Podcasts about Sota

Iván Martínez Sota repasa en Radio Rioja su participación en el Campeonato de Europa de Natación.

This Week on the Toy Power Podcast; we have Damian (aka Truly_Truly_Truly_Outrageous); Rocking back into the Studio to assist us with a 'Musical Spin' on our Regular Segment: The Team! This round we are covering the JEM Universe; so we are switching out the topic from a 'Special Forces Team'; to a 'Truly Outrageous Rock Group!' The Band! So in this case, we are picking a: Vocalist, Guitarist, Keyboard or Drummer, as well as a Wild-Card addition. Then Damian with his incredible passion & knowledge, gets to select not only the Band Manager for this Universal Group; but also an ideal Venue / Arena for them to play at! This was genuinely a lot of fun to discuss!! Then we chat towards the Latest Marvel film that is: Spiderman - Brand New Day. It's killing the box-office right now; so we go into full spoiler discussion & try to decipher that end credit scene too?!?! Rounding out the ep; we reveal our upcoming "Best Cartoon Intro Tournament". Frank breaks down the initial ladder, (subject to adjustment); & potentially what to expect over future eps! All this & more! Enjoy!! Support the show: http://patreon.com/toypowerpodcastSee omnystudio.com/listener for privacy information.

This Week on the Toy Power Podcast; we are quite Excited & equally Privileged for reaching our Milestone 10th Year of Podcasting Fun! So to Celebrate; we have several things lined up to Celebrate this! First off we are sharing our Annual Daily Collection Pics of Your Collections via Social Media- on Instagram & Facebook! Thankyou; & please submit a pic if you haven't already. Big reveal too; we unveil a New TPP Logo! Huge thanks to Cody Hulks aka SADC_TheClown for bringing this to life for us! But that's not all; we FINALLY have an avenue for you to purchase our Merch via the RedBubble website (See link bellow). And if that all wasn't enough; then listen out; as we have Special Guest: Damian, aka: Truly_Truly_Truly_Outrageous in the studio; to bring us up to speed on all the Exciting News & Collaborations he has been involved in for the world & passion of the Jem Universe!! Plus, as any good Birthday's are known for; some nice Presents! Well, Master Colin Betts has it in spades; as we open some beautiful & very very thoughtful gifts to round out the episode. Thankyou good sir!! A genuinely fun episode & wider Celebration of our little Podcast & the AMAZING Community we have built around us. Thank-you everyone for your on-going support!Support the show: http://patreon.com/toypowerpodcastSee omnystudio.com/listener for privacy information.

Paul VK3HN has been building homebrew radio gear since the late 1970s and activating SOTA summits in the Australian bush since around 2015 — and he'll tell you the two pursuits feed off each other perfectly. His Summit Prowler series of homebrew superhet transceivers were designed specifically for backpacking and activating, with every design decision driven by the constraints of the trail.In this episode Paul walks through his journey from a scratch built 2 meter FM receiver as a teenager to a series of increasingly refined portable CW and SSB transceivers, shares how his 20 year career as a software engineer made the jump to Arduino and Si5351 easier than most, and talks about choosing to stay a builder rather than become a full time YouTuber.Join us as we explore how you can get involved in portable radio, QRP, and more in this episode of the All Portable Discussion Zone (AP/DZ). Every aspect of portable operations is covered in this biweekly podcast, from news and gear to achievements, the workbench, contests, awards, and beyond.Paul's Blog: https://vk3hn.wordpress.com/Paul's YouTube Channel: https://www.youtube.com/c/PaulTaylorVK3HNPaul's GitHub Site: https://github.com/prt459**SolderSmoke DISCORD INVITE**: https://discord.gg/GYVRZSBVFCConnect with us:* Discord: https://discord.gg/WVE3vVveWU* YouTube: https://www.youtube.com/c/redsummitrf* TikTok: @redsummitrf* X (formerly Twitter): @NJ7V_Support the channel:* Buy us a Coke: https://www.buymeacoffee.com/RedSummitRF* Red Summit RF Amazon Storefront: https://www.amazon.com/shop/redsummitrf#apdz #HamRadio #QRP #Workbench #Electronics #homebrewradio #DIYradio #amateurradio #hamradiopodcast #scratchbuild #SOTA #PortableOps #superhet #Si5351 #Arduino #VK3HN #SummitProwler #CW #SSB

SUMMARY: We continue our Models and Money series. In this episode, Brian and Aaron explore the current state and future of AI models, focusing on model size, model harnessing, and intelligent model routing. They discuss whether bigger models are always better, the economics of AI, and how enterprise applications can benefit from tailored AI solutions.SHOW: 1051SHOW TRANSCRIPT: The Enterprise AI Show #1051 TranscriptSHOW VIDEO: https://youtu.be/tkJmeazn8BsSHOW SPONSORS:Nasuni - Activate your data for AI and request a demoTopic: When will the models/harnesses be good enough?Why now? Benchmark Maxxing - cost to build/host/maintain 1+ trillion parameter modelPast: The “wow” moments in versions really stopped around GPT4… (maybe?)Present: Race to the top/bottom, millions spent to gain SOTA for a few daysFuture: Will the pendulum swing back? Will bigger/faster always rule?FEEDBACK?Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow

This Week on the Toy Power Podcast; even though we are Celebrating our 10th Year of Podcasting; we unfortunately exhausted all our energy on the Previous SDCC Reveals & Announcements Ep. So we are giving you a ReIssue episode instead from the Vault. (Originally Posted as Ep #143 - from October 2019). Original blurb: It’s a very special ep as we’re joined by not one, but two of favourite guests in Davey Damaged and Scotty so that’s right – it’s a Homer episode! Trent takes us through a vintage retrospective look at The Addams family by Playmates. Perfectly timed for Halloween, we wonder what could have been with this short-lived line and look to the future of the franchise as a new movie approaches. What characters didn’t get made, which ones make the camera glow and where does MC Hammer fit into it all? We make the jump to Show and Tell as Davey tries to trump us all with a purchase so large, we doubt it will actually fit in his toy room! There’s some DC, MOTU and even Final Fantasy options to choose from. Frank gives a lovely tribute showing that sometimes, the value of a toy; lays in more than its sticker price. Then it’s Quiz time and with Davey onboard, anything is possible. To say this episode goes off the rails is putting it mildly – but on the plus side we learnt that Trent can’t hold his liquor, Ben thinks photos record sound, Space Jam was an event and Rick Moranis is Trent’s muse. So hold on for this crazy ride that also serves as a farewell for one of the team…Support the show: http://patreon.com/toypowerpodcastSee omnystudio.com/listener for privacy information.

podcast recorded with enacast.com

Hey, it's Alex (yeah, I'm finally back from my vacation!) What a freaking week to come back to! Just after our last episode was published, Anthropic releases Opus 5, Jensen joins X and drops the “Open Weights & AI Leadership” open letter, Kimi K3 is released the following Monday beating expectations, and then the AI hack (OpenAI model breaking sandbox and infiltrating HuggingFace) is on everyone's mind, another Open Letter, this time from over 1K employees inside the frontier AI companies all talk about pacing the pace of frontier AI development. We played with Opus 5 and Kimi K3, and had the great pleasure to chat with friends of the pod Elie Bakouch (Prime Intellect) and Philip Kiely (BaseTen) about this important open weights release, then covered our general thoughts on Opus 5, and made order of all the different open letters that came out this week. Finally we chatted with Max from Pangram about the next version of AI writing detection (their biggest yet) and finished with Zuckerbergs (also on X! what's going on with everyone joining X) op-ed on the vision of personal superintelligence for everyone. Let's dive into this (as always, all the links and sources at the end, please don't forget to sub to our podcast on your favorite podcast app!) Open Weights AIKimi K3 the king of open weights - 2.8T chonker MoE near frontier model (X, HF, Blog, Tech report)This has got to be the biggest news of this week, and maybe the open weights AI news since GLM 5.2. MoonShot came back with Kimi K3, and we haven't seen any models quite this large in the open. Even Grok 4.5 is around 1.5T, this model is nearly 2x the size. Coming in at close to 3T parameters (and 2.5terabytes of weights at MXFP4 format), this model comes in very close to frontier! This was such an important release that I invited 2 friends of the pod, Elie Bakouch (prev HuggingFace, now Prime Intellect) and Philip Kiely (Author of Inference Engineering book, BaseTen) to dive deep into what makes this special! Elie's take, from reading the tech report, there's no single secret sauce, it's a combination of already available in the open techniques. Like KDA (Kimi Delta Attention) that has been out for a while, attention residuals, NVIDIA's latent MoEs. The highlight for Elie was the scaling work they did that reported a 2.5x scaling efficiency over Kimi K2.5 (2.5 performance at the same compute)! They also skipped RoPE entirely in favor of NoPE (the report calls it No Positional Encoding) for long context.Serving 1.4TB on eight GB300s (Baseten blog)Philip's team at Baseten was a day-zero provider (we're still working on bringing this model to CW Inference, stay tuned!) so I invited him to tell us behind the scenes of hosting this beast. Philip said that just loading the weights takes about 1.5TB!! of VRAM, and that's before the KV cache allocation + 1M token windows, so they're serving it on 8 GB300s where NVL72 . Baseten worked with the vLLM and SGLang teams on kernels and he also said they contributed patches back upstream! The model was trained with MXFP4, which, unlike Nvidia's own NVFP4 is a more standard format per Philip. I enjoyed his deep dive analysis into the differences, but because of this and because they trained the model with quantization awareness, it's “only” 1.5TB vs the would-be 5-6 TB if that this model in FP16 would demand. One of the more favorite nerd snipes moments, Philip pointed out that his colleague discovered that with over 99% of the usage being cached (think harnesses that send millions of the same cached tokens back and forth), tokenization actually starts to become a bottleneck. So they released a custom “basetenkenizer” that reduces the latency to serve the first token significantly! Great job!The harness in question is very importantOne important callout with 2 evidence pieces - the way you inference this model really matters. Kimi trained K3 with preserving thinking history, so when your harness uses it, it must send back the full thinking and tool use into the API to get the best next response. If your harness strips that out, you're not getting the most intelligence out of Kimi (shoutout to Niels from HF team for pointing this out). Additionally, the Composio folks, tested K3 on 3 harnesses, Kimi Code, Hermes and Claude Code. The difference in outcome was negligible, but the different in cost and number of tokens is definitely surprising! Claude Code (as a harness only) took 9x more Kimi tokens to get the same responses! This is also why Kimi Vendor Verified exists, their own held back benchmark of how well model providers serve Kimi across different quantization, tokenizer and KV cache settings. Benchmarks and the license! Ok let's start with the ugly... this isn't MIT, not remotely. This model is suspiciously served by all providers with exactly the same price (check OpenRouter) and requires inference companies to sign a contract with Kimi (I've no internal knowledge of this except that CW folks are working on it). Not something I particularly like, but hey... we're still advancing the frontier here! Speaking of frontier, this model approaches the frontier very closely. On DeepSWE, K3 sits just behind Fable 5 and GPT-5.6 Sol at 67%, beating GPT-5.5 & Opus 4.8. On Terminal-Bench 2.1 it takes second place behind GPT 5.6 Sol! It's 4th overall on Agentic Arena, with frontend design being genuinely good across the board - 1st on Design Arena

There's a band in San Francisco comprising musicians from The City and whom I discovered on social media playing shows on a stoop here in SF. I can't quite be sure when I discovered them, or whether it was me or my wife, but either way, right from the start, we knew we'd stumbled upon something special. Something truly unique. Something joyful, beautiful, free … something for the people. People could gather around with neighbors, some they know, some they might be meeting for the first time. Community is something of a buzz word lately, especially in light of things like gentrification, neo-tech fascism, and just plain ol' fascism. But for good reason—it's one of the most powerful tools we have not only to resist all the nonsense being forced on us, but also to overcome that crap and move on toward creating a world of inclusion, equity, diversity … and good tunes. The band's name is Top Chefs. This last episode of Season 8 of Storied: San Francisco is, not by accident, all about them and the magic they create. We start things off with TJ Milan, lead arranger for Top Chefs and co-editor of what they call "Stoop Sessions." TJ was born and raised in San Francisco. His dad came out here from Connecticut, and his mom is from The Philippines. His parents lived in South of Market, and TJ lives in that neighborhood today. TJ's mom came to The City with her parents when she was young. She later joined the Army, serving with a support group for the Green Berets. His dad had moved to New York first for work. Then he got offered a job in San Francisco in the late Eighties and took it. In the mid-Nineties, the two met at a law firm where they both worked. In 2002, they had TJ, the youngest of their three kids—he has an older sister and an older brother. His dad is an amateur saxophone player. More on that in a bit. The family lore goes that his sax stylings played a part in his parents' union, because duh. I give TJ the opportunity to rattle off his SF schools, something he's proud to do—Clarendon Elementary, Hoover Middle School, and School of the Arts (SOTA). He describes his time at SOTA as "very chaotic." There was beef between the SOTA kids and the Academy kids in his days. Academy is sort of a school-within-a-school for SOTA, a majority/minority arrangement. Despite that situation, though, TJ met the person who's still his significant other to this day. The only time TJ left San Francisco for any significant time was to go to college at The New School in New York City, where he studied jazz. But he credits most of his education with Community Music Center in The Mission. The entire time TJ spent in NYC, he felt in his heart that he would return to his hometown. He played out a lot while he lived in New York, but the market for saxophonists was saturated. He came back to SF around September 2024. It started off rough, as he puts it. He started busking to make ends meet. Then we turn to Liam Berry, aka Loveberry, the head of audio production and guitar player in Top Chefs and co-editor of Stoop Sessions. His mom came here from San Diego. Her mom came to the US from Ecuador, where she was raised. His mom's dad is from Illinois. Loveberry's grandfather was on a work trip in South America when he met his grandmother. That couple went to Philadelphia before landing in San Diego. Their daughter, Loveberry's mom, moved to The Bay to go to UC Berkeley to study biology. I take us on a sidebar about how San Diego can be cool to visit for like two days, max. But there's only so much that good Mexican food and nice weather can do for me before I need to get back home. His dad is from Cupertino. His dad's dad is from Philadelphia and his dad's mom is from Houston. They met when his grandfather worked for NASA and was stationed at the Johnson Space Center in Texas. His grandfather was transferred to the Ames facility on the Peninsula and brought his wife with him. Loveberry's dad wanted to get out of the area where he grew up, but he didn't want to go too far. So he got into SF State for meteorology. He ended up working in the data science field after discovering that meteorologists don''t make great money. His parents met at a party through mutual Cupertino friends who'd moved up to Berkeley and Oakland. They were still together until a couple years ago, when Loveberry's dad passed away. He was almost raised in Oakland. His dad worked a low-paying job at a tech company and his mom, back then at least, worked different admin jobs (today, she's a librarian). They'd lived in the Inner Sunset and put bids in on a couple houses in Oakland, where they could afford to buy in the late Nineties/early 2000s. They also bid on one house here in The City, in The Castro. And that's the one they got. That's the house Loveberry grew up in, and when his parents first bought it, the house needed a lot of work. There are family stories about moving in and a mysterious tub of some sort of toxic goo. Schools he went to and music intertwine for Loveberry. He started at Rooftop K–8, where kids worked on a different art project every year. When he was in second grade there, Loveberry started to gravitate toward guitar and started taking lessons. He went to high school at Gateway, which didn't have a music program at the time. So he and some friends started the school's music club. In the summer between his sophomore and junior years of high school, Loveberry smoked pot for the first time. He also did an internship at NASA over that same summer. Going back and forth between those two wildly different worlds, he preferred the community that came with smoking pot with friends. Music became more and more a thing, but so did partying. Shit got out of control. He got out of high school to get himself into rehab. After rehab, he wanted to go to music school in Los Angeles, but his parents nixed that idea. Instead, he stayed in The City and worked at a few different local eateries. So he never left. A guy he met playing open mics in SF offered Loveberry $1,000 a month to open a recording studio and music business. It didn't work out, but it showed him that he could persue music and art as a lifestyle. He worked at some nonprofits and did visual art as well as music. He met Afterthough (Aft) in 2021. Top Chefs formed soon after that. Check back Thursday for Part 2 and the final podcast of Season 8. We recorded this podcast on Scott Street near Alamo Square in June 2026. Photography by Marcella Sanchez

The Shulhan Aruch (Orah Haim 2:6) writes that one must not walk "Be'koma Zekufa" – fully upright, with his head held high. This is based on the Gemara's teaching in Masechet Berachot (43b) that if a person walks in an overly confident, arrogant manner, he "crowds out" the Shechina. Elsewhere (Sota 4b), the Gemara states that an arrogant person is considered like a heretic, and like an idol-worshipper. G-d refuses to live together with an arrogant individual, because the world was created for His honor, not for the honor of human beings, and thus G-d's honor is meant to fill the entire earth. The Gemara later (Sota 5a) comments that a Torah scholar must have "one-eighth of one-eighth" – meaning, a smidgen – of arrogance, but only for the purpose of unyieldingly upholding the Torah's values and principles. Otherwise, arrogance has no place at all in the life and mindset of a Torah Jew. (The Vilna Gaon offered a different interpretation of the Gemara's comment, according to which even a Torah scholar is not permitted to have even a smidgen of arrogance.) The trait of humility must be reflected even in the way a person walks, and thus one should not walk with his head fully upright, appearing overly confident and self-assured. Some have noted that the Gemara's teaching seems to call into question Rashi's comment to a verse toward the end of the Book of Vayikra (26:13). There Hashem tells Beneh Yisrael, "Va'olech Etchem Komemiyut" – "I have led you upright," and Rashi explains, "Be'koma Zekufa" – that they walked tall and proud. How can Rashi's comment be reconciled with the Gemara's stern warning against walking about in such a manner? The Seda La'derech (work on Rashi's Torah commentary by Rav Yissachar Ber Eilenburg, c. 1550–1623) suggests that Rashi used this term figuratively, as a reference to Beneh Yisrael's unique stature. He draws a comparison to the difference between human beings – who walk upright – and animals, which walk on four legs. Animals walk looking toward the ground, because they are incapable of conceiving of lofty, spiritual matters or pursuing spiritual goals, whereas humans walk upright because they are to be focused on the heavens, on spiritual ambitions. We, who received the Torah, are to live with a special degree of "uprightness" in this sense, which our heads directed heavenward. According to the Seda La'derech, this is the meaning of the verse, "Va'olech Etchem Komemiyut" – that Hashem has granted us the privilege of living with a special connection to the heavens and spirituality. The Petah Ha'debir (Rav Binyamin Pontremoli, Turkey, d. 1784) answers differently, explaining that this verse foresees the time when the enemy nations will fear Beneh Yisrael. At that time, it will be permissible for Am Yisrael to walk "Be'koma Zekufa" in order to intimidate those peoples who might otherwise think to attack and persecute the Jewish Nation. The Petah Ha'debir also suggests an additional answer, explaining that we must display humility when we walk because of the sin of Adam and Hava in Gan Eden, which resulted in an inherent spiritual deficiency that we must always be cognizant of and that should bring us humility. In the future, however, when the sin of Adam and Hava will be fully rectified, this will no longer be necessary, and the verse "Va'olech Etchem Komemiyut" refers to that time, when we will, indeed, be allowed to walk with our heads held high. Hacham Ovadia Yosef asserted that this law, requiring one to avoid walking fully upright, applies only as a Midat Hasidut (measure of piety), and not on the level of a strict Halachic obligation. He draws proof to this conclusion from the story told of the time when Rav, the great Amora, passed away, and each of his leading disciples chose to adopt one of his unique religious practices. Rav Yehuda adopted the practice of ensuring not to walk four Amot (cubits) fully upright. This clearly proves that refraining from walking "Be'koma Zekufa" is a measure of piety, for if it constituted an outright Halachic requirement, then Rav Yehuda would have adopted this practice even before Rav's passing. Nevertheless, the Shulhan Aruch writes that this is a practice which everyone should follow. The definition of "Koma Zekufa" is walking with one's head turned upward such that his eyes cannot see the ground, thus requiring him to walk more slowly. The Aruch Ha'shulhan (Rav Yechiel Michel Epstein, 1829-1908) emphasizes that one does not need to walk with his back hunched; he must simply ensure not to walk with his head turned upward. Rav Shlomo Zalman Auerbach (Jerusalem, 1910-1995) similarly writes that one should avoid walking in a manner that expresses arrogance, but does not need to lower his head as he walks. A Torah scholar has an obligation to walk with a special degree of dignity. This means not only that he should avoid walking with his head held high, but also that he should not run in public. Some Rabbis would make a point of avoiding conversation in the street, deeming this undignified for a Torah sage. Although it is unbecoming for a Rabbi to run in public, he also should not walk about slowly and leisurely, as though he has nothing to do. He should walk quickly and with a sense of urgency, showing that he is constantly occupied with Torah learning, teaching, and Misvot. One must be especially careful with how he walks about in a synagogue or Bet Midrash, as these are, in a sense, the "houses" of the Almighty, and so one must conduct himself in these buildings with a sense of reverence and humility. One of the commentaries to Pirkeh Abot cites a passage from the Midrash relating that King David was once reprimanded by Ahitofel for walking with his head held upright in the Bet Midrash, in violation of the requirement to carry oneself reverently in the study hall, and David accepted the criticism. Several writers noted the seeming implication of the Midrash that walking upright is improper only in the Bet Midrash, but not in other places – in direct contradiction to the Halacha we have been discussing. Hacham Ovadia Yosef suggested that as a king, David was permitted to walk about with his held up high in order to earn the respect of the people, but in a Bet Midrash this is forbidden even for a king. The Hida (Rav Haim Yosef David Azulai, 1724-1806) explained that David walked about in the Bet Midrash not with his head held high, but rather in a loose, carefree manner which is not proper in a Bet Midrash, and for this was he criticized. Returning to Hacham Ovadia's explanation – that a king is allowed to walk with his head held high – this is the opinion of the Bet Obed, cited by the Petah Ha'debir. Some, however, challenged this view, noting that the Torah requires a king to exercise greater humility than all other people and to avoid all feelings of superiority (Ramban, Debarim 17:20). The answer, it would seem, is that a king must make a special effort to remain in his heart humble and subservient to the Almighty, but outwardly may – and in fact must – carry himself with an aura of authority in order to earn the reverence of his subjects.

SUMMARY: Brian and Aaron explore the current state and future of AI models, focusing on model size, model harnessing, and intelligent model routing. They discuss whether bigger models are always better, the economics of AI, and how enterprise applications can benefit from tailored AI solutions.SHOW: 1048SHOW TRANSCRIPT: The Enterprise AI Show #1048 TranscriptSHOW VIDEO: https://youtu.be/1IHXNrYlYCASHOW SPONSORS:ShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!Nasuni - Activate your data for AI and request a demoTopic: When will the models/harnesses be good enough?Why now? Benchmark Maxxing - cost to build/host/maintain 1+ trillion parameter modelPast: The “wow” moments in versions really stopped around GPT4… (maybe?)Present: Race to the top/bottom, millions spent to gain SOTA for a few daysFuture: Will the pendulum swing back? Will bigger/faster always rule?FEEDBACK?Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow

There's so so SO much stuff to cover, we just hit record and took our eyes off the clock. We go through all the big player companies (and a few surprises as well) to bring you the biggest review of San Diego Comic-Con 2026 ever! Who wins? Who surprises us? Who dissapoints us? How many Shuttup and Take my Money's are we throwing out there? Strap in because this is hands down our longest episode EVER! Best of luck to Lego Master Trent who finds out if he's winner today! Support the show: http://patreon.com/toypowerpodcastSee omnystudio.com/listener for privacy information.

Templah Invites: Patient Show: Templah Invites: Artist: Templah Guest: Patient Air Date: 24 July 2026 Genre: Drum & Bass Yes, yes! Welcome back for another episode of Templah Invites. This month we have Patient dropping in for a mix - expect nothing less than one of his legendary Full Spectrum sets! Remember to tune in every 4th Friday of the month at 5pm for the next episode, or check out Templah on Instagram for the latest notifications! Tracklist: Alice Wonderland - Good Enough (Patient Intro Bootleg) Camo & Krooked, Tiga x Zyntherius - Sunglasses At Night Metrik - Simulation (DJ Edit) Marie Vaunt - That Acid (Dimension & Subsonic Remix) Genic - Boppa Patient - Waterfalls (Patient Bootleg) Break - So Right ShockOne - Polygon (Circadian Remix) DLR - Don't Come Too Close Mind Vortex - Stand High - Original Tantrum Desire, Sophie-Grace - Moonlight Sound In Noise - Sugar Rush Celeste x MK - Stop This Flame (Patient Bootleg) Krakota - Move With Me Mob Tactics - 9mm (Original Mix) Sub Focus - Rock It (Wilkinson Remix) Grafix - Concentration SKIYE - Body Talk (Original Mix) Arcando/Sam Harper - Wide Awake Flux Pavilion - 2Fast2Stop Metrik - Utopia Funtcase - 50.cal (Patient DnB Edit) Kanine, Sota, Mila Falls - Touchdown A.M.C - I See You Circadian - Hold That Sucker Down (Extended Mix) SKIYE - Walkman (Original Mix) SKIYE - Walkman (El Pablo Remix) Chase & Status - BACKBONE DJ Fresh - Gold Dust (Fox Stevenson Remix Extended) Grafix, Rova - Let Me Down Cyantific - Alpine Fanatics Sero - Homunculus Patient & Offwrld - All My Love (DnB Vip) Sub Focus - Airplane (Culture Shock Remix) Rex Hooligan Adonai - In My Head Macky Gee/Kritikal - Here For You (Original Mix) 1991 - Full Send (Pirapus Remix) Patient & Offwrld - Aftershock F3NG - Contact Play Unglued - Who Dis Mefjus, Camo & Krooked - Sientelo (Sota & Circadian Remix) Culture Shock - Bunker (Original Mix) Patient, TLZ - Wonder Cyantific - Hardbody Patient & Edina - Out Of Time C-DU - Master Schemer Eric Prydz - Pjanoo (Changing Faces Bootleg) Rene LaVice & Raign - Fall From The Dark Unglued x Pola & Bryson - Warning (feat. Cimone) Sub Focus/Sota - Elevate (SOTA Extended Mix) Felix Delta Heavy - Don't You Want Me (Delta Heavy Remix) DJ Hazard - Bricks Don't Roll Netsky, Grafix - Come Alive (Grafix Remix) Friction - Never Know (Love You So) Spor - A1 Aztec Spor - Aztec (Calyx & Teebee Remix) René LaVice - Molten Euphoria Paul Oakenfold - Ready Steady Go (René LaVice Remix) Bad Company UK Latte - Planet Dust (Latte Remix) Eklyps Sound - been so long Kanine - Set It Off Linkin Park - Lying From You (Protostar Remix) TanTron, Wiguez, moneo - ICARUS Patient x Ofwrld - ID Hoax - Nebula Patient - Wind Up X Sub Focus & Dimension - Desire (Vocal Only) The Prodigy - Voodoo People (Pendulum Mix) Exception AROHA - Nefraid Patient - Headlights Rido Counterstrike DOVN - Let It Roll (DOVN Remix) Pirapus, Signum - What Ya Got 4 Me Bensley - Pinger Darkzy - Inside The Rider (Darkzy Remix) Disrupta - Inside The Rider (TC & Original Sin 2000's Remix) Basstripper - No Looking Back SOLAH & Hoax - Wings Fred V & Krakota - Weightless (ft. Lottie Jones) (DJ Edit) Dkn - The Peak Patient, Vindicate - Closer Mind Vortex - Gravity (Original Mix) Patient - Pure NRG So Dope - MEANT2B Patient - Can't See me The Prodigy - Warriors Dance (Odan Bootleg) MXTR - Give Me More (Original Mix) Kanine - Tell Me (Subsonic Remix) JET A-1 - Don't Let Go (I Miss You) Fred V - Horizon (ft. WHAT EVA) (DJ Edit) Sammy Virji - Never Let You Go (Gino Remix) Saint Riders - HMF Patient & Utelka - Past Life J Bookey - Luv U (Original Mix) Genic - Mysterons Avicii & Nicky Romero - I Could Be the One (Patient Bootleg) Gydra - Lava Run (VIP) S.P.Y, The Melody Men - Sweet Sound Calibre, High Contrast - Mr Majestic Fred V & Grafix - Rain Is Falling Fred V & Grafix - Major Happy (Original Mix) Doshi - Jump In The Pool (Patient Bootleg) vs Porter Robinson - Language (Doshi Bootleg) 1991 - Guiding Light Patient, JoyDoc - Lifeline and more

In recent months, the open vs closed, and US vs China discussions on model ownership and sovereign/local AI have heated up to a fever pitch. So it is very very good news that Poolside AI are finally emerging with new models, like Laguna S 2.1, that are beating Thinking Machines' recent release nearly 10 times their size.Poolside's recent tech report got a lot of praise due to their level of detail, and Vibhu first covered Laguna's recent technical report on our paper club:From spending $12 million building language models for code before the world cared to creating a Model Factory that can take a model from pre-training to release in eight weeks, Eiso Kant has spent more than a decade betting that code is the path to AGI. In this episode, the Poolside co-founder joins swyx and Vibhu to explain why ChatGPT felt like vindication, why Poolside embraced open weights and open research, and why he would rather live in a world with 100 foundation model companies than five even if Poolside were one of the five.We go deep on Poolside's Model Factory: the engineering systems behind 10,000–20,000 experiments per month, streaming data directly into training, reproducible experimentation, low-precision compute, and agents that increasingly write code, launch jobs, evaluate results, and modify the pipelines used to train future models. Eiso also unpacks their recent launch Laguna S, why persistence, verification, and backtracking may matter more than raw intelligence, how much capability remains inside smaller models, why reinforcement learning will move earlier into pre-training, and why next-token prediction is still extracting too little from the web.We also discuss model-harness co-design, Poolside's path from coding agents to AGI, why Eiso thinks MCP and traditional tool calls are “stupid,” the real economics behind frontier-model training, Poolside's $500 million raise, open-source AI, regulation, NVIDIA and TSMC's influence, engineering productivity in the agent era, high-agency teams, and hiring at Poolside.We discuss:* How Andrej Karpathy's RNN work inspired Eiso to start building language models for code in 2015* Why Eiso spent four years and $12 million pursuing an idea before the market cared* Why ChatGPT felt like vindication and brought Poolside back to open source* Why Eiso would prefer 100 foundation model companies over an oligopoly of five* The difference between releasing open weights and publishing genuinely open research* Why Poolside deliberately built a global research organization outside the Bay Area talent war* Why model building is ultimately 90% engineering* The Model Factory: Poolside's end-to-end system for rapidly training and improving models* How fewer than 70 researchers run roughly 10,000–20,000 experiments each month* How Poolside moved from six-month model cycles to five- and eight-week launches* Why streaming data directly into training unlocked faster experimentation* How immutable data, versioned code, and reproducibility enable rigorous model research* Why Eiso wants capable researchers to leave their labs and become Poolside's competitors* Why 95% of model building can be reduced to better data or compute efficiency* Laguna S and why persistence, verification, and backtracking can outperform raw intelligence* Why smaller models may handle far more knowledge work than previously expected* Why reinforcement learning will move earlier into pre-training* Why next-token prediction is still failing to extract enough knowledge from the web* Why distillation and environments have become the AI industry's favorite “drugs”* Why mid-training is really an early form of curriculum design* Low-precision training, networking bottlenecks, and the next gains in compute efficiency* Laguna S: 118 billion total parameters, 8 billion active, and eight weeks from training to launch* Why model builders can often evaluate a new checkpoint within its first 30 minutes* Model versus harness: where agent capabilities actually come from* Why Poolside sees coding and long-horizon software tasks as a path to AGI* Why Eiso thinks MCP and traditional tool calls are “stupid”* Why future agents will write scripts instead of choosing from dozens of predefined tools* The case for minimal harnesses, containers, and model freedom* Why Poolside is prioritizing vision but does not expect to work on audio soon* Why language may be the most compute-efficient modality for encoding knowledge and reasoning* The real cost of model development and why the final training run is anticlimactic* The story behind the Poolside name and why it represents refusing to lower ambitions* How Poolside raised $500 million while investors still questioned whether AGI was real* Why intelligence could become the world's most demanded and commoditized resource* When open models may become too capable to release without restrictions* Why unilateral AI safety does not work in a globally competitive environment* How regulation could accidentally lock in an oligopoly of two or three AI companies* NVIDIA, TSMC, and the hardware systems underpinning foundation-model progress* Why reinforcement-learning wall-clock time is one of Poolside's biggest bottlenecks* Why Poolside trains models from scratch instead of simply distilling larger models* How AI changes the way companies should measure engineering productivity* Why agency may become the most important quality for employees in the AI era* How leaders align high-agency people through shared goals and clear constraints* Hiring across research, post-training, pre-training, architecture, evals, and engineering at PoolsideEiso KantLinkedIn: https://www.linkedin.com/in/eisokantX: https://x.com/eisokantPoolside: https://poolside.aiTimestamps00:00:00 Introduction00:00:54 Karpathy, RNNs, and Building Code Models Before Transformers00:02:26 The $12M Failure and ChatGPT Vindication00:03:39 Open Source and the Case for 100 Foundation Model Companies00:09:22 Open Weights, Open Research, and Poolside's Global Team00:16:04 The Model Factory: Why Model Building Is 90% Engineering00:20:19 Agents, Automated Experiments, and Early Signs of RSI00:24:04 Streaming Data, Reproducibility, and Scientific Rigor00:30:35 Creating More Foundation Model Companies00:36:07 Laguna S: Persistence vs. Raw Intelligence00:43:01 Reinventing Pre-Training, RL, and Curriculum Design00:52:33 Low-Precision Training and Squeezing More From Smaller Models00:58:37 Model Harnesses, Coding Agents, and the Path to AGI01:09:26 Why MCP and Traditional Tool Calls Are “Stupid”01:13:04 Vision, Multimodality, and Why Language Still Matters01:18:15 Scaling Models and the Real Economics of Training01:20:40 Why Poolside Is Called Poolside and Raising $500M01:27:37 Open Models, AI Safety, and the Risk of an Oligopoly01:33:53 NVIDIA, TSMC, and the Reinforcement-Learning Bottleneck01:41:52 Smaller Models, Distillation, Engineering Productivity, and HiringTranscriptIntroduction: Eiso Kant, Poolside, and Open ModelsSwyx [00:00:00]: All right, we're here in the studio with Eiso Kant from Poolside, together with Vibhu. Welcome.Eiso Kant [00:00:08]: Thanks. Thanks for having me, guys. Good to be here.Swyx [00:00:10]: Yeah, fresh on the plane. You texted me, you were like, “Hey, I'm on my way to SF.” I was like, “You're on a plane right now, right?” Like, hey.Eiso Kant [00:00:16]: I know. After I texted you, I realized that probably coming in with major jet lag was gonna offer some fun experiences today, but let's do it.Swyx [00:00:23]: I mean, I think the thing I would tell guests is that they don't have to prepare that much because if you're truly working on this every single day, then even, like, what you hazily remember is going to be new for a lot of the audience that don't live in your world every day, right? so 10 years ago, you did a talk at Google Slush, talking about the democratization of AI. and, now here you are, like, open sourcing an incredible new model that we're gonna talk about. But I guess, like, what got you into democratization of AI? Like, it's not obvious from your LinkedIn or something.From Karpathy's RNN Post to SourcedEiso Kant [00:00:57]: No, it's not at all. I don't think it's obvious how I got in this space. I owe getting into this space to Andrej Karpathy.Eiso Kant [00:01:05]: In 2015, he wrote an article called “The Unreasonable Effectiveness of Recurrent Neural Nets.”Swyx [00:01:10]: Neural Nets, yep.Eiso Kant [00:01:11]: And that article, I read it, and I pivoted my startup at the time overnight to working on RNNs, and later LSTMs and Transformer models to be able to write code. If you go to this article and you scroll down, you can start seeing, like, this was the precursor to what ended up becoming language models. So, at least when he was character-level language models that were starting to predict letters, he has an example out here. There's a little Paul Graham generator, and you can read it, and the text makes sense, but it doesn't. and there's a little-- There's an example of code a little bit further down. Yeah, so Shakespeare.Swyx [00:01:47]: Shakespeare.Swyx [00:01:49]: CoolEiso Kant [00:01:49]: And for some reason, I read this, and I went down the rabbit hole of learning everything I could about RNNs and LSTMs, right? This is Transformer paper. And I had built a completely unreasonable belief, that neural nets should be able to generalize to anything and everything, and that language should be able to generalize, to a lot of things that are intelligent and the ability to write code. And so I started building Sourced, which was a fully open source company trying to build, what we used to call machine learning on code, language models on code. And we spent about four or five years on this, till the end of 2019. And that sounds really cool today, but back then, no one cared.Eiso Kant [00:02:29]: Right? Like, no one cared. We were in the dark. Like, we did things along the way. We tried applying convolutional neural nets to, like, the structure of code. We were. when attention came out, we were applying it to LSTMs, and then the Transformer paper came out. And it - it wasn't obvious, and what we missed throughout that entire journey, that we were on the right track, but we should have just kept scaling up. And today, to all of us, the scaling laws and scaling up seems like the most obvious thing. But having spent four or five years of my life on working on language models on code, it wasn't obvious. So I have a lot of respect to folks at Google and OpenAI and others who took that confidence and kept going. we failed ultimately at the time, and it was, like, biggest failure of my career, right? You blew $12 million of investors' money, which was a lot back then.Swyx [00:03:18]: Yep.Eiso Kant [00:03:19]: You spent, still a lot, but, And you spent years with, like, a group of 40 people just obsessing over this problem. And life took a different turn, And it was, and family became a focus, and I kept my heads down and really, didn't really look at language models for the following two years. big mistake considering Following years are gonna be really interesting. And then ChatGPT came out And it was like a vindication. It's like people started texting me. I found, like, my old, work decks and these old talks. And throughout that whole journey, we,ChatGPT, Vindication, and Returning to Open SourceEiso Kant [00:03:56]: We really had a strong point of view at the time that, like, as you're building more capable intelligence, it should be open and open source.Eiso Kant [00:04:04]: When we started Poolside, that wasn't the case at all, and I wanna be very open about it. When we started Poolside, we were like, there was a premise of two things. One is this technology is not gonna stop compounding in capabilities. I think to most people obvious today, but three-plus years ago when we started, most people were still arguing if these were stochastic parrots or not.Eiso Kant [00:04:23]: And the second was that reinforcement learning was gonna be the biggest driver for LLM capabilities. Today, very obvious. Three years ago, was not an opinion held or direction held at either OpenAI or Google or Anthropic or others. And so people looked down on us a little bit. They were like, “ is this really gonna work?” And so we just started working the problem, and we never really thought about open source again. We just kept our heads down and we built our, like, knowledge, understanding from scratch, right? We didn't roll out of an existing lab. So we picked up the papers and started writing code and figuring things out.Eiso Kant [00:04:59]: And it wasn't until the beginning of this year that me and my founder, Jason, picked up the open source conversation again.Eiso Kant [00:05:07]: And if you go back to some of the early things on our website, it was very straightforward. It was we wanna get to AGI, we wanna support a world of abundance, and we wanna be the first company that gets there.Eiso Kant [00:05:20]: But we started talking at the beginning of this year because it became obvious that the world was going in a direction that was starting to like, pick at us a little bit. Like, it didn't, this didn't happen overnight. It was, like, a little bit we were seeing this and we're like, “Okay, The world's going down a path.” And Throughout this journey, there was something that I used as a, as an analogy or thing. So I said well, if I go back to back in those days, 2015 or 2016, we're working on this, and I picked up a fi book off the shelf, and I was reading the book about 2035. AGI is achieved, and the story would be over the following, decades. And it would have that first chapter where everyone's trying to figure things out. You'd get the chapter of ChatGPT coming out And then you would get to the chapter where the world was at a fork in the road, and the one that it picked was one where three or four or a handful of companies were going to create all of intelligence moving forward.Eiso Kant [00:06:21]: And when I thought about that story, it felt like a dystopian fi book, not a utopian fi book. And the reality is, I'm a utopian fi guy. Like, and so We took a step back and said, “Hey, can we play a role here?” Now it was easy for us to do so because we were not at the frontier.Eiso Kant [00:06:41]: If we were at the frontier, I don't think we could have changed our mind. and I don't mean this like it's when the moment there's too much capital involved, too much expectations, you've built up things, right? We're a small team, just improving and improving. And so we knew that we could make that decision now, but it would be a lot harder to make as we got closer and closer to the frontier and caught up to others. And did a lot of soul-searching and a lot of conversations, and said, “No, this makes sense,” Even if there's big unanswered questions, like how the hell do you build a business model with foundation models about open source? Big open-ended question that we do not fully have the answer to yet, right? At what point do you no longer wanna release open source models because misuse of models has, real potential risks associated with it? how is the government gonna respond to open source? but I think it all just came down to one thing, and I'll stop the monologue, is the fact that I rather live in a world that has 100 foundation model companies than a world that has five, even if I was one of the five. And the smallest and most meaningful contribution we can make for 100 to exist is to open up our research and open up, like, our weights right now and figure out along the way how we can, like, do more.Neo-Labs, Model Choice, and the Token EconomySwyx [00:08:01]: Yeah. I think if anything, over the past three years, that has become a bit more true. you are one of a cohort of Neo labsEiso Kant [00:08:10]: YeahSwyx [00:08:10]: That people are now calling that. And, we're, we're doing this on the day that Thinky launched their, new model and you are outperforming them on their, on some benchmarks that they released, right? Like, they just don't have it yet. so it goes to show that I think, like, this is one of those things where, like, there is room for multiple players, and you are seeing a little bit more of the future. Maybe more like 20, not 100, but, like, you are one of the 20.Eiso Kant [00:08:36]: I really hope so, right? I think we I'm, I'm excited about their release, and I'm excited about everyone releasing because, like, ultimately, like, choice competition is both gonna drive progress in the right direction. But the fact that like, we create models and while we all, drink out of the same well of data effectively, we do introduce very different behaviors and biases in our models. Some are intended biases, some are completely unintended biases.Swyx [00:09:03]: Yeah.Eiso Kant [00:09:03]: And if we shape up in an ecosystem in the world where open models are gonna be a part of the token economy, like, I don't think there's any question about it anymore Then we want to be able to live in a world where companies, countries, people can choose and say, “Hey, I am most aligned and I trust most this provider for these things.”Swyx [00:09:25]: Yeah.Vibhu [00:09:26]: I think more than just one of the 20 Neo labs, up until recently, most of open source innovation was coming from the Chinese labs, right? So there's the DeepSeek of the West. Is it today? Okay, maybe it's thinking machines reflection, but there aren't many, right? So, one of the things you guys started in France, Europe, but very much now you're taking that American standpoint and more than just that, the point is the Chinese models that we see, they're not super open research. the work you put out is, I think, some of the best. So every few months you get not only frontier models, but also here's a breakdown blog, paper, technical report of here's everything for state of the art to build, frontier intelligence and you're filling that gap too, right? So not just only open weight, not just Western, but also pretty open research.Open Weights vs. Open ResearchEiso Kant [00:10:20]: No, I appreciate it. Look, I think it's, I think it's the most meaningful contribution, right? Weights are a binary. Let's call them what they are. Yes, we can modify them, we can change them, but, like, giving someone the weights does not allow them ultimately to recreate what you're doing, right? And so now there's challenges around releasing data sets, challenges around like releasing certain things, but being able to share your research, like, right, how do we do it? What are the lessons we learned that we spent, tens of thousands of experiments of compute on? I think very much so. One correction though, Vibhu, and I say this because it's been haunting us for quite a few years. We from day zero were an American company.Swyx [00:10:55]: Yeah. They movedPoolside's Global Team and American Company StorySwyx [00:10:56]: To France.Eiso Kant [00:10:56]: So the story once and for all is very. We start as an American company. We have always been an American company, and early on we made a very conscious decision. We said, “We're not gonna hire any researchers in the Bay Area. We're gonna look for talent everywhere else in the world.” and that is everything from Middle Americas, Seattle to, Serbia, and to Taiwan and Singapore and other places. And it was because we took a view that this was gonna become a talent war for this, and I think it has over the years now. Three years ago, that wasn't fully obvious yet. I think today it very much is. And we also realized that, like, some of the world's most capable people with, like, the most interesting, innovative ideas were not just gonna be here. And so it led us to create like a fully remote company. and we ended up opening an office in Paris and London and different places and we have a lot of the team in the US and a lot of team outside. But we always took this view of like, we're an American company, but if we want the best of the best to work with us, we need to take a global view. Now we do also have people here in Silicon Valley, like the company's grown and others, but I think one of the things that, it slowed us down at the beginning, but it has sped us up now, and it's why you're seeing like the progress, I think, on our models and the cadence at which we release, is because we didn't roll out of an existing lab. Right? we didn't, we didn't have a lot of the information that's freely flowing around here at the time. We just took this point of view as like, “Okay, well, let's just work the problem. Let's just go and, like, read the few papers that are out there, and let's just figure this stuff out.” And we made some hilarious mistakes in model training because of that over the yearsEiso Kant [00:12:35]: Like especially in the first 12 months. there's a few that I think still haunt me and scare me. We can talk about them later. but it created a, like, a resiliency and persistency in the team, right? with extremely few people have left us over the years, that, like, told us, “Okay, we can do this.” When we first wrote our first training code base completely from scratch, it wasn't a fork of any open source. It was just like, “Okay, let's build it from scratch.” I remember we had this one moment where we spent three weeks working out an optimizer bug. Like, it was like training just couldn't get stable. We, like, obsessed over it, and we thought, like, maybe we were wrong. Maybe we should have just forked this repo, or we should have. But then when we solved it, I still remember at the time we were like five people in the company. when we solved it, we were like, “Oh, we can do things,” like if we're just willing to work hard. and I think that culture with a very strong engineering bias has helped us, like, get to where we were. And so there's this notion of open source and talent and these things. I think we, We just took different decisions from a different starting point. and I think we are lucky. I do want to definitely call it lucky. And there was a lot of hard work at the team that now, like, that's starting to show up in results.Swyx [00:13:52]: Just ‘cause we probably won't revisit this again, but, and this is a fun recruiting challenge if someone knows the answer. What was the bug? And then we won't tell the solution, but we'An Optimizer Bug and the Value of Building From ScratchEiso Kant [00:14:01]: So the - This - You're gonna test my memory here,Swyx [00:14:04]: Oh, okayEiso Kant [00:14:04]: So but I thinkSwyx [00:14:05]: DirectlyEiso Kant [00:14:05]: I think I can recall. So if you, so if you look at, So if you take like Adam as an optimizer, you have epsilonSwyx [00:14:12]: YeahEiso Kant [00:14:13]: Which is, right, like in the denominatorSwyx [00:14:14]: Momentum and weights. YeahEiso Kant [00:14:15]: Is exactly, in the denominator. And at the time, if I recall, you looked at like the early Llama papers and things like that. People were juicing epsilon, like, quite a bit. Like, they were, like, adding, I don't know if it was E minus four or whatever, like a high value for epsilon.Eiso Kant [00:14:31]: And if you think about this during training, it's like a bit weird and counterintuitive that we're adding noise to our optimizer by just adding effectively, like, a random number in the denominator, right? Like behind the decimal point. And I don't recall the exact bug, but it had - What I remember is once we solved it, we no longer had to juice epsilon as much as, like, was happening in the Llama paper and other places. and it was like one of those fundamental moments where we had trusted this paper that was out there, and we're like, “Oh, no, it has to be this way. It has to have this high value of epsilon.” But it made no sense to us intuitively. Like, why do you have to have this so high? Like, if you're just trying to avoid division by zero, why can't the value be extremely small? and that was like one of those moments where you realize like, okay, finding things out from scratch yourself builds a better intuition. Because the one thing you learn very quickly with model building is that your intuitions that you start with are gonna get beaten up so hard.Eiso Kant [00:15:33]: Right? Like - It's such an experimental science, that the things that seem obvious, you very quickly get to learn, like, you were wrong, and hopefully you figure out why, and sometimes you don't even.Swyx [00:15:45]: Yeah. yeah, so, one of the reasons that you, when you released your new models, Vibhu got really excited. I mean, everyone got really excited. But Vibhu led our paper club on it, and you guys sawEiso Kant [00:15:58]: YeahSwyx [00:15:58]: Obviously. maybe talk through some lessons learned in that, whatever you can disclose. we can focus on the model factory stuff, whatever you think is a good starting point.Model Building as EngineeringEiso Kant [00:16:08]: So I would say that our view from very early on in the company was that model building is ultimately 90% engineering.Eiso Kant [00:16:18]: And I think we all know it in the industry because if you look at where's every researcher spending their time, they're spending their time writing code, right? Looking at data and writing code. And so we said, okay, The state at the moment, like three years ago, was bash scripts and Slurm and spaghetti code bases for training and, like, data pipelines that were patched together. And we looked at this and said, “Well, ultimately, model building is a process.” You're going from raw data, right? Like training raw material, the web, et cetera. you're doing a whole bunch of filtering, cleaning up, transformations, analyzing. These days, that's, far more complex than it was three years ago. then you're training a model, which is effectively a large distributed systems problem, right? Across hardware that has still-- It's become a lot more reliable. It was extremely flaky back then. and now with every new generation, we get our new sets of challenges. And then you go into the next stages, right? There was no training back then, but, like, you got, your post-training and then your reinforcement learning. And so we looked at this and we said, “Well, this looks like an industrialized process. This looks like an end process, that every single part of it has its machinery,” right? If it's your big data pipelines, if it's your crawling ingestion of the web, if it's your, large-scale distributed training, and then you've got your reliability. And we said, “Well, why don't we take some of the world's smartest distributed systems engineers that we knew and make them part of the process of research from day zero?” Not retrofitting it later on, but, like, really from the beginning. And that became our model factory. And so our model factory started with a handful of components. Today, it's thousands of components, and I try to equate it to, if you think about, like, someone who was at the very early days of Foxconn, if they had been there for the following, decade, they would be able to rebuild Foxconn because they saw every decision that led to building that system and all the complexity. If you and I walk into Foxconn today, no chance.The Model Factory and Experiment VelocityEiso Kant [00:18:18]: Right? Because we don't have the lineage and history of decisions that led to that. And so we built early on from the beginning- with a team that really understood that, well, the metric that we are optimizing for is the speed of an idea from a researcher to an experimental result that we can trust to then being part of the next model training.Eiso Kant [00:18:42]: And in the. And because it's such an experimental science, ultimately, in the beginning when it wasn't that complex, you could patch your way around it, right? But now, at any foundation model company, you are running. I mean, we're a small team, right? We're less than 70 researchers, another 35 engineers. and we are running, I haven't checked the latest count, but far more than 10,000, maybe 10 to 20,000 experiments a month that we cut. And so if you look at that scale of every model run that is, like it's ultimately it's, it's you need to be able to trust it as an infra problem. And so what we have now done over the years is gotten really good at that, and just by working it and improving it and obsessing over those end decisions. So now what that means is that you looked up Laguna XS 2 that we launched. It was five weeks from the beginning of training to launch. The model that we're gonna talk about today was eight weeks from start of training, to launch. We started the next model literally yesterday because we now finished the post-training required for the model we're launching, next week or by the time this comes out today. and we move that compute to the much larger Laguna M model that we're now training. And so the model should be an artifact of someone's process. It shouldn't be really a thing in itself. Like, and we treat this like the way you would look at like a SpaceX factory where, yes, the first rocket, really hard to build, but the much harder challenge was building the factory. And now they're rolling off, and no one is really thinking about the next launch anymore. So it's just another launch, it's another launch, another rocket comes off. And that's what we're trying to do with model building.Eiso Kant [00:20:22]: And what has been, which was not planned from day zero, it was in the back of our mind like this will happen one day, is that when you build a really good end model factory with really good APIs and really good engineering systems, Well, what is it perfect for? It's perfect for agents.Agents Inside the Model FactoryEiso Kant [00:20:40]: Because agents are now starting to take over more and more work in our model factory.Vibhu [00:20:43]: Yeah.Eiso Kant [00:20:44]: So I look at the screens when I walk, like when we're, we come together, in our monthly, we do monthly onsites, and I walk behind people's screens and I stop by and I talk to our researchers. And the default is all of these different agents running on their screen that are writing the code. They're launching the jobs. They're evaluating the results that are coming back from the model runs. They are, making the changes. And we're still in the driver's seat. We're still coming up with the ideas. We're still helping with the debugging. But more and more, and this is right now very profound on the data side of our pipelines in both pre and post and the synthetic data pipelines, it's starting to become more on the architecture side as well. You're starting to see these twinklings of what RSI is gonna look like.Eiso Kant [00:21:27]: And that's. So when we talk about, like to your question about our models, every talk about the model factory, And my coolest example of these things is always that when we kick off a new run, doesn't matter if it's a training like big run or if it's now a post, like one of 10 post-training versions we do for like release or many experiments, is that at any given moment, the changes that somebody made that they had experimental results from the day before make it into that run.Eiso Kant [00:21:57]: So there's not like a cutoff 90 days before. Like no, it's like literally from that moment because we can now trust the machine enough. And then you also have to invest in the reliability. So one of my favorite metrics about like Laguna S is that there was no call events, Right? Like completely zero. And we haven't had a meaningful call event, like something to wake up for, as far as I recall this entire year. now there is one asterisk to that. In usually the first six hours of launching a new model run, something breaks because you set a config wrong, you made a small mistake, et cetera. So that's usually there's a little bit of intervention, but that's always within like call periods, right? Not on call. And I think that's starting to now compound. So the model we're releasing now, I love it. It's amazing, but we're already onto the next one. and I think that's the way it should be.Laguna, Five-Week Builds, and Zero On-Call EventsVibhu [00:22:50]: Hey, I also just wanna point out, so for context, this was like a month ago. we found it in the tech report, so we just came in with, “Okay, new model's dropped. Haven't heard about it.” We wereEiso Kant [00:23:02]: Yeah, we're very used to doing this every few months.Vibhu [00:23:03]: We're, we're very much like, “ okay, look, it's like, on par with Kimi, DeepSeek, whatnot, the small ones, Gemma level. Oh, it's a very cool paper on what goes into building.” And then we hit this page, right? Like literally page two of tech report is, “This process allowed us to build the small model from scratch to delivery within five weeks applying the lessons”. And then I'm like, oh, this paper is not about here's a tech report of benchmarks and here's how many tokens it was trained on. Like for people that wanna dive more from what we're not gonna discuss on the podcast, it's all laid out here, right? FromEiso Kant [00:23:38]: YeahVibhu [00:23:39]: Custom software that agents can use to interface with training code, training data.Eiso Kant [00:23:45]: Yeah. Well, link the paper correctly, so yeah.Vibhu [00:23:47]: Yeah. All that stuff. read the paper here, but,Technical Report Principles and Streaming Training DataEiso Kant [00:23:50]: But I would like to. I love principles, and I think that is a good starting off point for maybe telling some stories. Maybe we can go one by one past the principles. I'll just call out that Dagster just got bought by a Prefect.Vibhu [00:24:01]: Yeah.Eiso Kant [00:24:01]: Isn't it fun? But yes, I'm very familiar with Dagster. just anything where like they trigger some story.Vibhu [00:24:07]: So, well, I would say, well, experiments code's obvious, but I think one of my favorite things is, I don't know where it is in here, but early on, and I still think this is the case a lot of foundation model companies, people prepare their training data sets, they get packaged up, then they get copied over to a training cluster distributed across all of the nodes, and then training starts.Vibhu [00:24:30]: And we looked at this like three years ago and we were like That makes no senseEiso Kant [00:24:36]: You lose so much time because the moment you have to rematerialize the data set, you have to make a change, you have to fix something, et cetera, you've got all this time of like repackaging it, right? Toca- tokenizing it, repacking it, moving it over to a cluster, then distributing it across the nodes. The bigger your clusters are, you start using fancy like torrent-like algorithms to like distribute your data. So why aren't we streaming data into training? Right? Something that's very common and like just basicVibhu [00:25:00]: Like just in timeEiso Kant [00:25:01]: Just in time, like good computer science like principle. And that was one of the first things that I think unlocked - the model factory. Because the moment you start thinking about, well, a training job, it doesn't matter if it's a big hero run or a small like, post-training experiment, consumes a certain number of tokens per second, right? And it's not a lot, right? From a like a data, moving data perspective. So we said, well, we have our training cluster, and then we've got like our AWS kinda setup where we can build these amazing big data pipelines. We can set things up. We use Spark underneath the hood, like all these things.Vibhu [00:25:36]: But when you say AWS, it's not actual AWS, it's your internal AWS.Eiso Kant [00:25:39]: It's our internal-- No, it's our internal like just running like our infrastructureVibhu [00:25:42]: Site web servicesEiso Kant [00:25:43]: Exactly. Our stuff running on like an AWS account or on like any hardware, right?Vibhu [00:25:47]: Yeah.Eiso Kant [00:25:48]: And so once we made that shift into I can stream data into training, all of a sudden you realize a lot of things unlock. Because now you don't have to wait for the whole data set to materialize.Immutable Data, Experiments as Code, and Scientific RigorEiso Kant [00:26:00]: You now all of a sudden when you're running data experiments about mixing data, it's a config. Because you've got these data sources that are coming in, and you just - we have this service called Blender that's in the report, where we then say, “Okay, for this run, I want 20% of this source, 10% of this source. I want this much, so many epochs of repetition. I want this to be, shuffled in a certain way,” and your training job can start while the rest of the data is even still materializing. also what it does is because all of this underneath-- So for us, we treated the data layer underneath as like an immutable data layer, and that was really important. Like experiments as code, immutable data layer means that you can always go back and understand literally down to the single token at which cursor it went in on which version of the code.Vibhu [00:26:47]: Yeah.Eiso Kant [00:26:48]: And it took us a I have to admit, like the first year of Poolside, we understood that engineering had to get great, But we didn't understand yet, that this is ultimately in support of like a good rigorous scientific progress. We were quite a - We were a very small number of people, so a lot of it was YOLO ideas and YOLO runs.Vibhu [00:27:08]: Yeah.Eiso Kant [00:27:09]: And we built great infra for the YOLO runs. But once we realized that we treated data as immutable and code as always versioned, and you could always track and trace every experiment end to end perfectly, you could repeat everything perfectly, right? You have perfect reproducibility. I can still reproduce runs from two years ago if I wanted to, right? It enables the scientific progress, like the scientific process, and I think that took us probably about a year and a half into the company to figure out. We also had some great hires, like our head of applied research, Nikolai, who joined us from Yandex, who'd been working on language models since like the early 2020s, I think brought that into the company of like, “Hey, we wanna have even more rigor.” And then once we kinda had the combination of like increasingly more capable platform that allowed people to do more, but had this immutability, we were able to start “Okay, every experiment is truly an ablation. We truly need to understand it.” And I think we became much more scientifically rigorous in the last couple of years, and the infra underneath enabled it. and then there's just fun stuff like, andVibhu [00:28:16]: Yeah, a lot of it's fun, like even just the, one, you share all the ablations, two, picking the data sets, right? There's like a random small paragraph in here where it's just like, “Oh yeah, training data, we have some, we have an auto mixer.” it trains eight small models, scales them up, picks the training data set. We don't even need to look at it. I'm like, “Wow, a lot of engineering rigor there.” And there's just, there's just a lot in here.Publishing Research and Giving BackEiso Kant [00:28:40]: Yeah, and it'- and look, and we wanna put out more. Like we, We treat writing papers as something that we haven't earned the right for yet for a long time. So you earn the right to spend time, publishing research once you're at the frontier, because until then, you're catching up, and every minute and hour in this industry matters. Like I obsess over it, not just the wall clock time from idea to result, but just general like time every day that we, waste is one that doesn't allow us to catch up. But in this case, we said, “Okay, we're gonna give ourselves.” I think we gave the team like three or four days while still doing their work, like give everything in there. And to your point earlier, if your stuff, it's easy to like put it out. And so there's so many more things that we wanna talk about over time, and we will definitely start doing. And as we earn more of the right, but also now have like added to our mission that we want more foundation model companies to exist, you'll see us like be way more proactive, and just trying to keep dropping some of those like things that we've learned along the way that can help others like speed up.Vibhu [00:29:40]: Which is the other cool side of this, right? It's, it's not like, back to your point, it's not just here's the benchmarks of our training. If you want to replicate, here's experiments of optimizers, data sets, post-training. you lay out a lot of it here alongside here's your system for how to do it? So it's, it's really like promotingEiso Kant [00:29:59]: No, thank youVibhu [00:29:59]: Other people can do the same.Eiso Kant [00:30:00]: And by the way, I also wanna make clear, right, we have been incredible-- Like we've taken a lot of advantage of the fact of all the open research that others have published, Right? And you mentioned, the Chinese labs, and we I think it's important that there's, from every country and every culture and background, including like Western companies like us, there's different models that come out that people can choose to trust. But I think we do have to give credit where credit's due, right? The incredible Chinese lab have done an amazing job at sharing their research, and we have definitely like been on the receiving end of taking advantage of that. So when you're on the receiving end of something coming to you, I think it's, you also have an obligation to give back.Swyx [00:30:39]: Do you have a favorite or underrated Chinese lab that you wanna shout out? Everyone shout outs DeepSeek.Chinese Labs, Zhipu, and PersistenceEiso Kant [00:30:44]: That's a good question.Swyx [00:30:45]: Moaan obviously for Therapsi. Yeah.Eiso Kant [00:30:48]: Yeah, look, I think, I think obviously everyone's been talking about Zhipu lately, with 5.2. I think what most people don't realize is when they started.Swyx [00:30:59]: Yeah.Eiso Kant [00:30:59]: Right? They started years before ChatGPT.Swyx [00:31:02]: They just rebranded. YeahEiso Kant [00:31:03]: And so, I've like, I remember how hard it was to work on these things Before the rest of the world got excited about it. And so I have an immense amount of respect for people, who were working on improving models when it wasn't the sexy thing to do, when believing in LLMs, was gonna get you ridiculed. I remember like back in 2016 when we were doing what we'd call, machine learning on code with some of these models. we would-- people would just laugh at us, like they'd be like, “This makes no sense. Like why are you wasting all these, like, millions of dollars on trying to figure this out?” And so I would say they're probably the one that, I think deserves a shout-out, not just because their latest model is very good, but because they fought to get here. And I think, I think every foundation model company it takes time to get here, right? It took us three years to get to the model that we're, that we're now gonna be releasing. and now the time in between the models is coming, is counted in weeks. It's no longer counted in months or years. But this stuff's hard. and if we can make it a little bit easier for the next person, like we should all do so. Because if we don't do so, we're, we've got a small window before models are really impacting recursive self-improvement to a level where catching up otherwise might become unfeasible. And we should try to, in that window, encourage as many labs or however we wanna call them, like to start. And so one of my currentEiso Kant [00:32:36]: Mission, but qualm is like I wanna encourage whoever is a researcher right now who thinks they can tackle this to go and leave and become my competitor.Eiso Kant [00:32:45]: Like start another foundation model company because I think we need it. I think otherwise we're not gonna be in the world where, I don't want to just be the fifth or the sixth company that wins. I wanna look at a world where there's lots of choice.Starting a Foundation Model CompanyVibhu [00:32:57]: What else do people not see in starting a foundation model? it's, there's a lot of compute, there's a lot of capital required, a lot of compute. You lay out model factory and how to do the training, but there's a lot there, right? That's,Eiso Kant [00:33:10]: Well, look, it's, I in turn-- this is an oversimplification, and I always asterisk it with that because it can land a little bit the wrong way in people's minds. But I think you can sum down, And I saw it, 95% of model building to just doing, you're just doing two things. You're improving data or you're improving compute efficiency. And I know that feels like an oversimplification for the incredible, like, Gifted and skilled work people do. But if you really look at it, like what are we doing? We are looking at data, we're generating new data, we're improving data. and the only way to do that is to look at the data, right? That's a big part of foundation model building. And on the other hand, we come up with these incredible breakthroughs in inference, in architecture, and new attention mechanisms. But what are they really doing? They're bringing compute efficiency. Now, we have definitely had some breakthroughs over the years that allow for more model capabilities. But at the limit, if you could train a large enough model, right, like, and you had infinite compute, we probably-- if you had infinite compute, you'd be at AGI probably already tomorrow.Eiso Kant [00:34:12]: Right? Like it's not. And so, and let me say that infinite compute with infinite ability of much faster networking because networking ends up being more of the bottleneck than compute. But, so I do think that's, those are the main things. And to just realize that this is engineering. I think it's become more obvious, but I think for quite a few years, people have held foundation model companies and researchers and others on this pedestal of like you're doing incredible magic or rocket science, or only like, Nobel laureate physicists can do this. And don't get me wrong, there are some really hard problems that need to be solved, but a lot of the work that all of us are doing on a day Is not sitting down trying to solve a math theorem. A lot of the work that we're doing is just really doing the basics right, writing good code, looking at data, improving it, running experiments, looking at plots, trying to see like, hey, trying to shape our intuitions. And a lot more people could be highly capable researchers. and I think that's, it feels far for people to do so. But I've seen in our own company, we've seen engineers become researchers because the model factory allowed them to be, have a much lower hurdle of running experiments and trying things. And one of the guys on our team who started as an engineer building our agents is a legit reinforcement learning researcher now, making real progress. and that happened in the span of like six months. that would've not been what I think most people assumed was possible, a couple of years ago.Swyx [00:35:46]: Yeah. I think one of the interesting moments is when you can self-host, like, if in a programming language, like if you can compile the language in the language, the equivalent is can you use your own tools, right? You have the pool CLI, you have your own models. presumably you're not only using your own models. There's no way. But like, what's that percentage over time?Laguna S, Persistence, and Behavioral GainsEiso Kant [00:36:10]: This is the first model that we're releasing that is starting to meaningfully contribute to our own work. It's not a it's not state-art model yet. Fable and other, they're, they're very capable models, but Laguna S Is really interesting. I'm gonna pull up the quote. Peng Ming, one of our heads of applied research, said something, last week as the model came out about 10 days ago, much better than we had hoped for or expected. And he said, I have the feeling that a lot of the gains in Laguna S come not from more intelligence, but more from different behavior, more verification, less taking things for granted, not declaring victory early, and being way more persistent. And to be honest, those are more predictive than raw intelligence for success in human also to some degree. And this was, he wrote me this on 5th of July on a Sunday, and it's been burned in my brain ever since because the Laguna S model, as you'll see it and why it does so well on benchmarks and why it does so well in using it on a day basis, is that it's just incredibly persistent. It reasons a lot. I do call that out. We have work to do on making it more efficient. We have to work to do on offering different reasoning modes. But this is the model that has been able to do things that I never thought it could do. A hundred eighteen billion 8B active model, which is not that large. It fits on a DGX Spark and still runs at, thirty, forty tokens a second on a Spark, is able to solve Erdős 397 independently. It's able to do complex programming tasks. It's able to. I asked it this morning to make me a Fi scanner without using any external libraries on my Mac, and it's, like, figuring out, like, the core WLAN API by really persistently trying to understand it without access to the internet. And more, I love vibe checking. I've probably spent eight to ten hours a day with this model for the last ten days.Eiso Kant [00:38:05]: I'm not exaggerating. I was on my eleven-hour flight yesterday. I spent ten hours reading trajectories and traces and, like, of the model.Eiso Kant [00:38:12]: And what I take away from it is exactly what Peng Ming said. We are gonna be able to squeeze so much more out of smaller models than I think we had imagined in the industry because, yes, there's intelligence and larger models are more intelligent. Like, no doubt about it. We should continue to scale up. but the behaviors of being really persistent, of being able to backtrack when you're wrong, of, like, understanding how to interact with your environment show us that we can get a lot more out of it. And this, for me, has created a bit of a Question in my mind the last couple of days. If you think about where we're using models today, right? We are using models, say, for knowledge work. Represents twenty-five percent of the global economy, twenty-five trillion dollars of work.Eiso Kant [00:39:00]: As we scale up models and they become more intelligent, we are excited about using them more and more for pushing the frontier of science.Small Models, Knowledge Work, and CommoditizationEiso Kant [00:39:08]: And if you look at the frontier of science, like true breakthroughs in science, they have been linked, they are linked to more intelligence in many places. Einstein figuring out general relativity is able to bring ideas together that other people would have not brought together. And I think one of the many dimensions of intelligence is the ability to do that, and it's something we clearly see that as models get larger and more capable, they're able to pull more ideas and threads together that a smaller model wouldn't be able to.Eiso Kant [00:39:36]: And we're starting to see examples of that in medicine and, like, in bio and other things. But if you think about the majority of knowledge work that we do, and it includes building software. I'm a software developer at heart first and foremost probably, although I probably can't say it that much anymore as I don't write production code in years, is that what makes us good is our persistence. It's our ability to encounter a problem and backtrack and say, “I need to go figure out this bug. I need to go research this. I need to go look at the documentation. I need to, like, try different, five different ways to see, like, if I can solve it.” But it is not necessarily bringing three ideas together from radically different fields. And so if we are now seeing, and I think Laguna S is an example, that we are able to make a relatively small model much more capable than I had definitely predicted or any previous, like, benchmarks had shown for any model remotely this size or even larger, At least on coding tasks, that it's because of the behaviors. And so now the question I have, and I don't have an answer, it is I know at the limit, so infinite model size, right, extremely large model, and the cost of that model is gonna be very expensive to run. We know this, right? So larger model ROI.Eiso Kant [00:40:52]: So I know that at the very limit, I'm not gonna use the world's largest model one day, quadrillion parameter, whatever crazy, like, scale we scale up, to do a basic coding task. Already today, I'm starting to size down for certain tasks.Eiso Kant [00:41:07]: So it means that there is an optimal. It means there's some curve that goes as we go up to model size for knowledge work, at some point we're at the peak, and after that, the return on investment of using a bigger model, just doesn't make sense.Eiso Kant [00:41:22]: Now, I think the question is, before I would have thought that peak was extremely very far away.Eiso Kant [00:41:30]: This model for me is the first sign that Maybe that peak is At a trillion, five trillion, ten trillion. Maybe we can just squeeze way more out of these models. I'm no longer thinking that we need two or three orders of magnitude on the largest models to be able to, solve knowledge work, the accounting, the legal, the code that we write. And so if that holds true, It is an argument for the commoditization of models. It's an argument that open source can win and, like, succeed in this world. And now it's of course a self-serving argument and it's a hopeful argument, but theoretically at the limit it works. We just have to go discover in the next couple of years of how much more we can squeeze out. Now, I do want to put a big asterisk. This does not mean I'm against scaling models. I think we ultimately only succeed if we scale our models as large as our competition. I do not like. I think we should not put our head in the sand and say we're gonna be king of open source small models. I think that's, It's a out. It's trying to be king of your own kingdom, but not realizing what the rest of the world's doing. All of us rather use a smarter, faster, more model. It's a sign of hope. And so I don't wanna overly state this is a good model. We have a long way to go to get to the state-art. But what hopefully people take away when they use this model is that the behaviors inside of it are what push it to be far more capable, less than necessarily the number of parameters.Pre-Training, Mid-Training, and RL Moving EarlierVibhu [00:43:03]: Is that mostly post-training? LikeEiso Kant [00:43:05]: YesVibhu [00:43:05]: Right.Eiso Kant [00:43:06]: It's entirely post-training.Vibhu [00:43:08]: Are we done improving anything on training? Is, like, training done?Eiso Kant [00:43:12]: No.Vibhu [00:43:12]: Okay.Eiso Kant [00:43:13]: SoVibhu [00:43:13]: I just wanted to cover training, and then we go post-trainingEiso Kant [00:43:15]: Training is not done. I mean, look, there's a part of training of just dealing with skill, right? Every new order of magnitude of model skill, you are going to get new things you gotta solve for. That'- but those are ultimately, engineering challenges.Eiso Kant [00:43:31]: I have a, I would say, a not commonly held opinion that reinforcement learning Will move earlier and earlier into training.Vibhu [00:43:42]: Yeah, training.Eiso Kant [00:43:44]: Not even training. Like training today, right, is, like if you look at - So we've been working on this for years already. and I think the best-- I think the first time we saw it out in public was the DeepSeek Zero paper. this is a year and a half ago, I think, if I recall correctly. where, you can Very early on in a model as it starts capable of being able to use language, et cetera, induce reasoning. and so the question that I have is like, we have this- we have the dataset that's the web. and the web, I think we could arguably say probably has The totality of humanity's knowledge somewhere encoded in different places. It's a huge variance degree of quality, from garbage data, and like once you look at training data, you really get humbled of like what the web is, to like, the most greatest scientific papers and best blog posts and like, best transcripts and whatnot.Eiso Kant [00:44:39]: And so now What we are trying to figure out, and have been doing a lot of work on, and it's a place where maybe not as open as we're on other things, but we will become more over time. we've been spending a couple of years really doing research on how can we turn the web into not just next token prediction, but into a way to teach the model to think earlier in its training. and I think there's a huge amount of gold to be found there. I think we are right now in, we've got some drugs in the industry. One of the drugs is distillation. Another drug is, more environments. Like, and they're great, and they make us feel good, and they make the models better, and like we're all addicted to them, and we'll use them, right? in various different ways. and but ultimately, I think we are still barely squeezing out of the web what we should be getting out of the web.Eiso Kant [00:45:33]: I think just next token prediction during training is not enough.Eiso Kant [00:45:36]: AndVibhu [00:45:38]: YeahEiso Kant [00:45:38]: I think we'll see some very interesting things still happen. and that RL in post-training to induce behaviors, to improve things, like I think - the whole world knows how to do this now. I think we're, we're scaling it up. Everyone is. But I wonder if we need to go as far as we're going today with environments. I'm not sure yetVibhu [00:46:01]: You mean we're going too far?Eiso Kant [00:46:02]: I'm, I'm not sure if the path to AGI is justVibhu [00:46:06]: Is more environmentEiso Kant [00:46:07]: More environments.Vibhu [00:46:08]: It seems like a never-ending, “Okay, I want instruction manual for this table, right? Am I gonna environment out building furniture? Or are we just gonna tail end like we need some general solution?”Eiso Kant [00:46:19]: I think there is, I think there's an ability to generalize more from the web. but I also am very encouraged, like when I look at Laguna S and, which is post-training is, well, is the big impact there. and I see like, oh, wait a second, just by making some of these behaviors much better, we're able to get so much more out of it. It just changes a little bit the way you think about intelligence.Vibhu [00:46:40]: Yeah. The analogy people draw often is the RL phase is where you don't learn as much new knowledge. You shiftEiso Kant [00:46:46]: Yeah.Vibhu [00:46:46]: Yeah. So, you shift distribution, and you can have it reason towards what you want. on your point about training, a lot of training is still just continue training in a domain, say medicine, then you do RL. So still justEiso Kant [00:47:00]: It's just better data, right? Like, I mean, training, ooh, I like how we invented this word. Like it's effectively just like,Vibhu [00:47:06]: Second phaseEiso Kant [00:47:07]: It's the second phase of training With like a really dumb way to do a curriculum. But like ultimately, what you'd want is a curriculum from token zero to token 30 whatever or 40 trillion tokens that really truly is the optimal curriculum for the model to learn. But training is essentially a stage curriculum on the web because we do not have to compute, And, effectively to try to ablate the perfect curriculum, right? And so I'm pretty sure that you'll start to see people talking soon about some other term, and there's two or - ‘cause now we do this, right? We talk stage two and stage three and stage four training and like. But ultimately, all we're doing is we're trying to assign a curriculum to the web data that we have to allow the model to learn better. I think at some point, as things get compute, as models get cheaper to run, as the next generations of compute, this will become more of a continuous spectrum. I also think the reason, by the way, you have training and like stage two and stage three is organizational, Right? It'- this is, I think, a thing where-- that we really try to avoid with the model factory is like Training exists because there's a training team now, right? There's people, or like people in training decide to focus on like a training effort. but what you really want is engineering and scale of experiments that allows for a much more continuous spectrum that you don't, you have infinite stages. Now, we're not there. Compute's not there. Organization design is not there for it yet. but I think we'll get there. we'll look back on a couple of years and be like, “Oh my God, it was so cute that we did our training data like this in such a like naïve way. Like we barely ordered it. We didn't really do a good job at likeCurriculum, Auto Research, and New ObjectivesVibhu [00:48:48]: The building that curriculum will get you that in the industry.Eiso Kant [00:48:51]: And I'll confirm that, when I talk to some researchers that this is a lot of the focus now is like how does training change and what is the next objective other than, next token prediction. I assume you don't have the answers, but you have some ideas.Vibhu [00:49:02]: We have some ideas. We're not ready to talk about it yet.Eiso Kant [00:49:05]: Yeah.Vibhu [00:49:05]: We've been working on them for years, and I think that's the one thing that's also like you asked earlier about, like what's not obvious about building a foundation model company is that you are constantly balancing the table stakes work, the recipe worksEiso Kant [00:49:19]: Yeah.Vibhu [00:49:19]: Versus like your, my crazyEiso Kant [00:49:22]: Pure researchVibhu [00:49:22]: Breakthrough.Eiso Kant [00:49:22]: Yeah.Vibhu [00:49:22]: Pure research and finding that balance and adjusting the percentage to it based on where you are in the race is really important.Eiso Kant [00:49:31]: I mean, so like, this is a nice way. I was gonna bring up auto research at some pointVibhu [00:49:35]: YesEiso Kant [00:49:35]: As another Andrej invention, or coinage, which is like, I honestly, like how many objective functions can there be, right? Like just try 1,000 of them, set it running, whatever.Vibhu [00:49:47]: Man, it's alsoEiso Kant [00:49:48]: Like what you're looking for. You're looking for loss curves like that, likeVibhu [00:49:51]: It's also a thing people take bets on, right? When you say more Neo labs, you're doing a version of we'll do foundation models, scale them up, next token predictors. A lot of other Neo labs that we see want to take a completely different approach, right? At some level, you're right. It's all, compute efficiency, and that's the net objective. But some are okay, different architecture, like vastly different amounts of compute spend. So some are different. They're not justEiso Kant [00:50:19]: YeahVibhu [00:50:19]: They're like, 99% not balancing, here's the vanilla and scale up. They're 99% on, here's novel research that'll change everything.Eiso Kant [00:50:27]: And I think, Luke, I think you. It depends when you started as well, right?Pure Research vs. Table StakesVibhu [00:50:30]: Yeah.Eiso Kant [00:50:30]: When we started, like the novel thing we did was reinforcement learning on code. No long- that's no longer novel by far, but we were like, - that's where we obsessed over when no one believed in RL. So you have to when you start the company, you have to have your own idea. You have to have something that's different that allows you to speed up, right? For us, it was RL to LLMs that later became common, like, Knowledge. But in the beginning, it wasn'tVibhu [00:50:53]: It's cool. this was like your original 2023 blogEiso Kant [00:50:57]: YeahVibhu [00:50:57]: Of purpose.Eiso Kant [00:50:58]: Yeah.Vibhu [00:50:59]: And like you do lay it all out here.Eiso Kant [00:51:01]: We laidVibhu [00:51:01]: The blog is pretty underrated, right? The whole RL on code was very early on.Eiso Kant [00:51:06]: Very early. And even we had to argue with people, like we say here things like to push beyond current capability, to train your own foundation model. We had to argue with people that it mattered that you had your own like, base model. you can fine-tune your way to success, right? major capabilities emerge from training a base model made accurate and useful during fine-tuning.Vibhu [00:51:23]: Which like, for perspective at the time, we knew closed models, OpenAI, Anthropic were huge. The open models we had were like Mistral 7B, a 30B, a 70B.Eiso Kant [00:51:35]: When weVibhu [00:51:35]: YeahEiso Kant [00:51:36]: The date on this thing is wrong. When we published this, it was April 2023. I think this was justVibhu [00:51:42]: YeahEiso Kant [00:51:42]: Happened on a migration, probably found it on archive.org.Vibhu [00:51:45]: Mistral.Eiso Kant [00:51:46]: Mistral had started, we started on the same month, right?Vibhu [00:51:49]: Yeah.Eiso Kant [00:51:49]: So this wasn't even, there was only, I think, Llama out at the timeVibhu [00:51:52]: SnellEiso Kant [00:51:52]: And that's it, right? And so, but I agree. I think we wan

"I need to stop using Opus. This doesn't work." That was Heitor Lessa's conclusion after a refactor cost him 200 million tokens, and it forced him to rebuild the entire agent workflow now available for 1400 engineers. Heitor spent 11 years at AWS, built Lambda Powertools to 230 billion API calls a week, and in this episode he walks through the full SDLC workflow on screen, from discovery to merge check.In this episode, we cover:The product loop: discovery, whiteboarding, and the /roadmap commandSpec-driven development with Open Spec and why vanilla setups failThree model tiers: SOTA for planning, mid-tier for implementation, cheap models for reviewsMerge checks with adversarial reviewers and attestations that catch agents fabricating test resultsThe /retro command: using the Socratic method to make your workflow more deterministicIf you're an engineer figuring out how to work with agents at team scale without losing trust in your codebase, this is the workflow to steal. This is also the first Beyond Coding episode with visuals on screen, so let me know what you think of the format.Timestamps:00:00:00 - The Math Doesn't Add Up00:00:43 - Amazon Hypergrowth: 11 Years, 8 Different Roles00:03:29 - Learning From the Trenches as a Technical Account Manager00:08:38 - Developer Identity and the Birth of Lambda Powertools00:10:20 - The Hard Parts of Working in Public00:13:12 - How Powertools Hit 230 Billion API Calls a Week00:16:42 - Career Advice: Learn Adjacent Roles, Not More Tech00:19:37 - When Leadership Decisions Don't Make Sense to You00:23:21 - The Product Loop Starts With Discovery00:25:22 - From Whiteboard to /roadmap00:27:37 - Why Humans Plan First and Agents Come Second00:30:33 - Commands vs Skills Across 32 Different Models00:33:38 - Adversarial Reviewers on Every Plan00:36:07 - The Socratic Method, Explained00:40:29 - Why He Only Takes Paper Notes00:44:43 - The Five-Line Paper Trick for High-Stakes Meetings00:48:18 - /new-work: Capturing Scope Creep Without Derailing00:54:03 - The Dev Loop Begins: Open Spec Explore00:56:34 - Three Model Tiers: SOTA, Mid, Cheap00:57:43 - The $5,000/Month Per Engineer Question00:58:57 - Guardrails vs Autonomy for 1,400 Engineers01:04:22 - Auto-Sizer: Does This Task Even Need a Spec?01:07:26 - Decision Fatigue and Why Frameworks Win01:09:10 - The Plan Phase: Specs, Design, Formal Verification01:13:07 - The Refactor That Cost 200 Million Tokens01:15:11 - When Agents Forge Evidence They Ran Your Tests01:17:27 - Local-First Architecture Explained01:23:04 - The Apply Phase: Fully Autonomous Loops01:24:30 - Coding Was Never the Bottleneck01:26:39 - Why This Workflow Is an Investment01:27:39 - Decision Logs and the /onboarding Command01:29:06 - Running Agents Locally With Enterprise Governance01:32:42 - Hooks: Making Quality Gates Deterministic01:36:02 - Merge Checks: 15 Adversarial Reviewers Per Change01:38:30 - /retro: Interviewing Yourself to Improve the Loop01:43:12 - Trust, Loss of Trust, and Recovery With Agents01:48:02 - Experience, Scars, and Critical Thinking01:49:32 - Why Right Now Is the Time to Experiment01:52:04 - Conviction Comes From Being in the Loop#softwareengineering #aiagents #aws

This Week on the Toy Power Podcast; we dive back into our regular News Segment - with quite a bit to cover! First off, a recap from Episode 1 (of 4) from Trent's latest Lego Masters Bricktacula Event! Then, an exciting announcement from Cards & Collectables season 2 - now with an official release date! A New 4-Armed Beast is announced for the MOTU Origins line; who is hyped for this?! New Movie MOTU characters announced plus some awesome TMNT plus Ghostbusters multi-packs from Mondo. S7 Silverhawks keep on delivering more characters; & Blokees tease an Absolute Batman. McFarlane announces more Batman related characters; then Robosen showcase the most Superior Decepticon of them all! The Cross-Over nobody asked for? -Transformers X Scooby Doo offer. Sometimes you think that some modern toys can't do any better; but somehow, SH Figuarts said "Hold My Beer!" Lego showcase the first round of Pokémon Mini-Figures plus in scale Pokémon Characters too; all in a very nice looking Build. Then Ben tests the lads with the return of The Quiz! Is it too hard? How many did you know? Support the show: http://patreon.com/toypowerpodcastSee omnystudio.com/listener for privacy information.

In this episode we meet Becky Spiceland N4BKY, Mike Spiceland N4FFF and Barb Asuroglu WB2CBA the amateur radio operators behind the Pebble radio project.  The Pebble is a small 20m HF QRP transciever.  Becky and Mike are avid portable radio operators and wanted a low cost radio that would be great for hams doing POTA and SOTA.  They teamed up with Barb, who led the development of the Pebble radio.  Met the team and their journey from an idea to a working radio.    

We go live on Facebook to celegrate anotehr milestone episode and without burying the lead, there is chaos. And maybe a Lord of Destruction or two. We go card-based for this ep and unboxed a sealed pack of Batman Returns cards from 1992. Can we get the full set? Who has the best costume? Who had the best comment? Is Scoty over-worked and under paid? If the audio sounds odd then check out the mid on Facebook to see our faces fit for radio! Thanks to all our wonderful Patreons and longtime supporters - we couldn't have done this without you! Support the show: http://patreon.com/toypowerpodcastSee omnystudio.com/listener for privacy information.

GGROSSY guestmix: 01. Intro 02. GGrossy - Beware The Shadows 03. GGrossy - Cold Blood 04. GGrossy - Anomaly 05. GGrossy - Blaze Break 06. GGrossy - Outside 07. GGrossy - No Turning Back 08. GGrossy - Brave 09. GGrossy - Signal Code 10. GGrossy - Elephant Stomp 11. GGrossy - Heavy 12. GGrossy - Do My Thing 13. GGrossy - Push It 14. GGrossy - Hold It 15. GGrossy - First Contact 16. GGrossy - Reload 17. GGrossy - Toxic 18. GGrossy - Take A Chance 19. GGrossy - Dance With Me 20. GGrossy - Out Of Tune Flute 21. GGrossy - Want It 22. GGrossy - Bass Line 23. GGrossy - Never Back Down 24. GGrossy - Spirit Speaks 25. GGrossy, M.L.D - Soundz Crazy 26. GGrossy - Destroy 27. GGrossy - Understand 28. GGrossy - Spend The Night 29. GGrossy - Face Fuckts 30. GGrossy - Crazy Enough 31. GGrossy - Spinback 32. GGrossy - Say What You Want GVOZD vibez: 01. Koven - hEarTbeAt 02. Clsm, Foor, Kora Wang - Sometimes 03. Pantherium - Hold On 04. Special Guests! - In The Dark 05. Gntlman & Chillhomers - Rise 06. 5x, Reconnect, Jess Robyn - Otherside 07. Method, Diandra Faye - Dopamine 08. 10xx - I Know 09. Irontype - Want You 10. 1991, Luude - All We Do Is Dance 11. Kanine - Wide Awake 12. Gaja - Think About It 13. Lexed - How (we doin' it) 14. Lixed - Levitate 15. Blackman - BAD GIRL 16. Krasia - Body Fall 17. Plago - Basscharger 18. State Of Disorder - Addictive 19. Dread Mc, Maedm - PSYCHO 20. Sub Cat - Orbit 21. Event Horizon, Kaizah, The Landlord, Madrush Mc - Warning Calling 22. Bad Syntax - Database 23. Alpha Rosa - Caught Up 24. Moekel - Sun Dips Low 25. Phasebound - Smashbro 26. Parallel - Filter Freak 27. Macoumba - Original 28. Rift - Mercury 29. Molecular - My Way 30. Cutworx - Stubborn 31. Milo - Roughest 32. Fabric8 - Fake Gods V.I.P 33. Visulant - Gangster 34. Celestial & Cinet - Finger Trace 35. Dlr - From The Inside 36. Need For Mirrors - Roly Poly 37. Clipz, Shy Fx, Vybz Kartel - Just Love 38. DJ Die - New Way 39. Sub Killaz - No Better Than This 40. Bou, Toxinate - Bounce (Kefrennnn Remix) 41. Bou, Sota, B Live - Bodybag 42. Benny Page, Mc Sye - Dubplate Secrets 43. Benny L, Shimon - Sharks VIP 44. Solix, Bunnerz - Risk It All 45. Higher Sector, Maddy V - Welcome To The Trip 46. Nu Elementz & Wavysof - Feel It Now 47. Kenna - Stop That feat. Gifta 48. Flint & Figure - See The Light 49. Syphon, Qua Rush, Wyr - Shelby Moves (Qua Rush Remix) 50. Veak - We All Need That Funk 51. Jody Sternberg - Shine (Zero T Remix) 52. Catching Cairo, Turno - Fingerprints 53. Jaise - Strange Ways 54. Justin Hawkes & Audioscribe - Wayward 55. Jflux - Stingwing 56. Syran - OSG 57. Logistics - Chant (Lens & Unglued Remix) 58. Mish - Modern Jungle 59. Kara & DJ Millz - Alley Cat 60. Encryption - Jungle Insanity 61. P Money, Whiney - New Life 62. Mage - Let Me Love 63. Grace Barton - Loved By You 64. Bcee, Javeon - What You're Missing 65. Cyber Posix - Rooftop Jumping 66. Leaf Dog - Hear To Remind You (Shiny Radio Bootleg) 67. Etherwood, Lottie Jones - Roam 68. Hiraeth - Magic Harp 69. Mockbravado - Blue Notes & Basslines 70. Lsb - Without You 71. Hyrah - Too Late 72. Keylo - Take My Hand

GGROSSY guestmix: 01. Intro 02. GGrossy - Beware The Shadows 03. GGrossy - Cold Blood 04. GGrossy - Anomaly 05. GGrossy - Blaze Break 06. GGrossy - Outside 07. GGrossy - No Turning Back 08. GGrossy - Brave 09. GGrossy - Signal Code 10. GGrossy - Elephant Stomp 11. GGrossy - Heavy 12. GGrossy - Do My Thing 13. GGrossy - Push It 14. GGrossy - Hold It 15. GGrossy - First Contact 16. GGrossy - Reload 17. GGrossy - Toxic 18. GGrossy - Take A Chance 19. GGrossy - Dance With Me 20. GGrossy - Out Of Tune Flute 21. GGrossy - Want It 22. GGrossy - Bass Line 23. GGrossy - Never Back Down 24. GGrossy - Spirit Speaks 25. GGrossy, M.L.D - Soundz Crazy 26. GGrossy - Destroy 27. GGrossy - Understand 28. GGrossy - Spend The Night 29. GGrossy - Face Fuckts 30. GGrossy - Crazy Enough 31. GGrossy - Spinback 32. GGrossy - Say What You Want GVOZD vibez: 01. Koven - hEarTbeAt 02. Clsm, Foor, Kora Wang - Sometimes 03. Pantherium - Hold On 04. Special Guests! - In The Dark 05. Gntlman & Chillhomers - Rise 06. 5x, Reconnect, Jess Robyn - Otherside 07. Method, Diandra Faye - Dopamine 08. 10xx - I Know 09. Irontype - Want You 10. 1991, Luude - All We Do Is Dance 11. Kanine - Wide Awake 12. Gaja - Think About It 13. Lexed - How (we doin' it) 14. Lixed - Levitate 15. Blackman - BAD GIRL 16. Krasia - Body Fall 17. Plago - Basscharger 18. State Of Disorder - Addictive 19. Dread Mc, Maedm - PSYCHO 20. Sub Cat - Orbit 21. Event Horizon, Kaizah, The Landlord, Madrush Mc - Warning Calling 22. Bad Syntax - Database 23. Alpha Rosa - Caught Up 24. Moekel - Sun Dips Low 25. Phasebound - Smashbro 26. Parallel - Filter Freak 27. Macoumba - Original 28. Rift - Mercury 29. Molecular - My Way 30. Cutworx - Stubborn 31. Milo - Roughest 32. Fabric8 - Fake Gods V.I.P 33. Visulant - Gangster 34. Celestial & Cinet - Finger Trace 35. Dlr - From The Inside 36. Need For Mirrors - Roly Poly 37. Clipz, Shy Fx, Vybz Kartel - Just Love 38. DJ Die - New Way 39. Sub Killaz - No Better Than This 40. Bou, Toxinate - Bounce (Kefrennnn Remix) 41. Bou, Sota, B Live - Bodybag 42. Benny Page, Mc Sye - Dubplate Secrets 43. Benny L, Shimon - Sharks VIP 44. Solix, Bunnerz - Risk It All 45. Higher Sector, Maddy V - Welcome To The Trip 46. Nu Elementz & Wavysof - Feel It Now 47. Kenna - Stop That feat. Gifta 48. Flint & Figure - See The Light 49. Syphon, Qua Rush, Wyr - Shelby Moves (Qua Rush Remix) 50. Veak - We All Need That Funk 51. Jody Sternberg - Shine (Zero T Remix) 52. Catching Cairo, Turno - Fingerprints 53. Jaise - Strange Ways 54. Justin Hawkes & Audioscribe - Wayward 55. Jflux - Stingwing 56. Syran - OSG 57. Logistics - Chant (Lens & Unglued Remix) 58. Mish - Modern Jungle 59. Kara & DJ Millz - Alley Cat 60. Encryption - Jungle Insanity 61. P Money, Whiney - New Life 62. Mage - Let Me Love 63. Grace Barton - Loved By You 64. Bcee, Javeon - What You're Missing 65. Cyber Posix - Rooftop Jumping 66. Leaf Dog - Hear To Remind You (Shiny Radio Bootleg) 67. Etherwood, Lottie Jones - Roam 68. Hiraeth - Magic Harp 69. Mockbravado - Blue Notes & Basslines 70. Lsb - Without You 71. Hyrah - Too Late 72. Keylo - Take My Hand

In this roundtable episode, Dan KC7MSU, Brian W7JET, and I debrief on Field Day, touch on the 13 Colonies special event, and I share highlights from a three day SOTA excursion with my wife W7NRS just before Field Day.Superstition Amateur Radio Club: https://superstitionarc.org/Morsle CW Practice: https://morsle.fun/Join us as we explore how you can get involved in portable radio, QRP, and more in this episode of the All Portable Discussion Zone (AP/DZ). Every aspect of portable operations is covered in this biweekly podcast, from news and gear to achievements, the workbench, contests, awards, and beyond.**SolderSmoke DISCORD INVITE**: https://discord.gg/GYVRZSBVFCConnect with us:* Discord: https://discord.gg/WVE3vVveWU* YouTube: https://www.youtube.com/c/redsummitrf* TikTok: @redsummitrf* X (formerly Twitter): @NJ7V_Support the channel:* Buy us a Coke: https://www.buymeacoffee.com/RedSummitRF* Red Summit RF Amazon Storefront: https://www.amazon.com/shop/redsummitrf

This Week on the Toy Power Podcast; we have Special Guest: Sean in the Studio to chat all things DC related, with a big focus on Batman & the wider Justice League themselves. Kicking things off, we have our routine Get-To-Know-You questions. Then with a Fantastic recent visit to see Sean's Awesome Collection in person; we highlight a few things to chat towards; including the very controversial & quickly banned Jarts Toy from the early 1960's. Then we get into a discussion about Batman. Everything from Batman 66' & why that particular TV Run is so special to Sean. Highlights about the Batmobile & it's Origins; all the way covering the dynamic characters in the Show! We next pivot across to the Ground-Breaking Toy-line that was Kenner Super Powers. With Sean's expertise about the line, the variants & waxing on just why this particular Toyline out-shines many other interpretations of Super Heroes in Toy form. Then backing up that Super Powers discussion; we entertain another segment of The Team! This round focusing purely on the Heroes from the Kenner Super Powers Run! (with a purpose adjustment to the line-up, so that some certain characters don't get all the Votes!) - Who would you have picked?? Enjoy this fun filled ep! In this episode we promote Sean's good friend Glenn Pluck - whom crates Fantastic Acrylic Cases for your Toys & Collectables. Be sure to check out his Business site: Ultimate Figure Protection on all the social sites or directly via his Website: www.ultimatefigureprotection.com Milestone Episode #450 will be Broadcast LIVE on Facebook & potentially other streams, on Saturday July 11th @8pm (Adelaide, South Australian time). Tune in for Visual Fun, Interaction & Give Aways! Support the show: http://patreon.com/toypowerpodcastSee omnystudio.com/listener for privacy information.

Väkivaltaiset kriisit rapauttavat ihmiskunnan kulttuuriperintöä. Toisaalta yhteiset kokemukset ja muistot auttavat selviytymään vaikeina aikoina. Maailmanpolitiikan arkipäivää-ohjelmassa tarkastellaan tällä viikolla sitä, miten kulttuuria voidaan suojella sodan tuhoilta. Ukrainassa apuna on digitalisaatio. Lähi-Idässä tuhottujen kohteiden jälleenrakennukseen ja entisöintiin yritetään päästä mahdollisimman pian kriisin laannuttua. Epävakaat ajat lisäävät riskejä kulttuurikohteisiin myös vakaissa maissa. Mielipide halutaan esiin mahdollisimman näyttävästi esimerkiksi värjäämällä monumentteja tai tuhrimalla taideteoksia. Kuulemme, miten turvajärjestelyjä hoidetaan Oslon kuulussa Munck-museossa. Maailmanpolitiikan arkipäivää -ohjelman toimittavat Maxim Fedorov ja Erja Tuomaala. . Äänitarkkailijana on Juha Hjelm. Tunnusmusiikki: Petri Alanko, kuva: Tuuli Laukkanen/Yle.

AI is now uncovering and fixing thousands of hidden software bugs faster than humans can keep up, but not everyone is playing by the rules. Find out how state-sponsored attackers and careless disclosures are turning the cybersecurity playbook upside down. Win10's popularity forces another year of free updates. CISA directs all federal agencies to update their UniFi OS devices. CISA gave federal agencies "the weekend" to update Cisco devices. Australia is disturbed by a deeply compromised infrastructure provider. OpenAI introduces Daybreak-powered "Patch the Planet" initiative. Meta's employee monitoring-for-AI-training backfired badly. Script Kiddies figure out how to use AI to find vulnerabilities. AI improves with "looping", "repeating" or "iterating". A wonderful story about Kevin Mitnick. Serious hackers mistakenly left a server directory accessible Show Notes - https://www.grc.com/sn/SN-1085-Notes.pdf Hosts: Steve Gibson and Leo Laporte Download or subscribe to Security Now at https://twit.tv/shows/security-now. You can submit a question to Security Now at the GRC Feedback Page. For 16kbps versions, transcripts, and notes (including fixes), visit Steve's site: grc.com, also the home of the best disk maintenance and recovery utility ever written Spinrite 6. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: threatlocker.com/twit hoxhunt.com/securitynow cohesity.com/Resilience zscaler.com/security

AI is now uncovering and fixing thousands of hidden software bugs faster than humans can keep up, but not everyone is playing by the rules. Find out how state-sponsored attackers and careless disclosures are turning the cybersecurity playbook upside down. Win10's popularity forces another year of free updates. CISA directs all federal agencies to update their UniFi OS devices. CISA gave federal agencies "the weekend" to update Cisco devices. Australia is disturbed by a deeply compromised infrastructure provider. OpenAI introduces Daybreak-powered "Patch the Planet" initiative. Meta's employee monitoring-for-AI-training backfired badly. Script Kiddies figure out how to use AI to find vulnerabilities. AI improves with "looping", "repeating" or "iterating". A wonderful story about Kevin Mitnick. Serious hackers mistakenly left a server directory accessible Show Notes - https://www.grc.com/sn/SN-1085-Notes.pdf Hosts: Steve Gibson and Leo Laporte Download or subscribe to Security Now at https://twit.tv/shows/security-now. You can submit a question to Security Now at the GRC Feedback Page. For 16kbps versions, transcripts, and notes (including fixes), visit Steve's site: grc.com, also the home of the best disk maintenance and recovery utility ever written Spinrite 6. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: blackhat.com/us-26 and use code TWIT XBOW.com hoxhunt.com/securitynow cohesity.com/Resilience zscaler.com/security

AI is now uncovering and fixing thousands of hidden software bugs faster than humans can keep up, but not everyone is playing by the rules. Find out how state-sponsored attackers and careless disclosures are turning the cybersecurity playbook upside down. Win10's popularity forces another year of free updates. CISA directs all federal agencies to update their UniFi OS devices. CISA gave federal agencies "the weekend" to update Cisco devices. Australia is disturbed by a deeply compromised infrastructure provider. OpenAI introduces Daybreak-powered "Patch the Planet" initiative. Meta's employee monitoring-for-AI-training backfired badly. Script Kiddies figure out how to use AI to find vulnerabilities. AI improves with "looping", "repeating" or "iterating". A wonderful story about Kevin Mitnick. Serious hackers mistakenly left a server directory accessible Show Notes - https://www.grc.com/sn/SN-1085-Notes.pdf Hosts: Steve Gibson and Leo Laporte Download or subscribe to Security Now at https://twit.tv/shows/security-now. You can submit a question to Security Now at the GRC Feedback Page. For 16kbps versions, transcripts, and notes (including fixes), visit Steve's site: grc.com, also the home of the best disk maintenance and recovery utility ever written Spinrite 6. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: blackhat.com/us-26 and use code TWIT XBOW.com hoxhunt.com/securitynow cohesity.com/Resilience zscaler.com/security

AI is now uncovering and fixing thousands of hidden software bugs faster than humans can keep up, but not everyone is playing by the rules. Find out how state-sponsored attackers and careless disclosures are turning the cybersecurity playbook upside down. Win10's popularity forces another year of free updates. CISA directs all federal agencies to update their UniFi OS devices. CISA gave federal agencies "the weekend" to update Cisco devices. Australia is disturbed by a deeply compromised infrastructure provider. OpenAI introduces Daybreak-powered "Patch the Planet" initiative. Meta's employee monitoring-for-AI-training backfired badly. Script Kiddies figure out how to use AI to find vulnerabilities. AI improves with "looping", "repeating" or "iterating". A wonderful story about Kevin Mitnick. Serious hackers mistakenly left a server directory accessible Show Notes - https://www.grc.com/sn/SN-1085-Notes.pdf Hosts: Steve Gibson and Leo Laporte Download or subscribe to Security Now at https://twit.tv/shows/security-now. You can submit a question to Security Now at the GRC Feedback Page. For 16kbps versions, transcripts, and notes (including fixes), visit Steve's site: grc.com, also the home of the best disk maintenance and recovery utility ever written Spinrite 6. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: blackhat.com/us-26 and use code TWIT XBOW.com hoxhunt.com/securitynow cohesity.com/Resilience zscaler.com/security

AI is now uncovering and fixing thousands of hidden software bugs faster than humans can keep up, but not everyone is playing by the rules. Find out how state-sponsored attackers and careless disclosures are turning the cybersecurity playbook upside down. Win10's popularity forces another year of free updates. CISA directs all federal agencies to update their UniFi OS devices. CISA gave federal agencies "the weekend" to update Cisco devices. Australia is disturbed by a deeply compromised infrastructure provider. OpenAI introduces Daybreak-powered "Patch the Planet" initiative. Meta's employee monitoring-for-AI-training backfired badly. Script Kiddies figure out how to use AI to find vulnerabilities. AI improves with "looping", "repeating" or "iterating". A wonderful story about Kevin Mitnick. Serious hackers mistakenly left a server directory accessible Show Notes - https://www.grc.com/sn/SN-1085-Notes.pdf Hosts: Steve Gibson and Leo Laporte Download or subscribe to Security Now at https://twit.tv/shows/security-now. You can submit a question to Security Now at the GRC Feedback Page. For 16kbps versions, transcripts, and notes (including fixes), visit Steve's site: grc.com, also the home of the best disk maintenance and recovery utility ever written Spinrite 6. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: blackhat.com/us-26 and use code TWIT XBOW.com hoxhunt.com/securitynow cohesity.com/Resilience zscaler.com/security

AI is now uncovering and fixing thousands of hidden software bugs faster than humans can keep up, but not everyone is playing by the rules. Find out how state-sponsored attackers and careless disclosures are turning the cybersecurity playbook upside down. Win10's popularity forces another year of free updates. CISA directs all federal agencies to update their UniFi OS devices. CISA gave federal agencies "the weekend" to update Cisco devices. Australia is disturbed by a deeply compromised infrastructure provider. OpenAI introduces Daybreak-powered "Patch the Planet" initiative. Meta's employee monitoring-for-AI-training backfired badly. Script Kiddies figure out how to use AI to find vulnerabilities. AI improves with "looping", "repeating" or "iterating". A wonderful story about Kevin Mitnick. Serious hackers mistakenly left a server directory accessible Show Notes - https://www.grc.com/sn/SN-1085-Notes.pdf Hosts: Steve Gibson and Leo Laporte Download or subscribe to Security Now at https://twit.tv/shows/security-now. You can submit a question to Security Now at the GRC Feedback Page. For 16kbps versions, transcripts, and notes (including fixes), visit Steve's site: grc.com, also the home of the best disk maintenance and recovery utility ever written Spinrite 6. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: blackhat.com/us-26 and use code TWIT XBOW.com hoxhunt.com/securitynow cohesity.com/Resilience zscaler.com/security

AI is now uncovering and fixing thousands of hidden software bugs faster than humans can keep up, but not everyone is playing by the rules. Find out how state-sponsored attackers and careless disclosures are turning the cybersecurity playbook upside down. Win10's popularity forces another year of free updates. CISA directs all federal agencies to update their UniFi OS devices. CISA gave federal agencies "the weekend" to update Cisco devices. Australia is disturbed by a deeply compromised infrastructure provider. OpenAI introduces Daybreak-powered "Patch the Planet" initiative. Meta's employee monitoring-for-AI-training backfired badly. Script Kiddies figure out how to use AI to find vulnerabilities. AI improves with "looping", "repeating" or "iterating". A wonderful story about Kevin Mitnick. Serious hackers mistakenly left a server directory accessible Show Notes - https://www.grc.com/sn/SN-1085-Notes.pdf Hosts: Steve Gibson and Leo Laporte Download or subscribe to Security Now at https://twit.tv/shows/security-now. You can submit a question to Security Now at the GRC Feedback Page. For 16kbps versions, transcripts, and notes (including fixes), visit Steve's site: grc.com, also the home of the best disk maintenance and recovery utility ever written Spinrite 6. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: blackhat.com/us-26 and use code TWIT XBOW.com hoxhunt.com/securitynow cohesity.com/Resilience zscaler.com/security

In this weeks episode, Dottie and Brandon talk about the finality of screenplays and other unfinished work. Are films ever really finished? What makes any art complete? As an artist, it is often difficult to find where a project ends and that sentiment is regarded heavily in this episode. New upload schedule lets get hype. As of this weeks episode, we will transition to Wednesday uploads rather than Mondays to better fit our schedules and keep uploads consistent. Thanks for sticking around with us so far. Leave us a comment about some of your unfinished projects or when you think a piece is really finished! Intro shot by Reagan Lawson with music by Zeke Jones. #recap #review #video #hashtag #comedyvideo #podcast #interview #artist #artwork #awesome #campuslife #art #community #comedy #creativeadvice #collegeartist #college #collegelife #video #videos #funny #funnyvideo #silly #students #studentlife #artwork #beautiful #community #design #education #explore #entertainment #explorepage #editing #foryou #fun #hosts #highlights #happy #improvement #instagram #inspiration #jokes #like #motivation #memes #movie #viral #viralvideo #viralvideos #2026 #trending #trendy #trendingvideo #trends

AI is now uncovering and fixing thousands of hidden software bugs faster than humans can keep up, but not everyone is playing by the rules. Find out how state-sponsored attackers and careless disclosures are turning the cybersecurity playbook upside down. Win10's popularity forces another year of free updates. CISA directs all federal agencies to update their UniFi OS devices. CISA gave federal agencies "the weekend" to update Cisco devices. Australia is disturbed by a deeply compromised infrastructure provider. OpenAI introduces Daybreak-powered "Patch the Planet" initiative. Meta's employee monitoring-for-AI-training backfired badly. Script Kiddies figure out how to use AI to find vulnerabilities. AI improves with "looping", "repeating" or "iterating". A wonderful story about Kevin Mitnick. Serious hackers mistakenly left a server directory accessible Show Notes - https://www.grc.com/sn/SN-1085-Notes.pdf Hosts: Steve Gibson and Leo Laporte Download or subscribe to Security Now at https://twit.tv/shows/security-now. You can submit a question to Security Now at the GRC Feedback Page. For 16kbps versions, transcripts, and notes (including fixes), visit Steve's site: grc.com, also the home of the best disk maintenance and recovery utility ever written Spinrite 6. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: blackhat.com/us-26 and use code TWIT XBOW.com hoxhunt.com/securitynow cohesity.com/Resilience zscaler.com/security

Apple i Microsoft pugen els preus. En Xose Anton ens ho explica perquè ho entenguem. Novetats al reglament de trànsit a partir de l'octubre. En Jep Cabestany hi està molt en contra. Una onada de calor recorre Europa. Hem tingut accés a un reportatge de la BBC sobre el tema.

This Week on the Toy Power Podcast; we have HEAPS of awesome Pop Culture News & Announcements to cover!! But at the very Top of our list, is the extremely exciting announcement that Trent is a contestant again on Lego Masters Australia! This Bricktacular Event will be held over four episodes & he is a part of a team of three - consisting of himself, Alex & Felix! We wish him good-luck in advance! Then onto Toy News with McFarlane Super Powers Officially revealing their wave 14 line-up; plus One:12 Collective announce an awesome Vintage style Punisher figure with ALL the Guns! Another Godzilla figure from Super7 & so many G.I. Joe figures, its hard to keep up with! Star Wars Black Series 'Expands their Universe', plus a neat addition to the Archie Comic Book Figure line-up that rounds out the set of Mighty Mutanimals. A Blind-Box offer of TMNT figs from Playmates that doesn't impress us much; but Batman media announcements & release dates certainly has us hyped indeed. Then we round out the ep with a Classic segment of Reading, Watching, Playing. Ben praises The Absolute Batman Comics, Frank appreciates the Supergirl film, Scot is impressed by the 'Obsessions' film; & Trent is flying the Aussie Flag for the World Cup Soccer! All this & More! Enjoy!! Milestone Episode #450 will be broadcsast LIVE on Facebook & potentially other streams, on Saturday July 11th @8pm (Adelaide, South Australian time). Tune in for Visual Fun, Interaction & Give Aways! Support the show: http://patreon.com/toypowerpodcastSee omnystudio.com/listener for privacy information.