Podcasts about gustavs

Gustavs sommerhus er blevet solgt - men minderne har vi vel lov at have. Emilie er gået i sort foran en journalist fra JP, og så har vi en gæst med i studiet, som overbringer en ganske særlig nyhed til Gustav (og menneskeheden).

När börsen skakar och investerarna söker efter råd är det folket med spetskunskaper som leder vägen. Sällan har det varit mer aktuellt att komplettera sin aktiehandel med derivat för att kunna hantera en alltmer oberäknelig aktiemarknad. Vi har bjudit tillbaka Gustav Sällberg från Prior Nilsson som dagligen handlar med derivat för att hantera - och tjäna på - en skakig aktiemarknad. Han delar med sig av hur man kan tänka i rådande läge, vilka strategier han använder sig av och hur man kan planera sin handel för att öka sannolikheten att nå sina mål. Värdefulla tips utlovas som du inte vill missa.

We are working with Amplify on the 2025 State of AI Engineering Survey to be presented at the AIE World's Fair in SF! Join the survey to shape the future of AI Eng!We first met Snipd over a year ago, and were immediately impressed by the design, but were doubtful about the behavior of snipping as the title behavior:Podcast apps are enormously sticky - Spotify spent almost $1b in podcast acquisitions and exclusive content just to get an 8% bump in market share among normies.However, after a disappointing Overcast 2.0 rewrite with no AI features in the last 3 years, I finally bit the bullet and switched to Snipd. It's 2025, your podcast app should be able to let you search transcripts of your podcasts. Snipd is the best implementation of this so far.And yet they keep shipping:What impressed us wasn't just how this tiny team of 4 was able to bootstrap a consumer AI app against massive titans and do so well; but also how seriously they think about learning through podcasts and improving retention of knowledge over time, aka “Duolingo for podcasts”. As an educational AI podcast, that's a mission we can get behind.Full Video PodFind us on YouTube! This was the first pod we've ever shot outdoors!Show Notes* How does Shazam work?* Flutter/FlutterFlow* wav2vec paper* Perplexity Online LLM* Google Search Grounding* Comparing Snipd transcription with our Bee episode* NIPS 2017 Flo Rida* Gustav Söderström - Background AudioTimestamps* [00:00:03] Takeaways from AI Engineer NYC* [00:00:17] Weather in New York.* [00:00:26] Swyx and Snipd.* [00:01:01] Kevin's AI summit experience.* [00:01:31] Zurich and AI.* [00:03:25] SigLIP authors join OpenAI.* [00:03:39] Zurich is very costly.* [00:04:06] The Snipd origin story.* [00:05:24] Introduction to machine learning.* [00:09:28] Snipd and user knowledge extraction.* [00:13:48] App's tech stack, Flutter, Python.* [00:15:11] How speakers are identified.* [00:18:29] The concept of "backgroundable" video.* [00:29:05] Voice cloning technology.* [00:31:03] Using AI agents.* [00:34:32] Snipd's future is multi-modal AI.* [00:36:37] Snipd and existing user behaviour.* [00:42:10] The app, summary, and timestamps.* [00:55:25] The future of AI and podcasting.* [1:14:55] Voice AITranscriptswyx [00:00:03]: Hey, I'm here in New York with Kevin Ben-Smith of Snipd. Welcome.Kevin [00:00:07]: Hi. Hi. Amazing to be here.swyx [00:00:09]: Yeah. This is our first ever, I think, outdoors podcast recording.Kevin [00:00:14]: It's quite a location for the first time, I have to say.swyx [00:00:18]: I was actually unsure because, you know, it's cold. It's like, I checked the temperature. It's like kind of one degree Celsius, but it's not that bad with the sun. No, it's quite nice. Yeah. Especially with our beautiful tea. With the tea. Yeah. Perfect. We're going to talk about Snips. I'm a Snips user. I'm a Snips user. I had to basically, you know, apart from Twitter, it's like the number one use app on my phone. Nice. When I wake up in the morning, I open Snips and I, you know, see what's new. And I think in terms of time spent or usage on my phone, I think it's number one or number two. Nice. Nice. So I really had to talk about it also because I think people interested in AI want to think about like, how can we, we're an AI podcast, we have to talk about the AI podcast app. But before we get there, we just finished. We just finished the AI Engineer Summit and you came for the two days. How was it?Kevin [00:01:07]: It was quite incredible. I mean, for me, the most valuable was just being in the same room with like-minded people who are building the future and who are seeing the future. You know, especially when it comes to AI agents, it's so often I have conversations with friends who are not in the AI world. And it's like so quickly it happens that you, it sounds like you're talking in science fiction. And it's just crazy talk. It was, you know, it's so refreshing to talk with so many other people who already see these things and yeah, be inspired then by them and not always feel like, like, okay, I think I'm just crazy. And like, this will never happen. It really is happening. And for me, it was very valuable. So day two, more relevant, more relevant for you than day one. Yeah. Day two. So day two was the engineering track. Yeah. That was definitely the most valuable for me. Like also as a producer. Practitioner myself, especially there were one or two talks that had to do with voice AI and AI agents with voice. Okay. So that was quite fascinating. Also spoke with the speakers afterwards. Yeah. And yeah, they were also very open and, and, you know, this, this sharing attitudes that's, I think in general, quite prevalent in the AI community. I also learned a lot, like really practical things that I can now take away with me. Yeah.swyx [00:02:25]: I mean, on my side, I, I think I watched only like half of the talks. Cause I was running around and I think people saw me like towards the end, I was kind of collapsing. I was on the floor, like, uh, towards the end because I, I needed to get, to get a rest, but yeah, I'm excited to watch the voice AI talks myself.Kevin [00:02:43]: Yeah. Yeah. Do that. And I mean, from my side, thanks a lot for organizing this conference for bringing everyone together. Do you have anything like this in Switzerland? The short answer is no. Um, I mean, I have to say the AI community in, especially Zurich, where. Yeah. Where we're, where we're based. Yeah. It is quite good. And it's growing, uh, especially driven by ETH, the, the technical university there and all of the big companies, they have AI teams there. Google, like Google has the biggest tech hub outside of the U S in Zurich. Yeah. Facebook is doing a lot in reality labs. Uh, Apple has a secret AI team, open AI and then SwapBit just announced that they're coming to Zurich. Yeah. Um, so there's a lot happening. Yeah.swyx [00:03:23]: So, yeah, uh, I think the most recent notable move, I think the entire vision team from Google. Uh, Lucas buyer, um, and, and all the other authors of Siglip left Google to join open AI, which I thought was like, it's like a big move for a whole team to move all at once at the same time. So I've been to Zurich and it just feels expensive. Like it's a great city. Yeah. It's great university, but I don't see it as like a business hub. Is it a business hub? I guess it is. Right.Kevin [00:03:51]: Like it's kind of, well, historically it's, uh, it's a finance hub, finance hub. Yeah. I mean, there are some, some large banks there, right? Especially UBS, uh, the, the largest wealth manager in the world, but it's really becoming more of a tech hub now with all of the big, uh, tech companies there.swyx [00:04:08]: I guess. Yeah. Yeah. And, but we, and research wise, it's all ETH. Yeah. There's some other things. Yeah. Yeah. Yeah.Kevin [00:04:13]: It's all driven by ETH. And then, uh, it's sister university EPFL, which is in Lausanne. Okay. Um, which they're also doing a lot, but, uh, it's, it's, it's really ETH. Uh, and otherwise, no, I mean, it's a beautiful, really beautiful city. I can recommend. To anyone. To come, uh, visit Zurich, uh, uh, let me know, happy to show you around and of course, you know, you, you have the nature so close, you have the mountains so close, you have so, so beautiful lakes. Yeah. Um, I think that's what makes it such a livable city. Yeah.swyx [00:04:42]: Um, and the cost is not, it's not cheap, but I mean, we're in New York city right now and, uh, I don't know, I paid $8 for a coffee this morning, so, uh, the coffee is cheaper in Zurich than the New York city. Okay. Okay. Let's talk about Snipt. What is Snipt and, you know, then we'll talk about your origin story, but I just, let's, let's get a crisp, what is Snipt? Yeah.Kevin [00:05:03]: I always see two definitions of Snipt, so I'll give you one really simple, straightforward one, and then a second more nuanced, um, which I think will be valuable for the rest of our conversation. So the most simple one is just to say, look, we're an AI powered podcast app. So if you listen to podcasts, we're now providing this AI enhanced experience. But if you look at the more nuanced, uh, podcast. Uh, perspective, it's actually, we, we've have a very big focus on people who like your audience who listened to podcasts to learn something new. Like your audience, you want, they want to learn about AI, what's happening, what's, what's, what's the latest research, what's going on. And we want to provide a, a spoken audio platform where you can do that most effectively. And AI is basically the way that we can achieve that. Yeah.swyx [00:05:53]: Means to an end. Yeah, exactly. When you started. Was it always meant to be AI or is it, was it more about the social sharing?Kevin [00:05:59]: So the first version that we ever released was like three and a half years ago. Okay. Yeah. So this was before ChatGPT. Before Whisper. Yeah. Before Whisper. Yeah. So I think a lot of the features that we now have in the app, they weren't really possible yet back then. But we already from the beginning, we always had the focus on knowledge. That's the reason why, you know, we in our team, why we listen to podcasts, but we did have a bit of a different approach. Like the idea in the very beginning was, so the name is Snips and you can create these, what we call Snips, which is basically a small snippet, like a clip from a, from a podcast. And we did envision sort of like a, like a social TikTok platform where some people would listen to full episodes and they would snip certain, like the best parts of it. And they would post that in a feed and other users would consume this feed of Snips. And use that as a discovery tool or just as a means to an end. And yeah, so you would have both people who create Snips and people who listen to Snips. So our big hypothesis in the beginning was, you know, it will be easy to get people to listen to these Snips, but super difficult to actually get them to create them. So we focused a lot of, a lot of our effort on making it as seamless and easy as possible to create a Snip. Yeah.swyx [00:07:17]: It's similar to TikTok. You need CapCut for there to be videos on TikTok. Exactly.Kevin [00:07:23]: And so for, for Snips, basically whenever you hear an amazing insight, a great moment, you can just triple tap your headphones. And our AI actually then saves the moment that you just listened to and summarizes it to create a note. And this is then basically a Snip. So yeah, we built, we built all of this, launched it. And what we found out was basically the exact opposite. So we saw that people use the Snips to discover podcasts, but they really, you know, they don't. You know, really love listening to long form podcasts, but they were creating Snips like crazy. And this was, this was definitely one of these aha moments when we realized like, hey, we should be really doubling down on the knowledge of learning of, yeah, helping you learn most effectively and helping you capture the knowledge that you listen to and actually do something with it. Because this is in general, you know, we, we live in this world where there's so much content and we consume and consume and consume. And it's so easy to just at the end of the podcast. You just start listening to the next podcast. And five minutes later, you've forgotten everything. 90%, 99% of what you've actually just learned. Yeah.swyx [00:08:31]: You don't know this, but, and most people don't know this, but this is my fourth podcast. My third podcast was a personal mixtape podcast where I Snipped manually sections of podcasts that I liked and added my own commentary on top of them and published them as small episodes. Nice. So those would be maybe five to 10 minute Snips. Yeah. And then I added something that I thought was a good story or like a good insight. And then I added my own commentary and published it as a separate podcast. It's cool. Is that still live? It's still live, but it's not active, but you can go back and find it. If you're, if, if you're curious enough, you'll see it. Nice. Yeah. You have to show me later. It was so manual because basically what my process would be, I hear something interesting. I note down the timestamp and I note down the URL of the podcast. I used to use Overcast. So it would just link to the Overcast page. And then. Put in my note taking app, go home. Whenever I feel like publishing, I will take one of those things and then download the MP3, clip out the MP3 and record my intro, outro and then publish it as a, as a podcast. But now Snips, I mean, I can just kind of double click or triple tap.Kevin [00:09:39]: I mean, those are very similar stories to what we hear from our users. You know, it's, it's normal that you're doing, you're doing something else while you're listening to a podcast. Yeah. A lot of our users, they're driving, they're working out, walking their dog. So in those moments when you hear something amazing, it's difficult to just write them down or, you know, you have to take out your phone. Some people take a screenshot, write down a timestamp, and then later on you have to go back and try to find it again. Of course you can't find it anymore because there's no search. There's no command F. And, um, these, these were all of the issues that, that, that we encountered also ourselves as users. And given that our background was in AI, we realized like, wait, hey, this is. This should not be the case. Like podcast apps today, they're still, they're basically repurposed music players, but we actually look at podcasts as one of the largest sources of knowledge in the world. And once you have that different angle of looking at it together with everything that AI is now enabling, you realize like, hey, this is not the way that we, that podcast apps should be. Yeah.swyx [00:10:41]: Yeah. I agree. You mentioned something that you said your background is in AI. Well, first of all, who's the team and what do you mean your background is in AI?Kevin [00:10:48]: Those are two very different things. I'm going to ask some questions. Yeah. Um, maybe starting with, with my backstory. Yeah. My backstory actually goes back, like, let's say 12 years ago or something like that. I moved to Zurich to study at ETH and actually I studied something completely different. I studied mathematics and economics basically with this specialization for quant finance. Same. Okay. Wow. All right. So yeah. And then as you know, all of these mathematical models for, um, asset pricing, derivative pricing, quantitative trading. And for me, the thing that, that fascinates me the most was the mathematical modeling behind it. Uh, mathematics, uh, statistics, but I was never really that passionate about the finance side of things.swyx [00:11:32]: Oh really? Oh, okay. Yeah. I mean, we're different there.Kevin [00:11:36]: I mean, one just, let's say symptom that I noticed now, like, like looking back during that time. Yeah. I think I never read an academic paper about the subject in my free time. And then it was towards the end of my studies. I was already working for a big bank. One of my best friends, he comes to me and says, Hey, I just took this course. You have to, you have to do this. You have to take this lecture. Okay. And I'm like, what, what, what is it about? It's called machine learning and I'm like, what, what, what kind of stupid name is that? Uh, so you sent me the slides and like over a weekend I went through all of the slides and I just, I just knew like freaking hell. Like this is it. I'm, I'm in love. Wow. Yeah. Okay. And that was then over the course of the next, I think like 12 months, I just really got into it. Started reading all about it, like reading blog posts, starting building my own models.swyx [00:12:26]: Was this course by a famous person, famous university? Was it like the Andrew Wayne Coursera thing? No.Kevin [00:12:31]: So this was a ETH course. So a professor at ETH. Did he teach in English by the way? Yeah. Okay.swyx [00:12:37]: So these slides are somewhere available. Yeah. Definitely. I mean, now they're quite outdated. Yeah. Sure. Well, I think, you know, reflecting on the finance thing for a bit. So I, I was, used to be a trader, uh, sell side and buy side. I was options trader first and then I was more like a quantitative hedge fund analyst. We never really use machine learning. It was more like a little bit of statistical modeling, but really like you, you fit, you know, your regression.Kevin [00:13:03]: No, I mean, that's, that's what it is. And, uh, or you, you solve partial differential equations and have then numerical methods to, to, to solve these. That's, that's for you. That's your degree. And that's, that's not really what you do at work. Right. Unless, well, I don't know what you do at work. In my job. No, no, we weren't solving the partial differential. Yeah.swyx [00:13:18]: You learn all this in school and then you don't use it.Kevin [00:13:20]: I mean, we, we, well, let's put it like that. Um, in some things, yeah, I mean, I did code algorithms that would do it, but it was basically like, it was the most basic algorithms and then you just like slightly improve them a little bit. Like you just tweak them here and there. Yeah. It wasn't like starting from scratch, like, Oh, here's this new partial differential equation. How do we know?swyx [00:13:43]: Yeah. Yeah. I mean, that's, that's real life, right? Most, most of it's kind of boring or you're, you're using established things because they're established because, uh, they tackle the most important topics. Um, yeah. Portfolio management was more interesting for me. Um, and, uh, we, we were sort of the first to combine like social data with, with quantitative trading. And I think, uh, I think now it's very common, but, um, yeah. Anyway, then you, you went, you went deep on machine learning and then what? You quit your job? Yeah. Yeah. Wow.Kevin [00:14:12]: I quit my job because, uh, um, I mean, I started using it at the bank as well. Like try, like, you know, I like desperately tried to find any kind of excuse to like use it here or there, but it just was clear to me, like, no, if I want to do this, um, like I just have to like make a real cut. So I quit my job and joined an early stage, uh, tech startup in Zurich where then built up the AI team over five years. Wow. Yeah. So yeah, we built various machine learning, uh, things for, for banks from like models for, for sales teams to identify which clients like which product to sell to them and with what reasons all the way to, we did a lot, a lot with bank transactions. One of the actually most fun projects for me was we had an, an NLP model that would take the booking text of a transaction, like a credit card transaction and pretty fired. Yeah. Because it had all of these, you know, like numbers in there and abbreviations and whatnot. And sometimes you look at it like, what, what is this? And it was just, you know, it would just change it to, I don't know, CVS. Yeah.swyx [00:15:15]: Yeah. But I mean, would you have hallucinations?Kevin [00:15:17]: No, no, no. The way that everything was set up, it wasn't like, it wasn't yet fully end to end generative, uh, neural network as what you would use today. Okay.swyx [00:15:30]: Awesome. And then when did you go like full time on Snips? Yeah.Kevin [00:15:33]: So basically that was, that was afterwards. I mean, how that started was the friend of mine who got me into machine learning, uh, him and I, uh, like he also got me interested into startups. He's had a big impact on my life. And the two of us were just a jam on, on like ideas for startups every now and then. And his background was also in AI data science. And we had a couple of ideas, but given that we were working full times, we were thinking about, uh, so we participated in Hack Zurich. That's, uh, Europe's biggest hackathon, um, or at least was at the time. And we said, Hey, this is just a weekend. Let's just try out an idea, like hack something together and see how it works. And the idea was that we'd be able to search through podcast episodes, like within a podcast. Yeah. So we did that. Long story short, uh, we managed to do it like to build something that we realized, Hey, this actually works. You can, you can find things again in podcasts. We had like a natural language search and we pitched it on stage. And we actually won the hackathon, which was cool. I mean, we, we also, I think we had a good, um, like a good, good pitch or a good example. So we, we used the famous Joe Rogan episode with Elon Musk where Elon Musk smokes a joint. Okay. Um, it's like a two and a half hour episode. So we were on stage and then we just searched for like smoking weed and it would find that exact moment. It will play it. And it just like, come on with Elon Musk, just like smoking. Oh, so it was video as well? No, it was actually completely based on audio. But we did have the video for the presentation. Yeah. Which had a, had of course an amazing effect. Yeah. Like this gave us a lot of activation energy, but it wasn't actually about winning the hackathon. Yeah. But the interesting thing that happened was after we pitched on stage, several of the other participants, like a lot of them came up to us and started saying like, Hey, can I use this? Like I have this issue. And like some also came up and told us about other problems that they have, like very adjacent to this with a podcast. Where's like, like this. Like, could, could I use this for that as well? And that was basically the, the moment where I realized, Hey, it's actually not just us who are having these issues with, with podcasts and getting to the, making the most out of this knowledge. Yeah. The other people. Yeah. That was now, I guess like four years ago or something like that. And then, yeah, we decided to quit our jobs and start, start this whole snip thing. Yeah. How big is the team now? We're just four people. Yeah. Just four people. Yeah. Like four. We're all technical. Yeah. Basically two on the, the backend side. So one of my co-founders is this person who got me into machine learning and startups. And we won the hackathon together. So we have two people for the backend side with the AI and all of the other backend things. And two for the front end side, building the app.swyx [00:18:18]: Which is mostly Android and iOS. Yeah.Kevin [00:18:21]: It's iOS and Android. We also have a watch app for, for Apple, but yeah, it's mostly iOS. Yeah.swyx [00:18:27]: The watch thing, it was very funny because in the, in the Latent Space discord, you know, most of us have been slowly adopting snips. You came to me like a year ago and you introduced snip to me. I was like, I don't know. I'm, you know, I'm very sticky to overcast and then slowly we switch. Why watch?Kevin [00:18:43]: So it goes back to a lot of our users, they do something else while, while listening to a podcast, right? Yeah. And one of the, us giving them the ability to then capture this knowledge, even though they're doing something else at the same time is one of the killer features. Yeah. Maybe I can actually, maybe at some point I should maybe give a bit more of an overview of what the, all of the features that we have. Sure. So this is one of the killer features and for one big use case that people use this for is for running. Yeah. So if you're a big runner, a big jogger or cycling, like really, really cycling competitively and a lot of the people, they don't want to take their phone with them when they go running. So you load everything onto the watch. So you can download episodes. I mean, if you, if you have an Apple watch that has internet access, like with a SIM card, you can also directly stream. That's also possible. Yeah. So of course it's a, it's basically very limited to just listening and snipping. And then you can see all of your snips later on your phone. Let me tell you this error I just got.swyx [00:19:47]: Error playing episode. Substack, the host of this podcast, does not allow this podcast to be played on an Apple watch. Yeah.Kevin [00:19:52]: That's a very beautiful thing. So we found out that all of the podcasts hosted on Substack, you cannot play them on an Apple watch. Why is this restriction? What? Like, don't ask me. We try to reach out to Substack. We try to reach out to some of the bigger podcasters who are hosting the podcast on Substack to also let them know. Substack doesn't seem to care. This is not specific to our app. You can also check out the Apple podcast app. Yeah. It's the same problem. It's just that we actually have identified it. And we tell the user what's going on.swyx [00:20:25]: I would say we host our podcast on Substack, but they're not very serious about their podcasting tools. I've told them before, I've been very upfront with them. So I don't feel like I'm shitting on them in any way. And it's kind of sad because otherwise it's a perfect creative platform. But the way that they treat podcasting as an afterthought, I think it's really disappointing.Kevin [00:20:45]: Maybe given that you mentioned all these features, maybe I can give a bit of a better overview of the features that we have. Let's do that. Let's do that. So I think we're mostly in our minds. Maybe for some of the listeners.swyx [00:20:55]: I mean, I'll tell you my version. Yeah. They can correct me, right? So first of all, I think the main job is for it to be a podcast listening app. It should be basically a complete superset of what you normally get on Overcast or Apple Podcasts or anything like that. You pull your show list from ListenNotes. How do you find shows? You've got to type in anything and you find them, right?Kevin [00:21:18]: Yeah. We have a search engine that is powered by ListenNotes. Yeah. But I mean, in the meantime, we have a huge database of like 99% of all podcasts out there ourselves. Yeah.swyx [00:21:27]: What I noticed, the default experience is you do not auto-download shows. And that's one very big difference for you guys versus other apps, where like, you know, if I'm subscribed to a thing, it auto-downloads and I already have the MP3 downloaded overnight. For me, I have to actively put it onto my queue, then it auto-downloads. And actually, I initially didn't like that. I think I maybe told you that I was like, oh, it's like a feature that I don't like. Like, because it means that I have to choose to listen to it in order to download and not to... It's like opt-in. There's a difference between opt-in and opt-out. So I opt-in to every episode that I listen to. And then, like, you know, you open it and depends on whether or not you have the AI stuff enabled. But the default experience is no AI stuff enabled. You can listen to it. You can see the snips, the number of snips and where people snip during the episode, which roughly correlates to interest level. And obviously, you can snip there. I think that's the default experience. I think snipping is really cool. Like, I use it to share a lot on Discord. I think we have tons and tons of just people sharing snips and stuff. Tweeting stuff is also like a nice, pleasant experience. But like the real features come when you actually turn on the AI stuff. And so the reason I got snipped, because I got fed up with Overcast not implementing any AI features at all. Instead, they spent two years rewriting their app to be a little bit faster. And I'm like, like, it's 2025. I should have a podcast that has transcripts that I can search. Very, very basic thing. Overcast will basically never have it.Kevin [00:22:49]: Yeah, I think that was a good, like, basic overview. Maybe I can add a bit to it with the AI features that we have. So one thing that we do every time a new podcast comes out, we transcribe the episode. We do speaker diarization. We identify the speaker names. Each guest, we extract a mini bio of the guest, try to find a picture of the guest online, add it. We break the podcast down into chapters, as in AI generated chapters. That one. That one's very handy. With a quick description per title and quick description per each chapter. We identify all books that get mentioned on a podcast. You can tell I don't use that one. It depends on the podcast. There are some podcasts where the guests often recommend like an amazing book. So later on, you can you can find that again.swyx [00:23:42]: So you literally search for the word book or I just read blah, blah, blah.Kevin [00:23:46]: No, I mean, it's all LLM based. Yeah. So basically, we have we have an LLM that goes through the entire transcript and identifies if a user mentions a book, then we use perplexity API together with various other LLM orchestration to go out there on the Internet, find everything that there is to know about the book, find the cover, find who or what the author is, get a quick description of it for the author. We then check on which other episodes the author appeared on.swyx [00:24:15]: Yeah, that is killer.Kevin [00:24:17]: Because that for me, if. If there's an interesting book, the first thing I do is I actually listen to a podcast episode with a with a writer because he usually gives a really great overview already on a podcast.swyx [00:24:28]: Sometimes the podcast is with the person as a guest. Sometimes his podcast is about the person without him there. Do you pick up both?Kevin [00:24:37]: So, yes, we pick up both in like our latest models. But actually what we show you in the app, the goal is to currently only show you the guest to separate that. In the future, we want to show the other things more.swyx [00:24:47]: For what it's worth, I don't mind. Yeah, I don't think like if I like if I like somebody, I'll just learn about them regardless of whether they're there or not.Kevin [00:24:55]: Yeah, I mean, yes and no. We we we have seen there are some personalities where this can break down. So, for example, the first version that we released with this feature, it picked up much more often a person, even if it was not a guest. Yeah. For example, the best examples for me is Sam Altman and Elon Musk. Like they're just mentioned on every second podcast and it has like they're not on there. And if you're interested in it, you can go to Elon Musk. And actually like learning from them. Yeah, I see. And yeah, we updated our our algorithms, improved that a lot. And now it's gotten much better to only pick it up if they're a guest. And yeah, so this this is maybe to come back to the features, two more important features like we have the ability to chat with an episode. Yes. Of course, you can do the old style of searching through a transcript with a keyword search. But I think for me, this is this is how you used to do search and extracting knowledge in the in the past. Old school. And the A.I. Web. Way is is basically an LLM. So you can ask the LLM, hey, when do they talk about topic X? If you're interested in only a certain part of the episode, you can ask them for four to give a quick overview of the episode. Key takeaways afterwards also to create a note for you. So this is really like very open, open ended. And yeah. And then finally, the snipping feature that we mentioned just to reiterate. Yeah. I mean, here the the feature is that whenever you hear an amazing idea, you can trip. It's up your headphones or click a button in the app and the A.I. summarizes the insight you just heard and saves that together with the original transcript and audio in your knowledge library. I also noticed that you you skip dynamic content. So dynamic content, we do not skip it automatically. Oh, sorry. You detect. But we detect it. Yeah. I mean, that's one of the thing that most people don't don't actually know that like the way that ads get inserted into podcasts or into most podcasts is actually that every time you listen. To a podcast, you actually get access to a different audio file and on the server, a different ad is inserted into the MP3 file automatically. Yeah. Based on IP. Exactly. And that's what that means is if we transcribe an episode and have a transcript with timestamps like words, word specific timestamps, if you suddenly get a different audio file, like the whole time says I messed up and that's like a huge issue. And for that, we actually had to build another algorithm that would dynamically on the floor. I re sync the audio that you're listening to the transcript that we have. Yeah. Which is a fascinating problem in and of itself.swyx [00:27:24]: You sync by matching up the sound waves? Or like, or do you sync by matching up words like you basically do partial transcription?Kevin [00:27:33]: We are not matching up words. It's happening on the basically a bytes level matching. Yeah. Okay.swyx [00:27:40]: It relies on this. It relies on the exact match at some point.Kevin [00:27:46]: So it's actually. We're actually not doing exact matches, but we're doing fuzzy matches to identify the moment. It's basically, we basically built Shazam for podcasts. Just as a little side project to solve this issue.swyx [00:28:02]: Actually, fun fact, apparently the Shazam algorithm is open. They published the paper, it's talked about it. I haven't really dived into the paper. I thought it was kind of interesting that basically no one else has built Shazam.Kevin [00:28:16]: Yeah, I mean, well, the one thing is the algorithm. If you now talk about Shazam, the other thing is also having the database behind it and having the user mindset that if they have this problem, they come to you, right?swyx [00:28:29]: Yeah, I'm very interested in the tech stack. There's a big data pipeline. Could you share what is the tech stack?Kevin [00:28:35]: What are the most interesting or challenging pieces of it? So the general tech stack is our entire backend is, or 90% of our backend is written in Python. Okay. Hosting everything on Google Cloud Platform. And our front end is written with, well, we're using the Flutter framework. So it's written in Dart and then compiled natively. So we have one code base that handles both Android and iOS. You think that was a good decision? It's something that a lot of people are exploring. So up until now, yes. Okay. Look, it has its pros and cons. Some of the, you know, for example, earlier, I mentioned we have a Apple Watch app. Yeah. I mean, there's no Flutter for that, right? So that you build native. And then of course you have to sort of like sync these things together. I mean, I'm not the front end engineer, so I'm not just relaying this information, but our front end engineers are very happy with it. It's enabled us to be quite fast and be on both platforms from the very beginning. And when I talk with people and they hear that we are using Flutter, usually they think like, ah, it's not performant. It's super junk, janky and everything. And then they use it. They use our app and they're always super surprised. Or if they've already used our app, I couldn't tell them. They're like, what? Yeah. Um, so there is actually a lot that you can do with it.swyx [00:29:51]: The danger, the concern, there's a few concerns, right? One, it's Google. So when were they, when are they going to abandon it? Two, you know, they're optimized for Android first. So iOS is like a second, second thought, or like you can feel that it is not a native iOS app. Uh, but you guys put a lot of care into it. And then maybe three, from my point of view, JavaScript, as a JavaScript guy, React Native was supposed to be there. And I think that it hasn't really fulfilled that dream. Um, maybe Expo is trying to do that, but, um, again, it is not, does not feel as productive as Flutter. And I've, I spent a week on Flutter and dot, and I'm an investor in Flutter flow, which is the local, uh, Flutter, Flutter startup. That's doing very, very well. I think a lot of people are still Flutter skeptics. Yeah. Wait. So are you moving away from Flutter?Kevin [00:30:41]: I don't know. We don't have plans to do that. Yeah.swyx [00:30:43]: You're just saying about that. What? Yeah. Watch out. Okay. Let's go back to the stack.Kevin [00:30:47]: You know, that was just to give you a bit of an overview. I think the more interesting things are, of course, on the AI side. So we, like, as I mentioned earlier, when we started out, it was before chat GPT for the chat GPT moment before there was the GPT 3.5 turbo, uh, API. So in the beginning, we actually were running everything ourselves, open source models, try to fine tune them. They worked. There was us, but let's, let's be honest. They weren't. What was the sort of? Before Whisper, the transcription. Yeah, we were using wave to work like, um, there was a Google one, right? No, it was a Facebook, Facebook one. That was actually one of the papers. Like when that came out for me, that was one of the reasons why I said we, we should try something to start a startup in the audio space. For me, it was a bit like before that I had been following the NLP space, uh, quite closely. And as, as I mentioned earlier, we, we did some stuff at the startup as well, that I was working up. But before, and wave to work was the first paper that I had at least seen where the whole transformer architecture moved over to audio and bit more general way of saying it is like, it was the first time that I saw the transformer architecture being applied to continuous data instead of discrete tokens. Okay. And it worked amazingly. Ah, and like the transformer architecture plus self-supervised learning, like these two things moved over. And then for me, it was like, Hey, this is now going to take off similarly. It's the text space has taken off. And with these two things in place, even if some features that we want to build are not possible yet, they will be possible in the near term, uh, with this, uh, trajectory. So that was a little side, side note. No, it's in the meantime. Yeah. We're using whisper. We're still hosting some of the models ourselves. So for example, the whole transcription speaker diarization pipeline, uh,swyx [00:32:38]: You need it to be as cheap as possible.Kevin [00:32:40]: Yeah, exactly. I mean, we're doing this at scale where we have a lot of audio.swyx [00:32:44]: We're what numbers can you disclose? Like what, what are just to give people an idea because it's a lot. So we have more than a million podcasts that we've already processed when you say a million. So processing is basically, you have some kind of list of podcasts that you will auto process and others where a paying pay member can choose to press the button and transcribe it. Right. Is that the rough idea? Yeah, exactly.Kevin [00:33:08]: Yeah. And if, when you press that button or we also transcribe it. Yeah. So first we do the, we do the transcription. We do the. The, the speaker diarization. So basically you identify speech blocks that belong to the same speaker. This is then all orchestrated within, within LLM to identify which speech speech block belongs to which speaker together with, you know, we identify, as I mentioned earlier, we identify the guest name and the bio. So all of that comes together with an LLM to actually then assign assigned speaker names to, to each block. Yeah. And then most of the rest of the, the pipeline we've now used, we've now migrated to LLM. So we use mainly open AI, Google models, so the Gemini models and the open AI models, and we use some perplexity basically for those things where we need, where we need web search. Yeah. That's something I'm still hoping, especially open AI will also provide us an API. Oh, why? Well, basically for us as a consumer, the more providers there are.swyx [00:34:07]: The more downtime.Kevin [00:34:08]: The more competition and it will lead to better, better results. And, um, lower costs over time. I don't, I don't see perplexity as expensive. If you use the web search, the price is like $5 per a thousand queries. Okay. Which is affordable. But, uh, if you compare that to just a normal LLM call, um, it's, it's, uh, much more expensive. Have you tried Exa? We've, uh, looked into it, but we haven't really tried it. Um, I mean, we, we started with perplexity and, uh, it works, it works well. And if I remember. Correctly, Exa is also a bit more expensive.swyx [00:34:45]: I don't know. I don't know. They seem to focus on the search thing as a search API, whereas perplexity, maybe more consumer-y business that is higher, higher margin. Like I'll put it like perplexity is trying to be a product, Exa is trying to be infrastructure. Yeah. So that, that'll be my distinction there. And then the other thing I will mention is Google has a search grounding feature. Yeah. Which you, which you might want. Yeah.Kevin [00:35:07]: Yeah. We've, uh, we've also tried that out. Um, not as good. So we, we didn't, we didn't go into. Too much detail in like really comparing it, like quality wise, because we actually already had the perplexity one and it, and it's, and it's working. Yeah. Um, I think also there, the price is actually higher than perplexity. Yeah. Really? Yeah.swyx [00:35:26]: Google should cut their prices.Kevin [00:35:29]: Maybe it was the same price. I don't want to say something incorrect, but it wasn't cheaper. It wasn't like compelling. And then, then there was no reason to switch. So, I mean, maybe like in general, like for us, given that we do work with a lot of content, price is actually something that we do look at. Like for us, it's not just about taking the best model for every task, but it's really getting the best, like identifying what kind of intelligence level you need and then getting the best price for that to be able to really scale this and, and provide us, um, yeah, let our users use these features with as many podcasts as possible. Yeah.swyx [00:36:03]: I wanted to double, double click on diarization. Yeah. Uh, it's something that I don't think people do very well. So you know, I'm, I'm a, I'm a B user. I don't have it right now. And, and they were supposed to speak, but they dropped out last minute. Um, but, uh, we've had them on the podcast before and it's not great yet. Do you use just PI Anode, the default stuff, or do you find any tricks for diarization?Kevin [00:36:27]: So we do use the, the open source packages, but we have tweaked it a bit here and there. For example, if you mentioned the BAI guys, I actually listened to the podcast episode was super nice. Thank you. And when you started talking about speaker diarization, and I just have to think about, uh, I don't know.Kevin [00:36:49]: Is it possible? I don't know. I don't know. F**k this. Yeah, no, I don't know.Kevin [00:36:55]: Yeah. We are the best. This is a.swyx [00:37:07]: I don't know. This is the best. I don't know. This is the best. Yeah. Yeah. Yeah. You're doing good.Kevin [00:37:12]: So, so yeah. This is great. This is good. Yeah. No, so that of course helps us. Another thing that helps us is that we know certain structural aspects of the podcast. For example, how often does someone speak? Like if someone, like let's say there's a one hour episode and someone speaks for 30 seconds, that person is most probably not the guest and not the host. It's probably some ad, like some speaker from an ad. So we have like certain of these heuristics that we can use and we leverage to improve things. And in the past, we've also changed the clustering algorithm. So basically how a lot of the speaker diarization works is you basically create an embedding for the speech that's happening. And then you try to somehow cluster these embeddings and then find out this is all one speaker. This is all another speaker. And there we've also tweaked a couple of things where we again used heuristics that we could apply from knowing how podcasts function. And that's also actually why I was feeling so much with the BAI guys, because like all of these heuristics, like for them, it's probably almost impossible to use any heuristics because it can just be any situation, anything.Kevin [00:38:34]: So that's one thing that we do. Yeah, another thing is that we actually combine it with LLM. So the transcript, LLMs and the speaker diarization, like bringing all of these together to recalibrate some of the switching points. Like when does the speaker stop? When does the next one start?swyx [00:38:51]: The LLMs can add errors as well. You know, I wouldn't feel safe using them to be so precise.Kevin [00:38:58]: I mean, at the end of the day, like also just to not give a wrong impression, like the speaker diarization is also not perfect that we're doing, right? I basically don't really notice it.swyx [00:39:08]: Like I use it for search.Kevin [00:39:09]: Yeah, it's not perfect yet, but it's gotten quite good. Like, especially if you compare, if you look at some of the, like if you take a latest episode and you compare it to an episode that came out a year ago, we've improved it quite a bit.swyx [00:39:23]: Well, it's beautifully presented. Oh, I love that I can click on the transcript and it goes to the timestamp. So simple, but you know, it should exist. Yeah, I agree. I agree. So this, I'm loading a two hour episode of Detect Me Right Home, where there's a lot of different guests calling in and you've identified the guest name. And yeah, so these are all LLM based. Yeah, it's really nice.Kevin [00:39:49]: Yeah, like the speaker names.swyx [00:39:50]: I would say that, you know, obviously I'm a power user of all these tools. You have done a better job than Descript. Okay, wow. Descript is so much funding. They had their open AI invested in them and they still suck. So I don't know, like, you know, keep going. You're doing great. Yeah, thanks. Thanks.Kevin [00:40:12]: I mean, I would, I would say that, especially for anyone listening who's interested in building a consumer app with AI, I think the, like, especially if your background is in AI and you love working with AI and doing all of that, I think the most important thing is just to keep reminding yourself of what's actually the job to be done here. Like, what does actually the consumer want? Like, for example, you now were just delighted by the ability to click on this word and it jumps there. Yeah. Like, this is not, this is not rocket science. This is, like, you don't have to be, like, I don't know, Android Kapathi to come up with that and build that, right? And I think that's, that's something that's super important to keep in mind.swyx [00:40:52]: Yeah, yeah. Amazing. I mean, there's so many features, right? It's, it's so packed. There's quotes that you pick up. There's summarization. Oh, by the way, I'm going to use this as my official feature request. I want to customize what, how it's summarized. I want to, I want to have a custom prompt. Yeah. Because your summarization is good, but, you know, I have different preferences, right? Like, you know.Kevin [00:41:14]: So one thing that you can already do today, I completely get your feature request. And I think it just.swyx [00:41:18]: I'm sure people have asked it.Kevin [00:41:19]: I mean, maybe just in general as a, as a, how I see the future, you know, like in the future, I think all, everything will be personalized. Yeah, yeah. Like, not, this is not specific to us. Yeah. And today we're still in a, in a phase where the cost of LLMs, at least if you're working with, like, such long context windows. As us, I mean, there's a lot of tokens in, if you take an entire podcast, so you still have to take that cost into consideration. So if for every single user, we regenerate it entirely, it gets expensive. But in the future, this, you know, cost will continue to go down and then it will just be personalized. So that being said, you can already today, if you go to the player screen. Okay. And open up the chat. Yeah. You can go to the, to the chat. Yes. And just ask for a summary in your style.swyx [00:42:13]: Yeah. Okay. I mean, I, I listen to consume, you know? Yeah. Yeah. I, I've never really used this feature. I don't know. I think that's, that's me being a slow adopter. No, no. I mean, that's. It has, when does the conversation start? Okay.Kevin [00:42:26]: I mean, you can just type anything. I think what you're, what you're describing, I mean, maybe that is also an interesting topic to talk about. Yes. Where, like, basically I told you, like, look, we have this chat. You can just ask for it. Yeah. And this is, this is how ChatGPT works today. But if you're building a consumer app, you have to move beyond the chat box. People do not want to always type out what they want. So your feature request was, even though theoretically it's already possible, what you are actually asking for is, hey, I just want to open up the app and it should just be there in a nicely formatted way. Beautiful way such that I can read it or consume it without any issues. Interesting. And I think that's in general where a lot of the, the. Opportunities lie currently in the market. If you want to build a consumer app, taking the capability and the intelligence, but finding out what the actual user interface is the best way how a user can engage with this intelligence in a natural way.swyx [00:43:24]: Is this something I've been thinking about as kind of like AI that's not in your face? Because right now, you know, we like to say like, oh, use Notion has Notion AI. And we have the little thing there. And there's, or like some other. Any other platform has like the sparkle magic wand emoji, like that's our AI feature. Use this. And it's like really in your face. A lot of people don't like it. You know, it should just kind of become invisible, kind of like an invisible AI.Kevin [00:43:49]: 100%. I mean, the, the way I see it as AI is, is the electricity of, of the future. And like no one, like, like we don't talk about, I don't know, this, this microphone uses electricity, this phone, you don't think about it that way. It's just in there, right? It's not an electricity enabled product. No, it's just a product. Yeah. It will be the same with AI. I mean, now. It's still a, something that you use to market your product. I mean, we do, we do the same, right? Because it's still something that people realize, ah, they're doing something new, but at some point, no, it'll just be a podcast app and it will be normal that it has all of this AI in there.swyx [00:44:24]: I noticed you do something interesting in your chat where you source the timestamps. Yeah. Is that part of this prompt? Is there a separate pipeline that adds source sources?Kevin [00:44:33]: This is, uh, actually part of the prompt. Um, so this is all prompt engine. Engineering, um, uh, you should be able to click on it. Yeah, I clicked on it. Um, this is all prompt engineering with how to provide the, the context, you know, we, because we provide all of the transcript, how to provide the context and then, yeah, I get them all to respond in a correct way with a certain format and then rendering that on the front end. This is one of the examples where I would say it's so easy to create like a quick demo of this. I mean, you can just go to chat to be deep, paste this thing in and say like, yeah, do this. Okay. Like 15 minutes and you're done. Yeah. But getting this to like then production level that it actually works 99% of the time. Okay. This is then where, where the difference lies. Yeah. So, um, for this specific feature, like we actually also have like countless regexes that they're just there to correct certain things that the LLM is doing because it doesn't always adhere to the format correctly. And then it looks super ugly on the front end. So yeah, we have certain regexes that correct that. And maybe you'd ask like, why don't you use an LLM for that? Because that's sort of the, again, the AI native way, like who uses regexes anymore. But with the chat for user experience, it's very important that you have the streaming because otherwise you need to wait so long until your message has arrived. So we're streaming live the, like, just like ChatGPT, right? You get the answer and it's streaming the text. So if you're streaming the text and something is like incorrect. It's currently not easy to just like pipe, like stream this into another stream, stream this into another stream and get the stream back, which corrects it, that would be amazing. I don't know, maybe you can answer that. Do you know of any?swyx [00:46:19]: There's no API that does this. Yeah. Like you cannot stream in. If you own the models, you can, uh, you know, whatever token sequence has, has been emitted, start loading that into the next one. If you fully own the models, uh, I don't, it's probably not worth it. That's what you do. It's better. Yeah. I think. Yeah. Most engineers who are new to AI research and benchmarking actually don't know how much regexing there is that goes on in normal benchmarks. It's just like this ugly list of like a hundred different, you know, matches for some criteria that you're looking for. No, it's very cool. I think it's, it's, it's an example of like real world engineering. Yeah. Do you have a tooling that you're proud of that you've developed for yourself?Kevin [00:47:02]: Is it just a test script or is it, you know? I think it's a bit more, I guess the term that has come up is, uh, vibe coding, uh, vibe coding, some, no, sorry, that's actually something else in this case, but, uh, no, no, yes, um, vibe evals was a term that in one of the talks actually on, on, um, I think it might've been the first, the first or the first day at the conference, someone brought that up. Yeah. Uh, because yeah, a lot of the talks were about evals, right. Which is so important. And yeah, I think for us, it's a bit more vibe. Evals, you know, that's also part of, you know, being a startup, we can take risks, like we can take the cost of maybe sometimes it failing a little bit or being a little bit off and our users know that and they appreciate that in return, like we're moving fast and iterating and building, building amazing things, but you know, a Spotify or something like that, half of our features will probably be in a six month review through legal or I don't know what, uh, before they could sell them out.swyx [00:48:04]: Let's just say Spotify is not very good at podcasting. Um, I have a documented, uh, dislike for, for their podcast features, just overall, really, really well integrated any other like sort of LLM focused engineering challenges or problems that you, that you want to highlight.Kevin [00:48:20]: I think it's not unique to us, but it goes again in the direction of handling the uncertainty of LLMs. So for example, with last year, at the end of the year, we did sort of a snipped wrapped. And one of the things we thought it would be fun to, just to do something with, uh, with an LLM and something with the snips that, that a user has. And, uh, three, let's say unique LLM features were that we assigned a personality to you based on the, the snips that, that you have. It was, I mean, it was just all, I guess, a bit of a fun, playful way. I'm going to look up mine. I forgot mine already.swyx [00:48:57]: Um, yeah, I don't know whether it's actually still in the, in the, we all took screenshots of it.Kevin [00:49:01]: Ah, we posted it in the, in the discord. And the, the second one, it was, uh, we had a learning scorecard where we identified the topics that you snipped on the most, and you got like a little score for that. And the third one was a, a quote that stood out. And the quote is actually a very good example of where we would run that for user. And most of the time it was an interesting quote, but every now and then it was like a super boring quotes that you think like, like how, like, why did you select that? Like, come on for there. The solution was actually just to say, Hey, give me five. So it extracted five quotes as a candidate, and then we piped it into a different model as a judge, LLM as a judge, and there we use a, um, a much better model because with the, the initial model, again, as, as I mentioned also earlier, we do have to look at the, like the, the costs because it's like, we have so much text that goes into it. So we, there we use a bit more cheaper model, but then the judge can be like a really good model to then just choose one out of five. This is a practical example.swyx [00:50:03]: I can't find it. Bad search in discord. Yeah. Um, so, so you do recommend having a much smarter model as a judge, uh, and that works for you. Yeah. Yeah. Interesting. I think this year I'm very interested in LM as a judge being more developed as a concept, I think for things like, you know, snips, raps, like it's, it's fine. Like, you know, it's, it's, it's, it's entertaining. There's no right answer.Kevin [00:50:29]: I mean, we also have it. Um, we also use the same concept for our books feature where we identify the, the mention. Books. Yeah. Because there it's the same thing, like 90% of the time it, it works perfectly out of the box one shot and every now and then it just, uh, starts identifying books that were not really mentioned or that are not books or made, yeah, starting to make up books. And, uh, they are basically, we have the same thing of like another LLM challenging it. Um, yeah. And actually with the speakers, we do the same now that I think about it. Yeah. Um, so I'm, I think it's a, it's a great technique. Interesting.swyx [00:51:05]: You run a lot of calls.Kevin [00:51:07]: Yeah.swyx [00:51:08]: Okay. You know, you mentioned costs. You move from self hosting a lot of models to the, to the, you know, big lab models, open AI, uh, and Google, uh, non-topic.Kevin [00:51:18]: Um, no, we love Claude. Like in my opinion, Claude is the, the best one when it comes to the way it formulates things. The personality. Yeah. The personality. Okay. I actually really love it. But yeah, the cost is. It's still high.swyx [00:51:36]: So you cannot, you tried Haiku, but you're, you're like, you have to have Sonnet.Kevin [00:51:40]: Uh, like basically we like with Haiku, we haven't experimented too much. We obviously work a lot with 3.5 Sonnet. Uh, also, you know, coding. Yeah. For coding, like in cursor, just in general, also brainstorming. We use it a lot. Um, I think it's a great brainstorm partner, but yeah, with, uh, with, with a lot of things that we've done done, we opted for different models.swyx [00:52:00]: What I'm trying to drive at is how much cheaper can you get if you go from cloud to cloud? Closed models to open models. And maybe it's like 0% cheaper, maybe it's 5% cheaper, or maybe it's like 50% cheaper. Do you have a sense?Kevin [00:52:13]: It's very difficult to, to judge that. I don't really have a sense, but I can, I can give you a couple of thoughts that have gone through our minds over the time, because obviously we do realize like, given that we, we have a couple of tasks where there are just so many tokens going in, um, at some point it will make sense to, to offload some of that. Uh, to an open source model, but going back to like, we're, we're a startup, right? Like we're not an AI lab or whatever, like for us, actually the most important thing is to iterate fast because we need to learn from our users, improve that. And yeah, just this velocity of this, these iterations. And for that, the closed models hosted by open AI, Google is, uh, and swapping, they're just unbeatable because you just, it's just an API call. Yeah. Um, so you don't need to worry about. Yeah. So much complexity behind that. So this is, I would say the biggest reason why we're not doing more in this space, but there are other thoughts, uh, also for the future. Like I see two different, like we basically have two different usage patterns of LLMs where one is this, this pre-processing of a podcast episode, like this initial processing, like the transcription, speaker diarization, chapterization. We do that once. And this, this usage pattern it's, it's quite predictable. Because we know how many podcasts get released when, um, so we can sort of have a certain capacity and we can, we, we're running that 24 seven, it's one big queue running 24 seven.swyx [00:53:44]: What's the queue job runner? Uh, is it a Django, just like the Python one?Kevin [00:53:49]: No, that, that's just our own, like our database and the backend talking to the database, picking up jobs, finding it back. I'm just curious in orchestration and queues. I mean, we, we of course have like, uh, a lot of other orchestration where we're, we're, where we use, uh, the Google pub sub, uh, thing, but okay. So we have this, this, this usage pattern of like very predictable, uh, usage, and we can max out the, the usage. And then there's this other pattern where it's, for example, the snippet where it's like a user, it's a user action that triggers an LLM call and it has to be real time. And there can be moments where it's by usage and there can be moments when there's very little usage for that. There. So that's, that's basically where these LLM API calls are just perfect because you don't need to worry about scaling this up, scaling this down, um, handling, handling these issues. Serverless versus serverful.swyx [00:54:44]: Yeah, exactly. Okay.Kevin [00:54:45]: Like I see them a bit, like I see open AI and all of these other providers, I see them a bit as the, like as the Amazon, sorry, AWS of, of AI. So it's a bit similar how like back before AWS, you would have to have your, your servers and buy new servers or get rid of servers. And then with AWS, it just became so much easier to just ramp stuff up and down. Yeah. And this is like the taking it even, even, uh, to the next level for AI. Yeah.swyx [00:55:18]: I am a big believer in this. Basically it's, you know, intelligence on demand. Yeah. We're probably not using it enough in our daily lives to do things. I should, we should be able to spin up a hundred things at once and go through things and then, you know, stop. And I feel like we're still trying to figure out how to use LLMs in our lives effectively. Yeah. Yeah.Kevin [00:55:38]: 100%. I think that goes back to the whole, like that, that's for me where the big opportunity is for, if you want to do a startup, um, it's not about, but you can let the big labs handleswyx [00:55:48]: the challenge of more intelligence, but, um, it's the... Existing intelligence. How do you integrate? How do you actually incorporate it into your life? AI engineering. Okay, cool. Cool. Cool. Cool. Um, the one, one other thing I wanted to touch on was multimodality in frontier models. Dwarcash had a interesting application of Gemini recently where he just fed raw audio in and got diarized transcription out or timestamps out. And I think that will come. So basically what we're saying here is another wave of transformers eating things because right now models are pretty much single modality things. You know, you have whisper, you have a pipeline and everything. Yeah. You can't just say, Oh, no, no, no, we only fit like the raw, the raw files. Do you think that will be realistic for you? I 100% agree. Okay.Kevin [00:56:38]: Basically everything that we talked about earlier with like the speaker diarization and heuristics and everything, I completely agree. Like in the, in the future that would just be put everything into a big multimodal LLM. Okay. And it will output, uh, everything that you want. Yeah. So I've also experimented with that. Like just... With, with Gemini 2? With Gemini 2.0 Flash. Yeah. Just for fun. Yeah. Yeah. Because the big difference right now is still like the cost difference of doing speaker diarization this way or doing transcription this way is a huge difference to the pipeline that we've built up. Huh. Okay.swyx [00:57:15]: I need to figure out what, what that cost is because in my mind 2.0 Flash is so cheap. Yeah. But maybe not cheap enough for you.Kevin [00:57:23]: Uh, no, I mean, if you compare it to, yeah, whisper and speaker diarization and especially self-hosting it and... Yeah. Yeah. Yeah.swyx [00:57:30]: Yeah.Kevin [00:57:30]: Okay. But we will get there, right? Like this is just a question of time.swyx [00:57:33]: And, um, at some point, as soon as that happens, we'll be the first ones to switch. Yeah. Awesome. Anything else that you're like sort of eyeing on the horizon as like, we are thinking about this feature, we're thinking about incorporating this new functionality of AI into our, into our app? Yeah.Kevin [00:57:50]: I mean, we, there's so many areas that we're thinking about, like our challenge is a bit more... Choosing. Yeah. Choosing. Yeah. So, I mean, I think for me, like looking into like the next couple of years, like the big areas that interest us a lot, basically four areas, like one is content. Um, right now it's, it's podcasts. I mean, you did mention, I think you mentioned like you can also upload audio books and YouTube videos. YouTube. I actually use the YouTube one a fair amount. But in the future, we, we want to also have audio books natively in the app. And, uh, we want to enable AI generated content. Like just think of, take deep research and notebook analysis. Like put these together. That should be, that should be in our app. The second area is discovery. I think in general. Yeah.swyx [00:58:38]: I noticed that you don't have, so you

Vi fortsätter vår uppladdning, den här gången med en vidöppen frågelåda! Vem förtjänade inte att vinna Robinson? Hur kommer man med? Och vem vill Pelle ta en titel från? Dessutom ger Gustavs mormor sin syn på stekheta Farmen!!Vi är tokigt stolta över att du vill vara med oss i det här forumet! Så TACK för att du lyssnar!!Kramar till tusen!/Pelle och Gustav

Kan man lave radio om Knoppers og Danonino yoghurt? Gustav har ihvertfald tænkt sig at prøve. Emilie sammenligner Monster energidrik med Marvel-karakterer, og sammen taler de om ugens alvorlige begivenhed på den anden side af atlanten, der har sat statsledere og diplomater på overarbejde.Tak fordi du flyver med BEL AIR

Långt fängelsestraff för mord på Mikael. Vansbro kommun är anmälda för att Gustavs assistans tagits bort. Musik väcker minnen. Lyssna på alla avsnitt i Sveriges Radio Play.

Långt fängelsestraff för mord på Mikael. Vansbro kommun är anmälda för att Gustavs assistans tagits bort. Musik väcker minnen. Lyssna på alla avsnitt i Sveriges Radio Play.

Stāsta mākslas zinātniece, mākslas muzeja "Rīgas birža" vadītājas vietniece, izstāžu kuratore Vita Birzaka Stāsts būs par tēlnieku un Valsts mākslas muzeja direktoru Burkardu Dzeni. Ar uzlecošas saules emblēmu, ko nosacīti uzskata par pirmo valsts ģerboni, Dzenis 1918. gada nogalē uzvarēja konkursā par jaunās Latvijas Republikas nacionālo simbolu. Šis ģerbonis tika lietots līdz 1921. gada 15. jūnijam, kad Satversmes sapulce pieņēma grafiķu Riharda Zariņa un Vilhelma Krūmiņa izstrādātu valsts ģerboni – tādu, kādu mēs to zinām šodien. 20. gadsimta sākumā Burkards Dzenis (1879–1966)  kļuva par vienu no latviešu profesionālās tēlniecības modernizētājiem un akmens tēlniecības tradīciju ieviesējiem. Līdzīgi kā brālēns Teodors Zaļkalns un kolēģis Gustavs Šķilters, mākslinieks Sanktpēterburgā pabeidza Štiglica Centrālo tehniskās zīmēšanas skolu (1898–1905), specializējies dekoratīvajā tēlniecībā. Atjaunotā mākslinieku pulciņa “Rūķis” ideāli sekmēja Dzeņa nacionālo pašapziņu. Studiju beigās piešķirtā ārzemju stipendija viņam deva iespēju franču tēlnieka Ogista Rodēna mācību darbnīcā Parīzē (1906) apgūt impresionisma principus un Maskavā (1907) papildināt pieredzi bronzas liešanā. Pēc atgriešanās no ārzemēm Dzenis Ķīšezera krastā Staņģos uzcēla sev darbnīcu un lietuvi. Līdzās radošajam darbam, Dzenis vienmēr bijis arī sabiedriski aktīvs un nav vairījies no brīžiem grūtas atbildības nastas. Jau 1910. gadā Rīgā viņš iesaistījās pirmās latviešu mākslas izstādes rīkošanā, kas pārauga Latviešu mākslas veicināšanas biedrības dibināšanā. Liktenīgā saskare ar mākslas darbu vākšanu sākās, darbojoties tās valdē. Pirmā pasaules kara laikā tēlnieks piedalījās gan latviešu mākslas izstāžu organizēšanā Petrogradā un Maskavā, gan leģendāro nacionālās mākslas albumu sagatavošanā, ar kuriem diplomāts Zigfrīds Anna Meierovics 1918. gada augustā devās uz Rietumeiropu, cenšoties panākt Latvijas neatkarības starptautisku atzīšanu. 1920. gada 15. martā Dzeni apstiprināja par jaundibinātā “Izglītības ministrijas Mākslas muzeja pagaidu pārzini”. Muzejam ierādīja telpas toreiz vēl karā izpostītajā Rīgas pilī, un kopš šī brīža Rīgā darbojās divi mākslas muzeji – jau agrāk dibinātais Rīgas pilsētas mākslas muzejs gleznotāja Vilhelma Purvīša vadībā un tagad arī – Valsts mākslas muzejs. Dzenis rūpējās gan par muzeja krājuma papildināšanu turpmākos divdesmit gadus, gan iesaistījās valstiski reprezentatīvu izstāžu organizēšanā un dažādu komisiju darbā, kā arī meklēja muzejam piemērotākas, plašākas telpas. Jau 1929. gada 8. novembrī Dzenis rakstīja izglītības ministram par to oficiālu vēstuli, norādot – ja nevar uzcelt speciālu jaunu ēku, tad, piemēram, armijas arsenāls Jēkaba laukumā varētu tikt pārbūvēts muzeja vajadzībām (kopš 1988. gada LNMM izstāžu zāle “Arsenāls”). Vienlaikus Burkards Dzenis vadīja arī Lietišķās tēlniecības meistardarbnīcu Latvijas Mākslas akadēmijā un darbojās mākslinieku un audzēkņu biedrībā “Sadarbs”. Tēlniecībā viņu interesēja portreta žanrs. Memoriālajā tēlniecībā radīti Emīla Dārziņa, Rūdolfa Blaumaņa, Rūdolfa Pērles, Viļa Olava un citi kapu pieminekļi. Pie Latvijas Nacionālā mākslas muzeja ēkas atrodas viņa veidotais gleznotāja Jaņa Rozentāla piemineklis. Nacionālā romantisma garā stilizētās Dzeņa etnogrāfiskās saktas savulaik valkājušas dzejniece Aspazija, operdziedātāja Malvīne Vīgnere-Grīnberga, aktrises Jūlija Skaidrīte, Lilija Ērika un Marija Leiko. Vēl joprojām šodien Mākslas muzeja “Rīgas Birža” ekspozīcijās un ātrijā lietošanā ir Dzeņa savulaik Valsts mākslas muzeja apmeklētājiem dizainētie ozolkoka soli. 1944. gadā mākslinieks ar ģimeni emigrēja uz Vāciju, bet 1950. gadā pārcēlās uz Amerikas Savienotajām Valstīm. Otrā pasaules kara beigās Valsts mākslas muzejā atstāto tēva privātkolekciju, godinot viņa piemiņu, jaunākais dēls Atis Dzenis 1993. gadā uzdāvināja tagadējam Latvijas Nacionālajam mākslas muzejam. Paldies manai muzeja kolēģei un kopā veidotās izstādes “Mūsu muzejs. Valsts mākslas muzejam 100” līdzkuratorei Dr. art. Aijai Brasliņai par palīdzību šī materiāla tapšanā!

Värmedöd, fjärilsvingar, mutanter och biologins bad boy – Idéer som förändrar världen firar hundra med ett specialavsnitt där komikern Emma Knyckare snor åt sig programledarrollen från Gustav Källstrand och ställer honom till svars. Med förändring som röd tråd lyfts exempel på upptäckter, nytänkande och idéer hämtade från Gustavs bok ”Tänk som en Nobelpristagare”.. Hosted on Acast. See acast.com/privacy for more information.

Get ready for a brand new season of Building One. Tune in Jan 14 as we kick things off with Nike's own Jason Mayden, the Chief Design Officer of the Jordan brand. But that's hardly all - we've got a great lineup of guests for you this season: Sal Khan of Khan Academy, Jenna Johnson of Patagonia, Arvind Jain of Glean, and Will Ahmed of WHOOP are just a few of the new voices we'll hear from! And if you can't wait, it's never been a better time to catch up on great interviews from our past season, like our interviews with Spotify's Gustav Söderström, Plaid's Zach Perret, or even Michelin-starred Chef, Dan Barber.

Eilon Reshef is the co-founder and chief product officer at Gong, one of the most ubiquitous B2B products in the world. In our conversation, we discuss:• Gong's unique approach to working with design partners• Their unique pod model• Why Eilon makes big decisions quickly• Lessons learned from being early in AI• The power of extreme focus• His “spiral method” for learning complex topics quickly• How to maintain quality while optimizing for speed—Brought to you by:• WorkOS—Modern identity platform for B2B SaaS, free up to 1 million MAUs• Think Fast Talk Smart—Tools and techniques to help you communicate more effectively• Vanta—Automate compliance. Simplify security—Find the transcript at: https://www.lennysnewsletter.com/p/inside-gong-eilon-reshef—Where to find Eilon Reshef:• LinkedIn: https://www.linkedin.com/in/eilonreshef—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Eilon's background(04:20) The pod model(06:33) Working with design partners(09:13) Finding and coordinating design partners(13:12) Balancing customer feedback and vision(15:10) Gong's 95% feature adoption(17:05) The importance of autonomy and trust(23:30) How to implement this unique way of working(27:15) Speed and decision-making(31:47) Early AI adoption and lessons learned(35:50) Building effective AI teams(38:16) The spiral method for learning(41:36) Narrowing down the initial customer profile(44:24) Failure corner(46:35) Lightning round—Referenced:• Gong: https://www.gong.io• Cisco: https://www.cisco.com/• How Gong builds product: https://www.lennysnewsletter.com/p/how-gong-builds-product• What is Montessori education?: https://amshq.org/About-Montessori/What-Is-Montessori• Isaac Asimov: https://en.wikipedia.org/wiki/Isaac_Asimov• Amit Bendov on LinkedIn: https://www.linkedin.com/in/amitbendov/• Lessons from scaling Spotify: The science of product, taking risky bets, and how AI is already impacting the future of music | Gustav Söderström (Co-President, CPO, and CTO at Spotify): https://www.lennysnewsletter.com/p/lessons-from-scaling-spotify-the• Nvidia: https://www.nvidia.com• Figma: https://www.figma.com• The Spiral Method: https://www.gong.io/blog/using-the-spiral-method/• Webex: https://www.webex.com/• L'Oréal: https://www.lorealparisusa.com/• American Express: https://www.americanexpress.com/• Slow Horses on AppleTV+: https://tv.apple.com/us/show/slow-horses/umc.cmc.2szz3fdt71tl1ulnbp8utgq5o• Dishwasher basket: https://www.amazon.com/Munchkin-High-Capacity-Dishwasher-Basket/dp/B07ZPMYKKS/• What most people miss about marketing | Rory Sutherland (Vice Chairman of Ogilvy UK, author): https://www.lennysnewsletter.com/p/what-most-people-miss-about-marketing• Occam's razor: https://en.wikipedia.org/wiki/Occam%27s_razor• Hanlon's razor: https://en.wikipedia.org/wiki/Hanlon%27s_razor• Sabich: https://en.wikipedia.org/wiki/Sabich#Ingredients_and_description• Careers at Gong: https://www.gong.io/careers—Recommended books:• Marty Cagan's books: https://www.amazon.com/stores/Marty-Cagan/author/B00J21JTNM• “The Machine That Won the War”: https://www.goodreads.com/book/show/18402398-the-machine-that-won-the-war• Crossing the Chasm: Marketing and Selling Disruptive Products to Mainstream Customers: https://www.amazon.com/Crossing-Chasm-3rd-Disruptive-Mainstream/dp/0062292986• The Ideal Executive: https://www.amazon.com/Ideal-Executive-Ichak-Kalderon-Adizes/dp/0937120030/• Crucial Conversations: Tools for Talking when Stakes Are High: https://www.amazon.com/Crucial-Conversations-Tools-Talking-Stakes/dp/1260474186/—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe

Radioteātrī ierakstīts koncertuzvedums "Ziemassvētki manā ielā". Raimonda Paula mūzika, Ineses Zanderes dzeja. Par jauno ierakstu stāsta Radioteātra direktore Māra Eglīte. "Šoreiz galveno lomu spēlēja Gundars Grasbergs, kurš arī sarindoja tos dzejoļus, kā nu vajadzētu," stāsta Māra Eglīte. "Man likās, ka šis ir stāsts, es teiktu, maza bērna, Ziemassvētku redzējums, kas reizē ir ļoti mīļš, ļoti viegls, ļoti patīkams, reizēm draisks, reizēm mazliet skumjš, bet, lūk, caur šiem dzīvniekiem, caur šiem visiem tēliem, mēs nonākam līdz tam galvenajam līdz cilvēkam, līdz viņu izjūtām. Tā man likās." Bet vēl pirms dzejoļu rindošanas bija saruna ar maestro Raimondu Paulu, kurš jau gadiem Ziemassvētku laikam gatavo jaunu programmu Radioteātrī. Sākumā viņš atteicis, ka šogad nebūs, jo viss jau uzrakstīts. Tomēr domājot par svētku laika jauniestudējumu, aktieris Gundars Grasbergs piedāvājis Ineses Zanderes krājumu "Sapnis par Ziemassvētkiem". Māra Eglīte lūgusi Raimnodam Paulam uzrakstīt vienu dziesmu. Nepagāja ne divas nedēļas, kad bija uzrakstītas 12 jaunas dziesmas! Koncertuzvedumā piedalās: Gundars Grasbergs - vokāls un dzeja, Ērika Eglija-Grāvele - vokāls, dzeja, Raimonds Pauls - klavieres, Raimonds Macats - taustiņinstrumenti, Jānis Kalniņš - ģitāra, Andris Grunte - bass, Māris Briežkalns - sitaminstrumenti, vokālais trio "Limonāde". Skaņu režisors Gustavs Ērenpreiss.

Norges rikeste United-fan er på besøk i Uno-studio! Gustav Witzøe forteller om oppveksten på Frøya, Beckham-plakatene på rommet, og hvordan han sjonglerer livet som laksemilliardær, modell og United-supporter. Vi snakker om Gustavs opplevelser på Old Trafford, den gangen han rømte fra en pub med Liverpool-fans, og om han klarte å gjøre Jan Thomas til en rød djevel. Litt business blir det også. For har trønderen noen gang vurdert å kjøpe favorittklubben? Gustav forteller om sin lidenskap for stiftelsen W Initiative, og gir oss også et knallsterkt 5-a-side-lag.I studio: Gustav Magnar Witzøe, Fredrik N. Filtvedt og Eivind Holth Hosted on Acast. See acast.com/privacy for more information.

How Bluesky, Alternative to X and Facebook, Is Handling Explosive Growth Netflix's Live Mike Tyson Vs. Jake Paul Fight Battling Sound & Streaming Glitches In Lead-Up To Main Event Biden Asked Microsoft to "Raise the Bar on Cybersecurity." He May Have Helped Create an Illegal Monopoly. CFPB looks to place Google under federal supervision, setting up clash Apple's Tim Cook Has Ways to Cope With the Looming Trump Tariffs Apple Removes Another RFE/RL App At Request Of Russian Regulator Here's Why I Decided To Buy 'InfoWars' Elon Musk's X Corp. files notice in Alex Jones' Infowars bankruptcy case Spotify's Plans For AI Generated Music, Podcasts, and Recommendations, According To Its Co-President, CTO, and CPO Gustav Söderström This 'AI Granny' Bores Scammers to Tears Congress ponders underwater alien civilizations, human hybrids, and other unexplained stuff In Memoriam: Thomas E. Kurtz, 1928–2024 Host: Leo Laporte Guests: Alex Kantrowitz, Daniel Rubino, and Iain Thomson Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech Get episodes ad-free with Club TWiT at https://twit.tv/clubtwit

How Bluesky, Alternative to X and Facebook, Is Handling Explosive Growth Netflix's Live Mike Tyson Vs. Jake Paul Fight Battling Sound & Streaming Glitches In Lead-Up To Main Event Biden Asked Microsoft to "Raise the Bar on Cybersecurity." He May Have Helped Create an Illegal Monopoly. CFPB looks to place Google under federal supervision, setting up clash Apple's Tim Cook Has Ways to Cope With the Looming Trump Tariffs Apple Removes Another RFE/RL App At Request Of Russian Regulator Here's Why I Decided To Buy 'InfoWars' Elon Musk's X Corp. files notice in Alex Jones' Infowars bankruptcy case Spotify's Plans For AI Generated Music, Podcasts, and Recommendations, According To Its Co-President, CTO, and CPO Gustav Söderström This 'AI Granny' Bores Scammers to Tears Congress ponders underwater alien civilizations, human hybrids, and other unexplained stuff In Memoriam: Thomas E. Kurtz, 1928–2024 Host: Leo Laporte Guests: Alex Kantrowitz, Daniel Rubino, and Iain Thomson Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech Get episodes ad-free with Club TWiT at https://twit.tv/clubtwit

How Bluesky, Alternative to X and Facebook, Is Handling Explosive Growth Netflix's Live Mike Tyson Vs. Jake Paul Fight Battling Sound & Streaming Glitches In Lead-Up To Main Event Biden Asked Microsoft to "Raise the Bar on Cybersecurity." He May Have Helped Create an Illegal Monopoly. CFPB looks to place Google under federal supervision, setting up clash Apple's Tim Cook Has Ways to Cope With the Looming Trump Tariffs Apple Removes Another RFE/RL App At Request Of Russian Regulator Here's Why I Decided To Buy 'InfoWars' Elon Musk's X Corp. files notice in Alex Jones' Infowars bankruptcy case Spotify's Plans For AI Generated Music, Podcasts, and Recommendations, According To Its Co-President, CTO, and CPO Gustav Söderström This 'AI Granny' Bores Scammers to Tears Congress ponders underwater alien civilizations, human hybrids, and other unexplained stuff In Memoriam: Thomas E. Kurtz, 1928–2024 Host: Leo Laporte Guests: Alex Kantrowitz, Daniel Rubino, and Iain Thomson Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech Get episodes ad-free with Club TWiT at https://twit.tv/clubtwit

How Bluesky, Alternative to X and Facebook, Is Handling Explosive Growth Netflix's Live Mike Tyson Vs. Jake Paul Fight Battling Sound & Streaming Glitches In Lead-Up To Main Event Biden Asked Microsoft to "Raise the Bar on Cybersecurity." He May Have Helped Create an Illegal Monopoly. CFPB looks to place Google under federal supervision, setting up clash Apple's Tim Cook Has Ways to Cope With the Looming Trump Tariffs Apple Removes Another RFE/RL App At Request Of Russian Regulator Here's Why I Decided To Buy 'InfoWars' Elon Musk's X Corp. files notice in Alex Jones' Infowars bankruptcy case Spotify's Plans For AI Generated Music, Podcasts, and Recommendations, According To Its Co-President, CTO, and CPO Gustav Söderström This 'AI Granny' Bores Scammers to Tears Congress ponders underwater alien civilizations, human hybrids, and other unexplained stuff In Memoriam: Thomas E. Kurtz, 1928–2024 Host: Leo Laporte Guests: Alex Kantrowitz, Daniel Rubino, and Iain Thomson Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech Get episodes ad-free with Club TWiT at https://twit.tv/clubtwit

How Bluesky, Alternative to X and Facebook, Is Handling Explosive Growth Netflix's Live Mike Tyson Vs. Jake Paul Fight Battling Sound & Streaming Glitches In Lead-Up To Main Event Biden Asked Microsoft to "Raise the Bar on Cybersecurity." He May Have Helped Create an Illegal Monopoly. CFPB looks to place Google under federal supervision, setting up clash Apple's Tim Cook Has Ways to Cope With the Looming Trump Tariffs Apple Removes Another RFE/RL App At Request Of Russian Regulator Here's Why I Decided To Buy 'InfoWars' Elon Musk's X Corp. files notice in Alex Jones' Infowars bankruptcy case Spotify's Plans For AI Generated Music, Podcasts, and Recommendations, According To Its Co-President, CTO, and CPO Gustav Söderström This 'AI Granny' Bores Scammers to Tears Congress ponders underwater alien civilizations, human hybrids, and other unexplained stuff In Memoriam: Thomas E. Kurtz, 1928–2024 Host: Leo Laporte Guests: Alex Kantrowitz, Daniel Rubino, and Iain Thomson Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech Get episodes ad-free with Club TWiT at https://twit.tv/clubtwit

How Bluesky, Alternative to X and Facebook, Is Handling Explosive Growth Netflix's Live Mike Tyson Vs. Jake Paul Fight Battling Sound & Streaming Glitches In Lead-Up To Main Event Biden Asked Microsoft to "Raise the Bar on Cybersecurity." He May Have Helped Create an Illegal Monopoly. CFPB looks to place Google under federal supervision, setting up clash Apple's Tim Cook Has Ways to Cope With the Looming Trump Tariffs Apple Removes Another RFE/RL App At Request Of Russian Regulator Here's Why I Decided To Buy 'InfoWars' Elon Musk's X Corp. files notice in Alex Jones' Infowars bankruptcy case Spotify's Plans For AI Generated Music, Podcasts, and Recommendations, According To Its Co-President, CTO, and CPO Gustav Söderström This 'AI Granny' Bores Scammers to Tears Congress ponders underwater alien civilizations, human hybrids, and other unexplained stuff In Memoriam: Thomas E. Kurtz, 1928–2024 Host: Leo Laporte Guests: Alex Kantrowitz, Daniel Rubino, and Iain Thomson Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech Get episodes ad-free with Club TWiT at https://twit.tv/clubtwit Sponsors: expressvpn.com/twit Fundrise.com/TWIT 1password.com/twit canary.tools/twit - use code: TWIT NetSuite.com/TWIT

In this episode of Grumpy Old Geeks, we break down the latest chaos in tech, pop culture, and humanity's never-ending quest to out-stupid itself. Hackers are raiding Ticketmaster accounts (again), and Chegg is circling the drain after ChatGPT obliterated its business model. Meanwhile, Amazon is hawking boner pills that tanked Hims' stock, 23andMe is laying off nearly half its staff, and Bluesky is quietly stealing users who've had it with Elon's dumpster fire over at X.Driverless Waymo cars take over LA, nuclear safety gets outsourced to AI, and Apple wants to track your luggage with AirTags (but only on planes they like). Oh, and let's not forget Mark Zuckerberg's bizarre karaoke moment with Get Low.On the pop culture front, The Penguin finale cements it as TV's best comic book adaptation, Moana smashes Disney streaming records, and The Beatles are back—kind of—thanks to AI. Plus, Mattel accidentally linked kids' toys to a porn site, and some genius in a bear costume is faking car accidents for insurance money. It's all as dumb, messy, and oddly entertaining as you'd expect. Tune in for the snark, stay for the schadenfreude!Sponsors:HelloFresh - Get 10 FREE meals at HelloFresh.com/freegog1Password Extended Access Management - Check it out at 1Password.com/grumpyoldgeeks. Secure every sign-in for every app on every device.DeleteMe - Head over to JoinDeleteMe.com/GOG and use the code "GOG" for 20% off.Private Internet Access - Go to GOG.Show/vpn and sign up today. For a limited time only, you can get OUR favorite VPN for as little as $2.03 a month.SetApp - With a single monthly subscription you get 240+ apps for your Mac. Go to SetApp and get started today!!!1Password - Get a great deal on the only password manager recommended by Grumpy Old Geeks! gog.show/1passwordShow notes at https://gog.show/674FOLLOW UPHackers Keep Stealing Tickets From People's Ticketmaster AccountsTrump Media Execs Dumped Millions in Stock After the ElectionStudy Finds Eggs Might Protect Brain Health And Lower CholesterolIN THE NEWSFTX bankruptcy estate sues Anthony Scaramucci, FWD.us, othersChegg Is On Its Last Legs After ChatGPT Sent Its Stock Down 99%AI travel influencers are here. Human travelers hate it.Bluesky reaches 15 million users; people look for alternatives to XJason on BlueskyBrian on BlueskyGrayskyBluesky scoops up the developer of popular third-party app, Graysky23andMe is laying off 40 percent of its staffAmazon Gets Into the Boner Pill Business, Hims Stock Goes LimpWaymo's driverless cars in LA County are now available to everyoneTelecom Builds AI ‘Grandmother' Bot to Talk to Phone Scammers and Waste Their TimeAnthropic, feds test whether AI will share sensitive nuclear infoNew artificial intelligence search solution deployed at Diablo Canyon Power PlantThe Manhattan Project (1986)AI Expert Warns Crash Is Imminent As AI Improvements Hit Brick Wall'Hawk Tuah' girl launches Pookie Tool, an AI-powered dating advice app, and it's fine?The Onion Purchases Alex Jones' InfoWars in Bankruptcy AuctionMEDIA CANDYCountdown: Paul vs. TysonPaul vs. TysonSuper League: The War for FootballShrinkingNobody Wants ThisSilo Season TwoMoana is Disney's Biggest Movie on Streaming EverThe Penguin Finale Cemented It as One of the Best Comic Book Shows EverThe Beatles are nominated for two Grammys thanks to AIGates McFadden InvestiGates: Who do you think you are?ERIC SAYSThe story of Love And Rockets, the former Bauhaus members who helped sell goth to AmericaMark Zuckerberg Just Released a Cover of Lil Jon's "Get Low" and Yes, He Says the Line About BallsSpotify's Plans For AI Generated Music, Podcasts, and Recommendations, According To Its Co-President, CTO, and CPO Gustav SöderströmAPPS & DOODADSGood News: Instagram Chief Says App Won't Refresh Right When You Open It AnymoreMeta Will Get Its Unwanted Day in Antitrust CourtApple Has Begun Selling iPhone 16 Replacement PartsApple teams up with airlines for new ‘Share Item Location' AirTags feature in iOS 18.2"Wicked" toys from Mattel featuring link to porn site pulled from shelves, now available on eBaySound Studio 4RedactTangle NewsAT THE LIBRARYThis photography book shows people just standing in front of Danzig's houseBeacon 23: The Complete Novel by Hugh HoweyTHE DARK SIDE WITH DAVEDave BittnerThe CyberWireHacking HumansCaveatControl LoopOnly Malware in the BuildingAndor Will Gloriously Rebel Once More in April 2025Andor Season 2 Will Pit Your Favorite Miserable Imperials Against Each OtherHow to migrate from X to Bluesky without losing your followersPerson dressed in bear costume to fake attacks on cars for insurance payout, California officials say.CLOSING SHOUT-OUTSTony Todd, ‘Candyman' and ‘Final Destination' Actor, Dead at 69Tony Todd Made One of Deep Space Nine‘s Greatest Episodes ShineHappy Birthday Gigi Edgley!See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Gustav Söderström is Spotify's co-president, chief technology officer, and chief product officer. He joins Big Technology Podcast — as we debut video episodes on Spotify — for discussion of Spotify's approach to AI generated content, algorithmic recommendations, and more. Tune in for a deep conversation covering whether Spotify wants AI-generated music and podcasts on its platform, how it can lean on AI recommendations to enhance discovery while sustaining human choice, and its long term AI vision. Stay tuned for the second half where we discuss Spotify's plans for podcasts, audiobooks, and other new formats. --- Enjoying Big Technology Podcast? Please rate us five stars ⭐⭐⭐⭐⭐ in your podcast app of choice. For weekly updates on the show, sign up for the pod newsletter on LinkedIn: https://www.linkedin.com/newsletters/6901970121829801984/ Want a discount for Big Technology on Substack? Here's 40% off for the first year: https://tinyurl.com/bigtechnology Questions? Feedback? Write to: bigtechnologypodcast@gmail.com

Ģenerālis šodien uz interviju uzaicinājis Gustavu Mellenbergu. Kopā viņi aprunāsies par koledžas basketbolu, Gustavs atklās savu pieredzi mācoties Amerikā un spēlējot basketbolu, kā arī ieteiks, kāpēc tieši ir jāizvēlās NCAA ceļš.

Sestdienas rīta "Atsperi" vadām kopā ar režisori, TV raidījumu vadītāju un producenti Martu Selecku. Runājam par jauno dokumentālo seriālu "Garainis", kas no 31. oktobra būs vērojams Latvijas Televīzijas 1. kanālā un tīmekļa platformā "REplay", ģimenes piedzīvojumu filmu "Bum!", kas no 3. novembra būs skatāma kinoteātros, kā arī superspējām un arī "Tutas lietām"... Ilze Medne: Sāksim ar "Garaini", kas pirmizrādi piedzīvos jau pavisam drīz. Esat tā pati komanda, kas sešus gadus veidojusi "Literatūri". Nu ejat solīti tālāk. Kā tapa ideja par tēmas paplašināšanu? Marta Selecka: Iniciatīva nāca gan no mums pašiem, gan arī skatītājiem. Daudzi prasīja: kāpēc runājat tikai par literatūru? Šādu raidījumu taču var veidot par visām mākslām! Piemēram, kāds mākslinieks izvēlas sev ceļabiedru no kāda cita laika un dodas ceļojumā kopā ar viņu. Nospriedām, ka patiešām varam mēģināt paplašināt darbības lauku. Kultūrkapitāla fonds mūs atbalstīja, uzrakstījām projektu, tad arī Latvijas Televīzija mūs ņēma savā paspārnē un nu mums būs ļoti labs raidlaiks: ēterā būsim tūlīt pēc "Panorāmas"! Raidījums būs arī daudz garāks nekā "Literatūre" – tas būs 45 minūšu garumā, un, kā teica arhitekte Zaiga Gaile, katrs raidījums būs kā filma. Cik vienkārši bija izvēlēties šīs personības? Tas princips, pēc kura izvēlamies, ir līdzīgs princesei uz zirņa... (smejas) Raidījuma varoņus uzrunājot, gribam, lai viņi tiešām ir visīstākās mākslas personības, kas sevi ir pamatīgi pierādījušas. Komplekts, kas būs pirmajā sezonā, manuprāt, iznācis ļoti, ļoti stiprs. Vai visi uzrunātie teica "jā"? Jā gan, visi uzrunātie uzreiz teica "jā"! Būs arī mans onkulis Ivars Seleckis – pat viņš teica "jā". Jo šis viņam ir ļoti, ļoti saspringts rudens, tikko kā bija "Turpinājuma" otrās daļas pirmizrāde, viss laiks vienās intervijās, un arī "kino gadu", kā viņš pats saka, viņam ir diezgan daudz. Šo raidījumu vēl neesam nofilmējuši, vēl gatavojamies. Bet pats pirmais pāris vien jau kaut ko nozīmē – Māra Ķimele un Ilmārs Blumbergs...  Tas būs ļoti jauks raidījums. Tas arī ir mūsu pirmais raidījums, ko šajā sērijā esam nofilmējuši. Lai skatītāji skatās un vērtē. Tas būs ļoti dziļš, daudz pārdomu raisošs. Ceļavārdi tam varētu būt šādi: "Darījām, ko varējām..." "Literatūrē" Gustava Terzena uzdevums ir būt ar grāmatu rokā. Kāds "Garainī" ir viņa uzdevums?  Gustavs skatās un meklē faktus par un ap abām personībām. Vai nu viņš dodas uz izstādi un skatās darbus, vai – kā Ilmāra Blumberga gadījumā – sarunājas ar laikabiedriem un meklē viņa pēdas pilsētvidē, runājas ar cilvēkiem uz ielas, aicina viņus baudīt kādus mākslas faktus. Katrā gadījumā tas ir tāds radošs process arī no Gustava puses. Katru reizi tas ir kaut kas cits, ko viņš izdomā. Visas izraudzītās ir ļoti liela mēroga personības, un, jo lielāks mērogs, jo vairāk tajā var iegrimt. Iegrimes process ir neaptverami liels! Ja "Literatūres" gadījumā tev bija jālasa grāmatas, Māras Ķimeles un Ilmāra Blumberga gadījumā, piemēram, izpētes process nav tikai viena mēneša garumā. Protams, izpētes process var būt ļoti garš, bet es dažkārt praktizēju tādu žurnālistisku paņēmienu, izpēti mazliet dozējot. "Literatūrē" tas nebija iespējams, bet šeit var būt arī tā, ka uz interviju ej mazliet kā balta lapa un skatītājam kaut kādā ziņā tas pat vairāk patīk, jo viņš kopā ar tevi var izzināt mākslas procesus, to, kā rodas māksla, kas ir māksla un kas ir tie jautājumi, ko mākslinieks risina, kur māksla rodas un kurš ir tas punkts, kad mēs uz pasauli sākam skatīties mākslas acīm. Tāpēc "Literatūre" man pašai personīgi ir grūtāks projekts, jo tur tā gatavošanās ir pamatīgāka. Nevaru aiziet un pateikt, ka konkrēto grāmatu neesmu lasījusi. Šeit tomēr ir citādāk. Mārai, piemēram, bijušas vairāk nekā astoņdesmit izrāžu, un man nav bijis iespējas tās visas noskatīties, taču varu paklausīties viņas intervijas, palasīt viņas domas kādos rakstos.  Kāda bija kopābūšana ar šīm personībām? Jo tas tomēr ir diezgan ilgs laiks, ko pavadāt kopā, kamēr top raidījums. Ļoti viegla! Bet tas tā ir ar daudzām lielām radošajām personībām. Šie cilvēki ikdienas komunikācijā ir ļoti brīnišķīgi un viegli sarunbiedri arī. Jo viņiem ir, ko teikt. Tas ir svarīgi. Jebkurš žurnālists to pateiks, ka ir ļoti svarīgi, lai viņa varonim ir, ko teikt. Un viņu teiktajam ir jēga. Tieši tā. Jēgpilnās sarunas arī ir viena no lietām, kas vieno visas "Garaiņa" filmēšanas. Vai, izvēloties personības, skatījāties arī uz žanrisko atšķirību? Jā gan. Atšķirībā no "Literatūres", "Garainī" uzdevums ir šāds: mēs izvēlamies cilvēku no konkrētas mākslas sfēras, un viņš nedrīkst sev blakus izvēlēties personību no tās pašas sfēras, ar kuru doties ceļojumā. Jāizvēlas personība no kādas citas nozares. Un tas bija ļoti interesanti! Piemēram, komponists Krists Auzinieks izvēlējās Mildu Pilēviču, kura ir filozofe un aprakstošās estētiskās domas pamatlicēja Latvijā. Zaiga Gaile stāstīs par Augustu Sukutu.  Un Laila Pakalniņa dosies vēl dziļāk vēsturē – pievērsīsies Jānim Jaunsudrabiņam. Viņu izvēles bija interesantas, turklāt mēs tās neietekmējām.  Man personīgi ir milzīgs prieks par to, ka Helēna Heinrihsone izvēlējusies muzikoloģi un režisori Ligitu Viduleju, kuras vārds ir nepelnīti piemirsts, jo viņas devums ir liels gan muzikoloģijā, gan televīzijas vēsturē – viņas muzikālās filmas ir tik brīnišķīgas! Ligitas un Helēnas raidījums vēl man priekšā, tā ka ļoti labi, ka tu man tagad to saki – tas man jāpatur prātā! (smejas) Līdz gada beigām paredzēti astoņi raidījumi. Vai ir cerības uz turpinājumu? Domāju, ka jā. Tie jaukie projekti, ja labi iesākas, parasti arī turpinās. Nav tā, ka pusceļā pazūd.  Un kas notiks ar "Literatūri"? Arī tai būs jauna sezona – pēc jaunā gada. Vēl astoņi raidījumi būs par literatūru, tā ka šis posms arī vēl nav noslēdzies. Redzēsim, protams, būs nākotnē, bet mums nebija tādas domas, ka pabeidzam vienu projektu un sākam nākošo. Mēs vienkārši paplašināmies. Tāda ir tā iecere.  Raidījumam ir ļoti zīmīgs nosaukums, un arī publicitātes fotogrāfijā šis vārds sadalīts divās daļās, vedinot pamanīt arī pirmos trīs burtus atsevišķi – ka tur klāt ir arī gars.  Mums bija ļoti ilgas diskusijas par to, kā mēs varētu nosaukt šo raidījumu. Un tad Uģa Oltes dzīvesbiedre Lāsma Olte, māksliniece un dzejniece, izdomāja tieši garaini – pirmām kārtām jau tas ir no Ziedoņa – "garainis, kas veicina vārīšanos". Un vēl man likās, ka arī tas stils, kādā runājam ar skatītāju, ir tādas garas ainas. Un vēl tas radošais gars. Dievišķais gars, kas ir mākslas darbā. Tā ka nosaukums, lai arī uzradās pēc ilgiem meklējumiem, atbild uz visiem jautājumiem, kas sagaidāms šajā raidījumā. Komanda tā pati, kas "Literatūrē"? Jā, tas ir mūsu komandas autordarbs, kurā jūtamies ērti. Mums šīs tēmas pašiem arī ir ļoti svarīgas. Prieks, ka raidījumi tikuši un tiks filmēti ne tikai Rīgā, bet arī ārpus tās. Jā, protams, protams! Un patiesībā gan "Literatūrē", gan arī "Garainī" tieši mūsu viesi ir scenārija autori: viņi saka – dosimies turp, tad dosimies turp, un tad – turp. Tajā brīdī mēs atpūšamies, kad kāds cits mūsu vietā saka, ka mums jābrauc tur un tur. (smaida) Esat jau devušies arī uz Liepāju? Turp brauca Gustavs, es nē. Viņš brauca uz Krista Auznieka operu. Tieši ar Liepāju saistīts tavs otrais radošais darbs – ģimenes piedzīvojumu filma "Bum!", kuras pirmizrāde būs jau 3. novembrī. Tā filmēta Liepājā, un tajā piedalās jauni, talantīgi cilvēki no Liepājas. Vai tev pašai ir kāda saistība ar Liepāju? Man pašai lielas saistības ar Liepāju nav, bet noteicošais faktors, kāpēc filmai "Bum!" izvēlējāmies tieši Liepāju kā galveno lokāciju, bija tas, ka mums vajadzēja atrast smuku skolu, jo visa filmas darbība notiek skolā un ap to. Liepājas Pirmā ģimnāzija filmēta daudzās filmās, arī "Homo novus" un vēl viskautkur. Tā ir ļoti skaista ēka, un arī mēs nevarējām atturēties – sapratām, ka Liepāja būs tā vieta, kur filmēsim savu filmu par skolu. Bet kā vispār tev radās ideja par šo lēcienu mazdrusciņ aukstā ūdenī, jo šī ir tava pirmā pilnmetrāžas filma! Līdz šim tavs vārds vairāk saistīts ar mazām distancēm, televīzijas raidījumiem, un nu pēkšņi kaut kas tāds liels un nozīmīgs kā filma!  Tas ir nejaušību ķēdes rezultāts... Ar Andru Doršu, kura ir filmas otra režisore, jau bijām kārtīgi ievingrinājušas roku bērnu saturā: filmējam tādu slavenu raidījumu kā "Tutas lietas", un mums ir arī bērnu detektīvseriāls "Emī un Rū", ko filmējam TET televīzijai. Tur mums bija 62 sērijas, piecas sezonas, un sapratām, ka varam to izdarīt, ka tas ir mūsu spēkos, jo caur šo seriālu un "Tutas lietām" sapratām, ko bērni no mums gaida, un nodomājām – labi, varētu pamēģināt uzfilmēt arī filmu. Radās iespēja, varēja pieteikties, bija kovids, bija nauda pilnmetrāžas filmām – un tieši bērnu filmām, tā ka visas kārtis bija mūsu rokās! Dabūjām finansējumu, un varējām ķerties klāt šādai pilnmetrāžas filmai... Kas tad ir tas, ko bērni gaida? Viņi gaida, ka mēs viņus cienīsim un par viņiem domāsim. Un nevis domāsim tā: ai, viņi jau tāpat sēž telefonos, un pusi no tā, par ko viņi sarunājas, mēs nesaprotam, puse vārdu ir angļu valodā un vispār nevar saprast, par ko viņi komunicē. Man šķiet – ir ļoti, ļoti svarīgi viņiem pievērst uzmanību! Teikt – hei, jūs te esat, un jūs mums esat svarīgi! Un mēs jums varam uzfilmēt filmu, kas domāta tieši jums! Kāds bija darba process? Ļoti grūts... Bet arī ļoti, ļoti interesants. Kad pabeidzu filmēšanas procesu, šķita, ka tas ir visforšākais, bet vienlaikus arī visgrūtākais darbs pasaulē, ko esmu darījusi. Un laikam nebūt nav viegli strādāt ar nepieredzējušiem aktieriem, vai ne? Jā… Mēs pašas gan ļoti ilgi gatavojāmies tam, lai tiktu galā ar šo uzdevumu, un rezultātā, domāju, sanāca labi. Iespējams, ka pašā sākumā, kad filmu sāk skatīties, liekas – tā kā īsti nevar saprast, kas tur notiek… Bet es jau iepriekš varu pabrīdināt, ka tas ir Liepājas dialekts, un ir tik jauki klausīties, kā mazie aktieri tajā runā! Bet kā bija strādāt? Bija tā, ka mēs ļoti gatavojāmies šim procesam. Tikām pat uzaicinātas uz Amsterdamu, lai veselu nedēļu mācītos pie slaveniem režisoriem, kā filmēšanas laukumā strādāt tieši ar bērniem. Jo mums patiešām bija tikai bērni-aktieri. Bija gan dažas epizodes, kurās bija arī mūsu mīļie, slavenie, lielie, pieaugušie aktieri, bet pārsvarā visā filmā piedalās tikai bērni.  Un kādas ir tās lielākās atšķirības? Lielākās atšķirības – filmēšanas laukumā nedot kolu un aizliegt lietot mobilos telefonus. Tie bija divi galvenie principi. (smejas)  Bet tie no bērnu skatupunkta nav ļauni principi? Bišķi ir. Bet – lai mazie aktieri varētu darboties, saņemties un būt fokusēti uz to, ko mēs no viņiem gribam, mums bija tāds noteikums – ka to dara visi. Arī asistenti, arī operatori: neviens nedzer kolu un nelieto mobilo telefonu filmēšanas laukumā! Un vēl no Amsterdamas bijām pārņēmuši tādu rīta apli: ka visa grupa, kas konkrētajā dienā ir laukumā, sanāk no rīta visi kopā un izstāsta kaut ko par savu bērnību: piemēram, ko tu kolekcionēji, kāda bija tava mīļākā dziesma, kam tu gribēji līdzināties, kāds bija tavs nedarbs… Tas mūs visus satuvināja un mūs, lielos, arī noskaņoja uz bērnības viļņa, bērna domāšanu. Par ko stāsta filma? Filma ir stāsts par draudzību. Jauniešiem, bērniem šis stāsts mazliet ir arī pamācošs, kā draudzēties. Bet šis ir arī tāds brīnumstāsts, jo tas ir par diviem puikām, no kuriem viens traumas rezultātā iegūst trīs dažāda veida superspējas, ko viņš var izmantot. Skolas dzīve tiek apgriezta kājām gaisā, jo viņš sāk mainīt bērniem atzīmes, ieviest brīvstundas, sekotāji soctīklos tiek samainīti vietām... Caur tehnoloģijām runājam par draudzības tēmu. No bērniem, kuri tika uzaicināti uz preses un dažādiem testa seansiem, kuros vērojām, vai bērni to visu saprot un vai viņiem interesē šī tēma, pagaidām esam saņēmuši ļoti labas atsauksmes – tātad tieši no tās auditorijas, kurai mēs šo filmu veidojām!

För många svenskar börjar historien med Gustav Vasa – vår förste ”riktige” kung, nationalstatens grundläggare och rikets befriare från Kalmarunionens danska förtryck. Verkligheten var annorlunda: Sverige hade haft egna kungar i hundratals år, nationalstaten växte fram långt senare och Kalmarunionen var mycket mer än ett danskt projekt. Trots att detta idag är välkänt inom historikerkretsar har Gustav Vasa fortsatt att vara den enskilt mest kända härskaren i vårt förflutna. Varför? Vad gjorde Gustav som gör att han fortfarande sätter sin prägel på våra historieböcker?Frågorna går att besvara genom att vi närmar oss Gustav Vasa från två håll: genom att vi skapar oss en överblick av hans politiska gärning och sätter den i relation till både medeltidens Norden och 1500-talets Europa, och genom att vi detaljanalyserar Gustavs egen korrespondens, till exempel med fruar och barn. I så fall framträder bilden av en kung som med en blandning av effektivitet, hänsynslöshet, framsynthet och försiktighet byggde upp ett stabilt välde åt sig själv och sin familj. Vi möter en mångsidig gestalt: ena dagen misstänksam envåldshärskare med gott om fiender, andra dagen ömsint familjefar. Eftersom han fick regera så länge, från 1523 till 1560, hann Gustav verkligen göra skillnad. Han byggde kanske inte vår nationalstat, men han lade flera av grundstenarna.I detta avsnitt av podden Harrisons dramatiska historia samtalar Dick Harrison, professor i historia vid Lunds universitet, och fackboksförfattaren Katarina Harrison Lindbergh om Gustav Vasa, som för många svenskar fortfarande är en portalfigur i historien.Bild: Gustav Vasa, porträtt från omkring 1558 i porträttsamlingen på Gripsholm. Beskuret. Okänd konstnär, men med mycket snarlikt utförande trärelief av kungen, gjord av Willem Boy.Klippare: Emanuel LehtonenProducent: Urban Lindstedt Hosted on Acast. See acast.com/privacy for more information.

Gustavs Zilgalvis is a technology and security policy fellow within RAND's Global and Emerging Risks Division, a Ford Dorsey Master's in International Policy candidate at Stanford's Freeman Spogli Institute for International Studies, and a founding Director at the Center for Space Governance. At RAND, he is specializing in the geopolitical and economic implications of the development of artificial intelligence. Previously, Zilgalvis has written about the interface of space and artificial intelligence in Frontiers of Space Technology, held a Summer Research Fellowship on artificial intelligence at Oxford's Future of Humanity Institute, and his research in computational high-energy physics has appeared in SciPost Physics and SciPost Physics Core. Zilgalvis holds a Bachelor of Science with First-Class Honors in Theoretical Physics from University College London, and graduated first in his class from the European School Brussels II.About Foresight InstituteForesight Institute is a research organization and non-profit that supports the beneficial development of high-impact technologies. Since our founding in 1987 on a vision of guiding powerful technologies, we have continued to evolve into a many-armed organization that focuses on several fields of science and technology that are too ambitious for legacy institutions to support.Allison DuettmannThe President and CEO of Foresight Institute, Allison Duettmann directs the Intelligent Cooperation, Molecular Machines, Biotech & Health Extension, Neurotech, and Space Programs, alongside Fellowships, Prizes, and Tech Trees. She has also been pivotal in co-initiating the Longevity Prize, pioneering initiatives like Existentialhope.com, and contributing to notable works like "Superintelligence: Coordination & Strategy" and "Gaming the Future".Get Involved with Foresight:Apply to our virtual technical seminars Join our in-person events and workshops Donate: Support Our Work – If you enjoy what we do, please consider this, as we are entirely funded by your donations!Follow Us: Twitter | Facebook | LinkedInNote: Explore every word spoken on this podcast through Fathom.fm, an innovative podcast search engine. Hosted on Acast. See acast.com/privacy for more information.

Spotify's personalized recommendations have set it apart from the pack of music streaming platforms for years. Those curated recommendations have only gotten stronger with the onset of AI, with the ability to power things like your own personal AI DJ. This week on the podcast, we're revisiting a conversation with Spotify's Co-President, CPO & CTO, Gustav Söderström. Gustav joins host and Lightspeed Partner Michael Mignano to talk about building UX that can power more efficient AI, the future of AGI, and what the technology could look live over the next few iterations.   Episode Chapters (00:00) An introduction to Gustav Söderström (01:29) How Spotify became one of the first consumer AI companies (05:57) A philosophical shift: UI that works for AI, not the other way around (12:52) “Talk is cheap, so do a lot of it” (15:00) Building for the tech we will have, not the tech we do have (19:43) What will AI do for media creation? (25:00) The Wild West of model training (29:45) How anthropomorphizing AI can help you understand its potential (37:04) What happens when cognitive labor gets automated? (40:42) How much more intelligent could AI get? (47:45) Is AGI on the horizon, or already here? (53:48) Should we be worried about AGI? Stay in touch: www.lsvp.com X: https://twitter.com/lightspeedvp LinkedIn: https://www.linkedin.com/company/lightspeed-venture-partners/ Instagram: https://www.instagram.com/lightspeedventurepartners/ Subscribe on your favorite podcast app: generativenow.co Email: generativenow@lsvp.com The content here does not constitute tax, legal, business or investment advice or an offer to provide such advice, should not be construed as advocating the purchase or sale of any security or investment or a recommendation of any company, and is not an offer, or solicitation of an offer, for the purchase or sale of any security or investment product. For more details please see lsvp.com/legal.

Last week we welcomed Spotify Co-President and CPO Gustav Söderström to the show to discuss his professional journey and the evolution of Spotify. We're back this week with a few more questions for Gustav to get to know him better. Follow Gustav Söderström on LinkedIn. Follow Tomer Cohen on LinkedIn and check out his newsletter Building LinkedIn

I Sommarsverige är Pelle irriterad över något han hört! Och nu letar han rätt på ett test för att kolla Gustavs sexualtiet? Men också om glädjen som sommaren ger! Tack och åter tack för att DU är med. Det värmer. Kramar! Pelle och Gustav

Dagens episode er med vores gode kammerat, favorit-jurist og genganger, Gustav Thybo, som for nyligt har haft 10-års jubilæum i Flyhjælp og stopper på toppen, TILLYKKE! Derfor skal vi selvfølgelig høre om Flyhjælps genfødsel efter corona, salget af Flyhjælp til Flightright, Gustavs oplevelse af at sælge til en tysk-ejet koncen og de kommende planer i Flyhjælp.  Husk endelig at gøre brug af rabatkoden "FREMTIDSFABRIKKEN", der giver alle førstegangsbrugere af GoMore 150 DKK til brug på plaftformen. Lease, udlej eller lej jeres bil på gomore.dk - det kører bare! Link til FAQ omkring beskatning på Gomore.dk Connect med os på LinkedIn: https://www.linkedin.com/in/larshorsbol/  https://www.linkedin.com/in/eskegerup/ 

Spotify's personalized recommendations have set it apart from the pack of music streaming platforms for years. Those curated recommendations have only gotten stronger with the onset of AI, with the ability to power things like your own personal AI DJ. In this episode, Spotify's Co-President, CPO & CTO, Gustav Söderström, joins host and Lightspeed Partner Michael Mignano to talk about building UX that can power more efficient AI, the future of AGI, and what the technology could look live over the next few iterations.   Episode Chapters (00:00) An introduction to Gustav Söderström (01:29) How Spotify became one of the first consumer AI companies (05:57) A philosophical shift: UI that works for AI, not the other way around (12:52) “Talk is cheap, so do a lot of it” (15:00) Building for the tech we will have, not the tech we do have (19:43) What will AI do for media creation? (25:00) The Wild West of model training (29:45) How anthropomorphizing AI can help you understand its potential (37:04) What happens when cognitive labor gets automated? (40:42) How much more intelligent could AI get? (47:45) Is AGI on the horizon, or already here? (53:48) Should we be worried about AGI? Stay in touch: www.lsvp.com X: https://twitter.com/lightspeedvp LinkedIn: https://www.linkedin.com/company/lightspeed-venture-partners/ Instagram: https://www.instagram.com/lightspeedventurepartners/ Subscribe on your favorite podcast app: generativenow.co Email: generativenow@lsvp.com The content here does not constitute tax, legal, business or investment advice or an offer to provide such advice, should not be construed as advocating the purchase or sale of any security or investment or a recommendation of any company, and is not an offer, or solicitation of an offer, for the purchase or sale of any security or investment product. For more details please see lsvp.com/legal.

Gustav Söderström is the Co-President, CPO & CTO at Spotify. Gustav has been instrumental in taking Spotify from a 30-person operation in Sweden when he joined to being the global leader of the space. Scott Belsky is Adobe's Chief Product Officer and Executive Vice President, Creative Cloud. Scott oversees all of product and engineering for Creative Cloud, as well as design for Adobe.  Tomer Cohen is the Chief Product Officer @ Linkedin where he is responsible for setting and executing the global product strategy at LinkedIn. In Today's Episode on How AI Changes The Future of Product and Design We Discuss: 1. Why AI Is Now the Product that UI Serves: Why does Gustav believe that AI is now the product? How has the importance of UI changed with the rise of AI? How did TikTok change the product paradigm over the last few years? 2. What Matters More Models or Data: What is more important the size of the model or the amount of data a company has? Will companies use many models at the same time? Why will companies using many models at once create a huge opportunity for startups? Will every company have their own model? What will be the decision-making framework of whether to have your own model or leverage another? How does the rise of AI change how companies approach data acquisition, collection and cleaning? 3. The Workforce Needs to Change with AI: How do product leaders and teams need to change in an AI-first world? What do designers need to do to stay up to date in an AI-first world? What does it mean to be good at prompting? How can people get good at prompting? Why will AI kill companies that charge by the hour? Why will seat pricing die in a world of AI? What will be the business model for AI? 4. Incumbents vs Startups: Who Wins: Do incumbents win in a world of AI or do startups? Why is AI primed for incumbents to win and move fast in a way they could not in prior technology cycles? What are the biggest hurdles and challenges incumbents have to face that startups do not? What are the biggest barriers that startups have to win in a world of AI that incumbents do not have?

Anuj Rathi is the Chief Product and Marketing Officer at Jupiter Money, where he leads product management, marketing, design, growth, and analytics. Before Jupiter Money, Anuj served as the Senior Vice President of Revenue and Growth at Swiggy, VP of Product at SnapDeal, a Senior PM at Walmart Labs and the first-ever PM at Flipkart. He's also one of the most beloved and respected product leaders in India. In this episode, we discuss:• How product management is different in India• How to rethink your approach to new users• How Anuj operationalizes the “working backwards” framework• Why Anuj thinks PMs should be more full-stack than they are• How to use Anuj's “4BB” framework to get better at product strategy and prioritization• Advice on developing innovative roadmap ideas• The three essential skills of a successful PM• Three reasons why leadership fails• Why OKRs don't work in marketplaces—Brought to you by Sanity—The most customizable content layer to power your growth engine | Vanta—Automate compliance. Simplify security | Wix Studio—The web creation platform built for agencies—Find the transcript for this episode and all past episodes at: https://www.lennyspodcast.com/episodes/. Today's transcript will be live by 8 a.m. PT.—Where to find Anuj Rathi:• X: https://twitter.com/anujrathi• LinkedIn: https://www.linkedin.com/in/anujrathi1—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Anuj's background(04:28) How product differs in India (08:34) When modern product thinking started to gain traction in India(14:01) How Anuj thinks about new-user experiences(15:07) Scott Belsky's “lazy, vain, and selfish” framework (19:59) Why PMs must understand category consumers(22:30) Anuj's philosophy on the PM job(23:59) How Anuj applies the working-backwards framework(28:36) The importance of FAQs(30:10) The full-stack PM mindset(33:06) Anuj's “show don't tell” framework(36:19) How to use the show-don't-tell framework(39:14) The impact of using this framework(41:27) Anuj's “4BB framework” for product strategy(48:59) Contrarian corner(50:49) Anuj's “framework of 3” for great PMs(52:34) How to develop grit and influence(54:00) Three reasons why leaders fail (56:21) AI corner(57:51) Lessons from building a successful marketplace(1:02:19) How to balance and maintain stability on all sides of a marketplace(1:07:48) Lightning round—Referenced:• MakeMyTrip: https://www.makemytrip.com/• Shaadi.com: https://www.shaadi.com/• Bharat Matrimony: https://www.bharatmatrimony.com/• Flipkart: https://www.flipkart.com/• Ola: https://www.olacabs.com/mobile• Swiggy: https://www.swiggy.com/• Jio: https://www.jio.com/• UPI: http://cashlessindia.gov.in/upi.html• The First 15 Seconds by Scott Belsky: https://medium.com/positiveslope/the-first-15-seconds-9590d7dabc• Jupiter: https://jupiter.money/• How to get better at influence: https://www.lennysnewsletter.com/p/how-to-get-better-at-influence#• Working Backwards: https://www.workingbackwards.com/• Range: Why Generalists Triumph in a Specialized World: https://www.amazon.com/Range-Generalists-Triumph-Specialized-World/dp/0735214484• In Search of Greatness on Prime Video: https://www.amazon.com/Search-Greatness-Wayne-Gretzky/dp/B07P5X99P5• Team Topologies: Organizing Business and Technology Teams for Fast Flow: https://www.amazon.com/Team-Topologies-Organizing-Business-Technology/dp/1942788819• Conway's Law: https://www.atlassian.com/blog/teamwork/what-is-conways-law-acmi• Lessons from scaling Spotify: The science of product, taking risky bets, and how AI is already impacting the future of music | Gustav Söderström (Co-President, CPO, and CTO at Spotify): https://www.lennyspodcast.com/lessons-from-scaling-spotify-the-science-of-product-taking-risky-bets-and-how-ai-is-already-impac/• Taobao: https://world.taobao.com/• Alibaba: https://offer.alibaba.com/• Working Backwards: https://www.amazon.com/Working-Backwards-PB/dp/1529033845• How Brands Grow: What Marketers Don't Know: https://www.amazon.com/How-Brands-Grow-What-Marketers/dp/0195573560• The Luxury Strategy: Break the Rules of Marketing to Build Luxury Brands: https://www.amazon.com/Luxury-Strategy-Break-Marketing-Brands/dp/0749464917• The Office on Peacock: https://www.peacocktv.com/stream-tv/the-office• Rise: https://www.risescience.com/—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe

I slutet av 1500-talet råder ett kallt krig mellan den svenska kungen Sigismund och hans farbror Karl. Den yngre katoliken, Gustavs barnbarn, är Sveriges och Polens kung. Den hänsynslöse hertig Karl vill gärna göra någonting åt den situationen.Brödrafejden efter Gustav Vasa är en av svensk historias mest dramatiska och rafflande berättelser. I dess skugga finns hundratals andra lika intressanta berättelser som till stor eller liten del hänger samman med den.Som i Finland där Sigismunds överståthållare Klas Fleming satte. Som en annan romersk prokonsul lät han Finlands bönder bära hela bördan att försörja rytteriet. Missnöjet hade pyrt länge, i slutet av seklet skulle eldan flamma upp och snart brann det överallt.Äntligen blir det klubbekrig i Historiepodden. Vi pratar om storpolitiken, om böndernas ekonomiska situation och förfasas över hur järnmasken Klas Fleming. Mycket nöje!Läslistan:Ylikangas, Heikki & Hulden, Mats, Klubbekriget: det blodiga bondekriget i Finland 1596-97, Atlantis, Stockholm, 1999Adolfsson, Mats, Fogdemakt och bondevrede [Elektronisk resurs], Natur & Kultur, 2014Larsson, Lars-Olof, Arvet efter Gustav Vasa: berättelsen om fyra kungar och ett rike, Prisma, Stockholm, 2005Hårdstedt, Martin, Finlands svenska historia, Svenska litteratursällskapet i Finland, Helsingfors, 2023 Lyssna på våra avsnitt fritt från reklam: https://plus.acast.com/s/historiepodden. Hosted on Acast. See acast.com/privacy for more information.

Fluent Fiction - Swedish: Café Calamity: A Spill Sparks Friendship Find the full episode transcript, vocabulary words, and more:fluentfiction.org/cafe-calamity-a-spill-sparks-friendship Story Transcript:Sv: Det var en krispig höstmorgon när Elsa bestämde sig för att träffa sin bästa vän Sofia på deras favoritkafé.En: It was a crisp autumn morning when Elsa decided to meet her best friend Sofia at their favorite café.Sv: Kaféet hade små röda bord och stolar och doften av nybakat bröd fyllde luften.En: The café had small red tables and chairs, and the smell of freshly baked bread filled the air.Sv: På väggarna hängde tavlor av lokala konstnärer och i hörnet stod ett gammalt piano.En: Paintings by local artists hung on the walls, and an old piano stood in the corner.Sv: Elsa kom först och köpte en stor kopp kaffe.En: Elsa arrived first and bought a large cup of coffee.Sv: Hon såg sig omkring och noterade att det var trångt och många satt ner och njöt av sin morgon.En: She looked around and noticed that it was crowded, with many people sitting down and enjoying their morning.Sv: Hon vinkade åt Sofia som just kommit in genom dörren.En: She waved to Sofia, who had just walked in through the door.Sv: Elsa började gå mot sitt bord, men hon var så uppspelt att hon inte såg Gustav som kom gående med sin laptop i högsta hugg.En: Elsa started walking towards her table, but she was so excited that she didn't see Gustav coming with his laptop in full swing.Sv: Plötsligt snubblade Elsa och kaffet flög ur koppen, rakt ner i Gustavs knä.En: Suddenly, Elsa stumbled, and the coffee flew out of the cup, straight into Gustav's lap.Sv: Gustav hoppade upp så fort att hans stol föll omkull.En: Gustav jumped up so quickly that his chair fell over.Sv: Han började dansa runt, viftade med händerna och försökte fläkta bort den heta kaffedropparna från byxorna.En: He began to dance around, waving his hands and trying to fan away the hot coffee drops from his pants.Sv: "Åh, nej!En: "Oh no!Sv: Förlåt, förlåt!"En: I'm sorry, I'm sorry!"Sv: utbrast Elsa och såg förskräckt på Gustav.En: exclaimed Elsa, looking horrified at Gustav.Sv: Kaféets andra besökare började skratta och några applåderade till Gustavs ofrivilliga dansnummer.En: The café's other visitors started laughing, and some applauded Gustav's involuntary dance number.Sv: Sofia rusade fram med servetter i handen och hjälpte till att torka upp röran.En: Sofia rushed forward with napkins in hand and helped clean up the mess.Sv: Gustav var först arg, men när han såg Elsas ledsna ansikte och hörde skratten runt omkring kunde han inte låta bli att le.En: Gustav was initially angry, but when he saw Elsa's sad face and heard the laughter around him, he couldn't help but smile.Sv: "Det är okej, det var en olyckshändelse," sa han till sist och skakade huvudet.En: "It's okay, it was an accident," he finally said, shaking his head.Sv: Elsa kände sig lite bättre och erbjöd sig att köpa en ny kopp kaffe åt Gustav samt betala för hans tvätt.En: Elsa felt a little better and offered to buy Gustav a new cup of coffee and pay for his dry cleaning.Sv: Han accepterade erbjudandet och snart satt de alla tre tillsammans och skrattade.En: He accepted the offer, and soon all three of them were sitting together, laughing.Sv: Sofia berättade skämt och Gustav glömde bort sin blöta byxor.En: Sofia told jokes, and Gustav forgot about his wet pants.Sv: När de skiljdes åt den dagen, var det med löften om att träffas igen.En: When they parted ways that day, it was with promises to meet again.Sv: Och kaféet, som hade varit vittne till en pinsam situation, blev istället platsen för en ny vänskap.En: And the café, which had witnessed an embarrassing situation, instead became the place for a new friendship.Sv: Elsa, Gustav och Sofia skulle komma att skämta om kaffehändelsen i många år framöver.En: Elsa, Gustav, and Sofia would joke about the coffee incident for many years to come.Sv: Gustavs dans hade inte bara spätt på roset, utan också fört dem närmare varandra.En: Gustav's dance not only lightened the mood, but also brought them closer together. Vocabulary Words:crisp: krispigautumn: höstdecided: bestämde sigmeet: träffafavorite: favoritcafé: kafésmall: småtables: bordchairs: stolarsmell: doftfreshly baked: nybakatbread: brödfilled: fylldeair: luftenpaintings: tavlorlocal: lokalaartists: konstnärerhung: hängdewalls: väggarnaold: gammaltpiano: pianoarrived: komfirst: förstbought: köptelarge: storcup: koppcoffee: kaffecrowded: trångtpeople: människorsitting down: satt ner

Vēsturniekam Gustavam Strengam tapusi monogrāfija, kas veltīta mirušo piemiņai un komemorācijas praksēm viduslaiku Livonijā. Pētījums skaidro 'memoria' kā kolektīvās atmiņas formas nozīmi dažādām grupām un institūcijām: pilsētu valdībai un ģildēm, Vācu ordenim, bīskapiem un katedrāles kapituliem un klosteru kopienām. Viduslaiku 'memoria' jeb mirušo piemiņa bija gan kolektīvās atmiņas forma, gan sociāla prakse, kas bija sastopama visās dzīves jomās. Tā veidoja identitāti un veidoja grupas, un tādējādi piemiņas prakses izpēte var mums daudz pastāstīt par viduslaiku kopienām. Šis ir teksts no kopsavilkuma pētījumam ar nosaukumu “Pieminot mirušos: kolektīvā atmiņa un piemiņa vēlo viduslaiku Livonijā” (Remembering the Dead: Collective Memory and Commemoration in Late Medieval Livonia). Pētījums skaidro 'memoria' kā kolektīvās atmiņas formas nozīmi dažādām grupām un institūcijām: pilsētu valdībai un ģildēm, Vācu ordenim, bīskapiem un katedrāles kapituliem un klosteru kopienām. Ko tad viduslaikos nozīmēja 'memoria', kā tā izpaudās šīm atšķirīgajām sabiedrības grupām un vai memoria mūsdienās ir kaut kas pilnīgi cits, skaidro pētījuma autors vēsturnieks Gustavs Strenga. Grāmata ikvienam brīvi pieejama tīmeklī.  

Today on the flagship podcast of machine learning-based recommendation systems: 03:31 - The Verge's David Pierce chats with Spotify's co-president and chief product officer Gustav Söderström about recommendations, audiobooks, app design, what Spotify wants to be, and whether it's possible to do it all well.  Spotify - The Verge 44:08 - Alex Cranz joins the show to discuss a bunch of recent streaming news, including the plan to combine Disney Plus and Hulu.  Streaming - The Verge 1:10:28 - Chris Welch joins the show to help answer this week's Vergecast Hotline question about mp3 players.  The Mighty — an iPod shuffle for Spotify — finally arrives for $85 bemighty.com Email us at vergecast@theverge.com or call us at 866-VERGE11, we love hearing from you. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Brought to you by Sidebar—Catalyze your career with a Personal Board of Directors | Wix Studio—The web creation platform built for agencies | LinkedIn Ads—Reach professionals and drive results for your business—Maya Prohovnik is currently Spotify's Head of Podcast Product. She was employee #1 at Anchor, which was acquired by Spotify in 2019 and now powers more than 80% of all new podcasts created in the world. In 2023, Maya was named one of the Most Important People in Podcasting by The Hollywood Reporter. In today's episode, we discuss:• How Maya operationalizes “dogfooding”• How to balance data-driven decision-making and intuition• Strategies for preserving startup culture in a large organization• Tactical tips to improve at public speaking• How Radical Candor and the Eisenhower matrix transformed her approach to managing people• What's next at Spotify for Podcasters—Find the full transcript at: https://www.lennyspodcast.com/building-anchor-selling-to-spotify-and-lessons-learned-maya-prohovnik-spotifys-head-of-podcast/—Where to find Maya Prohovnik:• Threads: https://www.threads.net/@mayafish• LinkedIn: https://www.linkedin.com/in/mayaprohovnik/—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Maya's background(04:34) Spotify's podcasting platform(06:24) Maya's personal podcasts(11:36) The importance of “dogfooding” (13:24) How Maya operationalizes dogfooding(16:31) How to balance data-driven decision-making and trusting your gut(21:38) Building Anchor 2.0(26:24) The beginning of Anchor's hockey stick growth(28:08) How Anchor utilized interns to make the Apple Podcasts integration “magical”(35:36) Anchor and Spotify's successful integration(37:50) Maintaining a startup culture within a large organization(39:20) Transitioning from a startup to a large company(42:02) Challenges brought on by the acquisition(48:49) How Maya's leadership approach is guided by Radical Candor(51:53) The Eisenhower matrix for prioritization and task management(52:46) Productivity tips(55:10) How to get better at public speaking(59:38) The future of Spotify for Podcasters(1:00:58) Lightning round—Referenced:• What is “Dogfooding”?: https://www.nytimes.com/2022/11/14/business/dogfooding.html• The Derry Connection: A Stephen King Podcast: https://open.spotify.com/show/6ixSiYlj3A9NqEXZDBgycf• Blood on Their Hands: A Big Brother Fancast: https://open.spotify.com/show/4VP16lTL8sUniQXCFeBInv• Time Share: A Children of Time Podcast: https://open.spotify.com/show/38yhl2lNOUajccfsdluh5j• The End of the World as We Know It: A First-Time Parenting Podcast: https://open.spotify.com/show/3TUr0LxcueYo2nvnyR5rML• Forgotify (stream Spotify songs that have never been played): https://forgotify.com/ • Michael Mignano on LinkedIn: https://www.linkedin.com/in/mignano/• Lessons from scaling Spotify: The science of product, taking risky bets, and how AI is already impacting the future of music | Gustav Söderström (Co-President, CPO, and CTO at Spotify): https://www.lennyspodcast.com/lessons-from-scaling-spotify-the-science-of-product-taking-risky-bets-and-how-ai-is-already-impacting-the-future-of-music-gustav-soderstrom-co-president-cpo-and-cto-at-spotify/• Radical Candor: https://www.amazon.com/Radical-Candor-Revised-Kick-Ass-Humanity/dp/1250235375• What is the Eisenhower matrix?: https://www.figma.com/resource-library/what-is-the-eisenhower-matrix/• Todoist: https://todoist.com/• Getting Things Done: The Art of Stress-Free Productivity: https://www.amazon.com/Getting-Things-Done-Stress-Free-Productivity/dp/0143126563/• Spotify for Podcasters: https://podcasters.spotify.com/• Children of Time: https://www.amazon.com/Children-Time-Adrian-Tchaikovsky/dp/0316452505• It: https://www.amazon.com/Novel-Stephen-King/dp/1982127791/• Poker Face on Peacock: https://www.peacocktv.com/stream-tv/poker-face• Barbie on Prime: https://www.amazon.com/Barbie-Margot-Robbie/dp/B0CB1TMKR6• Deadly Games: https://www.imdb.com/title/tt0096741/• 1-800 Contacts: https://www.1800contacts.com/• Lovevery: https://lovevery.com/• CoopCrate: https://www.coopcratechickens.com/—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe

Fluent Fiction - Swedish: Cobblestone Coffee Tales: A Whimsical Love Brew Find the full episode transcript, vocabulary words, and more:fluentfiction.org/cobblestone-coffee-tales-a-whimsical-love-brew Story Transcript:Sv: I en idyllisk liten stad, där kobbarna på gatorna glänste i solskenet och blommorna i fönsterrutorna doftade sött, låg ett pittoreskt litet café vid torgets hörn.En: In an idyllic little town, where the cobblestones on the streets glistened in the sunlight and the flowers in the window panes smelled sweet, there was a picturesque little café on the corner of the square.Sv: Det var inte något vanligt café, utan en plats där tid verkade stanna upp, där varje kaffekopp kändes som en saga.En: It wasn't just an ordinary café, but a place where time seemed to stand still, where each cup of coffee felt like a fairy tale.Sv: Det var här Sofia, en ung kvinna med livliga ögon och ett varmt leende, brukade tillbringa sina morgnar.En: It was here that Sofia, a young woman with lively eyes and a warm smile, used to spend her mornings.Sv: Gustav, ägaren och baristan på caféet, hade alltid ett varmt välkomnande för alla kunder.En: Gustav, the owner and barista of the café, always had a warm welcome for all customers.Sv: En ljus och solig morgon stod Sofia framför Gustav vid disken.En: On a bright and sunny morning, Sofia stood in front of Gustav at the counter.Sv: Hon log mot honom och sa, "Hej!En: She smiled at him and said, "Hello!Sv: Jag skulle vilja beställa en kaffe med mjölk, tack."En: I would like to order a coffee with milk, please."Sv: Gustav, med sina vänliga ögon och sitt konstanta leende, svarade, "Absolut!En: Gustav, with his kind eyes and constant smile, replied, "Certainly!Sv: Vilken sorts kaffe önskar du?"En: What kind of coffee would you like?"Sv: "En vanlig cappuccino, tack," svarade Sofia.En: "Just a regular cappuccino, please," answered Sofia.Sv: Kaffemaskinens surr överröste snabbt caféets lugna atmosfär när Gustav började brygga Sofias cappuccino.En: The humming of the coffee machine quickly overshadowed the calm atmosphere of the café as Gustav started brewing Sofia's cappuccino.Sv: Efter några minuter, räckte han över den ångande koppen till Sofia och frågade, "Här har du din cappuccino med mjölk.En: After a few minutes, he handed over the steaming cup to Sofia and asked, "Here's your milk cappuccino.Sv: Vill du ha något mer?"En: Would you like anything else?"Sv: "Nej, tack, det är allt," svarade Sofia, nöjd med sin beställning.En: "No, thank you, that's all," replied Sofia, pleased with her order.Sv: "Varsågod!En: "You're welcome!Sv: Din totala kostnad blir 35 kronor.En: Your total comes to 35 kronor.Sv: Hur betalar du?"En: How will you be paying?"Sv: frågade Gustav, och mötte Sofias glada ögon.En: Gustav asked, meeting Sofia's joyful eyes.Sv: "Jag betalar med kort, tack," sa Sofia och räckte över sitt kort till Gustav.En: "I'll pay with card, please," said Sofia, handing her card over to Gustav.Sv: "Perfekt, ge mig kortet så fixar jag betalningen åt dig," sa Gustav och tog emot hennes kort.En: "Perfect, give me the card and I'll take care of the payment for you," said Gustav, accepting her card.Sv: Några ögonblick senare var transaktionen komplett.En: A few moments later, the transaction was complete.Sv: "Tack så mycket!En: "Thank you so much!Sv: Ha en bra dag!"En: Have a great day!"Sv: sa Sofia medan hon tog sitt kaffekort och gick mot ett bord vid fönstret.En: said Sofia as she took her coffee and headed towards a table by the window.Sv: "Varsågod!En: "You're welcome!Sv: Du med, ha en trevlig dag!"En: You too, have a lovely day!"Sv: ropade Gustav efter henne och vände sig sedan mot nästa kund med sitt eviga leende.En: called Gustav after her, then turned to the next customer with his eternal smile.Sv: Denna rutin fortsatte i Sofia och Gustavs liv.En: This routine continued in Sofia and Gustav's lives.Sv: Deras morgnar började alltid med samma gamla rutin, samma vänliga exchange, och samma cappuccino med mjölk.En: Their mornings always began with the same old routine, the same friendly exchange, and the same milk cappuccino.Sv: Det var en enkel historia om två personer som, oaveta till dem, blev staplar av varandras vardagsliv på caféet vid torget.En: It was a simple story of two individuals who, unbeknownst to them, became pillars of each other's everyday lives at the café on the square. Vocabulary Words:idyllic: idylliskcobblestones: kobbarnastreets: gatornaglistened: glänstesunlight: solskenetflowers: blommorwindow panes: fönsterrutornasmelled: doftadesweet: söttpicturesque: pittoresktcafé: caféordinary: vanligtplace: platstime: tidstand still: stanna uppcoffee: kaffefairy tale: sagayoung woman: ung kvinnalively eyes: livliga ögonwarm smile: varmt leendemornings: morgnarowner: ägarenbarista: baristancounter: diskorder: beställakind eyes: vänliga ögonconstant smile: konstanta leenderegular: vanligcappuccino: cappuccinobrewing: brygga

Brought to you by Sidebar—Catalyze your career with a Personal Board of Directors | Superhuman—The fastest email experience ever made | Vanta—Automate compliance. Simplify security.—Noah Weiss is Chief Product Officer at Slack, where he leads all aspects of the product organization, including the self-service SMB business, the team that launched huddles and clips, and the search and machine-learning teams. Prior to Slack, Noah served as SVP of Product at Foursquare. He started his career at Google, leading the structured data search team and working on display ads. In today's episode, we discuss:• The top 10 traits of great PMs• How “complaint storms” helped Slack teams foster empathy• How Slack's product team is approaching AI• “Comprehension desirability” and other key factors leading to Slack's success• Why you should be customer-aware but not customer-obsessed• Important areas of growth for both new PMs and senior PMsCurious to learn more about Slack? You can try Slack Pro and get 50% off using this link.—Find the transcript at: https://www.lennyspodcast.com/the-10-traits-of-great-pms-how-ai-will-impact-your-product-and-slacks-product-development-process/—Where to find Noah Weiss:• Twitter: https://twitter.com/noah_weiss• LinkedIn: https://www.linkedin.com/in/noahw/—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• Twitter: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Noah's background(04:22) Noah's advice on new parenthood(07:23) Lessons learned from leading product at Foursquare(11:33) Advice for working with strongly opinionated founders(14:14) Thinking of involvement on a U-shaped curve(16:53) Principles at Slack(19:32) Implementing ML, AI, and LLMs in meaningful ways(25:11) How Slack structures AI teams(26:59) Complaint storms and how they help foster empathy(30:01) Slack's approach to prioritization (32:26) How delight is baked into the DNA of Slack(34:41) How Slack thinks about competition (38:04) Building a culture that takes big bets(41:40) Rituals at Slack(44:51) How Slack unlocked new levers of growth and revived their self-serve business(52:01) Slack's early success and the factors that made them successful (58:08) Slack's pilot programs for testing new features(1:02:03) Noah's famous blog post: “The 10 Traits of Great Product Managers”(1:10:15) Book recommendations to improve your writing(1:12:30) Managing up and the importance of data fluency(1:14:54) The most important skills to improve as an early-career PM and as a senior PM(1:17:16) Lightning round—Referenced:• Emily Oster: https://emilyoster.net/• Dennis Crowley: https://denniscrowley.com/• Stewart Butterfield on Twitter: https://twitter.com/stewart• Don't Make Me Think, Revisited: A Common Sense Approach to Web Usability: https://www.amazon.com/Dont-Make-Think-Revisited-Usability/dp/0321965515• Gustav Söderström on Lenny's Podcast: https://www.lennyspodcast.com/lessons-from-scaling-spotify-the-science-of-product-taking-risky-bets-and-how-ai-is-already-impacting-the-future-of-music-gustav-soderstrom-co-president-cpo-and-cto-at-spotify/• Seth Godin: https://seths.blog/• Noah's blog post on the 10 traits of great PMs: https://medium.com/@noah_weiss/10-traits-of-great-pms-a7776cd3d9cd• Five Dangerous Myths about Product Management: https://medium.com/@noah_weiss/five-dangerous-myths-about-product-management-d1d852ed02a2• Paul Graham: http://paulgraham.com/• Ben Horowitz on Twitter: https://twitter.com/bhorowitz• On Writing: A Memoir of the Craft: https://www.amazon.com/Writing-Memoir-Craft-Stephen-King/dp/1982159375• On Writing Well: The Classic Guide to Writing Nonfiction: https://www.amazon.com/Writing-Well-Classic-Guide-Nonfiction/dp/0060891548• Nobody Wants to Read Your Sh*t: And Other Tough-Love Truths to Make You a Better Writer: https://www.amazon.com/Nobody-Wants-Read-Your-Tough-Love/dp/1936891492• Several Short Sentences About Writing: https://www.amazon.com/Several-Short-Sentences-About-Writing/dp/0307279413• Paige Costello on Twitter: https://twitter.com/paigenow• Creative Selection: Inside Apple's Design Process During the Golden Age of Steve Jobs: https://www.amazon.com/Creative-Selection-Inside-Apples-Process/dp/1250194466• The Innovator's Dilemma: When New Technologies Cause Great Firms to Fail: https://www.amazon.com/Innovators-Dilemma-Technologies-Management-Innovation/dp/1633691780• Radical Candor: https://www.amazon.com/Radical-Candor-Revised-Kim-Scott/dp/1250258405• Leadership: In Turbulent Times: https://www.amazon.com/Leadership-Turbulent-Doris-Kearns-Goodwin/dp/1476795924• Succession on HBO: https://www.hbo.com/succession• The Bear on Hulu: https://www.hulu.com/series/the-bear-05eb6a8e-90ed-4947-8c0b-e6536cbddd5f• Nanit: https://www.nanit.com/• Snoo: https://www.happiestbaby.com/products/snoo-smart-bassinet• Uppababy: https://uppababy.com/—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe

7 juni. Fyllde Sverige 500 år i veckan? Knappast, men att frågan ens kan ställas visar på vilken stor betydelse Gustav Vasas regeringstid anses ha haft för landet. Andreas Ericson diskuterar Gustavs liv och gärning med Olle Larsson, författare och historiker.

Brought to you by Microsoft Clarity—See how people actually use your product | Eppo—Run reliable, impactful experiments | Eco—Your most rewarding app—Gustav Söderström is the Co-President and Chief Product and Technology Officer at Spotify. He is responsible for Spotify's global product and technology strategy, overseeing the product, design, data, and engineering teams. Prior to Spotify, he founded 13th Lab, a startup that was later acquired by Facebook's Oculus. He also served as the Director of Product and Business Development for Yahoo Mobile and founded Kenet Works, a company focused on community software for mobile phones, which was acquired by Yahoo in 2006. In today's episode, we discuss:• How Spotify structures product teams to promote freedom of thought• Lessons on thinking long-term and navigating negative feedback• Why Gustav started a podcast and what he's learned• How AI has impacted the work PMs, engineers, and designers do within Spotify• AI-generated music and its impact on artists• What's next for Spotify and Spotify Podcasting—Find the full transcript at: https://www.lennyspodcast.com/lessons-from-scaling-spotify-the-science-of-product-taking-risky-bets-and-how-ai-is-already-impacting-the-future-of-music-gustav-soderstrom-co-president-cpo-and-cto-at-spotify/#transcript—Where to find Gustav Söderström:• Twitter: https://twitter.com/GustavS• LinkedIn: https://www.linkedin.com/in/gustavsoderstrom/—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• Twitter: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Gustav's background(04:08) The various roles Gustav has occupied at Spotify(06:54) Why Gustav launched a podcast and what he learned(12:37) How PMs and product teams should think about AI(21:23) AI-generated music(26:19) Will AI continue to be a magic trick for products?(28:27) How Spotify organizes product teams(34:33) How Spotify operationalized autonomy(35:45) Why Spotify uses a centralized model for structuring their organization(43:34) The big bet Spotify took with redesigning its interface, and what they learned(57:26) How they tested their hypothesis before launch(1:02:35) Gustav's “10% planning time” methodology (1:03:53) How to bring energy and clarity to your work(1:08:07) How to systematize deep thinking(1:10:29) The peeing-in-your-pants analogy (1:11:38) Thoughts on how the Swedish culture is portrayed in Succession (1:13:30) What's next for Spotify and Spotify Podcasting(1:15:52) Lightning round—Referenced:• Spotify: https://open.spotify.com/• Daniel Ek: https://www.linkedin.com/in/daniel-ek-1b52093a/• Spotify: A Product Story podcast: https://open.spotify.com/show/3L9tzrt0CthF6hNkxYIeSB• Spotify's AI DJ: https://newsroom.spotify.com/2023-02-22/spotify-debuts-a-new-ai-dj-right-in-your-pocket/• Avicii: https://avicii.com/• DALL-E: https://openai.com/product/dall-e-2• Stable Diffusion: https://stability.ai/• Midjourney: https://www.midjourney.com/• Brian Chesky: https://www.linkedin.com/in/brianchesky/• Succession on HBO: https://www.hbo.com/succession• Fjällräven: https://www.fjallraven.com/us/en-us• 7 Powers: The Foundations of Business Strategy: https://www.amazon.com/7-Powers-Foundations-Business-Strategy• Charlie Munger: The Complete Investor: https://www.amazon.com/Charlie-Munger-Tren-Griffin• The Mystery of the Aleph: Mathematics, the Kabbalah, and the Search for Infinity: https://www.amazon.com/Mystery-Aleph-Mathematics-Kabbalah-Infinity• Something Deeply Hidden: Quantum Worlds and the Emergence of Spacetime: https://www.amazon.com/Something-Deeply-Hidden• Helgoland: Making Sense of the Quantum Revolution: https://www.amazon.com/Helgoland-Making-Sense-Quantum-Revolution• The Beginning of Infinity: Explanations That Transform the World: https://www.amazon.com/The-Beginning-of-Infinity• The Fabric of Reality: The Science of Parallel Universes—and Its Implications: https://www.amazon.com/The-Fabric-of-Reality• The Case Against Reality: Why Evolution Hid the Truth from Our Eyes: https://www.amazon.com/The-Case-Against-Reality-audiobook/dp/B07VL5TCVF/ref=sr_1_1• Gödel's Proof: https://www.amazon.com/G%C3%B6dels-Proof-Ernest-Nagel• The Demon in the Machine: How Hidden Webs of Information Are Solving the Mystery of Life: https://www.amazon.com/Demon-Machine-Information-Solving-Mystery• Halt and Catch Fire on Apple TV: https://tv.apple.com/us/show/halt-and-catch-fire/umc.cmc.5s15r46uj0wx044tipm2zoh88• Duolingo: https://www.duolingo.com/—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe

Wille och Jacob gästas denna vecka av Gustav Nilsonne, docent i neurovetenskap på KI. Ämnet för dagen är framför allt meta-vetenskap vilket i princip är forskning på forskning. De senaste 15 åren har det skett en form av revolution kring det fältet då det har uppdagats att hur man utför forskning har stor potential till förbättring så att resultat blir mer tillförlitliga och att mer av den viktiga informationen blir tillgänglig för alla. I dagens avsnitt får du bland annat höra om bristerna i hur det fungerar med de vetenskapliga tidskrifterna idag där det i princip är stora företag som tjänar massa pengar på universitet och forskare. Det blir också en hel del diskussion kring hur de friheter som forskar har när de ska analysera data påverkar deras resultat. Det är visat i flera studier att olika forskargrupper kan komma fram till olika resultat med precis samma data eftersom de väljer olika sätt att analysera den. Till sist blir det också en kort diskussion kring hur fenomenet regression to the mean kan leda till att man hittar ”falska” korrelationer i vissa observationsstudier. I just det fallet som vi diskuterar i avsnittet så handlar det om sambandet mellan amning och IQ. På Tyngre Träningssnacks instagram kan du hitta bilder relaterat till detta och tidigare avsnitt. Hålltider (00:00:00) Gudiol har tappat rösten (00:03:40) Wille sprang 10 km på Kungsholmen runt (00:07:09) Veckans gäst, Gustav Nilsonne (00:08:20) John Ioannidis som en av de som öppnade upp fältet kring meta-vetenskap (00:10:24) Hur man blir neurovetenskapare och Gustavs väg till forskningen (00:14:57) Byte av fält direkt efter doktorandstudierna gav mycket extra jobb (00:19:15) Försök att studera emotionell reglering hos människor (00:24:35) Hur Gustav kom in på att forska om forskning (00:25:48) Ett av de första stora projekten där man försökte replikera tidigare kända/välciterade studier (00:30:47) Hur blir mottagandet när man är forskare som granskar andra forskare? (00:36:25) Betydelsen av förhandsregistrering och ännu bättre, registered reports (00:42:38) Problemet som är vetenskapliga tidskrifter och hur det fungerar i dag (00:53:51) Peer Review fångar sällan upp rent fusk (00:58:38) Vad är förslagen som alternativ till vetenskapliga tidskrifter (01:02:45) Olika forskargrupper kan få fram olika resultat med exakt samma data (01:11:22) Pandemin blev ett bra exempel på att samma data kan analyseras olika av olika personer (01:17:14) Alla möjliga analyser som kan göras inom nutritionsepidemiologi (01:23:18) Studier som bör vara utförda men inte publicerade (01:29:07) Regression to the mean och en analys som tittar bakåt i tiden (01:33:15) Observationsstudier som visar att amning ger smartare barn påverkas av regression to the mean

Jag kunde inte riktigt släppa Karl X Gustav än så vi tar hans begravning och riksdagen 1660 också.Bli patron för att lyssnaLitteraturKarl XI av Göran RystadKarl X Gustav av Björn Asker Hosted on Acast. See acast.com/privacy for more information.

Kung Karl X Gustav dör i Göteborg 1660 under den pågående riksdagen. För att lyssna på hela måste du bli månadsgivare på patreon.com/kungarochkrig Litteratur:Riksdagen i Kronhuset och Carl X Gustafs död i Göteborg 1660, Curt Weibull, utgiven av Göteborgs historiska museum 1957 Hosted on Acast. See acast.com/privacy for more information.

Normally, trust = consistency + time. But when you're scaling fast, you have to find shortcuts to trust, with your partners and your users. Spotify CEO Daniel Ek knows a thing or two about this. When he founded Spotify, he did what few disruptors ever do: He worked with the industry he was trying to reinvent. How did Ek build a relationship with a music industry wary of piracy? He found shortcuts to trust. And not just with the music industry, but users to. With cameo appearances from Gustav Söderström (Spotify's Chief Research & Development Officer) and Miles Daisher (Red Bull Air Force).Read a transcript of this episode: https://mastersofscale.com/Subscribe to the Masters of Scale weekly newsletter at http://eepurl.com/dlirtXSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Gustav Söderström has worked at Spotify for a long time; his first big project was leading the launch of its mobile app back in 2009. That makes him the perfect company leader to talk to about Spotify's recent redesign, which introduces a visual, TikTok-like feed for discovering new content on the app's homepage. As his boss CEO Daniel Ek put it last week, it's “the biggest change Spotify has undergone since we introduced mobile.” With the title of co-president and chief product and technology officer, Söderström is responsible for not only how Spotify looks and feels but also all the AI work happening behind the scenes to power its increasingly important recommendations. According to Söderström, it turns out that improving those recommendations is actually at the heart of the big redesign. “I think companies that don't have an efficient user interface for a machine learning world are not going to be able to leverage machine learning,” he told Alex Heath on the newest episode of Decoder. Links: https://en.wikipedia.org/wiki/Napster Spotify is laying off 6 percent of its global workforce, CEO announces Spotify's new design turns your music and podcasts into a TikTok feed Alex Heath's Tweet Functional versus Unit Organizations Two-Pizza Teams Transcript: https://www.theverge.com/e/23402123 Credits: Decoder is a production of The Verge and part of the Vox Media Podcast Network. It was produced by Creighton DeSimone and Jackie McDermott and it was edited by Callie Wright.  The Decoder music is by Breakmaster Cylinder. Our Editorial Director is Brooke Minters and our Executive Director is Eleanor Donovan. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Audio is no longer just audio anymore -- podcasts now pull from new video platforms like TikTok and older ones like radio, user experience is growing and changing, and it's easier than ever to create audio content. Where do all these mediums converge and where do they diverge -- when it comes to user experience, product design, recommendations, discovery, and more?In this episode from October 2020, a16z general partner Connie Chan and Spotify's chief R&D officer Gustav Söderström join host Sonal Choksi to discuss the past, present and future of audio. They dig into everything from what the past in radio can tell us about the future, what audio can and will borrow from mediums like video and platforms like TikTok, the role for more interactivity and increased use of tools like machine learning and AI, and more. 

Gustav Söderström is Spotify's Chief Research & Development Officer. He has the CPO & CTO responsibility, overseeing the product, design, data, and engineering teams at Spotify and is responsible for Spotify's product strategy. Gustav is also an entrepreneur and investor who has founded and sold startups that he co-founded to Meta's Oculus in 2014 and then also his first startup which he co-founded and led as CEO, up until their acquisition by Yahoo! Gustav is also the host of the podcast mini-series  -- Spotify: A product story -- which offers a glimpse into the decisions that have guided Spotify's product evolution. In Today's Episode with Gustav Söderström 1.) From Selling Companies to Yahoo and Meta to Leading Spotify's Product: How did Gustav first make his way into the world of tech and startups? What was it that made Gustav so compelled to join Daniel Ek and build Spotify? What does Gustav know now that he wishes he had known when he started? 2.) "Never Fight a Macro Wind": What does Gustav mean when he says "never fight a macro wind"? What models can product leaders construct to measure the size, importance and timing of a macro wind? What can product leaders do to change the macro wind and have it blowing in their back and not their face? When did Gustav experience this? What did he change to have the wind blow in his back? How did this alter his mindset and mentality? 3.) "Do Something Completely Different to the Competition": Why does Gustav believe startups should do the complete opposite to the competition? Does this change if your competition is other startups vs incumbents? What is the story of how Spotify did the complete opposite to Youtube? Why did it work? On the flip side, when did Spotify do the complete opposite and it did not work out? 4.) Mastering the Learning Process: How does Gustav approach the learning process for all new skills and disciplines? Why does Gustav believe that all technology leaders have to be the master of their domain? How did this lead to Gustav going back to University to study machine learning? What are the single biggest mistakes people make in the learning process? 5.) Gustav: The Product Leader: Why does Gustav believe that product is 100% science and not art? What does Gustav mean when he says, "talk is cheap and so we should do more of it"? How does Gustav structure internal debates? Who sets the agenda? Who is invited? What makes a good vs a bad internal debate? How does Gustav make everyone feel safe? What can leaders do to ensure an environment where everyone feels they can debate with the boss? 6.) Spotify: The Crucible Moments: What is Gustav's favourite near-death experience in the Spotify journey? Why did Spotify decide to make the move into podcasting and video? How does that additional complexity change the product paradigm of an audio-only platform? How do the single most impactful platforms in the world approach market expansion and when to add new products? What are the best companies in the world not merely technology innovations but also business model innovations?

I Was There When is an oral history project that's part of the In Machines We Trust podcast. It features stories of how breakthroughs and watershed moments in artificial intelligence and computing happened, as told by the people who witnessed them. In this episode we meet Gustav Söderström, who helped create algorithms aiming to understand our taste in music.  CREDITS: This project was produced by Jennifer Strong, Anthony Green and Emma Cillekens. It was edited by Michael Reilly and mixed by Garret Lang, with original music by Jacob Gorski. Artwork by Eric Mongeon.