Podcasts about liquids

Sam is having only clear liquids today as he prepares for a FUN day at the doctor. See omnystudio.com/listener for privacy information.

This episode we are joined by Mr. Bevin Wirzba - CEO of South Bow - a TSX listed infrastructure company with a market cap of ~$10 billion. Mr. Wirzba was an integral part of the TC Energy Executive Leadership Team. He oversaw the strategy and corporate development teams and led TC Energy's Canadian Natural Gas and Liquids transportation businesses. During his time at TC Energy, Bevin was responsible for the successful mechanical completion of the Coastal GasLink pipeline project, in addition to his many contributions to the company's strategy and corporate development efforts. Before joining TC Energy, Mr.Wirzba served as Senior Vice-President, Business Development and Capital Markets of ARC Resources Ltd., was Managing Director of RBC Dominion Securities, and served in multi-disciplinary roles across North America and internationally with Chevron Corp. Mr. Wirzba holds a Bachelor of Science in Civil Engineering from the University of Alberta and a Master of Business Administration from the Edinburgh School of Business. Mr. Wirzba is a member of the Association of Professional Engineers and Geoscientists of Alberta (APEGA) and sits on the Board of Directors for STARS. Among other things we learned about Reviving Keystone: ~1M BBL Prairie Connector.Enjoy. Thank you to our sponsors.Without their support this episode would not be possible:Connate Water SolutionsATB Capital MarketsBunch ProjectsWarren ValveAstro Oilfield Rentals-*This podcast is for informational and educational purposes only, and is not intended as investment advice. Please do your own research, and consult professionals directly before making any investment decisions.Support the show

Fala cambada de Nakeds, Liquids, Solids, Solidus e Venoms Snakes!! O COFFCAST TÁ DE VOLTA!! Nosso time de elite dos games (menos o Host) se reúne para comentar a respeito da grande saga criada por Hideo Kojima "METAL GEAR/SOLID", mas como o assunto é complexo e gigantesco, dividimos o programa em duas partes. Em breve a Parte 2 estará publicada. Aguardem.⁠Acesse o Mapingua Nerd aqui⁠⁠Acesse o Aldeia Geek aqui⁠⁠Acesse o Última Ficha aqui⁠Mas, contudo, porém, entretanto, todavia, o que está esperando? Escute o CoffCast 213 e também assine o nosso feed!!!E se caso você queira enviar algum email para a gente é só mandar para: coffcast@gmail.comCoffCasters: Davi Cardoso (Sr. Dharma), ⁠Yuri Hupsel⁠ e ⁠Anderson Carlos⁠.LINK DO CAST NO ANCHOR – ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://anchor.fm/asociedadedepodcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠LINK DO SPOTIFY – ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://open.spotify.com/show/6UjBjDjRJc5IAgdIyZQ9Yl⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Edição: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Davi Cardoso⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Arte: Davi Cardoso

Shell's plan to acquire Western Canadian E&P ARC Resources Ltd. affirms the global energy giant's new strategic focus, enhances the prospects for Phase 2 of LNG Canada, and supports the view that the Montney Shale may be replacing the Permian as the epicenter of oil and gas M&A.

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

As embolization tools continue to expand, operators are increasingly adopting hybrid approaches that combine coils with liquid and other embolic agents. In this episode, Dr. Raj Ramaswamy, Dr. Nevin de Korompay, and Dr. Seetharam Chadalavada, interventional radiologists from diverse practice settings discuss how embolization has evolved in their practices and how they approach tool selection on a case‑by‑case basis. Through real‑world examples and shared experiences, the panel highlights clinical reasoning, evolving practice patterns, and differing perspectives on where embolization is headed—grounded in practical, everyday cases rather than device‑driven debate.

master the pronunciation of rhotics and liquids

In this Peskies Pest Control Podcast episode, hosts Michael Wienecke and Travis McGowin assist a Georgia homeowner, Laura, with a persistent Asian Lady Beetle infestation. We explain that these “occasional invaders” swarm southern-facing walls in autumn to overwinter, often returning to the same structures due to lingering pheromone trails. To address the problem, we recommend an Integrated Pest Management (IPM) strategy that combines structural exclusion—sealing cracks and poor door seals—with the application of insecticidal dusts in wall voids and fast-acting liquids on high-activity exterior surfaces. While Laura was previously told that vacuuming was the only solution, the Peskies team asserts that a professional protocol can achieve a 90% to 95% reduction in the beetle population. Watch this YouTube Video! Podcast Transcript:Michael Wienecke: All right, so here today on the Peskies Pest Control Podcast, we have got a guest—Laura from Georgia. She put a form out on our online fill-out form and said her issue, what she was having an issue with. No one in Georgia could take care of it. Saw a YouTube video that Travis and I had made, and so she came, she wants to come on the podcast and just talk about this issue, which is Asian Lady Beetles. So here she is.Travis McGowin: All right. Welcome, Laura.Laura K: Thank you! Nice to be here.Travis McGowin: So Laura, we understand that you’ve got a pretty extreme—I was about to say German cockroaches! We were talking about those earlier—but Asian Lady Beetles. I mean, who knew? Now is this your first run-in with them that you've ever really had?Laura K: Yes. I’ve never had them—you think they’re ladybugs until you have hundreds or thousands of them and they bite, and you know that's not a ladybug.Michael Wienecke: Did you have any reaction to the bite?Laura K: No, just hurts. And they're not aggressive; they just bite if they get stuck, like under a sleeve or something, but it's still not good.Travis McGowin: Now have you been in Georgia your entire life or…?Laura K: No, I grew up in Virginia… in rural Virginia, so I’m used to bugs and rural areas. And then lived in New Jersey for 10 years, and then we got cold and it was expensive, and we moved to Georgia in 2004. So we’ve been in Georgia since then.Travis McGowin: That's pretty impressive though that you've lived several different places and still up to this point haven’t really had any kind of run-ins with these things. And it feels like—Michael, see if you agree—but I do feel like now over the last probably four or five years, they’ve actually seemed to be getting worse. That's just from my take on how many I've treated and seen. Would you agree with that?Michael Wienecke: 100%. Me and my wife went to Georgia, I do not know exactly where, but it was very similar to your pictures of your home—beautiful home by the way, up in the mountains and all that—but we were bombarded by them. My wife, obviously, it's not something that… they controlled them a lot better than what was outside than what was inside the condo/cabin where we were at. So I think they were doing something. But yes, like Travis said, we’ve had such hot winters. It’s just been extremely hot winters and we normally see a much larger pest infestation or we start seeing a lot more aggregation of pests inside homes in the summer.Travis McGowin: So these Asian Lady Beetles, they fall under a category in our world of trying to overwinter in a warm place. They’re one of a couple—you’ve got Asian Lady Beetles, you’ve got the Brown Marmorated Stink Bugs…Laura K: Stink bugs are all there too, yeah.Travis McGowin: Right, absolutely. So they come into September, October, they start to look for a place to overwinter and ride out the potentially colder weather, which in Alabama we've had like three days of cold weather and the rest has been miserably warm or humid. And I'm sure North Georgia might be better, but Georgia itself is probably not too far off base of what we've been seeing.Laura K: No, it's been a mild winter, yeah.Travis McGowin: Yeah, they creep in and they just try to take over. They're just looking for a place to hang out and they come in droves.Laura K: They do. They fly, yeah.Michael Wienecke: Well, they're called occasional invaders for this reason, that they come in right around that time and then they drop off at a certain time, and then the next year they come right back.Travis McGowin: Right, so, but from what we’ll discuss and from what you’ve experienced, I feel like come next season for these pests, you’ll probably be a lot more well-equipped to deal with them, especially having some understanding about what it is you’re dealing with.Laura K: I hope so. I've got… that’s why I wanted to talk to you guys about it, but also just to come up with a plan for the next six months to start the prevention because I know they’ll come back. I think they’re trying to get out of the house from what I’ve read. And so they get trapped inside now and they die everywhere. And so they are, I guess, trying to get out, but then they’re going to come back in the fall and swarm again. And they swarmed like… you had to walk like you’re swatting them to get up to the house. It was terrible. And I don't think the previous homeowners did anything. I think they were very much more laid back than we are.Travis McGowin: Well, and as with most any pest, on the very basic level, the first thing we always like to take a look at is how are they getting in and what can you do about that? Before any type of chemical application, before any type of pesticide or what have you. I don't know if you're familiar with the term “Integrated Pest Management” or IPM, but in your research you may have seen it or heard it. So IPM is really, really big on cutting the access points off for whatever it is you’re having a problem with even before using anything to basically harm them or kill them or whatever it is. So, judging by what we saw from some of the pictures that you sent us for your house there… you’ve probably got a decent amount of access points around.Laura K: Oh yeah, for sure, yeah. And we’ve tried to seal up windows and doors as best we can, but I think our next step is going to be to hire a painter to just come and caulk the entire house. Like everywhere—outside, inside, if you guys think that would help. But just all those little cracks and crevices because it's an older home and it's got a lot in it.Michael Wienecke: Well, and that's what I wanted to pull up the pictures and talk about too, because there are a few areas, and I’m really hoping because I’ve bet a lot on this in myself, but where these little guys are hanging out mostly in your house. So, right-hand side right there on the deck ledge, is that where you're getting most of the activity?Laura K: Yes, around those doors and that window on the right. Both of the two windows on the right side, but really the one on the right side mostly—right is… they're everywhere. That corner. That lighter colored wood right there.Michael Wienecke: Yes, that shingles, uh-huh.Travis McGowin: Now which direction—can you advise us which direction that part of the house faces?Laura K: That is… it faces south.Travis McGowin: Okay, so being that it’s a southern-facing direction, of course sun rises in the east and then sets towards the west, it's probably, I would dare say, probably receives the most amount of sunlight more so than the rest of the house. Obviously more so than the opposite side.Laura K: Yes, it's been a mild winter.Michael Wienecke: So tell me what your other pest control companies have done to try to get rid of this problem for you as far as treatment?Laura K: They have come out and just sprayed. They sprayed the eaves, the windows, around the doors… they were just out last week.Travis McGowin: So when did they start doing that?Laura K: We’ve only owned the home since November. So they came shortly after when we moved in with the ladybugs and were needing help. So they came probably late November and sprayed, and then I called them and said come back and they did and it didn’t, you know…Travis McGowin: So that—that's the kicker right there, and I'm glad you—I'm not glad you're dealing with this, but I'm glad you said November because it probably hit the nail on the head of what I was thinking in my mind, which is: so they start to migrate in in that September-October range. So by the point that you guys purchased the house, they were already there. Now I'm not going to say you didn't end up with more like you said, they've swarmed and you're swatting at them and all that, but if you don't catch it from a chemical application standpoint on the exterior of the house before they get there, then they're already inside.Michael Wienecke: It is much more difficult to deal with them once they’re already, like he said, established. Because they’re going to put off that pheromone and they’re going to just start kind of coming in there in droves.Travis McGowin: Right. So this is kind of the point in question that we're talking about, that second floor. How are the door seals around that bottom?Laura K: They’re really bad. The doors need to be replaced, we just can’t afford to do it yet. But both doors are in pretty rough shape. I mean, you can see daylight through one of them—like through the crack in the middle.Travis McGowin: And they probably get baked by the sun a lot. The black trim attracts them, I think, because it's warm through there.Michael Wienecke: My other one was the inside—it's beautiful, but you don't have an attic space. So I would imagine that there's some pretty good cracks and crevices between the tongue and groove where they may be getting in through that.Laura K: They are definitely. And in this corner where the fan to the right is… yeah, that corner, there's tons of them. All over there, all through there. And we tried caulking on our own some of those cracks and crevices and it just got to be too much. So… yeah, I think it needs to be done anyway, it'll look better.Travis McGowin: But let me ask you this. So, I know you had—let’s see if I can find the picture that’s in my mind. All right, so I'm going to show two. So first of all you got this one. Yeah, this was a big one. Right. So I'm going to look at this and then transitioning to this picture. That's why I took that for y’all. Right. So this void space that goes up above the living space of the house right there, how open is that past what we can see?Laura K: I mean, there's definitely places for bugs to get in there. We actually got a Starlink and when we had to kind of put it through this… a different area but same idea, put it through the space between the—that we’re looking at, those empty gaps between the boards there… there were like, you know, layers of them in there.Travis McGowin: The Asian Lady Beetles? Yeah, uh-huh. And so think about this too. So if you've got them in large layers or in large groups inside of those void spaces, something similar to this, and there is any type of gaps, cracks, crevices that look into the living space… so think about what happens at night when daylight disappears but the lights come on in the house. Now the only light that these bugs can see is inside your house and what's typically attractive to insects? Right. So, you know, you've got that kind of working against you too. So would you say that in those little void spaces between the boards right there, that any of that sits directly above that tongue and groove that we were looking at right here and that there could be some direct access through those gaps from there?Laura K: There could be, yeah, for sure.Travis McGowin: That's what we were kind of speculating when we first looked at the pictures was that that could also be an issue too. So our speculations have kind of actually been kind of spot on from what we've already been kind of discussing while looking at them. So, but I know that ultimately you're searching for how to make this more tolerable obviously for the upcoming season. So, we kind of hit point number one: that initial exterior application—or for starters, exclusion. Being able to prevent them from entering in the first place is always your best medicine, so to speak, for the problem. And then of course we already talked about the chemical application side being at an appropriate time to catch them before they start migrating to the warm surfaces on the outside of your house.Travis McGowin: Now, if you find yourself in the point where you were behind the eight ball on that and they're already here, Michael, what are some things that we can recommend to her? What are some things that a company might be able to do to help her to help the here and now, now that they're already here?Michael Wienecke: So I mean honestly, the one thing… the spraying, everything that they're doing is fine, but the one thing that I'm seeing they're missing is dust. I mean, you've got dust between the cracks and crevices of—if you want to pull those pictures back up I can kind of talk about that. Which one are you wanting? All of them. All of them.Michael Wienecke: So, to go to the extreme, there are tools that we have that we can put a duster and dust the gable vents, we can dust around the roofline. We've had situations in the past where a customer's had a hole in their roof and they've had Asian Lady Beetles and we're having to combat with water getting in a home, and the dust really does a really good job. So I'd dust around those cracks and crevices where that beam's coming out on that other picture, Travis. That one right there. So where the beam is coming out of the house itself, I'd wonder if you could get some dust behind that, behind those light fixtures if there's any way that we could pull that off, dust that, and get a good bit of dust behind all these areas that these beetles and other pests—roaches and other things—are going to congregate.Travis McGowin: And when he says dust, what we're referring to is an insecticidal dust. Very, very fine powder, doesn’t absorb moisture. Once it goes into a void space like an attic or a wall void or wherever, it is there for a really, really long time.Michael Wienecke: Well, our breakdown of products—and I believe I told you over the phone—is that, you know, the sunlight is going to be the biggest breakdown of our products. UV light is going to break that product down faster than moisture, rainwater, any of that. UV light. So being in that attic space or that dark environment, it's like Travis just said, it's going to be there for a long time.Travis McGowin: So, some other things too right here that I'm just while I'm pointing this out: so, you know, you've got the light fixtures on either side of the double doors, looks like you've maybe got an outlet right there on the lower right wall there. So those are some other places too, if they're not caulked around very well, that you could dust behind those as well because these insects will go ahead and go past these fixtures where they come out and work their way down into the wall voids and hang out in the wall voids too.Laura K: Does the dust have something that attracts them to it or do they just happen upon it?Michael Wienecke: It’s a contact poison. So there’s no attraction to it.Travis McGowin: No, there's no attraction, yep. Basically, the way this works is that it's puffed into wherever it's going to be applied to and then it floats and settles on whatever surfaces are in there. So if it's in a wall void, it's going to float and settle on the sides and down into the bottom of the wall void or whatever insulation's there. And so basically these insects, whether it's Asian Lady Beetles, whether it's roaches, they're going to track through that product and then a lot of insects groom themselves. So when they track through it and they walk through it, they groom themselves and it gets all over their body—or they may just walk through it and it scrapes their body across it, and then their body will absorb it and then that's what eventually kills the insect, yep.Michael Wienecke: It starts to kind of dry them out too. I mean, if the exoskeleton gets damaged, like Travis just said, they’ll start to not retain water as well and all that kind of stuff.Travis McGowin: Right. So definitely I'm glad you were talking about the door seals needing to be remedied.Laura K: We thought about even just putting plastic over them. A lot of people do that in the wintertime.Michael Wienecke: Well, my question too is how much insulation is that between that cedar board and that brick? Because that's on the other side of the wood, correct?Laura K: I guess, we have no idea. Probably not much. I think the previous owner did like a foam… because there’s other parts in the room that we could see that were unfinished and it was like a spray foam, hard, you know, it hardened. I think that was insulation he had in there.Michael Wienecke: Okay. All right, and so let's move on. Can you kind of give us a descriptor of what where this might be? Is this still up on the second floor?Laura K: Yes, all the living space is on the second floor. And this is in a bedroom. If you’re looking at that picture of the outside of the house and the deck was all the way in the right, this is the window counting from right to left, it’s the third window—right before you get to the smaller window there to the far left. And that's a bedroom. And then yeah, and so it got better when we sealed up the window with some—I don’t know what you call it, like sealant caulk—but the stuff you buy in a roll. But they were in there… we were up there last weekend and they were coming in or trying to get out or whatever they're doing, there was a lot more because it was warm last week.Travis McGowin: Right. Okay. And then now I'm assuming this is also off the deck, so off the second floor? So this is a kitchen area?Laura K: East. And there's not too many that come in there. There's a couple, but not terribly bad. And this is still in that same area as that last picture then? Yep, and that's in the kitchen area too and same thing. We get a couple in there but not like we do in the other room.Travis McGowin: All right, so certainly dust applications are great, especially for void space. You can’t just go dust everything in the house, that’s frowned upon for sure. But chemical application around gaps, cracks, crevices, eaves, doors, windows and all that on the outside, dust in void spaces. But then, of course, obviously like in your situation, you still run into that little problem is that they were there already invading before you guys got there in November.Travis McGowin: So one other thing that I like to point out too is that unfortunately, yeah, we do run into those problems to where the insect is already there and it’s like, okay, well what do we do now? They’re going to have to vacuum them up when they die, but we want to expedite their funeral process, so to speak. Because we know they're going to find their—they're going to try to find their way to a light source, which is usually going to be a window or door. Liquids and aerosols around those areas where they're trying to congregate are great.Travis McGowin: One of the liquids that we use, I promise you after I apply it, if there's active Asian Lady Beetles in those areas, I promise you within a minute or two they're starting to drop and they're starting to die. And so if you're going to be stuck with them, at least having to clean them up or vacuuming them up, you might as well do it when they're dead. They're not flying and crawling everywhere. And that usually works out really well in starting to knock down the population because that's where they're going to go.Laura K: Where and how do they reproduce? Are they laying eggs in the walls or are they like out in the woods?Travis McGowin: So I would say that I have never personally seen them like babies in a house at all, ever.Michael Wienecke: It’s going to be coming out of the woodland of the trees. I think their breeding cycle is all completely done before this invasion ever starts. Because I've never seen any type of larva or anything like that. They've always been those the same size, that red to off-red orangeish color.Michael Wienecke: This is not something that, like mosquitoes or something, where you can cut off the life cycle—it’s an every 21-day life cycle, every 30-day life cycle, something of that nature—it's a seasonal issue. So that's why they call it an occasional invader, because it's something that we just don't see. And if we had a proper winter—1993 here in Birmingham for us or something—then we might not have near as a problem like we talked about here at this time of the year.Laura K: Was going to ask would mosquito fogging help prior to, but I guess not, yeah.Michael Wienecke: No. And the protocol really, I mean, it's fairly simple. You're going to come in, we're going to treat around all the windows interior and exterior, we're going to dust in the cracks and crevices and the voids, we're going to treat around the outside perimeter, we're going to make sure to do a really nice spot treatment on that sunny side of the house that we kind of talked about before. I have done three or four this month for the same issue that you've had, kind of the same “oh, nobody can get rid of them,” and the first treatment we've got a 90% reduction. The second treatment I haven't had a callback yet, so I would hope it's a 95% reduction because that's what we're aiming for.Laura K: That’s awesome.Michael Wienecke: Well, we're already looking at opening a branch and going over there and all that, you know, we're ready to go.Laura K: I have told everybody I've run into—because this is a new part of town for us up in Jasper—and I met with the tax assessor about something with our property and I'm like, “Hey, okay now we did that, can we talk about these Asian Lady Beetles?” I'm asking everyone who comes over—the propane guy—like, “What do you do?” And the solution, I'm like, the person who figures this out is going to be very wealthy.Michael Wienecke: Well, we talk about on the podcast information that's to help you as the customer and just to be able to help anybody that wants to do it themselves or anything like that. And we're just honored that you would reach out from Georgia over and even about an Asian Lady Beetle. We didn't even understand that this was really that big of a—I mean, we get it every year where people call about it, but more and more people are telling us this year like companies are saying there's nothing they can do about it. And we're getting that in Birmingham too, they're just giving up. I think it comes down to a liability standpoint, honestly. I think that it comes down to a time and a liability standpoint. Most companies won’t cover yellow jackets because of the liability and the time. I built this company on customer service and customer satisfaction.Laura K: Exactly. There's no way in down in Atlanta in some of those old historic homes where they have old money, there's no way they would put up with it. I don't know where they pull people in from, but they don't settle for this stuff.Michael Wienecke: Well, I'd love to talk to your company that's doing it currently and just if they need any help—I don't mean this in a bad way—but any direction on what maybe they could use that they don't know about. Because again, Georgia and Alabama, we have different rules that we have to follow.Laura K: Okay. I'll ask them about it. I'm not sure I'm going to keep them, so give me a quarter.Travis McGowin: If we ever end up in that area though, we will be more than happy to service your home there.Michael Wienecke: That is the first thing I told Travis when I saw your—I was like, “We gotta find a way to get up there and treat this house.”Laura K: Everybody would be… I just met a new neighbor the other day and I asked her of course, “Do you have this problem?” “Oh, they’re terrible, the whole street has them.” And everybody's been told the same thing: vacuum them up, don’t step on them, they release pheromones, they attract more. I'm like, there's so many, how do I not step on them?Travis McGowin: And I'm glad you said that too, because the fact that this can be a yearly problem… pheromones are definitely—you're talking about something that can raise a beacon and say, “Hey, we got a great place, this is a great hotel for us to accommodate during the winter.”Laura K: Which that's one of my other thoughts was: is there any research or anything out there about attracting them away from property? Instead of just the prevention, it'd be a great plan as if you could find a way to lure them somewhere else through pheromones even. I don’t know, I’m daydreaming about this.Michael Wienecke: I would be on an EPA standpoint then where they would be what they could and couldn't put in the air. Because there's so many… I mean, there is millions and millions of dollars that goes into a product's just invention, you know, being thought of.Laura K: UGA extension office, their research and labs, I read everything at the extension office at UGA… and they said the same thing: vacuuming.Michael Wienecke: Wow, that's interesting.Travis McGowin: Well, again Laura, like I said, we greatly appreciate you taking your time and discussing those photos. It was kind of neat to take those assumptions and make them a reality.Michael Wienecke: Yeah, we had fun.Laura K: Glad I could help. Well, I'll send you our… you know, we’re in Georgia, I was my kids are at UGA, so I’ll send you our… I’ll be sure to pass your names along.Michael Wienecke: How about that?Laura K: You guys have a great day, take care. Bye Laura.Travis McGowin: Hey listen, if you guys watching this podcast, if anybody is having an issue with Asian Lady Bugs just like Laura is, I hope that some of the information that Michael and I shared and discussed with Laura, I hope it helps you. And of course if you’re in our coverage area here in Central Alabama or Northern part of Alabama, North Central Alabama, give us a call. I'll go ahead really quick and put our information up: if you're in the Birmingham area, give us a call (205) 470-8161; and then if you are in the Montgomery area, (334) 595-9055. We would love to talk to you just like we talked with Laura. You'll be 100% happy or you won't pay a penny and we're going to do our very best to get to the bottom of your problem and keep you bug-free. The post Listener Deep Dive: Reviewing Laura's Infestation (Part 2) appeared first on Peskies Pest Control.

This episode of the Oil and Gas Measurement Podcast explores the evolution and future of liquid flow computers, featuring insights from industry veteran Galen Cotton. The conversation highlights how measurement technologies have advanced over time and examines the role of modern flow computing, data communication, and system design in improving accuracy and operational visibility. It also touches on emerging trends that are shaping the future of measurement in the oil and gas industry.   Visit PipelinePodcastNetwork.com for a full episode transcript, as well as detailed show notes with relevant links and insider term definitions.

Episode: 1539 In which a thin layer of fluid determines whether an airplane flies.  Today, a wind blows by us.

* Blades: Pure Liquids* Interlude: Last Words* Rank 8 Key Knowledge and Deed* Very open framing; PCs just need to get the info in some way* Prophetic vision by the last surviving member of the Seven Spokes* Deed: Visit site of the last battle against Betrayer by the Seven Spokes* Adventure by Robin Laws* Destination: Pale Ones dome below the Tylon Mountains * Escorted by the Vodanicus family* True Water miners, strongly disliked* Opportunity for dark comedy; poor, hillbilly-style * Overland trek with large rafts* Dangerous journey; ork scorchers, underground rivers, rapids, waterfalls* Betrayer pits the Vodanicus against each other just before encountering the Pale Ones* Meet with the Shivalahala; she had a dream about the group's arrival* PCs must learn a blood ritual to summon Betrayer out of the Blades* During the week the group spends with the t'skrang, Betrayer continues to work* Uprising against the Shivalahala led by Vodanicus* Complete the Deed and may weave the last thread.* Escorted by adolescent t'skrang to the ritual site* Face the ghosts of the Seven Spokes, now under the Horror's influence* Ghosts can only be harmed by the Blades* Breakdown and analysis of Betrayer's game stats and combat tactics* Play Betrayer intelligently; make it a difficult fight* Climax of the story arc, could be a strong campaign ending* Discussion of Karma Tap* Closing thoughts about adventure difficulty* Final thoughts about the overall Blades campaignFind and Follow:Email: edsgpodcast@gmail.comYouTube: https://www.youtube.com/@EDSGPodcastFind and follow Josh: https://linktr.ee/LoreMerchantGet product information, developer blogs, and more at www.fasagames.comFASA Games on Facebook: https://www.facebook.com/fasagamesincOfficial Earthdawn Facebook Group: https://www.facebook.com/groups/officialearthdawnFASA Games Discord Channel: https://discord.gg/uuVwS9uEarthdawn West Marches: https://discord.gg/hhHDtXW

Medicine [Part 1]: Edibility; Three categories; Ach'shvei; Vitamins; Gelcaps; Liquids and chewables. See seforim by Rabbi Cohen at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.kashrushalacha.com

In the fourth episode of Down the Garden Path's "Houseplant Chat" series, Joanne discusses the basics of fertilizing houseplants. Topics Covered When to start fertilizing Wait until days are noticeably longer (sometime after daylight savings time) when plants begin active growth again. How to read fertilizer labels (N-P-K) The three numbers represent Nitrogen (N), Phosphorus (P), Potassium (K). Leafy plants may benefit from higher nitrogen; flowering plants often need more phosphorus. Choosing an "all-purpose" fertilizer Balanced blends (e.g., 10-10-10) can work for many houseplants, especially as a baseline. Three common fertilizer formats Water-soluble liquid: mix into watering can; easy to apply while watering. Granular: sprinkle on the soil surface; dissolves as you water (Joanne notes it can feel less precise). Slow-release pellets: dissolve gradually; may only need reapplying every few months. Frequency & control Liquids are immediate and routine-friendly; granular and slow-release can be less frequent but require awareness of plant needs. Start gently Use half-strength for the first few feedings at the start of the season to ease plants in. Organic vs. big-box options Joanne prefers organic options (often lower N-P-K numbers) and mentions the appeal of local/smaller brands, while acknowledging "use what you have" if it's already in your cupboard. Check expiry dates Expired fertilizer is usually not harmful—just potentially less effective. Know your special plants Spot-check care requirements for "can't-risk-it" plants: heirlooms, cuttings, orchids, sentimental favourites (she shares the example of a long-loved rubber tree). Homemade fertilizer recipes (with caution) DIY approaches (tea/coffee grounds/fish emulsion) exist, but beginners should stick to products with known N-P-K values. Takeaways and Tips Don't rush it: Start fertilizing when daylight increases (typically after daylight savings), not in the dead of winter. Half-strength first: For the first few fertilized waterings, go 50% strength to avoid shocking plants waking up from slower winter growth. Match fertilizer to the goal: Leaf growth: consider a more nitrogen-forward option. Flowering: look for a higher middle number (phosphorus). Pick a method you'll actually remember: If you're consistent with watering, liquid can be easiest. If you forget steps easily, slow-release may be safer; just add a reminder so it doesn't get missed. Follow the label (seriously): Package directions matter more than brand debates. Research your "VIP plants": If a plant is sentimental or pricey, do a quick care check so you're not guessing at feeding needs. Use what you have, smartly: If you find old fertilizer, check the expiry date; it may still work, just a bit weaker. Keep it measurable: If you're newer to houseplants, prioritize fertilizers with clear N-P-K numbers over DIY mixes until you've got a baseline routine. Other Houseplant Chat episodes Getting the Light Right Soil and Watering Tips Propagation Made Simple Have a topic you'd like Joanne to discuss? Email your questions and comments to downthegardenpathpodcast@hotmail.com, or connect with Joanne on her website: down2earth.ca Find Down the Garden Path on Instagram, Facebook, and YouTube: @downthegardenpathpodcast. Down the Garden Path Podcast On Down The Garden Path, professional landscape designer Joanne Shaw discusses down-to-earth tips and advice for your plants, gardens and landscapes. As the owner of Down2Earth Landscape Design, Joanne Shaw has been designing beautiful gardens for homeowners east of Toronto for over a decade. She does her best to bring you interesting, relevant and useful topics to help you keep your garden as low-maintenance as possible.  In Down the Garden Path: A Step-By-Step Guide to Your Ontario Garden, Joanne and fellow landscape designer Matthew Dressing distill their horticultural and design expertise and their combined experiences in helping others create and maintain thriving gardens into one easy-to-read monthly reference guide. Get your copy today on Amazon. Don't forget to check out Down the Garden Path on your favourite podcast app and subscribe! You can now catch the podcast on YouTube.

This episode explores the laws of extracting juice from fruits on Shabbos and when liquids that emerge are permitted or prohibited. We discuss olives and grapes crushed before Shabbos, when their juice is automatically allowed, and how nullification works when juice mixes immediately with existing liquid. The shiur clarifies key distinctions between juice that appears independently versus juice that blends right away, and why that difference matters.We also cover practical scenarios: placing ice or snow into drinks, soaking raisins or grape remnants to create a beverage, filtering liquids prepared before Shabbos, and squeezing fruit directly onto food versus into an empty dish. Special attention is given to unripe or inedible fruits, differing rabbinic opinions, and when stringency is recommended. The episode concludes with everyday applications such as squeezing lemon onto food and how intent and timing affect permissibility.

Hour 2 of A&G features... Iran's leader & negotiations A what up his what?! Plus, Trump nicknames over the years Hillary in 08 on immigration, US headed to divorce w/ China & Super Bowl bets Inflation over the last 25 years See omnystudio.com/listener for privacy information.

Hour 2 of A&G features... Iran's leader & negotiations A what up his what?! Plus, Trump nicknames over the years Hillary in 08 on immigration, US headed to divorce w/ China & Super Bowl bets Inflation over the last 25 years See omnystudio.com/listener for privacy information.

A screencast from Chapter 10 in CH 222 entitled “Liquids and Vapor Pressure”

There's only one Charlie Austin and he joined us this weekend in New York for the Wrexham Watch Party...- R's fill the Factory Floor from as far as Boston, Philly, D.C., Toronto, Austin, Houston and London. - Rangers took the lead twice but caught as cold as New York was in injury time- Highlights from a day full of highlights- Hugs, Limbs and Liquids.- We reply to some correspondence- Snow and ICE in New York City- Did everyone get home OK?- Ant's Kit Korner. It's one of the Specials- Predictions. Will Andy remember who we're playing?- A Triptych from Jacob- Remembering Dan McQuade, Lily and Charlie, and this, us. - Looking to start your own American chapter? We're happy to help- Coventry and Charlton on Paramount+Rate, review, send us a message on insta, follow,

This episode continues the laws of Borer as applied to filtering liquids on Shabbos. We explore when filtering wine, water, and other drinks is permitted—focusing on the key distinction between clear and cloudy liquids. If a drink is already fit to consume without filtering, straining it may be allowed, even if small particles remain. The discussion contrasts using a designated filter versus ordinary clothing, highlighting concerns of laundering (libun) and weekday-like activity. Differences between opinions, including the Rambam and the Shulchan Aruch, are clarified, along with practical cases such as fermenting wine and the broader implications for modern questions like filtering beverages. The episode concludes with guidance on making a proper shinui when required.

97 [1.20] Kitzur Shulchan Aruch Yomi 49:1-6 [Hagafen Basics. When It Exempts Other Liquids. Don't Mix Water In]

The Plan-B Show with Brock & Kiki - January 16th 2026See omnystudio.com/listener for privacy information.

TALK TO ME, TEXT ITA list of the world's most expensive liquids isn't just trivia—it's a window into how markets, scarcity, and hype collide. We open with a fast, funny rundown of price tags that range from nail polish and penicillin to horseshoe crab blood and cobra venom, and we explore what those numbers actually say about extraction, demand, and whether some figures are more headline than reality.Then we take on a charged report about a potential season-long suspension for a coach accused of using slurs toward officials. Where should the line fall between free speech and workplace consequences? We wrestle with proportionality, deterrence, and the example set for fans, without turning nuance into a cop-out. Accountability matters, but so does context—and punishment should fit both the words and the role.We also unpack a viral claim about five “liar phrases,” from “as far as I can recall” to “to be honest.” Most of these look like normal speech habits under stress or uncertainty, not automatic deceit. The real tell? Evasive answers that dodge facts. Along the way we contrast pop-psych tips with what actually helps: aligning stories with evidence, spotting clusters of cues, and asking better, simpler questions.And because reality outpaces headlines, we tell the story of a giant bear living under a house for 37 days. The eventual eviction used paintballs with vegetable oil—annoying, not lethal—and a patchwork fix that likely won't hold. Urban wildlife problems need prevention, not plywood. Close crawl spaces properly, cut attractants, and stop moving the issue to someone else's yard.We close by asking why big-name hosts dominate “independent” media and what incentives shape their takes. If you care about real independence, demand transparency, consistency, and the courage to self-correct. If this conversation hit a nerve, follow the show, share it with a friend, and drop your take—what's fair punishment, and which “liar tell” do you actually watch for?Buzzsprout - Let's get your podcast launched!Start for FREE Thanks for listening! Liberty Line each week on Sunday, look for topics on my X file @americanistblog and submit your 1-3 audio opinions to anamericanistblog@gmail.com and you'll be featured on the podcast. Buzzsprout - Let's get your podcast launched!Start for FREESupport the showTip Jar for coffee $ - Thanks Music by Alehandro Vodnik from Pixabay Blog - AnAmericanist.comX - @americanistblog

66 [12.20] Kitzur Shulchan Aruch Yomi 33:1-6 [Sakanah Fish Meat. Sweat. Giluy: Uncovered Liquids. Food Under Bed]

In this episode of the Industry Pro Pod, I chat with Tonya Cornett! We discuss dry yeast, creating balance, the wide range of beverages that UPP offers, hazy IPA, and more!#probrewer #professionalbrewer #howtobrew #brewery #homebrew #waterchemistry #ipa #dryhop #neipa If you are interested in my consultation & marketing services, or just want to know more, please check out www.adammakesbeer.com Adam Makes Beer Podcast: Spotify: https://open.spotify.com/show/4Si7TqiEY7ZeTq3D7CwqMUApple Podcast: https://podcasts.apple.com/us/podcast/adam-makes-beer/id1695229502Instagram: @adammakesbeer Equipment Sponsor: Blichmann Engineering Pro BrewingWebsite: https://www.blichmannengineering.com/pro-brewingEmail: Probrewing@Blichmannengineering.com#howtobrew #probrewer #brewerylife #howtobrewbeer #howtomakebeer #craftbeerbrewing ---Hello, I am Adam! I am professional brewer and educator outside of Cincinnati, OH. I am a former high school and university educator, and I have been making beer for a living for over a decade. My goal here is to give a behind-the-scenes look into the craft brewing industry, and to share any knowledge I have. I am not the perfect brewer, but I am always pushing myself to get better and to learn more. Our goal in the brewhouse is to always aim for the bullseye, knowing we will never hit it. That mantra keeps us focused on continual growth, and helps us appreciate the journey of improving as brewers.If you have questions like: How to keg beer in a brewery - How to make beer in a commercial brewery - How to harvest yeast in a brewery - How to dry hop in a brewery - How to can beer in a brewery - How to clean a fermenter in a brewery - How to transfer beer in a brewery - How to purge a tank in a brewery - How to add fruit to a beer in a brewery - How to brew beer in a microbrewery - How to add coffee to a beer in a brewery - How to become a professional brewer, you have come to the right place!

This episode reviews the laws of rescuing wine, liquids, or food when a barrel breaks on Shabbos. We examine when a person may place a single vessel to save as much as possible, and when using multiple vessels is restricted due to concerns of weekday-like behavior or accidental carrying.We clarify the differences between rescuing up to three meals' worth versus larger amounts, how invited guests affect permitted quantities, and why one cannot invite guests merely as a legal workaround. The episode also analyzes cases of dripping grape juice that is not yet considered usable liquid, the issue of making a vessel designated for prohibited material, and permissible workarounds to prevent loss.Finally, we address the rules for gathering scattered fruit on Shabbos—when it may be collected, how it must be eaten, and the limits created by concerns of weekday practice and selecting.

Even a mixture of liquid may depend on majority, though the appearance of the mixture may make the difference -- for example, if the color of the liquid is lighter than it would have been without being mixed with another liquid. With different treatment of spit as compared to urine. But the substance itself can't be nullified by the same kind of substance. Plus, an impure person's urine that is nullified by several mixings with (pure) water (how many times is a matter of dispute). Also, once the impure liquid comes in contact with flax, the impurity remains.

Rauchen gilt längst als gesundheitsschädlich – das ist bekannt. Doch vor allem die junge Generation greift immer häufiger zur E-Zigarette. Hersteller bewerben sie als „rauchfreie“ Alternative, die weniger schädlich sei. Aber: In den meisten Liquids der Vapes steckt trotzdem Nikotin – und das macht bekanntlich schnell abhängig. Und auch noch andere gesundheitsschädliche Stoffe stecken in den Liquids. Kann Vapen also wirklich eine gesündere Alternative zur Zigarette sein oder ist das nur schlaues Marketing? Und wie steht es um das Suchtpotential bei Vapes? Das beantwortet in dieser Folge von "Aha! Zehn Minuten Alltagswissen“ der Pneumologe und Chefarzt der Lungenklinik Köln Prof. Dr. Wolfram Windisch. Im zweiten Teil geht es um den Mythos des vermeintlichen Chinarestaurant-Syndroms. Hier geht es zur Folge “Wie es gelingt, mit dem Rauchen aufzuhören”: https://open.spotify.com/episode/3XebTv2QNhpEBWYwKG5tZ0 Hier findet ihr die deutsche Erhebung zum Rauchverhalten: https://www.debra-study.info/ Hier geht es zu einer Übersichtsarbeit zum Mythos des “Chinese Restaurant Syndrome” von 2024: https://ijprajournal.com/issue_dcp/Chinese%20Restaurent%20Syndrome%20CRS%20In%20Depth%20Analysis%20of%20Myths,%20Mechanism%20and%20Public%20Perception.pdf "Aha! Zehn Minuten Alltags-Wissen" ist der Wissenschafts-Podcast von WELT. Wir freuen uns über Feedback an wissen@welt.de. Produktion: Sermet Agartan Redaktion: Sophia Häglsperger Impressum: https://www.welt.de/services/article7893735/Impressum.html https://www.welt.de/services/article157550705/Datenschutzerklaerung-WELT-DIGITAL.html

Earlier this month, a woman from west London was fined £150 for pouring the dregs of her morning coffee down a street drain! So in this weeks sustainability slot Jo Linehan shares the best way to manage liquids at home, the do's and don't of disposal.

Have you ever been held up in airport security because some knucklehead broke probably every liquid rule there is? Going through security is hard enough, so why on Earth have they come up with this ridiculous liquid thing to slow us all down? What's so dangerous about a bottle of water or your favorite perfume or cologne? Does it have something to do with the pressure in the cabin? Can my liquids somehow mess with the plane's navigational system? That's why they ask you to put your cellphone on airplane mode – maybe the two are related? I'm sure you know by now that it all comes down to security. Learn more about your ad choices. Visit megaphone.fm/adchoices

When it's extremely difficult to make up your mind.  Having the power of choice can actually weaken someone.  On this episode we're going to explore why states like North Carolina are making the choice to change the rules in the department of increased THC infused liquid sales.  Plus…how many people are actually making the choice to move away from hard alcohol and get into THC infused drinks?  I'm Arroe.  Life is a series of choices.  Who decides when you're not making it the right choice?  Is it the fear of going wrong?  The greatest lessons in life are often lost inside hidden away attempts and concepts.  It's time to reopen your heart. Having a choice is a daily gift.  On this highway we learn to trust mirages… What is the choice?  Become a supporter of this podcast: https://www.spreaker.com/podcast/arroe-collins-unplugged-totally-uncut--994165/support.

When it's extremely difficult to make up your mind.  Having the power of choice can actually weaken someone.  On this episode we're going to explore why states like North Carolina are making the choice to change the rules in the department of increased THC infused liquid sales.  Plus…how many people are actually making the choice to move away from hard alcohol and get into THC infused drinks?  I'm Arroe.  Life is a series of choices.  Who decides when you're not making it the right choice?  Is it the fear of going wrong?  The greatest lessons in life are often lost inside hidden away attempts and concepts.  It's time to reopen your heart. Having a choice is a daily gift.  On this highway we learn to trust mirages… What is the choice?  Become a supporter of this podcast: https://www.spreaker.com/podcast/arroe-collins-like-it-s-live--4113802/support.

Meeting assets for Seager HM with Dr. Sara Seager, Sept. 10, 2025Dr. Seager focused on a groundbreaking discovery of an ionic liquid compound that could potentially support life on planets without water, made accidentally during research for a Venus space mission. The discussion explored the scientific implications of this discovery, including its potential to expand our understanding of habitable zones and life beyond Earth, while highlighting ongoing research and experiments in this area. The conversation concluded with a discussion of space exploration strategies and the announcement of a privately funded mission to Venus scheduled for 2026, which will investigate cloud particles and ionic liquids.Professor Sarah Seeger from MIT was welcomed to Hotel Mars to discuss a recent discovery about a planet without water that may be able to sustain life. David noted that this discovery was unexpected and not widely publicized. The conversation began with introductions and background information about Professor Seeger's role at MIT, focusing on planetary science.Dr Seager discussed the discovery of an ionic liquid compound in a laboratory setting that could potentially exist on planets. He explained that this liquid, held together by ionic bonds, has a very low vapor pressure and doesn't evaporate easily, making it a promising candidate for supporting life beyond water. The discovery was made accidentally by her postdoc while working on a Venus space mission project where they were trying to collect and evaporate sulfuric acid cloud particles to search for signs of life.Our guest discussed the concept of recognizing life beyond Earth, particularly focusing on ionic liquids as potential habitats for life on planets where water cannot exist. He explained that while biomolecules are stable in ionic liquids, no planets with such conditions have been found yet, making this part of a long-term research journey. David noted that the current habitable zone model needs refinement with adjectives like "water habitable" or "ionic liquid habitable" zone, and mentioned ongoing experiments in his lab that could be replicated by others. She also discussed the possibility of ionic liquids existing below Venus's clouds, though more research is needed to confirm this possibility.Sara discussed the concept of ionic liquids as potential life-preserving agents in space, particularly in the context of panspermia theory. He explored the possibility of finding ionic fluids on Earth, including in ocean vents and volcanic areas, and considered their potential to sustain life beyond water. The discussion highlighted that while these substances could last for millions of years in space, they might be vulnerable to high-energy particles. The conversation concluded with a note about needed further research on ionic fluids and their potential discovery on planets.Sarah discussed her vision for space exploration with an unlimited budget, proposing two approaches: sample return missions from solar system planets, and the development of Solar Gravitational Lens Telescopes for distant planet observation. She emphasized the importance of studying Venus's atmosphere as a more practical near-term goal, leading to the formation of the Morningstar missions consortium. She announced a privately funded Rocket Lab mission to Venus scheduled for 2026, which will investigate cloud particles and ionic liquids, noting that this mission is sponsored by Schmidt Sciences and built by Rocket Lab.Special thanks to our sponsors:Northrup Grumman, American Institute of Aeronautics and Astronautics, Helix Space in Luxembourg, Celestis Memorial Spaceflights, Astrox Corporation, Dr. Haym Benaroya of Rutgers University, The Space Settlement Progress Blog by John Jossy, The Atlantis Project, and Artless EntertainmentOur Toll Free Line for Live Broadcasts: 1-866-687-7223For real time program participation, email Dr. Space at: drspace@thespaceshow.comThe Space Show is a non-profit 501C3 through its parent, One Giant Leap Foundation, Inc. To donate via Pay Pal, use:To donate with Zelle, use the email address: david@onegiantleapfoundation.org.If you prefer donating with a check, please make the check payable to One Giant Leap Foundation and mail to:One Giant Leap Foundation, 11035 Lavender Hill Drive Ste. 160-306 Las Vegas, NV 89135Upcoming Programs:Special thanks to our sponsors:Northrup Grumman, American Institute of Aeronautics and Astronautics, Helix Space in Luxembourg, Celestis Memorial Spaceflights, Astrox Corporation, Dr. Haym Benaroya of Rutgers University, The Space Settlement Progress Blog by John Jossy, The Atlantis Project, and Artless EntertainmentOur Toll Free Line for Live Broadcasts: 1-866-687-7223For real time program participation, email Dr. Space at: drspace@thespaceshow.comThe Space Show is a non-profit 501C3 through its parent, One Giant Leap Foundation, Inc. To donate via Pay Pal, use:To donate with Zelle, use the email address: david@onegiantleapfoundation.org.If you prefer donating with a check, please make the check payable to One Giant Leap Foundation and mail to:One Giant Leap Foundation, 11035 Lavender Hill Drive Ste. 160-306 Las Vegas, NV 89135Upcoming Programs:Broadcast 4434 ZOOM Lynn Harper | Sunday 21 Sep 2025 1200PM PTGuests: Harper, LynnZOOM Biomedical science in space, commercial space profitability, ISS and moreLive Streaming is at https://www.thespaceshow.com/content/listen-live with the following live streaming sites:Stream Guys https://player.streamguys.com/thespaceshow/sgplayer3/player.php#FastServ https://ic2646c302.fastserv.com/streamStream Guys https://player.streamguys.com/thespaceshow/sgplayer3/player.php#FastServ https://ic2646c302.fastserv.com/stream Get full access to The Space Show-One Giant Leap Foundation at doctorspace.substack.com/subscribe

Liquids! Like solids but better because they can sploosh about and stuff! But which are the best of all the liquids? Find out here! Also, we discuss eye eating, Orbital, spiders, Wormbursters, Mrs Potts who gives out the sweets, the butter-bog gods and worms.TTP Tndex (Time To Parasites): One minute and 15 secondsTIMESTAMPS:0:11:06 - Water0:20:51 - Olive Oil0:26:39 - Tea0:41:13 - Cone Snail venom0:47:43 - Custard0:49:40 - Horseshoe Crab blood0:55:38 - Liquid Helium-20:58:27 - Alcohol1:00:28 - Soup1:01:13 - Crude oil1:03:13 - Ranking! Hosted on Acast. See acast.com/privacy for more information.

It's been nearly 19 years to the day since we have had restrictions on taking liquids through onto the aircraft. Almost 2 decades of decanting our toiletries into small containers, of getting held up because we forgot to empty our water bottles, and countless expensive bottles of perfume have no doubt been confiscated. For pilots and flight attendants, it changed the way we work!Do you remember why though? It's something we are so used to now that many of us don't remember what led us to that place. And it all started in the UK. A local terrorist plot that had consequences worldwide and changed aviation and travel forever.In today's episode of The Red Eye, we have a dramatization of the events of that day. The day that the liquids were banned on planes.Music Credits for The Liquids BanOminous & Gloomy - Music by ViraMiller from PixabaySnowy Peaks pt I - Chris HaugenYacoby - SchwartzySound Effects by freesound_community from Pixabay Sound Design by Ally MurphySend us a text! If you'd like a reply, please leave an email or number Kaylie has written 6 other fictional novels about the lives of cabin crew! Amazon UKAmazon USABarnes and NobleSupport the showThe Red Eye Podcast is written by Kaylie Kay, and produced and narrated by Ally Murphy.To subscribe to the monthly newsletter and keep up to date with news, visit www.theredeyepod.com. Or find us on Facebook, YouTube, TikTok & Instagram @theredeyepod, for behind the scenes stories and those funny short stories that only take a minute or less!If you'd like to support the podcast you can "buy us a beer" and subscribe at https://www.buzzsprout.com/2310053/support, we'd be happy to give you a shout out on our newsletter!Ally Murphy is a former flight attendant, and a British voice over artist based in the USA, visit www.allymurphy.co.ukKaylie Kay is a flight attendant and author based in the UK. You can find more of her work at www.kayliekaywrites.comTo buy The Red Eye's first book click on the following links:Amazon UK Amazon USABarnes and Noble Other E Book Platforms

No trend has taken the private equity industry by storm quite like that of semi-liquid and evergreen funds. Data from consultancy Bfinance shows that at least $30 billion has been raised via private equity semi-liquid funds since 2020 – a figure that represents just 10 percent of the overall semi-liquids universe. In this episode, Ajay Pathak, a partner and co-chair of Goodwin's UK business, joins PEI senior editor Adam Le to discuss how management fees and carried interest are calculated; whether the typical 2-and-20 model prevalent in traditional drawdown funds make sense to apply to semi-liquid funds; whether charging carried interest on net asset value on both a realised and unrealised basis really make sense; and more.

In this episode, we speak with Etosha Cave, Co-Founder and Chief Science Officer of Twelve, who shares how the carbon transformation company harnesses carbon dioxide from industrial waste streams to produce efuels. Twelve is one of the visionary companies featured in our new book Sustainability in the Air: Volume Two. You can learn more about the book and order a copy here.Cave discusses:Twelve's carbon transformation technology that mimics photosynthesis, taking CO2 from industrial emissions, air, and landfills, combining it with water and renewable electricity through metal catalysts to create jet fuel and other products.Strategic partnerships with airlines like Alaska Airlines and International Airlines Group (IAG) and tech companies like Microsoft, leveraging growing consumer demand for carbon-neutral travel solutions and the scalability advantages of power-to-liquids.Twelve's Moses Lake, Washington plant location choice, capitalising on abundant hydropower, state incentives, geographic proximity to partners, and the emerging cleantech hub.How the company navigates political risks around climate incentives by focusing on carbon management as a bipartisan issue that creates jobs and economic value.Cave also shares her vision for a future with completely closed carbon cycles and explains how Twelve aims to transform waste CO2 into the building blocks for everything from consumer products to aviation fuel.If you LOVED this episode, you'll also love the conversation we had with Nicholas Flanders, Co-Founder & CEO of Twelve, who discusses the crucial role of clean technology in addressing environmental challenges. Check it out here. Feel free to reach out via email to podcast@simpliflying.com. For more content on sustainable aviation, visit our website green.simpliflying.com and join the movement. It's about time.Links & more:E-Jet® Sustainable Aviation Fuel - Twelve Twelve and IAG sign historic long-term multi-million gallon SAF offtake agreement - Twelve Alaska Airlines, Microsoft and Twelve partner to advance new form of sustainable aviation fuel - Alaska Airlines United Airlines Invests in Twelve for Sustainable Aviation Fuel - Carbon Credits 

On this episode of Bounced From The Roadhouse:Special Guests in 4B:My Little Pony1,000 Waters from TBDog Lost & Found in AlaskaWould You Leave for $1 MillionDarth VaderFlashback Friday: Most Embarrassing School MomentLiquids in AirportsWyatt's LemonadeThat's a Great QuestionApplaud a 66-Year-Old at Lowe'sVaselineaQuestions? Comments? Leave us a message! 605-343-6161Don't forget to subscribe, leave us a review and some stars Hosted on Acast. See acast.com/privacy for more information.

Millions of people living along the Gulf Coast are getting ready for severe flooding as a tropical storm approaches. Republicans are racing to get DOGE cuts approved. We'll tell you why the US is set to destroy 500 metric tons of food meant to go to starving people. New York City Mayor Eric Adams is facing his fifth lawsuit in 2 weeks. Plus, rules could be changing around how much liquid you can take on a flight. Learn more about your ad choices. Visit podcastchoices.com/adchoices

D&P Highlight: Liquids on a plane? full 414 Wed, 16 Jul 2025 18:57:00 +0000 BLYYvIfRLEmYUBFAva8ZcE7mIRSkJ2gZ news The Dana & Parks Podcast news D&P Highlight: Liquids on a plane? You wanted it... Now here it is! Listen to each hour of the Dana & Parks Show whenever and wherever you want! © 2025 Audacy, Inc. News False https://player.amperwavepodcasting.com?feed-link=https%3A%

Beef Stew has more meanings than you think! Some great Would you Rathers followed up by some insightful That's a Great Question and a draft you don't want to miss… Worst Things to See Come Out of Your Shower make this an episode to remember.  Re-brand Mondays with some comedy! Subscribe and tell your friends about another funny episode of The Spitballers Comedy Podcast!Connect with the Spitballers Comedy Podcast:Become an Official Spitwad: SpitballersPod.comFollow us on X: x.com/SpitballersPodFollow us on IG: Instagram.com/SpitballersPodSubscribe on YouTube: YouTube.com/Spitballers

Joining Carlos this week are Nev, Captain Al and Armando. In this week's show: Spirit Airlines grounds 5 Aircraft amid Detroit hangar foam incident;  Airport liquids rule change risking extra delays and confusion as the 100ml restriction is scrapped at some airports; and Airbus gives the green light to the use of a ‘Taxibot' to tow planes to the runway.    In the military: RAF Honington near Bury St Edmunds unveils a Tornado at their gate entrance; and the US Air Force adds more aircraft to the RIAT event. Take part in our chatroom to help shape the conversation of the show. You can get in touch with us all at : WhatsApp +447446975214 Email podcast@planetalkinguk.com or comment in our chatroom on YouTube.

This is Part 2! For Part 1, check the feed!Liquids, ay?! Where would we be without them!? This week we're discussing coffee, drinks in Ancient Rome and.. drum roll please… custard. YES, CUSTARD. Get ready for the best custard facts you'll ever hear.And what did we do before industrial production of clothing? Nothing at all? Is this why the loincloth was such a hit in the past? Well, if you know, do let us know: hello@ohwhatatime.comIf you fancy a bunch of OWAT content you've never heard before, why not treat yourself and become an Oh What A Time: FULL TIMER?Up for grabs is:- two bonus episodes every month!- ad-free listening- episodes a week ahead of everyone else- And much moreSubscriptions are available via AnotherSlice and Wondery +. For all the links head to: ohwhatatime.comYou can also follow us on: X (formerly Twitter) at @ohwhatatimepodAnd Instagram at @ohwhatatimepodAaannnd if you like it, why not drop us a review in your podcast app of choice?Thank you to Dan Evans for the artwork (idrawforfood.co.uk).Chris, Elis and Tom xSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Liquids, ay?! Where would we be without them!? This week we're discussing coffee, drinks in Ancient Rome and.. drum roll please… custard. YES, CUSTARD. Get ready for the best custard facts you'll ever hear.And what did we do before industrial production of clothing? Nothing at all? Is this why the loincloth was such a hit in the past? Well, if you know, do let us know: hello@ohwhatatime.comIf you fancy a bunch of OWAT content you've never heard before, why not treat yourself and become an Oh What A Time: FULL TIMER?Up for grabs is:- two bonus episodes every month!- ad-free listening- episodes a week ahead of everyone else- And much moreSubscriptions are available via AnotherSlice and Wondery +. For all the links head to: ohwhatatime.comYou can also follow us on: X (formerly Twitter) at @ohwhatatimepodAnd Instagram at @ohwhatatimepodAaannnd if you like it, why not drop us a review in your podcast app of choice?Thank you to Dan Evans for the artwork (idrawforfood.co.uk).Chris, Elis and Tom xSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Author and podcast host Malcolm Gladwell immigrated to Canada when he was young, the child of an English father and Jamaican mother. He's always felt like an outsider. He hated maple syrup, in a town that hosts the largest one-day maple syrup festival in the world. That outsider's perspective served him well as he became a cultural observer, and over the years, Malcolm's trained his critical eye on food. He calls flavored seltzer “an abomination” and Earl Grey tea “a bridge too far.” He also talks with Dan about his food-related work, from what chunky tomato sauce says about our food preferences, to the supposed merits of appropriating food, and he explains why he only drinks five liquids.This episode originally aired on September 12, 2022, and was produced by Dan Pashman, Emma Morgenstern, Andres O'Hara, Johanna Mayer, Tracey Samuelson, and Jared O'Connell. The Sporkful team now includes Dan Pashman, Emma Morgenstern, Andres O'Hara, Giulia Leo, Kameel Stanley, and Jared O'Connell. This update was produced by Gianna Palmer. Publishing by Shantel Holder.Every other Friday, we reach into our deep freezer and reheat an episode to serve up to you. We're calling these our Reheats. If you have a show you want reheated, send us an email or voice memo at hello@sporkful.com, and include your name, your location, which episode, and why.Transcript available at www.sporkful.com.Right now, Sporkful listeners can get three months free of the SiriusXM app by going to siriusxm.com/sporkful. Get all your favorite podcasts, more than 200 ad-free music channels curated by genre and era, and live sports coverage with the SiriusXM app.

Merriam-Webster's Word of the Day for April 2, 2025 is: effusive • ih-FYOO-siv • adjective Someone or something described as effusive is expressing or showing a lot of emotion or enthusiasm. // Jay positively glowed as effusive compliments on the meal echoed around the table. See the entry > Examples: "More recently, Billboard ranked Grande, who also writes and produces her own work, high on its list of the greatest pop stars of the 21st century. ... Rolling Stone has been similarly effusive, praising 'a whistle tone that rivals Mariah Carey's in her prime.'" — Lacey Rose, The Hollywood Reporter, 11 Feb. 2025 Did you know? English speakers have used effusive to describe excessive outpourings since the 17th century. Its oldest and still most common sense relates to the expression of abundant emotion or enthusiasm, but in the 1800s, geologists adopted a specific sense characterizing flowing lava, or hardened rock formed from flowing lava. Effusive can be traced, via the Medieval Latin adjective effūsīvus ("generating profusely, lavish"), to the Latin verb effundere ("to pour out"), which itself comes from fundere ("to pour") plus a modification of the prefix ex- ("out"). Our verb effuse has the same Latin ancestors. A person effuses when speaking effusively. Liquids can effuse as well, as in "water effusing from a pipe."

If you know Tori you know she is water drinking adverse! Tori is a one sip wonder that for reasons we explore today, is able to live her life in a state of dehydration. Is it because she believes each sip of liquid could be her last? That any beverage not from a can might be poisoned? Even by those closest to her? Join Misspelling as she opens the faucets and pours over the reasons why she is such a selective sipper, and how she will spill this truth about herself on a dating app!See omnystudio.com/listener for privacy information.

If you know Tori you know she is water drinking adverse! Tori is a one sip wonder that for reasons we explore today, is able to live her life in a state of dehydration. Is it because she believes each sip of liquid could be her last? That any beverage not from a can might be poisoned? Even by those closest to her? Join Misspelling as she opens the faucets and pours over the reasons why she is such a selective sipper, and how she will spill this truth about herself on a dating app!See omnystudio.com/listener for privacy information.