Podcasts about qps

Today's guest is Robert Cheatham https://www.linkedin.com/in/rcheetham/ and we are talking about Japan Earth Observer, https://www.japanearthobserver.com/. This is a newsletter on the space, Earth observation, and geospatial industry in Japan. It is based on a fantastic effort by Robert to systematically collate every last company in the space and earth observation supply chain in that country. Naturally I was drawn to it due to my efforts to do this not only for Japan but every country in the world as the GEO500. We got in touch and exchanged our lists which led to the discussion recorded here today.The GEO500 has 56 Japanese companies since inception. 7 of them are delisted. So the index contains 49 live positions for this country. All GEO500 positions start with $100. The entire Japanese part of the GEO500 stands at $AUD13,000. This is because the 56 have compounded at 8% annually since inception. In the case of this country, starting as a position in Fujitsu in 2000. The top three performers in terms of capital growth rate are Terra Drone (125% annually), Synspective (71%) and QPS (59%). The top 3 in terms of value are Sony ($1100 - bear in mind the $100 starting position, equating to a 20% annual growth rate since entering the index in 2013), NEC ($960, 10%) and Raito Kogyo ($760, 11%). Note that the 3 fastest growers are all earth observation companies, and 2 of those are synthetic aperture radar satellite constellations. This is an exciting part of our industry to watch and I have recorded a couple of great episodes on the power of it. For example, - Jamon Van Den Hoek's work as an academic partnering with some of the world's largest media organisations to monitor building destruction in warzones and after fires: https://open.spotify.com/episode/0RQNjzDtTQTNqv8IiMy2SU- Umbra, a pioneering American SAR satellite manufacturer, they also operate their own constellation: https://open.spotify.com/episode/2QM3OeglXI1naiSgrKod17- Iceye, the equivalent from Finland: https://open.spotify.com/episode/04S3fsdL4cus2tOEbq4KBN- Ursa Space Systems, a commodity intelligence firm that uses SAR to monitor everything from iron ore stockpiles at ports to oil farm tank lids around the world: https://open.spotify.com/episode/3ltO0Bv8Mtq7dgnLhXWGxO- SeerAI, another analytical platform where I coaxed the guest to use SAR as a way to detect surface change on the NEOM excavation, the world's largest (now abandoned) building site: https://www.geospatial.fm/p/seerai-responds-to-johnny-harrisBut this wasn't just about current companies. We were privileged in this episode to be given a history of Japan's trading houses since the 1860s to today. This led to several insights around the longevity, for example, of the social groups that comprise such firms and how they can persist even through a trust busting effort by the winning country after a world war. Another insight was the benefit to society of trust busting and how it unleashed a wave of new companies and from that household names such as Honda and Sony. Honda recently launched a rocket that was able to land itself. This has relevance to possible future earth observation constellation launches. Sony released LiDAR solution in 2013 and also sells other geospatial products like a GNSS chip. We have come full circle.A final note about Robert himself. We are presented here with a cultivated and successful entrepreneur. He grew a geospatial software development firm to 50 people then sold it. He has learnt a foreign language well enough to spontaneously translate things for me during a podcast recording without warning. He also is able to offer a comprehensive historical view on the emergence, development, setbacks and modern day expression of several dominant companies in a vibrant foreign economy, Japan. He shows us the way, then, on multiple fronts. I am grateful for the chance to put an inspiring industry figure in front of you today. Thanks Robert.

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

Turbopuffer came out of a reading app.In 2022, Simon was helping his friends at Readwise scale their infra for a highly requested feature: article recommendations and semantic search. Readwise was paying ~$5k/month for their relational database and vector search would cost ~$20k/month making the feature too expensive to ship. In 2023 after mulling over the problem from Readwise, Simon decided he wanted to “build a search engine” which became Turbopuffer.We discuss:• Simon's path: Denmark → Shopify infra for nearly a decade → “angel engineering” across startups like Readwise, Replicate, and Causal → turbopuffer almost accidentally becoming a company • The Readwise origin story: building an early recommendation engine right after the ChatGPT moment, seeing it work, then realizing it would cost ~$30k/month for a company spending ~$5k/month total on infra and getting obsessed with fixing that cost structure • Why turbopuffer is “a search engine for unstructured data”: Simon's belief that models can learn to reason, but can't compress the world's knowledge into a few terabytes of weights, so they need to connect to systems that hold truth in full fidelity • The three ingredients for building a great database company: a new workload, a new storage architecture, and the ability to eventually support every query plan customers will want on their data • The architecture bet behind turbopuffer: going all in on object storage and NVMe, avoiding a traditional consensus layer, and building around the cloud primitives that only became possible in the last few years • Why Simon hated operating Elasticsearch at Shopify: years of painful on-call experience shaped his obsession with simplicity, performance, and eliminating state spread across multiple systems • The Cursor story: launching turbopuffer as a scrappy side project, getting an email from Cursor the next day, flying out after a 4am call, and helping cut Cursor's costs by 95% while fixing their per-user economics • The Notion story: buying dark fiber, tuning TCP windows, and eating cross-cloud costs because Simon refused to compromise on architecture just to close a deal faster • Why AI changes the build-vs-buy equation: it's less about whether a company can build search infra internally, and more about whether they have time especially if an external team can feel like an extension of their own • Why RAG isn't dead: coding companies still rely heavily on search, and Simon sees hybrid retrieval semantic, text, regex, SQL-style patterns becoming more important, not less • How agentic workloads are changing search: the old pattern was one retrieval call up front; the new pattern is one agent firing many parallel queries at once, turning search into a highly concurrent tool call • Why turbopuffer is reducing query pricing: agentic systems are dramatically increasing query volume, and Simon expects retrieval infra to adapt to huge bursts of concurrent search rather than a small number of carefully chosen calls • The philosophy of “playing with open cards”: Simon's habit of being radically honest with investors, including telling Lachy Groom he'd return the money if turbopuffer didn't hit PMF by year-end • The “P99 engineer”: Simon's framework for building a talent-dense company, rejecting by default unless someone on the team feels strongly enough to fight for the candidate —Simon Hørup Eskildsen• LinkedIn: https://www.linkedin.com/in/sirupsen• X: https://x.com/Sirupsen• https://sirupsen.com/aboutturbopuffer• https://turbopuffer.com/Full Video PodTimestamps00:00:00 The PMF promise to Lachy Groom00:00:25 Intro and Simon's background00:02:19 What turbopuffer actually is00:06:26 Shopify, Elasticsearch, and the pain behind the company00:10:07 The Readwise experiment that sparked turbopuffer00:12:00 The insight Simon couldn't stop thinking about00:17:00 S3 consistency, NVMe, and the architecture bet00:20:12 The Notion story: latency, dark fiber, and conviction00:25:03 Build vs. buy in the age of AI00:26:00 The Cursor story: early launch to breakout customer00:29:00 Why code search still matters00:32:00 Search in the age of agents00:34:22 Pricing turbopuffer in the AI era00:38:17 Why Simon chose Lachy Groom00:41:28 Becoming a founder on purpose00:44:00 The “P99 engineer” philosophy00:49:30 Bending software to your will00:51:13 The future of turbopuffer00:57:05 Simon's tea obsession00:59:03 Tea kits, X Live, and P99 LiveTranscriptSimon Hørup Eskildsen: I don't think I've said this publicly before, but I just called Lockey and was like, local Lockie. Like if this doesn't have PMF by the end of the year, like we'll just like return all the money to you. But it's just like, I don't really, we, Justine and I don't wanna work on this unless it's really working.So we want to give it the best shot this year and like we're really gonna go for it. We're gonna hire a bunch of people. We're just gonna be honest with everyone. Like when I don't know how to play a game, I just play with open cards. Lockey was the only person that didn't, that didn't freak out. He was like, I've never heard anyone say that before.Alessio: Hey everyone, welcome to the Leading Space podcast. This is Celesio Pando, Colonel Laz, and I'm joined by Swix, editor of Leading Space.swyx: Hello. Hello, uh, we're still, uh, recording in the Ker studio for the first time. Very excited. And today we are joined by Simon Eski. Of Turbo Farer welcome.Simon Hørup Eskildsen: Thank you so much for having me.swyx: Turbo Farer has like really gone on a huge tear, and I, I do have to mention that like you're one of, you're not my newest member of the Danish AHU Mafia, where like there's a lot of legendary programmers that have come out of it, like, uh, beyond Trotro, Rasmus, lado Berg and the V eight team and, and Google Maps team.Uh, you're mostly a Canadian now, but isn't that interesting? There's so many, so much like strong Danish presence.Simon Hørup Eskildsen: Yeah, I was writing a post, um, not that long ago about sort of the influences. So I grew up in Denmark, right? I left, I left when, when I was 18 to go to Canada to, to work at Shopify. Um, and so I, like, I've, I would still say that I feel more Danish than, than Canadian.This is also the weird accent. I can't say th because it, this is like, I don't, you know, my wife is also Canadian, um, and I think. I think like one of the things in, in Denmark is just like, there's just such a ruthless pragmatism and there's also a big focus on just aesthetics. Like, they're like very, people really care about like where, what things look like.Um, and like Canada has a lot of attributes, US has, has a lot of attributes, but I think there's been lots of the great things to carry. I don't know what's in the water in Ahu though. Um, and I don't know that I could be considered part of the Mafi mafia quite yet, uh, compared to the phenomenal individuals we just mentioned.Barra OV is also, uh, Danish Canadian. Okay. Yeah. I don't know where he lives now, but, and he's the PHP.swyx: Yeah. And obviously Toby German, but moved to Canada as well. Yes. Like this is like import that, uh, that, that is an interesting, um, talent move.Alessio: I think. I would love to get from you. Definition of Turbo puffer, because I think you could be a Vector db, which is maybe a bad word now in some circles, you could be a search engine.It's like, let, let's just start there and then we'll maybe run through the history of how you got to this point.Simon Hørup Eskildsen: For sure. Yeah. So Turbo Puffer is at this point in time, a search engine, right? We do full text search and we do vector search, and that's really what we're specialized in. If you're trying to do much more than that, like then this might not be the right place yet, but Turbo Buffer is all about search.The other way that I think about it is that we can take all of the world's knowledge, all of the exabytes and exabytes of data that there is, and we can use those tokens to train a model, but we can't compress all of that into a few terabytes of weights, right? Compress into a few terabytes of weights, how to reason with the world, how to make sense of the knowledge.But we have to somehow connect it to something externally that actually holds that like in full fidelity and truth. Um, and that's the thing that we intend to become. Right? That's like a very holier than now kind of phrasing, right? But being the search engine for unstructured, unstructured data is the focus of turbo puffer at this point in time.Alessio: And let's break down. So people might say, well, didn't Elasticsearch already do this? And then some other people might say, is this search on my data, is this like closer to rag than to like a xr, like a public search thing? Like how, how do you segment like the different types of search?Simon Hørup Eskildsen: The way that I generally think about this is like, there's a lot of database companies and I think if you wanna build a really big database company, sort of, you need a couple of ingredients to be in the air.We don't, which only happens roughly every 15 years. You need a new workload. You basically need the ambition that every single company on earth is gonna have data in your database. Multiple times you look at a company like Oracle, right? You will, like, I don't think you can find a company on earth with a digital presence that it not, doesn't somehow have some data in an Oracle database.Right? And I think at this point, that's also true for Snowflake and Databricks, right? 15 years later it's, or even more than that, there's not a company on earth that doesn't, in. Or directly is consuming Snowflake or, or Databricks or any of the big analytics databases. Um, and I think we're in that kind of moment now, right?I don't think you're gonna find a company over the next few years that doesn't directly or indirectly, um, have all their data available for, for search and connect it to ai. So you need that new workload, like you need something to be happening where there's a new workload that causes that to happen, and that new workload is connecting very large amounts of data to ai.The second thing you need. The second condition to build a big database company is that you need some new underlying change in the storage architecture that is not possible from the databases that have come before you. If you look at Snowflake and Databricks, right, commoditized, like massive fleet of HDDs, like that was not possible in it.It just wasn't in the air in the nineties, right? So you just didn't, we just didn't build these systems. S3 and and and so on was not around. And I think the architecture that is now possible that wasn't possible 15 years ago is to go all in on NVME SSDs. It requires a particular type of architecture for the database that.It's difficult to retrofit onto the databases that are already there, including the ones you just mentioned. The second thing is to go all in on OIC storage, more so than we could have done 15 years ago. Like we don't have a consensus layer, we don't really have anything. In fact, you could turn off all the servers that Turbo Buffer has, and we would not lose any data because we have all completely all in on OIC storage.And this means that our architecture is just so simple. So that's the second condition, right? First being a new workload. That means that every company on earth, either indirectly or directly, is using your database. Second being, there's some new storage architecture. That means that the, the companies that have come before you can do what you're doing.I think the third thing you need to do to build a big database company is that over time you have to implement more or less every Cory plan on the data. What that means is that you. You can't just get stuck in, like, this is the one thing that a database does. It has to be ever evolving because when someone has data in the database, they over time expect to be able to ask it more or less every question.So you have to do that to get the storage architecture to the limit of what, what it's capable of. Those are the three conditions.swyx: I just wanted to get a little bit of like the motivation, right? Like, so you left Shopify, you're like principal, engineer, infra guy. Um, you also head of kernel labs, uh, inside of Shopify, right?And then you consulted for read wise and that it kind of gave you that, that idea. I just wanted you to tell that story. Um, maybe I, you've told it before, but, uh, just introduce the, the. People to like the, the new workload, the sort of aha moment for turbo PufferSimon Hørup Eskildsen: For sure. So yeah, I spent almost a decade at Shopify.I was on the infrastructure team, um, from the fairly, fairly early days around 2013. Um, at the time it felt like it was growing so quickly and everything, all the metrics were, you know, doubling year on year compared to the, what companies are contending with today. It's very cute in growth. I feel like lot some companies are seeing that month over month.Um, of course. Shopify compound has been compounding for a very long time now, but I spent a decade doing that and the majority of that was just make sure the site is up today and make sure it's up a year from now. And a lot of that was really just the, um, you know, uh, the Kardashians would drive very, very large amounts of, of data to, to uh, to Shopify as they were rotating through all the merch and building out their businesses.And we just needed to make sure we could handle that. Right. And sometimes these were events, a million requests per second. And so, you know, we, we had our own data centers back in the day and we were moving to the cloud and there was so much sharding work and all of that that we were doing. So I spent a decade just scaling databases ‘cause that's fundamentally what's the most difficult thing to scale about these sites.The database that was the most difficult for me to scale during that time, and that was the most aggravating to be on call for, was elastic search. It was very, very difficult to deal with. And I saw a lot of projects that were just being held back in their ambition by using it.swyx: And I mean, self-hosted.Self-hosted. ‘causeSimon Hørup Eskildsen: it's, yeah, and it commercial, this is like 2015, right? So it's like a very particular vintage. Right. It's probably better at a lot of these things now. Um, it was difficult to contend with and I'm just like, I just think about it. It's an inverted index. It should be good at these kinds of queries and do all of this.And it was, we, we often couldn't get it to do exactly what we needed to do or basically get lucine to do, like expose lucine raw to, to, to what we needed to do. Um, so that was like. Just something that we did on the side and just panic scaled when we needed to, but not a particular focus of mine. So I left, and when I left, I, um, wasn't sure exactly what I wanted to do.I mean, it spent like a decade inside of the same company. I'd like grown up there. I started working there when I was 18.swyx: You only do Rails?Simon Hørup Eskildsen: Yeah. I mean, yeah. Rails. And he's a Rails guy. Uh, love Rails. So good. Um,Alessio: we all wish we could still work in Rails.swyx: I know know. I know, but some, I tried learning Ruby.It's just too much, like too many options to do the same thing. It's, that's my, I I know there's a, there's a way to do it.Simon Hørup Eskildsen: I love it. I don't know that I would use it now, like given cloud code and, and, and cursor and everything, but, um, um, but still it, like if I'm just sitting down and writing a teal code, that's how I think.But anyway, I left and I wasn't, I talked to a couple companies and I was like, I don't. I need to see a little bit more of the world here to know what I'm gonna like focus on next. Um, and so what I decided is like I was gonna, I called it like angel engineering, where I just hopped around in my friend's companies in three months increments and just helped them out with something.Right. And, and just vested a bit of equity and solved some interesting infrastructure problem. So I worked with a bunch of companies at the time, um, read Wise was one of them. Replicate was one of them. Um, causal, I dunno if you've tried this, it's like a, it's a spreadsheet engine Yeah. Where you can do distribution.They sold recently. Yeah. Um, we've been, we used that in fp and a at, um, at Turbo Puffer. Um, so a bunch of companies like this and it was super fun. And so we're the Chachi bt moment happened, I was with. With read Wise for a stint, we were preparing for the reader launch, right? Which is where you, you cue articles and read them later.And I was just getting their Postgres up to snuff, like, which basically boils down to tuning, auto vacuum. So I was doing that and then this happened and we were like, oh, maybe we should build a little recommendation engine and some features to try to hook in the lms. They were not that good yet, but it was clear there was something there.And so I built a small recommendation engine just, okay, let's take the articles that you've recently read, right? Like embed all the articles and then do recommendations. It was good enough that when I ran it on one of the co-founders of Rey's, like I found out that I got articles about, about having a child.I'm like, oh my God, I didn't, I, I didn't know that, that they were having a child. I wasn't sure what to do with that information, but the recommendation engine was good enough that it was suggesting articles, um, about that. And so there was, there was recommendations and uh, it actually worked really well.But this was a company that was spending maybe five grand a month in total on all their infrastructure and. When I did the napkin math on running the embeddings of all the articles, putting them into a vector index, putting it in prod, it's gonna be like 30 grand a month. That just wasn't tenable. Right?Like Read Wise is a proudly bootstrapped company and it's paying 30 grand for infrastructure for one feature versus five. It just wasn't tenable. So sort of in the bucket of this is useful, it's pretty good, but let us, let's return to it when the costs come down.swyx: Did you say it grows by feature? So for five to 30 is by the number of, like, what's the, what's the Scaling factor scale?It scales by the number of articles that you embed.Simon Hørup Eskildsen: It does, but what I meant by that is like five grand for like all of the other, like the Heroku, dinos, Postgres, like all the other, and this then storage is 30. Yeah. And then like 30 grand for one feature. Right. Which is like, what other articles are related to this one.Um, so it was just too much right to, to power everything. Their budget would've been maybe a few thousand dollars, which still would've been a lot. And so we put it in a bucket of, okay, we're gonna do that later. We'll wait, we will wait for the cost to come down. And that haunted me. I couldn't stop thinking about it.I was like, okay, there's clearly some latent demand here. If the cost had been a 10th, we would've shipped it and. This was really the only data point that I had. Right. I didn't, I, I didn't, I didn't go out and talk to anyone else. It was just so I started reading Right. I couldn't, I couldn't help myself.Like I didn't know what like a vector index is. I, I generally barely do about how to generate the vectors. There was a lot of hype about, this is a early 2023. There was a lot of hype about vector databases. There were raising a lot of money and it's like, I really didn't know anything about it. It's like, you know, trying these little models, fine tuning them.Like I was just trying to get sort of a lay of the land. So I just sat down. I have this. A GitHub repository called Napkin Math. And on napkin math, there's just, um, rows of like, oh, this is how much bandwidth. Like this is how many, you know, you can do 25 gigabytes per second on average to dram. You can do, you know, five gigabytes per second of rights to an SSD, blah blah.All of these numbers, right? And S3, how many you could do per, how much bandwidth can you drive per connection? I was just sitting down, I was like, why hasn't anyone build a database where you just put everything on O storage and then you puff it into NVME when you use the data and you puff it into dram if you're, if you're querying it alive, it's just like, this seems fairly obvious and you, the only real downside to that is that if you go all in on o storage, every right will take a couple hundred milliseconds of latency, but from there it's really all upside, right?You do the first go, it takes half a second. And it sort of occurred to me as like, well. The architecture is really good for that. It's really good for AB storage, it's really good for nvm ESSD. It's, well, you just couldn't have done that 10 years ago. Back to what we were talking about before. You really have to build a database where you have as few round trips as possible, right?This is how CPUs work today. It's how NVM E SSDs work. It's how as, um, as three works that you want to have a very large amount of outstanding requests, right? Like basically go to S3, do like that thousand requests to ask for data in one round trip. Wait for that. Get that, like, make a new decision. Do it again, and try to do that maybe a maximum of three times.But no databases were designed that way within NVME as is ds. You can drive like within, you know, within a very low multiple of DRAM bandwidth if you use it that way. And same with S3, right? You can fully max out the network card, which generally is not maxed out. You get very, like, very, very good bandwidth.And, but no one had built a database like that. So I was like, okay, well can't you just, you know, take all the vectors right? And plot them in the proverbial coordinate system. Get the clusters, put a file on S3 called clusters, do json, and then put another file for every cluster, you know, cluster one, do js O cluster two, do js ON you know that like it's two round trips, right?So you get the clusters, you find the closest clusters, and then you download the cluster files like the, the closest end. And you could do this in two round trips.swyx: You were nearest neighbors locally.Simon Hørup Eskildsen: Yes. Yes. And then, and you would build this, this file, right? It's just like ultra simplistic, but it's not a far shot from what the first version of Turbo Buffer was.Why hasn't anyone done thatAlessio: in that moment? From a workload perspective, you're thinking this is gonna be like a read heavy thing because they're doing recommend. Like is the fact that like writes are so expensive now? Oh, with ai you're actually not writing that much.Simon Hørup Eskildsen: At that point I hadn't really thought too much about, well no actually it was always clear to me that there was gonna be a lot of rights because at Shopify, the search clusters were doing, you know, I don't know, tens or hundreds of crew QPS, right?‘cause you just have to have a human sit and type in. But we did, you know, I don't know how many updates there were per second. I'm sure it was in the millions, right into the cluster. So I always knew there was like a 10 to 100 ratio on the read write. In the read wise use case. It's, um, even, even in the read wise use case, there'd probably be a lot fewer reads than writes, right?There's just a lot of churn on the amount of stuff that was going through versus the amount of queries. Um, I wasn't thinking too much about that. I was mostly just thinking about what's the fundamentally cheapest way to build a database in the cloud today using the primitives that you have available.And this is it, right? You just, now you have one machine and you know, let's say you have a terabyte of data in S3, you paid the $200 a month for that, and then maybe five to 10% of that data and needs to be an NV ME SSDs and less than that in dram. Well. You're paying very, very little to inflate the data.swyx: By the way, when you say no one else has done that, uh, would you consider Neon, uh, to be on a similar path in terms of being sort of S3 first and, uh, separating the compute and storage?Simon Hørup Eskildsen: Yeah, I think what I meant with that is, uh, just build a completely new database. I don't know if we were the first, like it was very much, it was, I mean, I, I hadn't, I just looked at the napkin math and was like, this seems really obvious.So I'm sure like a hundred people came up with it at the same time. Like the light bulb and every invention ever. Right. It was just in the air. I think Neon Neon was, was first to it. And they're trying, they're retrofitted onto Postgres, right? And then they built this whole architecture where you have, you have it in memory and then you sort of.You know, m map back to S3. And I think that was very novel at the time to do it for, for all LTP, but I hadn't seen a database that was truly all in, right. Not retrofitting it. The database felt built purely for this no consensus layer. Even using compare and swap on optic storage to do consensus. I hadn't seen anyone go that all in.And I, I mean, there, there, I'm sure there was someone that did that before us. I don't know. I was just looking at the napkin mathswyx: and, and when you say consensus layer, uh, are you strongly relying on S3 Strong consistency? You are. Okay.SoSimon Hørup Eskildsen: that is your consensus layer. It, it is the consistency layer. And I think also, like, this is something that most people don't realize, but S3 only became consistent in December of 2020.swyx: I remember this coming out during COVID and like people were like, oh, like, it was like, uh, it was just like a free upgrade.Simon Hørup Eskildsen: Yeah.swyx: They were just, they just announced it. We saw consistency guys and like, okay, cool.Simon Hørup Eskildsen: And I'm sure that they just, they probably had it in prod for a while and they're just like, it's done right.And people were like, okay, cool. But. That's a big moment, right? Like nv, ME SSDs, were also not in the cloud until around 2017, right? So you just sort of had like 2017 nv, ME SSDs, and people were like, okay, cool. There's like one skew that does this, whatever, right? Takes a few years. And then the second thing is like S3 becomes consistent in 2020.So now it means you don't have to have this like big foundation DB or like zookeeper or whatever sitting there contending with the keys, which is how. You know, that's what Snowflake and others have do so muchswyx: for goneSimon Hørup Eskildsen: Exactly. Just gone. Right? And so just push to the, you know, whatever, how many hundreds of people they have working on S3 solved and then compare and swap was not in S3 at this point in time,swyx: by the way.Uh, I don't know what that is, so maybe you wanna explain. Yes. Yeah.Simon Hørup Eskildsen: Yes. So, um, what Compare and swap is, is basically, you can imagine that if you have a database, it might be really nice to have a file called metadata json. And metadata JSON could say things like, Hey, these keys are here and this file means that, and there's lots of metadata that you have to operate in the database, right?But that's the simplest way to do it. So now you have might, you might have a lot of servers that wanna change the metadata. They might have written a file and want the metadata to contain that file. But you have a hundred nodes that are trying to contend with this metadata that JSON well, what compare and Swap allows you to do is basically just you download the file, you make the modifications, and then you write it only if it hasn't changed.While you did the modification and if not you retry. Right? Should just have this retry loops. Now you can imagine if you have a hundred nodes doing that, it's gonna be really slow, but it will converge over time. That primitive was not available in S3. It wasn't available in S3 until late 2024, but it was available in GCP.The real story of this is certainly not that I sat down and like bake brained it. I was like, okay, we're gonna start on GCS S3 is gonna get it later. Like it was really not that we started, we got really lucky, like we started on GCP and we started on GCP because tur um, Shopify ran on GCP. And so that was the platform I was most available with.Right. Um, and I knew the Canadian team there ‘cause I'd worked with them at Shopify and so it was natural for us to start there. And so when we started building the database, we're like, oh yeah, we have to build a, we really thought we had to build a consensus layer, like have a zookeeper or something to do this.But then we discovered the compare and swap. It's like, oh, we can kick the can. Like we'll just do metadata r json and just, it's fine. It's probably fine. Um, and we just kept kicking the can until we had very, very strong conviction in the idea. Um, and then we kind of just hinged the company on the fact that S3 probably was gonna get this, it started getting really painful in like mid 2024.‘cause we were closing deals with, um, um, notion actually that was running in AWS and we're like, trust us. You, you really want us to run this in GCP? And they're like, no, I don't know about that. Like, we're running everything in AWS and the latency across the cloud were so big and we had so much conviction that we bought like, you know, dark fiber between the AWS regions in, in Oregon, like in the InterExchange and GCP is like, we've never seen a startup like do like, what's going on here?And we're just like, no, we don't wanna do this. We were tuning like TCP windows, like everything to get the latency down ‘cause we had so high conviction in not doing like a, a metadata layer on S3. So those were the three conditions, right? Compare and swap. To do metadata, which wasn't in S3 until late 2024 S3 being consistent, which didn't happen until December, 2020.Uh, 2020. And then NVMe ssd, which didn't end in the cloud until 2017.swyx: I mean, in some ways, like a very big like cloud success story that like you were able to like, uh, put this all together, but also doing things like doing, uh, bind our favor. That that actually is something I've never heard.Simon Hørup Eskildsen: I mean, it's very common when you're a big company, right?You're like connecting your own like data center or whatever. But it's like, it was uniquely just a pain with notion because the, um, the org, like most of the, like if you're buying in Ashburn, Virginia, right? Like US East, the Google, like the GCP and, and AWS data centers are like within a millisecond on, on each other, on the public exchanges.But in Oregon uniquely, the GCP data center sits like a couple hundred kilometers, like east of Portland and the AWS region sits in Portland, but the network exchange they go through is through Seattle. So it's like a full, like 14 milliseconds or something like that. And so anyway, yeah. It's, it's, so we were like, okay, we can't, we have to go through an exchange in Portland.Yeah. Andswyx: you'd rather do this than like run your zookeeper and likeSimon Hørup Eskildsen: Yes. Way rather. It doesn't have state, I don't want state and two systems. Um, and I think all that is just informed by Justine, my co-founder and I had just been on call for so long. And the worst outages are the ones where you have state in multiple places that's not syncing up.So it really came from, from a a, like just a, a very pure source of pain, of just imagining what we would be Okay. Being woken up at 3:00 AM about and having something in zookeeper was not one of them.swyx: You, you're talking to like a notion or something. Do they care or do they just, theySimon Hørup Eskildsen: just, they care about latency.swyx: They latency cost. That's it.Simon Hørup Eskildsen: They just cared about latency. Right. And we just absorbed the cost. We're just like, we have high conviction in this. At some point we can move them to AWS. Right. And so we just, we, we'll buy the fiber, it doesn't matter. Right. Um, and it's like $5,000. Usually when you buy fiber, you buy like multiple lines.And we're like, we can only afford one, but we will just test it that when it goes over the public internet, it's like super smooth. And so we did a lot of, anyway, it's, yeah, it was, that's cool.Alessio: You can imagine talking to the GCP rep and it's like, no, we're gonna buy, because we know we're gonna turn, we're gonna turn from you guys and go to AWS in like six months.But in the meantime we'll do this. It'sSimon Hørup Eskildsen: a, I mean, like they, you know, this workload still runs on GCP for what it's worth. Right? ‘cause it's so, it was just, it was so reliable. So it was never about moving off GCP, it was just about honesty. It was just about giving notion the latency that they deserved.Right. Um, and we didn't want ‘em to have to care about any of this. We also, they were like, oh, egress is gonna be bad. It was like, okay, screw it. Like we're just gonna like vvc, VPC peer with you and AWS we'll eat the cost. Yeah. Whatever needs to be done.Alessio: And what were the actual workloads? Because I think when you think about ai, it's like 14 milliseconds.It's like really doesn't really matter in the scheme of like a model generation.Simon Hørup Eskildsen: Yeah. We were told the latency, right. That we had to beat. Oh, right. So, so we're just looking at the traces. Right. And then sort of like hand draw, like, you know, kind of like looking at the trace and then thinking what are the other extensions of the trace?Right. And there's a lot more to it because it's also when you have, if you have 14 versus seven milliseconds, right. You can fit in another round trip. So we had to tune TCP to try to send as much data in every round trip, prewarm all the connections. And there was, there's a lot of things that compound from having these kinds of round trips, but in the grand scheme it was just like, well, we have to beat the latency of whatever we're up against.swyx: Which is like they, I mean, notion is a database company. They could have done this themselves. They, they do lots of database engineering themselves. How do you even get in the door? Like Yeah, just like talk through that kind of.Simon Hørup Eskildsen: Last time I was in San Francisco, I was talking to one of the engineers actually, who, who was one of our champions, um, at, AT Notion.And they were, they were just trying to make sure that the, you know, per user cost matched the economics that they needed. You know, Uhhuh like, it's like the way I think about, it's like I have to earn a return on whatever the clouds charge me and then my customers have to earn a return on that. And it's like very simple, right?And so there has to be gross margin all the way up and that's how you build the product. And so then our customers have to make the right set of trade off the turbo Puffer makes, and if they're happy with that, that's great.swyx: Do you feel like you're competing with build internally versus buy or buy versus buy?Simon Hørup Eskildsen: Yeah, so, sorry, this was all to build up to your question. So one of the notion engineers told me that they'd sat and probably on a napkin, like drawn out like, why hasn't anyone built this? And then they saw terrible. It was like, well, it literally that. So, and I think AI has also changed the buy versus build equation in terms of, it's not really about can we build it, it's about do we have time to build it?I think they like, I think they felt like, okay, if this is a team that can do that and they, they feel enough like an extension of our team, well then we can go a lot faster, which would be very, very good for them. And I mean, they put us through the, through the test, right? Like we had some very, very long nights to to, to do that POC.And they were really our biggest, our second big customer off the cursor, which also was a lot of late nights. Right.swyx: Yeah. That, I mean, should we go into that story? The, the, the sort of Chris's story, like a lot, um, they credit you a lot for. Working very closely with them. So I just wanna hear, I've heard this, uh, story from Sole's point of view, but like, I'm curious what, what it looks like from your side.Simon Hørup Eskildsen: I actually haven't heard it from Sole's point of view, so maybe you can now cross reference it. The way that I remember it was that, um, the day after we launched, which was just, you know, I'd worked the whole summer on, on the first version. Justine wasn't part of it yet. ‘cause I just, I didn't tell anyone that summer that I was working on this.I was just locked in on building it because it's very easy otherwise to confuse talking about something to actually doing it. And so I was just like, I'm not gonna do that. I'm just gonna do the thing. I launched it and at this point turbo puffer is like a rust binary running on a single eight core machine in a T Marks instance.And me deploying it was like looking at the request log and then like command seeing it or like control seeing it to just like, okay, there's no request. Let's upgrade the binary. Like it was like literally the, the, the, the scrappiest thing. You could imagine it was on purpose because just like at Shopify, we did that all the time.Like, we like move, like we ran things in tux all the time to begin with. Before something had like, at least the inkling of PMF, it was like, okay, is anyone gonna hear about this? Um, and one of the cursor co-founders Arvid reached out and he just, you know, the, the cursor team are like all I-O-I-I-M-O like, um, contenders, right?So they just speak in bullet points and, and facts. It was like this amazing email exchange just of, this is how many QPS we have, this is what we're paying, this is where we're going, blah, blah, blah. And so we're just conversing in bullet points. And I tried to get a call with them a few times, but they were, so, they were like really writing the PMF bowl here, just like late 2023.And one time Swally emails me at like five. What was it like 4:00 AM Pacific time saying like, Hey, are you open for a call now? And I'm on the East coast and I, it was like 7:00 AM I was like, yeah, great, sure, whatever. Um, and we just started talking and something. Then I didn't know anything about sales.It was something that just comp compelled me. I have to go see this team. Like, there's something here. So I, I went to San Francisco and I went to their office and the way that I remember it is that Postgres was down when I showed up at the office. Did SW tell you this? No. Okay. So Postgres was down and so it's like they were distracting with that.And I was trying my best to see if I could, if I could help in any way. Like I knew a little bit about databases back to tuning, auto vacuum. It was like, I think you have to tune out a vacuum. Um, and so we, we talked about that and then, um, that evening just talked about like what would it look like, what would it look like to work with us?And I just said. Look like we're all in, like we will just do what we'll do whatever, whatever you tell us, right? They migrated everything over the next like week or two, and we reduced their cost by 95%, which I think like kind of fixed their per user economics. Um, and it solved a lot of other things. And we were just, Justine, this is also when I asked Justine to come on as my co-founder, she was the best engineer, um, that I ever worked with at Shopify.She lived two blocks away and we were just, okay, we're just gonna get this done. Um, and we did, and so we helped them migrate and we just worked like hell over the next like month or two to make sure that we were never an issue. And that was, that was the cursor story. Yeah.swyx: And, and is code a different workload than normal text?I, I don't know. Is is it just text? Is it the same thing?Simon Hørup Eskildsen: Yeah, so cursor's workload is basically, they, um, they will embed the entire code base, right? So they, they will like chunk it up in whatever they would, they do. They have their own embedding model, um, which they've been public about. Um, and they find that on, on, on their evals.It. There's one of their evals where it's like a 25% improvement on a very particular workload. They have a bunch of blog posts about it. Um, I think it works best on larger code basis, but they've trained their own embedding model to do this. Um, and so you'll see it if you use the cursor agent, it will do searches.And they've also been public around, um, how they've, I think they post trained their model to be very good at semantic search as well. Um, and that's, that's how they use it. And so it's very good at, like, can you find me on the code that's similar to this, or code that does this? And just in, in this queries, they also use GR to supplement it.swyx: Yeah.Simon Hørup Eskildsen: Um, of courseswyx: it's been a big topic of discussion like, is rag dead because gr you know,Simon Hørup Eskildsen: and I mean like, I just, we, we see lots of demand from the coding company to ethicsswyx: search in every part. Yes.Simon Hørup Eskildsen: Uh, we, we, we see demand. And so, I mean, I'm. I like case studies. I don't like, like just doing like thought pieces on this is where it's going.And like trying to be all macroeconomic about ai, that's has turned out to be a giant waste of time because no one can really predict any of this. So I just collect case studies and I mean, cursor has done a great job talking about what they're doing and I hope some of the other coding labs that use Turbo Puffer will do the same.Um, but it does seem to make a difference for particular queries. Um, I mean we can also do text, we can also do RegX, but I should also say that cursors like security posture into Tur Puffer is exceptional, right? They have their own embedding model, which makes it very difficult to reverse engineer. They obfuscate the file paths.They like you. It's very difficult to learn anything about a code base by looking at it. And the other thing they do too is that for their customers, they encrypt it with their encryption keys in turbo puffer's bucket. Um, so it's, it's, it's really, really well designed.swyx: And so this is like extra stuff they did to work with you because you are not part of Cursor.Exactly like, and this is just best practice when working in any database, not just you guys. Okay. Yeah, that makes sense. Yeah. I think for me, like the, the, the learning is kind of like you, like all workloads are hybrid. Like, you know, uh, like you, you want the semantic, you want the text, you want the RegX, you want sql.I dunno. Um, but like, it's silly to like be all in on like one particularly query pattern.Simon Hørup Eskildsen: I think, like I really like the way that, um, um, that swally at cursor talks about it, which is, um, I'm gonna butcher it here. Um, and you know, I'm a, I'm a database scalability person. I'm not a, I, I dunno anything about training models other than, um, what the internet tells me and what.The way he describes is that this is just like cash compute, right? It's like you have a point in time where you're looking at some particular context and focused on some chunk and you say, this is the layer of the neural net at this point in time. That seems fundamentally really useful to do cash compute like that.And, um, how the value of that will change over time. I'm, I'm not sure, but there seems to be a lot of value in that.Alessio: Maybe talk a bit about the evolution of the workload, because even like search, like maybe two years ago it was like one search at the start of like an LLM query to build the context. Now you have a gentech search, however you wanna call it, where like the model is both writing and changing the code and it's searching it again later.Yeah. What are maybe some of the new types of workloads or like changes you've had to make to your architecture for it?Simon Hørup Eskildsen: I think you're right. When I think of rag, I think of, Hey, there's an 8,000 token, uh, context window and you better make it count. Um, and search was a way to do that now. Everything is moving towards the, just let the agent do its thing.Right? And so back to the thing before, right? The LLM is very good at reasoning with the data, and so we're just the tool call, right? And that's increasingly what we see our customers doing. Um, what we're seeing more demand from, from our customers now is to do a lot of concurrency, right? Like Notion does a ridiculous amount of queries in every round trip just because they can't.And I'm also now, when I use the cursor agent, I also see them doing more concurrency than I've ever seen before. So a bit similar to how we designed a database to drive as much concurrency in every round trip as possible. That's also what the agents are doing. So that's new. It means just an enormous amount of queries all at once to the dataset while it's warm in as few turns as possible.swyx: Can I clarify one thing on that?Simon Hørup Eskildsen: Yes.swyx: Is it, are they batching multiple users or one user is driving multiple,Simon Hørup Eskildsen: one user driving multiple, one agent driving.swyx: It's parallel searching a bunch of things.Simon Hørup Eskildsen: Exactly.swyx: Yeah. Yeah, exactly. So yeah, the clinician also did, did this for the fast context thing, like eight parallel at once.Simon Hørup Eskildsen: Yes.swyx: And, and like an interesting problem is, well, how do you make sure you have enough diversity so you're not making the the same request eight times?Simon Hørup Eskildsen: And I think like that's probably also where the hybrid comes in, where. That's another way to diversify. It's a completely different way to, to do the search.That's a big change, right? So before it was really just like one call and then, you know, the LLM took however many seconds to return, but now we just see an enormous amount of queries. So the, um, we just see more queries. So we've like tried to reduce query, we've reduced query pricing. Um, this is probably the first time actually I'm saying that, but the query pricing is being reduced, like five x.Um, and we'll probably try to reduce it even more to accommodate some of these workloads of just doing very large amounts of queries. Um, that's one thing that's changed. I think the right, the right ratio is still very high, right? Like there's still a, an enormous amount of rights per read, but we're starting probably to see that change if people really lean into this pattern.Alessio: Can we talk a little bit about the pricing? I'm curious, uh, because traditionally a database would charge on storage, but now you have the token generation that is so expensive, where like the actual. Value of like a good search query is like much higher because they're like saving inference time down the line.How do you structure that as like, what are people receptive to on the other side too?Simon Hørup Eskildsen: Yeah. I, the, the turbo puffer pricing in the beginning was just very simple. The pricing on these on for search engines before Turbo Puffer was very server full, right? It was like, here's the vm, here's the per hour cost, right?Great. And I just sat down with like a piece of paper and said like, if Turbo Puffer was like really good, this is probably what it would cost with a little bit of margin. And that was the first pricing of Turbo Puffer. And I just like sat down and I was like, okay, like this is like probably the storage amp, but whenever on a piece of paper I, it was vibe pricing.It was very vibe price, and I got it wrong. Oh. Um, well I didn't get it wrong, but like Turbo Puffer wasn't at the first principle pricing, right? So when Cursor came on Turbo Puffer, it was like. Like, I didn't know any VCs. I didn't know, like I was just like, I don't know, I didn't know anything about raising money or anything like that.I just saw that my GCP bill was, was high, was a lot higher than the cursor bill. So Justine and I was just like, well, we have to optimize it. Um, and I mean, to the chagrin now of, of it, of, of the VCs, it now means that we're profitable because we've had so much pricing pressure in the beginning. Because it was running on my credit card and Justine and I had spent like, like tens of thousands of dollars on like compute bills and like spinning off the company and like very like, like bad Canadian lawyers and like things like to like get all of this done because we just like, we didn't know.Right. If you're like steeped in San Francisco, you're just like, you just know. Okay. Like you go out, raise a pre-seed round. I, I never heard a word pre-seed at this point in time.swyx: When you had Cursor, you had Notion you, you had no funding.Simon Hørup Eskildsen: Um, with Cursor we had no funding. Yeah. Um, by the time we had Notion Locke was, Locke was here.Yeah. So it was really just, we vibe priced it 100% from first Principles, but it wasn't, it, it was not performing at first principles, so we just did everything we could to optimize it in the beginning for that, so that at least we could have like a 5% margin or something. So I wasn't freaking out because Cursor's bill was also going like this as they were growing.And so my liability and my credit limit was like actively like calling my bank. It was like, I need a bigger credit. Like it was, yeah. Anyway, that was the beginning. Yeah. But the pricing was, yeah, like storage rights and query. Right. And the, the pricing we have today is basically just that pricing with duct tape and spit to try to approach like, you know, like a, as a margin on the physical underlying hardware.And we're doing this year, you're gonna see more and more pricing changes from us. Yeah.swyx: And like is how much does stuff like VVC peering matter because you're working in AWS land where egress is charged and all that, you know.Simon Hørup Eskildsen: We probably don't like, we have like an enterprise plan that just has like a base fee because we haven't had time to figure out SKU pricing for all of this.Um, but I mean, yeah, you can run turbo puffer either in SaaS, right? That's what Cursor does. You can run it in a single tenant cluster. So it's just you. That's what Notion does. And then you can run it in, in, in BYOC where everything is inside the customer's VPC, that's what an for example, philanthropic does.swyx: What I'm hearing is that this is probably the best CRO job for somebody who can come in and,Simon Hørup Eskildsen: I mean,swyx: help you with this.Simon Hørup Eskildsen: Um, like Turbo Puffer hired, like, I don't know what, what number this was, but we had a full-time CFO as like the 12th hire or something at Turbo Puffer, um, I think I hear are a lot of comp.I don't know how they do it. Like they have a hundred employees and not a CFO. It's like having a CFO is like a runningswyx: business man. Like, you know,Simon Hørup Eskildsen: it's so good. Yeah, like money Mike, like he just, you know, just handles the money and a lot of the business stuff and so he came in and just hopped with a lot of the operational side of the business.So like C-O-O-C-F-O, like somewhere in between.swyx: Just as quick mention of Lucky, just ‘cause I'm curious, I've met Lock and like, he's obviously a very good investor and now on physical intelligence, um, I call it generalist super angel, right? He invests in everything. Um, and I always wonder like, you know, is there something appealing about focusing on developer tooling, focusing on databases, going like, I've invested for 10 years in databases versus being like a lock where he can maybe like connect you to all the customers that you need.Simon Hørup Eskildsen: This is an excellent question. No, no one's asked me this. Um, why lockey? Because. There was a couple of people that we were talking to at the time and when we were raising, we were almost a little, we were like a bit distressed because one of our, one of our peers had just launched something that was very similar to Turbo Puffer.And someone just gave me the advice at the time of just choose the person where you just feel like you can just pick up the phone and not prepare anything. And just be completely honest, and I don't think I've said this publicly before, but I just called Lockey and was like local Lockie. Like if this doesn't have PMF by the end of the year, like we'll just like return all the money to you.But it's just like, I don't really, we, Justine and I don't wanna work on this unless it's really working. So we want to give it the best shot this year and like we're really gonna go for it. We're gonna hire a bunch of people and we're just gonna be honest with everyone. Like when I don't know how to play a game, I just play with open cards and.Lockey was the only person that didn't, that didn't freak out. He was like, I've never heard anyone say that before. As I said, I didn't even know what a seed or pre-seed round was like before, probably even at this time. So I was just like very honest with him. And I asked him like, Lockie, have you ever have, have you ever invested in database company?He was just like, no. And at the time I was like, am I dumb? Like, but I think there was something that just like really drew me to Lockie. He is so authentic, so honest, like, and there was something just like, I just felt like I could just play like, just say everything openly. And that was, that was, I think that that was like a perfect match at the time, and, and, and honestly still is.He was just like, okay, that's great. This is like the most honest, ridiculous thing I've ever heard anyone say to me. But like that, like that, whyswyx: is this ridiculous? Say competitor launch, this may not work out. It wasSimon Hørup Eskildsen: more just like. If this doesn't work out, I'm gonna close up shop by the end of the mo the year, right?Like it was, I don't know, maybe it's common. I, I don't know. He told me it was uncommon. I don't know. Um, that's why we chose him and he'd been phenomenal. The other people were talking at the, at the time were database experts. Like they, you know, knew a lot about databases and Locke didn't, this turned out to be a phenomenal asset.Right. I like Justine and I know a lot about databases. The people that we hire know a lot about databases. What we needed was just someone who didn't know a lot about databases, didn't pretend to know a lot about databases, and just wanted to help us with candidates and customers. And he did. Yeah. And I have a list, right, of the investors that I have a relationship with, and Lockey has just performed excellent in the number of sub bullets of what we can attribute back to him.Just absolutely incredible. And when people talk about like no ego and just the best thing for the founder, I like, I don't think that anyone, like even my lawyer is like, yeah, Lockey is like the most friendly person you will find.swyx: Okay. This is my most glow recommendation I've ever heard.Alessio: He deserves it.He's very special.swyx: Yeah. Yeah. Yeah. Okay. Amazing.Alessio: Since you mentioned candidates, maybe we can talk about team building, you know, like, especially in sf, it feels like it's just easier to start a company than to join a company. Uh, I'm curious your experience, especially not being n SF full-time and doing something that is maybe, you know, a very low level of detail and technical detail.Simon Hørup Eskildsen: Yeah. So joining versus starting, I never thought that I would be a founder. I would start with it, like Turbo Puffer started as a blog post, and then it became a project and then sort of almost accidentally became a company. And now it feels like it's, it's like becoming a bigger company. That was never the intention.The intentions were very pure. It's just like, why hasn't anyone done this? And it's like, I wanna be the, like, I wanna be the first person to do it. I think some founders have this, like, I could never work for anyone else. I, I really don't feel that way. Like, it's just like, I wanna see this happen. And I wanna see it happen with some people that I really enjoy working with and I wanna have fun doing it and this, this, this has all felt very natural on that, on that sense.So it was never a like join versus versus versus found. It was just dis found me at the right moment.Alessio: Well I think there's an argument for, you should have joined Cursor, right? So I'm curious like how you evaluate it. Okay, I should actually go raise money and make this a company versus like, this is like a company that is like growing like crazy.It's like an interesting technical problem. I should just build it within Cursor and then they don't have to encrypt all this stuff. They don't have to obfuscate things. Like was that on your mind at all orSimon Hørup Eskildsen: before taking the, the small check from Lockie, I did have like a hard like look at myself in the mirror of like, okay, do I really want to do this?And because if I take the money, I really have to do it right. And so the way I almost think about it's like you kind of need to ha like you kind of need to be like fucked up enough to want to go all the way. And that was the conversation where I was like, okay, this is gonna be part of my life's journey to build this company and do it in the best way that I possibly can't.Because if I ask people to join me, ask people to get on the cap table, then I have an ultimate responsibility to give it everything. And I don't, I think some people, it doesn't occur to me that everyone takes it that seriously. And maybe I take it too seriously, I don't know. But that was like a very intentional moment.And so then it was very clear like, okay, I'm gonna do this and I'm gonna give it everything.Alessio: A lot of people don't take it this seriously. But,swyx: uh, let's talk about, you have this concept of the P 99 engineer. Uh, people are 10 x saying, everyone's saying, you know, uh, maybe engineers are out of a job. I don't know.But you definitely see a P 99 engineer, and I just want you to talk about it.Simon Hørup Eskildsen: Yeah, so the P 99 engineer was just a term that we started using internally to talk about candidates and talk about how we wanted to build the company. And you know, like everyone else is, like we want a talent dense company.And I think that's almost become trite at this point. What I credit the cursor founders a lot with is that they just arrived there from first principles of like, we just need a talent dense, um, talent dense team. And I think I've seen some teams that weren't talent dense and like seemed a counterfactual run, which if you've run in been in a large company, you will just see that like it's just logically will happen at a large company.Um, and so that was super important to me and Justine and it's very difficult to maintain. And so we just needed, we needed wording for it. And so I have a document called Traits of the P 99 Engineer, and it's a bullet point list. And I look at that list after every single interview that I do, and in every single recap that we do and every recap we end with.End with, um, some version of I'm gonna reject this candidate completely regardless of what the discourse was, because I wanna see people fight for this person because the default should not be, we're gonna hire this person. The default should be, we're definitely not hiring this person. And you know, if everyone was like, ah, maybe throw a punch, then this is not the right.swyx: Do, do you operate, like if there's one cha there must have at least one champion who's like, yes, I will put my career on, on, on the line for this. You know,Simon Hørup Eskildsen: I think career on the line,swyx: maybe a chair, butSimon Hørup Eskildsen: yeah. You know, like, um, I would say so someone needs to like, have both fists up and be like, I'd fight.Right? Yeah. Yeah. And if one person said, then, okay, let's do it. Right?swyx: Yeah.Simon Hørup Eskildsen: Um. It doesn't have to be absolutely everyone. Right? And like the interviews are always the sign that you're checking for different attributes. And if someone is like knocking it outta the park in every single attribute, that's, that's fairly rare.Um, but that's really important. And so the traits of the P 99 engineer, there's lots of them. There's also the traits of the p like triple nine engineer and the quadruple nine engineer. This is like, it's a long list.swyx: Okay.Simon Hørup Eskildsen: Um, I'll give you some samples, right. Of what we, what we look for. I think that the P 99 engineer has some history of having bent, like their trajectory or something to their will.Right? Some moment where it was just, they just, you know, made the computer do what it needed to do. There's something like that, and it will, it will occur to have them at some point in their career. And, uh. Hopefully multiple times. Right.swyx: Gimme an example of one of your engineers that like,Simon Hørup Eskildsen: I'll give an eng.Uh, so we, we, we launched this thing called A and NV three. Um, we could, we're also, we're working on V four and V five right now, but a and NV three can search a hundred billion vectors with a P 50 of around 40 milliseconds and a p 99 of 200 milliseconds. Um, maybe other people have done this, I'm sure Google and others have done this, but, uh, we haven't seen anyone, um, at least not in like a public consumable SaaS that can do this.And that was an engineer, the chief architect of Turbo Puffer, Nathan, um, who more or less just bent this, the software was not capable of this and he just made it capable for a very particular workload in like a, you know, six to eight week period with the help of a lot of the team. Right. It's been, been, there's numerous of examples of that, like at, at turbo puff, but that's like really bending the software and X 86 to your will.It was incredible to watch. Um. You wanna see some moments like that?swyx: Isn't that triple nine?Simon Hørup Eskildsen: Um, I think Nathan, what's calledAlessio: group nine, that was only nine. I feel like this is too high forSimon Hørup Eskildsen: Nathan. Nathan is, uh, Nathan is like, yeah, there's a lot of nines. Okay. After that p So I think that's one trait. I think another trait is that, uh, the P 99 spends a lot of time looking at maps.Generally it's their preferred ux. They just love looking at maps. You ever seen someone who just like, sits on their phone and just like, scrolls around on a map? Or did you not look at maps A lot? You guys don't look atswyx: maps? I guess I'm not feeling there. I don't know, butSimon Hørup Eskildsen: you just dis What about trains?Do you like trains?swyx: Uh, I mean they, not enough. Okay. This is just like weapon nice. Autism is what I call it. Like, like,Simon Hørup Eskildsen: um, I love looking at maps, like, it's like my preferred UX and just like I, you know, I likeswyx: lotsAlessio: of, of like random places, soswyx: like,youswyx: know.Alessio: Yes. Okay. There you go. So instead of like random places, like how do you explore the maps?Simon Hørup Eskildsen: No, it's, it's just a joke.swyx: It's autism laugh. It's like you are just obsessed by something and you like studying a thing.Simon Hørup Eskildsen: The origin of this was that at some point I read an interview with some IOI gold medalistswyx: Uhhuh,Simon Hørup Eskildsen: and it's like, what do you do in your spare time? I was just like, I like looking at maps.I was like, I feel so seen. Like, I just like love, like swirling out. I was like, oh, Canada is so big. Where's Baffin Island? I don't know. I love it. Yeah. Um, anyway, so the traits of P 99, P 99 is obsessive, right? Like, there's just like, you'll, you'll find traits of that we do an interview at, at, at, at turbo puffer or like multiple interviews that just try to screen for some of these things.Um, so. There's lots of others, but these are the kinds of traits that we look for.swyx: I'll tell you, uh, some people listen for like some of my dere stuff. Uh, I do think about derel as maps. Um, you draw a map for people, uh, maps show you the, uh, what is commonly agreed to be the geographical features of what a boundary is.And it shows also shows you what is not doing. And I, I think a lot of like developer tools, companies try to tell you they can do everything, but like, let's, let's be real. Like you, your, your three landmarks are here, everyone comes here, then here, then here, and you draw a map and, and then you draw a journey through the map.And like that. To me, that's what developer relations looks like. So I do think about things that way.Simon Hørup Eskildsen: I think the P 99 thinks in offs, right? The P 99 is very clear about, you know, hey, turbo puffer, you can't run a high transaction workload on turbo puffer, right? It's like the right latency is a hundred milliseconds.That's a clear trade off. I think the P 99 is very good at articulating the trade offs in every decision. Um. Which is exactly what the map is in your case, right?swyx: Uh, yeah, yeah. My, my, my world. My world.Alessio: How, how do you reconcile some of these things when you're saying you bend the will the computer versus like the trade

Chegamos ao final da nossa série "Probióticos em Foco"! Para fechar com chave de ouro, o Dr. Dan Waitzberg recebe a farmacêutica Natália Wagner, Gerente de Suporte Técnico da IFF Health Sciences, para revelar o que acontece ANTES de um probiótico chegar às suas mãos.Você já se perguntou:Como uma cepa é escolhida?Como se garante que bilhões de bactérias cheguem vivas ao consumidor?E o que são todos aqueles outros nomes no rótulo?Este episódio é uma imersão na ciência, tecnologia e no rigoroso controle de qualidade por trás de um suplemento probiótico eficaz e seguro.ReferênciasHill C, Guarner F, Reid G, Gibson GR, Merenstein DJ, Pot B, et al. The International Scientific Association for Probiotics and Prebiotics consensus statement on the scope and appropriate use of the term probiotic. Nat Rev Gastroenterol Hepatol. 2014;11(8):506–14.FAO/WHO. Guidelines for the Evaluation of Probiotics in Food. London: WHO; 2002.Sanders ME, Merenstein DJ, Reid G, Gibson GR, Rastall R. Probiotics and prebiotics in intestinal health and disease: from biology to the clinic. Nat Rev Gastroenterol Hepatol. 2019;16(10):605–16.Vinderola G, Ouwehand A, Salminen S, von Wright A. Lactic Acid Bacteria: Microbiological and Functional Aspects. 5th ed. Boca Raton: CRC Press; 2019.Champagne CP, Ross RP, Buckley N, Fitzgerald GF. Carnitine, trehalose and maltodextrin as cryoprotectants during freeze-drying of probiotic bacteria. J Food Sci. 2005;70(9):M500–7.Anal AK, Singh H. Recent advances in microencapsulation of probiotics for industrial applications and targeted delivery. Trends Food Sci Technol. 2007;18(5):240–51.Reid G. Regulatory considerations of: (i) probiotics in foods; and (ii) therapeutic uses of probiotics. Curr Opin Clin Nutr Metab Care. 2016;19(6):506–11.EFSA Panel on Biological Hazards (BIOHAZ). Scientific Opinion on the maintenance of the list of “QPS” microorganisms intentionally added to food or feed. EFSA J. 2020;18(2):5966.Lehmann FS, Jehle EC, Drewe J. Safety assessment of probiotic strains: focus on acquired antimicrobial resistance. Hum Microbiome J. 2018;8-9:20–34.

Here's the thing. Most enterprise AI talk today starts with chatbots and ends with glossy demos. Meanwhile, the data that actually runs a business lives in rows, columns, and time stamps. That gap is where my conversation with Vanja Josifovski, CEO of Kuma.ai, really comes alive. Vanja has spent two and a half decades helping companies turn data into decisions, from research roles at Yahoo and Google to steering product and engineering at Pinterest through its IPO and later leading Airbnb Homes. He's now building Kuma.ai to answer an old question with a new approach: how do you get accurate, production-grade predictions from relational data without spending months crafting a bespoke model for each use case? Vanja explains why structured business data has been underserved for years. Images and text behave nicely compared to the messy reality of multiple tables, mixed data types, and event histories. Traditional teams anticipate a prediction need, then kick off a long feature engineering and modeling process. Kuma's Relational Foundation Model, or RFM, flips that script. Pre-trained on a large mix of public and synthetic data warehouses, it delivers task-agnostic, zero-shot predictions for problems like churn and fraud. That means you can ask the model questions directly of your data and get useful answers fast, then fine-tune for another 15 to 20 percent uplift when you're ready to squeeze more from your full dataset. What stood out for me is how Kuma removes the grind of manual feature creation. Vanja draws a clear parallel to computer vision's shift years ago, when teams stopped handcrafting edge detectors and started learning from raw pixels. By learning directly from raw tables, Kuma taps the entirety of the data rather than a bundle of human-crafted summaries. The payoff shows up in the numbers customers care about, with double-digit improvements against mature, well-defended baselines and the kind of time savings that change roadmaps. One customer built sixty models in two weeks, a job that would typically span a year or more. We also explore how this fits with the LLM moment. Vanja doesn't position RFM as a replacement for language models. He frames it as a complement that fills an accuracy gap on tabular data where LLMs often drift. Think of RFM as part of an agentic toolbox: when an agent needs a reliable prediction from enterprise data, it can call Kuma instead of generating code, training a fresh model, or bluffing an answer. That design extends to the realities of production as well. Kuma's fine-tuning and serving stack is built for high-QPS environments, the kind you see in recommendations and ad tech, where cost and latency matter. The training story is another thread you'll hear in this episode. The team began with public datasets, then leaned into synthetic data to cover scenarios that are hard to source in the wild. Synthetic generation gives them better control over distribution shifts and edge cases, which speeds iteration and makes the foundation model more broadly capable upon arrival. If you care about measurable outcomes, this episode shows why CFOs pay attention when RFM lands. Vanja shares examples where a 20 to 30 percent lift translates into hundreds of thousands of additional monthly active users and direct revenue impact. That kind of improvement isn't theory. It's the difference between a model that nudges a metric and a model that moves it. By the end, you'll have a clear picture of what Kuma.ai is building, why relational data warrants its own foundation model, and how enterprises can move from wishful thinking to practical wins. Curious to try it yourself? Vanja also points to a sandbox where teams can load data and ask predictive questions within a notebook, then compare results against in-house models. If your AI plans keep stalling on tabular reality, this conversation offers a way forward that's fast, accurate, and designed for the systems you already run.

The Magistrates' Court has found in favour of the QPS and the protest has been deemed unauthorised. See omnystudio.com/listener for privacy information.

Around 7000 Pro-Palestine protestors had planned to march across the Story Bridge with the QPS now saying that this demonstration will be unsafe. One of the organisers of this event, Omar Ashour from Justice For Palestine joined Peter Fegan on 4BC Breakfast to discuss the planned protest.See omnystudio.com/listener for privacy information.

On Sunday, August 24, thousands of protesters are expected to take part in a pro-Palestine event planned for the Story Bridge. Peter Fegan had his say on the matter including what the QPS have said about the planned protest.See omnystudio.com/listener for privacy information.

Heute spricht Max Kraft über ein Thema, das aktueller nicht sein könnte: Selbstständigkeit – oder wie Max es nennt: "I Love Selbstständigkeit". Gemeinsam mit Michael Paar, Apotheker und seit über zehn Jahren erfolgreicher freiberuflicher QP, geht es um die Herausforderungen, Chancen und Missverständnisse rund um das Thema. Ausgangspunkt ist eine Kampagne des Bundesverbands der Selbstständigen, die mehr Wertschätzung und bessere Bedingungen fordert. Max und Michael diskutieren die aktuelle Lage, rechtliche Hürden, mentale Blockaden und warum die Angst vor Haftung oft unbegründet ist. Michael teilt offen seine Learnings, seine Beweggründe für die Selbstständigkeit und warum er überzeugt ist, dass mehr Menschen diesen Schritt wagen sollten – gerade in der Pharma- und Medizintechnikbranche, wo Selbstständige oft die Lücken füllen, die festangestellte Strukturen nicht abdecken können. Außerdem sprechen die beiden über die neue QP-Schulung am 4. Dezember, die speziell für angehende oder unsichere QPs konzipiert ist.

View the Show Notes Page for This Episode Become a Member to Receive Exclusive Content Sign Up to Receive Peter's Weekly Newsletter In this quarterly podcast summary (QPS) episode, Peter summarizes his biggest takeaways from the last three months of guest interviews on the podcast. Peter shares key insights from his discussions with Paul Turek and Paula Amato on male and female fertility; Rachel Rubin on menopause and hormone replacement therapy; Brian Kennedy on the biology of aging; Tanuj Nakra and Suzan Obagi on facial aging and skin rejuvenation; and BJ Miller and Bridget Sumser on lessons we can learn from the dying about how to live. Peter highlights the most important insights from each episode and any behavioral changes he's made for himself or his patients as a result of these fascinating discussions. If you're not a subscriber and are listening on a podcast player, you'll only be able to hear a preview of the AMA. If you're a subscriber, you can now listen to this full episode on your private RSS feed or our website at the episode #358 show notes page. If you are not a subscriber, you can learn more about the subscriber benefits here. We discuss: Summary of episode topics [1:15]; Episodes on fertility with Paul Turek and Paula Amato: insights on all things male and female fertility [4:45]; How men can optimize fertility [20:15]; How women can optimize fertility [26:00]; Rachel Rubin episode: insights on women's sexual health, menopause, and HRT [31:45]; How women can prepare for menopause: proactive care, evidence-based HRT, and more [41:45]; Brian Kennedy episode: understanding aging, role of inflammation and mTOR, and current limitations of aging clocks and biomarkers [46:30]; Advice from Brian Kennedy on testing longevity interventions [56:45]; Tanuj Nakra/Suzan Obagi episode: causes of facial aging and practical strategies for prevention and treatment [57:30]; Skincare: making sense of the wide range of skin resurfacing treatments [1:06:45]; How to create a realistic, sustainable skincare routine [1:12:30]; The dangers of following unqualified aesthetic advice online and the importance of getting professional medical guidance for cosmetic treatments [1:18:00]; BJ Miller/Bridget Sumser episode: lessons about living from the dying [1:21:45]; and More. Connect With Peter on Twitter, Instagram, Facebook and YouTube

Taxa de quadros acima de 400 QPS

This week, Peter Deegan sits down with newly qualified QP Mike Hadebe, who shares his inspiring path from NHS pharmacist to QA specialist and ultimately to QP success. In this episode, Mike opens up about: - How he built a support network of QPs to learn from diverse experiences - The value his clinical background brought to the QP application process - Why staying sharp on regulatory changes is essential ahead of your Viva Mike's journey is a must-listen for anyone on the QP path or for QP mentors. - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - If you'd like to follow in Mike's footsteps you can start with us by checking out our suite of QP courses here: https://www.rssl.com/life-science-training-consultancy/qualified-person-training-programme/

View the Show Notes Page for This Episode Become a Member to Receive Exclusive Content Sign Up to Receive Peter's Weekly Newsletter In this quarterly podcast summary (QPS) episode, Peter summarizes his biggest takeaways from the last three months of guest interviews on the podcast. Peter shares key insights from his discussions with Jeff English on the journey to healing from trauma; Ashley Mason on improving sleep and CBT-I; Sanjay Mehta on misconceptions around radiation and its use in cancer therapy and treating inflammatory conditions (such as arthritis and tendonitis); Sean Mackey on understanding and treating acute and chronic pain; and Susan Desmond-Hellmann on insights from her extraordinary career that pertain to the use of AI in medicine, understanding cancer, and the development of cancer therapeutics. Additionally, Peter shares any behavioral changes he's made for himself or his patients as a result of these fascinating discussions. If you're not a subscriber and are listening on a podcast player, you'll only be able to hear a preview of the AMA. If you're a subscriber, you can now listen to this full episode on your private RSS feed or our website at the episode #347 show notes page. If you are not a subscriber, you can learn more about the subscriber benefits here. We discuss: Summary of episode topics [1:45]; Jeff English episode: how trauma shapes behavior and identity, and the value of understanding personal adaptations and working through unresolved emotional wounds [3:45]; Practical behavioral changes and emotional tools Peter has applied since the Jeff English episode [13:00]; Ashley Mason episode: treating insomnia using CBT-I and practical behavioral techniques for improving sleep quality [19:15]; When to seek professional care for sleep issues [30:30]; Sanjay Mehta episode: radiation therapy's evolution, its underused potential in treating inflammatory conditions, and the cultural misconceptions surrounding radiation exposure [33:45]; Peter's predictions and insights for the upcoming Formula 1 season [43:15]; Sean Mackey episode: the neuroscience, classifications, and treatment strategies for chronic pain, and the importance of personalized care [57:45]; Susan Desmond-Hellmann episode: how AI is revolutionizing medicine through advancements in drug development, biomarker discovery, and the potential of training models on private clinical data [1:05:45]; More from Susan Desmond-Hellmann: why cancer is so difficult to treat with drugs, the promise of immunotherapy, and the long-term hope for systemic treatments [1:14:00]; and More. Connect With Peter on Twitter, Instagram, Facebook and YouTube

View the Show Notes Page for This Episode Become a Member to Receive Exclusive Content Sign Up to Receive Peter's Weekly Newsletter In this quarterly podcast summary (QPS) episode, Peter summarizes his biggest takeaways from the last three months of guest interviews on the podcast. Peter shares key insights from his discussions on diverse topics such as aerobic efficiency and VO2 max with Olav Aleksander Bu; insulin resistance with Ralph DeFronzo; economics of the US healthcare system and cost-saving strategies with Saum Sutaria; diagnosis and treatment of autism, ADHD, and anxiety in children with Trenna Sutcliffe; and strength training with Mike Israetel. Additionally, Peter shares any personal behavioral adjustments or modifications to his patient care practices that have arisen from these fascinating discussions. If you're not a subscriber and are listening on a podcast player, you'll only be able to hear a preview of the AMA. If you're a subscriber, you can now listen to this full episode on your private RSS feed or our website at the episode #338 show notes page. If you are not a subscriber, you can learn more about the subscriber benefits here. We discuss: Overview of topics to be discussed [2:00]; Olav Aleksander Bu Pt.2 episode: metrics to track aerobic efficiency and insights about VO2 max, and the ability of increased carbohydrate consumption to boost performance [4:30]; The best practices for performing a VO2 max test, the differences between VO2 max training and all-out efforts, and the role of energy intake in endurance performance [14:45]; Ralph DeFronzo episode: the pathophysiology of insulin resistance and type 2 diabetes—how they impact different organs, flaws in conventional diabetes treatment, and more [24:30]; Understanding type 2 diabetes beyond the traditional triumvirate of features: the “ominous octet” describes changes in other organs [31:45]; Pharmacological treatments for insulin resistance and type 2 diabetes [41:30]; The importance of early detection and intervention in insulin resistance [50:30]; Saum Sutaria episode: the economic and systemic drivers of high healthcare costs in the U.S. [54:00]; Reducing health care costs: redefining health insurance, lowering drug prices while maintaining innovation, leveraging AI for efficiency, and more [1:07:15]; Trenna Sutcliffe episode: insights on autism, ADHD, and anxiety in children—definitions and diagnosis [1:11:45]; Exploring the rising prevalence of autism spectrum disorder [1:17:15]; Trenna's views on caring for children with autism [1:21:15]; Misconceptions around vaccines and autism [1:26:00]; Mike Israetel episode: insights about strength training, minimum effective dose, troubleshooting plateaus, tips for beginners, and more [1:28:15]; Topics Peter is interested in exploring in future podcasts [1:40:15]; and More. Connect With Peter on Twitter, Instagram, Facebook and YouTube

The rise of antisemitism in Australia is apparent, particularly in the last few months. For the most part, Queensland has avoided this rise in hate thanks to the state's security and counter terrorism team. Assistant Commissioner Charysse Pond told Peter Fegan on 4BC Breakfast, "I want to assure our community that the QPS is doing everything in our power to detect, disrupt and investigate all acts associated with terrorism and that the community can feel safe," AC Pond said.See omnystudio.com/listener for privacy information.

Yesterday, Defenders for Hope, a domestic violence charity, revealed many victims were living in tents in a Logan park. Acting Mayor of Logan, Scott Bannon, told Gary Hardgrave on 4BC Drive, "We didn't know about it, as soon as we heard the interview [on 4BC Drive], we had to contact QPS and YFS and the critical response team from Queensland Housing straight away." "As early as yesterday we had a crisis team go through the park in Beenleigh and coordinated with the critical response team from Queensland Housing, YFS, and QPS." "We're very proactive and seeing what we can do to help these people, especially, the men and women that are victims of DV," Acting Mayor Bannon continued.See omnystudio.com/listener for privacy information.

The largest seafood distributor on the Mississippi Gulf Coast and two of its managers have been sentenced on federal charges of mislabeling inexpensive imported seafood as local premium fish weeks after a restaurant and its co-owner were also sentenced. “This large-scale scheme to misbrand imported seafood as local Gulf Coast seafood hurt local fishermen and consumers,” said Todd Gee, the U.S. attorney for southern Mississippi. “These criminal convictions should put restaurants and wholesalers on notice that they must be honest with customers about what is actually being sold.” Sentencing took place in Gulfport for Quality Poultry and Seafood Inc. (QPS), sales manager Todd A. Rosetti, and business manager James W. Gunkel. QPS and the two managers pleaded guilty on August 27 to conspiring to mislabel seafood and commit wire fraud. QPS was sentenced to five years of probation and was ordered to pay $1 million in forfeitures and a $500,000 criminal fine. Prosecutors said the misbranding scheme began as early as 2002 and continued through November 2019. Rosetti received eight months in prison, followed by six months of home detention, one year of supervised release, and 100 hours of community service. Gunkel received two years of probation, one year of home detention, and 50 hours of community service. Mary Mahoney's Old French House and its co-owner/manager Anthony Charles Cvitanovich, pleaded guilty to similar charges May 30 and were sentenced November 18. Mahoney's was founded in Biloxi in 1962 in a building that dates to 1737, and it's a popular spot for tourists. The restaurant pleaded guilty to wire fraud and conspiracy to misbrand seafood. Mahoney's admitted that between December 2013 and November 2019, the company and its co-conspirators at QPS fraudulently sold about 58,750 pounds (26,649 kilograms) of frozen seafood imported from Africa, India, and South America as local premium species. The court ordered the restaurant and QPS to maintain at least five years of records describing the species, sources, and cost of seafood it acquires to sell to customers, and that they make the records available to any relevant federal, state, or local government agency. This article was provided by The Associated Press.

QPS and the Arts

View the Show Notes Page for This Episode Become a Member to Receive Exclusive Content Sign Up to Receive Peter's Weekly Newsletter In this quarterly podcast summary (QPS) episode, Peter summarizes his biggest takeaways from the last three months of guest interviews on the podcast. Peter shares key insights from his discussions on diverse topics such as dopamine and addiction with Anna Lembke, the current state and exciting future of CRISPR-mediated gene editing with Feng Zhang, how to build and maintain strong bones from youth to old age with Belinda Beck, how calorie restriction may influence longevity and metabolic health with Eric Ravussin, and the role of testosterone and TRT in prostate cancer with Ted Schaeffer. Additionally, Peter shares any personal behavioral adjustments or modifications to his patient care practices that have arisen from these engaging discussions. If you're not a subscriber and are listening on a podcast player, you'll only be able to hear a preview of the AMA. If you're a subscriber, you can now listen to this full episode on your private RSS feed or our website at the episode #325 show notes page. If you are not a subscriber, you can learn more about the subscriber benefits here. We discuss: Overview of topics to be covered [1:45]; Anna Lembke episode: addiction, dopamine's role in pleasure and pain, and managing addictive behaviors [4:15]; Follow-up questions about addiction: heritability, cold therapy, exercise, and strategies for breaking addictive behaviors [14:45]; Feng Zhang episode: the potential of gene editing with CRISPR technology for treating diseases, and the challenges ahead [21:00]; Feng Zhang's impactful education experience, and how early exposure and curiosity-driven learning can develop scientific interest for kids [28:30]; The future of CRISPR: weighing the scientific potential to combat complex diseases against ethical considerations around genetic modification [33:45]; Belinda Beck episode: how to build and maintain strong bones from youth to old age [37:30]; How both nutrition and exercise are crucial for bone health at all ages, and why it's never too late to start [54:45]; Eric Ravussin episode: calorie restriction, energy expenditure, exercise for weight maintenance, and more [59:00]; Measuring energy intake and energy expenditure: techniques and challenges [1:09:45]; ed Schaeffer episode: the nuance role of testosterone in prostate cancer, TRT, and the need for better cancer biomarkers [1:14:30]; Peter's favorite bands [1:25:45]; and More. Connect With Peter on Twitter, Instagram, Facebook and YouTube

Queensland Police Service Commissioner Steve Gollschewski joined Peter Fegan on 4BC Breakfast to discuss the big issues in the QPS headlined by a triple-zero ramping crisis.See omnystudio.com/listener for privacy information.

View the Show Notes Page for This Episode Become a Member to Receive Exclusive Content Sign Up to Receive Peter's Weekly Newsletter In this quarterly podcast summary (QPS) episode, Peter summarizes his biggest takeaways from the last three months of guest interviews on the podcast. Peter shares key insights from each episode, covering diverse topics such as liver health with Julia Wattacheril, heart rate variability with Joel Jamieson, artificial intelligence with Zak Kohane, klotho for brain health with Dena Dubal, and lactate and lactate metabolism with George Brooks. Additionally, Peter shares any personal behavioral adjustments or modifications to his patient care practices that have arisen from these engaging discussions. If you're not a subscriber and are listening on a podcast player, you'll only be able to hear a preview of the AMA. If you're a subscriber, you can now listen to this full episode on your private RSS feed or our website at the episode #319 show notes page. If you are not a subscriber, you can learn more about the subscriber benefits here. We discuss: Overview of topics, and the positive feedback on the quarterly podcast summary format [2:00]; Julia Wattacheril episode: liver health and disease [4:00]; Noninvasive methods to diagnose liver conditions, and how to manage and improve liver health [16:00]; Joel Jamieson episode: heart rate variability (HRV) for training and health [27:15]; Practical tools for measuring HRV and how it informs training and recovery decisions [37:00]; Zak Kohane episode: artificial intelligence and medicine [47:15]; The current role of AI in medicine and how it could revolutionize medicine in the future [53:45]; The limitations and concerns pertaining to AI [1:00:15]; Dena Dubal episode: the potential benefits of klotho for brain health [1:05:00]; Animal studies on klotho and brain health [1:11:00]; Genetics-based variations in klotho levels in humans and their impact on cognition, disease risk, and longevity [1:14:15]; Testing klotho levels, the significance of the KL-VS variant, the role of exercise in increasing klotho, and more [1:17:30]; The potential of klotho as a treatment for cognitive decline and Alzheimer's disease [1:23:15]; George Brooks episode: a new paradigm to think about lactate and lactate metabolism [1:27:45]; The potential for lactate infusions to aid in brain recovery following a head injury [1:34:00]; The relationship between lactate and cancer, and the impact of exercise on lactate levels and cancer risk [1:36:30]; and More. Connect With Peter on Twitter, Instagram, Facebook and YouTube

Queensland Police Union President Shane Prior joined Peter Gleeson on 4BC Drive with the QPS in mourning after the death of Senior Sergeant Brendan Wiblen on the annual remembrance motorcycle ride.  See omnystudio.com/listener for privacy information.

 New documents reveal Queensland Police waited days to follow up a complaint that might have seen paedophile Ashley Paul Griffith exposed sooner. Find out more about The Front podcast here. You can read about this story and more on The Australian's website or on The Australian's app. This episode of The Front is presented and produced by Kristen Amiet, and edited by Lia Tsamoglou. Our regular host is Claire Harvey and original music is composed by Jasper Leak.See omnystudio.com/listener for privacy information.

View the Show Notes Page for This Episode Become a Member to Receive Exclusive Content Sign Up to Receive Peter's Weekly Newsletter In this quarterly podcast summary (QPS) episode, Peter introduces a new format aimed at summarizing his biggest takeaways from the last three months of guest interviews on the podcast. Peter shares key insights from each episode, covering diverse topics such as protein and muscle building with Luc van Loon, toe strength with Courtney Conley, VO2 max with Olav Aleksander Bu, liquid biopsies for cancer with Alex Aravanis, gut health and probiotics with Colleen Cutcliffe, and road safety with Mark Rosekind. Additionally, Peter shares any personal behavioral adjustments or modifications to his patient care practices that have arisen from these engaging discussions. If you're not a subscriber and are listening on a podcast player, you'll only be able to hear a preview of the AMA. If you're a subscriber, you can now listen to this full episode on your private RSS feed or our website at the episode #304 show notes page. If you are not a subscriber, you can learn more about the subscriber benefits here. We discuss: How Peter keeps track of his takeaways from each podcast episode [5:15]; Luc van Loon episode: fat utilization, muscle protein synthesis, dietary protein, aging and inactivity, and more [8:45]; Behavioral changes that have come about from the conversation with Luc van Loon [23:45]; Courtney Conley episode: importance of toe strength and the impact of dedicated foot training [26:45]; Olav Aleksander Bu episode: the importance of VO2 max for lifespan, and the practicalities of measuring and improving VO2 max [36:45]; Behavioral changes that have come about from the conversation with Olav [56:00]; Alex Aravanis episode: liquid biopsies for cancer detection [1:01:30]; Colleen Cutcliffe episode: the importance of gut bacteria balance, and the potential therapeutic uses of probiotics, particularly Akkermansia [1:16:45]; Mark Rosekind: the significant issue of road fatalities and injuries, their causes, and practical safety measures to reduce risks [1:27:00]; and More. Connect With Peter on Twitter, Instagram, Facebook and YouTube

Acting Assistant Commissioner Andrew Massingham, joined Peter Gleeson on 4BC Drive to chat youth crime including the latest incidents and how well the QPS is cracking down on repeat offenders.  See omnystudio.com/listener for privacy information.

This episode was recorded live at the IMAPS Device Packaging Conference – helping celebrate the event's 20th year. The record turnout included many of our 3D InCites Community members. Françoise von Trapp spoke with several of them who were exhibiting and presenting, and in some cases, simply attending.  Alex Ospina of ACM Research discussed the latest technologies in wafer-level packaging, and the company's focus on developing novel IP technologies to address industry challenges. You'll hear about the company's new vacuum cleaning tool designed to remove flux from bonds in smaller chiplets. You'll also learn about the company's approach to reducing its environmental impact. Tim Olson, of Deca, shares big news about the company's collaboration with ASU to create an open lab for innovation for innovation, licensing its M-Series and Adaptive Patterning technology, and working with ASU to outfit a fab with unique equipment.Laura Mirkarimi and Oliver Zhao, of Adeia, explain the important role optical interferometry plays in atomic-level hybrid bonding. Zhao explains how they are using AI-powered neural networks to identify defects in the hybrid bonding process, with a focus on categorizing defects based on their relevance to certain process steps.Manuela Junghähnel, of Fraunhofer IZM-ASSID,  explains her new role taking over the leadership of IZM-ASSID, from Jürgen Wolf.  She talks about learning the pilot scale to the production line created by Wolf. She also explains the relationship from the parent IZM and IZM ASSID.Brian Riley, of QP Technologies, shares a history of advanced packaging technologies, and the company. He describes QPs' proprietary process for flip chip packages, the use of open mold plastic packages, and overmold QFNs.Justin Locke, of Siemens EDA discusses innovations in functional verification of 3D Heterogeneous integration connectivity. He explains about the importance of formal verification in the design process, highlighting its ability to catch errors early on and prevent physical implementation issues. Peter Cronin, of MRSI Mycronic talks about new technologies and interconnects in optical packaging, highlighting the need for active alignment tools. He introduces the Active AClimate ConfidentWith a new episode every Wed morning, the Climate Confident podcast is weekly podcast...Listen on: Apple Podcasts SpotifySupport the showBecome a sustaining member! Like what you hear? Follow us on LinkedIn and TwitterInterested in reaching a qualified audience of microelectronics industry decision-makers? Invest in host-read advertisements, and promote your company in upcoming episodes. Contact Françoise von Trapp to learn more. Interested in becoming a sponsor of the 3D InCites Podcast? Check out our 2023 Media Kit. Learn more about the 3D InCites Community and how you can become more involved.

Listen to this episode if you or a friend: Are nervous or unsure how to report domestic violence or coercive control to the police. Want to understand the process and what happens after you report it. Need guidance on evidence collection and seeking support. Are interested in safety planning and available resources. Seek reassurance and empowerment in navigating the challenging situations involving domestic violence. TRIGGER WARNING: This episode contains discussions of Domestic Violence. If you feel triggered please call 13 11 14 Lifeline and someone is there to listen. Call 1800 Respect if you or someone you know needs support in this area. Call 000 if in immediate danger. In this episode, we speak with Inspector Dwyer, Manager of State Domestic Family Violence and Vulnerable Persons Unit in the Queensland Police. We discuss the step-by-step process of reporting domestic violence and coercive control to the police. Learn what happens inside a police station so you can take those next steps with confidence instead of being afraid of the unknown. Inspector Dwyer provides valuable insights into the steps that people can take, the evidence required, and the support available to them. Don't Miss Out On These Key Points: Understanding the Process: Inspector Dwyer outlines the process of reporting domestic violence incidents, emphasising the importance of collecting evidence such as witness statements or medical reports. Follow-up and Communication: Victims should expect proactive communication from police officers, who are responsible for keeping them informed about the progress of their case. Dealing with Insufficient Evidence: If you feel your case has been dismissed prematurely, seek advice from a domestic violence coordinator or specialist within the police station. Coercive Control: Report patterns of coercive behaviour to the police. Safety Planning: Create a safety plan with the help of domestic violence service providers to mitigate risks associated with reporting abuse. Interstate Orders: Orders issued in one state are recognized nationally, ensuring protection for victims even if they move or travel to another state. Training and Awareness: Details on how the Queensland Police Service has invested in extensive training for its officers to recognize and respond effectively to domestic violence situations, including victim-centric, trauma-informed practices. Empowerment and Hope: Inspector Dwyer underscores the commitment to eradicating domestic violence and offers reassurance that victims survivors have support and resources available to them. For Full Show Notes, go to www.thedivorcecourse.com.au/blog For more support, visit www.thedivorcecourse.com.au Inspector Melissa Dwyer Bio: Melissa joined the Queensland Police Service in 1991, following her completion of secondary education the year prior. Initially serving in uniform, Melissa was later appointed to the Redcliffe Criminal Investigation Branch, becoming the first female appointed to the CIB in that District. She gained extensive experience as a regional Detective, specializing in investigating rape offenses, including those committed within marriage, and leading investigations into domestic and family violence (DFV) homicides. After 13 years as a regional Detective, Melissa became the Officer in Charge of a Prosecution Corp, serving as the senior prosecutor for DFV and serious and violent crimes. In 2015, she was promoted to Senior Sergeant in the Strategic Policy Branch, where she played a key role in leading the Queensland Police Service's implementation activities associated with the recommendations of the Not Now: Not Ever Report. Currently, Melissa serves as the Inspector Manager of the State DFV & Vulnerable Persons Unit, where she was instrumental in implementing the DFV Specialist Courts, earning joint agency Prime Minister's awards and Commissioners Outstanding Awards. She also developed and implemented the Queensland Drug & Alcohol Court. Melissa has provided evidence at the Commission of Inquiry into Police responses to DFV and led the development of DFV frontline and specialist training for the QPS, earning accolades at the 2023 International ACWAP awards. Her training was endorsed by ANROWS as best-practice and shared to assist interstate training development. Melissa holds post-graduate qualifications in DFV and is actively involved in enhancing collective responses to DFV through membership on external Boards. Her experiences have fueled her desire to improve the system and change outcomes for women and children while holding perpetrators accountable.   Please note that this podcast provides general education only and is not legal advice. This is just one lawyer's opinion of the family court's views in Australia. Do not base your case on anything mentioned in this episode unless it is first discussed and approved by your personal lawyer. Always seek independent legal advice, as every situation is different. By listening to this show, you are agreeing that it and the company that runs it are not liable for the outcome of your case.   Other Podcast Episodes Mentioned  Domestic Violence and how to get out of it Part 1   Domestic Violence and Family Violence Part 2   Coercive Control in Divorce and after Separation   Stalkers gonna stalk - How to deal with coercive control, tracking & harassment   Post Separation Abuse and what you can do about it.   Coercive Control: how you can help yourself or a friend & what you need to know about the legal changes coming   Child Abuse, Family Violence or Risk and the notice you need to fill in for the Family Court.   What you might be doing that you don't yet realize may have significant ramifications to your property, parenting or domestic violence matters   Family & Domestic Violence and how it impacts on your case in The Family Court   Four things you might not realise are coercive control and the questions to ask yourself to see if it might be happening to you.   Delay Tactics in Divorce: How to counter then & How it can affect your Property, Chlildren's & Domestic Violence Matters.   The Post Separation Abuse Playbook and what you can do about it. Part 1   The Post Separation Abuse Playbook and what you can do about it. Part 2 Useful Resources:   Click here for our free before you leave checklist  Click here for our free mediation checklist Sign up here for our next free online webinar Emergency Contacts: WOMEN'S SHELTER SERVICE  LEGAL AID Lifeline 13 11 14 Mensline Australia 1300 789 978 Kids Help Line 1800 551 800 Aboriginal Family Domestic Violence Hotline 1800 019 123 Relationships Australia  Police on 000 DVConnect Womensline on 1800 811 811 (24 hours, 7 days a week) Note: This number is not recorded on your phone bill DV Connect Mensline on 1800 600 636 National DV line on 1800 737 732.1800RESPECT 1800 737 732   Don't forget to hit SUBSCRIBE so you don't miss out on our upcoming practical steps and guidance for your divorce or de facto separation. If you found this episode helpful, please leave a rating and a review to help others. Thank you.   Please note that this podcast provides general education only and is not legal advice. Always seek independent legal advice, as every situation is different.

Interested in learning about the challenges of police leadership in the 21st Century?The day before she retired from the role of Commissioner of the Queensland Police Service (QPS), Katarina Carroll APM spared some time to talk to Jason Byrnes about a range of topics. These included the challenges facing police now and into the future, the skills future leaders should possess, how the QPS responded to the COVID-19 pandemic, and the impact on the organisation in the wake of the murder of two police officers in December 2022. Ms Carroll also revealed the roles in her career she found challenging, gave an insight into her experience in previously leading Queensland Fire and Emergency Services, and also how she dealt with government, parliament and the opposition.This episode continues the APJ's efforts to inform and educate police and the community about the challenges of policing at all levels. It is particularly relevant for serving police from any agency, keen to gain an understanding of the priorities, pressures and opportunities that face senior police leadership. Host: Jason Byrnes APMGuest: Katarina Carroll APM

Marcey Webb w/QPS, Mecki Kosin w/Quincy Tea Party

Cheers to 2024! HAPPY NEW YEAR! Today on QPS- we dive in to the two things I will be intentionally focused on this year- the 80/20 principle and the internal thermostat theory. Check it out. feel free to share. and CHEERS to all we have to look forward to and create this year. Let's go set the world on fire!

We all know that the more prospects we're pursuing, the more potential revenue we have, but how do we manage great outreach and awesome conversations at scale? To learn the secret of relentless selling at scale, we sat down with Nicole Williams, a former intelligence analyst with the US Army, and now the regional vice president of operations and sales for the QPS employment group. She showed us how any salesperson can plan their work and work their plan in a way that doesn't just sell more but allows us to serve more prospects and clients along the way! It's all in this week's Bulletproof Selling podcast!

On today's Zero Limits Podcast I have a chat with Jeff Casson former Australian Army Armoured Corps Cavalryman and Queensland Police Officer.After being rejected on his first attempt to enlist into the defence force due to a previous knee injury after successful appeal Jeff enlisted into the Australian Defence Force in 2005 as a cavalryman within the Royal Australian Armoured Corps. After the completion of basic training and initial employment training at the school of armour he was posted to 2nd Cavalry Regiment in Darwin. During his posting at 2 cav he deployed to Iraq and Afghanistan and shortly after his Afghan deployment he posted to 3/4 cav in Townsville and again deployed to Afghanistan. In 2012 Jeff discharged from defence and decided to join the Queensland Police however during his police service Jeff's mental health declined where after a few years as a QPS officer he was involved in a incident where he was charged by the corruption and crime commission for theft in which the theft of 100 dollar was going to be used as a means to fund his suicide. Listen in for the rest of the story.Website - www.zerolimitspodcast.comInstagram - https://www.instagram.com/zero.limits.podcast/?hl=en

On this episode of QPS, we unpack what the four core areas are of our life and how what we have created as belief systems can impact how we show up for ourselves and others  I refer to belief systems as B.S. and sometimes what happens is we have created our own belief systems based on someone else's BS.... ;) Often as we mature and age, we encounter situations that will challenge an existing belief system, usually causing some internal dysregulation and this will usually have a domino effect of massive discomfort.  This is not wrong, in fact it actually often means incredible growth.  We talk about what this means, how we can work through this and create new systems that will serve us. Don't be afraid to challenge your thoughts and your B.S. We are always changing and evolving, its in our DNA.  As always, I love you and go light the world on fire! 

In this episode Graeme discusses the RTI file received from QPS and the extraordinary measures the QPS have taken to prevent any criticism of their investigation.He provides examples on the facebook pages of the material released by QPS under RTI.He provides the answer received from the Attorney General in response to his email of April 2023.He discusses the RTI information with Christine Day, sister of Sandrine.This is the link to those documents:https://www.facebook.com/groups/missingsandrinejourdanThere is more of this story to come out.The email address is graeme5353@live.com Hosted on Acast. See acast.com/privacy for more information.

On today's QPS, I dive in to the idea of other people's opinions and how those often stunt our growth and willingness to take a leap and change. We discuss that we WILL suck at things we do for the first time, if we can just get over that hump, allow ourself to suck and fail we will be stronger when we get back up and do it again, knowing we won't die! We can change! We don't have to stay the same. We often fear the change because of two things; one we are going to most likely suck at it at first because its new and two others will see us suck at it  ESPECIALLY those who know us We will be judged by others for changing and you'll be judged by yourself for not so we might as well do it anyway Id rather judge myself and be proud in the pursuit of who I am becoming than allow others to stunt my growth and judge me anyway. Create yourself. One step at a time. When we can get excited about this journey and not fear it, that's where the magic happens. go set the world on fire love always e

Transcript: Good afternoon. Either one, I can just get the time change. I was just in Ohio as I'm still trying to figure out what time it is. I just forget every time I visit my friend he's just barely over that timezone like line. Like, so he is maybe like 45 minutes away from that line and you're traveling and you have that weird timezone change where you like, I feel like when I drove across the country I expected but like when I get to where he lives I'm like this doesn't make any sense to me. It's like oz. Gone, gentlemen, good. How are you? I'm just swell. talk amongst ourselves with recordings on. Quite sure I feel free to talk at my leisure for the recording is that where they said that? Yeah, the note taker in there too. So I have a call I do with my marketing company. And the note taker, or the recording agent that they have starts the meeting beforehand. So I always think I'm late to the meeting. That's already there already in the room. I'm missing it. And I get there, just me and a bot. I was like, This is worse, like at least like you would hear we have other people but I'm sitting there with just one thing. Like, I can't hit the mute button fast enough, because I don't know I'm gonna sit down with technicians. And obviously here I'm not in on this call yet. It's a it's a bit of a funny spot to be. Worked out some sort of like, go to a chat TV team come up with a script, and then just start reading it and see what it comes up with in the interpretation. Right? Because lot of times when I'm doing bot recordings. Yeah. It doesn't really get it right. Depending on what it is and how I'm saying what I'm saying. Sounds you there Jack. I always feel like any recording thinks I have marbles in my mouth. And I just I can't do voice of text that can't be voiced anything. And I think I'm understandable but it does not it never has. And I I've seen people who mumble much worse than me be impossible to be translated as good. I'm actually a keep tweaking my presentation because we added one more security suite options for people. But I don't want to make my presentation too long. Because I want to show people Option A and Option B is I don't want to do them a disservice. I'm only showing everyone one solution. But the problem is it when I when I added the other one in my my PowerPoint went up to I think it was it was at 37 slides. So this is your presentation. It was only about a half an hour. So now I gotta get back and contribute things things back out of it too, because some of them are today I've actually cut out a couple other slides. So I'm down to 33. Now it's one of the last ones just like the thank you in the beans the intro So I've cut down a lot on it. Right? Like a one slide I was walking through our ticketing system and showing them all the stuff now I'm like that's more of an onboarding thing. They don't need to know some of these things in here like how to use this icon. It's just taking up space in here so well and time and really you're right. They don't even know that doesn't that doesn't even matter. Until they're Yes. Yep. I didn't have one they updated today. They really liked it was the GPT slides that were so we have a including one of our security stacks, they get the password manager so you know how people have it on their phone and have the thing that stores all the passwords we have. One that we include, and certainly as a point of I thought this was an interesting fact that in 2020, if you had a character password that was 11 characters, with numbers, letters, capitals, symbols, it would be safe for 400 years. Now in 2023, with Chad's UBT, it's down with four days. So the amount that it actually changed how safe passwords were is just, it's ridiculous. And so I kind of showed the generations of that password manager. So I have that little n Have you ever seen it's like little, little chart where it shows like, okay, four characters here, a teens here and a little like, little chart for you to see how safe your password is? No. Is it a chart? When you say that though? It reminds me of the amortization schedule? Is it a flat graph? Or is it a chart that moves? It's, I'm trying to think with it, it's what's the word? I'm thinking almost like if you have four quadrants of something like so you can see, you know, safest to most like, most secure to least secure, right? And it shows you based off of how many characters and how many other variables you have, how safe so I'm trying to remember the name of that chart is ahead. But you know, what would be cool with that? Have you seen like, ECM on Facebook a lot, where it's, it talks about things over time. That's why do something like that, like the population of the United or the, you know, top 10 countries of the world, right, or number one saw our highest selling artists, and then they'll go from like, you know, 60 to 21. And it'll just, you'll see people move up and down calling off the chart. I wonder what it would look like to have a chart like that, that talked about security? And overlap the last 10 years? I don't, I'm able to channel able to share my screen. Yeah, I think I was open to where anybody can share. And so maybe I can get some feedback on this one then too, because I just, I just changed this. Good morning, everybody. Morning. Come on, Jack. We're all waiting. Yeah, no pressure. Stop yelling at me in front of my friends. So you see in this, yeah, looks great. So this was 2020. And this is again, how safe a password was. And when you switch this. So it goes from 2020 skips over 2021 for whatever reason. So you can see how this chart had this, this red is actually going farther down. In 23, here's where it is. And then here's now with using GPT. So this is where people used to think their stuff was safe. So that's where you go from that 400 years to four days and show the different years and how the point of using a password managers 100% of their r&d is on encryption and password management. That's what everything they do. It's very different than your business where you're working on whatever you are unless you are a password manager. And I think they said they blame that almost 80% of breaches on just weak passwords nowadays, and weak passwords has changed a lot over time where people used to think, you know, weak was 12345. Weak is changed a lot in the last couple of years. So Right. I would my I would reverse the graph. Yeah. Yeah, I want to see that. What I what I'm doing is getting worse. Right, that graph almost. And I'm not sure that that's what I would think do I want to see what I'm doing is getting worse. So if I have a seven digit password, I would be looking at that, because I probably have the same, you know, same seven digit password, which is, you know, for the last three years, right, or not the same password, but I change it but it's, you know, the same amount, which is why if you look at almost all things that require a password, they've gone from six to eight to 11 to 15 to 24 characters are characters. So that says this, and I show I show what it used to be and what it is and actually does get worse as you keep going. So what was saved was not saved. Right. And I do have the ability to just scroll back like I use the little Senator on dialer, to roll forwards and backwards to show that 400 years and that four days, and I made sure that the lines lined up the same. So if someone's eyes were in the same spot, when I got to the last page, they're still looking at that same location, because they might think, well I use that patch. What's my address cat's name and how big of that how much of your business is the manager of passwords? How big is that of the whole piece of the pie what we do of what you're trying to sell it? That's a small part of this of the second security stack. So it's a very small All piece is a part that I recommend people have. Absolutely, yes. But then consider that in your proportion of slides and time. Yeah, I did. That's why I think it's one slide and one rolling thing. And they're gonna go right through it. And one piece of one portion of that time, right? If it's 5%, that of my 30 minute presentation, it gets 5% of that piece. I think I have this as like a 32nd. Slide. So I have some of these that have time that look, it's worth a mention. It's not the whole thing. We're talking about cybersecurity, and it has an industry I spend much more time on that one too. Because that's the bigger thing of what we focus on. Good. It's good thought, though. Thank you. But But the great thing about it is it's something everybody understands. Yeah, that's something I mean, if you're giving a 30 minute presentation, I would think you have, you know, 1015 slides, because I don't want you watching the slide, I want you to pay attention to what I'm saying. The slide when we talk about presentations, the slide is to remind me of where I'm going next. Yeah, right. And then I'll give you the slide deck later, or send you more information or answer your questions. But I want you with me, because really, the sales conversation isn't about the product, the sales conversation is about your needs. You're getting clear that I hear your needs, understand your needs, and can solve your needs. And then be our relationship. So you trust my knowledge, and feel comfortable moving forward with me. Right, I know that you're like a sales person. So yeah, but I'm just saying too much information. Usually, it's like, like picking choices, right? It's like getting somebody like you have to now it's like the people will give you five choices. You got your homework and come back and talk to me. Yep. So that's why I keep trimming things down. And I have couple more interactive questions in here too. So like when I asked like these to show how people could get support, and I instead say, Hey, Kevin, how would you like to reach your IT company? Because they might say, hey, I want to email you. That's great. You know, a lot of companies do that. But we have we have four different ways they can send something in, but I want to see how someone wants to interact with us. And so I do that as a question in that part. You know, it's definitely the first questions I ask people in my business, because I want to know how best to support you. If you're a person who texts and I send you emails. Right, we're gonna do this. Or if your personal emails and I send you text and you'll look at your phone all day. Yeah, that's smart. And like I said, you kind of have this new with my pin out, Jack, you know what's up? I'm taking notes. So do understand. I'm still young, so I'm still figuring a lot of stuff out. But like to think I do a lot of potential power trying to improve my craft. Yeah, absolutely. It's impressive. All right, John. Sorry. We just probably talking amongst ourselves without you. No worries. So what, what have you guys covered so far? today? We're talking about Jack's slides. He's got 37 slides and a half an hour presentation. And he's wheedling it down. And he showed us one that was really cool about that he thought was interesting, little fun fact about password that used to take 400 years to crack with chat TBT now thanks for days. Got it. Because I returned that same question for you, Jack. Because you know, I trained speakers. Is that what you would like some coaching on today? I wouldn't mind it. Because I've been, I've been looking at changing phrasings and how I go through this whole thing I actually finished so one of the books we had, where we did fix this next, he also is referring to the storytelling brand. So let me let me stop you there real quick. So I'll make a list here some jack and talk about presentation. Sure. Because we can do today's fourth, Monday, we can do administrative and finance. Or we go around the room and go okay, what is the thing you would like to work on today? Okay, so, Jack presentation and Mike. Any specific thing you'd like to work on today? No, nothing specific. I I need to go through the I forget what it's called the, the 20 contacts eight weeks. I need to go through that. You'll go through those modules go through those trainings, kind of burn that out. And then the other thing I want to do is I've been texting agents once a week of intense texting about 200 agents once a week, industry updates. And I want to try to go to each of their offices and try to meet them in person so they know who's sending the text. That's the other thing I want to do. But other than that, no nothing specific I just need to need to get some business back. Kevin, I would like to create a $7,500 a month coaching program in 90 days right Vanessa, yes, I need to finish my business plan for my new business Okay, so let's start with who you Jack. So the presentation who is the target audience this is my slide deck for for any target audience. But the the addition of the time was I we added an advanced security suite in response to compliance and cyber insurance. And it had added on a couple of products that were probably necessary. But again, I'm trying to condense the time because it is hard to shorten up some of the items in that actual slide going through DNS, spam filter, sock, MFA, play check. So each thing I try not to talk too much on, but kind of give a value proposition to that item. Right? So the reason I asked who the intended audience is, is if you're talking to a group of engineers, this is awesome. Right? Like this is like, yeah, it's gonna be unbelievably great for them. Okay, if you're talking to a small business owner, like myself, like Kevin, like the neasa, like, Michael, I'm going to want to hear more stories. And I'm not going to want to hear too much technical stuff, I'm going to want to hear, what's the problem? How's it affect me? I want you to dig as much as you can into that pain. So I can now relate. And then I want you to give me a solution. That's that's the way you're talking to the audience or when you're crafting a talk, right? Or you're crafting any type of social media message? Who's the target audience? Knowing that first, what's the intended outcome of the post of the presentation? What is the outcome, like any of you guys can attend this webinar that I'm doing with Adrian boy sell at 12pm, today is going to be a two hour. It's not even really a webinar, it's going to be like a dialogue between two business owners, because we're launching a $10,000 a month, guaranteed revenue program. And we're charging 10 grand for it. But you're gonna see how we banter back and forth. Because right now, I think there's 2530 people registered for the thing. We decided we're not going to do this long drawn out PowerPoint presentation. And we're just going to have a dialogue, we may share some of the shoot a few things on the screen, but it's going to be very, very packed. So when we were planning this, and this mon bring this home to you when we were planning this, because Adrian hasn't done as much of this as I have. Okay. I just point blank said, What is the ultimate outcome here? Where do you want to lead this audience to at the end of your talk? And that determines how the talk goes, right? So if your slide deck is for informing other engineers, then you got to look at okay, can I split this up into three talks? part one, part two, part three? Okay. Is the audience business owners, small business owners to educate them on the importance of password management? Right? And in that case, I'll tell you what my mentors always told me is, every single slide in there gets deleted, and it has to find its way back to the presentation. If it's not absolutely necessary, then it doesn't get put on there. Right. And especially if you're talking to for business owners, no more than three or four bullet points per slide. Okay, cuz you want them listening to you, not reading the slide. So I want to talk about something and talk about it in I want to add the bullet point. Okay, so I'm going to give you a resource. I'll just, I'll just drop this in the Dropbox folder for everybody's benefit. The number one PowerPoint, instructor, present presentation guru that worked for Microsoft created a talk on how to deliver a PowerPoint presentation slide deck. Okay. And I have the recording. And so I'm going to put that in the in the deal. Ad. PowerPoint. slide deck. Okay. So are you wanting them to buy something at the end? Are you wanting them to contact you? What is your ultimate outcome of your talk? Sign the contract, are no neck or no next steps. In some cases where I know I'm gonna be doing several presentations, because I might do one for the office manager first and then get to the C suite depends on the company and size, like you said, Sure. I think my biggest client, I think I had to do like a dozen presentations for every VP and everyone in the C suite. Sure. And so I'm in the in the C suite, office manager level. The format is I would do like intro outcomes. Chunk one, chunk two, is a 30 minute deal. Call to action, right? If you got 45 minutes to an hour, it's intro outcomes, chunk one chunk to chunk three call to action. Okay. And so an intro no more than two to three minutes, and its credibility building. Okay, here's who I am. Here's what I've done. Here's what we've done. Here is the reason you should listen to me my favorite thing, and I discovered this years ago, when I was giving lots and lots and lots of talks, is I came up with this thing called three questions. Okay. And all three of you are in this talk that I did, which is how you landed up in this program. Right? So when I first started that talk, I said, there's basically three questions somebody has when when, you know, when they're listening to somebody speak number one, who is this dude? Number two, what's, you know, why should I listen to him? And number three, what's in it for me? Is that a fair statement? Everybody in the room went? Yes. Right? Same thing here, if you're giving a presentation, but it's one on one, okay? You want to craft your talk in such a way where you're giving it to an audience, and then it's easier to take out the stuff that doesn't apply when you're doing a one on one thing? Right? So in this case, is if it's the office manager, and you've never spoken to, they're not an existing client. Okay. Same three questions. Who are you agreeing to take the meeting for a reason? Okay. Why should I listen to you? And what's in it for me? Okay. So, by answering those three questions will help craft this talk. Again, if I'm sitting with a bunch of engineers, I'm going to have some pretty darn technical slides. Okay, for them to geek out on, right? If I'm talking to an office manager, I'm going to paint a real world scenario of, you know, Joe Smith, new employee coming in and setting up their account and using a weak password. And later on, coming to work and get an email from somebody that says click here and he goes ahead and clicks. Okay, next thing, you know, I get a virus spreading around my entire office. Okay, next thing you know, I'm in the office manager running around, like a chicken with my head cut off trying to fix everything I'm calling the IT person, I'm panicky, because, you know, we're getting attacked here. Does that sound familiar? Yes. Okay. So what we do at outsource my it is we prevent that nightmare from ever happening to you as an office manager. Z. I didn't talk about anything technical. Okay, what I did do with the owner, and the office manager and the C level people unless they're a computer engineer, I'm going to talk about pain. I'm going to talk about the fact that if there is a computer problem, if somebody actually hacks the password, it's going to take down the whole network and because you are networked, it's going to it's going to allow that person to grow have anything that's on that computer network, because once they hack into the domain, they have control over everything. And now like, you know that that spreadsheet that you guys did that you guys, you know, you had multiple people in your company work on for over 100 hours times all the employees all that's now gone. That's going to scare the living hell out of them to the point where they're not going to be open to a solution. Okay. So, again with your talk is who what is the ultimate outcome? Who is the intended audience? Okay, you put it together. And then you create one master talk. If you look at Tony Robbins, if you look at Les Brown, if you look at myself, if you look at any unique speaker, there's usually about two to three signature talks that they have. Okay, and their full blown talks. But now because I'm talking to you, I'm taking out this slide, I'm taking out this slide, I'm taking out this slide, I'm taking out this slide. And I'm whittling it down directly to my audience. Okay, so I'll give you some advanced training. If you really want to nail down this talk thing. I'll give you some advanced training that I've done with other coaches, or other people on speaking and, and really nail it down on how to do the talk. Okay, yeah, yes. That the other company I trained with originally was with HR tech to their original presentation, and they trained me on was an hour long, like, at least and I'm thinking, hey, I'm sorry, but you don't understand what jerseys like, like, not people don't give you an hour here, especially even for a sales presentation. Like, we're an hour. You haven't, we have to break this up. So I took a lot of what they originally did. And like you said, I had to start what I do, instead of deleting the slides, I would I would hide them and then revisit, do I need that? Can I add add that add that somewhere else do I need that graphic? And that's a lot of what I've been doing. So HR Tech Academy, because I know Alex Rogers personally. I was there when they started shark sec. Okay, when was Buck 25% Shark sec. I was actually in Alex's office when that happened. So I have a lot of their stuff. And and so again, it's the full talk, but whittle it down to whoever the audience is the least amount of slides the better. I agree, talking to an audience the least amount of slides, the better. Because stories are what sells like when you when you this afternoon between 12 and 2pm. When as Adrienne and I are talking the one thing you will notice if you're there, we're going to record it I'll give you as a recording, we are going to do a tremendous amount of storytelling. Like most of our time is going to be telling story after story after story after story after story. Because we're going to create an evergreen situation where that's going to be promoted out for the next week on an automated webinar type thing. Okay, but it's the stories that are going to sell any questions? Mike, marketing type system. So do you have questions about the actual system? No, don't have questions about the system. I understand the system. It's just a matter of me kind of coming up with things to touch points to use. I know Kevin and I've talked about it before and Toastmasters is one of them, you know inviting them to Toastmasters. And I just I haven't been going to toastmasters for the last month. So I haven't, I haven't wanted to invite anybody. So now that things are starting to calm down with my wife, and I know I'm gonna start going again so that y'all have that to add. So I don't really have questions on it. It's just a matter of me, you know, creating the content and then doing it. So with we go to this and I'm going to share the screen here. Now while you're doing that you and maybe Jack can answer this. You said there are four things I wrote down, who's the audience? What's the outcome? Like? There are a couple other things that I didn't write down. Remember that Jack? What was he saying? I wrote who? Why? And what's in it for them too as the three questions for that, too. Okay. Who is this? Why should I listen to him? What's in it for me? He also, there's one thing that you also said to that I, I'm gonna guess his right out QPS, or question based selling, that's what we talked about, the order of solutions that you want to do with people is problem, alternate solutions, which is also important. And that's a lot of what I think about my slides. Because a lot of times people start with, here's my solution, then they give you a problem. And they showed what you were using as an alternate, but the buying process and your brain is wired a little differently. So if you start with a problem, give the alternate and then show the solution to so you're showing kind of a different direction of those things to they would call it pass versus spa thinking on that one to say that, again, you've said when you're whether it's on the overall presentation, or on the short parts of the slides, when in the book, question based selling time for us talks about doing you go with the problem, the ultimate, the solution, not leading with your solution first, where people start a lot of times with the solution. And that's the easiest order of operations. When you say the alternate you mean, what they're doing now. Use the alternate is more of just the pivot step. That's your transition part two, right? It's how do you do that. But sometimes people put the transition that comparison part at the end to so they have problem solution, or they'll do solution problem. And then in the at the end, and they say well, here's the verses, here's this versus that. So they do comparison at the end of it instead of so here's your problem. Here's what some of those things, here's how you would solve these problems. Here is the solution that does solve those problems, too. So ultimately, I don't know if it's always the best word, but it's usually just your pivot, pivot step about one two. That makes sense. I'm just trying to understand what you said alternate was almost like, obviously, you know, the problem is, is obviously, efficiently at first, right? Because we have a problem, the solution, you know, and we start talk about the solution. First, it gets me thinking about the solution, as opposed to feeling the pain of the problem, and not fixing the solution. I don't have the pain yet. Yeah. So if I said, Hey, we specialize in a Kevin, we specialize in cybersecurity, here's why we're so great at cybersecurity, you might actually have passwords that are out there, and then start going through how to do it. It's just a different order of operations, instead of talking about, hey, a lot of people have, you know, at companies have reached out there because the passwords aren't very strong. So that's the problem. You know, the ultimate is just visually how do you pivot from one to the other, but tactically, and you don't say words like but are things that can be seen more combative, by nature? verbiage? Hey, I know you like this, but because you want people to insinuate that the wrongs Hey, I know you've been doing this for a while at the same time, can I show you how this could be a solution that can save time for you and make your company more safe, you know, something that's not combative. Alright, so on the screen, I just now uploaded this to the folder and didn't find it in the Dropbox folder. Okay, so this is in this folder right here, where it's appointment setting, because I teach the marketing tech system on video, but I actually have a step by step document for you to follow. And in pretty much it's laid out, boom, boom, boom. So the only time you actually have to get creative is twice a month. Okay? So you need one piece of content, it could be, you know, the top 10 things in real estate, the top 10 things and mortgage it can be a sales tip, it can be, you know, if you're going after realtors, you want to solve the number one problem they have. Okay, and that is getting leads. Okay, the number one problem realtors have is leads. The number one problem realtors have is consistency. Okay, now I'm talking new realtors. I'm not talking about seasoned veterans. Okay. So seasoned veterans have all different products like in the VSAs case, she's a seasoned veteran. Okay, her number one problem right now is starting her own brokerage. Getting her business plan up to date, she already has a massive following of people relationships, etc. And her her actual next big move, okay. In fact, all of you should I have let me go and find the actual name of this thing because the number one realtor In the world at the time, I learned from Dan Sullivan wrote a book called Well, him and his buddy have a book called 10x is easier than 2x. Very, very powerful book, I got it on the audio audio version, but she, she told, he told the story of this realtor who did not even want to get in real estate. And next thing, you know, became the number one realtor in Keller Williams nationwide. Okay, they millions and millions of millions of dollars in commission income. Okay. And he goes through exactly how you do it, he goes, okay. It's the I'm not gonna get into that. It's just, it's a squirrel long rabbit hole right there. But this is the marketing Touch system right here. You just follow it step by step once you have about 12 pieces of content, usually recycling, recycling. So what I tell people, hey, you know what your group number one. And let's just say you only have 50 leads, well, now you got two groups to start with. And over the next week, you're going to, you know, maybe you take the 50 leads, and you divide them up into eight groups that you got maybe 10 people in each group or less than that, your job is to grow it to 200 qualified leads. Okay, once you have two under qualified leads, if you follow this system to the tee, the fastest I've been able to get the email sent down is nine minutes and 36 seconds. Okay. And on average, about 10 and a half minutes. And but if you use the system, as I've laid it out here, it's very, very productive, and it's very consistent creates a consistency. Right? You get one new piece of content again, what is the number one thing challenging a realtor? Now one thing, challenges in a realtor most Realtors is I need to lead. You know, I need to be introduced. Okay, I need a relationship. I learned this from Marguerite Chris Villa when I was working with her, and even how to go and as long as it's like too many people chase active listings. You know, to me, people want to time a listing, when we're in reality, they just need to go out and develop a couple of new relationships per day. Over time, they have enough people, where somebody is going to start referring them to somebody that they just heard in the wants to list their home. He knows but you know, I remember Brent telling me this when I trained his team, he goes, he goes and I look back at my real estate career. The only thing I've done is I've gone out and made friends consistently. I've gone out on I've added new friends to my database, every single day I go on, I meet people, I cannot get become a rock star stain in the office, I go out and like meet people, I shake their hand. Yeah, I get I get a contact, I need a contact, I need a warm body. And then I need to determine if I like them or not. And then I need to figure out how I can help them and keep in touch with them in a systematic way. So when they do know of somebody, or they themselves are interested in real estate, I'm the first person they think about. Yeah. And ultimately, you can outsource and have an assistant do all that stuff. But this marketing type system, you do it yourself first. And the five times I've implemented this to start my companies, I get it to the point where I'm too buried to do this anymore, and then I hire an assistant to do it. So that's how it's laid out right there. And feel free to add me to your marketing type system treat me as a prospect, add me to group number one. So that way, you're sending me a piece of content, you can add me to every group, if you want just practice on me every day, I don't care. Okay, you send me a marketing touch and then you follow up with a phone call. So what that's how the system works is you can add whatever content you want here. Right, but the key is, is consistency. So in real estate, you need a warm body as a connection, and even the mortgage, you need a warm body as a connection. The other thing that the two of you can do as a mortgage company, is you can host a webinar, you can say, Hey, come here, John Pyron for 45 minutes talk about referral systems. Right? You promote it out to every realtor that you know. And you set up a time where you want me to be a speaker for you for free. Right, this is what I used to do. I used to go around the mortgage companies and I used to go around the real estate companies. Why? Because they have a meeting every say A week and they always have a guest speaker and I was a guest speaker every week like clockwork somewhere at some place in this town. Okay. So if you go to my YouTube channel and you'll see many talks up there Coldwell Banker there's all kinds of places I've gone and spoken to. The number one requested talk is my talk on how to 10x your referrals that resonates with every mortgage person, every realtor like clockwork and it works. Yeah. And you guys can be the people that host it. And it's just going to be a magnet. Yeah, so that's an idea. Okay, any questions about that? No. All right, Kevin, created a 770 $500 per month coaching business in 90 days and you want to coach you real estate mortgage real estate mortgage. So you want to be a real estate mortgage coach and be able to create $7,500 in 90 days per month Yes sir. Right there's two ways to go about it number one, you need content you see my screen still yes Right. Actually, you know what we do this the benefits of being a part of this group is. That, honestly give it all to you guys. In a six month mentorship program, teaching Coaches and Consultants how to make 100 grand as a coaching consultant, okay. And in order to graduate from the program, you had to create a six figure income and in six months, Okay, step by step, I recorded everything. workbook templates, everything. Okay? So I'm just going to give it to all of you, if you have an ambition of wanting to do that, because it is laid out, boom, boom, boom, workbook everything. Okay? So workbook right here. I paid I don't know, I take pain, Steve Knoppix on $75,000 to learn this. Okay. And with his permission, I just duplicate because I love training coaches and consultants and they've done a lot of that. So this is one path. The other path, you and I are going to have a one on one conversation about how to work together and I can get you there 1015 times faster. So because $7,500 a month and 90 days is a lot of work. I've done it, I can help you do it. It's entirely up to you. Okay, but if I was going to if you want to do a self paced type thing, just follow this workbook step by step. Okay, and all the recordings 12 People in this mastermind that I did for six months, I made the mistake of giving to, to, to the spots to to friends and didn't make make or pay for. And of course, they dropped out. As you know, they didn't have any skin in the game. How did the other remaining 10, eight of them graduated meaning eight of them creating a six figure coaching consulting business in less than six months. Okay, and but I didn't leave anything out, this is exactly what they paid for it. They all the calls are here, step by step speaker training, all that stuff. And you all have access to it now. But really for to make it simple. It's figuring out who do you want to coach real estate and mortgage people? If I put you on a stage right now, and said, Hey, here's a bunch of real estate mortgage people, what are the top 10 Things you can share with them right now? That would have the biggest impact on their business? Here's the mic. That's your first 10 pieces of content you really need to create. Because when you're building a coaching, consulting business, content is everything. Having enough content out there to show that you are an expert and having some success stories, okay. So in your case, if it's coaching real estate and mortgage people, you got Mike, who's worked with you, he's your first call client. Okay? And treat him like he's paying you a lot of money. Okay, and you mentor him for free, because he's already on your team. But you do it on Zoom, so you guys can record your conversations, and that becomes training content. Okay, then you go out and you find a couple other realtors to do the same thing. You put social media posts out there, what's the biggest challenge you have as a realtor? The biggest number one problem you have as a mortgage first. You can even ask a couple of people, what is your number one problem ago, you created a little survey going out of lead generation, at least four areas of business, marketing, sales, lead generation, whatever those bullet points are, what is your biggest challenge that you have as a realtor? And whatever they give you you teach content on that. Because it's the content, it's your talking and I did a lot of training in there on that about that. But it's your your content that is gonna attract people to you. And you go from there. So a classic example here, let me go here Stephanie shell are here, same thing came to me and said, Hey, I want to be a coach. I want to be a speaker. I want to be a consultant. Okay. And it took me three and a half months of working with her one on one to get her to fire her boss, which she did. Now, if you go through any of her stuff here, well, best selling author of 12 books. She has a workshop that we designed. I designed it because I wanted to be an attendee of a great workshop. So we designed it. It's coming up in January. It's called the rare retreat. It's usually in San Antonio. And, but now she has does well over a million dollars a year as the author, speaker trainer, coach that a whole team, etc. Right. We worked together for seven years. And but it was just step by step lumbo She wanted to get she's one of the stories I'm going to be telling between 12 and two today is probably one of the going to be the one one of the stories that resonates the most is she tried for four months to hit 10k a month. And you know there's a video up there called how to give yourself a clean slate. That is a direct result of Stephanie shower. There she calls me up February 9 that says hey, I I'm not going to hit it again. I know you are why it's February 9 Steph what? Well I did the numbers I use see what I have my A pipeline and I just don't see how many hit it today and you'll win. Do you want to hit it? Yeah. Do you really want to hit it? Yeah, you're gonna do exactly what I tell you to do. Yeah, great. Okay, here's what you're gonna do. And I gave her that clean slate strategy. Within seven days, she passed 10k a month. And I said, you're not done. You got another eight or nine days, I forget what the what the actual number is on the video. But she ultimately came in at $14,865. Because it was a mental breakthrough that she needed. She had to how to exact how to give you to get to $7,500 a month, which actually can go a whole lot faster now, because of the amount of people I've done this with now. You know, it works. And the format is this. I give elite. I've got enough content to convict me at what I do as a consultant, or coach, I send you an email. My signature has enough information there for you to be curious enough to go and click OK. Then I have an appointment link on there. You're going to set a 10 minute phone call with me. I'm going to do a 10 minute phone call mash you three things. Number one, what's your big goal that you're trying to hit by the end of this current year? You're gonna tell me, I'm gonna say okay, you'd like to hit it by the end of the year. Yeah. Okay, let's just say you're there and you're, you're achieved it. And I'm gonna walk you through a two minute phone call. The questions I'm gonna ask them the dialogue I'm gonna ask. The most important thing is, is mastering that 10 minute call, she was stubborn as hell about. Right, and she kept failing and failing and failing and failing for about three weeks in a row. She finally calls me ever says this, there's something wrong, I'm not succeeding with this 10 minute call thing. And she was given away 45 minutes to 60 minutes of her time with every prospect and it's just not sustainable. And so I went and we roleplay and she treated me like a prospect says stuff you're not following the 10 minute sprint. Follow 10 minute script over the next week. Verbatim. Don't add live. Stop being stubborn and do it. Can you do that? Yeah. Well, we hold it work. And the rest is history. I'm going to call it a strategy session which led to a ticket to the garage reader which led to a ticket to a workshop which led to a ticket to speaking engagements. And he is having a funnel like I have here on the wall that you can have touch points with them all over the place. So once you have that process and system down is just duplication it's it's it's repetition over and over and over again. Okay, so take a look at the resources that I've shared here. Okay, and and then if you want to talk you know about working together one on one to get you there quicker just let me know you're more than happy to start in the resources huh? You shared when I look when you shared it because there's quite a bit there where do I start and those resources just starting the beginning? Or is there a start creating content because I feel like content seems like the key content is the key. And as a content outline, I would start with this workbook and walk you through step by step how to get your content based started. Because and humor Oh. Sales foundation sales message sales voice This is a funnel, right and this whole workbook walks you through step by step how to create that. Okay. Lisa English business plan. Yes. Do you need there? Where are you stuck? I am stuck at how big I want to go. How what? How big do I want to go or how small do I want to keep my my practice or my my brokerage? So like, I'm thinking okay, do I'm just going to be on my own do this for a year or two? Or should I just you know, just start like hiring people. I go I go back and forth. Sure. Did you get your business plan done? No. Okay. You follow the one page PLAN strategy? Are you doing a full blown 1520 page business plan? I'm actually I'm going back and forth. I tried to do the one foot one page, and then I get distracted, and then start, you know, going through through my mission and objectives. And then I come back down. And, you know, try to do that again. So, yeah. So it starts off with I mean, it's, it's easier to go after a bigger role. And Miss, versus going after a smaller goal. I mean, smaller goals don't really motivate us. Yeah. Yeah. Well, you want something that's, that's like, unrealistic, that you feel like there's just no way I can hit that. Okay. Because the person you're, you don't want to build a business, or set a vision, that doesn't require you to change. Because nothing's gotten your hit the goal, it would be very easy to hit the goal, you're going to be the same in the process, and your targets are going to be set low. And it's going to be easy to not do it. Yeah. So when you set an intention, you know, you're starting a real estate company from scratch. And, you know, what's it going to be like to have, you know, 50 agents. As a real jerk, you're, you're going to be the person that is going to be the Rainmaker in the beginning. And I can tell you beyond a shadow of a doubt, the very first hire you're going to hire is going to be an assistant. Yeah, take all that stuff off your plate that you don't want to do. Right? And then even then you might need a second assistant before you hire your first agent. Right? If you get that book 10x is easier than to X. entire chapter one is about a realtor, I forget her name. But she started from nothing. And, and she followed that model, he laid it out step by step how she went from nothing to a million dollars in commission. I mean, step by step. And she built it in increments, big, big leaps. Okay, so out of the 12, out of the 100% of the work that I do 80% of it needs to be given to somebody else. So I can make room for the 20%, which is going to give me the biggest leap, and the biggest jump, and he walks through the psychology of the growth of that. So when you set small goals, you show up in a small level, when you set big goals, you have to grow into that because you don't have the skill set to hit it right now. So the first thing I would do is, is get your business plan done, you don't need unless you're gonna go out and get investor dollars. A one page plan is going to be just fine. Okay, first milestone, how do I get to how do I get to $8,333 a month? How do I get to a six figure income consistently in my new business? And if that doesn't motivate you, to under 50,000? You know, how do I get there? And it'll become very apparent of what you need to do. You can always bring it here and go, Hey, can we look at the plant and they can we can we talk about it? Any questions about that? I mean, the other thing we can do if you if you want is we can schedule a strategy session between the two of us and we can just get it done. Okay, okay. It's always an option for every one of you. There are going to be times where you're going to want to make these big leaps. And there's not enough time on this call to get it done. And any of you can book a strategy session with me, obviously, I'm not going to do it for free. But doesn't mean you have to work with me one on one. Maybe you just need two hours, four hours of one on one time. It's totally fine. You have that option. Okay, because once we get that plan done, then all you got to do is show up here. Here's where I'm at. There's one that hold me accountable here. Hold me Hello there, what's the next step here? What's the next step there? Okay. But I would get that one page plan done first. So you know exactly what that next step is. Because each day that goes by that you don't have it is lost opportunity. Does that make sense? Yeah. Cool. Anybody else have anything else that they want to discuss? Alright guys, I gotta run. And if you want to be a part of this deal here at 12 to two, I will send you a link on that. Just let me know. So all right, I gotta run. I'll see you guys. Can you send me that John? Yes, I will. Talk to you guys later.

Season 5 of the Pot Moms Podcast has a new twist on parenting and cannabis. In this week's episode, Kait and Natalie launch the second-to-LAST of a multi-part series around Cannabis Education! They covered A-P, and are now coming for QRST! They cover QPs and other difficult cannabis math weight, RSO or Rick Simpson Oil, S for our girl SATIVA, and finally, a mini deep dive into THC! Education is a huge way to break down stigma and find common ground and topics to speak with cannabis consumers and non-consumers alike. Tune in each week as they provide an open and honest perspective on cannabis-use as it relates to parenting, mental health, and life in general. Connect with the Pot Moms Podcast on Instagram @PotMomsPodcast, or reach out PotMomsPodcast@gmail.com with questions. 

Having attended a range of emergency incidents, examining major crime scenes, bombings, profile serial killings and a myriad of suspicious deaths, organised crime gangs etc, Gordon Drage was forced to leave QPS purely b/c he'd turned 60. That's the rules.Gordon loved his work & excelled in his field of forensic examinations. He was at the top of his game, with so much knowledge & insight into forensic examinations. What a waste…. Hosted on Acast. See acast.com/privacy for more information.

Having attended a range of emergency incidents, examining major crime scenes, bombings, profile serial killings and a myriad of suspicious deaths, organised crime gangs etc, Gordon Drage was forced to leave QPS purely b/c he'd turned 60. That's the rules.Gordon loved his work & excelled in his field of forensic examinations. He was at the top of his game, with so much knowledge & insight into forensic examinations. What a waste…. Hosted on Acast. See acast.com/privacy for more information.

Mayor Koch on the Quincy Public Schools as the year closes, Updates on New Public Safety Building, Roads and More..

Cameron is back with another episode of the Fifth Estate Podcast.In this episode he talks about...Is podcasting a sham?Which is better, value for value, or advertising?Possible war with China, and what the Australian government should be doing instead of spending $200billion on subs.QPS negotiator on scene in a Townsville seige.

Top cop apologises to LGBTIQ+ community Police Commissioner Katarina Carroll apologised to Brisbane Pride leaders at a private ceremony. The Queensland Police Service has apologised to the LGBTIQ+ community for the “profound hurt and pain” it has caused by mistreating and discriminating against them. This is QPS propaganda attempting to take the focus off all the negative stories that have taken up so many of the headlines so far this year, and it's only January. This is just my opinion. PS: If you enjoy my content, I will think of you while drinking my coffee. – Buy Me a Coffee The Slippery Slope Spotify J Fallon Apple Music J Fallon Spotify J Fallon YouTube The Slippery Slope Apple Podcasts The Slippery Slope YouTube The Slippery Slope Stitcher --- Send in a voice message: https://podcasters.spotify.com/pod/show/jason-fallon/message

Police officer charged with computer hacking Queensland police have charged another of their own, this time for alleged computer hacking and improper disclosure of information. “This does not mean that the allegations against the officer have been substantiated,” the QPS said in a statement. It did not release further information. It followed news this week that other Queensland members had been stood down following separate alleged incidents involving domestic violence, stealing and drink-driving. The details provided by the QPS were generally vague, but in “keeping with our commitment to high standards of behaviour, transparency and accountability”. When it comes to their own, QPS will always be vague, because high standards of behaviour, transparency and accountability are just mottos. They don't lead to actual standards. This is just my opinion. PS: If you enjoy my content, I will think of you while drinking my coffee. – Buy Me a Coffee The Slippery Slope Spotify J Fallon Apple Music J Fallon Spotify J Fallon YouTube The Slippery Slope Apple Podcasts The Slippery Slope YouTube The Slippery Slope Stitcher --- Send in a voice message: https://podcasters.spotify.com/pod/show/jason-fallon/message

Shaun O'Gorman is a Human Behaviour, High Performance and Resilience consultant, keynote speaker, and author. After joining the Queensland Police Service in 1989, Shaun worked in the Police Dog Squad for many years as well as the Covert and Surveillance unit working on Major and Organised Crime. While in the K9 unit Shaun was involved in daily high-risk critical incidents ranging from violent street brawls, high-speed pursuits, barricaded suspects, domestics, and other serious shootings. The majority of these placed him at high risk of serious injury or worse. He also performed duties with the Special Emergency Response Team (SWAT) as a tactical K9 handler.Shaun left the QPS and was diagnosed with Post Traumatic Stress Disorder (PTSD). The years of exposure to serious police call outs, resulted in clinical depression. To overcome his mental health injuries, Shaun spent the next 17 years immersed in the study of personal development, human behaviour and high performance with a goal of healing himself and living a happy life. He now devotes his life to helping others using the knowledge and education that helped him help himself.While forging his successful corporate executive career Shaun developed an interest in mentoring and coaching others and now continues to follow his passion for helping others to create high performing lives for themselves. He established “The Strong Life Project” to deliver High-Performance Mentoring,  Workshops, Keynote Speeches, daily podcasts, and articles focused on providing tools and strategies to empower people to conquer challenges, manage stress, and create happy and fulfilled lives.As an accomplished author, Shaun's most recent book, “My Dark Companion”, chronicles his own highly personal fight with PTSD, depression and how he has come out the other side as a role model for people of all walks of life on how to create a life that you love.TRIGGER WARNINGFollow the podcastOnemomentpleasepodcast.comIG:@onemomentpleasepodcastFB: OneMomentPleasewww.thestronglifeproject.com podcast: The Strong Life Project

Queensland police whistleblower goes public to argue commissioner Katarina Carroll should lose her job The commissioner says she has only been in the role for three years and she needs more time to implement change within Queensland Police Service (QPS). Yet what changes was she attempting to implement while she was in the upper echelon of leadership prior to being appointed commissioner? The only reason the commissioner is now concerned about change to systemic cultural issues with the QPS is due to the inquiry asking her some tough questions. Before this she was quite happy for the status quo, because it was the status quo that helped her rise through the ranks. She was quite happy to deny issues within the ranks because QPS leadership is an unchecked authority. This is just my opinion. PS: If you enjoy my content, I will think of you while drinking my coffee. – Buy Me a Coffee The Slippery Slope Spotify J Fallon Apple Music J Fallon Spotify J Fallon YouTube The Slippery Slope Apple Podcasts The Slippery Slope YouTube The Slippery Slope Stitcher --- Send in a voice message: https://anchor.fm/jason-fallon/message

Queensland police let off with a talking to after inquiry's scathing report Throughout the inquiry into Queensland police responses to domestic violence, the public learned about cases where officers who were found to have engaged in serious racism or sexism were let off with a “local management resolution”, which is basically a stern talking to and a demand to do better in future. Premier Palaszczuk says Commissioner Carroll is a “strong leader” but that the QPS needs to do better. “Let me say very clearly: I expect the reforms to be implemented. Very, very clearly,” Palaszczuk says. The subtext: the police leadership has had a stern talking to, and a demand to do better in the future. As one former police officer puts it: “It's just one giant government local management resolution.” This is just my opinion. PS: If you enjoy my content, I will think of you while drinking my coffee. – Buy Me a Coffee The Slippery Slope Spotify J Fallon Apple Music J Fallon Spotify J Fallon YouTube The Slippery Slope Apple Podcasts The Slippery Slope YouTube The Slippery Slope Stitcher --- Send in a voice message: https://anchor.fm/jason-fallon/message

This is a Quit Playing Small episode! If you want more QPS episodes head over to the QPS feed. What are you giving your attention to? I'm sharing one of my stories on being laser focused on a destination and how that turned out. Quit Playing Small Book - https://amzn.to/2EENAbbWebsite - https://www.iquitplayingsmall.com/Instagram - https://www.instagram.com/AHYIANA.ANGEL/

First up, J+M discuss Bob Iger joining Thrive Capital as a venture partner (2:09), then they break down the most interesting moments from the Twitter whistleblower hearings (24:48). After that, they discuss KKR tokenizing part of its new healthcare fund (57:43), before wrapping on the Launch House allegations. (1:06:22) (0:00) J+M tee up today's topics! (2:09) Legendary former Disney CEO Bob Iger joins Thrive Capital as venture partner (12:23) Revelo - Get 20% off the first 3 months by mentioning TWIST at https://revelo.io/twist (13:45) How Iger linked up with Josh Kushner, false perceptions of VCs (23:32) Dell For Startups - Apply for Dell for Startups and get an additional 10% off at http://dell.com/twist (24:48) J+M react to clips from Twitter whistleblower Peiter "Mudge" Zatko's congressional hearing today (33:13) Assure - To get 20% off your first Special Purpose Vehicle (SPV) visit https://Assure.co/twist (34:33) More clips from the hearing and reflections on Twitter's security issues (57:43) KKR tokenizes part of its new healthcare fund to allow QPs to invest more easily (1:06:22) Launch House allegations FOLLOW Jason: https://linktr.ee/calacanis FOLLOW Molly: https://twitter.com/mollywood Subscribe to our YouTube to watch all full episodes: https://www.youtube.com/channel/UCkkhmBWfS7pILYIk0izkc3A?sub_confirmation=1

Ep. 43 Shaun O'Gorman former 1990's Queensland Police K9 Unit Police Officer LIVE NOW LINK IN BIO SPOTIFY - APPLE PODCAST - AMAZON MUSIC - GOOGLE PODCAST - STITCHER and many more platformsOn today's Zero Limits Podcast we chat to Shaun O'Gorman former K9 Queensland Police Officer. After joining the Queensland Police Service in 1989, Shaun worked in the Police Dog Squad for many years as well as the Covert and Surveillance unit working on Major and Organised Crime. While in the K9 unit Shaun was involved in daily high-risk critical incidents ranging from violent street brawls, high-speed pursuits, barricaded suspects, domestic violence, and shooting incidents. The majority of these placed him at high risk of serious injury or even death. He also performed duties with the Special Emergency Response Team (SWAT) as a tactical K9 handler.Shaun left the QPS and was diagnosed with Post Traumatic Stress Disorder (PTSD). The years of exposure to violence and tragedy resulted in clinical depression and a battle with suicide. Shaun spent three nights in a row, a Glock pistol in his hand, laying in bed trying to think of reasons not to end his life. To overcome his mental health injuries, Shaun spent the next 17 years immersed in the study of personal development, human behaviour and high performance with a goal of healing himself and living a happy life. He now devotes his life to helping others using the knowledge and education that helped him help himself. Currently he is a Human Behaviour, High Performance and Resilience consultant, keynote speaker, and author. Let's Go!!

Your day in 30 seconds The happiest cities in the U.S. and where Charlotte fits in Top ways cheaters get caught Maney hides snacks from his family LauRen's Aupair's parents can't visit because of the Corona virus  ST Patrick's day at QPS! Code Support the show: https://www.mrlshow.com/See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.