Podcasts about andrej

Srečen narod in srečna družba, ki imata pesnika, kot je Andrej Rozman - Roza. Pesnik namreč stoji v areni življenja že leta in desetletja ter še kako brezkompromisno, igrivo, izzivalno, izvirno, humorno ustvarja poezijo in še marsikaj. Pred njim ni varna literarna tradicija, nacionalna patologija, politika itd., njegovi verzi zarežejo natančno in zdravijo s humorjem. Ob izidu knjige zbranih pesmi za odrasle 1982-2025 z naslovom Zdravilo za čas (izšla je pri Mladinski knjigi) Andrej Rozman - Roza več pove o svoji poeziji in še čem v pogovoru v živo z Markom Goljo v Izšlo je. Nikar ne zamudite.

Piše Marija Švajncer, bereta Maja Moll in Bernard Stramič. Knjiga Andreja Capudra Nedokončani spisi je sestavljena iz dveh delov: Esejev o Danteju in Breviarija. Avtorjeva soproga Majda Capuder v uvodnem zapisu Knjigi na pot pravi, da je zbirka esejev nastala v zadnjih letih moževega življenja. Njegova literarna pot se je začela z Dantejem in z njim se je tudi končala. Božanska komedija ga je spremljala ves čas: zanj je bila univerza, temeljna izobrazba in kažipot ob profesuri, politiki, diplomaciji in nikakor nazadnje ob družini. Plodovito življenje pisatelja, esejista, prevajalca in pesnika se je izteklo leta 2018. Načrtoval je po pet esejev za vsako kantiko, Pekel, Vice in Raj, vendar se zaradi smrti to ni uresničilo. Nedokončani spisi niso labodji spev, temveč izvrstni eseji, v katerih Andrej Capuder, čeprav že v zrelih letih, piše kot kak mladenič, poln ustvarjalne moči, iskrivih domislic in novih spoznanj. Njegova življenjska energija je nezaustavljiva, toliko vsega ima povedati. Zastavlja si vrsto vprašanj, kopiči dokazne razloge in kar naprej ugotavlja, da je še vse preveč tem, o katerih bi se bilo treba razpisati. Z veliko filozofsko vednostjo in obsežnim znanjem vstopa v Dantejevo Božansko komedijo. Že res, da je v naslovu omenjena komedija, toda, poudarja Capuder, v tem resnobnem delu zaman iščemo humor. Avtor osupne nad izdajalci in samomorilci v Peklu, zazre se v oči grešnikov v Vicah in se pošali na račun Dantejevega podrejanja pametni, vsevedni in moralno neoporečni Beatrice. Pekel, vice in raj Capuder prepoznava v starih časih in miselnosti visokega srednjega veka, vse skupaj pa s kritičnim pogledom prenese v naš čas in izreče par gorkih na račun demokracije, razbrzdane tehnologije in slabih medčloveških odnosov. Z izbranim besediščem, literarno bogatim slogom in miselno izvirnostjo vsebinsko ostaja tudi filozof in subtilen psiholog. Kot poznavalec človekovega duha, zavesti in njegove duše, ki ji pravi skrivnostna entiteta, prodira v skrito notranjost in razkriva obe plati duševnosti in ravnanja – tako dobro kot zlo. Edino dobro je samo resnica!, vzklikne. Pesniška resnica, tako je prepričan, pa ni nič manj čvrsta od zgodovinske. Capuder pripomni, da se nam danes tehtnica zla čedalje bolj guga in pri tem grozi, da nas bo vrgla s sedla, kamor smo se postavili v izbiri na vse ali nič. Dante Alighieri je marsikaj doživel. Kompromitiral se je v državljanski vojni med »belimi« in »črnimi« gvelfi, se pravi privrženci papeževe politike, usmerjene proti cesarjevi politiki. Večkrat je menjal stran in se zameril vsem, tako papežu kot cesarju, za katerega je na koncu gorel z dušo in telesom, kot opiše Capuder. Ker so rodne Firence Danteja zavrgle, je begal po svetu in kot večni prosjak umrl v cesarski Raveni leta 1321 v svojem šestinpetdesetem letu. Bil je pesnik, politik in državljan. Še predobro se je zavedal svoje nepriznane veličine. Uspelo pa mu je, da je združil čar besede in domišljije ter se hkrati postavil na stališče sodnika, ki mu nič človeškega ni tuje. Capuder mu pravi »naš pesnik«, pa tudi »dobričina«, kljub temu pa ugotavlja, da je v njegovih čustvih več negativnega kot pozitivnega in je pravzaprav bolj pesnik razuma kot pesnik srca. Ko se Capuder vrača k razkošju Dantejevega pesniškega sveta, v njegove misli vstopajo filozofi, ki so veliko vedeli o življenju in notranjih nasprotjih minljivega bivanja. Spet je slišati Kierkegaardove besede o bolezni za smrt in slutiti njegovo tesnobo, Nietzschejev nihilizem ni pozabljen in je vsiljivo sedanji, Bergsonovo sporočilo o intuiciji in smehu je nenavadno živo, ohranjeno je Camusovo občutje absurda. Dante je sprejemal preinterpretacijo Aristotelove filozofije, kakršne se je v svoji krščanski maniri lotil Tomaž Akvinski. Pred Capudra stopi vitez žalostne postave Don Kihot, širi se omamni vonj Baudelairovih rož zla. Drugi del knjige, Breviarij, je sestavljen iz maksim, aforizmov, dvogovorov s filozofi in kratkih modrosti. Besedo breviarij Capuder pojasnjuje kot misel, spoznanje in očiščenje, skoraj molitev in laični brevir, povzetek za nazaj, odprtje v prihodnost, vdor svetlobe v sajasto izbo, prepih, okna in notranji prostor. Te besede, že skoraj verzi, povedo dovolj, v nadaljevanju pa se pisec ne brani sočnega humorja in blagega posmeha. Kar precej ima povedati o človekovih nraveh in lastnostih, kot bogoiskatelj odkriva nova obzorja ter pred božjim obličjem v veliki meri ohranja individualno svobodo in izbiro. Dotakne se tudi politike in jo ima za umetnost kako zasebni interes pokazati kot javno dobro. »To varanje se začne in konča pri samem sebi, vmes je lahko milijone trupel. Edina 'resničnost' pri tej stvari so pravila igre. Največji prevaranti se najbolj sklicujejo nanje.« Nedokončani spisi so nadvse vznemirljivo in aktualno pisanje. Vsa ta domiselnost, izvirnost in miselna gibkost so tako žive, da je toliko bolj žalostno, da je ostala samo zakladnica idej, njihovega avtorja pa ni več med nami. Besede ostajajo in zvenijo s svojo prepričljivostjo, globino in zrcaljenjem človekovega bivanja, tako veličastnega kot tragičnega, peklenskih muk in iskanja raja. Andrej Capuder ni bil samo esejist, filozof in prevajalec, temveč tudi pesnik. Njegovo izražanje je prečiščeno in povedno v svoji poetičnosti. Tu in tam je žlahtno konservativen ter obuja vznemirljivo in lepo zveneče, že skoraj pozabljene besede, kot so: temnica, gorečnost, ošabnost, razjarjenost, stremuh … Rad tudi vzklika, na primer: Pazimo!, in navaja italijanske, latinske in angleške besede, tujk pa bolj malo. Kar naravnost svetuje: »Splača se brati velike mojstre umske gimnastike, zlasti tiste, ki so svoja spoznanja pridelali na lastni koži.« Knjigo Andreja Capudra Nedokončani spisi sta uredili klasična filologinja Sonja Weiss in filozofinja Ignacija Fridl Jarc. Sonja Weiss v spremni besedi piše, da sta obe deli, Eseji o Danteju in Breviarij, dovršeni v jasnosti misli in odprti za nadaljnje razmišljanje. Capuder je mladost kot enačbo za resnicoljubnost obdržal do konca, ker je ohranjal notranjo transparenco, moralni pogum in zvestobo samemu sebi. Razgaljal je nevarno samoumevnost nekaterih temeljev modernega sveta, od demokracije kot »nujnega zla mislečega človeštva« in navidezne družbene solidarnosti. Avtorjevo razmišljanje, tako Sonja Weiss, se ponuja kot vrelec večne mladosti, iz katerega je sam pogosto pil, s čimer je ohranjal napet lok, pokončno držo in svoj jaz, vsekakor pa je vztrajal v pogumu.

Andrej Velkavrh, meteorolog, ki je napovedovanje vremena med prvimi obogatil s poljudnimi razlagami, humornimi odtenki in izobraževalnimi razmisleki ter postal eden najprepoznavnejših obrazov Agencije za okolje. Minuli teden je vreme napovedal še zadnjič pred odhodom v pokoj. Uradne vremenske napovedi je vseskozi nadgrajeval s kolumnami in s številnimi medijskimi sodelovanji, s tem pa pomembno populariziral tudi meteorologijo. Kandidata sta bila še: - Peter Tomaž Dobrila, predsednik Kulturno-izobraževalnega društva (KID) Kibla, ki je pred 30 leti v Mariboru nastal kot pionirski multimedijski center za področja kreativnih industrij, interdisciplinarne, intermedijske, multimedijske, vizualne, glasbene in avdiovizualne umetnosti. V treh desetletjih obstoja je društvo ustvarilo številne pomembne prostore, publikacije in festivale. Med vidnejšimi programskimi vsebinami sta bila tudi festival elektronske in elektroakustične glasbe MED in časopis za sodobno umetnost, kulturo in veselje do življenja Folio - Boštjan Kobe, raziskovalec na področju strukturne biologije, ki so ga izvolili za člana britanske znanstvene akademije The Royal Society, najstarejše neprekinjeno delujoče znanstvene akademije na svetu. Kobe je s tem postal drugi Slovenec v zgodovini po Janezu Vajkardu Valvasorju, ki je član postal leta 1687. Gre za eno najvišjih mednarodnih priznanj za znanstveno odličnost, ki predstavlja pomemben mejnik za slovensko znanost in potrjuje mednarodno prepoznavnost slovenskih raziskovalcev.

V brlog sta prišla kolesarska strokovnjaka, ki poznata vse prodajne in tehnološke trende sodobnega kolesarstva. Kako izbrati prvi "bajk", kaj so novi kolesarski varnostni sistem, ki ti na cesti lahko rešijo življenje, se na elektriko res lahko konkurečno "topi" kile? Kaj je v Sloveniji narobe? Imamo idelni kolesarsko pokrajino in najboljše kolesarje na svetu, a smo vse bolj prisiljeni kolesariti u tujini? Zakaj? Pa bonus podkasta: Klemen si je na Siolu redakcijo delil s Cirilom in Juretom. Veliko smeha! Ciril je na kolesarsko turo prišel v basket opremi! Tokratnega podcasta ne smete zamuditi, če razmišljate o elektromobilnosti na pedalih..Zapiski:Kristjan Vreček - Ukore zamenjal za gorsko kolo - Podcast #92https://youtu.be/US-Eb2g_920Matej Mohorič - 30.000 kilometrov na žgance - Podcast #55https://youtu.be/SAtJ7RZmx-oPogi, koliko koles zamenjaš vsako leto? KVIKI #332https://www.instagram.com/reel/DP29xXTCuMC/?igsh=MXgxeDN2aXczcHZrKolesarski center Valy Žagarhttps://www.instagram.com/valy_zagar_kolesarskicenter?igsh=MTZmcDE0eXFqeHcyNA==.IGRALNE KARTE "KONJE NA MIZO Mk2" - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://app.vibeit.co/en/atmosferci/product/karte-konje-na-mizo-mk2⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠PODPRI ATMOSFERCE - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://app.vibeit.co/en/atmosferci⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠PODPRI KOMOTAR MINUTO - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠http://shop.komotarminuta.com/en⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠JURE GREGORČIČ INSTAGRAM - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.instagram.com/jure_gregorcic/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠CIRIL KOMOTAR INSTAGRAM - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.instagram.com/komotar_minuta/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠SEBASTJAN PLEVNJAK INSTAGRAM - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.instagram.com/sebastjan_plevnjak/⁠⁠⁠⁠⁠⁠⁠⁠⁠

V oddaji smo gostili Andreja Pavlovića, Srba, ki se je odločil za študij na ljubljanski ekonomski fakulteti. V oddaji je spregovoril o študiju in življenju pri nas ter razlikah, ki jih zaznava v primerjavi z domovino. V pogovoru z njim smo se dotaknili tudi političnega dogajanja v Srbiji, ki je že nekaj časa soočena s študentskimi protesti.

Četrt tisočletja Združenih držav Amerike minuli konec tedna ni moglo prikriti aktualne razdeljenosti tamkajšnje družbe. Predsednik Trump se še naprej trudi razdeljevati poleg domače tudi svetovno javnost. Minule dni je spet napadel Iran, na vrhu NATO zveze v Turčiji grozil evropskim zaveznicam in se med drugim prelevil še v nogometnega sodnika. In tudi mešanje ogromnih zasebnih zaslužkov z opravljanjem predsedniške funkcije mu zadnje čase tudi ne lošči medijske podobe.

We've been running a bit of an Agent Cloud series surveying all the top inference/compute/cloud providers, from Databricks to Daytona to Railway and, even further back, E2B, but we're excited to conclude this series returning to Modal, which has just raised a monster $355M Series C.The cloud was built for developers. But agents are now changing that.The old infra stack was designed for a human who could read docs, reason through YAML, and understand dashboards to figure out what they need when something broke. While this was painful for developers, it worked since they could fill in missing context in their heads.However, agents don't have that luxury. Now in this new era of agents, everything has to be tighter.They need a place to write code, run it, inspect the output, change the environment, debug failures, and try again. Fast iteration and feedback loops with all the necessary context are crucial for agents to operate properly. Furthermore, sandboxes are a clear representation of this shift as agents can easily spin up isolated environments. This programmatic infra even extends to research:Two years ago, we were one of the first to cover Modal with CEO Erik Bernhardsson and Alessio designed our favorite LS thumbnail of all time:At the time, Modal was just a teeny little company with a $17M Series A.Today, fresh off their $355M Series C, Modal is one of the clearest examples of the agent cloud future being built in real time: a cloud platform moving past traditional web app assumptions toward the workloads AI actually creates such as elastic inference, sandboxes, GPU burst, post-training, background agents, and infrastructure that agents themselves can operate.In this episode, Modal CTO Akshat Bubna joins swyx and Vibhu to unpack why AI applications don't fit traditional cloud assumptions, why Kubernetes was never designed for bursty compute-heavy workloads, and why Modal is now shifting from developer experience to agent experience.We go deep on Modal's AI infra stack: serverless functions, decorator-based infrastructure, elastic inference for custom models, GPU snapshotting, DeFlash, speculative decoding, Auto Endpoints, sandboxes, persistent storage, networked containers, private IPv6, RDMA, multi-node training, and Modal's capacity pool across 17 cloud providers. Akshat also explains why RL rollouts can require 100,000 sandboxes, why production agents need hard guardrails, why observability may matter more than reading code, and why AI has made infrastructure exciting again.We discuss:* Why Kubernetes wasn't built for bursty AI workloads* How Modal started as a better runtime before becoming an AI cloud* Why Modal added GPUs before ChatGPT* The shift from developer experience to agent experience* Why observability matters when agents are writing the code* Elastic inference for custom models across audio, video, robotics, and comp bio* GPU snapshotting, cold starts, and why inference workloads are so bursty* Why RL rollouts can require 100,000 sandboxes* DeFlash, speculative decoding, and frontier-level inference performance* Auto Endpoints and making optimized inference easier to deploy* What Modal adds beyond vLLM, SGLang, and raw GPU rental* Modal's 17-cloud capacity pool and supercloud strategy* Networked sandboxes, sidecars, private IPv6, and RDMA* Serverless multi-node training for post-training and research workloads* Auto-research, model-guided sweeps, and agents launching GPU experiments* Compute strategy, capacity planning, and batch tiers* Why production agents need specialized sandboxes and hard guardrails* Modal's take on managed agents, CI, Gitpod/Ona, Python, TypeScript, and Modal BenchAkshat Bubna* LinkedIn: https://www.linkedin.com/in/akshat-bubna-188885103* X: https://x.com/akshat_bModal* Website: https://modal.comTimestamps00:00:00 Introduction00:00:39 Modal's origin and why Kubernetes wasn't enough00:04:32 Developer Experience → Agent Experience00:06:21 Modal's AI cloud primitives00:09:14 Sandboxes, agent loops, and proto-Cognition00:12:12 Elastic inference, GPU snapshotting, and 100,000 sandboxes00:15:24 DeFlash, speculative decoding, and Auto Endpoints00:19:59 Production-grade inference beyond raw GPUs00:22:00 Background agents, Ramp Inspect, and the agent lifecycle00:24:08 Modal's 17-cloud supercloud strategy00:26:40 Networked sandboxes, private IPv6, and RDMA00:32:48 Multi-node training, post-training, and auto research00:37:36 Compute strategy, capacity planning, and batch tiers00:40:55 Open models, real-time AI, and production agent infra00:43:06 Hard guardrails, managed agents, and specialized sandboxes00:46:06 Why AI made infrastructure exciting again00:48:30 Model APIs, differentiated products, and agentic video00:51:50 CI, coding-agent infra, SDKs, and Modal Bench00:57:28 Closing ThoughtsTranscriptIntroduction: Modal, Series C, and the Art PartySwyx [00:00:00]: We're here with Akshat, CTO of Modal, together with Vibhu. Congrats on your Series C.Akshat [00:00:10]: Thank you.Swyx [00:00:11]: Your party yesterday was amazing.Akshat [00:00:15]: Yeah.Swyx [00:00:15]: From all the photos and all the swag.Akshat [00:00:17]: We had a bunch of art installations, which was fun, seeing, like, our products on pedestals next to, like, Rodin.Swyx [00:00:25]: Very nice. Very nice. When you started, it was not the GPU inference company. Maybe it was in your mind. Take us back to the origin story.Modal's Origin: A New Runtime Beyond KubernetesAkshat [00:00:39]: I first met Eric, who's the CEO, through an investor. Back then Eric was already thinking about building, a new runtime, and he got there thinking through why are workflow orchestration products so hard to use. It's because you have to run them on Kubernetes. Kubernetes is hard to manage. It's not built for burstiness and, custom images,Swyx [00:01:03]: YeahAkshat [00:01:03]: It has a terrible developer experience.Swyx [00:01:05]: And I'll, I'll interjectAkshat [00:01:06]: YeahSwyx [00:01:07]: For listeners, who are new, we interviewed Eric two years ago, and there's a bit more of the story there from Spotify and all those things.Swyx [00:01:14]: And I came across Eric through Data Council because he did that talk on the serverless container stack that you guys did, which was like, that was my first like, “Okay, I need to take Modal very seriously” moment.Akshat [00:01:26]: Yeah.Swyx [00:01:26]: But it was still very unclear, like, do I need all this for just my data pipelines?Akshat [00:01:33]: Yeah. initially what we were thinking about was if we build a better runtime, it's a very useful primitive in itself. It's There's a lot of things that, get solved by serverless functions, like you can do, ETL stuff, you can do job queues, you can do all this, like, bursty processing, which it turns out every company had needs for. but then we also were thinking about this as like, this is a primitive that we can build a whole collection of products on, which are very verticalized. So perhaps data engineering would've been the first one, but we were thinking about inference. Back then it was more classical inference, like computer vision stuff and running XGBoosts and whatnot. But we added GPUs to the product a year before ChatGPT came out.From Serverless Containers to GPU WorkloadsSwyx [00:02:19]: Nice.Akshat [00:02:19]: We just didn't think it would be that big of a deal.Swyx [00:02:22]: Yeah, just like add A100.Vibhu [00:02:23]: Was there any, like, early key problem that really sparked off why you built it?Akshat [00:02:28]: Yeah. Primarily it's just, none of the tooling that was out there was built for, one, a really great developer experience, and also there's a general trend of, a lot of the workloads that we were seeing were very. I wish there was a better word for it, but compute-heavy. Like, they need, one, like, need a lot more resources, so you need to burst up and down a lot, versus like Kubernetes designed for, like, slow scaling and, more for, like, web server use cases. And also there's just a lot more specialization in, like, what kinds of environments these workloads run in. Like, we had sometimes they need accelerators, sometimes they need different kinds of images, and this is just like a consistent thing that we saw across a lot of companies. That would be the next step.Software-Defined Infrastructure and Decorator-Based DXSwyx [00:03:13]: Yeah. Yeah. Be nice. I don't know how much this factored into the early story, but I wrote a post when I was at Temporal about infrastructure, software-defined infrastructure or something like that.Akshat [00:03:22]: Yeah, the self-provisioningSwyx [00:03:23]: Self-provisioning.Akshat [00:03:24]: Yeah.Swyx [00:03:24]: Yeah. I can't even remember my own post.Swyx [00:03:26]: And then you put me on the landing page.Akshat [00:03:28]: Yeah. We really like, the term and so we stole it.Swyx [00:03:32]: Because you had the insight that everything can just be in decorators co-located with the code, right?Akshat [00:03:37]: Yeah.Swyx [00:03:37]: Was that a big part of the originalAkshat [00:03:39]: YesSwyx [00:03:39]: Story or it was just like a DX layer?Akshat [00:03:41]: That was, really important because we really didn't want people to spend, so much time, writing YAML, and it seemed like you could really condense the surface area of what you're doing, put it in code so you can operate on it just like you operate on other code, and like build stuff that's more expressive and dynamic. and so yeah, that was always a very important part.Swyx [00:04:04]: Then the pushback is this is a DSL.Akshat [00:04:07]: Yeah.Swyx [00:04:07]: It's you're closed source. I am locked into Modal.Akshat [00:04:11]: Yeah. We never really got pushback for that because the nice thing about Modal is you can bring whatever code you have, and sure, the DSL is at the configuration layer for, what hardware you're using, how you're scaling things up, but you still own the code.Akshat [00:04:27]: And that's, that's been an important, part of our story, even as we do inference now.Swyx [00:04:32]: Yeah.Vibhu [00:04:32]: How much of do you think still stays the same today? Like if you were to build something today, DevX very important, but I feel like, a lot of this has been changed with just hook it up to an agent, have Claude Code, have Codex implement a tool. there's very agent native primitives that are different than if I'm doing this myself, right?Developer Experience → Agent ExperienceAkshat [00:04:54]: We've changed our SDK team to think about agent experience instead of, developer experience and we think that the same benefits that apply for DX also apply for AX, which is why would you have an agent read through hundreds of Kubernetes files and like write YAML that's not even typed when it can make a couple of changes in a decorator and it gets this self-provisioning runtime of, being able to see its changes live in action? yeah, it just seems from the customers we talk to, they find Modal is much faster for agents to use versus operating on a different substrate.Swyx [00:05:34]: Yeah, because like you, again, you co-locate the infrastructure requirements to the code that runs it.Akshat [00:05:38]: Yeah.Swyx [00:05:38]: Well, the negative thesis now is that nobody's looking at their code anymore, so there's no point.Akshat [00:05:44]: Yeah, people aren't looking at code. one thing we still see is really important is observability.Swyx [00:05:51]: Yeah.Akshat [00:05:51]: Like how good is your dashboard? And of course, like we have, we push a lot of it to the CLI so the agents can do their own investigation, but you still need humans to go interpret what's going on and, make judgment calls and whatnot. and that's I feel like, Maybe more important now than looking at the code itself.Swyx [00:06:11]: Yes, because like, you can try to treat the code as a black box and then use, see the observable action that comes out of it, and then just prompt a change.What Modal Is For: AI Cloud PrimitivesAkshat [00:06:21]: Yeah.Swyx [00:06:22]: So I think it takes a bit of restraint to not specialize, to say, “I want to ship a new primitive,” and then just be general purpose.Swyx [00:06:31]: People ask you, “What are you for?” You're like, “ I don't know. We can do this, we can do that.”Vibhu [00:06:36]: Well, I'd be curious to see, like, okay, if we were to ask you, like, what is Modal for even at a high level? There's a lot you guys do, sandboxes, GPUs, everything. How do you answer?Akshat [00:06:46]: Modal is a cloud platform that's built for, where we've built the primitives from scratch for AI applications. and right now it covers, inference, training, batch processing, and sandbox workloads.Akshat [00:07:00]: But we're building a lot moreSwyx [00:07:02]: I noticed you didn't say web server, so there is still a role for, like, the always-on large-scale Kubernetes type things.Akshat [00:07:09]: Yeah, absolutely. We're, we're not trying to compete with the renders of the world, because yeah, we think the differentiator for us is the, are the workloads that need specialized compute, need to scale up and down a lot. yeah, they're, they're, they're just shaped differently.Working Alongside Frontier StartupsVibhu [00:07:26]: I think you're building a lot of it alongside the startups, right? They're innovating quite a bit, even in your, like, latest blog post. Like, even in the series C, the customers that you mention here, the cognitions, technical ones, ramps and whatnot, they're, they're innovating with you, right? And that's not something AWS is doing directly with.Akshat [00:07:45]: Yeah, absolutely. I think, this is again classic. We're a small team. We can move really fast. our engineers are working with our customers and figuring it out. Yeah.Swyx [00:07:54]: So my first week at Cognition, I walked in, there was someone wearing a Modal shirt. I was like, “What are you doing here?” They're like, “Yeah, I just. I am embedded inside of Cog.”Akshat [00:08:05]: Yeah, I think that was Peyton. We sent him overSwyx [00:08:07]: Yeah.Akshat [00:08:07]: Because, the latency of communication was too high otherwise.Swyx [00:08:12]: Yeah, distributed node, you have to - you have to place one and collocate.Vibhu [00:08:16]: Yeah.Swyx [00:08:16]: So I had a, I had direct personal experience, right? So I worked on smol developer three years ago. it was inspired by Claude 1. I think you onboarded me at some point, like, just before, and I was like, “Oh, like, I need some bursty compute. Like, I was just gonna try using Modal.” And it was a, it was a pretty pleasant experience. apparently, I showed up in the board meeting, like the analytics.smol developer, Sandboxes, and Proto-CognitionAkshat [00:08:39]: Yeah, you blew up on Hacker News and,Swyx [00:08:41]: YeahAkshat [00:08:41]: We got a big traffic spike. I. I think the way you used smol developer was Modal functions for running stuff, which was. Like, the, that was a good use case. but then, yeah.Swyx [00:08:53]: Yeah. That - So to me, that was proto-cognition.Akshat [00:08:55]: Right.Swyx [00:08:56]: If only I had, like, stuck to it.Swyx [00:08:58]: Like, that was like, if - did you say draw the tech treeAkshat [00:09:00]: AbsolutelySwyx [00:09:00]: You're just like, “Yeah, like, probably this will happen.”Akshat [00:09:02]: Yeah. Like, he was so close. You were just rebuilding upon usSwyx [00:09:04]: I just didn't realize.Akshat [00:09:05]: But the funny story there is at the same time, we were talking to a bunch of customers who needed something like sandboxing.Swyx [00:09:14]: Yeah.Akshat [00:09:14]: This is like twenty-three.Swyx [00:09:15]: Yeah.Akshat [00:09:16]: So we builtSwyx [00:09:17]: You introduced a new API right after that.Akshat [00:09:18]: Yeah.Swyx [00:09:19]: Yes.Akshat [00:09:19]: Like, we built sandboxes in May of twenty-three before anyone was even knew this was gonna be a thing. And the first example we published was, we took smol developerSwyx [00:09:28]: Smol developerAkshat [00:09:28]: And put it in a loop, so the agent can iterate on itself.Swyx [00:09:33]: Loops are hot these days.Vibhu [00:09:34]: It's the looper.Akshat [00:09:34]: Yeah.Vibhu [00:09:35]: Loops in. When was this, twenty-three?Akshat [00:09:38]: Yeah.Vibhu [00:09:39]: A small check.Akshat [00:09:39]: Yeah.Swyx [00:09:39]: It's like twenty-three. so the. the, those for listeners, like, the problem was the models are not built for any of this, right?Swyx [00:09:46]: Like, you're just trying to like. They're not post-training to understand, like, looping and, like, self-correction and tool calling was there, but, like, also not that great.Akshat [00:09:55]: Yeah.Akshat [00:09:55]: I don't remember if you used tool calling in this one, but yeah, the models would just diverge after like ten iterations and not produce anything meaningful.Swyx [00:10:03]: Yeah. But like, then. So okay, like now talking to myself three years ago, the answerVibhu [00:10:08]: Of course they will get betterSwyx [00:10:09]: Collect all the failures, build benchmark, and then collect all the, examples, build the RL environmentAkshat [00:10:15]: RightSwyx [00:10:15]: Sell it for like ten billion dollars to Meta.Swyx [00:10:17]: And then also train a model and then sell that for sixty billion dollars to Elon. And this isAkshat [00:10:23]: Yeah, of courseSwyx [00:10:23]: The funny machine. Like, it's like, it's about the hardware.Akshat [00:10:28]: It's hard to have that inherent conviction that the stuff will get that much better.Swyx [00:10:33]: In retrospect, it's so f*****g obvious.Akshat [00:10:36]: Fair enough.Swyx [00:10:37]: Like, what else were we doing back then? I don't know. anyway. Yeah. So this. That was the start of your sandboxing journey, right? I feel like it didn't blow up until, like, last year.Akshat [00:10:49]: Yeah.Swyx [00:10:50]: So there was like a couple years of quietness.Akshat [00:10:52]: Exactly, yeah. We wereVibhu [00:10:53]: I think very underrated product value. Like, my experience with Modal, Charles, before he had joined Modal, met this guy at a hackathon, and he really insisted we wanted to run some small model, not hosted anywhere, and he's like, “ there's this cool company, Modal. They'll like spin up a GPU sandbox, we can throw it on there. They'll take a Hugging Face link.” And like there's so much value just right there, right? Like instant hosting, spin it up, spin it down. It'll stay cold, but we run the demo a few days later, it'll come back up and like all this stuff in retrospect, like it's still what we needed like today.Akshat [00:11:27]: Yeah, it's still needed today. workload shapes have changed a lot as, we run stuff for people with really massive production scale and, there it's it's not about scaling from zero to one, but it's how do we scale really elastically, from like thousand to fifteen hundred GPUs very quickly in a given region. It's the same shape problem.Elastic Inference, GPU Autoscaling, and Custom ModelsVibhu [00:11:50]: Okay. So you look at, say, Cursor Composer, right?Akshat [00:11:53]: Yeah.Vibhu [00:11:53]: They had a. “We'll do RL on a model every couple hours.” you guys have a whole version of RL inference gym and whatnot.Vibhu [00:12:01]: When you look at workloads like that, you're doing train runs where you need to scale up, scale down every hour thousands of GPUs, right? That's the example for we do need it, right?Akshat [00:12:12]: Yeah. Well, so I'll, I'll take a step back and, maybe talk about like how people use Modal today. because our biggest use case is, elastic inference. And the thing we first found product market fit, with was inference for custom models. So we stayed away from the LLM space, and we were serving companies like Suno for audio, Runway for video, robotics, comp bio companies that train their own model elsewhere. But Modal is the best black box that for deployment, scaling to however many GPUs you need as your traffic pattern changes. And we saw all of them like have a very unpredict- predict- predictable, traffic pattern. it's like diurnal. It's Some days, like the company will do a launch and, they'll need like, way more. And it's not just one model that they deploy. They-- all these companies deploy, lots of different models in different regions, and so the autoscaling problem becomes even harder because then you have to scale within a certain region, and those cycles are offset. So different times you scale up in different regions.Akshat [00:13:20]: So that's like our sortVibhu [00:13:22]: And thatAkshat [00:13:22]: YeahVibhu [00:13:22]: That in and of itself is a huge category. There's a bunch of inference providers which, provide this fireworks, does this as a service together, whatnot, Base10. that's carved into its own niche for language models, at least right now.Akshat [00:13:36]: Yeah. the thing that we have specialized in is the autoscaling aspect.Vibhu [00:13:41]: Yeah.Akshat [00:13:41]: Because we found that it's not universally true that everyone else can autoscale, and we've gone deeper into it on the tech side by, we've incorporated GPU snapshotting into the product so we can take the GPU state, like your torch.compile model, snapshot it, and the next cold start is way faster. And so going back to your question, it's That's why you need a lot of burstiness for inference. But then people also do a lot of demand training, like for RL stuff, your rollouts are bursty, as you said. People also do a lot of batch jobs. So we'll see, a lot of companies, before they have a training run, they'll need thousands of GPUs to run encoding or something like that. And I think those things are much more bursty than. I agree that agents are not that bursty. sandboxes are, except when you're doing RL. RL is justRL, Batch Jobs, and 100,000 SandboxesVibhu [00:14:28]: Or commerceAkshat [00:14:28]: Insanely bursty.Vibhu [00:14:29]: Yeah.Akshat [00:14:30]: Yeah. Like when you're doing, rollouts, you sometimes need a hundred thousand sandboxes in your sandboxes.Vibhu [00:14:37]: Yeah. I'm curious if you've seen early sparks of continual learning. There are some people, like our friends, ngram, recently announced thisAkshat [00:14:45]: YeahVibhu [00:14:45]: They're, they're trying to do training. That also seems like a different workload, right? If you're doing training twenty-four/seven per se, there's a very weird dynamic of how you're using GPUs between people and whatnot, but seems like something you guys would work for.Akshat [00:15:00]: As you said, we're, we're fortunate to work with a number of, customers at the frontier and grab some of our customers. and they are taking the primitives we have, and trying to use them in very interesting ways, like continual learning. It's possible as the stuff gets better, some of that will be part of, our offering as well if, more people need it. but we're, we're just waiting to seeVibhu [00:15:23]: YeahAkshat [00:15:23]: How it shakes out.Vibhu [00:15:24]: Is there a primitive that you added after sandboxing that was the next step in the story?LLM Inference, DeFlash, and Speculative DecodingAkshat [00:15:32]: I guess we've been going much deeper into LLM inferenceVibhu [00:15:35]: YeahAkshat [00:15:35]: Because we realized that some of the advantages we have with like autoscaling, again, especially in different regions and whatnot, are, not present elsewhere. and the place where we had a gap was we weren't, working on the model layer itself. Like we were a black box. And, we realized that, we can get to frontier-level model performance, with, by having great people who work on this. And, we've been open sourcing a lot of our work, in terms of, Recently, we, shared our work on DeFlash, which is a block-based, speculator, and we've open sourced, all of it. So, you can - By using open source DeFlash, you can get the same performance as you would with one of the proprietary providers. And the next thing we're thinking about hereVibhu [00:16:23]: I thought this wasAkshat [00:16:24]: YeahVibhu [00:16:24]: An interesting blog post as well, right? Like, I think in here you make a claim that. Not a claim, just that how effective speculative deco-decoding really just get to.Akshat [00:16:33]: Yeah.Vibhu [00:16:33]: Anything you wanna point out from this around, what people should know?Akshat [00:16:39]: Yeah, absolutely. the high-level summary is, it would help to describe what speculative decoding is.Vibhu [00:16:44]: Yes.Akshat [00:16:44]: I will, yes.Vibhu [00:16:45]: I think, likeAkshat [00:16:46]: YeahVibhu [00:16:46]: So we've covered like Eagle and all thisAkshat [00:16:47]: YeahVibhu [00:16:47]: Like Hydra and all those things, but it was like two years ago.Akshat [00:16:51]: Yeah.Vibhu [00:16:51]: I think it doesn't hurt, right?Akshat [00:16:52]: Yeah. Speculative decoding is you have a smaller model, called a draft model, predict tokens ahead of the bigger model, and then you have the bigger model, verify all of this, all the tokens are predicted. And the reason it's faster is if you're predicting, one token at once, you're bound by memory bandwidth. But if you can batch the verification of, the draft model, then you're much more efficient using compute, and it's faster, and as long as your draft model is producing a lot of tokens that can get accepted, which is called the accept length, you can get a speed up that's, multiple times of, the original model speed. and well, that's what we highlight here. It's Like people talk a lot about we made these kernels faster and whatnot, but improving kernel will only give you like few percentage points of improvement, and, increasing accept length, literally is a multiplicative decreaseVibhu [00:17:47]: Like two to four X.Akshat [00:17:48]: Yeah, exactly.Vibhu [00:17:48]: Without much head-on performance.Akshat [00:17:50]: Yeah. I think it may - you are running a second model, right? So it may be something more expensive in the compute,Vibhu [00:17:57]: I meant quality performanceAkshat [00:17:58]: Probably not by muchVibhu [00:17:58]: But yeah. I thinkAkshat [00:17:59]: So there's no drop in quality performanceVibhu [00:18:01]: YeahAkshat [00:18:01]: Because you're always. You're never accepting a token that the big modelVibhu [00:18:04]: It's strictly betterAkshat [00:18:05]: YeahVibhu [00:18:05]: Or it's same.Akshat [00:18:06]: Exactly.Vibhu [00:18:07]: Right. Yeah.Akshat [00:18:08]: And so we've been working a bunch on DeFlash, which is a block-based speculator. so it's instead of predicting, one token at a time, it's predicting a block. And we've been open sourcing our work with it. The next thing for us here is for helping people train speculators and custom models. it's it's something that traditionally is very forward-deployed engineering driven, support deployed, engineer driven, like you work with customers and help them do that. And our vision for. This is why we launched Auto Endpoints, is we want to make frontier-level performance available to everyone. And so, we mentioned this in the announcement, we teased it. The next thing we're, we're launching is, as you run an auto endpoint, we shadow trafficAuto Endpoints and Frontier-Level PerformanceVibhu [00:18:54]: Do you want to explain what auto endpoints are?Akshat [00:18:57]: Yeah.Vibhu [00:18:57]: I lovely, yeah.Akshat [00:18:58]: Yeah. So, this is, I guess, going back to your Modal is you touch the code, but, sometimes people don't wanna touch the code, and they wanna get started with an endpoint that works and has all the great performance and, scalability that Modal has. So we've made that easier with, a way to create an endpoint from our UI, from the CLI, that has all of our optimizations that we talked about, like the DeFlash stuff already baked in, and there's full transparency. So we give you the code, you can go run it yourself, and if you want, you can eject out into the full Modal experience, which we see as people get sophisticated, they do wanna tweak the models, they wanna, fine-tune stuff. You can still do all of that. It's it's not a black box. And yeah, the next thing, as we teased later in the post, is how do we give you value even beyond this in terms of having your draft models evolve as your data distribution evolves, again, without having to talk to a person and, yeah.Vibhu [00:19:59]: I guess just to understand it directly, you have the GPUs, you have an endpoint that's compatible, you serve open model. If someone was to do this themselves, what's the delta that you guys provide? So you do a lot of open source great work on effective inference. how does it compare to, say, I take the same model, 5.2 FP8, take shelf inference engine, vLLM, SGLang, get compute of similar capacity, similar cost. What's the delta that plugging into something this, like this offers outside of the benefit of, scaling?Production Inference Beyond Raw GPUsAkshat [00:20:34]: It's interesting because we've taken the approach of open sourcing our contributions and upstreaming them. we work closely with the SGLang team. We want the improvements that our team, comes up with to be, there in open source for others to use, even outside of Modal. The benefit to us is we have a team that has significant expertise in terms of if you do have something that is not there, our team can help you get that performance, first. the other thing is with these endpoints, we are way more elastic, as you said, than, anyone else, and you have true scaling to zero. you have true, burstiness, and in practice, that matters a lot more to people than just finding, the GPU and, running Modal code on something.Vibhu [00:21:20]: Yeah. And I will say it's not that straightforward to just. like what I said is easier said than done, right?Akshat [00:21:26]: Yeah.Vibhu [00:21:27]: It's I think still for the average person, still hard to just gut check using different. There's, there's quite a bit of combinations you can make there. the trade-offs aren't really known at face value.Akshat [00:21:40]: Yeah. it's it's not just that. I think it's it's that running production-grade inference is a hard infer problem.Vibhu [00:21:49]: YeahAkshat [00:21:49]: Even if you subtract out the autoscalingVibhu [00:21:50]: YeahAkshat [00:21:51]: Is controlling things like tail latency and, making sure every, request is delivered at least once and whatnot.The Model and Agent LifecycleVibhu [00:22:00]: There's a lot of innovation that you can do here. I think, it's very interesting that you're starting to encroach on, like as you become a full cloud, you're starting to encroach on other people's turf.Vibhu [00:22:09]: What will you not do?Akshat [00:22:13]: Well, we wanna follow our users and, make sure they get like a platform that has everything that works well together. so right now we're focused on the model lifecycle and the agent, lifecycle. so both like going from data prep to training to inference, and then also if I want to deploy a background agent, let's say, sandbox, do persistent storage, a whole bunch of other stuff.Vibhu [00:22:38]: We talked to Cole, who did, OpenInspect. Yeah.Akshat [00:22:42]: Yeah.Vibhu [00:22:42]: And RealInspect also is on Modal.Akshat [00:22:44]: Yeah. So Ramp Inspect was a great example of a background agent that was really successful because they, were able to use some of the primitives like snapshotting and fast scaling to just have something that feels really reactive and works well.Ramp Inspect and Background AgentsVibhu [00:23:02]: Yeah. That's the new CTO of, Ramp right there.Akshat [00:23:05]: Yeah, Rahul.Vibhu [00:23:08]: It was really fun. yeah, okay, I think, all very bullish. Like, one of my reflections was also I did not originally. So when I met you guysThe Inference Inflection: CPU, GPU, and Co-LocationVibhu [00:23:19]: You weren't that much in the GPU game, and now you're all about, inference. And one of the points that I hinged on for Jensen's keynote at GTC this year was, what we're calling like the inference inflection, right? That let's say in AI workloads or machine learning workloads, it used to be like, let's call it eight to one GPU to CPU, and now it's more like one to one, which is like a interesting. Like, - because of how much agents are blocked or call out to this, to CPU heavy stuff the actual, like, limiting factor, like, swings back and forth from GPU to CPU a lot more than it used to be all GPU and then occasional CPU.Akshat [00:24:01]: Yeah.Vibhu [00:24:02]: GPU, CPU. And now it's like just constantly, and you just have to locate everything.Seventeen Clouds and the Supercloud StrategyAkshat [00:24:08]: Yeah. And that's one of the things that, again, we see as, something appealing about Modal, which is we've built this capacity pool that spans, 17 cloud providers, so we're, we're very good at Running on various kinds of cloud capacity across the worldSwyx [00:24:24]: You don't have your own data centers?Akshat [00:24:25]: We don't have our own data centers. We just run across a lot of neo cloudsSwyx [00:24:29]: Yeah. AreAkshat [00:24:30]: Metal providers.Swyx [00:24:30]: Yeah. Question mark.Swyx [00:24:31]: Yeah. You're, you're running the math, and you're like, “What's the cutover point where you're like.”Akshat [00:24:36]: Yeah, it's a good question. part of it is we see our differentiator in the software layer, and, being capital light and focusing on the software helps us move really fast. so far it's worked out well because there are so many other people building data centers that we're able to work effectively with them, and again, focus on what makes us, special.Swyx [00:24:55]: Yeah.Swyx [00:24:56]: 17 gets you into, like, the local providers sometimes. LikeAkshat [00:25:00]: The,Swyx [00:25:01]: Which was the most interesting one?Akshat [00:25:02]: There are a lot more neo clouds than you expect, and they all have various degrees of, various levels of reliability. And, that's why it's something we've invested a lot of time in, is building our own reliability layer on top. so if the GPU falls off the bus or something happens, we user workloads are not affected, and that lets us use a lot more capacity than,Swyx [00:25:30]: YeahAkshat [00:25:30]: You as a user would be able to.Swyx [00:25:32]: It's a useful thing to have because like now everyone knows, like, what layer you are and, like, you optimize for being the super cloud of all clouds.Akshat [00:25:41]: Yeah. That's, that's, that's the idea. and so I guess when you mentioned colocation, that's, that's another interesting thing where, one thing we've seen is people come to us when they want, very specifically located, CPUs or GPUs, like they wantSwyx [00:25:57]: Oh, they pin it in likeAkshat [00:25:58]: YeahSwyx [00:25:58]: EU?Akshat [00:25:59]: Exactly. Or EU, US.Swyx [00:26:01]: Right. Data resiliencyAkshat [00:26:02]: AustraliaSwyx [00:26:02]: Locality thing or performance or what?Akshat [00:26:04]: It's either data locality or latency, yeah.Swyx [00:26:07]: Yeah.Akshat [00:26:07]: Like, you want your. They're running sandboxes and model. They want them to be right next to aSwyx [00:26:10]: Yeah, it's easy thenAkshat [00:26:11]: YeahSwyx [00:26:12]: To. That is important in all those things. and so, like, you've accidentally, I don't know if it's accident, but, like, you've built the perfect primitive for agents to express themselves. And then, like, it's almost very funny how every extra development just involves more file system, just involves more CPU.Akshat [00:26:30]: Yeah.Swyx [00:26:31]: Just like the things that you already have. I don't know much about, if there's any, like, networking usages that are interesting, but you've also done some good work on networking.Networking, Sidecars, Private IPv6, and SandboxesAkshat [00:26:40]: Yeah, that's exactly right. Like, we're just taking compute storage and networking and building stuff on that layer, for, again, the stuff people need.Swyx [00:26:49]: YeahAkshat [00:26:50]: We see a few interesting networking things coming up. one is people want networked sandboxes. so we haveSwyx [00:26:57]: For like a Docker cluster type thing.Akshat [00:26:59]: Yeah.Swyx [00:26:59]: Sorry, Docker Swarm. Oh, f**k. What is it called?Akshat [00:27:02]: Compose.Swyx [00:27:03]: Compose type thing.Akshat [00:27:04]: Yeah. So if you want Docker Compose, our sandboxes now support, this thing called sidecars. So you can. A sandbox is a pod of containers, and you can run multiple containers in, a sandbox. also useful because, going back to networking, people want a lot of control over, outbound networking from a sandbox.Swyx [00:27:23]: Yeah.Akshat [00:27:23]: Like, they might wanna run a middle proxy for, like, maybe logging stuff for RL or, controlling how egress can happen to a domain, injecting credentials. and yeah. So we've, we've had to build a lot of that stuff ourselves.Swyx [00:27:38]: Yeah.Akshat [00:27:39]: But then also sometimes people want, sandboxes spanning multiple nodes to talk to each other, which is an emerging thing we're seeing. We have support for that for a different reason, and yeah, we'll see if that becomes stable.Swyx [00:27:52]: Like, just an open socket. It's a. This is directly like mTLS.Akshat [00:27:56]: We do support that, which is you can, expose a tunnel inside a sandbox.Swyx [00:28:01]: Yeah.Akshat [00:28:01]: And then you can either expose it to public internet or it can be, you can add like a HTTP, auth layer above it. But we have this thing called I6PN, which we haven't talked about, which is this, like, overlay network using IPv6 addresses. so if Modal containers, within the same workspace, when this is enabled, can address each other using this private IPv6 address, and no one else can.Akshat [00:28:28]: So it's like private networking, for containers. We built it because we needed it as a primitive for our distributed training product. so we have this other feature, which is you can add a decorator to a function, and you get a cluster of GPUs. and they have RDMA networking. so you can run a distributed training job, that's truly serverless. and we did the overlay network for that. But then we've seen that people are using it for other reasons, and, I'm intrigued to yeah, what would people do with it.Swyx [00:28:59]: Build primitives and let people figure it out, right?Akshat [00:29:01]: Yeah, exactly.Swyx [00:29:02]: You put out a pretty interestingAkshat [00:29:03]: They're like, they read the docs webpage. Let me use thatSwyx [00:29:06]: YeahAkshat [00:29:06]: Something they never intended to work. This is literally not even in our docs page. People somehow found it, and they're using it.RDMA, Memory Movement, and Distributed TrainingSwyx [00:29:12]: Huh.Swyx [00:29:14]: The way you portrayed it with, like, RDMA versus TCP, like, very well laid out, but just the transfer speed change at scale for RL, like yeah, you have it, you have it built in. I'm sure someone found it. It's found it to be a lot more efficient before you made a thing out of it, right?Akshat [00:29:32]: Yeah. And not to split hairs, I guess the overlay network is the TCP overlay network.Akshat [00:29:39]: The reason we have that is you need that to do the key exchange for RDMA before you set up the RDMA network on top of that. but then people found the TCP part.Swyx [00:29:48]: Can I tell you, this is like a big aha moment for me becauseAkshat [00:29:51]: YeahSwyx [00:29:51]: So I review 2,200 submissions for the World's Fair.Akshat [00:29:56]: Yeah.Swyx [00:29:57]: And then I got this from John OsterhoutAkshat [00:29:58]: HuhSwyx [00:29:59]: Who I don't know if. Do John Osterhout by name?Akshat [00:30:01]: The name sounds familiar.Swyx [00:30:02]: He published a. He's a well-known professor, published a lot of interesting software design books, and this is the talk he chose to submit, is on RDMA at Inference. And I'm like, you wouldn't think that this guy, who is like operating systems guy, would care about RDMA.Akshat [00:30:20]: I, it makes sense to me because I,Swyx [00:30:24]: This is the cloud, right? YeahAkshat [00:30:25]: Like, the way you move around your KV cache and how efficiently you can do it, how efficiently you move, your weights from your training GPUs to your inference GPUs in RL is there's a lot of degrees of freedom, and it is a systems problemSwyx [00:30:41]: YeahAkshat [00:30:41]: Moving memory aroundSwyx [00:30:42]: YeahAkshat [00:30:43]: Scheduling.Swyx [00:30:44]: This shows you how primitive my understanding of networking stuff is.Swyx [00:30:46]: Is this like the domain of WireGuard as well?Akshat [00:30:50]: Not quite.Swyx [00:30:51]: It's adjacent?Swyx [00:30:53]: Explain everything.Akshat [00:30:54]: Sure.Swyx [00:30:56]: How do we move memory around GPUs?Akshat [00:30:58]: Well, so sorry. Yeah, that is memory. Sorry, I was talking more, and maybe I was talking like five minutes back, about the private IPv6, addressing that you've set up.Swyx [00:31:09]: Yeah.Akshat [00:31:09]: Is it like it's a VPN?Swyx [00:31:10]: Yeah, it is like a VPN, and yeah, WireGuard is, yeah, you're right. It is,Akshat [00:31:16]: Right. Yeah, you already moved on to new topicsSwyx [00:31:17]: A similarAkshat [00:31:18]: OkaySwyx [00:31:19]: In the same space, WireGuard is, encrypted and this is,Akshat [00:31:23]: And you don't need encryption.Swyx [00:31:23]: Yeah.Akshat [00:31:24]: Yeah.Swyx [00:31:24]: This is not encrypted. that's the main difference. This is TCP and we have eBPF programs that will reject or allow the TCP connection based on whether you're allowed to do it.Akshat [00:31:35]: Used to involve a full sidecar, but now you have eBPF in the Linux kernel.Swyx [00:31:39]: Yeah.Akshat [00:31:40]: Yeah. I don't know if this is a natural follow-on to the topic of like my skepticism on distributed training is that while, like, people spend a lot of money on, like, cables to hook up GPUs, and even that is not, like, fast enough, and that's the bottleneck, is your networking fast enough?Swyx [00:31:59]: Yeah. So I guess you're talking about fully distributed training like, Dialog or something which is like cross data centerAkshat [00:32:06]: That would be, yes.Swyx [00:32:07]: That's the extreme.Akshat [00:32:08]: Yeah.Swyx [00:32:08]: You're in the middle, and then other people would have like the Mellanox cables up in, like, their actual data center.Akshat [00:32:14]: When you run multi-node training on Modal, RDMA, I think Mellanox, is, or InfiniBand is like a, is all seen as RDMA. but it's a way to bypass the TCP networking stack and, transfer, stuff much faster, between one node, to the other. And we have I think like 3 terabit per second, internal networkingSwyx [00:32:40]: OkayAkshat [00:32:40]: Which is the standard that's needed.Swyx [00:32:42]: Okay. So I misunderstood whatAkshat [00:32:43]: 50Swyx [00:32:43]: What part of the stack you wereAkshat [00:32:44]: 50 gigs overSwyx [00:32:45]: YeahAkshat [00:32:45]: If you wentSwyx [00:32:45]: YeahAkshat [00:32:46]: RDMA.Swyx [00:32:46]: Okay.Swyx [00:32:48]: Yeah. I, very impressive work.Multi-Node Training, Post-Training, and Auto ResearchSwyx [00:32:52]: So effectively you're extending like the model philosophy to the training cluster, like, yeah.Akshat [00:32:59]: Yeah. And we're, we're not going for like large scale training runs. the thing that we've built multi-node training for is, we see a lot of, smaller scale post-training. like, people are post-training like medium sized fund models, so they can, get higher quality on inference. this is a perfect fit, for something like that.Swyx [00:33:21]: Yeah. That is my impression of how a lot of these labs explore branches in post-training and then eventually merge whatever they find in.Akshat [00:33:31]: Yeah. The other use case we've seen for multi-node training is even if you have a big cluster, your researchers are still doing small runsSwyx [00:33:38]: YesAkshat [00:33:39]: Having elasticity thereSwyx [00:33:40]: Right, sureAkshat [00:33:40]: Matters a lot more.Swyx [00:33:41]: Yeah. the, like, this is like the current limiting factor for auto research, which is like you need to give your model some GPUs in order for it to completely run.Akshat [00:33:51]: We have a blog post on auto resource and model is,Swyx [00:33:55]: YeahAkshat [00:33:56]: Yeah, like, turns out to be pretty good substrate for that.Swyx [00:33:59]: So my impression is auto research means many things, likeAkshat [00:34:01]: YeahSwyx [00:34:01]: Anything that Andrej coins. Right now it's still science fair, right? Like not like, I don't know how many people are doing this.Akshat [00:34:08]: We're having a golf.Swyx [00:34:08]: Yeah.Akshat [00:34:09]: I thought the same thing.Swyx [00:34:11]: Yeah, you would know.Akshat [00:34:12]: We, like, our internal both training and inference teams use this the general shape of this quite a bit. like we have this one internal repo called auto inference, which essentially we've automated our own forward-deployed engineering efforts using, this harness, which is, the agent will just spin up a sweep of different things. It'll even run like, NVIDIA inside profiler and it'll like tweak configs and it'll arrive the right thing. it'll change your GPUs both from H200 to B200, and works really well.Swyx [00:34:47]: Nice.Akshat [00:34:47]: So yeah.Swyx [00:34:48]: By the way, I enjoy that your forward-deployed engineering is so technical that you have to do these things.Swyx [00:34:52]: It's very different from forward-deployed engineering from other people.Akshat [00:34:54]: Yeah. For our forward-deployed engineering team is, essentially they're like applied inference researchers or applied training researchers.Swyx [00:35:02]: Someone told me like they have to be able to build, but they also have to be able to sell. do they have to sell or are they like they're good, they're just like post-sale type of thing?Akshat [00:35:09]: It does, being able to talk to a customer and engage effectively with themSwyx [00:35:13]: YeahAkshat [00:35:13]: Matters a lot.Swyx [00:35:14]: They want the same thing.Akshat [00:35:15]: Yeah.Swyx [00:35:15]: ?Akshat [00:35:15]: But it's it's not really a sales, thing. We pair them with-- We have solution architects as well that are more on the sales side.Swyx [00:35:23]: Okay. Let's spend a bit more time on auto research. This is a big focus for for this year. Where does this go? like, have people explored enough? Like, there's all these beautiful charts of like improve and then level off a bit and then you find the next thing. Is this one abstraction up from normal training? Is that how we think about it, or do you think about it differently? Like model level training versus high, like driven hyperparameter search.Auto Inference and Modal BenchAkshat [00:35:51]: Yeah, like,Swyx [00:35:51]: Someone, some people call it like neural architecture search or whatever, right? Like.Akshat [00:35:54]: Yeah, - So the stuff I've seen people do with it is nowhere on the architecture level. It's pretty much tweaking parameters, but it's it's a hyperparameter sweep that's guided by some model intuition, so it's like much more efficient than, whatever other, sweep you would have.Swyx [00:36:12]: Yeah, it's just, it's just a question of where you want to spend your compute?Akshat [00:36:16]: Right.Swyx [00:36:16]: ‘Cause yeah, you can just throw infinite amounts of money on this and somehow you'll bang out Shakespeare?Akshat [00:36:22]: Yeah, infinite monkey.Swyx [00:36:24]: Yeah, so like the very good for model. and I think it's also very important that agents can spin up other agents, can spin up their infrastructure. Like very good for you. how good is our LLMs at generating model code? Like the benefit of existing LLMs is that you are in the data.Akshat [00:36:42]: Yeah. They're, they're surprisingly good. I think like pre Cloud 4 they were not, and then now they're able to shot, stuff out of the box. But we're playing around with releasing like a Modal Bench for like the harderSwyx [00:36:55]: YeahAkshat [00:36:55]: Things, that the LLMs cannot do yet and maybeSwyx [00:36:59]: What's an example of that?Akshat [00:37:01]: I think the things that- Sometimes agents struggle with, without right guidance and a skill is, how to, use the rest of our observability. Like how to. Something is failing, like how do you look at the logs and then update the right thing? It's reasoning about that. But they're able to shot, likeSwyx [00:37:23]: Yeah. You can just add a skill to it?Compute Strategy and Capacity PlanningAkshat [00:37:26]: Yeah. So we have a Modal skill now that. Which is why we built this Modal Bench. It's to find things like that, so we can address them in our tool.Swyx [00:37:35]: Tune a skill. Yeah.Akshat [00:37:36]: Yeah.Swyx [00:37:36]: No. it's it's good. are you facing any shortages? like we talk a lot about GPU shortages, but also CPU, also memory.Swyx [00:37:44]: Yeah.Akshat [00:37:45]: We have had a lot of growth, which means that, there's - we've had to be much better aboutSwyx [00:37:53]: PlanningAkshat [00:37:54]: Proactive capacity planning.Swyx [00:37:55]: Yeah.Akshat [00:37:55]: So we have,Swyx [00:37:57]: Which by the way, like it's like a MBA's like dreamAkshat [00:38:00]: YesSwyx [00:38:00]: Is like just planning this stuff. I think last time you and I talked about something maybe about this.Akshat [00:38:03]: Yeah. we have a really competent team of people that we call, The role is called compute strategy. so yeah, if anyone listening here or wants to work on thatSwyx [00:38:13]: Compute strategy?Akshat [00:38:13]: Yeah.Swyx [00:38:14]: I think,Akshat [00:38:14]: I feel like,Swyx [00:38:15]: I think the normies call it FP&A or something.Akshat [00:38:18]: Well, it's more It's it's not FP&A. It's it's There's a lot of interesting financial questions of like what is the blend between one year and three-year reservations? how do we forecast our own capacity? how do we. especially since our capacity is very fungible across different GPU types and different regions, like you have to model a lot of it. and you also have to have an opinion on how the supply chain is gonna evolve, and then you have to like, take bets,Swyx [00:38:49]: YeahAkshat [00:38:49]: Based on that.Swyx [00:38:50]: Tokenomics.Akshat [00:38:50]: Yeah.Swyx [00:38:51]: This is like probably a not a real point, but, I was trying to think about like what other industries. I was trying to think about like, we cannot be first to like these kinds of problems.Akshat [00:38:59]: Yeah.Swyx [00:39:00]: And what other industries have had this? And I was like, airlines with fuel and like they have to hedge their fuel and like, I think for a long time Southwest because they made like a hero fuel bet, they like were like super low cost becauseAkshat [00:39:12]: OhSwyx [00:39:12]: Compared to everyone else.Akshat [00:39:14]: Yeah. I hadn't thought about that.Vibhu [00:39:16]: We're at a fun time too?Akshat [00:39:18]: Yeah. It's. A lot of the compute business in general, for us is also about being very good about capacity management. That is how you have great unit, economics. but also over time it's how you can unlock more value for customers. Like, one of the things we're building now is like a way for customers to get, If they don't care about latency, like get much cheaper pricing and they'll get results back in like next 24 hours or something, like a batch tier essentially.Batch Tiers and Latency-Insensitive WorkloadsSwyx [00:39:47]: Yeah.Akshat [00:39:47]: And those are levers we have because we control the whole stack and scheduling and whatnot to give people a sufficientSwyx [00:39:53]: Yeah. I feel like they're not as popular. Like those, like the Frontier Labs have all those APIs. They're not as popular as they should be.Akshat [00:40:00]: The demand that we see for something like that is not for LLMs. although sometimes people wanna run evals andSwyx [00:40:08]: OkayAkshat [00:40:08]: Synthetic data prep and there it makes sense.Swyx [00:40:10]: Okay.Akshat [00:40:11]: But it's from a lot of LLM companies, like people who are doing computational bio, like they have to run really big batch jobs and they don't care about when they get it back.Swyx [00:40:22]: Yeah. And like they have a reasonable. It's it's also like a cousin to the stopping problem of like, will this finish in time?Akshat [00:40:30]: Yeah. You can bound it.Swyx [00:40:33]: Yeah.Akshat [00:40:33]: Like you can give peopleSwyx [00:40:34]: YeahAkshat [00:40:34]: SLAs on it.Swyx [00:40:35]: Yeah. I think what's, what's interesting is like the next phase of model.Swyx [00:40:38]: Like what, do people expect from you, now that you're established and you're like well-known compute player among all these leading companies. You had an inference launch week, and we talked a little bit about the launches. like what else? Like what else should people know?What Modal Builds NextAkshat [00:40:55]: We are building primitives that make our users' lives much easier. So, I think for example, with LLM inference, thousands more companies are gonna post-train their own models and, deploy open source models for inference. so we're thinking a lot about what is the best product shape for that. And, that involves everything from our training gym to, then, endpoints that get frontier-level performance. again, but I haven't talked to anyone. It looks somewhat different on other verticals. Like, we're also seeing a lot of real-time, audio-video stuff in there, which is why like, we're working on things like regional routing, with fallbacks. So you can get GPUs that are as close to users as possible. so you get like low latency for video streaming and whatnot. And then on the agent side, it's,Akshat [00:41:52]: We're still working very closely with our customers because stuff is changing so fast in terms of what they need. And, I think beyond sandboxes and persistent file systems, there's a lot of other things people will need from this agent stack as they build production agents. So yeah, we're thinking about those other things that fit in there.Swyx [00:42:13]: I want to ask what the other things are.Akshat [00:42:15]: Yeah. I probably should share right now.Swyx [00:42:17]: I think-- I think, okay, so, I do think a lot about the principal components of cloud, and you do talk about compute storage networking.Akshat [00:42:25]: Yeah.Swyx [00:42:25]: Because so far for me, it's fine. so far for the. the first couple generations of cloud, it's fine. What's different, qualitatively different about agents that you need some new permission level? Like a lot of people, okay, and I'll just kinda spew tokens at you until it like hopefully sparks something.Akshat [00:42:43]: Yeah.Swyx [00:42:44]: Like the new level now is whatever Claude Code does, which is dangerously scope permissions or like allow list by command or like whatever, right? And sometimes they're like, “Well, okay, we have like this adaptive thinking mode where like, just trust me, bro. I will make the calls for you.” Is that it? like mediated permissions.Hard Guardrails vs. LLM-Mediated PermissionsVibhu [00:43:03]: Now you're looping it with a goal and letting it roll.Akshat [00:43:06]: Yeah, I'm, I'm skeptical of LLM media permission for stuff that is at the sandbox level because you do want hard boundaries.Swyx [00:43:16]: Yeah.Akshat [00:43:16]: Otherwise, someone can exfiltrate stuff.Swyx [00:43:20]: But likeAkshat [00:43:20]: YeahSwyx [00:43:20]: Maybe that's old school thinking. Maybe we're the dinosaurs.Swyx [00:43:23]: Maybe the AI OS or the LLM OS is really the kernel is a goddamn LLM.Swyx [00:43:30]: Like it makes you feel uncomfortable.Akshat [00:43:31]: Yeah, I'm, I'm toldSwyx [00:43:32]: But that's what trusting the LLM is. Like imagine a spherical cow perfect LLM.Akshat [00:43:36]: Right.Swyx [00:43:37]: That it.Akshat [00:43:39]: Maybe.Swyx [00:43:41]: I wanna test the boundaries, right?Akshat [00:43:42]: Yeah.Swyx [00:43:42]: Like, and I don't believe that, but I wanna see where I'm wrong ‘cause that's, that's the consensus.Akshat [00:43:49]: Yeah. I think you always need hard guardrails when you want, And you can pair those with softer guardrails, right? And that's gonna be a lot of mediated.Managed Agents and Specialized SandboxesSwyx [00:44:00]: There. I'll also get you a end with a couple of your commentary on like the ecosystem outside of Modal. Manage agents. Everyone has one. Gemini, OpenAI, Claude, very useful for you, but also like it is their way of starting to edge into your space.Akshat [00:44:17]: Yeah.Swyx [00:44:17]: What's going on?Akshat [00:44:19]: Yeah, we're, very excited to partner with Anthropic and some of the other foundation labs, will not name who we're also working with. the way we see it is the manage agent thing is a great place to start if you're starting out building an agent and, But then when you get to, building something more production grade, like you're a company that's like Ramp that's building their own, Ramp also runs their accounting agent on us, so their external-facing agent. You need a lot more control over, your compute primitive on things like, what sort - how do you persist different files that the agent has access to, and how do you snapshot and restore? How do you control the networking? maybe you want GPUs. When you get to that point, you kinda want, a specialized sandbox provider, that gives you those things, and that's the role that we are trying to play.Swyx [00:45:15]: YeahAkshat [00:45:16]: We don't really have an opinion on the harness, whether it runs - it's a cloud-managed agent, and you hook it up to Model Sandbox, or you run the harness in Model Sandbox. We'll see where people converge with that.Swyx [00:45:26]: Yeah. Do you any opinions on like the meta harnesses, or just another layer on top of these things?Akshat [00:45:31]: You mean like the OpenPipeSwyx [00:45:33]: OpenPipe is one. I think Vercel had one, which I can't remember the name of right now. Fredshot had one. and then, to me, most recently was Data Databricks that had Omnigen. All these are meta harness. Like it's kinda pseudo agent cloud type things.Akshat [00:45:50]: I personally have not played around with them.Swyx [00:45:53]: Yeah.Akshat [00:45:53]: Build agents with them.Swyx [00:45:54]: Everything's bullish Modal, as long as it consumes more infra.Akshat [00:45:57]: That's why we're focusing on the infra layer. It's somewhere where our, relative competence is and, also it's a hard problem to solve.Swyx [00:46:06]: Yeah. I will say like just generally reflecting on that, I don't know if - if there's other topics on Modal, but like just generally reflecting as an infra person, not as intense as you, but in that field, this has like been the most exciting time in infra. Like it was boring for a while, and you couldn't really get people excited about data infrastructure. Like Eric would get on Data Console, everyone just watched the video and like say, “Look at how many sandboxes I can spin up,” and no one gave a crap.Why Infrastructure Became Exciting AgainAkshat [00:46:39]: Yeah.Swyx [00:46:40]: And like now everyone gives a crap.Akshat [00:46:42]: That's true. It is a very exciting time, and I think a lot of that's driven by just the amount of scale all of this stuff needs.Swyx [00:46:50]: I think the, like a lot of your initiatives or a lot of your like product directions make sense in retrospect, which is like the best kind, but I wouldn't necessarily have thought about it myself, which.Akshat [00:47:00]: We need the predictions.Swyx [00:47:02]: I think there's a lot that you just don't even see, right? Like you have the batch, you have the voice, you have the multimodal, but what else?Akshat [00:47:10]: What else is coming up for usSwyx [00:47:11]: Yeah. Where do you see things going?Akshat [00:47:13]: Yeah. I, in generalBiotech, Robotics, and Non-LLM AI WorkloadsAkshat [00:47:15]: It's it's clear that there's there's a huge shift happening. I think one thing that's not as obvious to people because LLM inference gets talked about so much and is also we work a lot of companies that are, doing things like drug discovery and computational bio, like the Chai Discoveries of the world. Big things are probably gonna happen there. we work a lot of robotics companies that are putting robots in like active deployments and getting good results out of them.Swyx [00:47:45]: Is there Air Gap Modal? Is there a version that is like prem air gapped whatever?Akshat [00:47:50]: No. We,Swyx [00:47:51]: You should cloud only.Akshat [00:47:51]: Yeah.Swyx [00:47:52]: Yeah. Okay. But yeah, so what you're saying is like because you're focused on primitives and they're good primitives, you find use cases in all these kinds of things.Akshat [00:48:01]: Yeah.Swyx [00:48:01]: Probably diversifies you a little bit away from LMS all the time.Akshat [00:48:05]: Yeah, absolutely. We're, we'- our goal isn't to only serve the LLM inference market.Swyx [00:48:10]: There are a lot just on the website, the audio,Akshat [00:48:12]: Yeah. We said both onSwyx [00:48:14]: Computational bio images. Yeah, there's a lot here. There's QTA TTS, customizing. Oh, Chatterbox. there was customizing Whisper.Akshat [00:48:24]: Okay. Yeah.Swyx [00:48:25]: This screen reminds me of a fallen competitor, which Replicate.Model APIs vs. Differentiated AI ProductsSwyx [00:48:31]: What's your postmortem on what happened?Akshat [00:48:34]: This is one thing we've stayed away from is providing an API for models because I think providing model APIs is some of it ends up serving like a really hobbyist market, which is much less sticky.Swyx [00:48:50]: Yeah.Akshat [00:48:50]: And we've always wanted to build for companies that are building products and need more flexibility that's not just an API.Swyx [00:48:57]: Which you can build an API for a model and this is clearly what it is. But you - but what you're saying, you can wrap it into a more fully functioning back end that you run.Akshat [00:49:06]: Yeah. So all of our examples, it's not that spin up this model, here's an API token, use it. They're all code.Swyx [00:49:13]: Okay.Akshat [00:49:13]: And so the point is that this is just an example.Swyx [00:49:16]: Starter code.Akshat [00:49:17]: Yeah. But you can tweak it however you want.Swyx [00:49:20]: Yeah.Akshat [00:49:21]: And if you're like a company building a product, like, computational bio whatnot, yeah.Swyx [00:49:26]: I guess I'm trying to tease out for listenersAkshat [00:49:28]: YeahSwyx [00:49:28]: When does it stop becoming, oh, you're just an API call and you're just a wrapper on API to becoming what you call a product, right?Swyx [00:49:36]: Like, what is that layer? Like what-- Like, more lines of code, but like beyond that, what is the substance that people add that qualifies it to be something more?Akshat [00:49:46]: I think there's a little bit of like a selection effect of like a lot of the companies who do wanna get deeper into that level are probably building something that's more differentiated. And, I think, an example is like - with LLM inference, originally we, worked with companies that were building their own post-training frameworks or they were, - Ramp early in the day was training their own tokenizer and like swapping out the tokenizer in Llama and whatnot. I'm not saying that's, that successful, in that case. But a better example is like, let's say Suno. because Suno, does not use Modal for training.Swyx [00:50:26]: Mikey on the pod. Yeah.Akshat [00:50:27]: But they use Modal for all their inference and that's because they have like a custom-- They have completely custom model architecture and that means that they have to be at the code level and tweak things that are not, just an API.Swyx [00:50:41]: It's interesting as well, like we had, Ethan, most recently on the xAI Groq team make a prediction that like the next tier in video gen is not a better video model, it's a better model or agent that orchestrates video models.Video Agents and Production WorkflowsAkshat [00:50:56]: Oh, interesting.Vibhu [00:50:56]: Language model backbone that can use toolsAkshat [00:50:58]: RightVibhu [00:50:59]: And write code.Akshat [00:51:00]: Like, yes, I can make my second video or my second video from Groq, but I want my minute video.Akshat [00:51:06]: And I'm not going there through normal video gen.Swyx [00:51:10]: Yeah, that's interesting. I - So we have GPU sandboxes and recently have seen a few companies doing agents that do video manipulation or,Akshat [00:51:22]: Yeah. Give it FFmpeg and just do it.Swyx [00:51:23]: Run FFmpeg. But likeAkshat [00:51:25]: That's not enough.Swyx [00:51:25]: Yeah.Akshat [00:51:26]: You need to give it Adobe.Swyx [00:51:27]: Yeah, I hadn't put it together with like it would be a video production thing. in my mind these things were going more towards editingAkshat [00:51:36]: Yeah.Vibhu [00:51:36]: Well, shout out Mantis.Akshat [00:51:37]: I think about this a lot.Swyx [00:51:38]: .Akshat [00:51:41]: Yeah. Sorry.Vibhu [00:51:41]: Luma. Luma Agent is a version of this for video production, but it's a off.Swyx [00:51:46]: I was gonna get your quick takes, on some other stuff that happensGitpod/Ona, CI, and Runtime SandboxesSwyx [00:51:50]: In recent news and just-just see if you have anything interesting. Gitpod, very li

Največja prednost novega demografskega sklada bo varnejši in stabilnejši pokojninski sistem, zagotavlja finančni minister Andrej Šircelj. Po njegovih besedah je prav, da imajo koristi od njega predvsem tisti, ki so vanj vlagali, torej upokojenci. Ob tem je prepričan, da bo upravljanje sklada tudi bolj strokovno in preprosto, politika pa da nanj ne bo imela vpliva.Kako bo na javne finance vplival novi način upravljanja sklada, bo znano na dolgi rok, pa meni Fiskalni svet. Preostali poudarki oddaje: Zbranih dovolj podpisov za razpis referenduma o parlamentarni preiskavi. Vrh Nata v znamenju krepitve obrambe in manjše odvisnosti od ZDA. Rusko-kitajske vojaške vaje povzročile zaskrbljenost v tihomorski regiji.

Ob upadanju rodnosti in podaljševanju življenjske dobe se bo število upokojencev v Sloveniji do sredine stoletja zvišalo za 100 tisoč, je opozoril finančni minister Andrej Šircelj. Dodatna sredstva za pokojnine bo država skušala pridobiti z demografskim skladom. Druge teme: - Pobudniki referenduma o parlamentarni preiskavi zbrali dovolj podpisov. - Ob začetku vrha Nata zaveza o izdatnem vlaganju v zaščito proti dronom - Obsežen požar v okolici Slovenske Bistrice pod nadzorom.

Táto relácia vznikla vďaka našim podporovateľom. Pridajte sa k nim, prosím, teraz aj vy na:https://podpora.postoj.sk/podporte-najsilnejsie-konzervativne-medium?referral_source=youtube&referral_campaign=youtube&referral_content=ziarovsky&utm_source=youtube. Ďakujeme. Spolupracovník Postoja Andrej Žiarovský a redaktor Christian Heitmann diskutujú o geopolitických ohniskách dneška. Údery ukrajinských ozbrojených síl na ciele v hĺbke ruského územia sú už na dennom poriadku, v desiatkach ruských regiónov obmedzujú predaj benzínu a Rusko muselo začať benzín dokonca dovážať. Môže ekonomický tlak prinútiť Rusko ukončiť vojnu a akú rolu pri ukrajinských útokoch hrá geografia? Sú ruské rafinérie dvetisíc kilometrov za frontom ešte bezpečné, a čo sa môže ukázať byť ruskou Achillovou pätou? Diskutujeme aj o tom, čo bude na Ukrajine nasledovať po vojne. Aká rola ešte čaká generála Zalužného, a možno od neho čakať prezidentské ambície? A aký veľký problém budú vzťahy s Poľskom po tom, ako sa rozhádali dvaja prezidenti Zelenskyj a Nawrocki?

Dve pomembni slovenski družbi ostajata brez vodstva. Po včerajšnjem slovesu Andreja Ribiča z vrha Darsa je odhod s čela uprave Slovenskega državnega holdinga napovedal še Žiga Debeljak. Usoda holdinga je bila v vsakem primeru negotova zaradi načrtov nove vlade. V oddaji tudi: - Radiologi po dogovoru z ministrom vendarle ostajajo v mariborskem kliničnem centru - Prejšnji mesec našteli najmanj brezposelnih od leta 1990. - Iran pred obsežnimi pogrebnimi slovesnostmi za ubitim ajatolo Hamenejem.

Gozdovi v mestu niso zgolj prostor, kjer se rekreiramo, po napornem dnevu v službi sprostimo, ali v vročih poletnih mesecih v senci dreves malce ohladimo. Te koščke narave lahko razumemo kot podaljšek našega življenjskega prostora – avno zaradi vsega, kar nam vsak dan nudijo,bi lahko rekli, da z njimi pravzaprav sobivamo. V takšnih gozdovih, kjer je človek vsakodnevno prisoten, se raziskovalcem porajajo drugačna vprašanja kot sicer: kako odmrla drevesa, ki so bivališča raznim žuželkam, pticam, ter glivam, ohraniti in hkrati zagotoviti varnost obiskovalcev? Kako človek vpliva na razširjanje invazivnih rastlin? Kje in kako urediti pešpoti, da bo človekov vpliv – tudi na zavarovana območja – čim manjši? In ali je lahko pritisk na gozd, zaradi skoraj množičnega obiska človeka, prevelik? Tokrat v Podobah znanja gostimo dr. Andreja Verliča, gozdarja in naravovarstvenika, ki trenutno deluje na Oddelku za gozdno ekologijo Gozdarskega inštituta Slovenije, v preteklosti pa je bil med drugim vodja upravljanja Krajinskega parka Tivoli, Rožnik in Šišenski hrib. Foto: Andrej Verlič (Klara Jurečič)

Spolupracovník Postoja Andrej Žiarovský a redaktor Lukáš Krivošík sa rozprávajú v tejto časti nášho vojensko-historického podcastu o bitke pri rieke Little Bighorn, ktorá sa odohrala v júni 1876. Americkú 7. kavalériu pod vedením podplukovníka Georga Armstronga Custera tu porazili indiánske kmene pod vedením Sediaceho býka (Sitting Bull) a Splašeného koňa (Crazy Horse). Čo k bitke viedlo? A ako mohla americká armáda utrpieť takú porážku? Vo videu sa venujeme aj osudu indiánskych kmeňov. Niektoré s bielymi osadníkmi a vládou vo Washingtone spolupracovali, iné sa rozhodli klásť odboj. Ktorá stratégia sa s odstupom času ukazuje ako lepšia?

Hey, it's Alex. Next month is my 40th b-day, and honestly, my wish for that month is to have a week like this week. A very chill, almost nothing announced week.This week started strong, with Sakana announcing FUGU (AI router) that can beat Fable (which we didn't get back yet), and then... quiet. The most important thing in AI this week from a release standpoint is that GLM 5.2 from Z.AI is having it's DeepSeek moment! Tons of new love for this model since last week! (+ we have the fastest GLM 5.2 deployment in the world with CW inference!) The rest we can quickly count on one hand, Anthropic added Claude to Slack (which made folks hate Andrej Karpathy), OpenAI announced their own inference chip, GPT 5.6 will be delayed and the US Gov will decide who gets it (yes really) and Sean Grove joined us to talk about Linzumi and his vision for running 10,000 agent hours per person per day. Oh and next week, is a special AI Engineer live stream from World's Fair! Don't miss itLet's get into it! Subscribe to never miss a beat! GLM 5.2 is having its DeepSeek moment (HF, CW Inference)We covered GLM 5.2 last week, but this week was when the rest verdict came in! We've never seen a better MIT licenced AI model! GLM 5.2 is scoring top scores on agentic benchmarks (Arena.ai), Design benchmarks, Legal tasks and full on software engineering tasks. The jump in generations from prevoius GLM is also massive and notable, as the lab is working on creating the next version of GLM (per the CEO's reply to Elon on X).Peter from Arena pulled up the Agent Arena numbers and they align with the vibe. GLM 5.2 sits above 5.1 but below Opus and Fable, which feels about right. Where it gets wild is Web Dev Arena: second place, right after Fable. Peter's take was that GLM has really good defaults. If you just say “give me a webpage” it gives you something nice. GPT models, by contrast, start off looking bad and need more steering.Last week, I asked my agents with GLM 5.2 to create a custom ThursdAI.news page for itself and it did a marvelous job! Look at that beautiful font, the castle it made... this is all just delignful. We also played Hassan's blind test on the show. It's a website that @nutlope built that lets you try and guess which webpage was built by which model. Nisten nailed it immediately by spotting Opus's circular buttons. Wolfram guessed right too. I got one wrong. The point isn't that GLM beats Opus, it's that you genuinely can't always tell which one costs 22 cents and which one costs 3 cents.Wolfram did flag that GLM is not good in German. First response already had mistakes. So if you're building for a non-English market, keep that in mind. It's a workhorse model, not a conversationalist. His approach: use GPT 5.5 for planning and discussion, GLM for the actual work, then GPT reviews. This weeks Buzz is all about GLM 5.2! First, we may have not been the fastest, but I'm glad to announce that we're the fastest provider to host GLM 5.2 on OpenRouter (at least at the time of writing this)! We're also not to shabby on the Artificial Analysis checks, clocking at #4 among the providers they tested for speed, TTFT and costAlso, Wolfram ran his WolfBench tests on GLM 5.2 and it's the best open model he's ever tested! In this new 3d view, wolfbench also shows the number of tokens it took for this test to run, and you can see that GLM 5.2 is fairly conservative with it's thinking budgets! Unsloth's 1-bit GLM 5.2 runs on a Mac Studio (X, HF)Shout out to Daniel Han and the Unsloth team, who took this 744B beast and quantized it down to a roughly 200GB GGUF that fits on a Mac Studio with 256GB of RAM. One bit still makes me laugh out loud. How does that even work. Nisten clarified it's a mixed quant, a true 1-bit would be under 100GB, but still.The wild part is the scores hold up. The 1-bit is within a point of GPT 5.5 on Frontier SWE, hits 62% on SWE-bench Pro, and 81% on Terminal-Bench. For a 1-bit quant that's incredible! AI's second-order effects: Apple is raising pricesThis one is AI news even though it doesn't look like it. Apple just raised prices across the board, base versions up around 20%, citing memory shortages. Same reason your RAM and SSDs cost two to three times what they did a year ago.We are so capacity constrained that memory is having its moment. Data center contracts are getting booked 18 months out, and here's the twist Nisten flagged: even open models you can run at home increase demand, because now a business says “great, we'll buy a rack of B200s and run it ourselves.” Sam Altman once said people saying “thank you” to ChatGPT costs them millions in generated “you're welcome” replies. Multiply that by a billion users. Even Intel is flying right now because anyone who can make a chip is winning.Is it worth it? I think yes. I love living in the era where Fable drops and we all get a taste of the future. But also I must admit this sucks and I hope that we'll unlock performance gains with the extra power all this AI is bringing to the world. But ask me again once the new iPhone hits and it's $300 more costly than the last one

Nato in Ucraina nel 1936, Andrej Chikatilo è considerato uno dei serial killer più feroci e inquietanti del Novecento. Cresciuto tra fame, guerra e repressione nell'Unione Sovietica staliniana, sviluppò fin da giovane profonde ossessioni sessuali e una personalità segnata da frustrazione, isolamento e violenza repressa. Dietro l'apparenza innocua da insegnante, marito e padre, si nascondeva però un assassino destinato a terrorizzare l'URSS per oltre dieci anni. Tra gli anni Settanta e Ottanta, Chikatilo uccise decine di donne e bambini, attirando le sue vittime nelle stazioni ferroviarie e nelle campagne isolate per poi massacrarle con una brutalità che sconvolse persino gli investigatori sovietici. Solo nel 1990, dopo una gigantesca caccia all'uomo e grazie a nuove tecniche investigative, Chikatilo venne finalmente arrestato. Il suo processo, trasmesso e discusso in tutta l'Unione Sovietica, mostrò al mondo il volto disturbante di un uomo che sembrava incarnare le paure più profonde di un intero sistema. Ma chi era davvero Andrej Chikatilo? E quanto il fallimento delle istituzioni sovietiche contribuì a trasformarlo nel “Mostro di Rostov”?Proviamo a scoprirlo insieme a Jim Bevilacqua: esperto di cronaca nera ed autore del podcast “La Fiamma Oscura”. Iscriviti al gruppo Telegram per interagire con noi e per non perderti nessuna delle novità in anteprima e degli approfondimenti sulle puntate: https://t.me/LucePodcast Se vuoi ascoltarci senza filtri e sostenere il nostro lavoro, da oggi è possibile abbonarsi al nostro canale Patreon e accedere a contenuti bonus esclusivi tramite questo link: patreon.com/LucePodcast

Andrej ima od vseh jedi najraje kašo, kot jo kuha babica iz Horjula. Pripoveduje: Majda Potokar. Napisala: Kristina Brenk. Posneto v studiih Radiotelevizije Ljubljana 1987.

For episode 743 of the BlockHash Podcast, host Brandon Zemp is joined by Andrej Bencic, CEO and Co-Founder of Tenderly, the simulation company for onchain institutions. An engineer by background, he co-founded Tenderly in 2018 and has spent the last eight years building it into the operational layer beneath crypto's most sophisticated protocols, enabling engineering, finance, and risk teams to model every onchain action against the live system before any capital or customer is exposed.  

Meyer, Clemens www.deutschlandfunkkultur.de, Lesart

Meyer, Clemens www.deutschlandfunkkultur.de, Lesart

Glenn Kinley from WCIA 3 Sports joins the show to discuss the IHSA Track & Field State Championships and what Andrej Stojakovic's return means for Illinois basketball. How real is the hype? We also give our Cardinals vs. Cubs series predictions and our Friday Toasts to wrap up the week! Follow The Drive on X, Instagram, and Facebook!

Brian Teague from Chicago State of Mind Sports talks Illini basketball, Andrej Stojakovic, football schedule; Brian Walton previews Cards vs Cubs Series. 

Majstrovský titul, enormný tlak a sezóna, v ktorej sa z očakávaní stala realita. Hosťom podcastu Góly z bufetu bol tréner Andrej Kmeč, ktorý v uplynulom ročníku doviedol Nitru k zisku majstrovského titulu. Moderátori Marek Marušiak a Tomáš Prokop s ním rozobrali cestu za titulom, psychológiu favorita aj zákulisie finálovej série, ktorá sa zapísala do dejín. Rozhovor vznikol ešte pred koncom angažmánu Andreja Kmeča v Nitre.Nitrania vstupovali do sezóny s nálepkou hlavného kandidáta na titul, čo so sebou prinieslo úplne iný druh tlaku. Stabilizovaný káder, vysoké ambície a očakávania okolia si vyžadovali dôslednú prácu nielen po hernej, ale najmä po mentálnej stránke. Andrej Kmeč v rozhovore vysvetľuje, že práve zvládanie tlaku bolo jednou z najväčších výziev ročníka, keďže súperi sa na Nitru pripravovali ako na tím, ktorý treba zdolať za každú cenu.Vyraďovacia časť sezóny tento fakt len potvrdila. Náročná séria s Michalovcami preverila vnútornú odolnosť mužstva. Finále proti Slovanu Bratislava zas prinieslo dramatický hokej plný zlomových momentov. Rozhodnutie v predĺžení siedmeho zápasu bolo vyústením dlhodobého procesu, v ktorom hrala dôležitú úlohu disciplína a trpezlivosť.„Play-off je čarovné. Človek nikdy nevie, aký príbeh napíše. Pokiaľ nie je odpískaný štvrtý víťazný zápas, tak stále žije a môže sa stať čokoľvek,“ zdôraznil slovenský tréner, pričom za kľúč k úspechu označil vyrovnanosť celej nitrianskej zostavy. Témou preto bola aj práca s brankármi. Rotácia nebola otázkou náhody, ale systémového prístupu, ktorým si Nitra udržiavala vysokú úroveň konkurencie počas celej sezóny.Andrej Kmeč sa dotkol aj mediálneho tlaku a emócií, ktoré sprevádzali najmä finálové zápasy, keď sú reakcie po prehrách často ostrejšie a menej racionálne. Aj to však podľa neho patrí k realite boja o titul. „Niekedy tam je frustrácia. Človek nie je úplne tak otvorený ako napríklad teraz, že som úplne v pokoji,“ vysvetlil úprimne.Treba dodať, že v čase nahrávania podcastu ešte nebolo známe, že po vypršaní zmluvy sa cesty Andreja Kmeča a HK Nitra rozídu. Pútavý rozhovor v podcaste Góly z bufetu sa tak nesie výlučne v duchu hodnotenia majstrovskej sezóny a procesu, ktorý viedol k zisku druhého titulu v rade v rodnom meste trénera.Najnovšia epizóda podcastu Góly z bufetu na ŠPORT.sk tak ponúka autentický pohľad do zákulisia šampióna, odhaľuje detaily trénerovej práce a pripomína, aké tenké sú hranice medzi očakávaním, tlakom a úspechom na najvyššej úrovni slovenského hokeja.

CBS Sports college basketball writer Isaac Trotter joins Illini Inquirer's Jeremy Werner to discuss Andrej Stojakovic withdrawing from the NBA Draft and officially returning to Illinois for the 2026-27 season. Trotter breaks down why Stojakovic made the decision, what it means for Illinois and how Stojakovic can get better in Year Two with the Illini. Trotter then breaks down the Big Ten landscape following the stay-or-go NBA Draft decisions. SUPPORT OUR SPONSORS Columbia Street Roastery: Head to ⁠⁠CSRcoffee.com⁠ and use code IlliniAllTheWay to get 10 percent off your first order and get free shipping on orders of over $45. Cheers Health: For a limited time our listeners are getting 20% off their entire order by using code ILLINI at CheersHealth.com. Just head to CheersHealth.com and use code ILLINI for 20% Off.  Follow the Illini Inquirer Podcast on: Apple: https://apple.co/3oMt0NP Spotify: https://spoti.fi/2Xan2L8  Other: https://bit.ly/36gn7Ct   Go VIP for just 50% OFF: https://tinyurl.com/2fkhmjdz 

Illini Headlines - Andrej Stojakovic is officially BACK with the Illini after withdrawing from the NBA Draft, giving Brad Underwood and Illinois another huge piece for a potential national title contender! Cubs vs Cardinals rivalry renewed with a big weekend series at Busch Stadium. Pierce the Intern will have live updates for us. MLB owners propose the league's first salary cap since the 1994-95 strike — which teams and players would be impacted the most? Plus, Evan Stone ranks the Illini football schedule from least exciting to most exciting. Follow The Drive on Twitter, Instagram, and Facebook.

Tristan Thomas joins the show to discuss Andrej Stojakovic officially returning to Illinois, previews of the IHSA State Track Meet, and his latest showdown with Evan Stone in a Champaign-Urbana celebrity basketball game. Can the San Antonio Spurs force a Game 7 against the Oklahoma City Thunder in the Western Conference Finals? Plus, Evan somehow finds himself on the wrong side of the law!  Follow The Drive on X, Instagram, and Facebook!      

They are officially running it back. LIKE AND SUBSCRIBE!Follow our Social Media Accounts:MERCH: https://illinibasketballpodcast-shop.fourthwall.com/- http://www.X.com/PodcastIllini- https://podcasters.spotify.com/pod/show/illini-basketball-podcast- http://www.facebook.com/illinibasketballpodcast- https://www.youtube.com/@illinibasketballpodcast- http://www.X.com/EthanCarterSW- http://www.X.com/tbramleyibp- https://www.instagram.com/illinibasketballpodcast/?igshid=Zjc2ZTc4Nzk%3D

The guys debate whether the New York Knicks would rather face the Oklahoma City Thunder or San Antonio Spurs in the NBA Finals after OKC grabbed a 3-2 series lead. Plus, Evan  will go head-to-head with WCIA 3's Tristan Thomas on the hardwood, more discussion on Andrej Stojaković's looming decision, Tuesday Draft results for the Best 90's Comedy Movies, and Pierce the Intern steals the spotlight! Follow The Drive on Twitter, Instagram, and Facebook.

Illinois basketball waits for the decision from Andrej Stojaković as he's expected to announce his future at 7 p.m. (he's coming back). Illini Headlines include football TV game times being announced and Quentin Coleman earning a spot on the U.S. U18 National Team. Plus, the St. Louis Cardinals fall to the Milwaukee Brewers while the Chicago Cubs drop their 10th straight game. Follow The Drive on X, Instagram, and Facebook.

Hour 2 featured Kyle Tausk from X, Instagram, and Facebook!

Rodáka z Piešťan videli rodičia ako recepčného v tamojších kúpeľoch. Andrej Zaťko sa však stal bankárom a úspech mu umožnil dlhodobo podporovať slovenských umelcov. Tvrdí, že úspešní ľudia by svoje pomáhanie nemali skrývať. Vypočujte si podcast Bod k dobru, v ktorom prinášame príbehy filantropov a filantropiek a búrame tabu spojené so slovom filantropia. Pred rokmi dostal Andrej Zaťko radu, aby s podporou umenia začal tým, že si namiesto dekorácie z obchodného reťazca kúpi obraz slovenského autora. „Robím to dodnes. Snažím sa každý rok vo všetkých relevantných galériách v Bratislave kúpiť aspoň jeden obraz, čo je akoby moja forma podpory,“ hovorí. Za necelé dve desaťročia vytvoril súkromnú zbierku súčasného vizuálneho umenia, ktorú vystavuje aj na verejnosti, aktuálne v prestížnom Ludwig Museum v Budapešti. V podcaste sa dozviete: prečo si Andrej Zaťko prvú výplatu takmer celú odložil, čo robí s väčšinou kúpených obrazov, ako sa pozerá na filantropiu kontroverzných firiem a ľudí z biznisu, čo ho sklamalo pri vstupnom zadarmo do národnej galérie, čím chce k pomáhaniu motivovať svoje deti. Kto je Andrej Zaťko? Vyštudoval hospodársku informatiku a už takmer 30 rokov pracuje vo finančnom sektore. Riadil privátne bankovníctvo J&T banky, neskôr bol CEO Poštovej banky a 365 banky, ktorú aj spoluvlastnil. Patrí medzi najvýznamnejších zberateľov umenia na Slovensku. Výber z jeho súkromnej zbierky Art Fond Collection, ktorá obsahuje vyše 500 diel slovenských umelcov a umelkýň od 60. rokov až po súčasnosť, sa aktuálne vystavuje v Ludwig Museum v Budapešti. Bod k dobru V podcaste Bod k dobru už tri roky predstavujeme inšpiratívne osobnosti z biznisu, kultúry a športu. Príbehy pomáhania sú pestré, od spontánnych činov až po premyslené projekty s veľkým spoločenským dopadom.Bod k dobru vám prináša Nadácia Pontis, ktorá sa dlhodobo venuje rozvíjaniu filantropie, sociálnych inovácií a zodpovedného podnikania.Podcast vzniká v spolupráci s portálom Aktuality.sk a moderuje ho Martin Staňo.

O pretanjenih kriminalcih, iznajdljivih slikarjih, naivnih šarlatanih in robinhoodovskih junakih ter celo o političnih in vojnih akterjih, ki so risali in tiskali lažne bankovce in kovali lažne kovanceDenar je od nekdaj sprepleten in povezan z oblastjo in večino človeške zgodovine so bili kralji, cesarji, kasneje pa države s svojimi centralnimi bankami tisti, ki so strogo nadzorovali tiskanje ali kovanje denarja, nadzor nad izdelavo lastne valute pa še danes velja za enega temeljev državne suverenosti. In vendar so se skozi zgodovino pojavljali posamezniki ali skupine, ki so - bodisi iz stike ali uporniškosti bodisi iz pohlepa - to pravico vzeli tudi v svoje roke. Prav o njih bomo govorili v tokratnih Sledeh časa. Skozi zgodovino ponarejanja denarja nas bodo pospremili dr. Andrej Šemrov iz Numizmatičnega kabineta Narodnega muzeja Slovenija ter Urška Purg in Matko Mioč iz Muzeja in galerije MUZA, kustosa tamkajšnje razstave Denar in kriminal: Tekma brez konca. Oddajo je pripravila Alja Zore. foto: ročno narisan ponarejen bankovec za 100 dinarjev, Kraljevina Jugoslavija, 1929, s pretiskom LAŽNA v cirilici, osebni arhiv Marka Mihajlovića, objavljeno z dovoljenjem Muzeja in galerije MUZA

The Water Colors team gathers around the table with special guest, Andrej Spec, to talk about aquarium plants. Andrej shares his aquarium journey and how he quickly went from hobbyist to passionate collector. He talks about his successes, some of his methods to that success, and so much more. Thank you so much for your kindness and inspiration, Andrej and we look forward to seeing you again. Looking for more content? Become a YouTube member for exclusive access to behind the scenes livestreams! https://www.youtube.com/@watercolorsaquariumgallery Enjoying the show? Support the gallery by shopping aquarium plants, merch, equipment, and more! https://watercolorsaquariumgallery.com/ Join the discussion on the Water Colors Aquarium Gallery Podcast Listeners Facebook group! https://www.facebook.com/groups/788428861825086/ Join our growing community on Discord! https://go.watercolors.shop/discord Sources mentioned in this episode: Andrej Spec Missouri Aquarium Society – https://missouriaquariumsociety.com/ Christel Kasselmann – https://www.instagram.com/christel.kasselmann/ Aquatic Gardeners Association – https://www.aquatic-gardeners.org/ Dennis Wong – www.2hraquarist.com Plants from Test Tubes: An Introduction to Micropropagation by Lydiane Kyte (with co-authors like John Kleyn, Holly Scoggins, and Mark Bridgen in later editions) Species mentioned in this episode: Crinum asiaticum – Crinum “Centorum” Cuphea anagalloidea Ludwigia inclinata var. verticillata – Ludwigia “Tornado” Rotala ramosior ‘Florida’ Rotala ramosior ‘Sunset’ Lobelia cardinalis – Cardinal Plant Lobelia kalmii – Kalm’s Lobelia Lobelia siphilitica – Blue Cardinal Plant Lobelia x Speciosa Oomycetes Xylaria Vallisneria spiralis var. denserrulata – Lake Tanganyika Vallisneria Cryptocoryne wendtii (‘Mi Oya’ and green) Barclaya motleyi Barclaya wellyi Nymphae – Water Lily Nymphaea sp. Peru Puerto Maldonado Nymphaea aff. dimorpha (minuta) Cryptocoryne keei Cryptocoryne nurii Cryptocoryne striolata Cryptocoryne spiralis Fenestratarum Bucephalandra ultramafica Bucephalandra kishii Cryptocoryne striolata – “Red Tiger” Crypt Osteogaster hephaestus – Fireball Cory Corydoras sp. CW113 Lagenandra ovata – “Mayan Sword” Pseudogastromyzon fasciatus “Zhejiang” Pseudogastromyzon lepidogaster

Tranzice v Maďarsku bude trvat dlouho, v jednokomorovém systému vždy hrozí monopolizace mociHostem Barbory Kroužkové v podcastu Tekutá společnost byl hungarolog Andrej Tóth. Jaké se odkrývají detaily fungování Orbánova režimu? Jakou podobu s maďarskými kroky vůči médiím vidí v plánech české vlády? Kam se posunou česko-maďarské vztahy a proč se nebojí používat termín Felvidék? ——Pořad Tekutá společnost můžete sledovat každý čtvrtek na:• Respektu: https://www.respekt.cz/tekuta-spolecnost a v mobilní aplikaci Respekt• YouTube: https://www.youtube.com/respekt• Spotify: https://open.spotify.com/show/4ozoQdrXPXo7WYKIIh9rMD • Apple Podcastech: https://podcasts.apple.com/us/podcast/tekuta-spolecnost/id1888505574

Carson Gourdie returns to break down the Illinois Fighting Illini men's basketball offseason, why Andrej Stojaković is expected to return, and expectations for Illinois Fighting Illini football this season. Plus, Kurtis' Curveballs make their return with plenty of random sports thoughts and facts about smells.

Brad Sturdy from Illini Guys joins the show to break down the latest with Illinois basketball's offseason, the buzz around Andrej Stojaković, and Brad Underwood's new contract extension, plus updates on the Illinois football roster. We also recap the Chicago White Sox taking down the Chicago Cubs, while the St. Louis Cardinals pick up another win.        

Wergelandforelesningen 2026Andrej Kurkov er en av Ukrainas fremste kulturelle og litterære ambassadører. Med titler som Døden og pingvinen og Grå bier har han fått lesere verden over. Men da Russlands fullskalainvasjon av Ukraina startet, klarte han ikke å skrive fiksjon. I stedet har han reist på kryss og tvers for å fortelle om krigens realitet og brutalitet, og skrevet essays og dagbok fra livet med krig. I 2022 kom Dagbok frå ein invasjon (til norsk ved Lasse Takle). Flere av tekstene her har tidligere vært trykket i Dag og tid, hvor Kurkov har vært fast spaltist siden krigen brøt ut.Kurkov vokste opp i Sovjet-tiden, og oppdaget forfattere som George Orwell og Aleksandr Solzjenitsyn gjennom ulovlige kopier. Han har fått erfare hva manglende ytringsfrihet innebærer, så vel som betydningen av språk og kultur i å bygge opp en nasjonal identitet. Selv skriver han på russisk, ukrainsk og engelsk. Ukraina som selvstendig nasjon, med egen kultur og eget språk er noe av det Putin vil til livs.Hvordan misbruker Putin historie og kultur i sitt narrativ om invasjonskrigen? Og hvordan har krigen ironisk nok styrket både Ukrainas og resten av verdens oppfatning av ukrainsk språk og kultur?Wergeland-forelesningen er en årlig begivenhet. Hvert år inviterer Stiftelsen Litteraturhuset internasjonale stemmer som gjennom sitt virke og sin litteratur viderefører Henrik Wergelands ånd. Hosted on Acast. See acast.com/privacy for more information.

The University of Illinois and Duke University officially announced a marquee home-and-home basketball series, giving Illini fans a massive non-conference matchup to look forward to over the next two seasons. The guys break down what the series means nationally for Illinois and how it could impact the program's growing reputation under Brad Underwood. Plus, the latest from the NBA Draft Combine on Andrej Stojaković and his future as scouts continue to evaluate his stock heading into the draft process. Chris Kwiecinski from FOX 32 Chicago joins the show to react to the Chicago Bears schedule release and break down the biggest matchups, toughest stretches, and prime-time storylines for the 2026 season. Follow The Drive on X, Instagram, and Facebook!

Andrej Stojakovic's NBA Draft stock continues to climb, and Brian Teague from Chicago State of Mind Sports joins the show to break down why scouts are becoming increasingly intrigued by his upside and overall fit at the next level. Are the chances of him leaving Illinois on the rise? The guys also dive into the latest outlook for the Illinois men's basketball schedule, highlighting key matchups, difficult stretches, and opportunities for the Illini heading into the season. Follow The Drive on X, Instagram, and Facebook!

Andrej Persolja built a product with a 4.9-star rating and real clinical proof it worked. He launched in one market and it took off. He then launched to the US and nothing happened. No customers. No conversions.Four years and tens of thousands in ad spend with almost nothing to show for it. He eventually figured out why. Then he ran a structured test. One thing changed. Revenue went up 200%. His cost to acquire a customer dropped by more than half. He left the startup and built a consulting practice around what he learned.He then went on to take another company from $200K to $2M in annual revenue in five months. And in this episode we cover the startup growth playbook, From research to market positioning to client acquisition.This conversation covers:what he learned about why products stop sellinghow he identifies growth levers most teams misswhat customer research actually looks like when it worksand what he did when things were at their worstEnjoy!

Illinois is well represented at the NBA Draft Combine as Keaton Wagler, Kylan Boswell, and Andrej Stojakovic all look to boost their draft stock in front of scouts and executives. The guys also react to top in-state recruit Cam Warner committing to Oregon over Illinois and what it means for the Illini moving forward. Plus, Evan previews the upcoming World Cup and discusses the biggest storylines and teams to watch heading into the tournament. Follow The Drive on Twitter, Instagram, and Facebook!

Mike Cagley and Brad Sturdy talk the NBA Draft: Keaton Wagler Andrej Stojakovic Kylan Boswell Whether you live in Champaign or Chicago, halfway across the country or halfway across the world, IlliniGuys.com keeps you in the know! Share this show on your social media & please give us a 5-star rating if you enjoyed the episode! We ask YOU to help the IlliniGuys Sports Spectacular & I on the Illini grow on social media by following us on all our social media and engaging with the content posted. Every like, love, comment & share help the IlliniGuys Sports Spectacular reach more people and establish our position as the leader in entertaining, fast-paced, non-political, all sports & guy-stuff programming. Thanks for listening! Don't miss our college sports focused podcasts: IlliniGuys Sports Spectacular I on the Illini Mike Cagley's Heat Checks & Hail Marys Follow the IlliniGuys Subscribe at IlliniGuys.com for just $99 annually Subscribe to our YouTube Channel: https://youtube.com/@illiniguys4844?si=oWtcpGPkAIYSBceM Follow us on X: Brad: https://x.com/Sturdy32 Mike: https://x.com/MikeCagley Larry: https://x.com/LarrySmithTV IlliniGuys: https://x.com/Illini_Guys Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Hour 2 featured Carson Gourdie breaking down Bret Bielema's latest press conference and why Illinois freshmen could see significant playing time this season. The guys also continued the discussion surrounding Andrej Stojaković and what his NBA Draft decision means moving forward. Don't worry - he's most likely coming back. Later, the crew made their picks for the Kentucky Derby before wrapping up the week with another edition of Friday Toasts! Follow The Drive on Twitter, Instagram, and Facebook!

Arhitekt Andrej Bernik vsakič znova odkriva Pariz, ki je njegov dom že dve desetletji. Skupaj s partnerjema, eden od njiju je tudi njegov življenjski sopotnik, vodi arhitekturni studio Fieldwork. Posveča se načrtovanju mest prihodnosti, s poudarkom na pomenu zelenja, ki bo ključno za ohlajanje poleti peklensko pregrete prestolnice. Tudi v Sloveniji je pred leti odmeval njihov projekt Terciarni gozd, pariško parkirišče so ozeleneli in spremenili v javni prostor, hibrid med parkom in trgom. Komentira tudi popkulturno prikazovanje Pariza kot romantičnega mesta počasnih užitkov in poudarja, da se croissant (rogljiček) poje takoj, svež, kar s prtička.

Hour 1 focuses on the latest developments surrounding Andrej Stojakovic declaring for the NBA Draft and what it means for fans moving forward. The show also covers key Illini headlines and wraps up with an update on the Cubs, Cardinals, and the latest movement in the NL Central.

Mike Cagley talks with Matt Stevens & Steve Sturm about Illini football & the NFL Draft. Then, Mike talks to Brad Sturdy & Ked Prince about Illini basketball recruiting and more! Whether you live in Champaign or Chicago, halfway across the country or halfway across the world, IlliniGuys.com keeps you in the know! Share this show on your social media & please give us a 5-star rating if you enjoyed the episode! We ask YOU to help the IlliniGuys Sports Spectacular & I on the Illini grow on social media by following us on all our social media and engaging with the content posted. Every like, love, comment & share help the IlliniGuys Sports Spectacular reach more people and establish our position as the leader in entertaining, fast-paced, non-political, all sports & guy-stuff programming. Thanks for listening!   Don't miss our college sports focused podcasts: IlliniGuys Sports Spectacular I on the Illini Mike Cagley's Heat Checks & Hail Marys Follow the IlliniGuys Subscribe at IlliniGuys.com for just $99 annually Subscribe to our YouTube Channel: https://youtube.com/@illiniguys4844?si=oWtcpGPkAIYSBceM Follow us on X: Brad: https://x.com/Sturdy32 Mike: https://x.com/MikeCagley Larry: https://x.com/LarrySmithTV IlliniGuys: https://x.com/Illini_Guys Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

We discuss the return of 4 key players and the commitment of Zavier Zens. LIKE AND SUBSCRIBE!Follow our Social Media Accounts:MERCH: https://illinibasketballpodcast-shop.fourthwall.com/- http://www.X.com/PodcastIllini- https://podcasters.spotify.com/pod/show/illini-basketball-podcast- http://www.facebook.com/illinibasketballpodcast- https://www.youtube.com/@illinibasketballpodcast- http://www.X.com/EthanCarterSW- http://www.X.com/tbramleyibp- https://www.instagram.com/illinibasketballpodcast/?igshid=Zjc2ZTc4Nzk%3D**We do NOT own the rights to the introduction video music** - MUSIC BY VLAD GLUSCHENKO (After a While)

Andrej Klokner, a Scale Up Coach, CEO, and licensed productivity expert with over 15 years of experience helping leaders grow from 5 to 30 plus team members with more ease. He's also the creator of the Integrated Man concept.  SHOWNOTES:

Illini Inquirer's Jeremy Werner and Kyle Tausk react to Andrej Stojakovic returning to Illinois basketball and three-star Class of 2026 forward Zavier Zens committing to the Illini. The guys discuss what Stojakovic's return means for the 2026-27 season, how Illinois was able to retain its Final Four core and what's next for the offseason. The guys also discuss what Zens, the 2026 Wisconsin Mr. Basketball brings to the program, and why Illinois went high school heavy this recruiting cycle.  Follow the Illini Inquirer Podcast on: Apple: https://apple.co/3oMt0NP Spotify: https://spoti.fi/2Xan2L8  Other: https://bit.ly/36gn7Ct  Go VIP for just 60% OFF: https://tinyurl.com/2fkhmjdz To learn more about listener data and our privacy practices visit: https://www.audacyinc.com/privacy-policy Learn more about your ad choices. Visit https://podcastchoices.com/adchoices

Big news for Illinois basketball as Andrej Stojakovic is officially back with the Illini, giving the roster a major boost heading into next season. Brad Sturdy joins the show to break down what Stojakovic's return means, along with insight on newcomer Zavier Zens and how he fits into the program. Plus, more reaction and analysis on what this all means for Illinois moving forward.

Sunny Verma from Locked On Illini joins the show to break down Andrej Stojakovic's return and what to expect from Illinois' incoming freshman class. The guys also have some fun with Evan “Stone Cold's” WrestleMania predictions, plus another edition of Around the Bases. And as always, they wrap things up with Friday Toasts. Follow The Drive on Twitter, Instagram, and Facebook!