Podcasts about tsv

Ganz anders, als eigentlich geplant verläuft die aktuelle Sommerpause im Hinblick auf unseren sechzger.de Talk. Aber das gilt ja irgendwie für den ganzen TSV 1860... Eigentlich wollten wir nach dem Livetalk in der vergangenen Woche nun mal ein paar Tage pausieren und dann kommenden Montag wieder mit einem - mehr oder weniger - zeitlosen Thema und einem externen Gast weitermachen. Doch dies ist nach der Entwicklung der vergangenen zehn Tage und insbesondere dem Mittwoch dieser Woche wohl nicht möglich. Daher finden sich am Abend des Fronleichnams-Feiertags fünf Redakteure zusammen, um einen besonderen sechzger.de Talk 265 aufzuzeichnen. In die Rekapitulation der Ereignisse der letzten Woche und des vorherigen Tages platzt dann die Bombe von der Kündigung des Kooperationsvertrags durch den e.V.... Verrückte Zeiten!Die zweite “4” – TSV 1860 München muss in die RegionalligaNach zweitem Zwangsabstieg: “Die Bayerische” kündigt Sponsorenvertrag mit dem TSV 1860 MünchenWelche Folgen hat der Zwangsabstieg?Nach dem Lizenz-Aus: KGaA prüft rechtliche Schritte gegen IsmaikNach Zwangsabstieg: Wird Ismaik eine Insolvenz der KGaA verhindern?TSV 1860 e.V. kündigt Kooperations-Vertrag mit HAM auf

Der dritte sechzger.de Livetalk, gleichzeitig schon die Ausgabe 264 unseres Podcast-Formats beschäftigte sich ausschließlich mit der einzigen Deutschen Meisterschaft, die die Löwenfußballer im Jahr 1966 erringen konnten. Die Moderatoren Peter und Christian führten – erneut im Bamboleo – durch die Sendung, in der vor allem die Zeitzeugen, die dem Titelgewinn vor sechzig Jahren auf den Rängen des Sechzgerstadions beiwohnen durften, zu Wort kamen. Die aktuellen Entwicklungen beim TSV 1860, was die Lizenz für die kommende Saison anbelangt, aber auch das erst am Wochenende zuvor verlorene Finale um den bayerischen Landespokal in Würzburg wurden für diesen Themenabend ganz bewusst ausgeklammert.Hier geht es zum Buch zur Meisterschaft:https://tsv1860shop.org/products/fa-buch-der-grosse-tag-der-lowen-der-tsv-munchen-von-1860-wird-deutscher-fussball-meister?variant=57566611112284

Der TSV 1860 beendet die Saison 2025/26 mit einer letzten Watschn. Der Löwe verliert das Totopokal-Finale gegen Regionalligist Würzburger Kickers im Elfmeterschießen und verpasst die letzte Chance auf ein versöhnliches Ende. Wäre ja auch zu schön gewesen, aber eigentlich musste es ja so kommen. Besonders bitter ist die Verletzung von Thore Jacobsen, was uns sehr bewegt. Aber welche Schlüsse zieht Sechzig jetzt aus dieser verkorksten Spielzeit, bzw. welche Stellschrauben können überhaupt gedreht werden und welche Hebel müssen in Bewegung gesetzt werden? Was erhoffen wir uns für die Löwen 2026/27? Außerdem beschäftigen wir uns damit, dass der TSV 1860 und der FFC Wacker München sich in "intensiven und konstruktiven Gesprächen über eine mögliche strategische Zusammenarbeit im Frauen- und Mädchenfußball" befinden. Was kann das heißen? Wie sehen Chancen und Risiken aus? Mit dem Ende dieser Saison verlassen wir auch zum letzten Mal diesen Stammtisch und beenden unseren Podcast im Zeichen des Löwen. Wir bedanken uns ganz herzlich bei allen Weggefährten und all unseren Gästen, aber vor allem bei allen Hörerinnen und Hörern, ohne die wir das "Giesinger Bergfest" niemals über diese wunderbaren 1903 Tage (etwas mehr als 5 Jahre) hätten machen können. Ein herzliches Dankeschön an Horst Haubrich alias Pete Winter, der zusammen mit Steve Summer und Mark Bender im Rahmen des "1860 Party Projekt" den Song "So ein Tag" anno 1997 geschrieben und uns diesen für diese Abschiedsfolge zur Verfügung gestellt hat. Bleibt's gsund, bleibt's freindlich, bleibt's optimistisch und bleibt's vor allem eines: Löwenslang weiß-blau. Schee war's mit euch beim "Giesinger Bergfest". Servus.

Was sollen wir sagen - eine, mal wieder, enttäuschende Löwen-Saison geht zu Ende. Zum Abschluss verliert der TSV 1860 München standesgemäß mit 0:3 in Verl. Nun bleibt nur noch das Totopokal-Finale in Würzburg, um das Kapitel halbwegs versöhnlich zu schließen. Und ein weiteres Kapitel wird sich nach diesem Spiel schließen: Wir beenden diesen Podcast nach mehr als fünf Jahren. Warum wir das tun, hört ihr in Folge 218.

Die Löwen sind wieder glücklich in Giesing – in dieser letzten Folge gehen wir der Frage nach, warum es mit dieser Rückkehr so lange gedauert hat. Warum haben die Löwen so lange in der Arena gespielt, obwohl viele Fans das gar nicht wollten? Wer hat sie immer wieder am Leben gehalten, und aus welchen Gründen? Wir suchen Antworten – und treffen dafür ein zweites Mal Uli Hoeneß. Windige Manager, interne Machtkämpfe und der Wirtschaftskrimi um die Allianz Arena: Zwei Sportjournalisten der SZ klären in diesem Doku-Podcast, wer schuld an der Misere des TSV 1860 München ist. „Inside 1860 - die Löwen, die Arena und das Geld“ ist ein FYEO-Original produziert von der Süddeutschen Zeitung. Moderation und Redaktion: Markus Schäflein und Philipp Schneider Autoren: Justin Patchett und Laura Terberl Mitarbeit: Moritz Eder Produktion, Sounddesign und Regie: Carlo Sarsky und Justin Patchett Teamleitung: Laura Terberl Redaktion FYEO: Isabel Lübbert-Rein und Tristan Lehmann Gesamtleitung FYEO: Luca Hirschfeld und Tristan Lehmann. Du möchtest Werbung in diesem Podcast schalten? Dann erfahre hier mehr über die Werbemöglichkeiten bei Seven.One Audio: https://www.seven.one/portfolio/sevenone-audio

Flo hat sich eingesaut. War auch viel zu voll im Block G und der Zaun war auch zu voll und überhaupt. Zeit also die Halbzeit gemütlich mit Harry und den Kids und alten neun30 Freunden in Block J zu verbringen. Es gibt auch viele Fragen zu klären. zB was mit neun30 in der nächsten Saison passiert? Was in Schweinfurt passiert ist? Wo die Choreo zu 60 Jahre Deutscher Meister war? Und so viel mehr. Und zum Saisonabschluss haben wir auch n Streetart Update dabei, eine Auswärtsgruß der Cabrios aus Frankreich und Hui & Pfui aus m Brunnenmiller. Pack ma's! TSV 1860 München gg FC Ingolstadt 04 / 09.05.2026 / Spielstand 1:0 (1:2) 00:00 Intro & Begrüßung 00:45 Erste Halbzeit & Stimmung im Block J 03:30 Spielanalyse mit Max und Franz 05:56 Grüße der Cabrio-Löwen aus Saarbrücken 08:59 Zukunft des Podcasts & kreative Pläne 15:30 Rückblick auf die Saison & Ausblicke 18:00 Verabschiedung & Outro

Investor Ismaik investiert jetzt richtig viel Geld – und führt den TSV 1860 dadurch in den Abgrund. Mit dem teuersten Kader der zweiten Liga und einer Mannschaft mit Spielern aus aller Welt steigen die Löwen ab. Sie landen im Amateurfußball und im alten Grünwalder Stadion. Aber für viele Fans ist das viel schöner, als es sich anhört. Windige Manager, interne Machtkämpfe und der Wirtschaftskrimi um die Allianz Arena: Zwei Sportjournalisten der SZ klären in diesem Doku-Podcast, wer schuld an der Misere des TSV 1860 München ist. „Inside 1860 - die Löwen, die Arena und das Geld“ ist ein FYEO-Original produziert von der Süddeutschen Zeitung. Moderation und Redaktion: Markus Schäflein und Philipp Schneider Autoren: Justin Patchett und Laura Terberl Mitarbeit: Moritz Eder Produktion, Sounddesign und Regie: Carlo Sarsky und Justin Patchett Teamleitung: Laura Terberl Redaktion FYEO: Isabel Lübbert-Rein und Tristan Lehmann Gesamtleitung FYEO: Luca Hirschfeld und Tristan Lehmann. Du möchtest Werbung in diesem Podcast schalten? Dann erfahre hier mehr über die Werbemöglichkeiten bei Seven.One Audio: https://www.seven.one/portfolio/sevenone-audio

Die anstehenden Abschiede von Morris Schröter, Maximilian Wolfram, David Philipp, Thore Jacobsen, Raphael Schifferl und Jesper Verlaat vom TSV 1860 haben Fragen aufgeworfen. Mit ein wenig Abstand kann man festhalten: Man kann sie verargumentieren. Nur ist einmal mehr die Frage, in welcher Form dies geschieht. Und einmal mehr muss man sagen, das wäre besser gegangen. Bei Sigurd Haugen besteht noch Hoffnung, dass er bleibt und den Löwen-Kosmos weiter mit Toren verzückt. Vielleicht ja auch im DFB-Pokal, den das Ticket könnte Sechzig am kommenden Samstag während des Spiels gegen Ingolstadt lösen – ganz ohne eigenes Zutun. Möglich macht es das parallel stattfindende Regionalliga-Duell der Würzburger Kickers gegen die SpVgg Unterhaching. Zudem blicken wir voraus auf die Saison 2026/27 und die Gegner, die runter- oder hochkommen (können) in Liga 3.

Drei Löwen und ein Schweinfurter versammeln sich am Montag nach dem Spiel zwischen den Schnüdeln und dem TSV 1860  zum sechzger.de Talk 261 um alles Besprechenswerte rund um die Partie vom Samstag zu bereden. Moderator Christian erhält Unterstützung vom Kollegen Peter. Außerdem hat sich erneut Thomas Spiesl eingefunden, der natürlich auch wieder eine Schnellraterunde im Gepäck hat. Und dank des externen Gasts vom gegnerischen Verein erhalten wir und unserer Zuhörer*innen auch noch die eine oder andere Information, wie es in Schweinfurt nach dem Abstieg weitergehen wird und was man aus diesem Gastspiel in der 3. Liga mitgenommen hat. Die abschließende Vorausschau auf das anstehende letzte Heimspiel dieser Saison gegen den FC Ingolstadt darf dann natürlich auch nicht fehlen.➡️ Spielbericht➡️Stimmen zum Spiel➡️Fotogalerie➡️Giesinger Gedanken⁠

Die Löwen haben mal wieder einen neuen Präsidenten – Gerhard Mayrhofer, einen erfahrenen Manager. Er versucht einen ganz neuen Kurs: Er will den Investor Hasan Ismaik stärker einbinden. Doch Mayrhofer hat sehr schnell sehr viele Feinde im Verein. Er wird verklagt, bekommt sogar Morddrohungen - und verzweifelt am Ende doch an Ismaik. Windige Manager, interne Machtkämpfe und der Wirtschaftskrimi um die Allianz Arena: Zwei Sportjournalisten der SZ klären in diesem Doku-Podcast, wer schuld an der Misere des TSV 1860 München ist. „Inside 1860 - die Löwen, die Arena und das Geld“ ist ein FYEO-Original produziert von der Süddeutschen Zeitung. Moderation und Redaktion: Markus Schäflein und Philipp Schneider Autoren: Justin Patchett und Laura Terberl Mitarbeit: Moritz Eder Produktion, Sounddesign und Regie: Carlo Sarsky und Justin Patchett Teamleitung: Laura Terberl Redaktion FYEO: Isabel Lübbert-Rein und Tristan Lehmann Gesamtleitung FYEO: Luca Hirschfeld und Tristan Lehmann. Du möchtest Werbung in diesem Podcast schalten? Dann erfahre hier mehr über die Werbemöglichkeiten bei Seven.One Audio: https://www.seven.one/portfolio/sevenone-audio

Nach dem Heimdreier gegen die Spatzen aus Ulm reden Peter, Christian, Thomas sowie Christoph über die letzten Personalentscheidungen und wagen eine Vorschau auf das kommende Auswärtsspiel in Schweinfurt.Weiterführende LinksSpielbericht Saarbrücken➡️ Sommerfußball in GiesingStimmen zum Spiel Saarbrücken - TSV 1860➡️ Stimmen zum Spiel

Der TSV 1860 München steht mal wieder kurz vor der Insolvenz. Aber auf den letzten Drücker wird offenbar ein Retter gefunden: Hasan Ismaik, der erste arabische Investor im deutschen Profifußball. Doch sofort geht das Drama los, denn der Verein versucht, Ismaiks Macht zu begrenzen – und die Lage eskaliert. Windige Manager, interne Machtkämpfe und der Wirtschaftskrimi um die Allianz Arena: Zwei Sportjournalisten der SZ klären in diesem Doku-Podcast, wer schuld an der Misere des TSV 1860 München ist. „Inside 1860 - die Löwen, die Arena und das Geld“ ist ein FYEO-Original produziert von der Süddeutschen Zeitung. Moderation und Redaktion: Markus Schäflein und Philipp Schneider Autoren: Justin Patchett und Laura Terberl Mitarbeit: Moritz Eder Produktion, Sounddesign und Regie: Carlo Sarsky und Justin Patchett Teamleitung: Laura Terberl Redaktion FYEO: Isabel Lübbert-Rein und Tristan Lehmann Gesamtleitung FYEO: Luca Hirschfeld und Tristan Lehmann. Du möchtest Werbung in diesem Podcast schalten? Dann erfahre hier mehr über die Werbemöglichkeiten bei Seven.One Audio: https://www.seven.one/portfolio/sevenone-audio

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

Wenig Sport, viel Drumherum: In Folge 215 sprechen wir nur am Rande über das tor- und trostlose Remis in Saarbrücken. Vielmehr sind es Aktionen und Aussagen abseits des Platzes, die beim TSV 1860 München für Gesprächsstoff sorgen. Viel Spaß beim Hören. **Bewertet und abonniert uns!** Lasst uns gerne eine Bewertung da und abonniert uns in eurem Podcast-Player, um keine Folge zu verpassen. Auf [YouTube](https://www.youtube.com/@giesingerbergfest) könnt ihr uns sogar sehen. Ach ja, und in den Sozialen Medien versorgen wir euch natürlich auch mit Inhalt bei [Facebook](https://www.facebook.com/giesingerbergfest), [Instagram](https://www.instagram.com/giesingerbergfest/), [Bluesky](https://bsky.app/profile/giesingerbergfest.bsky.social) und [Twitter/X](https://x.com/GiesingBergfest). Übers Bergfest-Fon erreicht ihr uns via WhatsApp unter der Nummer: 0162/1517854. **Unterstützt uns!** Euch gefällt der Löwen-Stammtisch und ihr wollt uns unterstützen? Schaut gerne mal bei [giesinger-bergfest.de/support](https://giesinger-bergfest.de/support/) vorbei und erfahrt, wie das geht!

Nach dem Auswärtsspiel gegen den 1.FC Saarbrücken, das mit einem torlosen Remis endete, und vor dem Heimspiel des TSV 1860 München gegen den SSV Ulm spricht Christian über die beiden Spiele mit drei Gästen. Das ist zum einen Thomas aus der Redaktion, zum anderen die Löwenfans Marco und Lukas, die beide im Gästeblock im Saarland live mit dabei waren.Weiterführende LinksSpielbericht SaarbrückenStimmen zum Spiel Saarbrücken - TSV 18600:00 Unentschieden in Saarbrücken58:43 Schnellraterunde01:08:25 Vorschau SSV Ulm

Der TSV 1860 München zieht als Zweitligist in seine neue Arena ein, muss bald seine Anteile verkaufen und ist nur noch Mieter. Das ist nicht nur die Folge von Misserfolg und Misswirtschaft, sondern auch von merkwürdigen Verträgen und Abreden mit dem FC Bayern. Windige Manager, interne Machtkämpfe und der Wirtschaftskrimi um die Allianz Arena: Zwei Sportjournalisten der SZ klären in diesem Doku-Podcast, wer schuld an der Misere des TSV 1860 München ist. „Inside 1860 - die Löwen, die Arena und das Geld“ ist ein FYEO-Original produziert von der Süddeutschen Zeitung. Moderation und Redaktion: Markus Schäflein und Philipp Schneider Autoren: Justin Patchett und Laura Terberl Mitarbeit: Moritz Eder Produktion, Sounddesign und Regie: Carlo Sarsky und Justin Patchett Teamleitung: Laura Terberl Redaktion FYEO: Isabel Lübbert-Rein und Tristan Lehmann Gesamtleitung FYEO: Luca Hirschfeld und Tristan Lehmann. Du möchtest Werbung in diesem Podcast schalten? Dann erfahre hier mehr über die Werbemöglichkeiten bei Seven.One Audio: https://www.seven.one/portfolio/sevenone-audio

Mit dem Wiesngastronom Karl-Heinz Wildmoser geht es für 1860 aufwärts. Als die WM in Deutschland vor der Tür steht, will er gemeinsam mit den Bayern eine Arena für München bauen. Wir sprechen mit Wildmosers Sohn, der für die großen Pläne einen hohen Preis bezahlt hat. Windige Manager, interne Machtkämpfe und der Wirtschaftskrimi um die Allianz Arena: Zwei Sportjournalisten der SZ klären in diesem Doku-Podcast, wer schuld an der Misere des TSV 1860 München ist. „Inside 1860 - die Löwen, die Arena und das Geld“ ist ein FYEO-Original produziert von der Süddeutschen Zeitung. Moderation und Redaktion: Markus Schäflein und Philipp Schneider Autoren: Justin Patchett und Laura Terberl Mitarbeit: Moritz Eder Produktion, Sounddesign und Regie: Carlo Sarsky und Justin Patchett Teamleitung: Laura Terberl Redaktion FYEO: Isabel Lübbert-Rein und Tristan Lehmann Gesamtleitung FYEO: Luca Hirschfeld und Tristan Lehmann. Du möchtest Werbung in diesem Podcast schalten? Dann erfahre hier mehr über die Werbemöglichkeiten bei Seven.One Audio: https://www.seven.one/portfolio/sevenone-audio

In der aktuellen Folge sprechen Christian, Thomas und Flo über drei Spiele des TSV 1860 München - nämlich die Duelle mit Waldhof Mannheim, Energie Cottbus und Jahn Regensburg in der 3.Liga.Statistikseite footystats

Aus und vorbei, der TSV 1860 München ist schon noch zwei Dritteln der Englischen Woche alle Aufstiegshoffnungen los. Wahnsinnig bitter, deswegen machen wir es an dieser Stelle kurz. Wir sprechen in Folge 213 über die letzten beiden Spieltage und auch ein bisschen über die Zukunft, die für die Löwen weiter in Liga 3 liegen wird. **Bewertet und abonniert uns!** Lasst uns gerne eine Bewertung da und abonniert uns in eurem Podcast-Player, um keine Folge zu verpassen. Auf [YouTube](https://www.youtube.com/@giesingerbergfest) könnt ihr uns sogar sehen. Ach ja, und in den Sozialen Medien versorgen wir euch natürlich auch mit Inhalt bei [Facebook](https://www.facebook.com/giesingerbergfest), [Instagram](https://www.instagram.com/giesingerbergfest/), [Bluesky](https://bsky.app/profile/giesingerbergfest.bsky.social) und [Twitter/X](https://x.com/GiesingBergfest). Übers Bergfest-Fon erreicht ihr uns via WhatsApp unter der Nummer: 0162/1517854. **Unterstützt uns!** Euch gefällt der Löwen-Stammtisch und ihr wollt uns unterstützen? Schaut gerne mal bei [giesinger-bergfest.de/support](https://giesinger-bergfest.de/support/) vorbei und erfahrt, wie das geht!

Ostern und Hoffnung und schaumamoi. Das passt doch alles ganz famos zusammen. Aber eins nach dem anderen: Heute steht Flo mit Franz und Max in J, und es schaut erstmal gar nicht so gut aus gegen den ungeliebten Waldhof. Genauso war es auch bei den Cabrios in Duisburg. Aber Laune hüben wie drüben wie immer bestens. Haben wir doch einen neuen Giesinger Bürgermeister, der jetzt das Stadion ausbaut. Und .... nach über 13 Jahren neun30 ... endlich endlich featuren wir mal sauber das schamamoi - mit einem ganz feinen Interview mit dem Lukas. Hört's rein, ist eine lange Folge mit ein bisschen Fussball und viel Giesinger Gefühl! TSV 1860 München gg SV Waldhof Mannheim / 04.04.2026 / Spielstand 0:1 (1:1)

Zwischen Atletico und Ampfing: Während der FC Bayern in den 70er und 80er Jahren immer höher steigt, fällt der TSV 1860 immer tiefer. Bayern-Manager Uli Hoeneß erzählt, wie er fast alles richtig gemacht hat - und Sechzig fast alles falsch. Und wie aus dem Lokalrivalen doch noch ein ernstzunehmender Konkurrent wurde. Windige Manager, interne Machtkämpfe und der Wirtschaftskrimi um die Allianz Arena: Zwei Sportjournalisten der SZ klären in diesem Doku-Podcast, wer schuld an der Misere des TSV 1860 München ist. „Inside 1860 - die Löwen, die Arena und das Geld“ ist ein FYEO-Original produziert von der Süddeutschen Zeitung. Moderation und Redaktion: Markus Schäflein und Philipp Schneider Autoren: Justin Patchett und Laura Terberl Mitarbeit: Moritz Eder Produktion, Sounddesign und Regie: Carlo Sarsky und Justin Patchett Teamleitung: Laura Terberl Redaktion FYEO: Isabel Lübbert-Rein und Tristan Lehmann Gesamtleitung FYEO: Luca Hirschfeld und Tristan Lehmann. Du möchtest Werbung in diesem Podcast schalten? Dann erfahre hier mehr über die Werbemöglichkeiten bei Seven.One Audio: https://www.seven.one/portfolio/sevenone-audio

Puh, das war ein ganz schön heißer Tanz im Toto-Pokal gegen Regensburg. Aber egal, der TSV 1860 München steht im Finale des bayerischen Landespokals und ist nur noch einen Sieg vom Einzug in den DFB-Pokal entfernt. Doch was gibt die Saison darüber hinaus noch her. Am Ende der nächsten Woche sind wir sicher schlauer. Mannheim, Cottbus und dann wieder Regensburg, alles binnen acht Tagen - englischer kann eine Woche kaum sein. In Folge 212 sprechen wir nochmal über den Pokalfight beim Jahn und die Woche der Wahrheit. Viel Spaß beim Hören! **Bewertet und abonniert uns!** Lasst uns gerne eine Bewertung da und abonniert uns in eurem Podcast-Player, um keine Folge zu verpassen. Auf [YouTube](https://www.youtube.com/@giesingerbergfest) könnt ihr uns sogar sehen. Ach ja, und in den Sozialen Medien versorgen wir euch natürlich auch mit Inhalt bei [Facebook](https://www.facebook.com/giesingerbergfest), [Instagram](https://www.instagram.com/giesingerbergfest/), [Bluesky](https://bsky.app/profile/giesingerbergfest.bsky.social) und [Twitter/X](https://x.com/GiesingBergfest). Übers Bergfest-Fon erreicht ihr uns via WhatsApp unter der Nummer: 0162/1517854. **Unterstützt uns!** Euch gefällt der Löwen-Stammtisch und ihr wollt uns unterstützen? Schaut gerne mal bei [giesinger-bergfest.de/support](https://giesinger-bergfest.de/support/) vorbei und erfahrt, wie das geht!

In dieser Episode ist Julian Stickdorn zu Gast. Er arbeitet aktuell als U16 Trainer beim TSV 1860 München und gibt spannende Einblicke zum Thema Mannschaftsführung. Wir sprechen über die „zwei Pizzen Regel“, integratives Entscheiden und wie wir aus einer Gruppe ein Team bilden. Warum ist Forming, Storming, Norming und Performing ein spannendes Thema für Teams? Was braucht man als guter Leader? Wir reden drüber!

Die Spurensuche nach den Gründen des Absturzes beginnt auf dem Gipfel: Werner Lorant, Erfolgstrainer um die Jahrtausendwende, wohnt heute auf einem Campingplatz in Waging am See. Dort erzählt er, warum die Löwen mit ihm Erfolg hatten – und welche folgenschweren Fehler sie nach seiner Zeit begingen. Windige Manager, interne Machtkämpfe und der Wirtschaftskrimi um die Allianz Arena: Zwei Sportjournalisten der SZ klären in diesem Doku-Podcast, wer schuld an der Misere des TSV 1860 München ist. „Inside 1860 - die Löwen, die Arena und das Geld“ ist ein FYEO-Original produziert von der Süddeutschen Zeitung. Moderation und Redaktion: Markus Schäflein und Philipp Schneider Autoren: Justin Patchett und Laura Terberl Mitarbeit: Moritz Eder Produktion, Sounddesign und Regie: Carlo Sarsky und Justin Patchett Teamleitung: Laura Terberl Redaktion FYEO: Isabel Lübbert-Rein und Tristan Lehmann Gesamtleitung FYEO: Luca Hirschfeld und Tristan Lehmann. Du möchtest Werbung in diesem Podcast schalten? Dann erfahre hier mehr über die Werbemöglichkeiten bei Seven.One Audio: https://www.seven.one/portfolio/sevenone-audio

Die Siegesserie ist gerissen, aber das ist schon die einzige negative Nachricht, die wir aus dem torlosen Remis des TSV 1860 München gegen Wehen Wiesbaden mitnehmen. Den nächsten Mitkonkurrenten in Schach gehalten, wenig zugelassen - insgesamt hat die Mannschaft gezeigt, dass sie bereit ist für die entscheidenden Wochen im Kampf um die Spitzenplätze der 3. Liga. Wir sprechen über die Lehren auf und neben dem Platz, das nächste Top-Duell in Duisburg und zwei prominente Löwen, die derzeit nicht zum Zug kommen. Außerdem kommt auch mal wieder ein Ex-Löwe als Bergfest-Experte zu Wort. Viel drin in Folge 210 - viel Spaß damit! **Bewertet und abonniert uns!** Lasst uns gerne eine Bewertung da und abonniert uns in eurem Podcast-Player, um keine Folge zu verpassen. Auf [YouTube](https://www.youtube.com/@giesingerbergfest) könnt ihr uns sogar sehen. Ach ja, und in den Sozialen Medien versorgen wir euch natürlich auch mit Inhalt bei [Facebook](https://www.facebook.com/giesingerbergfest), [Instagram](https://www.instagram.com/giesingerbergfest/), [Bluesky](https://bsky.app/profile/giesingerbergfest.bsky.social) und [Twitter/X](https://x.com/GiesingBergfest). Übers Bergfest-Fon erreicht ihr uns via WhatsApp unter der Nummer: 0162/1517854. **Unterstützt uns!** Euch gefällt der Löwen-Stammtisch und ihr wollt uns unterstützen? Schaut gerne mal bei [giesinger-bergfest.de/support](https://giesinger-bergfest.de/support/) vorbei und erfahrt, wie das geht!

  Ein halbes Schwein auf dem Grill (fast), Nieslregen und Wahlkampf nicht nur um den OB sondern auch um den Verwaltungsrat – das ist das heutige Potpourri bei neun30. Obwohl Harry und Flo den Spieltag getrennt voneinander verfolgen, wird doch die wichtige Frage diksutiert, ob Schweine wichtiger als die Löwen sind und ob man dafür wirklich ein 60er-Spiel - obacht Wortwitz! - sausen lassen darf. Aber erstmal kämpft Flo mit der Leitung, während Harry im Stadion fröstelt und auf bessere Zeiten hofft. Aber hey, die Null steht und Domi goes Verwaltungsrat (mehr dazu in einer der nächsten Folgen!). TSV 1860 München gg SV Wehen Wiesbaden / 14.03.2026 / Spielstand 0:0 (0:0)

Als „Bua aus Guiching“ gings für Gerald Hillringhaus nach Giasing. Der Fußballverrückte Gerry wechselte in der Jugend von Gilching zum TSV 1860 München. Was folgte? Eine Profikarriere mit Geschichten, die auf keine Kuhhaut passen. Zwangsabstieg mit den Löwen 1982 in die Bayernliga. Jahre später schießt Hillringhaus als erster Torhüter überhaupt das „Tor des Monats“. Uli Hoeneß lotst ihn in den 90ern zum FC Bayern München, wo er Bundesliga- und auch Europapokalluft schnuppert. Was Gerry während seiner Profizeit bei den verschiedenen Stationen erlebt hat, warum ihn der Sport nicht loslässt und wieso er auch mit über 60 Jahren bei Bad Heilbrunn in der Bezirksliga nochmal zwischen den Pfosten stand? Diese Fragen und noch so viel mehr beantwortet Gerald Hillringhaus in dieser Folge „Buchloh bewegt's“. 

Harry und Flo sind tatsächlich in Köln – live aus dem Gästeblock beim Viktoria-Spiel. Knapp 2.000 Löwen haben sich mit uns auf den Weg gemacht, es wurde sauber geKranzlt und das Kölsch schmeckt erstaunlicherweise auch aus dem Plastikbecher. Zur Halbzeit steht's 1:0 für die Blauen. Entsprechend aufgeladen ist die Stimmung beim Mob und bei den Jungs am Mikro. Auch wenn die Tribüne leider gefühlt acht Kilometer lang ist und der Druck aus der Westkurve ein bisschen fehlt. Aber wer will denn meckern bei der Serie, die wir gerade hinlegen? Weiter so, Löwen, kämpfen und siegen! TSV 1860 München gg Viktoria Köln / 06.03.2026 / Spielstand 0:1 (0:1)

Durchatmen und ruhig bleiben – gar nicht (mehr) so einfach, nachdem der TSV 1860 den vierten Sieg in Folge eingefahren hat und sich immer näher an die vorderer Plätze herankämpft. Und das im wahrsten Sinne des Wortes, sind die Punkte doch wahrlich nicht glanzvoll herausgespielt. Genau das aber ist derzeit ein wichtiger Faktor, finden wir. Warum, das besprechen wir in dieser Frühstücks-Folge. Der Fußballgott scheint mit Blick auf die Spiele gegen Hansa Rostock, TSG Hoffenheim II und nun auch Erzgebirge Aue derzeit weiß-blau zu tragen. Die Löwen profitieren von diskutablen Schiedsrichter-Entscheidungen (gegen Aue trotzen sie gar mal einer), gewissem Matchglück, steigenden Formkurven (Siemen Voet!) und einem echten Teamgefühl, das Raphael Schifferl am Dienstagabend in einem bemerkenswerten Interview betont. Zudem sind Giesinger "Ausgerechnet-Gesetze" zwar weiterhin wirksam, tangieren Sechzig aber nur peripher. Genießen wir die aktuelle Welle und schauen, wie lange sie läuft ...

Harry und Flo stecken mitten in den Vorbereitungen für den großen Auswärtstrip nach Köln. Von Schafkopfkarten bis hin zu dem Problem mit den Mikro-Akkus. Da keiner uns einen Schulausflugszettel zugeschickt hat, fragen wir halt in der Kurve rum - und bekommen Antworten. Und dann haben wir doppelt Nebel, den Hansi der das Spiel gegen Aue zerlegt und unsere Cabrios mit einem feinen Stelldichein aus Hoffenheim. Wir fragen uns: Geht die Reise gut aus? Findet's mit uns heraus! TSV 1860 München gg Erzgebirge Aue / 03.03.2026 / Spielstand 1:1 (2:1)

Im sechzger.de Talk Folge 251 spricht Jan mit Christian und Thomas aus der Redaktion sowie Bennet aus Rostock und Gerhard von den Unternehmern für Sechzig über......den Heimsieg gegen Hansa Rostock...die Unternehmer für Sechzig...den Kabarettabend im März, Karten sind unter tickets@unternehmerfuersechzig.de erhältlich...das Auswärtsspiel gegen Hoffenheim IIWeiterführende LinksSpielbericht Hansa Rostock➡️ https://sechzger.de/erster-sieg-seit-16-jahren-1860-erkaempft-sieg-gegen-hansa-rostock/Stimmen zum Spiel TSV 1860 - FCH➡️ https://sechzger.de/stimmen-zum-spiel-tsv-1860-hansa-rostock/Kabarettabend der Unternehmer für Sechzig➡️ https://sechzger.de/kabarettabend-der-unternehmer-fuer-60-am-17-03/0:00 Heimsieg gegen Rostock52:56 Die Unternehmer für Sechzig01:15:27 Quiz zu TSV 1860 - Hoffenheim01:34:23 Vorschau Hoffenheim II

Was braucht unser neues Stadion alles? Da is viel drüber geredet und geschrieben worden. Da müssen Harry und Flo natürlich auch was sagen dazu. Aber es is ganz einfach. Wenn s in Giesing bleibt ist doch eh alles super. Des war ned schwer sich da zu einigen drauf. Schwieriger ist die Frage mit dem Zaun, weil es ja schon Argumente FÜR den Zaun gibt. Ja, die Sachen hat einen Haken. Und dann gibt es noch krasse Neuigkeiten im neun30ger Umfeld. Die einen können wir noch nicht erzählen. Die andere is, das es jetzt 'ne echt schicke neuen neun30 Website gibt (neun30.de). Zum einen liegt die jetzt nicht mehr auf m Server von irgendeinem Tech-Bro und man kann die Podcast da auch ohne Spotify/Apple/trallala hören. Nein, es macht da richtig viel Lauen sich durch die knapp 200 Folgen zu scrollen, inklusive Filteroptionen wann es mal n Interview gab, wo die Cabrios von auswärts Grüßen oder wann wir mal über Rüscherl geredet haben. Schaut's rein und empfehlt's uns gern weiter. Merci! TSV 1860 München gg Hansa Rostock / 22.02.2026 / Spielstand 0:0 (1:0)

Fünf Tore für die Seele - der TSV 1860 München entledigt sich auf dem Kartoffelacker von Hannover des Frustes der letzten Wochen. Beim 5:0 gegen den TSV Havelse machten nicht nur die erwarteten Darsteller viel Spaß. Doch was ist dieser Kantersieg wert? Zumindest lässt er den Funken Hoffnung für eine weitere Woche am Leben. Die Lehren aus dem ersten Sieg im Kalenderjahr 2026 besprechen wir in Folge 206. Viel Spaß beim Hören! **Bewertet und abonniert uns!** Lasst uns gerne eine Bewertung da und abonniert uns in eurem Podcast-Player, um keine Folge zu verpassen. Auf [YouTube](https://www.youtube.com/@giesingerbergfest) könnt ihr uns sogar sehen. Ach ja, und in den Sozialen Medien versorgen wir euch natürlich auch mit Inhalt bei [Facebook](https://www.facebook.com/giesingerbergfest), [Instagram](https://www.instagram.com/giesingerbergfest/), [Bluesky](https://bsky.app/profile/giesingerbergfest.bsky.social) und [Twitter/X](https://x.com/GiesingBergfest). Übers Bergfest-Fon erreicht ihr uns via WhatsApp unter der Nummer: 0162/1517854. **Unterstützt uns!** Euch gefällt der Löwen-Stammtisch und ihr wollt uns unterstützen? Schaut gerne mal bei [giesinger-bergfest.de/support](https://giesinger-bergfest.de/support/) vorbei und erfahrt, wie das geht!

Nach dem deutlichen Sieg gegen den TSV Havelse sprechen Christian und Jan mit drei weiteren Redakteuren über das Spiel im Eilenriedestadion. Außerdem gibt es eine Vorschau auf das Heimspiel des TSV 1860 München gegen den FC Hansa Rostock.0:00 Deutlicher Sieg gegen den TSV Havelse56:06 Schnellraterunde01:19:23 Vorschau FC Hansa Rostock

Durch die Oma wurde er zum Fan, seit dem Sommer ist er der neue Präsident des TSV 1860 München: Gernot Mang hat Großes vor und bringt als Hobby-Triathlet auch viel Ausdauer mit. Über die Ziele mit dem Verein, über Werte und Tradition, die Bedeutung von Breitensport, aber auch über seine bodenständige Kindheit in Österreich und seine Karriere als Manager, spricht Gernot Mang im Gespräch mit Thorsten Otto. 

Die Hoffnung stirbt zuletzt – doch ehrlich gesagt ist sie am vergangenen Samstag so gut wie gestorben, dass beim TSV 1860 doch noch etwas im Aufstiegsrennen geht. Das 1:2 beim VfB Stuttgart hat bei vielen Löwen den Stecker gezogen. Wir greifen die Vorlage von Hörer Roland auf und blicken direkt nach vorne sowie auf die möglichen Kaderveränderungen im Sommer. Macht es nicht Sinn, schon jetzt die Weichen für einen neuen Anlauf zu stellen? 17 Verträge laufen aus – wer bleibt also? Der nächste Gegner des TSV 1860 heißt TSV Havelse, der ebenfalls in der 3. Liga bleiben will. Die Norddeutschen sind nicht mehr der Prügelknabe der Liga, sondern wehren sich mit allen Mitteln gegen den Abstieg. Also Vorsicht, Sechzig, das wird eine harte Auswärtsfahrt ... bei der es auch noch Überschneidungen mit der Anhängerschaft des Lokalrivalen gibt ...

Nach dem dritten Remis im dritten Rückrunden-Spiel ist beim TSV 1860 Ernüchterung eingekehrt. Nicht nur das 2:2 gegen Aachen an sich ist dafür verantwortlich, sondern auch die Art und Weise, denn es war selbstverschuldet und unnötig. Da passt auch das weiß-blaue Resultat auf dem Transfermarkt in diesem Winter dazu – da hat sich nämlich nichts getan. Was macht also Hoffnung auf Besserung? Vor allem ein Mann: Sigurd Haugen, der Hannibal-Löwe trotzt seinem Kieferbruch und sorgt für Schwung. Auch gegen den VfB Stuttgart II? Abseits des Rasens tut sich allerdings etwas, das auf lange Sicht Hoffnung macht. Der TSV 1860 hat die Machbarkeitsstudie für den Ausbau des Grünwalder Stadions in Auftrag gegeben. Ein wichtiges Signal des Klubs, beim Spieltag hatten die Fans ja schon deutliche Wünsche geäußert. Und weil der Humor ja nicht zu kurz kommen darf, haben wir noch einen Tipp für Euch, mit dem ihr dem NLZ auch noch etwas Gutes tun könnt.

Zweites Spiel 2026, zweites 1:1-Unentschieden, zweites Mal einen Rückstand egalisiert – zum Leben zu viel, zum Sterben zu wenig? Vielleicht, doch das Remis in Osnabrück unterstreicht die Resilienz des TSV 1860 in dieser schweren Phase der Saison. Allen voran sind dabei Kevin Volland, Max Reinthaler und Markus Kauczinski als Gesichter des Ganzen zu nennen. Extrem positiv: Es wird nicht lamentiert! Dennoch ist nach den zwei Punkten aus den ersten beiden Rückrundenspielen klar: Deren Wert wird in den kommenden drei Spielen definiert. Den Anfang macht das Heimspiel gegen Alemannia Aachen. Nicht vergessen wollen wir den starken dritten Platz von Linus Straßer in Kitzbühel! Pünktlich zu den Olympischen Winterspielen scheint der Ski-Löwe zurück in die Spur zu finden – das freut uns und unsere Anja ganz besonders, die demnächst nach Italien abreisen wird.

In der Jubiläumsfolge 200 lehnen wir uns zurück, lassen am Stammtisch einmal jegliche Sachlichkeit fallen und unseren persönlichen Geschmäckern freien Lauf. Heute ranken wir die Heim-Trikots des TSV 1860 der letzten 30 Jahre, genauer gesagt aus den Saisons 1994/95 bis 2024/25. Jeder von uns ordnet die Leiberl für sich in fünf vorgegebene Tiers, also Stufen, sodass wir am Ende drei persönliche Tier Lists (Ranglisten) haben. Wir haben insgesamt 15 Trikots aus besagtem Zeitraum vorab ausgewählt und uns dabei am Ranking auf dem Portal https://www.footballkitarchive.com/. Gesetzt waren die zehn von den Usern bestbewerteten Löwen-Heim-Trikots aus besagtem Zeitraum (Stand: 25.12.2025), hinzu wählten wir weitere fünf mit Kult- oder Diskussionspotenzial. Achtung, Vollständigkeit oder gar Objektivität können wir in dieser Runde nicht garantieren ... Für diese spezielle Stammtisch-Folge empfehlen wir Euch den Sprung auf unseren YouTube-Channel, auf dem ihr diese Folge als Video und mit allen Trikots eingeblendet findet.

Zum Jahresabschluss 2025 unterliegt der TSV 1860 dem SC Verl mit 0:2 auf Giesings Höhen – die Laune trübt das am Stammtisch aber nur bedingt. Der Grund: Vor einigen Wochen sah es zappenduster aus für die Löwen, doch mit Rang 8 zur Halbzeit und dem Punktestand von 30 Zählern sieht das dafür richtig ordentlich aus- Alex meldet sich aus Giesing, Flo und Anja vergeben vier persönliche Titel der Halbserie und ordnen die ersten 19 Spieltage ein. Was erwartet uns im Januar, kommt vielleicht ein Neu-Löwe? Wir sind gespannt. Das Giesinger Bergfest wünscht Euch allen frohe Weihnachten, besinnliche Tage im Kreis Eurer Liebsten und einen guten Rutsch in ein hoffentlich erfolgreiches weiß-blaues Jahr 2026!

Es ist schon unglaublich. Vor ungefähr fünf Wochen haben wir nach dem 0:4 in Regensburg an selber Stelle den Abstiegskampf ausgerufen. Vier Spiele später ist der TSV 1860 München auf einmal wieder eine ganze heiße Aktie im Aufstiegsrennen der 3. Liga. Weil die Mannschaft auf ihrem Tiefpunkt entscheidende Lehren gezogen hat und eine beeindruckende Entwicklung genommen hat. Bestes Beispiel: Der 2:1-Kampfsieg in Ingolstadt. Trotz Rückstand, trotz Platzverweis, trotz schwerer Verletzung des Topstürmers. Die Mannschaft ist bei sich geblieben, statt sich im Lamentieren zu verlieren. Was mit dieser neu entdeckten Überzeugung drin ist? Wir werden es beim Topspiel zum Jahresausklang gegen Verl sehen. Über all das und - wie gewohnt - ein bisschen mehr sprechen wir in Folge 198. Viel Spaß! **Bewertet und abonniert uns!** Lasst uns gerne eine Bewertung da und abonniert uns in eurem Podcast-Player, um keine Folge zu verpassen. Auf [YouTube](https://www.youtube.com/@giesingerbergfest) könnt ihr uns sogar sehen. Ach ja, und in den Sozialen Medien versorgen wir euch natürlich auch mit Inhalt bei [Facebook](https://www.facebook.com/giesingerbergfest), [Instagram](https://www.instagram.com/giesingerbergfest/), [Bluesky](https://bsky.app/profile/giesingerbergfest.bsky.social) und [Twitter/X](https://x.com/GiesingBergfest). Übers Bergfest-Fon erreicht ihr uns via WhatsApp unter der Nummer: 0162/1517854. **Unterstützt uns!** Euch gefällt der Löwen-Stammtisch und ihr wollt uns unterstützen? Schaut gerne mal bei [giesinger-bergfest.de/support](https://giesinger-bergfest.de/support/) vorbei und erfahrt, wie das geht!

Fifty years of Semicon Europa set a fitting backdrop for a conversation that feels both celebratory and unsentimental about the state of advanced packaging in Europe. We walk the floor in Munich and pull together a story that spans chemical metrology, panel plating, glass substrates, thermal materials, logistics resilience, and the push from R&D to production—plus a heartfelt goodbye.Dena Mitchell, Nova opens the curtain on chemical metrology for electroplating, showing how bath health drives TSV fill, hybrid bond grain structure, and environmental wins through longer bath life. Sally Ann Henry, ACM Research, explains why horizontal panel electroplating can deliver better uniformity than vertical as panel-level packaging grows. Thomas Uhrmann, EV Group zooms out to the strategy: Europe's strength in pilot lines and research consortia, the urgency to materialize large-scale packaging fabs, and how the EU Chips Act is knitting packaging into every node from photonics to logic.Henkel's Ram Trichur takes on thermals, from kilowatt-class data center processors with backside power delivery to mobile's shift from package-on-package to side-by-side for exposed die cooling, and the heat challenges inside HBM stacks. Comet's Isabella Drolz steps into glass panel territory with TGV inspection at 610 x 610 mm, aligning tools, standards, and timelines toward late-decade ramps. Martin Wynaendts van Resandt explains howLab14 brings agility with direct-write lithography for large substrates and optical interconnect masters—speeding iteration and trimming mask overhead as co-packaged optics advances. Jim Garstka, Shellback Semiconductor, talks about its Hydrozone product that is finding traction in photo mask cleaning.  We also get practical about moving all this innovation: Barry O'Dowd and Robin Knopf, of Kuehne+Nagel, detail how Europe's packaging supply chains remain global, and how sea-air blends can cut cost and time for non-sensitive, high-volume flows while building resilience against disruptions. ASE's Patricia MacLeod, Christophe Zinck, and Bradford Factor tie it together with automotive realities—centralized compute, heterogeneous integration, reliability constraints—and the enduring role of MEMS and sensors to feed the brain of the car.It's a grounded, forward-looking journey through the technologies and decisions that will determine whether Europe turns its R&D leadership into production momentum. Listen for clear takeaways, candid perspectives, and a final toast to the community that made the 3D InCites Podcast possible.If this conversation resonates, follow the show, share it with a colleague, and leave a review to help more listeners find it.Support the show

Was zur Hölle war das denn?! Diese Frage dürften sich alle im Löwen-Kosmos nach dem 0:4-Debakel des TSV 1860 bei Jahn Regensburg gedacht haben. Trainer Markus Kauczinksi sprach Klartext, wir tun es ihm gleich. Alex war live am Ground Zero in der Oberpfalz, schickt uns schonungslose Gedanken und eine steile These, die bei genauerem Betrachten für Flo und Anja sogar Sinn ergibt, bzw. ergeben kann. Stichwort Lothar Matthäus. So sehr wir uns in dieser Folge die Augen reiben und nach Erklärungen für den "Totalabsturz vom Anfang bis zum Ende" (Zitat Kauczinski) suchen, so sehr freuen wir uns aber schon jetzt auf die kommende Folge 195, die definitiv Gute-Laune-Charakter haben wird – ganz egal, was bis dahin auch passiert.

Send us a textThe floor in Phoenix was packed, and so were the ideas. We sat down with innovators across the stack—equipment makers, metrology experts, logistics strategists, and software leaders—to map the real state of advanced packaging and what it takes to build, measure, move, and power tomorrow's chips.EV Group kicked things off with a candid look at die-to-wafer realities: activation on film frame, then 100% overlay metrology that measures tens of thousands of points per hour so every die and corner is verified. They also unveiled LithoScale XT, a fully digital, maskless lithography system printing 300 mm at 60 wph—perfect for massive AI dies and fast design turns. Lab14 widened the frame with a portfolio approach: direct-write lithography, single-wafer processing, data prep, and analysis tools working as a coordinated line, with data sharing and AI feedback baked in.Resilience and regionalization came to life through Kuehne+Nagel's on-the-ground view: supplier clustering near fabs, cross-border trucking, time-critical services, and 4PL integration that gives real-time visibility and smarter capacity planning. ERS showed where throughput meets cost: photothermal debonding with lower stress and reusable glass carriers, demo centers in Taiwan (and planned in North America), plus surge demand for warpage repair as volumes rise.Process control is moving into packaging with front-end rigor. Nova detailed metrology for hybrid bonding, chemistry monitoring of plating baths, X-ray and XPS/SIMS material insights, and the handling know-how to measure framed wafers and panels reliably. Nordson Test & Inspection highlighted AI-driven inspection, ultra-fast acoustic scanning, automated X-ray metrology, and sensor wafers that cut tool downtime and sharpen process windows. Comet showcased its CT and CA20 upgrades for 3D IC and TSV analysis.Power dominated the later conversations. Siemens argued we need to design for energy from the chip through the blade, rack, and data center, simulating real workloads and cooling to slash gigawatts—then extend that thinking into the fab, where optimizing chillers and facilities already saves serious money. Onto Innovation brought it home with execution: the PACE Center now hosts partners' tools, accelerating experiments for glass, TGV, and panel processes without waiting on public funds.If you care about hybrid bonding, maskless lithography, CT for 3D ICs, panel-scale packaging, or cutting AI's energy bill, this one is dense with takeaways and hard truths. Subscribe, share with a colleague who lives in the fab or data center, and leave a review telling us which insight you'll act on first.Support the show

Da ist das Minipflänzchen der Hoffnung auch schon wieder zertreten, der TSV 1860 verliert bei Waldhof Mannheim. Und das dermaßen unnötig, dass der Ärger die Enttäuschung überwiegt. Nicht wegen der Löwen-Leistung, sondern auch wegen der des Unparteiischen – mal wieder. Trotzdem versuchen wir den Fokus auf dem zu belassen, was Sechzig beeinflussen kann und da stellen wir uns verstärkt die Frage, ob es nicht wirklich nötig ist, Kevin Volland oder Florian Niederlechner eine Pause zu gönnen – für sich, aber auch für das Team. Zusätzlich wirft das nächste Heimspiel gegen einen Tabellenführer der Dritten Liga seine Schatten voraus, diesmal wartet Energie Cottbus. Eigentlich schon wieder ein gutes Omen, oder? Zudem sprechen wir über die Pro-Olympia-Votum der Münchnerinnen und Münchner, die 1860-Dokumentation "Rise & Fall" des Bayerischen Rundfunks sowie die roten Stadionkauf-Pläne in Unterhaching.

Es gibt genügend traurige Themen, zu denen der Anchorman Markus Gaupp und der Producer Jens Huiber nicht beitragen können. Das trifft indes auch für die lustigen Themen zu - wie den TSV 1860 München.

My guest in this episode is Bret Morgan. Bret is an entrepreneur, software developer, and real estate investor based in Asbury Park, NJ. He provides strategic consulting to businesses looking to grow, innovate, and integrate traditional and digital business models.Bret is the founder of FRESH Markets, Monmouth County's top destination for local farmers, makers & food purveyors, where they annually operate over 150 high-traffic pop-up events to support budding entrepreneurs and revitalize local communities.He is also a co-founder and general partner for The Surf Village, a vacation rental community featuring 9 intentionally designed bungalows located in the heart of Bradley Beach.He co-founded Cowerks, a tech and startup hub providing workspace and community for entrepreneurs in Monmouth County, with locations in Asbury Park and Red Bank.He has co-founded and ran the Jersey Shore Tech Meetup, Asbury Agile, and BandsOnABudget.com.Bret is the father of 3 young children, husband, a long time student and teacher of yoga, amateur plant based chef, and world traveler.Interview Links:Bret Morgan Website: https://www.bretmorgan.me/Bradley Surf Village https://www.bradleysurfvillage.com/Offshore Wealth Summit Mastermind https://www.bradleysurfvillage.com/ows25Coupon Codes For $50 off is TSV and BEACHSubscribe To Our Weekly Newsletter:The Wealth Dojo: https://subscribe.wealthdojo.ai/Download all the Niches Trilogy Books:The 21 Best Cashflow NichesDigital: ⁠⁠https://www.cashflowninjaprograms.com/the-21-best-cashflow-niches-book⁠⁠Audio: ⁠https://podcasters.spotify.com/pod/show/21-best-cashflow-niches⁠The 21 Most Unique Cashflow NichesDigital: ⁠⁠https://www.cashflowninjaprograms.com/the-21-most-unique-cashflow-niches⁠⁠Audio: ⁠https://podcasters.spotify.com/pod/show/21-most-unique-niches⁠The 21 Best Cash Growth NichesDigital: ⁠https://www.cashflowninjaprograms.com/the-21-best-cash-growth-niches⁠⁠Audio: ⁠https://podcasters.spotify.com/pod/show/21-cash-growth-nichesThe 21 Next Level Cashflow NichesDigital: https://www.cashflowninjaprograms.com/the-21-next-level-cashflow-niches-book-free-downloadAudio: https://podcasters.spotify.com/pod/show/the-21-next-level-nichesListen To Cashflow Ninja Podcasts:Cashflow Ninja⁠https://podcasters.spotify.com/pod/show/cashflowninja⁠Cashflow Investing Secrets⁠https://podcasters.spotify.com/pod/show/cashflowinvestingsecrets⁠Cashflow Ninja Banking⁠https://podcasters.spotify.com/pod/show/cashflow-ninja-banking⁠Connect With Us:Website: http://cashflowninja.comPodcast: http://cashflowinvestingsecrets.comPodcast: http://cashflowninjabanking.comSubstack: https://mclaubscher.substack.com/Amazon Audible: https://a.co/d/1xfM1VxAmazon Audible: https://a.co/d/aGzudX0Facebook: https://www.facebook.com/cashflowninja/Twitter: https://twitter.com/mclaubscherInstagram: https://www.instagram.com/thecashflowninja/TikTok: https://www.tiktok.com/@cashflowninjaLinkedin: https://www.linkedin.com/in/mclaubscher/Gab: https://gab.com/cashflowninjaYoutube: http://www.youtube.com/c/CashflowninjaRumble: https://rumble.com/c/c-329875