Podcasts about Migs

Damon, Damo, and Aaron are joined by LCDR Jeff “Migs” Migdal. Jeff is a Cryptologic Warfare Officer, prior-enlisted Sailor, and longtime commissioning mentor who has helped countless Sailors navigate the path from enlisted service to the officer ranks. The conversation begins with Jeff sharing his journey from joining the Navy as an undesignated Airman to eventually earning a commission after multiple application attempts. He discusses the lessons he learned along the way, including how setbacks, preparation, and persistence shaped his career. The group explores common misconceptions about officer programs and why being eligible does not necessarily make someone competitive. Jeff provides insight into the commissioning process, discussing OCS, LDO, STA-21, motivational statements, appraisal interviews, OAR scores, and the importance of understanding program authorizations before submitting a package. The hosts examine the role mentorship plays in professional development and discuss the difference between mentors who guide and individuals who expect others to do the work for them. The discussion also turns to social media, information sharing, and how technology has changed the way Sailors seek career advice. Jeff explains why many applicants fail before their package ever reaches a selection board, while Damo reflects on lessons learned from his own experiences pursuing advancement and career opportunities. Later in the episode, the conversation broadens into leadership, standards, and accountability. The hosts discuss the importance of correcting Sailors, maintaining good order and discipline, and why seemingly small standards can have a larger impact on organizational culture. Damo introduces his “Get the Fries Right” analogy as the group explores the relationship between attention to detail and effective leadership. The episode closes with reflections on service, mentorship, and the responsibility leaders have to help others succeed. Jeff shares why he has dedicated so much time to helping future officers, while the hosts discuss the importance of preparation, ownership, and personal accountability. Jeff also shares the advice he would give any Sailor considering a commission and explains why, in the end, nobody is going to do it for you. These topics and more are covered in this episode.         Do you have a “Do Better” that you want us to review on a future episode? Reach out at ptsfpodcast@gmail.com       Stay connected with the PTSF Podcast: https://linktr.ee/Ptsfpodcast       PTSF Theme Music: Produced by Lim0

Once again it's just 2 of the lads on this week's episode and with Migs and Sen you know it's going to be World Cup themed. The boys talk World Cup songs, World Cup ads, KSI leaving the sidemen, a gnarly story out of the UK and Sen started a fight.The boys play some World Cup quizzes to test their ball knowledge.___________________________________________________________FULL PODCAST EPISODES

It's time for some Star Wars Questions! What is going on with Din Djarin's helmet? Does he have to kill anybody who sees him without it on? Or just the ones that shamed him? Did he and Migs lie about it? Is Din just talking tough to the Hutt Twins? The Mandalorian and his helmet are in focus today as Joseph Scrimshaw and Ken Napzok answer these questions and more on the 783rd episode of ForceCenterFrom the minds of Ken Napzok (comedian, host of The Blathering), Joseph Scrimshaw (comedian, writer, director of Dead Media), and Jennifer Landa (actress, YouTuber, crafter, contributor on StarWars.com) comes the ForceCenter Podcast Feed. Here you will find a series of shows exploring, discussing, and celebrating everything about Star Wars. Subscribe on Apple Podcasts and Google Podcasts. Listen on TuneIn, Amazon Music, Spotify, and more!Follow ForceCenter!Watch on YouTube!Support us on PatreonForceCenter merch!All from ForceCenter: https://linktr.ee/ForceCenter Hosted on Acast. See acast.com/privacy for more information.

Sen kicks us off this week with a new Maxibon rare unit - there seems to be dozens of people like this. Ro has restaurant stories out the wazoo, Migs has an update on his hike and finally Sen talks Tim Payne, Vivid drones and Harambe (RIP).Top 5's and 10's are back this week and we have some doozies, including ones that are completely vibes based.___________________________________________________________FULL PODCAST EPISODES

Rob “Z-Man” Zettel shares his story from inside one of the Air Force's most secret Cold War programs, Project Constant Peg.In this episode, Host Rick Crandall talks with Z-Man, a retired U.S. Air Force fighter pilot and former member of the legendary 4477th Test and Evaluation Squadron, the “Red Eagles,” about what it was like to fly real Soviet MiG fighters in the Nevada desert. From the F-4 Phantom and F-5 aggressors to the MiG-21 and MiG-23 at Tonopah Test Range, Zettel offers a firsthand look inside the classified program designed to train American pilots against the real thing. This one is going to be cool!

Happy Memorial Day Weekend 2026!! Never forget why we have the freedom to party! Let's remember those that have fallen in the name of freedom! We salute you!! Thank you for your service now lets club out with , Gianluca Zanna, Adam Darling, Bradeazy, Chance the Closer, Migs 718 and 20 plus more DJ's for the party event of the season! Get your summer started off right with the Halshack! Find the shacklist on Halshack.com See you soon for Destination Unknown 5 as we travel the highways of life and navigate the crossroads of our future! Halshack.com 

Bad week for Ro as he was left reeling on multiple occasions with no choice but to take it like a champ. Luckily for him, he's aware of someone else he knows who's also been left reeling for other reasons. Migs brings up Andros Townsend and Sen breaks down the budget.We're back with "Overrated/Underrated," as we once again let rip with some awful hot takes or otherwise.___________________________________________________________FULL PODCAST EPISODES

Jim “JB” Bell shares his story from inside one of the Air Force's most secret Cold War programs, Project Constant Peg.In this episode, Host Rick Crandall talks with Jim, a retired crew chief of the legendary 4477th Test and Evaluation Squadron, about what it took to keep MiG fighters flying in the Nevada desert. From maintaining MiG-17s, MiG-21s, and MiG-23s at Tonopah Test Range to flying on unmarked C-5s into China and bringing home F-7 fighters, Bell offers a rare perspective on one of the most classified adversary air programs in U.S. Air Force history. This one is going to be cool!

Slide Into Our VMs to hear robot Morgan Freeman read it.

Sen's back this week and comes full of yarns from his trip to Sri Lanka, his night out last Friday and he went to go scope out the competition, watching a live podcast. Migs then brings a follow up from last week about AI companions."Agree or Disagree" is back with the boys sharing some hot takes with each other.___________________________________________________________FULL PODCAST EPISODES

Another week, another duos episode with Sen coming and Sen going as we're back with Ro & Migs. This week, we're back to regular programming with the episode released on time on Wednesday - we give updates on why the past 2 were delay (Ro giving a half apology, half blame someone else). Migs chats about his absence last week and the etiquette behind parking in Mudgee & throwing yourself against glass walls. Ro pitches 2 outrageous ideas he stole from a tech party he attended recently, and an even more outrageous business model that creates AI smut.This week's game is "Heads Up" - no affiliation to Ellen or any alleged affiliations with Mrs. DeGeneres. Instead of an app or headbands, the boys place sticky notes on their heads whilst asking yes or no questions to guess who is on their head - as always, carnage ensues.___________________________________________________________FULL PODCAST EPISODES

Un festival et un salon comme le Montreux International Guitar Show alias le MIGS (migs.ch), ça tourne grace aux volontaires. Ils sont la cheville ouvrière pour faire tourner ce genre d'événement. Voilà ci-dessous une interview de Robert Ratini alias Roby qui est un des volontaires, responsable de l'équipe des volontaires. Bravo et Merci à Tous L'article MIGS : Bravo et Merci aux Volontaires ! est apparu en premier sur La Chaîne Guitare.

Check out Hey Bagel, award winning bagel shop right her in Seattle! @heybagel_wa

Lors de la dernière journée de l'édition 2026 du Montreux International Guitar Show alias le MIGS (migs.ch), j'ai alpagué les deux organisateurs de l'événement pour une interview express à chaud. Voilà Emmanuel Cottier et Alain Coppet au milieu du salon des luthiers. Interview Emmanuel Cottier et Alain Coppet Podcast de La Chaîne Guitare La version L'article MIGS : Interview des Organisateurs Emmanuel Cottier et Alain Coppet est apparu en premier sur La Chaîne Guitare.

Another week with Sen off mysteriously leaving Migs and Ro to hold down the fort. The boys talk about their time at the Sydney Comedy Festival (featuring Vietnamese/Cambodian Sen). Migs then talks about his upcoming South America trip at the end of the year including training for a trek, Ro continues to complain about the Sydney housing market, we have a few listener submissions and we end with the top work trends for 2026.Our segment this week is "Finding the Best," where we put head to head the greatest [insert name here]'s of all time i.e. who is the greatest Michael of all time?___________________________________________________________ FULL PODCAST EPISODES

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

Get the full episode here: https://www.10percenttrue.com/pricing-plans/list10PCT EP86 P2 Benji PrefontaineIn Episode 2, Benji Prefontaine moves from early operations into real combat experience—flying the Dassault Mirage F1 in Africa before transitioning to carrier aviation in the Dassault-Breguet Super Étendard.He describes the shock of adapting from Air Force flying to life on the carrier—where precision, discipline, and consistency are everything. Landing on the boat becomes a defining challenge, exposing the difference between being a good pilot and being an operational one.The episode also explores the limitations of the Super Étendard—an aging, analog jet forced into modern combat—and how that shapes tactics, workload, and risk. Benji highlights the realities of coalition warfare, early Afghanistan deployments, and the steep learning curve of operating in a far more complex and demanding environment.This is where the story shifts from “becoming a pilot” to learning how to survive and operate effectively in combat.0:00 Teaser – Pyjamas, Wine, Corkscrews and Cigarettes 1:45 Welcome Back Benji (Steve's Lost It!) + Episode Outline 3:20 First Operational Squadron – Tough Start to Mixed Force Ops 9:20 Post-9/11 Politics and Operational Reality 12:38 Red Flag – Flying with Mirage 2000D 15:36 Did Red Flag Validate the EW Suite? 18:41 How Red Flag Prepared Him for Combat 22:26 Inferiority Complex in Coalition Ops? 28:02 Social Life on Squadron 31:24 Old School vs New School – What Works? 35:38 Deployments to Chad 40:42 Threat Environment and Risk 44:48 Ferry Flight to Red Flag – Single-Engine Stress 47:43 Bird Strikes and Wildlife Hazards 52:09 Survival Kit – What's On Board? 55:10 CSAR – Expectations vs Reality 57:46 What Is a Pilot Worth? 59:05 Combat Psychology 1:04:18 Managing Pilots Doing “Cool Stuff” (Photos/Video) 1:09:54 Romania Deployment – Encounters with MiGs 1:18:09 QRA – Intercepts and Real-World Stories 1:24:45 French Air Force “Urban Legends” 1:27:27 Encounters with USAF Incursions? 1:30:08 End of First Tour – Seeking Exchange Opportunities 1:32:50 Carrier Tour Expectations – Charles de Gaulle 1:35:15 No Night Landings? 1:36:30 Targeting Pod (PDLCT) 1:37:52 FCLP – Carrier Landing Practice 1:42:12 The Hardest Part of Carrier Ops 1:45:15 Nuclear Strike Mission Explained 1:51:53 Super Étendard Capabilities (Including Exocet) 2:01:17 From Detection to Attack 2:05:00 Situation Display, Autopilot, Datalink 2:07:55 Tuning Exocet Targeting and Performance 2:09:12 How Do You Attack a Carrier Group? 2:13:00 Part 3 Preview – Combat and Command

Go 12s! Do you have what it takes to be like Norb?

Ro's biggest fear came true as he encountered another flat tyre, leaving Migs and Sen to their own devices. They lift the hood a little on the boys trip to the Easter Show, we discuss Trump's insane ramblings, the kit kat heist, an exuberant salesman, wild takes on IG Live and Sen wears a mental t-shirt.We have another game this week "Winner Stays On," where as the name suggests, the boys give each other a number of alternate options in a category and they must pick one until there is only one winner remaining.___________________________________________________________ FULL PODCAST EPISODES

Who's actually the Believer in this episode? Mando? Migs? Both? We forgot to talk about that, but we talk a lot more about the next-to-last episode of this season of The Mandalorian! Come listen!

Just when you thought we possibly couldn't have any more wedding yarns, here we are this week with back to back wedding tales to kick us off. Ro begins with a story of a wedding in the Hunter (if you can wrap your head around that) before Migs recounts a different wedding he attended over the weekend. Migs also went shooting and Sen hits the boys with a stream of consciousness.Our game this week is pretty topical - "Then vs Now" where we compare the prices of everyday items and larger purchases with prices from 10-20 years ago.___________________________________________________________ FULL PODCAST EPISODES

C'est le moment de la traditionnelle interview avec Emmanuel Cottier pour parler de son super événement guitare le Montreux International Guitar Show alias le MIGS (migs.ch) qui se tiendra du 24 au 26 avril. Pour rappel / info, le MIGS c'est un festival avec un programme riche de concerts et de masterclass, un salon des L'article MIGS 2026 : Interview Emmanuel Cottier est apparu en premier sur La Chaîne Guitare.

IRON EAGLE (1986) - Couch Potato Theater: Fandom Podcast Network Classics Listen: Couch Potato Theater Audio Podcast Link: https://fpnet.podbean.com/category/couch-potato-theater Welcome to Couch Potato Theater: 'Fandom Podcast Network Classics', where we celebrate our favorite movies on the Fandom Podcast Network!  We're re-releasing IRON EAGLE (1986), Couch Potato Theater. Originally recorded in 2019. Your Couch Potato Theater co-hosts and Fandom Podcast Network founders Kevin & Kyle are joined by guests Amy Nelson & Hayley Stoddart, our co-hosts from our Union Federation Star Trek & The Orville podcast. Sit back and relax on the couch and enjoy this special re-release presentation of IRON EAGLE (1986) - Couch Potato Theater. Iron Eagle is a 1986 action film directed by Sidney J. Furie who co-wrote the screenplay with Kevin Alyn Elders, and starring Jason Gedrick and Louis Gossett Jr.  Plot: When Doug's father, an Air Force Pilot, is shot down by MiGs belonging to a radical Middle Eastern state, no one seems able to get him out. Doug finds Chappy, an Air Force Colonel who is intrigued by the idea of sending in two fighters piloted by himself and Doug to rescue Doug's father after bombing the MiG base. Their only problems: Borrowing two fighters, getting them from California to the Mediteranean without anyone noticing, and Doug's inability to hit anything unless he has music playing. Then come the minor problems of the state's air defenses. Fandom Podcast Network Contact Information - - Fandom Podcast Network YouTube Channel:  https://www.youtube.com/c/FandomPodcastNetwork - Master feed for all FPNet Audio Podcasts: http://fpnet.podbean.com/ - Couch Potato Theater Audio Podcast Master Feed: https://fpnet.podbean.com/category/couch-potato-theater - Facebook: https://www.facebook.com/Fandompodcastnetwork - Email: fandompodcastnetwork@gmail.com - Instagram: https://www.instagram.com/fandompodcastnetwork/ - X: @fanpodnetwork / https://twitter.com/fanpodnetwork -Bluesky: @fanpodnetwork / https://bsky.app/profile/fanpodnetwork.bsky.social Host & Guest Contact Info: - Kevin Reitzel on X, Instagram, Threads, Discord & Letterboxd: @spartan_phoenix / Bluesky: @spartanphoenix - Kyle Wagner on X: @AKyleW / Instagram & Threads: @Akylefandom / @akyleW on Discord / @Ksport16: Letterboxd / Bluesky: @akylew Guests: - Amy Nelson on X: @MissAmyNelson / Instagram: @amynelson522 /  Blue Sky: @CounselorAmy - Hayley Stoddart on Instagram & Bluesky: @trekkie01D #IronEagle #CouchPotatoTheater #FandomPodcastNetwork #FandomPodcastNetworkClassics #JasonGedrick #LouisGossettJr  #IronEagle1986 #IronEagleMovie #CPT #FPNet #FPN #SidneyJFurie #DavidSuchet  #ShawneeSmith  #MeloraHardin  #LarryBScott  #LanceLeGault  #TimThomerson  #CarolineLagerfelt  #RobertJayne  #JerryLevine  #RobbieRist  #MichaelBowen  #KevinAlynElders #1986Movies #KevinReitzel #KyleWagner #AmyNelson #HayleyStoddart    

 We had a St Pats Weekend Blast party on all stations with over 15k people! Migs718 and Chance the Closer brought the house down! Both shows are available on the podcast feed! Party down with the Halshack! Find everything show related on Halshack.com****************BIG ANNOUNCEMENT!! *******************NO DIRECTION 2 Shackstop 117 airs March 16th week then Im taking a break!! Feeling the burn out in my life and feeling a bit like I have No Direction at the moment! Taking the next few weeks off. New episodes will resume April 6th week or April 13th. (Depending on life.... lol I may return the first or second week of April) I will keep uploading shows to the podcast feed for those that missed earlier shows or just want to recap the music with our popular retool series (Non Stop Pop) and (ReJam) Thanks for being great fans and helping out the show with love and support! Keep spreading the word! We can't grow unless you tell people!  

Time to spring into action! Time has jumped forward now time to get into action with a great theme show from yours truly the Halshack! Verbal Actions 2 is all about titles with verbs that create action whether its verbally speaking or verb type songs that move you. Lets GOOOOOOOOOOOOO!! Spring is on the way for your Shackstop 116 (Verbal Actions 2) March 9th week 2026! Happy Birthday to my mother! Her birthday was Mar 9th shared with one of our staff writers at Crews Views, Andrea Baskin! Happy birthday to both of you lovely souls! More Verbal Actions shows the next couple weeks then we will focus on our No Direction series! Club show this weekend Fri Mar 13th with Migs in the Shack and Chance the Closer 2 hour party for ST PATS WEEKEND BLAST! 4pm 9pm 12am 3am ET on Halshack Radio! See the website promo flyer for more times and stations! I will release Chance and Migs show separately to the podcast feed soon! BIG PARTY on MXTR FM for St Pats Friday March 13th (9pm-4am) ET! Find all details and links to radio station plus Crews Views weekly blog and more at Halshack.com 

Ro is back, fresh off his honeymoon, and we start this week by hearing his version of events at his wedding, including a behind the scenes look at all the highs and lows. We then talk about his honeymoon and his new found life of being a house husband. Sen then brings up a viral substack which outlines a very dystopian future regarding AI. We have 2 mini games this week with the first one being a game where Ro and Sen have to guess which are real KD burner tweets and which are AI. The second game is where Migs and Ro have to guess how many times different people show up in the Epstein files.___________________________________________________________ FULL PODCAST EPISODES

This special Italian-language episode of the Interventional Glaucoma podcast features Prof Fea and Dr Oddone as they explore the integration of minimally invasive glaucoma surgery (MIGS) into routine cataract procedures. They discuss how laser-based trabecular techniques can be effectively combined with phacoemulsification to enhance clinical outcomes for patients with glaucoma.   The ELIOS system (Bausch & Lomb) is manufactured by MLase GmbH, located at 82110 Germering, Industriestr. 17, Germany and by WEINERT Fiber Optics GmbH, Mittlere-Motsch-Strasse 26, 96515 Sonneberg, Germany.  ELIOS is CE marked for use in adult patients with glaucoma and is currently under investigational use in the US as part of an ongoing IDE study (FDA). The ExTra II (laser class 4) has the brand name ELIOS. The ExTra II is equivalent to ExTra and AIDA devices.  Find out more about ELIOS : http://bit.ly/4lWBJZ1

Lt Col Rob “Z-Man” Zettel is the author of American MiG Pilot - Inside the Top Secret USAF “Red Eagles. He tells the Red Eagles story for the first time through the experiences of a pilot who flew Soviet MiGs to their maximum performance in simulated combat engagements, often several times a day, against some of the very best fighter pilots hand-picked from the ranks of the USAF, US Navy and US Marine Corps. With controls labelled in Russian and the only spare parts being the ones they could salvage, the pilots who climbed into the MiGs - the Red Eagles - accepted all of the risks associated with operating these aircraft. Rob's vivid accounts of training engagements put the reader right in the cockpit as he describes what it was like to be there day in and day out at one of the most access-restricted airfields in the entire USAF, flying MiGs. In part two of our story, we join him for his first interview for the Red Eagles. Buy the book here and support the podcast⁠ Episode extras here ⁠https://coldwarconversations.com/episode445 ⁠ Go to ⁠https://surfshark.com/coldwardeal⁠ or use code COLDWARDEAL at checkout to get 4 extra months of Surfshark VPN! Help me preserve Cold War history via a simple monthly donation, You'll become part of our community, get ad-free episodes, and receive a sought-after CWC coaster as a thank-you, and you'll bask in the warm glow of knowing you are helping to preserve Cold War history. Just go to ⁠⁠⁠⁠⁠https://coldwarconversations.com/donate/⁠⁠⁠⁠⁠ If a monthly contribution is not your cup of tea, we also welcome one-off donations via the same link. Find the ideal gift for the Cold War enthusiast in your life! Just go to ⁠⁠⁠⁠⁠https://coldwarconversations.com/store/⁠⁠⁠⁠⁠ CONTINUE  THE COLD WAR CONVERSATION BlueSky ⁠⁠⁠⁠⁠https://bsky.app/profile/coldwarpod.bsky.social⁠⁠⁠⁠⁠ Threads ⁠⁠⁠⁠⁠https://www.threads.net/@coldwarconversations⁠⁠⁠⁠⁠ Twitter/X ⁠⁠⁠⁠⁠https://twitter.com/ColdWarPod⁠⁠⁠⁠⁠ Facebook ⁠⁠⁠⁠⁠https://www.facebook.com/groups/coldwarpod/⁠⁠⁠⁠⁠ Instagram ⁠⁠⁠⁠⁠https://www.instagram.com/coldwarconversations/⁠⁠⁠⁠⁠ Youtube ⁠⁠⁠⁠⁠https://youtube.com/@ColdWarConversations⁠⁠⁠ Learn more about your ad choices. Visit podcastchoices.com/adchoices

Sa pagkasiksik at paguumapaw ng reflections and wisdom ng mga guests ko, napahaba kami, buti na lang they're also very engaging and funny, kaya samahan niyo kaming paliparin ang oras sa isang kwentuhan tungkol sa mabuting paraan (at mga kakulangan) nating mga Katoliko pagdating sa ating pinakamahalagang misyon: ang pagbabahagi kay Kristo, kasama sina Fr. Migs Ramirez and Bro. Jonathan Yogawin

No Ro, no Kush which means once again it falls to Migs and Sen to keep this machine running. Sen outs himself for some rare behaviour, Migs and Sen then recount their Friday night at Leichhardt Oval before we spend way too much time talking about a bloke named Enrique.We put the call out for a Q&A and you guys delivered as we answered some serious and some not so serious questions across a wide range of topics.___________________________________________________________ FULL PODCAST EPISODES

It's Cirque Echo at Marymoor! And we got a great behind the scenes look! Tickets at: https://www.cirquedusoleil.com/usa/seattle/echo/buy-tickets

On this episode, we discuss geopolitical manuevers. — — — Become a valued and cherished Board Member today: https://www.patreon.com/timelineearth… Check out LineMart, our Official TLE Merchandise store: https://www.toplobsta.com/collections/timeline-earth — — — Recorded LIVE every Wednesday! (4/9/2025) Featuring, the "The Golden Throat", Car Campit: https://twitter.com/TLE_Car And the "Number One PTO User of the Year", Aaron: https://twitter.com/btwa_RETURNS And as always, the wise and Dionysian Birdarchist: https://twitter.com/TLEbirdarchist And of course, the team's erudite investigator Paz: https://twitter.com/TLEPaz Follow the show on Twitter: https://twitter.com/timelineearth — — — THE EARTH IS A LINE!

Kushy is back for the first time in 2026, but unfortunately Ro is off on his honeymoon (shout out Trip.com) so it's just the 3 musketeers today. Migs and Sen recount Ro's wedding last weekend, Kush has some tales from SF, including a quick mind your manners, we ran into some listeners during the week and we have a football update.We're back with "Things No One Says," this week as we rattle off niche sentences that have never been uttered.___________________________________________________________ FULL PODCAST EPISODES

We talk with Taryn and Steve from Daly Migs about their experience at the Super Bowl Trophy Celebration!

Steve and Taryn of Daly Migs join us to tell the rest of the story about their celebration experience!

This week on the Exciting & New podcast, Jason, Andy and Dana welcome Shamus back on the show to discuss the 1986 action flick Top Gun.  Tom Cruise leads this All-Star cast and their need for speed as they shoot down MIGs and spike volleyballs, all the while trying their best to show the world they're straight.  Once again, we have a woman cast just so we aren't confused by the sexuality of the cast, but no one is really fooled.  Ice Man, Goose, Maverick and Merlin all want to get into each other's cockpit.  DANGER ZONE!!  Enjoy the podcast!Jason, Andy and Dana will discuss a 1986 movie weekly, breaking down all the nonsense there within. The 3 hosts all work together and everyone else around them was getting really annoyed at all the movie talk, so they decided to annoy the world in podcast form.Check out previous seasons to hear them discuss 1982, 1983, 1984 & 1985 movies, as well as a full season of Love Boat episodes (if that is your thing). Plus one-off specials and a weekly mini "what are we watching" podcast.#jezoo74 #aegonzo1 #danacapoferri #exciting_new

Here we are, Ro's last few days of freedom before he ties the knot. He gives us some last minute updates for his wedding and then an epic battle he fought whilst trying to book his honeymoon. Ro also has some unethical life hacks for organising a wedding. Migs has a metro yarn, Sen talks about AI and has a sudden urge to buy an E-bike.We are back with "Compare and Contrast" as we tier list cuisines from around the world. We are sure people are chomping at the bit to hear 3 culinarily challenged men provide their opinions on food they couldn't cook.___________________________________________________________ FULL PODCAST EPISODES

In this 10-minute Master Class, we break down a practical, stepwise approach to glaucoma surgery, from knowing when it's time to intervene to managing expectations after MIGS.We'll cover how to think through surgical decision-making beyond drops, why SLT is often the right first step, and how to choose among MIGS options based on efficacy, safety, and preserving future options, not insurance-driven algorithms.You'll also get a clear framework for managing patients after minimally invasive glaucoma surgery, including:How to anticipate and manage early IOP spikesWhen and how to safely taper glaucoma medicationsSetting expectations around hyphema and visual recoveryWhy MIGS outcomes often aren't clear until 3 months post-opThe importance of post-operative gonioscopy for long-term successA concise, real-world Master Class designed to help you make confident surgical decisions and guide glaucoma patients through the post-op period with clarity.

This week we talk Ro's bucks, Polymarket, Migs saw a stereotype in China and Ro tries to tell a story about networking that descends into anus chat.We bring back the popular "Top 10's," with Migs putting Sen and Ro through their paces for the first time in 2026.___________________________________________________________ FULL PODCAST EPISODES

A Seahawks fan named Ben Conklin went viral for being featured during the Seahawks/Panthers game for being Sam Darnold's doppelganger! We have him on to chat about the new found fame!

It's our first BSE of 2026! So good to have Sam back -- and Leah is back too! Kicking the year off with a little Korean-French flavor that I'm sure you will enjoy. Let's go!Caller #1 is Migs 41yrs old from Geneva, Switzerland. Migs is struggling with her innate Asian school expectations from her 10yr old son vs the relaxed culture of Swiss schooling. Caller #2 is Yoshi 37yrs old from Pampanga. Yoshi is on the verge of setting up a new relationship but he doesn't liek the way the girl talks to him. She comes off as rude and disrespectful, but she on the other hand, says "this is who I am." GTWM and Good Times Radio are now streaming exclusively live on Discord!Join the Discord community by going to ⁠⁠www.discord.gg/goodtimesradio

We chat with Lisa Hughes! A girl from Auburn who just happens to grace the Deftones album cover for Around the Fur!

Episode 190 is what happens when you hand the mic to Captain CJ “Heater” Healy and then just try to keep up. Heater takes us from a childhood obsession with WWII airplanes to roller-coaster “G-training,” to flying—then teaching—at the highest levels of naval aviation. Along the way, we hit the $10 “Mexican Justice of the Peace” wedding that turned into a 53-year marriage, the fighter-pilot path that almost didn't happen, and the mind-bending world of MiGs at Area 51 — yes, the ones that “smelled like a hydraulic leak with an electrical fire.” — Heater also reveals how a single photograph helped spark Top Gun, plus what it was like being on set and shooting real missile events that almost ended VERY badly with a very non-digital camera… including mid-flight film surgery. This one's a top-five all-timer—no doubt. The Shot that Changed His Life Heater’s Paint Scheme

Links1. "For 250 years, it's been ‘change or lose' for our military. Here's what needs changing now," by Robert Neller and Peter Singer, Defense One, June 22, 2025.2. "Change or Lose: Past and Future War Lessons on 250th Birthday of the US Army and US Marine Corps," by Robert Neller and Peter Singer, Youtube, November 10, 2025.3. "Thinking First, Adapting Fast: Debating the Marine Corps' Need for the Information Group," by Brian Kerg, War on the Rocks, November 7, 2025.4. "Kill It or Fix It: Why Marine Corps Information Warfare Has Failed After a Decade of MIGs," by Dan Burns, Information Professionals Association, August 20, 2025.5. "Killing the MIG is the Last Thing We Should Do," by Colonel Ray Gerber, USMC (Ret.), Information Professionals Association, September 7, 2025.6. "Blinding First, Striking Fast: Why the Marine Corps Needs Information Groups," by Ben Jensen and Ian Fletcher, War on the Rocks, October 13, 2025.

King Kundra is still here gracing us with his presence. He begins this week with some yarns on Kundra Snr, including an update on the infrared vacuum and his negotiating skills. Kush and Migs had some run ins with fans, before we talk about El Jannah's sale to Private Equity and Ro met another bloke with 2 phones.For our game this week we have the "2025 Quiz" with Migs testing the rest of the boys knowledge on the events of 2025.We end with "Corporate Characters," where we discuss the archetypal characters you interact with on a daily basis at work and who you should avoid being / being seen with.___________________________________________________________FULL PODCAST EPISODES

What if the key to improving obstetrical surgery outcomes isn't a new technology, but rethinking who's in the operating room? In this episode of BackTable OBGYN, host Dr. Mark Hoffman and co-host Dr. Amy Park welcome Dr. Sony Singh, a prominent figure in the field of minimally invasive gynecologic surgery (MIGS) and obstetrics, to share perspectives on the emerging role of MIG surgeons in obstetrical surgery. --- SYNPOSIS Dr. Singh shares his extensive career journey, from his education in Canada and Australia to his current role as department chair of OBGYN at the Ottawa Hospital. The conversation delves into the integration of MIGS into obstetric surgery, including procedures like laparoscopic cerclages, placenta accreta management, and cesarean scar pregnancies. The hosts and guest discuss the challenges and importance of building a robust team, regionalization of care, maintaining work-life balance, and the eventual transition of leadership roles to sustain the high standards of care. This episode highlights the crucial role of minimally invasive specialists in advancing OBGYN practices while promoting a sustainable work culture. --- TIMESTAMPS 00:00 - Introduction 02:08 - Dr. Singh's Journey07:37 - The Role of MIG Surgeons in Obstetrical Surgery16:06 - Building a Collaborative Team18:38 - Challenges and Best Practices25:26 - Expanding the Scope of MIG Surgeons30:19 - The Evolution of Urogynecology and MIGS31:31 - Leadership and Building Programs37:54 - Scaling Up and Regionalization of Care42:53 - Balancing Work and Personal Life54:37 - Concluding Thoughts --- RESOURCES Canadian Society for Advancement of Gynecologic Excellencehttps://cansage.org/about/ From Strength to Strength, by Arthur Brookshttps://www.arthurbrooks.com/books

Beat Migs!! A listener left us a voice message about how much we argue and she might be right about that. Also, a listener suggested getting a matching Pearl Jam tattoo with Migs! Tune in to find out if he'll do it!

Absolute ripper of an episode this week as we are joined by young gun cricketer Harjas Singh, fresh off smoking the third highest score in Sydney Grade Cricket history. We chat early beginnings in cricket, his meteoric rise, winning a World Cup, the triple hundred, his life outside of cricket and plenty in between. Overall a fantastic yarn with Harjas and of course had to play a quick game with him as well. Despite audio evidence to the contrary, Migs was in fact in this episode.___________________________________________________________YOU CAN FIND HARJAS HERE:

Will Grant, Master Pizzaiolo and owner of Sourdough Willy's Pizzeria in Kingston and That's A Some Pizza on Bainbridge Island, has officially taken his craft to the world stage, earning a Lifetime Achievement Award at the Pizza World Cup in Rome. Instagram: @thatsasomepizza @sourdoughwillyspizzeria

Garrett is ready to Beat to Migs!! Only one way to find out!