.” Tobi, of course, is a big fan of QMD, and I'm more of a SQL, SQLite fan. But, uh, yeah, very similar things that we've already done here. The point is, yeah, we're very dynamic, you know, explosively growing company, and we have to be at the forefront of AI adoption, obviously.[00:02:29] swyx: Yeah. Yeah. Um, you, your team kindly prepared some slides actually that we were gonna bring up on to, uh, the screen. I think I can, I can screen share, and then we can kind of go through some of the shocking stats that maybe, maybe put some numbers to what exactly is going on. So here we have, uh- An internal AI tool adoption chart.What are we looking at here? What ?[00:02:54] Mikhail Parakhin: Yeah, this is very interesting statistics. Uh, this is number of daily active workers, you know, think of, uh, DAO, basically the active users of-[00:03:05] swyx: Yeah ...[00:03:05] Mikhail Parakhin: AI tool as a percentage of all the people in the company, right? And then- Yeah ... different AI tools. And, uh, you could see two things here is that one is the green is total.Uh, green is just total. So you could see that it approaches really % by now. It's hard not to do your job now without interacting deeply, at least with one tool. You could see another interesting thing is just as many people commented in December was the phase transition when suddenly models gotten good enough that, that everything took off and started growing.Uh, it, it was many people noticed that the thing is that small improvements accumulated into this big change in Sep- December roughly timeframe.[00:03:52] swyx: Yeah.[00:03:52] Mikhail Parakhin: The other thing I would claim you could see is that, uh, CLI-based tools and tools that don't require you to look at the code becoming more popular, and you could see, yeah, various versions of, uh, Cloud Code and Codex and Pi and internal development tools taking off.Uh, exactly, yeah, uh, and blue is our River, just internal agent for coding, where tools, uh, that require IDEs such as, uh, GitHub, Copilot or Cursor, they're not exactly shrinking, but they're not growing as fast. Like, uh, red, red line is, is the IDE kind of tools. So you could see that they're, they're not experiencing as, as fast of a growth.[00:04:37] swyx: As I understand it, basically, every employee has their choice, right? Of choose whatever tool you use, and then you're just kind of doing a, a daily sur-survey or something.[00:04:47] Mikhail Parakhin: Exactly. And, uh, we- Yeah ... the, the push is to get your job done, you can use any tool, and we effectively fund unlimited tokens for everybody.Uh, we, we do, we do try to control the models that, uh, people use, but from the bottom, not from top. Like we basically say, “Hey, please don't use anything less than Opus four point six.”[00:05:09] swyx: Oh .[00:05:10] Mikhail Parakhin: Some people, some people end up using GPT five point four extra high. Some people use Opus four point six. Um, uh, you know, uh, there are some, uh, there are plus and minuses in going for full one million context window versus not.But, uh, we try to discourage people from using anything less than that.[00:05:28] swyx: Yeah, yeah. Got it, got it. Uh, I mean, uh, that's, you know... The, the next chart here, it really kind of shows the expansion and the sort of December twenty twenty-five inflection, right? That, uh, people are using a lot of tokens. I think it's also really interesting that no one was kind of abusing it in twenty twenty-five.Like it was- Had comparatively, uh, to this year, there was almost no growth. I mean, it's still like, you know, probably, probably gave fifty percent.[00:05:56] Mikhail Parakhin: Yeah. This is just a different scale. It's still exponential- Yeah, yeah ...growth at just a different- ...rate of expansion. Uh, there was inflection point, and Sean, I would claim the, the super interesting part here is that you could see that the distribution becoming more and more skewed.Yes. The top percentiles grow faster. So that means- Yeah ...the people in the top ten percentile, they, their consumption grows faster than seventy-five and so forth. So, uh, the distribution skews more and more towards the highest users, which is... I don't know what it tells me. It's like it feels not ideal, to be honest.Or maybe it's okay. We'll see.[00:06:36] swyx: Why does it feel not ideal? Is, is it because of, um, quantity over quality, or what's the concern?[00:06:42] Mikhail Parakhin: Because take it to the limit. That means, you know, if, if this rate of separation continued- Ah, yes ...a year, there will be one person consuming all the tokens. So it's just, it's kinda strange.[00:06:54] swyx: Yeah, I mean, um, uh, I, I think internal like teaching and all that, uh, will, will help sort of distribute things more widely. But in, in the early days, of course, the people who are sort of more AI-pilled will obviously find more ways to use it than the people who are less AI-pilled. Maybe let's, let's call it that.I'll just, I'll just kinda quickly, uh, pause from the, the... You know, we will go back to the rest of the slides, but I just wanna, um, review, you know, there are a lot of CTOs of, of large companies like yourself where they're all considering some kind of token budget, right? Like I think it's something, something that Jensen Huang has been talking about, where like if your 200K engineer is not using 100K of tokens every year, like they're, they're underutilizing coding agents.Of course, Jensen Huang would say that, but like it seems a very quantity over quality approach and like some, some people are basically saying like, well, is this comparable to judging engineer quality by lines of code, right? Which we also know is like kind of flawed, but better than nothing. So I, I don't know if you have like a sort of management take here on, on how to view this kind of, uh, metrics.[00:08:02] Mikhail Parakhin: Well, I mean, you're, you're baiting me. I, I like... This is my favorite topic. Uh, if you let me, I'll probably talk for two hours on just this. I have a lot of things to say. Like I do think Jensen gotten a lot of bad press saying, “Oh, of course you're, you know, this, uh, the- ...the cake seller says you don't need enough cakes.”You know? Like, of course. Uh, but, uh, I actually, uh, think that's undeserved. I think he, he's actually right. Uh, I do think- He,[00:08:33] swyx: he's directionally correct.[00:08:35] Mikhail Parakhin: Yeah. Yeah. He's directionally correct for sure. Uh-[00:08:37] swyx: Who knows what the right number is? Yeah.[00:08:39] Mikhail Parakhin: The thing that I do Uh, want to say, and this is something that we learned through trial and error and very important is like two things.One is that it's not about just consuming tokens. Uh, you can consume tokens and, and in fact, the anti-pattern is running multiple agents, too many agents in parallel that don't communicate with each other. That's almost useless, uh, compared to just fewer agents and burns tokens very efficiently. Uh, setting up the right critique loop, especially with the high quality models, where one agent does something, the other one, ideally with a different model, critiques it, uh, suggests ways to improve it, the agent redoes it with this critique and, and so it takes much longer.So people don't like it because latency goes up. You know, they, they have to wait until this debate is happening. But, uh, the quality of the code is much higher. And another thing, just since you mentioned like, look, uh, uh, yeah, the overall budget is just like, uh, lines of codes. Lines of codes are exploding for everybody right now, or partially because AI is really mover balls, but partially just because AI can write a lot more code, you know, doesn't get tired.And so you have to have to have a very strong narrow waist during PR review. Otherwise, just the number of bugs will go through the roof. It's, uh, it's this unexpected consequence of the just volume trumping everything. I would claim by now good model writes code on average with fewer bugs than, than the average human.But since they write so much more of it, like more of it will make it into production. So you have to- You still[00:10:26] swyx: have[00:10:26] Mikhail Parakhin: more bugs. Yeah. Have to have a very rigorous PR reviews, also automated of course. But, uh, yeah, that to spend a lot budget there. Like this, this for me, for me, actually, the important metric is the ratio of budget spent during code generation versus, uh, spent, uh, expensive tokens like GPT, uh, five point four Pro or, uh, uh, Deep Think from Gemini, you know, checking on PR reviews.[00:10:55] swyx: Yeah, totally. Uh, I noticed in your chart you didn't have any review tools. Do you just use like, like let's say a Claude code to review tools? Or do you have another set of review tools like the Greptiles, the Code Rabbits, uh, Devin Reviews has a review tool. I don't know if you've had those specialist review tools.[00:11:13] Mikhail Parakhin: You are a little bit jumping on my store tool right now because the graphs I was only showing public tools. Uh, uh, the-- I haven't found a good PR review tool that, that does what I think should be done. And, uh, partially my, my thinking is because it's so... It just goes against both what people feel like emotionally they prefer and, uh, some of the, uh, you know, frankly Even business models that, that the companies run.At peer review tool, uh, time, you want to run the largest models. That means, I don't know, Codex or, or, uh, Cloud Code is not gonna cut it. You need to have pro-level models if you really want to, uh, stand the tide of bots from going into production. And you need us to spend a lot of time, the models taking turns, but you don't want, like, a big swarm of, uh, of, uh, agents.So in fact, you end up in a different dual-dualistic world where you generate not that many tokens. You, in fact, generate few tokens, but it takes f-a long time because these are expensive models taking turns rather than many, many agents trying to do many things in parallel. So that's, that's why I feel like I haven't found good tools, so we are using our own for peer review for now.[00:12:33] swyx: Yeah. Yeah. I mean, uh, I think a lot of companies are building their own, uh, especially to their needs, right?[00:12:38] Mikhail Parakhin: Mm-hmm.[00:12:38] swyx: Um, I, uh, you also have a chart here going back to the slides on, uh, PR merge growth, where we're now at thirty percent, uh, month on month rather than ten percent. Uh, and also the, the estimated complexity is going up.You know, this is productivity, right? ‘Cause y- presumably there's more stuff going into the code base and more, more features getting worked on. I'm curious about the backlog, right? Like the, the, the-- I actually don't mind a pro-level model taking an hour or two hours to review my PR, because I've dealt with humans who take a week to review my PR, right?And I keep pinging them on Slack, “Hey, hey, review my PR.” So, you know, I think there's some trade-off here where, like, it still doesn't make sense.[00:13:18] Mikhail Parakhin: Exactly. That, that's exactly m-my point. Uh, that on one hand, you can tolerate longer latencies at, uh, PR. On the other hand, like right now, the real problem is not in spending time waiting for PR.It's real problem is since there's so much more code than- Yeah ... uh, probability of at least some tests failing going up, and then you, like, keep de-failing, then you have to find the offending PR, evict it, retest it without that PR, and so deployment cycle becomes much longer. Uh, so it actually, in terms of the overall time to deploy, it's total time savings if you spend more time on a longer model, like thinking for an hour, because then, then you, you don't have to spend all that time during testing and rolling, you know, rolling back the deployment.[00:14:03] swyx: Yeah, totally. That's still worth it. You know, you don't look at the individual, look at the aggregate, and look at the, the, the change in the aggregate system.[00:14:11] Mikhail Parakhin: Exactly.[00:14:11] swyx: I'm kind of curious if, like, there's this PR mentality and, like, c-- the, the, the CICD paradigm will be changed eventually. Some people are like, obviously a lot of people want new GitHub, but I even wonder if, like, Git is the problem, right?Like, is that the bottleneck? Is the concept of a PR a bottleneck? Do you guys use stack diffs? I don't know if, uh, that's a, like, a merge queue stack diff type of thing.[00:14:34] Mikhail Parakhin: We, we use, we use Stacks, we u- we use Graphite. We worked with, uh, Graphite a lot. Uh, so we use Stack, uh, PRs. I think, uh, like that's clearly the overall CICD in general, and the interaction with the code repository right now is the, clearly the sort of the, the main issue and the bottleneck for us, uh, and highest top of mind.I would say we probably need a different metaphor or different whole design of how to process it in new agentic world. I haven't seen anything dramatically better yet. I, I think everybody right now is just trying to keep their head above the water ‘cause, ‘cause there, there's so many PRs and then everybody's CICD pipelines start creaking, the, the times are increasing, the number of bugs slipping by increasing, and you have to, have to clap on down.And so we are a little bit in this situation when we need to first stabilize that story and then start thinking, hey, what, what it could be a completely different and new world, which I haven't... I know some people working on it. I haven't seen something, like anything super compelling yet, but clearly the old thing were designed for humans will need to be morphed into something new.[00:15:53] swyx: One of the thing that I, I think about is kind of like the merge conflict is basically a global mutex on the whole system, right? And in, in hu- in human organizations, we do have something like that. It's the company standup. But like, other than that, it's like it's actually fitting for us to be somewhat decentralized, somewhat plugged into one stream of information source, but somewhat lossy.Like it's okay, you know, that, that not every delivery is like atomic consistency. Like we're not dealing with a database sometimes.[00:16:27] Mikhail Parakhin: This is a very good point, uh, because since humans don't write code too fast, you know that global mutex is not too bad. Once you-[00:16:36] swyx: Yes ...[00:16:37] Mikhail Parakhin: start writing code at the speed of machine, it becomes the, you know, the bottleneck.Then what do you do? Maybe, and I can't believe I'm saying this because I, I'm long-- lifelong opponent of, uh, microservices, and I always thought that was, like, a really bad idea. And now that you're saying it, like, maybe in new guys like microservices will make a comeback, you know, because then you, you can ship things independently in tiny things and, and the managing all that complexity automatically will be much easier.I don't know. Like, we'll s-- we'll have to see.[00:17:10] swyx: Yeah. I mean, I don't know what the Microsoft or, or Shopify thing is, but I, I read this paper from Google where they have a monorepo that deploys into microservices, right? And then, uh, the other concept that I think about a lot is the Chaos Monkey concept from, from Netflix.Being able to create, like, this robust system where, um, uh, you know, you, you have the service discovery, you have the, uh, the independent, independent microservices discovery and, and, uh, you know, probably going to be a fair amount of duplication. That's how an organic system sort of scales, uh, that, that you have that...I don't know how you call it. Slack? Robustness? Depend-- uh, d-duplication. I, I, I forget the-- I, I'm-- And this-- those-- these are not exactly the terms- Hmm ... I'm looking for, but I c-can't really think of the words. Okay. I was gonna go into Tangent and Tangle. Uh, so, uh, we, we sort of discussed the overall stats that, uh, Shopify has.Uh, but, you know, I, I think some, some pretty cool stuff that you guys are working on is your ML experimentation, uh, and your, your sort of auto tr-research training pipeline. Presumably you're much closer to this one because it's, it's a sort of personal hobby of yours. How, how would you explain them in, together?I thought we have a slide that, like, uh, has the s- the system diagram.[00:18:24] Mikhail Parakhin: Yeah. Tangle first and then Tangent as a-[00:18:27] swyx: Yeah ...[00:18:28] Mikhail Parakhin: as a thing on top of Tangle. And, uh, Tangle is the third generation, I claim, of, uh, systems of, uh, running any data processing, but a bit with a skew for ML experiments, but not necessarily. Any sort of data processing tasks where you need to iterate, share, and you have scale so that you want maximum efficiency.You know how, like, normally you would work, you would-- Imagine you're a data scientist or an ML practitioner, you would get Jupiter notebooks or, or maybe you would get, uh, you know, Pyth- your Python scripts, and you would manage the data, and you produce those TSV files, and you put them in some JFS or something.Then you would notice that, oh, it has this, uh, weird missing values. You go and write another script that, uh, goes and replaces them with, uh-[00:19:20] swyx: Ah ...[00:19:21] Mikhail Parakhin: dash S. And then, then you, then you run some, some, uh, “Oh, I need to filter bots.” And so you run some light GBM model that, uh, removes the bots. And then, then you like-- And then you, you kind of like get into shape, and then you start experimenting, and you run multiple experiments, and then you're like, “Oh my God,” like, “this experiment is worse.”You undo, and you cannot get to previous result. And like, “Ah, what did I do?” Like that. Again, then, then you finally like get everything working. Then you like start throwing it over the fence to production. You, you replicate it, those things don't work, and then sometimes you like don't notice that you forgot some feature naming and the, the features don't match.But then, like imagine you, you did everything, and then six months later you're like, have to repeat it because now there's more data, or you wanted to do another pass, and you're like, “What, what did I do?” Or like, or like, “This script crashes now,” or the, “the path has changed.” And then, then you're trying to, like you spend another month just doing ar- digital archeology on your own, you know, history, right?Now multiply that by many, many teams. Now imagine you got an intern that you wanna ramp up. Now you have to show that intern, “Oh, you know, look, here's the folder, there's the scripts, you know, ask your cloud agent to do, and then, uh, to, to figure it out.” And then cloud agent does something, and then you're, “Ah, yeah, right, right, it was the wrong folder.I forgot to tell you, I actually have this other thing I forgot myself.” And, and that's, that's the, like, the daily life we all, uh, all know it, uh, if, if you're a data scientist, machine practitioner, ma- machine learning practitioner or, uh, or even like any data managing, uh, person.[00:21:00] swyx: Yeah. So I, I used to do this, uh, f- uh, on the quant finance side, uh, in, in my hedge fund.So we did this before Airflow, and then, uh, obviously Airflow came along and, uh, then more recently Dagster, uh, I would say is like, in my mind, what I would use for that shape of problem, uh, where you had to materialize assets and create a pipeline.[00:21:19] Mikhail Parakhin: And that's, that's very good segue because... So Airflow is great, but Airflow is more about you, you have something and you wanna repeatedly run it in production on schedule.It's less about you as a team developing things and being able to share, and you grabbing the standard pipeline and saying, “Hey, I wanna change this tiny little component in the huge sea of data processing, and I don't wanna-- I wanna run ten experiments on this, and I wanna do hyperparameter optimization.”All that is very hard to do with Airflow. It's very easy to do with Tango. Tango is m- more about, it's everything about group of people Running experiments, it might be agents too nowadays. Uh, running experiments cheaply, collaborating, sharing results. Uh, you don't need to understand fully. You, you grab-- you clone somebody else's experiment or somebody else's pipeline, uh, run, uh, change small piece, run it, be, like, get it to production state, and then ship in one click.So then the... You don't have to port it into any other system to, to run in production. You can just run the same experiment. It's, it's fully production ready. And, and it's, uh, it has lots of... Again, as I said, it's third generation system. The original one was, I would claim there was Ether and then, uh, at least in my career, Ether was the first, first, uh, that pioneered this type of approach.And then there was, uh, Nirvana, which, uh, uh, at Yandex, which did kind of sec-second take on this. And now this one aggregates the, the learnings from all of those and, and Airflow as well to, to get to the state where you try it, it, it feels kind of magical. Uh, ‘cause now everything is based on content, uh, hashes.So even if the version changed, but if the output didn't change, nothing is being rerun. It's very efficient. If you... Multiple people start experiment that needs the same sort of data preprocessing, it's not repeated multiple times. It's automatically done only once. If you start ten experiments that all require, you know, some, some data preparation first as the first step, and you don't have to coordinate for that.Like, you don't have to know that other people are starting it. You now, it's very easy compos-, uh, composability, any language you can u- uh, you wanna use, and it's very visual. So you can see immediately, you can edit it easily, you can assemble small things with just even mouse clicks if you want to, and, uh, share, clone.And everybody knows also it's fully kind of static in the sense that we rerun it second time, it will exactly have the same results. Like, you will never have to do digital archeology. So full versioning and everything is also there.[00:24:06] swyx: Uh, so, so people can, uh... It's open source. Go to the GitHub repo and, and, uh, check it out.Uh, and it is also a really good, uh, blog post about it. I think all these is, like, really appealing. The, the, the, the thing that I think sells me the most about it is that, um, sort of development to production transition, right? Which I think, um, a lot of people haven't really solved that, uh, strictly, right?Like, we develop really, really well in, in Python notebooks, but then, you know, that's obviously not a sort of production ready process. I think that, like, any way in which that is solved, I think is, is very appealing. Then the other thing that you mentioned, which also raised my eyebrows, was content-based caching, which you mentioned is, is, um, you know, is ve-very much, uh, um, a sort of efficiency measure about, uh, you know, just like recalculation only on, on sort of content addressing Which I think makes sense.Uh, it surprised me that the savings could be this much, but maybe I just haven't worked at your scale where there's so much duplication, uh, that people just rerun because they change a single ID upstream.[00:25:10] Mikhail Parakhin: It does, yeah. But it's not only you rerun. The, the main savings are coming from the fact that you ran it, you got your job done, and you moved on.Then- Yeah ... somebody else in some department you don't know existed runs the same task, but on a newer version.[00:25:27] swyx: Yeah.[00:25:27] Mikhail Parakhin: Like right now, you can't, in, in most of the organizations, you can't even find out about it so that you can't even measure that you're spending that time twice, right? Here- Yeah ... if everybody's on Tango, that's detected automatically and detected that the output is the same.And then for that person, all it looks like is like experiment just suddenly moved, jumped forward, right? Uh, uh- Yeah ... so that's because, because the, there's network effect of multiple people helping each other.[00:25:51] swyx: Yeah. This is one of those things where it's designed to be a platform from the beginning rather than an individual developer's tool from the beginning, right?And, and everything's gonna streams down from there. That is the sort of Tango, uh, orchestrator, and it's, it manages jobs. We've seen a few versions of this, and this is obviously, uh, uh, the sort of, uh, unique approaches that you guys have, have, uh, figured out. And then there's Tangent.[00:26:14] Mikhail Parakhin: Yeah. And Tangent is basically an automatic auto research loop that can help and kind of do your work for you.Uh- ... you know, uh, effectively, effectively, Andrej Karpathy recently popularized it with auto research. Yes. Remember he said like he was, uh, speed running this, uh... Yeah, uh, you know the story. The, here we're basically bringing the same capability into Tango so that, uh, the, uh, Tangent can analyze it. It's just an agent that can run multiple experiments, figure out what can be changed, and keep on rerunning it, keep on modifying until, uh, maximizing some goal, some loss function, whatever you need to, to achieve.And in general, I would say if you're not using auto research-like approach in whatever you do, like literally whatever you do, then you're missing out. We saw at Shopify that taking like a wildfire, anything where you can put measurements can be done dramatically better. Our-[00:27:19] swyx: Mm-hmm ...[00:27:20] Mikhail Parakhin: uh, speed of, uh, templatization HTML, uh, completely new UX tem- uh, templatization of, uh, reducing latency for liquid themes.Uh, we-- Our, uh, search, uh, recently we moved from It's hard even, uh, quote from eight hundred QPS to forty-two hundred QPS with the same quality just by pure optimizations and not a research loop that kept running and changing code in our index serve on the same number of machines, just increasing the throughput.We, we managed to improve the quality of gisting and machine learning process. Uh, you know, gisting is the prompt compression technique that[00:27:59] swyx: allows for[00:28:00] Mikhail Parakhin: lower latency and, and lower and, uh, actually higher quality slightly. So like literally whatever different walks of life, and it doesn't have to be AI related.Uh, we, we had a reduction in, uh, storage because the agents would go and find data sets that clearly are derivative, uh, and then you don't need to store things twice. You know, we, we, we found somewhat embarrassingly that it was one of the largest tables was hashing random IDs into another random ID, and we literally- Oofput only one. So it was translating, yeah, two random IDs hashed[00:28:36] swyx: into[00:28:37] Mikhail Parakhin: each. So, so[00:28:37] swyx: it has access to the code as well, so it can, it can check the, like what, what the hell is it doing?[00:28:42] Mikhail Parakhin: So there, there cou- it could be run in two levels. You, uh, you know, at the superficial level, it could just use ex-existing components and, uh, reshuffle them.Uh, you know, like you can grab- Yeah ... uh, XGBoost, and you can grab some, some Py- PyTorch module, and then can grab some, you know, grab another tools and, and combine them. At a deeper level, since Tangle is all sort of CLI based underneath you, every, every component is a wrapped really CLI, uh, call and a YAML file, it can analyze code and create new components and, and, uh, keep on iterating as well.So, so you can, you can both have quick modifications of existing t- uh, pipelines with the, with components that are already there pre-baked, or you can create new components, uh, and-[00:29:29] swyx: Yeah ...[00:29:29] Mikhail Parakhin: keep iterating on those. So auto research is, again, this is probably the, the thing I was excited the most in the last two months happening, and we see it taking like, like totally like a wildfire.Just, uh, everybody, every day, every... well, every day, every minute, I would, uh, have somebody Slack message saying, “Oh, look how much better I made it.” And, uh, it's all throughout the research.[00:29:53] swyx: Is this democratized in some way in, in the sense that like is it your ML, uh, engineers and researchers doing this, or is it your regular PMs and software engineers also have the ability to auto-- to use Tangent?[00:30:07] Mikhail Parakhin: This is an awesome question. Like, Tango in general and Tangent in particular are extremely democratizing. Like they- Yeah ... they are the main tools for- ‘Cause I don't[00:30:15] swyx: need the details.[00:30:16] Mikhail Parakhin: Yeah. Exactly. Initially used by ML and AI engineers, but then literally, as you said, PMs are like the highest user right now is one of PMs on our org, uh, Sartak and he was, he was number one by, by usage of, of this ‘cause they're just, uh, energetic and knowledgeable, and now it, it unlocks a lot of capability where you don't have to co-change code manually.[00:30:39] swyx: I mean, I mean, because it kind of cuts out the ML, ML engineer from the process because the, the, the PMs have the domain knowledge and the ability to think about, uh, from first principles about, okay, what, what results do I want? And they can-- they even have the access to the data that, that needs to go in.So it's like in some ways, like this is the magic black box that we've always wanted for, for training and, and for, uh, I guess, uh, uh, hill climbing, whatever.[00:31:04] Mikhail Parakhin: It's basically cloud code for your AI development- ... uh, situation, right? Like now, now you don't have to know exactly how algorithms work. You can just, uh, bring your domain knowledge and expertise and product knowledge and iterate within Tangent until you've gotten the results that you need.[00:31:21] swyx: In my previous roles, every time that someone has pitched AutoML, you know, I've always been like, “Uh, this is not, this is not gonna work. It's, you know, it's, it's always gonna be a flop.” Somehow it's working now. I mean, presumably the answer is now we have LLMs and it's good enough, right? It's, it's an emergent property that we can do auto research, but like, it doesn't feel that satisfying that how come we didn't do this before, right?Like we just did like parameter search and like, I don't know. That's maybe that's it.[00:31:48] Mikhail Parakhin: Yeah. Bayesian optimization and hyperparameter optimization was, was the one that, or facet of AutoML that was used very actively, which incidentally also built into, uh, Tango. But, you know, I know Patrice Simard very well, and, uh, he was such a, uh, such a proponent of AutoML, and he put, like literally spent careers trying to democratize it.Without LLMs, it just turned out to be very hard. Like it, you, you would have flexibility within certain narrow domain, but it was hard to wider scale, and now with LLMs suddenly it's like magic wand, and so suddenly everybody- ... is an AutoML expert.[00:32:28] swyx: Yeah, I, I think it's multiple things, right? Like I'm, I'm just gonna bring up the, the, the chart again, right?Like LLMs can do the monitoring very well. That is the very potentially unbounded, super unstructured. It can do the analysis very well, it can do the... Uh, and basically it is much more intelligence poured into every single step. Uh, there's maybe nothing structurally changed about AutoML, but this is just m-more intelligent and more unstructured.[00:32:53] Mikhail Parakhin: Exactly.[00:32:54] swyx: Any flaws that you've run into? Like everyone is like drinking the Kool-Aid, oh my God, time savings, uh, you know, performance improvements. Like what, what, uh, issues have you have, uh, come up?[00:33:06] Mikhail Parakhin: This is really cool. It's not a solution to all the world's problems for sure. The limitations are usually the ones I-- And this is where we get into a bit of a subjective territory.Uh, I can only share what I've, I've seen so far, and I'm sure the situation, uh, is changing, and, you know, maybe after I say it, like many people will reach out and say, “Hey, what about this?” And you don't know that, and then, then we'll be probably right. But what I've seen is auto research is very good at doing kind of obvious things that you don't have bandwidth to do or you didn't notice or maybe you're not aware of like the-- some standard practices.It is not good at doing something completely out of distribution, something that, you know, you have to think for, for multiple days, uh, and, and do something like none of this. So, so it's, uh, I, uh, set an experiment once, uh, on, on my sort of, uh, hobby thing, and I let it run for, uh, ended up, uh, several weeks run, uh, you know, it's like full production kind of scale, so it, you know, slow runs and, and it ex-- it performed in the end, uh, over four hundred experiments, and only one was successful.I'm like, “Okay, that's, that's good.” But-[00:34:18] swyx: But it saved time.[00:34:19] Mikhail Parakhin: Yeah, I saved time. Like it, it was the, that thing. Yeah, if I, if I were doing four hundred experiments myself, my betting average, as I said, would have been much higher, I'm sure. But also, first of all, it would take me like three years to do four hundred experiments.And, uh, I didn't have to do them. Like the machines were just, uh, the price of electricity did that. So, and I got one improvement, uh, that in, uh, my, my-- Honestly, when I was starting that experiment, my thinking was to go and show that, “Hey, Andre, maybe you just don't know how to optimize.” And I was super smart because in, in my pro-problem, it was optimized for many years, and it was like fully improved.Uh, and I didn't expect it, you know, auto research to find anything at all. Yet it did. So instead of making fun of Andre, I ended up, uh, a big, big supporter. Yeah, that's exactly the tweet. Yes.[00:35:10] swyx: You and Toby really, really go back and forth on-online a lot, which is really funny. Uh, think of it as, as an eval for the optimalness of the code it's running on.Uh, it's almost like it reminds me of like a Kolmogorov complexity thing, but, uh, I guess it's-- there's some optimal thing that you're trying to sort of reduce down to, I guess. Um, and so, so you, you, you know, you should congratulate yourself that you had, uh, you know, uh, ninety-nine percent, uh, optimality.[00:35:36] Mikhail Parakhin: Exactly, yeah. I think Andre really deserves a lot of credit for popularizing this approach. This is, uh, this is incredibly, I think, powerful and cool and You know, the, uh, even him, him just mentioning it led to a lot of gains in a lot of places in the industry, so we should be thankful.[00:35:56] swyx: Yeah. I think he also has a just...I don't know what it is. Like, um, you know, it, it is a simple self-contained project that people can take and apply to other things, which is, is, is one thing, but also just the name. Just like somehow no one, no one managed to call their thing auto research. It's just naming things is very important. I think that that is mostly, uh, our coverage of Tango and, and, uh, Tangents.I think obviously, you know, there's a lot of, uh, ML infra at, at Shopify that people can, uh, dive into. We're about to go into SimGym, but before I do that, any, any other sort of broader comments around this whole effort? Like where is it, where is it leading to?[00:36:36] Mikhail Parakhin: As a segue to SimGym, like all those things start composing strongly.And, uh, you could see a huge unlock when you can look at each one of the tools and, and you see, oh, they're extremely useful. Uh, Tango is useful by itself. Auto Research is useful by itself. SimGym is useful by itself. If you combine all three, you create like synergetic effect. I think that's why we wanted to even, uh, cover them today is because this is something that if you go back even, you know, five years ago, would've been unthinkable.Uh, replicating that, uh, would, would be either incredibly costly or impossible, right? With probably thousands of people are required.[00:37:20] swyx: Well, we have serverless human, uh, serverless intelligence, right? Like, uh, so yes, you do have thousands of hu-- of, of intelligences, not just, not humans. And that's, that's close enough, right?Even if they're not AGI, they're, they're close enough to do the, the task that you need them to do. And, and, you know, that's, there's plenty for, for a lot of routine work, knowledge work. Okay, let's get into SimGym. Um, this is one of those things I, I was surprised to see actually it's apparently your, uh, one of your most popular launches, and I think something that, uh, I think Sim AI, I think Yunjun Park, who did the Smallville thing, there's a very small cottage industry of people trying to do like the simulate customer thing.I think a lot of people maybe don't super trust this yet because they're like, well, obviously they would just do what you prompt them to do, right? But maybe just think, uh, tell us about the sort of inspiration or origin story.[00:38:10] Mikhail Parakhin: That's exactly actually the thing I wanted to cover, because if you don't have the historical data, all you can do is prompt a-agents in a vacuum, and they will do exactly what you prompt them to do.In fact, when I first proposed it, and this is a bit of, um, my brainchild initially, if I, I can boast, even Toby said like, “But wouldn't they, they just repeat what, what you tell them?” And, uh, but I'm like, “Yes, except Shopify has decades of history of how people made changes and what there is, uh, there, what it resulted in terms of sales.”So now what we can do is we can-- we have this... It's not, it's a noisy data. There's a small, usually websites, uh, you know, like things, things are never in isolation. It's almost never AB experiment. It's always AA experiment when there's has two meanings, but basically, you know, in different time you run two different things.But if you aggregate in general, uh, like everything together, and you apply, uh, denoising and collaborative filtering like approach, you can extract a very clear signal. And then you can optimize your agents. And that's why it took so long. It took almost a year of that optimization of just us sitting and fiddling, and, and we had this internal goals of correlation of hitting-- internal goal was to hit zero point seven correlation with, uh, add to cart events, for example.Like that, that if we run real AB test experiment, that it should, it should go and, and rep-uh, replicate, uh, same sort of success that, that humans had or lack thereof. And it, it took forever, and I don't think that's easily replicatable because, uh, like who else would have that data? You have to have this historic, you know, decades, uh, worth of data.And now, now the, like the other thing you need is in-infrastructure and the scale, right? Because, uh, w- again, what we found, uh, stat sig results, you need to run a lot of simulations, a lot of agents, and, and it's-- Those are expensive things. Like you're, you're making actions in the browser because you want a real friction.You want to, to be able to get the image like of what humans will see because you wanna, uh, detect effects like, “Hey, if I make my images larger, will I have more sales or l- uh, fewer sales?” And like usually people's intuition here, by the way, is that I increase my images, I will have more because they look nicer.You know, designers all look sparse and big images. Like usually your sales tank, right? But, but, uh, you know, from HTML, all the characters look the same only the, the size tag looks different, right? So it's very hard. So you have to take visual information, you have to run this in simulated browser environment on the big farm and, and of course, you have to have, uh, like very, very expensive model, good model with multi-model model.So all this it's-- is what's taken so long and, uh, to share my personal fail a little bit there, Sean, is like, you know, we always had this bias to-- for like large company bias. You know, we always, uh, whenever you-- we do, we're like, “Hey, we'll run an experiment,” right? We make, make a change, and we will run an experiment and then, uh, see, uh, see which one's better or like, “No, this is worse,” and most of them are worse, so you discard it and keep iterating, hill climbing.And we're like, “Oh, like smaller merchants, they cannot get stat sig results. They cannot really run experiments simply because, you know, in a week there would be not enough data for them.” So we thought from this perspective. What we didn't realize is that most people don't have A and B, they just have one thing, and they need suggestions of What A and B should be.So, uh, we first build this, hey, we run simulation on two separate teams and, and, uh, say, “Hey, which one is better?” We then morphed it into, and very recently just released it, when you have just your site, your theme, we run over it and we say, “Hey, here's what predicted values of, of, uh, uh, conversions are, and here's how we think you should modify it to increase your conversions.”And then circling back to what you started with, the proof is in the pudding. Like, if we are not correlating with reality, like, people will not be using it. And, uh, thankfully, we see literally every day more users than the previous day. So, so right now, uh, right now- It's working. Yeah. I'm-- Right now my problem is how to pay for it all because the so our major thing is how to optimize the LLMs, do distillation, how to run the headless browsers, uh, and handful browsers, uh, uh, cheaper so that we can accommodate the increase in traffic.[00:42:47] swyx: Yeah. I, I understand that you, uh, you published a lot of technical detail at GTC, so I was just gonna bring it up a little bit. I think s- was this in, in con-conjunction with some kind of GTC presentation? Or something like that, right?[00:42:59] Mikhail Parakhin: Well, we, yeah, we, we did it in several place, but yeah, we had the engineering- Yeahblog, uh, as well. Yeah.[00:43:05] swyx: Yeah. So you're running, uh, GPT OSS. Uh,[00:43:08] Mikhail Parakhin: the, this is an older version. You know, now we run multimodal model. But yeah- Yeah ... GPT OSS, we still run GPT OSS as well for[00:43:15] swyx: And then you have the VMs, and you also have browser-based. I really like this one where it you said, “It violates almost every assumption that standard LLM serving is designed for.”And then you had like, basically orders of magnitude differences between everything.[00:43:29] Mikhail Parakhin: Exactly. Which is, which, uh, which was, you know, a bit of a challenge to implement, like when, like even simple things. Uh, be- since it violates all the assumptions, for example, multi-instance GPUs, like MIGs don't work as well.But we needed, uh, to get MIG to work because, ‘cause otherwise it's way too expensive. And so we had to deal with the, yeah, with, uh, lots of infrastructure and, and, uh, work with, uh, uh, Fireworks and CentML, uh, you know, to help with optimizations and browser-based, as you mentioned. Yeah, like, takes a village.[00:44:04] swyx: Okay. So there's a lot of like, I guess, experimentation in the infrastructure so far, and you've published more or less what you have here. I guess I'm, I'm less familiar with CentML. I, I don't do, uh, that much work in this, this part of the stack. But why was it the sort of preferred instance platform?[00:44:22] Mikhail Parakhin: There are really three probably top companies. There used to be, uh, uh- Three top companies, uh, at least I was aware of that did, uh, LM optimization. You know, together Fireworks and Santa ML, not necessarily in that order. Santa ML recently got acquired by NVIDIA. Uh, what they did is if you have a model and you want to optimize it to a specific prof-- uh, profile of usage, uh, they would go and do it.And, uh, we work with, with those companies, uh, this was work particularly in with Santa ML and NVIDIA to get them the best possible results out of it. And, and sometimes you, you have to retune depending on, like sometimes you want the maximum throughput, sometimes you want minimal latency, sometimes you want like the cheapest, right?And, yeah, or some combination. And so yeah, these are people who would come and help you.[00:45:14] swyx: I see. I see. Yeah, yeah. I'm familiar with these people for the LLM, you know, autoregressive stack. But the other interesting category of these optimizers is also the diffusion people, whereas like Fel and, you know, uh, Pruna recently has come up a lot as well, which I think is like really underappreciated, uh, at least by myself, because I, I thought, oh, all the workload would be LLMs, but actually there's a lot of diffusion as well.[00:45:38] Mikhail Parakhin: Exactly.[00:45:38] swyx: There's a lot here, so I, I, I... it's, it's, uh, it's, it's, it's hard to cover. But I, I do think like people underappreciate the importance of customer simulation, basically. I think this is something that I'm candidly still getting to terms with. Uh, you know, uh, you also-- your team also like prepared this, like, really nice diagram.Uh, I, I assume this is AI generated.[00:46:00] Mikhail Parakhin: Yeah, it looks-[00:46:01] swyx: Maybe it's not.[00:46:01] Mikhail Parakhin: Yeah, it looks, uh, Gemini-ish. Yeah, but, uh, uh, honestly, I, I don't know where, where the hell they generated. It looks, look, uh, looks like it's, uh, Google. But the interesting part, John, that, that, uh, we haven't covered, but I, I wanted to mention is if your store had previous customers, rather than it's a new store, you're like new merchant just launching things, it helps tremendously in just correlation and forecast.Yeah, we take your previous, uh, customer's behavior, and we create agents that replicate those specific distribution of, of customers that you get, and then we a- we apply those to your changes, and then that, that raised raw, you know, the re-- uh, just correlation with the add to cart events or to-- with conversion or whatever it, it, it may be, uh, quite dramatically.So, uh, replicating humans in general seems like an interesting, cool challenge.[00:46:58] swyx: As a shareholder, I think this is the-- like if people are Shopify shareholders, they should really deeply understand this because this is basically the moat. The, the more you use Shopify, the more it will just automatically improve, right?Like you're, you're doing the job for them.[00:47:13] Mikhail Parakhin: Yeah, that's what we started with. Like, uh- ... uh, otherwise, if you're just a startup, I wouldn't do it if, uh, you know, if it was my startup because Without the data, it, yeah, as, as you said, it's, it's exactly the case that, uh, whatever you say in prompt, that's, that's what the agents will be doing.[00:47:30] swyx: The statistician in me wants to like really satisfy the sort of, um, statistical intuition, I guess. Um, to me it's kind of, uh, the, the word that comes to mind is, um, ergodicity. Uh, so let's say a, a customer takes this path, customer takes this path, customer takes this path, right? Um, the... In my mind, the way I explain it is like, okay, here, here's the ninety-five percentile, here's the five percentile, and here's the median, right?Um, but to me, what SimGym is potentially doing is that it can, uh, modify... It can sort of model the sort of in-between sort of journeys as well, that, that maybe are dependent on the previous states. This may be like a very RL-type conclusion where like basically the summary statistics, if you only did naive AB testing, you only have the, the statistics at, at, at a certain point, and you only judge based on the sort of overall summary statistics.But here you can actually model trajectories. Does that make sense? Or-[00:48:31] Mikhail Parakhin: That makes total sense because like, well, that, that makes even more sense that maybe even you realize bec- because-[00:48:38] swyx: Okay. Please,[00:48:38] Mikhail Parakhin: please. Yes ... we do-- Yeah. The, so internally, uh, we have this system, we talked about it briefly once at NeurIPS.We have a huge HSTU-based system that models the whole companies, uh, and their possible paths. And like- Yeah ... what you are, what you are showing, like actually at any point of time, you can either model the user's behavior or you mo- can also think about, uh, the whole merchant as a company, as the entity that acts in the world.You can model that as well. And then you can do, can do counterfactuals. In your graph, like in your blue graph, uh, if you're... Imagine in the center there, uh, somewhere in the middle, you would have an intervention. I give that person a coupon, or I don't know, I send a personal thank you card, or give a discount in some- somewhere.And then you can, uh, then you can do forward rollouts from that counterfactual. So what would have happened with that intervention or without the intervention? And you can even ch- change where that intervention, uh, in time can happen, right? Like some- where, where in this journey. So we, we do this at the Shopify scale for our merchants, and then if we notice that something that they can be fixing, like there's a strong counterfactual, like we have Shopify policy, they basically get a notification like, “Hey, we think your...something is wrong with your-” I don't know, Canadian sales. Like, uh, it looks like it's misconfigured. Here's what you need to do. Or do you think like, uh, you have to set up this campaign with these parameters? And we do that at the buyer level to literally offer discounts or cashback or, or things to buyers.So this is-- I'm getting very excited. Like this is my sort of area of, uh, interest, I guess, and, and hobby. But being able to m-model something complex as human beings or companies and model counterfactuals on it, where you can have interventions in the future and optimize when to make intervention, what kind inter-- uh, what kind of intervention to make.It's such an unlock that previously was completely impossible. Like the-- it was, it was always dreamed of, but never... Like how would you even simulate it without LLMs or HTUs? I think very, very exciting times.[00:50:59] swyx: I just wanted to, uh, to maybe illustrate this. I, I'm not the best illustrator, but I, I am a conceptual statistics guy.And y-you know, you cannot just do this. Like this is a dimensionality AB test doesn't do, right? Like, uh, because it doesn't have the, the, the change over time, uh, stochastic nature, uh, and it doesn't have the sort of contextual like... Here's all the context to this point. Um, okay, cool. Um, that's SimGym.You're, you're gonna burn a lot of tokens on this thing. But you're, you're one of the, the only scale platforms in the world that can, uh, that can do this across a huge variety of workloads, right? I'm even curious on a sort of human, uh, research level of like, well, do, does retail behave d-differently from like clothing sales?D-does that behave differently from electronic sales? I, I don't know. I don't know what else you guys... The Kardashian shoppers, do they differ from like people who buy, uh, I don't know, cars and, uh, whatever.[00:51:55] Mikhail Parakhin: Well, very different, and different sensitivities and different modes of, uh, shopping and, and different levels of what's important.Now, to-totally, you can do aggregations at, uh, at a store level. You can do aggregations at a different, uh, category level. I don't know if, uh, you know, for our statisticians among us, I couldn't believe, but we-- recently we're looking at it, and we had to bring back, uh, CRPs, you know, Chinese restaurant process.It's a, like, way of aggregating and, like, naturally grow clustering. So across... Specifically to answer questions that, uh, like you were just posing on how, how if, if buyers behave different categories. And I'm like, “I haven't seen CRP since two thousand and one.” It's[00:52:37] swyx: so What? It's so- What is... No, I haven't, I haven't seen this.No. This is not in my training. Uh,[00:52:44] Mikhail Parakhin: but, but yeah, it, uh, uh, it actually, like the, the-- there was a very popular kind of theory, popular neurips HTML circles in early two thousands, uh, kind of nice. And now, now it has practical applications, uh- Yeah ... that we were resurrecting.[00:53:03] swyx: Yeah, amazing. Uh, I, I can see, I can see how this is like a, uh, a fun job for you where you get to apply all these things.Um, yeah, yeah, so super cool. Super cool. So, okay, so, so anyone who, who knows what CRPs are and has always wanted to use them at work, uh, they should, they should definitely join Shopify. Okay, so w-we have a lot and but I, I'm, I'm being mindful of the time. I, I do wanted to, to sort of cover some other things.Um, I-I'll give you a choice, UCP or Liquid?[00:53:30] Mikhail Parakhin: Liquid. I think, I think on UCP, you know, like UCP is very important for us and, and it just we are-- UCP, we have a structured, uh, discussions, and you can read about them, and we have, uh, blog posts, and we have a big release this week, in fact, like with our catalog.Oh,[00:53:46] swyx: okay.[00:53:46] Mikhail Parakhin: Uh, yeah,[00:53:46] swyx: but- Le-I mean, we, we can, we can discuss the, the, the release briefly because we'll release this after the-- after it's already announced so whatever. There's a catalog that you guys are doing?[00:53:55] Mikhail Parakhin: Yeah. So we are, we are- Okay ... we are bringing in capabilities of a whole, uh, Shopify catalog.Basically, you now you can search for products, you can do lookups by specific ID, you can do bulk lookups when you need to bring m-multiple products. You don't need to know in ad-in advance what you're trying to show or to sell or check out. Like, you can now, you can now have this decided at, at runtime, and this big area for investment for us for both non-personalized and personalized searches, trying to provide basically a win-window into whole universe of products that are being sold everywhere in the world.And Shopify is really not exactly, but almost like a super set of any-anything being sold. Now we are bringing it into UCP and, uh, and, uh, identity linking is another big thing for us, uh, so that you, you can use, uh, like Google or whatever, whatever identity you have, uh, they're minimizing friction.[00:54:56] swyx: Yeah. So[00:54:57] Mikhail Parakhin: yeah, big release for us.But Liquid AI of course we never talk about, and the problem might be more, more aligned with what we d-discussed previously on this chat.[00:55:07] swyx: Sure. The main thing that everyone understands about Liquid is that it is inspired by Worm, and I still don't know why. I'm curious on your explanation. I think you, you, uh, you can make things very approachable.And also I think like what is the potential of like the, the level of efficiency that you get out of Liquid?[00:55:23] Mikhail Parakhin: You- we all familiar with transformer architectures. And, uh, for the longest time, there was a competing architecture, it's called the state space models. So, so Sams, uh, you know, Chris, Chris Reyes, one of the pioneers and, and lots of startups, uh, trying to make those realities.They have, uh, significant benefits being main being, uh, being much faster and, uh, lower footprint and not quadratic in length, you know, sort of, uh, linear in, in, uh, in your context length. But with state space models- They never quite made it. Like they're used-- They have, uh, certain niches when they thrive, their hybrid architectures are useful, but they never quite made it.And liquid neural networks are, you can think of them as a next step, like, uh, sort of, uh, state-space model square. It's non-transformer architecture that's more complicated than sta-state space and really difficult to code if you-- if I'm being honest. But it's, um, very efficient. It's, uh, subline-- sub, uh, quadratic in, in length of your context.Uh, it's very compact way to represent things, and that's a liquid AI company. They... Their goal is to productize it, and very often you have this need, uh, when you need to have long context and small model, and you want to have low latency. Like in general, it's basically on par with transformers, and if you do hybrids with transformers, it's, it's even better.That's why we at Shopify, when we tried multiple and we constantly try multiple models, multiple companies, we found that for small, particularly with low latency applications, when you have low latency and/or if you need longer context lengths, liquid was the best. And so we still use the whole zoo and always like obviously test and use everything, uh, every open source model and, you know, it feels l

What does it take to fix the most broken system in America — from the outside?Ajay and Aniket, co-founders of Coral AI, had zero healthcare experience when they started. What they had was a burning problem, a one-way plane ticket to the US, and a relentless drive to understand healthcare from the ground up — visiting 17 cities in 30 days, becoming interns, and reading thousands of faxes doctors still send in 2026.In this episode of Zee47 Moments, Ashwin and Vikram sit down with the founders to unpack how Coral is using AI to eliminate the administrative chaos of patient referrals — cutting weeks of back-and-forth down to minutes — and how they're building one of the most AI-native companies in healthcare.

Franc Mamba: https://soundcloud.com/francmambamusic https://www.instagram.com/eteric.records/ RTS.FM • https://t.me/rtsfm • https://soundcloud.com/rtsfm • https://rts.fm/ • https://facebook.com/rtsfm • https://instagram.com/rts.fm • https://vk.com/rtsfm • https://youtube.com/user/rtsfmmoscow RTS.FM is the first international internet radio project with LIVE audio-visual broadcasting from 30+ studios around the world!

From the first breath of this game, you have a choice: stay the old version that suffocates or sprint toward a version that refuses permission. I've ridden through days when I wondered how I'd pay the bills, and I kept going while everything screamed quit. The secret isn't luck; it's a trail of decisions that compounds into domination. You shed skin and fear, and you commit to a version of you that asks for nothing and delivers everything. The cringe isn't a warning; it's a gateway to growth. Each morning in the mirror you remind yourself: this pain is the price of a bigger future. The Mamba mentality—constant improvement, ruthless focus, unflinching discipline—is not about perfection; it's about stacking wins until they roar. If you want leverage that turns ambition into reality, you must embrace isolation, doubt, and the long run. This is the story of a path forged through fire and a person who refuses to quit. You'll hear how to keep going when the storm hits, and why loving the process is the only way out.

Get AudioBooks for Free Best Self-improvement Motivation Outwork Everyone: Kobe Bryant's Mamba Mindset Learn the relentless work ethic of Kobe Bryant. Build discipline, stay focused, and outwork everyone to achieve elite success. ⁠We Need Your Love & Support ❤️ ⁠⁠⁠⁠⁠⁠⁠⁠⁠Get 3 Audiobooks Free -

Welcome back to the 40 Nickel Mixtape. The Invitational has broken down and gained focus. Just in time for Easter, the 3 related coaches are poised to grab the Mindgamez Hardware. Only Clockwork stands in their way. The last major 40 Nickel Event for 2026 is almost done. Can Clock survive the gauntlet? Can LJ be beaten two times for the Mindgamez chip? Will we see Skip's last game this week? Don't Worry. It's just the Mixtape... Today's Topics: Father, Son, and The Holy Ghost Skip Evolves Phone Booth Ball Chicagoland Changes to LoveLand "Poop Butts..." Mamba > Clockwork Skips Last Game (*Moved to Thursday) LJ, the new Freight Train Patriot Dayz The Spectre of Madden 27 "..Zippity Doo Dah..." Scores are an illusion Death of the Dub Clockwork Comeback "Skip's gonna beat the shit out of you..." Torch Passing Tourney Everyone is Rusty DuRagg....Future Nickelbowl Contender The Final Chapter  Enjoy!

Our 238th episode with a summary and discussion of last week's big AI news!Recorded on 03/18/2026Hosted by Andrey Kurenkov and Jeremie HarrisFeel free to email us your questions and feedback at andreyvkurenkov@gmail.com and/or hello@gladstone.aiRead out our text newsletter and comment on the podcast at https://lastweekin.ai/In this episode:* OpenAI released GPT-5.4 mini and nano with 400k-token context windows, higher per-token prices but claimed token-efficiency gains in Codex; nano is API-only and pitched for high-volume classification/data extraction despite a major price increase.* Mistral open-sourced the Small 4 model family (MoE, 119B total/6B active) combining reasoning, multimodal, and coding-agent capabilities, and announced Forge to help businesses train or post-train custom models.* Agent “operating system” competition intensified with Meta's acquired Manus launching a local Mac agent, Nvidia announcing NeMo/“Open Shell” sandboxed agent runtime, and Nvidia also unveiling DLSS 5 plus major hardware forecasts including Groq LPU integration.* Business and safety updates included OpenAI shifting focus toward productivity/enterprise amid competition, Microsoft reorganizing Copilot and frontier-model efforts, Meta delaying its next model, China-linked ByteDance deploying large Nvidia clusters abroad, and new safety work on steganography, chain-of-thought faithfulness, fine-tuning defenses, cyber-attack evals, and constitution/spec compliance.A thank you to our current sponsors:Box - visit Box.com/AI to learn moreODSC AI - go to odsc.ai/east and use promo code LWAI for an additional 15% off your pass to ODSC AI East 2026.Factor - head to factormeals.com/lwai50off and use code lwai50off to get 50 percent off and free breakfast for a yearTimestamps:(00:00:10) Intro / Banter(00:01:56) News PreviewTools & Apps(00:02:39) OpenAI ships GPT-5.4 mini and nano, faster and more capable but up to 4x pricier(00:08:04) Mistral's new Small 4 model punches above its weight with 128 expert modules(00:14:03) Meta's Manus launches 'My Computer' to turn your Mac into an AI agent - 9to5Mac(00:17:57) NVIDIA Announces NemoClaw for the OpenClaw Community | NVIDIA Newsroom + Nvidia boosts knowledge work with Open Agent Development Platform(00:24:09) DLSS 5 looks like a real-time generative AI filter for video games | The Verge(00:26:36) OpenAI to Launch ChatGPT 'Adult Mode' Despite Warnings From Its Own Advisers - CNETApplications & Business(00:33:46) OpenAI Reportedly Pivoting to a Focus on Business and Productivity Only(00:41:25) Nvidia GTC 2026: CEO Jensen Huang sees $1 trillion in orders for Blackwell and Vera Rubin through '27(00:45:44) Mistral launches Forge to help enterprises build their own AI models(00:54:17) China's ByteDance gets access to top Nvidia AI chips, WSJ reports(00:57:57) Meta Delays Rollout of New A.I. Model After Performance Concerns(01:02:50) Microsoft Shakes Up AI Division As Copilot Falls Behind Google and OpenAIPolicy & Safety(01:07:26) A Decision-Theoretic Formalisation of Steganography With Applications to LLM Monitoring(01:13:09) Reasoning Theater: Disentangling Model Beliefs from Chain-of-Thought(01:18:29) In-Training Defenses against Emergent Misalignment in Language Models(01:23:07) How do frontier AI agents perform in multi-step cyber-attack scenarios?(01:25:20) Eval awareness in Claude Opus 4.6's BrowseComp performance(01:29:49) Introducing Bloom: an open source tool for automated behavioral evaluations(01:32:26) How well do models follow their constitutions?(01:37:11) Nvidia's H200 License Stirs Security Concern Among Top DemocratsResearch & Advancements(01:40:050) [2603.15031] Attention Residuals(01:47:11) Mamba-3: Improved Sequence Modeling using State Space PrinciplesSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

It's #324st for 19ed March, 2026 or 3312! (33-Oh twelvenish)Squadron Briefing:  BGS highlightsThe Loose Screws are in 403 star systems, controlling 121!We are engaged in 7 conflicts - A new war in Alexandrinus is live and the states in that system are a hot mess for all the factions. That's the only war we care about.And Yes, there are Space CZ's this go around!We are in expansion out of NLTT 2969States of interest - Civil Unrest in Alexandrinus, Civil Liberty in 7ABoom in QamaBust/Lockdown in Cephei Sector MC-V b2-1Short PP Report:Lifted with unspoken consent from KrugerFive on the LS discordCycle 72:400t relics in full effect this weekPrincess Aisling getting 70 new toes, 15 new feet, 0 new spiked heelsLi Yong-Rui, while not getting as many systems picked up 70, but add 6 new strongholds! First to reach 200 strongholds.Arguably had the stronger cycle than Aisling because of thatPatreus getting blasted and losing 16 systems, including losing 5 fortifiedsGetting super tight between Archer and Antal, Antal may be able to pass in a few cycles for P6.Kaine also right behind and could also overtake Archer soonhttps://www.k5elite.com/Dev News: New Launcher Update 18 (yes, again)https://forums.frontier.co.uk/threads/elite-dangerous-launcher-update-18-march-2026.646228/Galnet News: Galnet News | Elite Dangerous Community Site Distant Worlds 3 Fleet Departs Beagle Point to Continue Deep Space SurveyHeading to the Abyss, Acheron and the Veils, oh my!Viral Wellness Trend Spreads Beyond Entertainment CirclesSocial Contagion of holovids regarding Clarity, Motivation, and Resilience in the AllianceDiscussion :Old Ships, New Tricks - Roy intro then guiding questionsCommunity Corner :“LARGE Player Instances made EASY in Elite Dangerous” by SiegfriedOrigin - https://youtu.be/mwUawYZSdxg"ED Wing Helper" - https://github.com/Siegfried-Origin/EDWingHelperRainbow's End Station is live in Roefoo ZE-H d10-0 (DS3 Waypoint 8). Store SaleEngine and Weapon Colors - 40% OffNew Plating Pack Ship Kit for Python Classic, Mamba, Krait Mk2, Krait Phantom, Cobra III, AspX - 0% OffNew Kestrel Mk II ‘Flare' holo-kits

Beabadoobee, que también forma parte del disco benéfico Help 2, con la versión de Say Yes” de Elliott Smith, presenta “All I Did Was Dream of You”, una preciosa canción en la que cuenta con la colaboración de The Marías y que se mueve por una atmósfera intima, etérea y muy sensual. Ellas protagonizan este podcast donde destacamos también "La Próxima Vez No Habrá Próxima Vez", nueva canción de Bunbury, con sonidos muy latinos, que conectan con los de The Animeros en "Mamba, Mambo". Aparte, escuchamos a Robyn con la canción que dedica a su hijo, "Blow My Mind" y a Sons con "Surfin'".   SEAN FRUTOS - Lo Volvería a HacerRUFUS T FIREFLY - La PlazaBLEACHERS - Dirty Wedding DressTHE LEMON TWIGS - I Just Can't Get Over Losing YouGINEBRAS - Con Las Chicas en BerlínPIPIOLAS - Feria CañeteREPION - El Sueño Dura Una SemanaSHEGO - amiamigaBEABADOOBEE - All I Did Was Dream Of You (ft. The Marías)ARLO PARKS - Get GoROBYN - Blow My MindBUNBURY - La Próxima Vez No Habrá Próxima VezTHE ANIMEROS - Mamba MamboTHE BLACK KEYS - You Got to LoseSONS - Surfin'EL KANKA - Las GanasEscuchar audio

"IS THAT NOT OUR JOB?!" From players like Kevin Durant and Giannis Antetokounmpo, to legendary broadcasters like Mike Breen, Jonathan Zaslow, and the ever-eloquent-and-definitely-not-too-white-to-say-Mamba-Mentality Jeremy Tache, people around the NBA are defending the validity of the excellence of Bam Adebayo's 83-point performance, and Dwyane Wade is taking a victory lap over Bam and the Heat's recent success. Today's cast: Dan, Zaslow, Chris, Jeremy, Mike, Roy, and Tony. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Oferta nucleară cu două fețe a Franței (Cotidianul) - Cât de pregătită este România pentru întreruperea timp de săptămâni a fluxurilor de petrol şi gaze din Golf. Alimentarea cu motorină pare în acest moment cea mai mare vulnerabilitate (Ziarul Financiar) - Blocați în Dubai. Cât de repede dispare o lume virtuală? (SpotMedia) „Nu ne apără scutul de la Deveselu, e exact invers”. Armele care ne-ar asigura împotriva unor rachete lansate din Iran (Adevărul) Escaladarea tensiunilor dintre SUA, Israel și Iran pune România într-o lumină nouă pe harta strategică. Cu baza de la Deveselu în alertă și experiența recentă a operatiunilor de la Mihail Kogălniceanu, capacitatea de interceptare a rachetelor balistice iraniene devine subiect de securitate națională. Generalul Bălăceanu vorbește, pentru „Adevărul”, despre riscuri din perspectiva militarului și explică pe ce tehnică militară ne-am putea baza pentru a ne apăra în cazul unui ipotetic atac venit dinspre Iran. Cu mențiunea că, în principiu, generalul Virgil Bălăceanu consideră că un asemenea atac este improbabil, dar nu poate fi exclus în totalitate. „Iranienii au rachete cu bătaie scurtă până în 1.000 km majoritatea și o parte cu bătaie până în 2.000 km. Sunt ideale pentru interceptarea cu sistemele Patriot sau sistemul francez din Capu Midia, MAMBA, al francezilor.  Avem capabilități pentru combaterea rachetelor iraniene”, mai spune generalul. Sistemul Aegis de la Deveselu este eficient împotriva rachetelor balistice iraniene. „Nu ne apără scutul, este, de fapt, exact invers. Noi apărăm scutul de la Deveselu. El este destinat pentru rachete balistice intercontinentale, pe care iranienii încă nu le au”, afirmă generalul Bălăceanu. Oferta nucleară cu două fețe a Franței (Cotidianul) Discursul președintelui Emmanuel Macron despre modificarea doctrinei nucleare a Franței a fost planificat demult. A fost o coincidență că a fost ținut la trei zile de la primele atacuri americane asupra Iranului. Coincidența susține cauza lui Macron. Franța vrea să-și extindă umbrela nucleară asupra Europei. Există însă un mare impediment în calea inițiativei președintelui Macron. Franța nu este integrată în planificarea nucleară a NATO. Deciziile se iau strict la nivel național, inclusiv cele legate de modernizarea arsenalului, vectori de transport la țintă. Costurile ar urma să fie împărțite cu statele care ar beneficia de ”umbrela” Franței.  Cine va plăti pentru mentenanța și modernizarea arsenalului nuclear al Franței, câtă vreme Franța și Germania, cel mai curtat posibil partener, nu au reușit să dezvolte proiectul unui nou avion multirol european, se întreabă Eurointelligence? Disputa ar fi și mai intensă, pentru că toate deciziile ar urma să se ia strict la Paris. Ar fi dispus președintele Franței să sacrifice Parisul pentru a apăra nuclear Berlinul sau Tallinnul? Este o întrebare pe care o pun mai multe ziare din Germania. O altă mare problemă a planului lui Macron este faptul că a anunțat ”descurajarea avansată” cu 14 luni înainte de finalul ultimului sau mandat. Cine va fi succesorul său la Elysee? Sondajele plasează pe prima poziție extrema dreapta – Jordan Bardella sau Marine Le Pen (care așteaptă până în iulie un verdict al justiției în cazul de fraudă cu fonduri UE la Parlamentul European). Integral în Cotidianul. Cât de pregătită este România pentru întreruperea timp de săptămâni a fluxurilor de petrol şi gaze din Golf. Alimentarea cu motorină pare în acest moment cea mai mare vulnerabilitate (Ziarul Financiar) România îşi asigură gazul necesar din producţia internă în cea mai mare parte, pentru alimentarea cu petrol, Kazahstanul este cea mai importantă sursă, dar pentru motorină, unde 40% din cererea internă sunt asigurate din importuri, există o vulnerabilitate. Mai bine de 20% din importurile de motorină au venit anul trecut din Arabia Saudită, ale cărei livrări sunt influenţate masiv de războiul din Iran şi închiderea strâmtorii Ormuz. Preţul carburanţilor a rezistat şi ieri sub nivelul de 9 lei pe litru, dar nimeni nu poate garanta acest nivel când deja sunt analişti care văd preţul petrolului escaladând la peste 150 de dolari pe baril. „România are stocuri pentru 90 de zile. În acelaşi timp, avem rezerve comerciale constituite de societăţile comerciale şi, nu în ultimul rând, avem încă o treime din ţiţei pe care îl prelucrăm aici, în România, ceea ce ne plasează într-o situaţie absolut favorabilă în Uniunea Europeană şi chiar în lume. Coroborând cu aceste stocuri, suntem liniştiţi din perspectiva cantităţii. Şi dacă statul vine cu măsuri corespunzătoare din punctul de vedere al constituirii preţului, cred că România nu trebuie să aibă nicio problemă din perspectiva preţului la pompă, pentru că 55% din ceea ce înseamnă preţul la pompă la benzină înseamnă acciză şi taxă pe valoarea adăugată, 50% înseamnă la motorină. Deci avem cumva o suficienţă unde putem oricând să lucrăm“, a spus Dumitru Chisăliţă, director la Asociaţia Energia Inteligentă, la ZF Live. Blocați în Dubai. Cât de repede dispare o lume virtuală? (SpotMedia) Mulți cetățeni români, surprinși de război în Orientul Apropiat, acuză autoritățile că nu i-au sprijinit și n-au reușit să-i repatrieze rapid, în condițiile în care în întreaga zonă sunt interzise cursele aeriene civile din cauza pericolului reprezentat de luptele în plină desfășurare. Lipsa de înțelegere a realității și incapacitatea cronică de a lua decizii în mod adecvat au legătură cu prăbușirea sistemului de educație, care a avut ca efect înlocuirea gândirii critice cu conținutul digital îndoielnic și, de multe ori, toxic de pe rețelele sociale, scrie jurnalistul Emilian Isăilă pe pagina SpotMedia.

In this episode, Eric sits down with serial fitness entrepreneur Anthony Geisler to unpack his new venture, Sequel, why he's doubling down on longevity, and how he sees the future of fitness shifting toward healthspan, tech-enabled training, and democratized wellness for the masses.​ ✨ Key takeaways

Get AudioBooks for FreeBest Self-improvement MotivationKobe Bryant's Best Inspirational Speech on Mamba MindsetKobe Bryant delivers legendary motivation on discipline, obsession, and greatness. This inspirational speech reveals the Mamba Mindset that wins championships.Get AudioBooks for Free⁠We Need Your Love & Support ❤️https://buymeacoffee.com/myinspiration#Motivational_Speech#motivation #inspirational_quotes #motivationalspeech Get AudioBooks for Free Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

This week, I talk with Michael Gendler about what it really takes to communicate at a high level without losing yourself in the process. Michael's story is more than “learn public speaking.” It's the journey of a shy Russian immigrant kid with a deep drive to prove himself, who eventually realized that the breakthrough isn't technique — it's self-acceptance. We talk about how grit can be both a gift and a cage, why striving doesn't always lead to happiness, and what happens when you start pursuing excellence from peace instead of pressure. We also unpack Michael's inside-out approach to speaking: flow over performance, presence over perfection, and why the ability to handle pressure is the real foundation of high-stakes communication. Let's get to it! In this episode: (00:00) Intro (00:33) Freestyle rap, improv, and learning to create on the fly (03:52) “Mamba mentality”: when proving yourself becomes your identity (08:27) Redefining happiness when achievement stops working (13:37) The hidden cost of growing up as an immigrant (and staying shy) (15:40) The “magic” of unlocking skills people already have (18:57) Grit is powerful… until it turns into a cage (37:38) The simple way to end strong (and why people remember the ending) (44:33) The moment Michael froze in a meeting—and everything changed (57:52) What Ultraspeaking is really teaching (01:02:23) Why most speaking advice fails (inside-out vs outside-in) (01:13:31) The real obstacle isn't skill—it's self-consciousness (01:16:04) What makes a keynote actually work (01:24:13) Teaching what you see: turning intuition into frameworks (01:34:41) The Accordion Method, and how great speakers expand + compress ideas (01:35:57) What Michael is world-class at (01:40:42) Building a future where people feel more free to express themselves Get full show notes and links at https://GoodWorkShow.com. Watch the episode on YouTube: https://www.youtube.com/@barrettabrooks.Apply for 1-on-1 Coaching with Barrett: https://barrettbrooks.com/coaching Subscribe to Tiny Leadership Lessons: https://barrettbrooks.com

The JBP kicks off its latest episode with a recap of the 2026 Golden Globes (33:18) which includes reactions to Anthony Anderson & Rocsi Diaz (52:50) as well as the award for best podcast (1:06:30). Worst Take returns as the cast covers Wild Card Weekend in the NFL including the Jaguars postgame press conference interaction between head coach Liam Coen & press reporter Lynn Jones-Turpin (1:09:00). Cam Newton interviews Lady London (1:26:43), Hot 97 announces Mero as the new host of its morning show (1:41:37), and Freekey Zekey releases old footage of an altercation between Dame Dash & Jim Jones (1:55:04). Also, LA Reid avoids trial (2:14:35), Druski's new skit (2:25:02), and much more! Become a Patron of The Joe Budden Podcast for additional bonus episodes and visual content for all things JBP! Join our Patreon here: http://www.patreon.com/joebudden 

We decided to leave 2025 behind by reheating an episode from 2023 that features the remarkable Hamissi Mamba, who came to Detroit from his native Burundi searching for a better life, leaving his wife Nadia behind not knowing she was pregnant with twins. Despite not speaking English and knowing no one in the motor city Mamba has in a few short years opened a successful restaurant Baobab Fare, a food truck Waka by Baobab, and Soko, a retail business selling East African curated sauces, coffee and beverages, chocolate jewelry, and apparel. Mamba's story is especially resonant given the cruel anti-immigrant policies of the Trump administration.  Learn about your ad choices: dovetail.prx.org/ad-choices

In this episode, I'm chatting with Mamba 100 Race Director, James Boler.  This is your deep dive into everything you want & need to know before hitting that sign up button.  Links to all the websites and groups James mentions in the show are linked below.  Join the Mamba Trail Runners Facebook GroupSign up for Mamba 100 on UltraSignUpCheck out the Mamba Trail Runners Website ⭐️ GET THE FREE ULTIMATE 50K TRAINING TOOLKIT

On this show we have espoused the general guiding principal of "when you hear hoofbeats, think horses, not zebras." Sometimes the hoofbeats actually are those of zebras. But what if sometimes there not even zebras, they're unicorns? On this episode of AMPED, our patient has been bitten by a Jameson's Mamba, one of the deadliest snakes in the world. Thankfully, he is an expert herpetologist who is able to talk our team through the steps needed to save his life. But what our team learns is that sometimes that which seems extremely rare results in care that isn't rare at all.    Interested in obtaining CE credit for this episode? Visit OnlineAscend.com to learn more. Listeners can purchase individual episode credits or subscribe to the Critical Care Review Bundle and gain access to all episode CE Credits.    We are joined by:     Samuel Hall MD (Picture)     Jim Harrison (patient) and Kristen Harrison     Courtney Martin NREMT-P   Sarah Crabrtree RN   Kristen Wiley   Kentucky Reptile Zoo Links:    Official site Facebook Instagram   Youtube channel Click here to download this episode today! As always thanks for listening and fly safe! Hawnwan Moy MD FACEP FAEMS John Wilmas MD FACEP FAEMS Nyssa Hattaway, BA, BSN, RN, CEN, CPEN, CFRN

We're doing something really fun next year and you're invited!In this episode, I'm spilling the details on the two destination races the She Runs Ultras crew will be tackling together in 2026: the Hypnosis Night Runs in Arizona

Entrevista con Fernando Zaplana, director del torneo más importante de veteranos en España: MAMBA Murcia.

Agónicos triunfos de Real Madrid y Valencia en una Liga Endesa que se rompe por abajo y problemas para Chris Paul y Giannis Antetokounmpo en la NBA. José Manuel Puertas repasa toda la actualidad del baloncesto, con los triunfos sobre la bocina del Real Madrid y el Valencia Basket para seguir dominando la Liga Endesa como puntos destacados en una gran semana para los equipos españoles en la Euroliga, que analizarán Lucas Sáez Bravo y Sergio Vegas. En el tiempo de NBA, la sorprendente salida de Chris Paul de Los Angeles Clippers será objeto de debate por parte de John Vázquez y Anastasio Ríos, así como la posible salida de Giannis Antetokounmpo de Milwaukee, tras un inicio de temporada en el que los Cleveland Cavaliers están decepcionando y LeBron James aceptando un papel de escudero en Los Angeles Lakers. Estará en el programa Fernando Zaplana, director del torneo MAMBA, la principal competición para veteranos en España, que recientemente reunión a 74 equipos de 16 países y más de 1.000 personas en Murcia. Y además, Andrea Blez repasará una intensa semana en el baloncesto femenino, con exhibición de Iyana Martín en la remontada del Perfumerías Avenida al Valencia Basket y la novedosa competición Project B apareciendo en el horizonte y generando incertidumbre sobre el futuro calendario europeo.

Kevin Shea is a Director/Senior Equity Strategist at BNY Mellon, where he focuses on individual security analysis, market structure, and emerging technologies—most notably artificial intelligence. Raised in a blue-collar household in Boston, Kevin learned the value of investing early and carried that discipline through Penn State University and into his professional life. He earned the CFA charter while beginning his career at Merrill Lynch Investment Management, eventually joining BNY Mellon's Equity Advisory Group, a team dedicated to helping wealth clients navigate complex equity markets and fast-moving innovation cycles. In this episode, Kevin joins Steve Curley and co-host Dan Fasciano to break down the state of AI, technology leadership, and the increasing concentration within U.S. equity markets. He explains why today's mega-cap technology firms continue to dominate—highlighting advantages in data scale, free-cash-flow margins, and unparalleled AI investment. The discussion explores whether we are in an "AI bubble," how current valuations compare to the late-1990s dot-com era, and the unprecedented capital-expenditure supercycle underway as companies race to build data-center infrastructure. Kevin also offers a global lens—comparing U.S. and Chinese capabilities, semiconductor constraints, and the geopolitical factors shaping the AI race. The conversation then pivots to how AI is transforming the investment-research process itself. Kevin walks through the tools BNY Mellon and industry analysts increasingly rely on—from ChatGPT and internal models like "Eliza" to AlphaSense, Sentieo, and Claude—and how these systems enable teams to process far more information than ever before. He also discusses how AI-driven productivity may help address demographic and inflation challenges over the long run. The episode closes with a memorable perspective on work ethic, drawing parallels between success in investing and Kobe Bryant's "Mamba mentality," emphasizing that excellence is built on consistent, behind-the-scenes effort. Today's hosts are Steve Curley, CFA (Co-Managing Principal at 55 North Private Wealth) & co-host Dan Fasciano, CFA (Principal at GW&K Investment Management) Please enjoy the episode. You can follow us on Twitter & LinkedIn or at investorsfirstpodcast.com

Episode 87 - Murdock and Marvel: 2014 Part 2 2014 saw some sales figures that had not been achieved in decades, and pop culture was filled with comic book related movies and merchandise.  Marvel was dominant again, and along with DC again climbed back to nearly 70% of the total market share in comic shops.  This is part 2 of the podcast. that will feature the year in Daredevil, the Spotlight story and the Takeaway for 2013.   The Year in Daredevil  Appearances: Daredevil v3 #33-36, Daredevil #1.5, Daredevil v4 #1-9, Daredevil: Dark Knights #6-8, Daredevil: Road Warrior Infinite Comic #1-4 (or #0.1), Fantastic Four #2 and 5-6, Ultimate Comics Spider-Man #200, Origin Sin #2-3 and #3.1, She-Hulk #4 and #8-9, Black Widow #7, Hulk #4, Captain America #25  Writer: Mark Waid and Chris Samnee (#33), Waid (#34-36 and #1-9)  Pencils: Jason Copeland (#33), Javier Rodriguez (#34 and #6-7), Chris Samnee (#35-36, #1-5 and #8-9),  Inks: Jason Copeland (#33), Alvaro Lopez (#34 and #6-7), Chris Samnee (#35-36, #1-5 and #8-9)  The year picks up right where we left off in 2012 — Matt Murdock was in Kentucky looking for info on the Darkhold, a ancient mystical book tied to the Sons of the Serpent. He was looking to track down Jack Russell, a.k.a. Werewolf by Night, hoping for answers. But before finding him, Daredevil ends up in the middle of an angry mob attacking some so-called “monsters.” He tries to stop it, gets shot, and the “monsters” — led by Russell — end up saving him. Even using some sort of restoration spell on him.  Once he's patched up, Daredevil learns about missing Darkhold pages connected to a sorcerer named Lucien Sinclair. After a few magical trials, Matt takes Sinclair down, destroys his house, and handed Sinclair over to Russell — but keeps the pages for himself before heading back to New York.  Back home, Matt and Kirsten McDuffie go bold — they take to the rooftops and broadcast a live message calling out the Sons of the Serpent. The Jester, who kicked this story off with a fake TV broadcast, is strung up on the side of a building which suggests Dardevil may be walking into a trap. Despite that, Daredevil wipes the floor with them and saves Kirsten from being gunned down by a helicopter for good measure.  Meanwhile, Foggy's cancer takes a bad turn. Two Serpent members step in with an experimental drug that temporarily saves him — but they want Daredevil's help in exchange. One has a son on death row for a fire that killed twelve people, and they're hoping Matt can help clear his name. Matt investigates the crime scene with Elektra's help, fights Mamba and Constrictor, and then surprises everyone in court by admitting under oath that he's Daredevil.  That confession sets off total chaos. The Sons of the Serpent storm the courtroom, a mistrial is called, and Daredevil fights them off. He exposes their infiltration of the Justice Department, but the fallout costs him his law license in New York. Kirsten steps in and says, “Let's get out of here,” and the two of them pack up for San Francisco.  In April, we get a Daredevil 1.5 issue with 3 stories that are celebrating Daredevil's 50th Anniversary and looking at possible futures for Matt Murdock. In “The King in Red” Daredevil takes on Jubula Pride (the Owl's Daughter) after the rest of New York is blinded. He takes down Jubula and breaks the machine causing their sight to return. There's a story from Brian Michael Bendis with art by Alex Maleev the form of a letter to a child from Stana Morgan – a woman who married Matt Murdock/Daredevil and was likely killed by Bullseye. Finally, in “The Last Will and Testament of Mike Murdock” has Matt and Foggy watching a video tape of Mike Murdock from back in the day. Written and Penciled by Karl & Kurt Kesel, Inked by Tom Palmer.  In Daredevil Road Warrior – a 4-part limited series released digitally in February and March (and later reprinted as Daredevil #0.1) in which we get the trip from New York to San Francisco for Kirsten and Matt Murdock which was far from ordinary and included several stops and plenty of fighting.  In May, we got the start of Volume 4 of Daredevil – with the same creative team that ended volume 3. Matt Murdock and Kirsten McDuffie are in San Francisco and started their own law office and Daredevil is fitting right in by saving little girls from kidnappings and/or bombs.    Over the next few issues, Daredevil goes up against SF's new kingpin of crime – The Owl – with the help of another blind vigilante The Shroud. At times it seems like Shroud and Daredevil are working together, other times they are fighting it out. It's complicated, I guess. They take down the Owl but Max/Shroud is still looking for Julia Carpenter whom he's in love with. Meanwhile Foggy, in a wheelchair and fake beard, comes to Matt's and Kirsten's new law office.   In issue 5, we get a funeral for Foggy Nelson (though he's not actually dead) after he confessed to wanting to “go out like a superhero” and then jumped into a big green machine Leapfrog used to attack San Francisco and was equipped with a bomb.  His jump allows the machine to explode safely while allowing Foggy to die a hero.  In issues 6-7, we get a two-part story as part of the Original Sin crossover event.  Daredevil is back in New York and has a vision of his father hitting his mother while he was a baby. He seeks out his mother for answers and finds out she's been arrested and is being sent to Wakanda. He investigates and uncovering a potentially illegal plot involving a US General and Wakandan officials, he confronts Queen Shuri about it and secures her release. Finally able to ask about the vision, he learns she left due to postpartum depression. The story ends with a heartfelt reconciliation between Maggie and her son.  The year wraps up by setting up the next big arc — Kilgrave, the Purple Man, and his superpowered children — which spills into 2015. Which we'll need to wait until then to discuss.  Lastly, we'll talk about the Daredevil: Dark Knights limited series. This 8-book collection contains three stories – the first story “Angels Unaware” is write and penciled by Lee Weeks and is about Daredevil braving a snowstorm to get a heart from a crashed helicopter meant for a young girl. The second story, titled “A man named Buggit” written and penciled by David Lapham in which Daredevil follows a 10-inch man who has stolen evidence needed to save a client. The final story is titled “In the Name of the King” and shows a team-up of Daredevil and Misty Knight in the Caribbean written by Jimmy Palmiotti and penciled by Thony Silas. Honestly the less said about the last story the better.  This Week's Spotlight: No Spotlight This Week! Sorry. Daredevil Rapid Fire Questions The Takeaway From ICv2: Data substantiates the emerging gender parity within geek culture. Questions or comments We'd love to hear from you!  Email us at questions@comicsovertime.com or find us on Twitter @comicsoftime. ------------------ THANKS TO THE FOLLOWING CREATORS AND RESOURCES  Music: Our theme music is by the very talented Lesfm.  You can find more about them and their music at https://pixabay.com/users/lesfm-22579021/.  The Grand Comics Database: Dan uses custom queries against a downloadable copy of the GCD to construct his publisher, title and creator charts.  Comichron: Our source for comic book sales data.  Marvel Year By Year: A Visual History  DC Comics Year By Year: A Visual Chronicle  https://en.wikipedia.org/wiki/List_of_films_based_on_English-language_comics  https://en.wikipedia.org/wiki/List_of_Marvel_Comics_superhero_debuts  https://comicbookreadingorders.com/marvel/event-timeline/  https://www.comic-con.org/awards/eisner-awards/past-recipients/past-recipients-1990s/ 

BIG news episode.

Advertising has always been a little out of control, but making people watch ads in order to wipe their ass might be just a bit over the line. Let's talk about that, redundant things we all say like stupid idiots, sticking your arm under the lap bar of a rollercoaster to keep a stranger from flying out, pouring coffee all over yourself... again, and more on today's episode of Can You Don't?!*** Wanna become part of The Gaggle and access all the extra content on the end of each episode PLUS tons more?! Our Patreon page is LIVE! This is the biggest way you can support the show. It would mean the world to us: http://www.patreon.com/canyoudontpodcast ***New Episodes every Wednesday at 12pm PSTWatch on Youtube: https://youtu.be/l5E1t02zAvgSend in segment content: heyguys@canyoudontpodcast.comMerch: http://canyoudontpodcast.comMerch Inquires: store@canyoudontpodcast.comFB: http://facebook.com/canyoudontpodcastIG: http://instagram.com/canyoudontpodcastYouTube Channel: https://bit.ly/3wyt5rtOfficial Website: http://canyoudontpodcast.comCustom Music Beds by Zach CohenFan Mail:Can You Don't?PO Box 1062Coeur d'Alene, ID 83816Hugs and Tugs.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Happy Halloween! Lazlo thinks Summer dressed up as an old jewish lady (to be clear, she's a minion). Lazlo gives an update on his son's recovery, and Luigi Mangione is a Swiftie and a Charlie XCX fan. What song would you permanently delete forever? Car troubles are so annoying. In Headlines, the guys discuss how Grok is turning sexual, a seatbelt that didn't work on The Mamba, a Michigan terrorist attack that was stopped, the oil in the engine scam, Prince Andrew being stripped of his titles, the emergency JetBlue landing, and much much more! Stream The Church of Lazlo podcast on Apple Podcasts, Spotify, or wherever you get your podcasts!

HOUR 2: Worlds of Fun responds after allegations of malfunctions on the Mamba. full 2224 Fri, 31 Oct 2025 20:00:00 +0000 w3mys3LflhbqIrd4P3p6zJvvhSVsbNrn news The Dana & Parks Podcast news HOUR 2: Worlds of Fun responds after allegations of malfunctions on the Mamba. You wanted it... Now here it is! Listen to each hour of the Dana & Parks Show whenever and wherever you want! © 2025 Audacy, Inc. News False https://player

HOUR 4: Trouble on the Mamba. full 2007 Thu, 30 Oct 2025 22:00:00 +0000 7Ze5h88xpTIaBLNcK9BYd6xVQ2Oox1qm news The Dana & Parks Podcast news HOUR 4: Trouble on the Mamba. You wanted it... Now here it is! Listen to each hour of the Dana & Parks Show whenever and wherever you want! © 2025 Audacy, Inc. News False https://player.amperwavepodcasting.com?feed-link=https%3A%2F%2F

Our last interview with Harold Reynolds brought out quite a few great Bo Jackson stories—and Tim has an update on what Bo himself thought about them. After that, of course, we dive straight into the World Series. We hope you're enjoying the action as much as we are—it's the perfect mix of dominant pitching and incredible hitting.Naturally, Tim got a little bored on the cross-country flight and started coming up with a few anagrams that had us scratching our heads. But hey, that's one way to pass the time at 35,000 feet!On This Date in Baseball History, we've got more great October moments for you to stash away and use to impress your friends and family later. Plus, the Quirkjians—though a bit slim this week—dig a little deeper into the mysterious eBay glove saga that we know everyone's still curious about. Visit GreatGameOrWhat.com to contact the show with your questions, quips and insights. Joy Pop Productions LLC Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Download Porter Here: https://app.adjust.com/1t90wasvGuest Suggestion Form: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://forms.gle/bnaeY3FpoFU9ZjA47⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Disclaimer: This video is intended solely for educational purposes and opinions shared by the guest are his personal views. We do not intent to defame or harm any person/ brand/ product/ country/ profession mentioned in the video. Our goal is to provide information to help audience make informed choices. The media used in this video are solely for informational purposes and belongs to their respective owners.Order 'Build, Don't Talk' (in English) here: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://amzn.eu/d/eCfijRu⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Order 'Build Don't Talk' (in Hindi) here: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://amzn.eu/d/4wZISO0⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow Our Whatsapp Channel: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.whatsapp.com/channel/0029VaokF5x0bIdi3Qn9ef2J⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Subscribe To Our Other YouTube Channels:-⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.youtube.com/@rajshamaniclips⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.youtube.com/@RajShamani.Shorts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

Welcome to Mysteries in the Machine! Ethan and Charlie once again encounter a, well, not so great episode. Thanks for getting through this with us lol.Send us an email at mysteriesinthemachinepod@gmail.com with your thoughts or any questions you have! We would love to hear from you. Make sure to subscribe so you know when our next episode drops and rate and review if you like what we are doing.Support us on Patreon! ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.patreon.com/MysteriesintheMachine⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠IG:⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.instagram.com/mysteriesinthemachinepod/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Tumblr: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.tumblr.com/mysteriesinthemachinepod⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow Ethan: ⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.instagram.com/ethan.t.hulen/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ and ⁠⁠⁠⁠⁠⁠https://bsky.app/profile/ethulen.does.chat⁠⁠⁠⁠⁠⁠⁠Follow Charlie: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.instagram.com/greenpixie12/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ and ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.instagram.com/greenpixiedraws/⁠⁠⁠⁠⁠⁠⁠⁠

Kobra, Mamba, Taipan – jede Schlange hat ihr eigenes Gift. Doch Immunologe Jacob Glanville ist überzeugt: Irgendwo verbirgt sich ein Angriffspunkt für ein universelles Gegengift. Das Blut des "Schlangemannes" Tim Friede könnte der Schlüssel sein. Walch-Nasseri, Friederike www.deutschlandfunk.de, Wissenschaft im Brennpunkt

Mamba Jalapeño Verde Fresco Hot Sauce Review Have Fun!!! –  Dry September!    Hot Sauce Review – My Big Fat Bloody Mary INTRO:  Welcome back to the award winning, Nationally syndicated My Big Fat Bloody Mary podcast where you will never drink alone. I missed you last week!  Hope your Sunday …

Sva writes in, asking whether stories of snakes being tamed by human milk have any grounding in science. James Tytko took on the question with the help of herpetologist Fortunate Mafeta Phaka, and Angela Julian of the Amphibian and Reptiles Group of the UK. Like this podcast? Please help us by supporting the Naked Scientists

The Gridiron Japan crew sits down with Tokyo Gas Creators' star defensive lineman, St. Louis native Matt McClellan (https://www.instagram.com/sackmamba/ | https://x.com/sackmamba & https://www.youtube.com/@GlobalSackboy), to talk about his journey to Japan where he is leading his team on a quest for championship glory.Gridiron Japan livestreams over at Gridiron Japan Television on YouTube at ⁠⁠www.gridironjapantv.net⁠⁠, on Facebook at ⁠⁠www.facebook.com/GridironJapan.jp⁠⁠, As well as on X, at ⁠www.x.com/GridironJapan⁠.

Fill out this short listener survey to help us improve the show: https://forms.gle/bbcRiPTRwKoG2tJx8 Tri Dao, Chief Scientist at Together AI and Princeton professor who created Flash Attention and Mamba, discusses how inference optimization has driven costs down 100x since ChatGPT's launch through memory optimization, sparsity advances, and hardware-software co-design. He predicts the AI hardware landscape will shift from Nvidia's current 90% dominance to a more diversified ecosystem within 2-3 years, as specialized chips emerge for distinct workload categories: low-latency agentic systems, high-throughput batch processing, and interactive chatbots. Dao shares his surprise at AI models becoming genuinely useful for expert-level work, making him 1.5x more productive at GPU kernel optimization through tools like Claude Code and O1. The conversation explores whether current transformer architectures can reach expert-level AI performance or if approaches like mixture of experts and state space models are necessary to achieve AGI at reasonable costs. Looking ahead, Dao sees another 10x cost reduction coming from continued hardware specialization, improved kernels, and architectural advances like ultra-sparse models, while emphasizing that the biggest challenge remains generating expert-level training data for domains lacking extensive internet coverage. (0:00) Intro(1:58) Nvidia's Dominance and Competitors(4:01) Challenges in Chip Design(6:26) Innovations in AI Hardware(9:21) The Role of AI in Chip Optimization(11:38) Future of AI and Hardware Abstractions(16:46) Inference Optimization Techniques(33:10) Specialization in AI Inference(35:18) Deep Work Preferences and Low Latency Workloads(38:19) Fleet Level Optimization and Batch Inference(39:34) Evolving AI Workloads and Open Source Tooling(41:15) Future of AI: Agentic Workloads and Real-Time Video Generation(44:35) Architectural Innovations and AI Expert Level(50:10) Robotics and Multi-Resolution Processing(52:26) Balancing Academia and Industry in AI Research(57:37) Quickfire With your co-hosts: @jacobeffron - Partner at Redpoint, Former PM Flatiron Health @patrickachase - Partner at Redpoint, Former ML Engineer LinkedIn @ericabrescia - Former COO Github, Founder Bitnami (acq'd by VMWare) @jordan_segall - Partner at Redpoint

In this episode of the En Factor, we are thrilled to be joined by Jake Colognesi, who is the founder of Mamba Growth Equity. Jake has spent numerous years as an investor and analyst in the technology space, working for firms like Fidelity Ventures, Volition Capital and Sageview Capital before he started his own growth equity firm, Mamba, just one year ago back in 2024. Jake has retained his focus on the technology world since Mamba's founding, investing in B2B software companies in their earlier stages and providing these entrepreneurs with focused and valuable strategic and financial advice and expertise. Jake's leadership experience goes beyond being a founder, as he also has spent over a decade as a board member for numerous different companies around the United States and Canada.  You won't want to miss out on this episode filled with meaningful and thoughtful insights and advice as Jake and Dr. Rebecca White dive into conversations such as Jake's professional journey and the start of his entrepreneurial journey, funding advice for early stage entrepreneurs, and best practices for building and operating successfully as a board.  Key Words - Funding, Investor

Use code RUFFTALKVR at checkout to save on any game or hardware on the Meta Quest store and help support the show!On this episode of the Ruff Talk VR podcast we are kicking off the week strong with another packed edition of VR news! Including Forefront doing an open alpha beta, Walkabout Mini Golf's Passport Tokyo DLC, "Tracked" announced, a new Ghosts of Tabor update, a bunch of games on display at the VR Games Showcase including Exoshock, ZIX, Flat2VR Studios, and many others. We also talk a new VR game studio, some Exorcist Sin news, a TMNT VR dev diary, and much more! Big thank you to all of our Patreon supporters! Become a supporter of the show today at https://www.patreon.com/rufftalkvrDiscord: https://discord.gg/9JTdCccucSPatreon: https://www.patreon.com/rufftalkvrIf you enjoy the podcast be sure to rate us 5 stars and subscribe! Join our official subreddit at https://www.reddit.com/r/RuffTalkVR/0:00 - Episode Start0:05 - Forefront14:30 - Walkabout Mini Golf Passport Tokyo24:20 - Ghosts of Tabor "Mamba"30:20 - Tracked announced37:45 - TMNT VR parkour preview41:00 - Exorcist: Sin on hold "indefinitely"43:45 - Exoshock new trailer45:30 - Noetic Studios announced48:10 - Glassbreakers announces return49:40 - My Monsters52:30 - Dimensional Double Shift New Joysey DLC58:20 - ZIX launch date1:00:25 - Clawball full launch1:02:00 - Ready or Not VR mod1:04:50 - Whispers of the Void1:07:50 - Cybrid1:09:30 - Slime Lab1:10:15 - Trombone Champ Unflattened Undertale and Deltarune DLC1:13:15 - Street Gods1:15:15 - Poppy Playtime1:18:10 - Among Us 3D Critical Cargo game mode1:19:45 - I Am Your Beast VR1:22:45 - Of Lies and Rain1:24:45 - Upcoming VR gamesSend us a text to the Ruff Talk VR fan mail line!Support the show

Directo del ring, nos acompañan dos íconos de la lucha libre mexicana... ¡PIMPINELA ESCARLATA Y MAMBA!

Join us on the Format Podcast as we dive into Paul Pierce's controversial claim of being the NBA's best pure scorer, surpassing legends like Kevin Durant and Michael Jordan. We unpack his bold statement, compare his scoring prowess, and debate his place among the greats. Plus, we address the recent disrespect toward the late Kobe Bryant, exploring his unmatched legacy and Mamba mentality. Don't miss this heated discussion! Subscribe, like, and comment to join the conversation. #NBAPodcast #PaulPierce #KobeBryant #BasketballDebate #FormatPodcastSit back, relax, and listen up!@OpinionStated @Mandownsports @sportsnfitnessrants @SportzTea @AngryOldHoops @DREAMERSPRO @UnCommonCents-865 If you want to support, every little bit helps!We appreciate SuperChats, or you can donate:CashApp: $TheFormatPodcastVenmo: TheFormatPodcast

Ropp the Casbah; Blazers & Ayton agree to buy out; Mamba mentality might be dead; Jerami Grant; Blazers remembered for non-basketball reasons; Stock Watch; In The News; Twitter algorithms; Steelers-Dolphins trade; NBA free agency; Impressive athletic things; The Club Hour

-Happy Spencer Strider day to all Braves fans who celebrate.-Plus the Falcons might have found their very own Mamba mentality.-And Aaron Rodgers, Kirk Cousins and the Saints have nothing in common.See omnystudio.com/listener for privacy information.

Want to Start or Grow a Successful Business? Schedule a FREE 13-Point Assessment with Clay Clark Today At: www.ThrivetimeShow.com   Join Clay Clark's Thrivetime Show Business Workshop!!! Learn Branding, Marketing, SEO, Sales, Workflow Design, Accounting & More. **Request Tickets & See Testimonials At: www.ThrivetimeShow.com  **Request Tickets Via Text At (918) 851-0102   See the Thousands of Success Stories and Millionaires That Clay Clark Has Helped to Produce HERE: https://www.thrivetimeshow.com/testimonials/ Download A Millionaire's Guide to Become Sustainably Rich: A Step-by-Step Guide to Become a Successful Money-Generating and Time-Freedom Creating Business HERE: www.ThrivetimeShow.com/Millionaire   See Thousands of Case Studies Today HERE: www.thrivetimeshow.com/does-it-work/  

White Mamba | Take the Scallenge And Experience the Ultimate Brian Scalabrine Highlight Video   To learn more about the man, the myth and the legend that is Brian Scalabrine click the links below:  https://en.wikipedia.org/wiki/Brian_Scalabrine  https://x.com/scalabrine?lang=en    To discover more original music by Brett Raio and Clay Clark click the links below: https://www.youtube.com/@BrettRaio/videos  https://www.thrivetimeshow.com/lyrical-miracles/  https://www.youtube.com/@thrivetimeshowbusinessscho5008/videos

Chad Hyams and Bob Stewart explore Kobe Bryant's "10 Rules" for success, highlighting lessons applicable to life, business, and personal growth. They discuss concepts like getting better every day, proving skeptics wrong, learning from wins and losses, practicing mindfulness, and fostering ambition. The episode reflects on Kobe's Mamba mentality, his influence on sports and beyond, and how these principles can translate into everyday achievements. Whether seeking inspiration in personal endeavors or professional environments, this episode offers valuable insights from Kobe's extraordinary life. ---------- Connect with the hosts:     •    Ben Kinney: https://www.BenKinney.com/     •    Bob Stewart: https://www.linkedin.com/in/activebob     •    Chad Hyams: https://ChadHyams.com/     •    Book one of our co-hosts for your next event: https://WinMakeGive.com/speakers/ More ways to connect:      •    Join our Facebook group at www.facebook.com/groups/winmakegive     •     Sign up for our weekly newsletter: https://WinMakeGive.com/sign-up     •     Explore the Win Make Give Podcast Network: https://WinMakeGive.com/ Part of the Win Make Give Podcast Network

Use code LOGAN10 for 10% off your SeatGeek order https://seatgeek.onelink.me/RrnK/LOGAN10 *Up to $25 off   Our ex-roommate Dwarf Mamba RETURNS to discuss life after Logan Paul, quitting social media for a 9-5 job, Hawk Tuah’s crypto scandal, bone-chilling truth about NJ drones, Logan’s family disaster at Thanksgiving, Trump ending daylight savings, backlash following Luigi Mangione’s m*rder, how to talk to aliens & more…   SUBSCRIBE TO THE PODCAST ► https://www.youtube.com/impaulsive   Watch Previous (AMP’s Biggest Member FANUM on iShowSpeed VS Kai Cenat, Taxing John Cena, Cops Stealing His Lambo) ► https://www.youtube.com/watch?v=oZziB35XSnw&t=145s   ADD US ON: INSTAGRAM: https://www.instagram.com/impaulsiveshow/   Timestamps: 0:00 Welcome Dwarf Mamba!