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emocleW, emocleW, emocleW to the Distraction Pieces Podcast with Scroobius Pip!This is your bonus FRIDAY REWIND episode! Today, we catch up with RICHARD E GRANT, originally episode 250 from 2019-01-30.Original writeup below:Hopefully you will have caught his perfect video reaction to being nominated for an Oscar, which is of course well overdue but greatly deserved, or maybe you will have read about or even seen his new film ‘Can You Ever Forgive Me?'. In either case, if you have missed those, by all means CATCH UP with the quickness. But you will surely know Richard for his many varied performances throughout his illustrious career, which will DEFINITELY include the classic ‘Withnail And I'. That is a given. Add to that his star turns in films and shows like ‘LA Story' (which forged a lifelong friendship with Steve Martin), ‘Girls', ‘Game Of Thrones' and a good four decades more, and there you have it. A carved in marble filmography of a true superstar right there. Covering everything including that Oscar nomination, the collaborative nature of film, how a testosterone-charged set is not always awesome, the grey area of memorabilia, the importance of casting directors, his booze allergy (believe it or not), his Swaziland origins, making theatres when he was kid and right up to the habit he shares with Pip of wearing two watches, it's a packed episode.PIP'S PATREON PAGE if you're of a supporting natureCAN YOU EVER FORGIVE ME?TWITTERTHE INTERNETIMDBPIP TWITCH • (music stuff)PIP INSTAGRAMSPEECH DEVELOPMENT WEBSTOREPIP TWITTERPIP IMDB Hosted on Acast. See acast.com/privacy for more information.
Welcome, welcome, welcome to the Distraction Pieces Podcast with Scroobius Pip!This week Pip and Buddy check in and catch up, with a little Q&A to round things off!PIP'S PATREON PAGE if you're of a supporting natureSPEECH DEVELOPMENT WEBSTOREPIP TWITCH • (music stuff)PIP INSTAGRAMPIP TWITTERPIP PATREONPIP IMDB Hosted on Acast. See acast.com/privacy for more information.
In Part 2, we pick up where we left off in Part 1, with Dave's time in Europe to get away from being drafted to fight in Vietnam. By that time, with a couple years at City College taking photo classes under his belt, Dave had built a portfolio. He took all his gear with him because he didn't know whether he was ever coming back. His French cousin had a friend whose mom worked at the US Embassy in Paris. Dave got about a dozen photos into a group show at the American Culture Center there, his first public showing. Through another connection, he was able to print photos for the show at the Sorbonne. The National Library of Paris ended up acquiring all of Dave's photos from that show, and they're still there today. Lodgings with his French family were barely adequate, and definitely crowded. He had a friend, also a draft dodger, living in Scotland. And so Dave decided to move to the UK. He and his cousin had bought a car together, and he ended up taking that car to Leeds in northern England. Locals there took Dave in, and so he stayed there, rather than Scotland, for another half-year. We go on a sidebar thanks to my out-of-left-field question about Dave smoking cigarettes when he lived in Europe. We also talk about partying as a young person over there. At the end of 1971, Dave returned to The Bay. It was safe by then, he says, because there were so many other draft dodgers. He shares a tidbit about how, before he left, if you had gone to the intersection of 19th Avenue and Lincoln, you would see at least half a dozen young men holding hitchhiking signs that said, "Canada." A few years down the road after Dave's return, Jimmy Carter granted amnesty to all the draft dodgers. At our live event, he next showed a photo from Mission and 18th Street that shows, among other things, Lakeside Liquors, Dave's family's business back then. His dad and a partner owned and ran the place, and Dave worked there. His dad also owned a laundromat on the block. His dad claimed that his was the first full-service, coin-op laundromat in The City. It was 1957, and we'll just go ahead and take his dad's word for it. As a young man, Dave learned to fix those washing machines and dryers, which he says "were always breaking down." And that's how he got his first taste of appliance repair, a career he spent 25 years in. When his dad retired in the Eighties, Dave opened his own joint—Little Hollywood Launderette on Market near Laguna. But by the time he opened Little Hollywood, unattended laundromats were fading in popularity, for various reasons. So Dave included attendants and wash-and-fold service at his place, as well as a dry cleaner next door. He ran Little Hollywood from 1989 until 2007. Dave shares the story of a music video that was shot mostly inside Little Hollywood. A then somewhat up-and-coming artist called Del the Funky Homosapien shot the video for his song "Made in America," directed by Michael Lucero, in a funky laundry spot on Market. Read all about that in this story from Hieroglyphics. Dave's behind-the-scenes story of that video shoot is fun. Back in his repairman days, Dave would travel from the Mission to the Western Addition regularly, as well as trips out to the Sunset and Richmond districts. He got to know his city even better. He knew all the laundromats, including the spot that would eventually be his own. In 2007, Dave decided to retire. That gave him more time with his cameras. Three years later, his parents really started aging and needed more and more care. To help out, Dave took over running his dad's businesses when he was no longer able to. He did continue taking photos during this time, though. In fact, he'd been taking photos since his return from Europe back in the early Seventies. Photographers from Dave's era didn't commonly show their work. They had boxes full of negatives instead. Eventually, though, someone turned Dave on to a photo-sharing site, and he acquired a scanner and went to work. He enjoyed it, and leveled up on the scanner. He set out to scan all of his negatives. He spent the next roughly 10 years scanning (in fact, he adds, he's still scanning to this day). Through those early photo-sharing websites, Dave started to make connections in the SF photo community. Eventually, Troy Holden reached out and asked Dave to be in a photo show at Cha Cha Cha, back when the Mission Street location was still open. A year later, he met Adrian Martinez at Michael Jang's open studio in the Richmond District. Adrian, Troy, and Dave formed the San Francisco Photography Club. He figured he could serve as a mentor to the many younger members of the group. But it turned out the other way around. Dave points to the young photographers in the club that came from punk rock and skateboarding backgrounds (communities that are near and dear to my heart, as I spent countless years of my childhood and early adulthood in both). Dave considered their art to be edgy and good. It reminded him of work by Ken Hayman, who in the Eighties published Hip Shot. Those young photographers' style wore off on Dave, and point-and-shoot street photography is what he mostly does today. At this point in the conversation, Dave points to the inspiration of Hamburger Eyes, a photo magazine founded by Ray Potes in San Francisco in 2001. There's an exhibit up at the SF Public Library's Main Branch through Sept. 24 celebrating a quarter-century of this amazing publication's work. Dave has one photo in that exhibition. Dave talks about another of the photos he shared that evening, which is also available in the gallery on this page. It's a photo he took of the moving of a Victorian house out of the Western Addition during the so-called redevelopment (read: gentrification) of that neighborhood. The next photo he showed was from Candlestick Park in 1989. Next is an example of Dave's street photography—a photo from a Nineties Pride Parade, taken near City Hall. I ask Dave whether he ever shoots color photos, because everything of his I see is black-and-white. He says that in the early days of digital cameras, he shot some color. He ended up shooting about 10 years of digital color, and he liked it. But the lure of film and black and white was too strong. Dave sold all his digital cameras. He says that, nowadays, smartphones do just as well as or better than cameras did back then. Today, he uses vintage cameras, vintages lenses, and black-and-white film, a combination that gives you a look and feel that you just cannot recreate with a digital camera. The last photo Dave shared that night at Warfield Commons is the most recent one. It's of folks and their cars at Ocean Beach, near to where Dave lives today. He says that his love of cars and car culture dates back to the Sixties. Back then, there were no stoplights on the Great Highway. It was like a drag strip (something you'll also hear about in our bonus episode on SF lowrider culture tomorrow). He shares fun stories of taking his mom's car, picking up friends, and joining the racers out near the ocean. They'd stay until cops chased them away, at which point they would head to Daly City and go to Pip's, a drive-in that existed back in the day. Please follow Dave on Instagram @daveglass_foto. Special thanks to Mel Baker and the team at SF Public Press for hosting and producing the live event where we recorded this episode. Look for Dave's exhibit at the San Francisco Historical Society next summer. It should stay up through the rest of 2027. Photography by Nate Oliveira/Shoot First SF
!#legislatura #medicaid #jenniffer La incertidumbre en Washington con relación a los fondos Medicaid podría ser catastrófico para PR y el gobierno de Jenniffer y los demás, en negación. El silencio de TRS en cuanto a Power Expectations. Conversamos con el representante del Partido Independentista, Denis Marquez, sobre el saldo de la vista con Osvaldo Carlo y el contrato billonario de energá temporera. ¡Conéctate, comenta y comparte! ¡Lo caliente! 6:05 Luis González se pasa por el porro a la Junta de Gobierno de la agencia 9:30 El PNP no cumple con la ley cuando está en el poder 13:22 Los Republicanos amenazan la existencia de la tarjetita de salud en PR 25:00 Hector Ferrer en la vista pública trabajó para obtener el titular 33:00 "Osvaldo Carlo dijo una jeringonza para no querer contestar la pregunta". Entrevista con Dennis Marquez del PIP #periodismoindependiente #periodismodigital #periodismoinvestigativo Síguenos en todas las redes: TikTok: tiktok.com Facebook: /bonitaradio Instagram: /bonitaradio X: https://x.com/Bonita_Radio
Over a year ago, an argument outside the old bone grinder. Two voices. One coaxing the other. Soothing assurance that whatever they might find inside will be no match for the two of them. It'll be fine Paul, it's only hags. The other voice, less sure, less certain, providing a laundry list of reasons they should probably turn around. The first voice lower now, more gentle. Trust me, I've got you. The second voice acquiesces.Inside, the pungent odour of rot and the throaty chirruping of an enormous toad. A collection of rags and bones coalesces into the floating form of a wretched old woman. Her voice like shattered glass. You're fucking dead you rat cunts! Fuck you! The sizzling sound of magical energy. Run! The first voice, panicked and self serving. It's got me, I'm trapped. The second voice, fearful and strained. The first voice does not respond. Pip? The second voice pleads. Pip.But he is gone.If you supported the Jarren's Outpost Board Game, check your emails to manage your pledge manager! Of course you can still grab a copy right here and we insist you do xx o. Hosted on Acast. See acast.com/privacy for more information.
Welcome, welcome, welcome to the Distraction Pieces Podcast with Scroobius Pip!This week Pip is joined by acclaimed rapper DENZEL CURRY!A lovely episode to catch you up to speed on all things Denzel, as he and Pip get involved in an expansive chat which goes into not only his current album with Kenneth Blume (fka Kenny Beats), but his life surrounding the album rollout as well as all things back in the day, upbringing, bringing personal views on world politics into the mix, Florida, touring and a ton of other goodies. Ideal listening if you're into Denzel's past and present work or those who want to discover someone awesome.PIP'S PATREON PAGE if you're of a supporting natureONLINEii ALBUM LINKSSPEECH DEVELOPMENT WEBSTOREPIP TWITCH • (music stuff)PIP INSTAGRAMPIP TWITTERPIP PATREONPIP IMDB Hosted on Acast. See acast.com/privacy for more information.
There's a squeeze on right now, and it's landing hardest on middle managers and senior leaders. Do more with less. Do more with the same. Lead the team and ship the code. The player-coach role used to be a startup thing, something you did because there were 6 of you and someone had to. Now it's being handed to leaders at scale, and most of them didn't ask for it.Bob comes out swinging. He's fine with it in a small company, but past a certain size, he wants you to wear one hat and be excellent at it. He walks through what actually breaks when a CTO is also a team member: it's less about time and more about the role conflicts nobody talks about, like having to put your own architect on a PIP while you sit next to them every day.Josh admits his first run as a leader was a failed player-coach experiment. He wasn't good at either job, and his team paid for it.The conversation turns to capacity, and that's where it gets uncomfortable. Bob's first question in any agile engagement was "do you know your capacity?" and almost nobody did. Josh tells a story from his college weight room, a strength coach, and a set of 25 squats that should've been mathematically impossible, to argue that people have more in them than they think. Bob answers with the difference between stretching internally and committing externally, and why he'll happily overload anyone who never says no.Then executive math. The belief that a million dollars or 10 new reqs turns into capacity on the day you sign the paperwork. Bob has a story from a financial services project where the plan assumed 120 people and the floor had 38, and nobody said a word until he did. Josh connects it to GitHub's outage problem in 2026: you can do more and still be getting worse.What's in this episode:Two contexts where player coach is fine, and the size where it stops being fineDilution vs tension: the hidden cost of leading people you also code withBob's rule: if you keep saying yes, he'll keep loading you up, and that's his rightThe 25 squats story and what it says about untapped capacityInternal stretch goals vs external commitments (and why they're different numbers)Why Bob hates reserves and gives extra capacity straight to the businessCapacity in 2026 is a moving target, and it doesn't only move upExecutive math, and how it wins when you can't defend your own numberBad news never ages wellBob's Badass Agile Coaching Days runs September 29 through October 1, 2026, virtual, 3 time zones, 12 speakers, all proceeds to Women in Agile and African Agility. Details at agile-moose.com.
emocleW, emocleW, emocleW to the Distraction Pieces Podcast with Scroobius Pip!This is your bonus FRIDAY REWIND episode! Today, we catch up with ALISTAIR GREEN, originally episode 482 from 2022-11-09.Original writeup below:This week Pip is joined by the hilarious and great comic ALISTAIR GREEN!You could also add actor to that list of accolades as Alistair is busy in front of the camera too, as you will hear. Kicking off with heavy metal nights and the tour t-shirt, it's a fun slip n' slide down the road of tangents as Pip and Al get down to the business of social media and rolling news being too quick for a comic hot take, making videos, creative process, making situations into comedy, the Prince Charles Cinema screening of his videos as a film (and the wonder of the cinema itself), conspiranoia, Alan Partridge and trying to resist goofing while a take is in progress, Ted Lasso and tons more. Awesome stuff and definitely one for Al heads and those who are yet familiar - much fun! Enjoy.PIP'S PATREON PAGE if you're of a supporting natureDON'T THINK SO SOMEHOWTHE BIG IDEAYOUTUBEINSTAGRAMPIP TWITCH • (music stuff)PIP INSTAGRAMSPEECH DEVELOPMENT WEBSTOREPIP TWITTERPIP IMDB Hosted on Acast. See acast.com/privacy for more information.
PIP rules differ from state to state, especially when it comes to how insurers can seek reimbursement for the benefits they have paid. On this week's episode, Rebecca is joined by Jason Sullivan, RG's partner who manages the firm's Florida litigation, to take a closer look at the ins and outs of subrogation of Florida PIP. Listen in to learn how no fault the right to recovery really is, when it comes to commercial vehicles, anyhow, and who is potentially responsible for the repayment of PIP benefits when it comes to Florida subrogation losses.
"It's very important to not notice the thing, because if you notice the thing, it becomes your responsibility. You don't want to be responsible for it, so it doesn't exist anymore." Hannah Wallen hosts with Lauren Brooks, Mike Stevenson and Richard the Lionhearted on what a health system does to the men inside it — assessment regimes, disability payments that come in under the wage they replace, and who is trusted to explain their own symptoms. Also: baby Alfie, held until he died rather than released for treatment abroad; the invisible disabilities that get you accused of faking; and why the panel thinks the male loneliness epidemic is misnamed. "They want him to apply for a job and report to work while he's dying. And the people who assess you aren't trained nurses or doctors. They're just random people they found off the street." Support the show: feedthebadger.com/support Send us a message: feedthebadger.com/justthetip Join the community: badgernation.online
Little Pip is embarrassed by the duck-like quacks she makes instead of proper penguin sounds. She tries everything to sound normal, but when a storm hits during the colony's First Dive ceremony and a young chick gets swept away, Pip's unique voice becomes exactly what everyone needs for the rescue. ✔️ Perfect for ages 5+ Sleep Tight!, Sheryl & Clark ❤️
Welcome, welcome, welcome to the Distraction Pieces Podcast with Scroobius Pip!This week Pip is joined by national treasure ROMESH RANGANATHAN!Weirdly it's been a long time coming, this one. It feels like Romesh has never been far away but in reality it's been a bunch of years since he was last on - and that appearance was a stone cold classic (which has come up more than a few times before). Well, consider the loop closed as Romesh and Pip are united once more, this time in a 100% sober environment which allows for, let's say, a far more focused and lucid conversation. But still fun and awesome as all hell. What do we have here... Well to start with, older rappers and how to age gracefully in Hip Hop, the Romesh glowup, where to put the binge day, the podcast arc from early mic and a recorder days to the current multi-cam industry, Rob & Romesh classics, being niche but also massive, performing, old rap, and a ton more. You know what it is. A genuine pick-me-up for your mid-week DPP needs. Go enjoy!PIP'S PATREON PAGE if you're of a supporting natureINSTAGRAMONLINE (everything is here)DRUNKCAST w/the gang Pt.1SPEECH DEVELOPMENT WEBSTOREPIP TWITCH • (music stuff)PIP INSTAGRAMPIP TWITTERPIP PATREONPIP IMDB Hosted on Acast. See acast.com/privacy for more information.
On today's episode, I'm joined by Mary Holland Nader, former Wall Street wealth manager and founder of Mary & Pip, to discuss overcoming money insecurity, building wealth, and why the biggest flex is what's in your investment account. We dive into how the way we grow up can shape our money mindset, overcoming the fear of investing, and why women should feel empowered to take control of their finances. Plus, Mary shares her weekly money ritual for freelancers and creatives, how to make financial wellness feel less intimidating and more approachable, the basics of investing and Roth IRAs, and why women shouldn't feel like they need to choose between being multifaceted and being taken seriously. Whether you're just starting to invest, navigating an unpredictable income, working toward financial independence, or looking to completely transform your relationship with money, this episode is packed with practical advice and empowering insights. Enjoy!To connect with Mary Holland Nader on Instagram, click HERE.To connect with Mary Holland Nader on Tiktok, click HERE.Listen to Mary Holland Nader's Podcast, Growing Interest, click HERE.To check out Mary and Pip, click HERE.To connect with Siff on Instagram, click HERE.To connect with Siff on Tiktok, click HERE.To learn more about Arrae, click HERE. To check out Siff's LTK, click HERE.To check out Siff's Amazon StoreFront, click HERE. This episode may contain paid endorsements and advertisements for products and services. Individuals on the show may have a direct, or indirect financial interest in products, or services referred to in this episode.Visit www.sleep.me/dreambigger to get up to $255 off your Chilipad 2.0 with code dreambigger. This special offer is available for Dream Bigger Podcast listeners – and only for a limited time! Order it today with free shipping and try it out for 30-days. You can return it for free if you don't like it with their sleep trial. Visit www.sleep.me/dreambigger and never wake up hot and tired again.Right now, you can save up to $230 on the 12 piece cookware set vs buying the products individually. And you can find even more set savings when you shop Caraway's full Kitchenware collection. Visit Carawayhome.com/BIGGER to take an additional 10% off your next purchase. This deal is exclusive for our listeners, so visit Carawayhome.com/BIGGER or use code BIGGER at checkout.Visit www.lilysilk.com/dreambigger and use code dreambigger for 20% off your LILYSILK order.Get $25 off your first purchase when you go to TheRealReal.com/dreambiggerVisit coyuchi.com/dreambigger for 15% off your first purchase of the most comfortable bedding you'll ever own. That coyuchi.com/dreambigger. Some exclusions apply.Produced by Dear MediaSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
#gobernadora #corrupción #populares Sigue rescabando Pablo José con el proyecto Esencia y la gobernadora arrastra los pies para confrontar a Josué Colón y Mary Carmen Za[ata con el toyo del contrato de $5,800 millones para energía temporera. | Norma Burgos no sabe como contestarle al Senado sobre los trámites de Itza y Paco con contratos antes de irse de La Fortaleza. Lo caliente: 7:00 Caqui los cristianos del Senado. Concilio Satánico está registrado en el Departamento de Estado 10:30 La corrupción, tema de la próxima campaña electoral 12:30 El alcalde PNP de Toa Baja le pone el nombre de un corrupto a la intersección Campanillas-Ingenio 18:33 Pablo José se enteró por el PIP de lo que dice la AAA y AEE sobre Esencia 30:43 La Payanada sobre el caso de Power Expectations ¡Conéctate, comenta y comparte! #periodismoindependiente #periodismodigital #periodismoinvestigativo Síguenos en nuestras redes sociales: tiktok.com: https://x.com/Bonita_Radio Facebook: / bonitaradio Instagram: / bonitaradio X: https://x.com/Bonita_Radio
#gobernadora #corrupción #populares Sigue rescabando Pablo José con el proyecto Esencia y la gobernadora arrastra los pies para confrontar a Josué Colón y Mary Carmen Za[ata con el toyo del contrato de $5,800 millones para energía temporera. | Norma Burgos no sabe como contestarle al Senado sobre los trámites de Itza y Paco con contratos antes de irse de La Fortaleza. Lo caliente: 7:00 Caqui los cristianos del Senado. Concilio Satánico está registrado en el Departamento de Estado 10:30 La corrupción, tema de la próxima campaña electoral 12:30 El alcalde PNP de Toa Baja le pone el nombre de un corrupto a la intersección Campanillas-Ingenio 18:33 Pablo José se enteró por el PIP de lo que dice la AAA y AEE sobre Esencia 30:43 La Payanada sobre el caso de Power Expectations ¡Conéctate, comenta y comparte! #periodismoindependiente #periodismodigital #periodismoinvestigativo Síguenos en nuestras redes sociales: tiktok.com: https://x.com/Bonita_Radio Facebook: / bonitaradio Instagram: / bonitaradio X: https://x.com/Bonita_Radio
Mary Holland Nader has lived two very different lives—and somehow built both at the same time. On this episode of Trading Secrets, Jason Tartick sits down with Mary, one of the Nader sisters from Hulu's Love Thy Neighbor, to unpack her unconventional journey from Wall Street to reality TV, modeling, content creation, and entrepreneurship. Mary shares what it was really like starting at Deutsche Bank fresh out of college with a $100K salary, working her way into wealth management, and secretly juggling her finance career with modeling and brand deals. She reveals the moment a $5,000 modeling job made her calculate her hourly rate—and realize her side hustle could eventually out-earn her Wall Street salary. The conversation goes deeper into Mary's decision to leave the security of a steady paycheck, healthcare, and corporate career to pursue entertainment and build her fintech startup, Mary & Pip. She opens up about the financial realities of becoming an entrepreneur, managing unpredictable income, negotiating alongside her sisters, and the freedom that came with finally controlling her own schedule. Jason and Mary also dive into the rapidly changing world of AI—from how she uses Claude, AI agents, and automation in her everyday life to why she believes emotional intelligence and relationships may become more valuable as technology reshapes the workforce. Mary also shares her perspective on protecting your financial information online and why you should think twice before giving sensitive data to an AI tool. Plus, Mary breaks down what it's like watching the Nader sisters' rise happen almost overnight, how Love Thy Neighbor changed her career, what goes on behind the scenes with executive producers, and where she wants to take her career next. From Wall Street to reality TV to fintech, this is a conversation about taking risks, following curiosity, and knowing when it's time to bet on yourself. Learn more about your ad choices. Visit podcastchoices.com/adchoices
It's almost never the work. Great engineers get passed over for promotion because they're all results and no reputation. Steal 200 ways to get known for the work you're already doing → https://toolkit.oasisofcourage.com/reach Built on a whiteboard. Watch it → https://youtu.be/pqVQgAwoz1k Your work does not speak for itself. It never has, and it never will. Reputation = Results × Reach. Results are the impact you create. Reach is the number of people who can explain your value when you're not in the room. It multiplies, which is what makes it brutal. Nine parts results and one part reach gives you nine. Five and five gives you twenty-five. And world-class results times no reach still equals nothing. Curtis was a senior software engineer at NASA Ames, already performing at a high level and afraid of another PIP. He didn't get better at his job. He got better at giving his managers clear information about what he was contributing. Best review of his career, new machine learning work, an award for a proposal. The results were already there. In this episode: why my work speaks for itself is the most expensive belief in engineering, the equation and why it multiplies instead of adds, more vs better vs different, what reach actually means, which handful of people matter more than everyone else combined, and how to talk about your contribution without it feeling like self-promotion. You built the results. Now go build the reach. New here? Follow the show. Ready to build the whole plan → https://calendly.com/oaco-client-success-team/career-clarity-call Zach White is the founder of Oasis of Courage. Engineering degrees from Purdue and Michigan, former engineering leader at Whirlpool, 300+ engineering leaders coached since 2019. You shouldn't have to sacrifice your life to reach your potential at work. Also in Season 2: How Engineering Managers Get Promoted Without Bragging · How to Get Promoted from Engineering Manager to Director
emocleW, emocleW, emocleW to the Distraction Pieces Podcast with Scroobius Pip!This is your bonus FRIDAY REWIND episode! Today, we catch up with KSI, originally episode 395 from 2021-07-14.Original writeup below:Pip is joined by a Youtube veteran, and definitely someone with an admirable work ethic. Inspired by the likes of Childish Gambino and early adopters, KSI has carved out his own unique space in the world of social media and by the sounds of it, not even a global pandemic can stop this train running… Hear the story of his path from Watford, and how it led to where he currently finds himself including his boxing hustle, balance, fear of losing, working harder than anyone, how Youtube accepts no defeats and how to ascend by way of constant grind.PIP'S PATREON PAGE if you're of a supporting natureEVERYTHING KSIPIP TWITCH • (music stuff)PIP INSTAGRAMSPEECH DEVELOPMENT WEBSTOREPIP TWITTERPIP IMDB Hosted on Acast. See acast.com/privacy for more information.
Taylor Johnson presents a mixtape of his personal selection of tracks from BBC Introducing, with music from Venus Grrrls, Dylan Bradley and the Heat, Phoebe Green, The Marches, Bea Malka, Iona Luke, Helena Gao, Isabella Richardson, The Wild Infinite, Two Blinks, Jo From School, Heron Red, Pip, Hannah‑Morgan and a new Track of the Week from Birdseed.Produced by BBC Audio for BBC Radio 6 Music.
Welcome, welcome, welcome to the Distraction Pieces Podcast with Scroobius Pip!This week is the digital debut of Pip's live spoken word rendition of the DISTRACTION PIECES album in full!For total clarity, the order of events was the solo album 'Distraction Pieces', and then this podcast. So it makes total sense that in this whole podcast ouroboros and infinity loop we have in front of us, the podcast itself is the perfect platform for the very first digital unveiling of the spoken word version! While Pip was filming in Canada in maximum pandemic, time marched on until boom, it was time for the 10 year anniversary of his solo album. With the initial album on deck as a proper reissue, it turned out that with all that isolation and solo time that was in fact legally mandated, it was the ideal moment to add a little special something to the reissue situation. What better moment to leap back in time to the days of rocking up to a venue with a notebook and not much else, to perform spoken word and get your name out there where the game was a good deal of word of mouth and not all socials. Pip packed up his mic and jetted to Stanley Park, up on the tippy top of Vancouver, recorded the whole album acapella, and this formed disc 2 of the vinyl reissue. It was never digitally released, no Bandcamps, no streamers, nothing, and so if you didn't cop the wax you won't have heard this. So it really is a very special thing indeed, and represents a really important era in the Pip career path. Enjoy, and as this isn't a regular sounding episode (ie. not an interview), maybe set aside some time to take it in properly and without - indeed - distractions.Recorded by Pip • mix/edit/master by Buddy PeacePIP'S PATREON PAGE if you're of a supporting natureSPEECH DEVELOPMENT WEBSTOREPIP TWITCH • (music stuff)PIP INSTAGRAMPIP TWITTERPIP PATREONPIP IMDB Hosted on Acast. See acast.com/privacy for more information.
Double Tap Double Tap - Ep 476 August 24, 2026 Presented by This episode of Double Tap is brought to you by: Foxtrot Mike (Code: WLSISLIFE) Medical Gear Outfitters (Code: WLSISLIFE) Bowers Group (Code: WLS) Flatline Fiber Co (Code: WLS15) Second Call Defense Giveaways!! GAW Text Dear WLS or Reviews +1 743 500 2171 Public Show Titles Dear WLS Question from Jakey Poo from Indiana Dear WLS Attention fustercluck, Jakey Poo here, I have a classic rifle I'd like to hang on my wall somewhere, but I don't want it to sit up there and deteriorate. What should I do to it, to make it last? I do want it to remain functional, even after a cleaning, not necessarily straight off the wall, and it has wooden parts. Not sure if that makes any difference in regards to oils etc. Thanks lady-boys. Question from Jeremy from Nebraska Jeremy from Nebraska. I haven't been listening long enough, but I'm curious what Shawn's problem is with Taurus. I know they're budget guns but their recent stuff seems to be pretty reliable. Show me on the doll where they hurt you.? Question from typicalpnwguy from Oregon Hey cult daddy's, I've noticed that Primary Arms hasn't been mentioned and is no longer the title sponsor. Why that be yo?Ps… I love you, say it back -typicalpnwguy WINNER Question from K.Y. Horseman from Oregon Dear WLS K.Y. Horseman SBR question Going to buy a Henry Supreme, and deck it out with Midwest furniture. I want to SBR it, but I don't know anyone around me that I trust to do it. Was going to send it to Jermey and let him do it, but don't know how to do the NFA paperwork since I'm sending it out of state to SBR it. Do I form 1 it before I send it to Jeremy? Can he ship it back to me after the work is done? Help me Thanks Question from Anonymous Coward from Texas Lucky BrainlessI have a question on how you identify the caliber of a barrel. I recently found several ARs I had put up pre-covid. The issue is I don't remember which are 556 and which are 300 blackout. How can I verify so I don't load them with the wrong ammo? I would rather not have to take them apart, or blow them up. Gun Industry News Shootingnewsweekly Vortex Venom Enclosed Micro Green Dot (VEN-MGD3-E) Vortex has expanded its Venom enclosed red dot line by adding a green dot variant, the VEN-MGD3-E. The optic features a 3 MOA green dot on a DeltaPoint Pro footprint, enclosed 6061 aluminum housing, and is designed for improved visibility in various lighting conditions or for users with astigmatism. It offers a large viewing window, aspherical lens, motion activation, and long battery life. The Gist: Available starting ~September 2024 Impact: MSRP $289.99 (street price ~$200) Bottom Line: 1x magnification, 3 MOA green dot, 20,000-hour CR2032 battery life, 1.84″ length, 1.75 oz, 1 MOA/click adjustment, motion-activated illumination with 10-min auto-off, top-mount brightness buttons, unlimited eye relief, enclosed design on DeltaPoint Pro footprint Bearingarms Pentagon Investigation Finds No Mechanical Issues with Sig Sauer M18 Pistol Following the death of an airman in an incident initially reported as an uncommanded discharge of an M18 pistol (the military version of the Sig Sauer P320), the Pentagon reviewed the Modular Handgun System program. Officials determined that all alleged uncommanded discharges were traced to the trigger being pulled, with no mechanically caused accidental discharges ever recorded. Firearms mishaps represent less than 0.006% of all MHS pistols issued; the Pentagon attributes safety concerns to public speculation and online misinformation. The Gist: The Gist: Pentagon investigation concluded no mechanical failures in M18; all incidents resulted from trigger pull, including the fatal airman case initially misrepresented (two airmen pleaded guilty to false statements). Impact: Market Impact: Affects U.S. military adoption and perception of Sig Sauer M18/P320 platform; continued confidence in the Modular Handgun System program with no indicated changes to procurement. Bottom Line: The Bottom Line: Extremely low mishap rate (
Fatherhood, Feeding & The Role No One Talks About - with Jim Chapman A powerful, honest conversation about modern fatherhood, parenting pressures, and what really matters. In this episode, Pip sits down with Jim Chapman @jimchapman to explore the realities of raising young children- from sleepless nights and feeding challenges to the emotional shifts that come with becoming a parent. Together, they unpack the myth of “perfect parenting,” the importance of trusting your instincts, and how to navigate judgement around feeding choices. Jim shares openly about his children's very different feeding journeys, the impact of reflux on family life, and how combination feeding transformed their experience. The conversation also dives into emotional intelligence in parenting, breaking generational patterns, and why being present matters more than getting it “right.” This episode is a reassuring reminder that parenting is not about perfection, it's about connection, flexibility, and figuring it out as you go. This episode is sponsored by Aptaclub. Aptaclub offers expert advice, practical guidance and trusted resources for parents throughout pregnancy, feeding and early parenthood, helping families navigate each stage with confidence. Extra Resources:
emocleW, emocleW, emocleW to the Distraction Pieces Podcast with Scroobius Pip!This is your bonus FRIDAY REWIND episode! Today, we catch up with Niamh Algar, originally episode 315 from 2020-03-04.Original writeup below:You can tell even like 5 minutes in that Pip was proper excited about this one… And naturally he was right to be, as Niamh is fantastic if you didn't already know! You may have seen her in the beautiful and powerful ‘The Virtues' alongside Distraction Pieces spiritual godfather Stephen Graham, and if you did you will adore this chat as Niamh goes IN on the whole process. As always, any Shane Meadows talk is fascinating and this time is no exception, as we get the pure goodness and intrigue from the process of making such a show and the deep personal digging involved in Niamh's transformation into Dinah. Not only that, there's further confirmation of that ‘being the lead in your own story' point that comes up here and there on the podcast, and some good chat about her role in Desiree Akhavan's ‘The Bisexual', working with Guy Ritchie and OF COURSE a good amount about her upcoming film ‘Calm With Horses', alongside Barry Keoghan and Cosmo Jarvis. Full on gold. Enjoy.PIP'S PATREON PAGE if you're of a supporting natureCALM WITH HORSESTHE VIRTUESTHE BISEXUALIMDBPIP TWITCH • (music stuff)PIP INSTAGRAMSPEECH DEVELOPMENT WEBSTOREPIP TWITTERPIP IMDBPOD BIBLE Hosted on Acast. See acast.com/privacy for more information.
This week, join us as we revisit our episode on No Fault/PIP for a refresher! Original Air Date: March 12, 2019. What does it mean to be in a No Fault state? Does it really mean no one is at fault? Join Rebecca and Steve as they dive into this often misunderstood area, where no fault doesn't mean without fault, damages may not be damages, and being a "PIP squeak" is problematic.
1. Continúan las pruebas en La Plata tras avería de una de las bombas desucción2. Gobernadora pide revisar proceso de adjudicación de energía temporera:defiende a Osvaldo Carlo y al zar de Energía3. Negociado de Energía se desliga de la controversia por el contrato dePower Expectations. 4. PIP solicita cancelar contrato de Genera e investigar plan parasustituir carbón por gas natural5. Avería en una de las bombas de La Plata afectará a unos 30,000 abonados6. LUMA deberá informar al Negociado cuánta generación temporera necesitael sistema7. Refinanciar la deuda le salió caro al Gobierno: $123 millones enpérdidas y $3,360 millones más en su costo 8. República Dominicana, segundo país de la región con más legisladores porhabitantes9. Incapaz de forzar la rendición de Teherán, el presidente Donald Trumpparece estar desahogando su frustración con mayor intensidad contra Omán yotros socios de Estados Unidos10. EE.UU. anuncia nueva salva de sanciones contra Cuba.11. Terremoto de 7,2 grados sacude el sur de Los Andes
We're hanging on to summer with the fragrance launches discussed, and in what we're wearing this week, but there is a noticeable change in the weather here in the U.K. There have even been sightings of socks, jackets, cardigans and knee-high boots on the streets! A week ago, such items were unthinkable, but now we're almost looking forward to the seasons changing again. ALMOST, but not quite yet. Not when there's a plethora of new perfume discoveries to delve in to (many of which will prove incredibly versatile in cooler weather anyway, we feel). Quite apart from some exquisite new fragrant finds to welcome Nicola back from holiday with, we've two more of your utterly brilliant Listener Prescriptions to answer, and an exciting update from Madeline – the musician / vineyard worker / medal-winning fighter we helped find new fragrances for last week! In this week's episode we discuss: Jo Malone London Sea Salt & Bergamot‘Plunge into the exhilarating surf of the Celtic Sea. Immersed in the wild waters, every wave carries a fresh minerality. Joining a burst of bergamot, driftwood anchors this sea-salt fresh scent. Experience the untamed beauty of the British coast. Top Note: bergamot(Brightens with its sparkling, citrus freshness) Heart Note: sea salt(The crunchy nature of sea salt accord brings both texture and a sense of freshness and purity to the heart of the fragrance) Base Note: driftwood(Suffused with the saltiness of the sea, this accord brings a light woodiness to the fragrance)' Jo Malone London Iris & White Musk Cologne Intense (recommend to listener in last week's episode seeking clean / soapy iris fragrances!) Harrods ‘The Iris and White Musk Cologne Intense from Jo Malone London is a light and airy scent that's perfect for everyday use. It marries powdery iris with creamy white musk, resulting in a distinctive aroma that's ideal for layering with other fragrances but can also be worn alone.' Ex Nihilo Demon Dancer (Harrods)‘Ex Nihilo recreates the defiant freedom of dance in motion.Inviting you to move to your own rhythm, Ex Nihilo's Demon Dancer is a call for ‘unapologetic self-expression'. Inspired by contemporary dance performance, the fragrance ignites an inner fire through energetic fruity notes that flow with freeing freshness and grounded intensity. Top Notes: rhubarb, bergamot, peppermint, blood orangeHeart Notes: orange blossom, blackcurrantBase Notes: georgywood, akigalawood, ambrofix, musks, tonka bean.' Une Nuit Nomade Sun Bleached ‘Clean Linen Accord • White Flowers • Bergamot •Ambrette AbsoluteMuscat, the capital of the Sultanate of Oman, also known as the White City. The sun drowns the immaculate walls with its rays. It is a bright light that makes eyes squint. Omanis wander the streets, elegantly wearing their dishdashas, a long white tunic decorated with a pom-pom that is delicately dipped in perfume. 'Sun Bleached' is adorned with freshness, in an accord of clean linen, white flowers and a zesty flight of citrus fruits as if biting into a piece of orange, with white musks and sandalwood rounding off its explosiveness.' Connock London Pelago‘A sun-drenched Mediterranean fragrance with bright citrus and aromatic herbs, evoking the warmth of golden afternoons along sunlit shores. Top Notes: orange, bergamot, lemon, mandarin, grapefruit.Heart Notes: rosemary, neroli, petitgrain.Base Notes: amber, vetiver.' Roger & Gallet Neroli Solid Perfume ‘Discover your Néroli Wellbeing Fragrant Water in a new solid format to put on the neck, the neckline and the wrists. An alcohol-free perfume merging soflty with the skin. A new way to boost and facet your trail all day long. Enriched with rose essential oil, known for its relaxing benefits, the solid fragrance is a real caress onto the skin. Its rich and unctuous texture with vegetal waxes encapsulates the olfactive notes and keep the trail longer. A small and nomad format with an addictive gesture, to bring everywhere, wherever you are.' French Cowboy Pear Pavlova‘Almond, Ambrette Seeds, Jasmine, Meringue, Pear, Pink Pepper, Powdery Musk, Vanilla. Ashley Santiago is the youngest perfumer at Givaudan and the co-founder of French Cowboy. California-born with Mexican-Caribbean roots, she honed her craft in Paris, New York & Singapore: blending global flavour with French savoir-faire.' PERFUME PRESCRIPTIONS: Update from Madeline (the musician / vineyard worker / medal-winning fighter from last week's Correspondence Special episode): ‘OH MY GOODNESS GRACIOUS!!Suzy and Nicola thankyou so so sooooo much for my prescription!!When I saw that you had dropped a new episode WITH MY NAME IN THE NOTES I screamed!!!! It's perfect perfect PERFECT beyond words.I have already ordered a bottle of the Map Of Hearts Valour and I am currently hunting for samples of the others you both suggested!!I CANNOT wait to let you know how I go, l am so so sooooo excited to try them all!!' (If we don't have time this week: NEXT WEEK'S EPISODE, another Perfume Prescription special?) Jessi - aka @grotesquex_o‘hi suzy and nicola, the day that you answered my cave perfume prescription was one of the best days of my life. i was hiking around the forest on a break at the museum in san francisco where i work, squealing and reveling in our shared zeal! ill remember the feeling forever. although i havent gotten my nose on that perfect perfume you found just yet, my sister and boyfriend know exactly what to get me for Christmas… I'm writing to you humbly with the hope of another perfume prescription. i love cold incense perfumes, and found that my favorites (andrea maack craft and CDG avignon) have a chlorine note (to my nose at least, tho unlisted) that i crave. like the way your skin and hair smell after a swim in the pool, not the smell of sunscreen or tropical fruits that so often characterizes pool culture-inspired perfumes. are there any other cold, chlorinated, incense perfumes you love?' Suzy's Answer: Serge Lutens L'Eau Froide‘By Christopher Sheldrake. The fragrance features Sea water, Pepper, Mint, Musk, Incense, Olibanum, Ginger and Vetiver.'‘"The situation was too hot for me to handle.” or “A chill ran down my spine.” I simply applied a similar concept to an Eau. The hero of this story is a tree during the dog days of summer. If an incision is made in its trunk, "tears of glass" come out. Without air conditioning, the heat would kill it. Frankincense helps this scent preserve an icy cool." - a note from the brand.' Bibbi Swimming Pool‘By Jérôme Epinette. Top notes are mint, basil and Ginger; middle notes are Eucalyptus and Geranium; base notes are Blonde Woods, Grass, White Wood, Ambroxan and Light Amber. You completely forget about time and space when you swim in the pool. The water forces you to go through all the darkest hours and brightest moments of your life. After swimming in the pool, you will be forever changed and have an intense basil-like scent stuck to your skin forever.' Heretic Nosferatu‘SMELLS LIKE: An encounter with an apparition in the cold, damp caves of Count Orlok's castle.'‘In partnership with Nosferatu and Focus Features, Heretic is proud to present Nosferatu Eau de Macabre, a fragrance inspired by the iconic vampire. A chilling scent of wilting lilacs, velvety vegan ambergris and strikes of lightning that fill the air with petrichor and electricity—It's both delicate and hedonistic. Samples available.Top Notes: lilac, ambretteHeart Notes: petrichor, violet absolute, orris concrete, cypriolBASE: vegan ambergris, oudh, labdanum.' Pip:‘Hi Suzie and Nicola. I'm looking for a perfume prescription. I've had a bit of a rollercoaster year, my partner and I were lucky enough to have a little boy together after IVF and it's been a really incredible (though exhausting!) time. One of my oldest and dearest friends died a month before we had him and there's not a day that goes by where I don't think about her. I'm incredibly sad that we never got to share any of our boy mum moments together. She was always so positive though and down to earth, and I want to remember her with happiness, as well as have a comforting fragrance that will make me think of my little boy when he's older and him of me. My memories of my friend when we were growing up together are scented with incense, books and the sticky floors of rock bars! I like vanilla, sandalwood and scents that have a bit of smoke. Thanks so much and thank you for keeping me company on my particularly sleep deprived days, I love listening to you both! Pip x' Suzy's answer: The Perfumer's Story Old Books by Azzi ‘Character: intelligent, eccentric, rock &...
Right, buckle up, trawlers... Trump's crypto company has been rubber-stamped as an actual bank (by his own government no less!) and Marina and Jemma wave a cheery bye Felicia to his press secretary Caroline Levitt (left to perish on the decoy plane while Trump and a "very very very close aide" swanned off on the catering flight). They boggle at the Tates' Temu lawyer, a shouting salami in sunglasses, vowing to take their extradition fight "to Trump" - plus the 30-odd bellends doing solidarity press-ups outside a Miami jail. Then it's back home to Blighty, where Reform reckon they'll save £50bn by whipping PIP off up to three million people and gutting the health bit of Universal Credit, which the BBC broadcast in full, of course. The ladies get stuck into why it's cruel, unworkable and really a mental health story, discuss their own personal experiences of welfare and then it's on to Netanyahu branding us the "Islamic Republic of Britain," and an Underrated of the Week for the actual Pope. Amen!Join The Trawl's Facebook BRAND NEW Facebook Group: https://www.facebook.com/profile.php?id=61590931382660Get your tickets for The Trawl Live from https://thetrawl.tix.to/ticketsThank you for sharing and please do follow us @MarinaPurkiss @jemmaforte @TheTrawlPodcast Patreonhttps://patreon.com/TheTrawlPodcast Youtubehttps://www.youtube.com/@TheTrawl Twitterhttps://twitter.com/TheTrawlPodcastIf you've even mildly enjoyed The Trawl, you'll love the unfiltered, no-holds-barred extras from Jemma & Marina over on Patreon, including:• Exclusive episodes of The Trawl Goss – where Jemma and Marina spill backstage gossip, dive into their personal lives, and often forget the mic is on• Early access to The Trawl Meets…• Glorious ad-free episodesPlus, there's a bell-free community of over 3,300 legends sparking brilliant chat.And it's your way to support the pod which the ladies pour their hearts, souls (and occasional anxiety) into. All for your listening pleasure and reassurance that through this geopolitical s**tstorm… you're not alone.Come join the fun:Patreon: https://www.patreon.com/TheTrawlPodcast?utm_campaign=creatorshare_creator Hosted on Acast. See acast.com/privacy for more information.
Welcome, welcome, welcome to the Distraction Pieces Podcast with Scroobius Pip!This week Pip is joined by Hardcore Listing homie and long term real life homie CHRIS GLASSON, and podcast producer / DJ / beatmaker BUDDY PEACE for the finalé of a 3 part series of celebration episodes!The third side of the pyramid is finally here, as we close in on the three part celebration featuring, indeed, three parts of the puzzle. A lovely way to bring this thing home and tie up loose ends, hang up phones, open new cans of worms (and then re-seal them?), and effectively conclude this three part extended hangout. Thanks for checking in and celebrating 20 years of Pip!PIP'S PATREON PAGE if you're of a supporting natureHARCORE LISTINGBUDDYSPEECH DEVELOPMENT WEBSTOREPIP TWITCH • (music stuff)PIP INSTAGRAMPIP TWITTERPIP PATREONPIP IMDB Hosted on Acast. See acast.com/privacy for more information.
Pip Northeast is the breakout star of Netflix’s My Brilliant Career, currently one of the Top 10 shows in the world. But forget everything you’ve been told about getting ahead at work. After chatting about taking on one of Australia’s most iconic stories - and the woman behind it all, Miles Franklin, we put the career advice we’ve all been fed to the test. Should you really “lean in”? Do you actually need a mentor? Can you be in love and have a career? Pip gives us her verdict on the best, worst and most outdated career advice, with insights into her own experiences. My Brilliant Career is out now on Netflix. Big Small Talk Instagram Sarah-Jane Adams InstagramSee omnystudio.com/listener for privacy information.
Philippa and Katie are back together for the week in Ambridge — Sunday 16th to Tuesday 18th August — and there is a lot to process.Monty's story moves to a heartbreaking conclusion as Lynda and Robert say goodbye and sign the documents. Katie cannot cope and Philippa has questions about the rehoming process, the DNA fingerprints, and whether there is more to come from that evidence. Meanwhile Harrison and Fallon's relationship continues to unsettle, a possibly flirty exchange between Harrison and Lily gets noted, and Mick is becoming CSI Ambridge.Khalil is struggling and Keira has been quietly keeping his secret — until Susan unknowingly spills the beans. Now Azra knows, and neither host is convinced she is going to handle it well. There are real concerns about Khalil bottling things up, particularly given his history.Eddie is filling in for George at Meadow Farm and is cutting every corner available to him. George, remarkably, was right. Ruth still has not booked the genetic test and both hosts are furious about it. The Cruck Barn is taking shape with an exciting programme of rural crafts, jewellery making and creative writing — but can Ambridge actually afford it? And nobody has acknowledged the heatwave or drought even once, which is bothering everyone.Plus Lynda's DNA fingerprints, Rosie and Pip moving into the bungalow, who is now paying rent on Rickyard Cottage, and a key that would not work properly.Star of the Week, Twitter of the Week and predictions including Lynda being unmasked as a mafia gang leader.Topics covered: Monty | Lynda Snell | Robert Snell | Khalil | Keira | Azra | Eddie Grundy | George Grundy | Ruth Archer | Harrison Burns | Fallon Rogers | Lily Pargetter | Cruck Barn | Elizabeth Pargetter | Susan Carter | The Archers August 2026 Hosted on Acast. See acast.com/privacy for more information.
Joining me for this episode is cartoonist and writer Jen de Oliveira. We have a chat about her latest early reader graphic novel, Pip & Pals: Otter Space! A sweet and funny story about Pip (River Otter), his cast of animal friends and one great adventure. Jen also shares how she creates all the cartoons and storylines for this and all of her books. Have a listen to this fun and educational chat with one of the best, Jen de Oliveira. Enjoy!EPISODE NOTES: Jen de Oliveira - Pip & Pals: Otter Space!Become a supporter of this podcast: https://www.spreaker.com/podcast/animal-writes-animal-writers-and-best-selling-authors-pets-animals--6666984/support.
In this solo episode, Bradley Hamner opens a series on developing and coaching team members with a case for performance improvement plans. Most small business owners treat a PIP as a cover-your-back document and a signal that someone is already on the way out. Bradley argues that stigma costs owners good people and real money.He walks through four reasons owners skip PIPs, including corporate red tape stigma, close personal relationships with staff, and the absence of any HR infrastructure. He then lays out four reasons to use them: forcing concrete clarity on expectations, protecting the business legally and financially, relieving the invisible strain that unaddressed underperformance puts on A players, and keeping people worth keeping. He shares a client who built automatic PIP triggers into the culture and retains more than 80 percent of the people who go on one.This conversation moves beyond documentation and into how owners actually develop people. What does good performance look like in concrete numbers? Who on your team is quietly absorbing someone else's workload? And what does it cost to replace a person you could have coached? If you have avoided a performance conversation because the paperwork felt like corporate overhead, this episode is for you.Visit https://workshop.blueprintos.com to register for the upcoming Above The Business workshop.Thanks to our sponsorsCoach P ConsultingCoach P found great success as an insurance agent and agency owner, leading a large and stable team of top-performing professionals. Today, he shares the systems, delegation strategies, and specialization methods he developed along the way. Gain access to weekly training calls and mentoring at:https://coachpconsulting.comBe sure to mention you heard about it on the Above The Business Podcast.Autopilot RecruitingAutopilot Recruiting helps small business owners solve staffing challenges by taking the stress out of hiring. Their dedicated recruiters work on your behalf every business day. They optimize your applicant tracking system, post job listings, and source candidates through social media and local communities.With their continuous recruiting approach, you can save time, reduce hiring costs, and receive pre-screened candidates without paying hiring fees or commissions.https://www.autopilotrecruiting.comMention Above The Business Podcast when you reach out.Direct ClicksDirect Clicks specializes in digital marketing solutions designed for business owners who want measurable results. Their team supports companies through paid advertising, SEO, and strategic marketing systems that help generate consistent leads.Exclusive offer for listeners:https://directclicksinc.com/abovethebusinessGet a free marketing campaign audit where their team reviews your website, SEO, content, social media, and paid advertising, then provides actionable recommendations. If you partner with them, all setup fees will be waived.About Above The BusinessAbove The Business is hosted by Bradley Hamner, founder of BlueprintOS, and focuses on helping small business owners transition from Rainmaker to Architect by building systems, teams, and operations that scale without their constant involvement.
Akram's excited for what he might enter in the Flower & Produce Show, and invites Khalil to come to the allotment to help him choose. Khalil declines; he and Keira will be gaming instead. Azra and Akram agree Khalil isn't himself, probably still affected by the situation with Lynda's dog, Monty. They speculate that he might be feeling guilty about his rant at Brookfield last week. Azra resolves to go over there and apologise on Khalil's behalf. Later she and Ruth chat about Pip and Stella's wedding before Azra gently broaches the subject of the attack offers her apology for Khalil's actions. Ruth tells her not to worry, brushing the incident with Khalil aside. She admits she's still feeling shocked over the attack on the sheep. Azra sympathises, and they agree it's a horrible situation for everyone. Meanwhile Khalil complains to Keira that his family is breathing down his neck, whilst admitting he's having flashbacks to the images of the sheep. He loved Monty like a friend and can't get his head round what he did. Keira suggests Azra might be able to help, but Khalil's adamant his family mustn't know he's seen the pictures. They already worry too much about him. Later Azra and Akram agree it's a relief how understanding Ruth and David were about Khalil. There's an awkward moment for Keira when they quiz her about what might be bothering Khalil, but she successfully diverts them. They hope they're worrying about nothing.
emocleW, emocleW, emocleW to the Distraction Pieces Podcast with Scroobius Pip!This is your bonus FRIDAY REWIND episode! Today, we catch up with Steven Knight, originally episode 256 from 2019-03-06.Original writeup below:Chances are that if you are a Distraction Pieces Podcast listener (god bless you), you will be fully versed on Steven's work. Pip references his work with a great and contagious fondness and for very good reason - he makes some incredibly fine film and television! This is a perfect opportunity to hear from the man in that director seat behind the curtain on a huge amount of topics and angles including the rule-based world of film making, his work on Locke, Peaky Blinders, Taboo and all the rest in his insane body of work, his new film Serenity and how he lucked out on getting his first choice cast picks, playing with reality, how we are all the lead roles in our own realities, the BBC leaving him alone in creating, the far and wide fans of Peaky, how one gets a cast together and that old unfinished story of Florence Pugh trying to smuggle she and Pip into Peaky too! Let's see if the inception works, shall we? Great one here folks, enjoy!PIP'S PATREON PAGE if you're of a supporting natureSERENITYIMDBSTEVEN on ROTTEN TOMATOESSTEVEN on DEN OF GEEKPIP TWITCH • (music stuff)PIP INSTAGRAMSPEECH DEVELOPMENT WEBSTOREPIP TWITTERPIP IMDBPOD BIBLE Hosted on Acast. See acast.com/privacy for more information.
Anthropics Investoren erwarten für Oktober einen Börsengang bei zwei Billionen Dollar, was der größte IPO aller Zeiten wäre. Pip erklärt, warum der Termin geschickt gewählt ist. Für OpenAI sieht er die Lage umgekehrt. Dort kommt der zweite Vertriebschef in einem Jahr. Zusammen mit Cerebras hat OpenAI dafür eine Variante gebaut, die vierzehnmal schneller antwortet. Danach vier Modellstarts in einer Woche, bei denen ausgerechnet DeepSeek die Preise um bis zu das Zwölffache erhöht, und Elon Musk sein neues Grok für objektiv das beste Modell hält. Bei den Finanzierungsrunden geht es um Databricks, Lovable, Legora und Cognition, dazu um die Frage, ob man das Geld gerade nehmen und liegen lassen sollte. Silver Lake holt Workday von der Börse. In der Schmuddelecke erlaubt die Trump-Regierung privaten Firmen offensive Cyberangriffe und beruft sich dabei auf Kaperbriefe aus der Verfassung. Unterstütze unseren Podcast und entdecke die Angebote unserer Werbepartner auf doppelgaenger.io/werbung. Vielen Dank! Philipp Glöckler und Philipp Klöckner sprechen heute über: (00:00:00) Aus der Community (00:02:05) OpenAI wechselt den Vertriebschef (00:12:20) Ultrafast mit Cerebras (00:17:16) Anthropic-IPO (00:29:10) Anthropic kauft Decart (00:30:34) Braucht man ein KI-Device? (00:32:36) Gemini 3.7 Flash (00:34:10) DeepSeek V4-Pro (00:34:47) Grok 4.6 (00:37:11) SpaceX (00:38:49) Databricks und Snowflake (00:42:40) Workday geht von der Börse (00:44:11) Lovable (00:49:26) Legora (00:52:28) Cognition (00:55:18) Mistral (00:57:22) Kaperbriefe (01:02:05) Truth API (01:03:28) Chronext (01:06:55) Apple zahlt Verlage Shownotes OpenAI holt den zweiten Vertriebschef in einem Jahr - bloomberg.com GPT-5.6 Sol läuft mit Cerebras bis zu 14-mal schneller - 9to5mac.com Anthropic peilt einen Börsengang bei 2 Billionen Dollar an - ft.com Anthropic verhandelt über Decart für 6 Mrd. - bloomberg.com Google stellt Gemini 3.7 Flash vor - blog.google DeepSeek bringt V4-Pro und erhöht die Preise um bis zu das Zwölffache - theinformation.com Grok 4.6 startet zuerst in Cursor - gizmodo.com SpaceX-Leerverkäufern gehen die Kugeln aus - cnbc.com Databricks sammelt 5 Mrd. bei 190 Mrd. Bewertung ein - cnbc.com Silver Lake verhandelt über eine Übernahme von Workday - reuters.com Lovable verdoppelt die Bewertung auf 13,3 Mrd. - trendingtopics.eu Legora verhandelt bei mindestens 10 Mrd. - ft.com Cognition verhandelt bei 40 Mrd. - bloomberg.com Mistral will bis 2030 ein Gigawatt in Europa bauen - aibusiness.com Trump lässt private Firmen offensive Cyberangriffe fahren - bloomberg.com Kaperbriefe stehen in der Verfassung - xcancel.com KI-Agenten greifen Taiwans Regierungssysteme an - ft.com Presseverbände klagen gegen Trumps Truth API - ft.com Chronext-Kunden warten auf Zahlungen und Lieferungen - wiwo.de Apple verhandelt mit Verlagen über Nachrichten für Siri - techcrunch.com
Is your performance improvement plan designed to improve performance—or is it simply documenting an exit you already decided to make? Too many leaders use a PIP to compensate for months of vague expectations, delayed feedback, inconsistent accountability, and conversations they refused to have. The paperwork may be complete, but the leadership is missing. In Episode 143 of the Leadership Sandbox, Tammy J. Bond exposes the five signs a PIP has become corporate theater and explains what actually changes workplace behavior: clear standards, honest dialogue, consistent communication, documented expectations, and real follow-through. She also challenges leaders to recognize the ripple effect of every PIP: the entire team is watching to see whether leadership uses accountability to help people win—or merely checks HR's boxes on the way to termination. Topics explored in this episode: [00:31] When the PIP becomes theater [02:00] Avoiding discomfort is not caring for the employee [03:40] The entire team is watching what the leader does next [05:45] How vague expectations create performance problems [08:55] Stop using paperwork as a predetermined exit strategy [9:20] Five signs your PIP is a theatrical production [14:10] People don't want to fail at work [16:15] Dialogue—not documentation—is the behavior-change strategy [17:21] Lead the human being, not only the human doing Explore COMMAND™ at https://www.bondgroupenterprises.com/command-leadership.
Welcome, welcome, welcome to the Distraction Pieces Podcast with Scroobius Pip!This week Pip is joined by Hardcore Listing homie and long term real life homie CHRIS GLASSON, and podcast producer / DJ / beatmaker BUDDY PEACE for the second in a 3 part series of celebration episodes!The continuation in the three part saga, on some Lord Of The Rings / Back To The Future / Godfather type ishh but right there in the middle. A lot more ground to cover here including the later stages of the music making, progressing into podcasting via radio, the London clubnight, and how the podcast itself spawned many other podcasts to follow... So much to get through - ENJOY!PIP'S PATREON PAGE if you're of a supporting natureHARCORE LISTINGBUDDYSPEECH DEVELOPMENT WEBSTOREPIP TWITCH • (music stuff)PIP INSTAGRAMPIP TWITTERPIP PATREONPIP IMDB Hosted on Acast. See acast.com/privacy for more information.
Troy Jackson is joined by Rev. Traci Blackmon to discuss election protection, what it is and why it matters. They reflect on the importance of voting access and what a healthy, faithful culture of civic participation would look like in our communities.Each August, we focus on civic engagement as a CCDA Family. Learn more about engaging with your local government at ccda.org/civic.Learn more about The Faithful Witness Campaign at faithfulwitness.us.The Rev. Traci Blackmon is the former associate general minister of justice and local church ministries for the United Church of Christ, a position she held for eight years. She earned a bachelor of science in nursing from Birmingham-Southern College and a master of divinity degree from Eden Theological Seminary.Blackmon's work focuses on communal resistance to systemic injustice through the redemptive power of love. Her response in Ferguson, Missouri, to the killing of Michael Brown resulted in national and international recognition.She was appointed to the Ferguson Commission by Missouri Gov. Jay Nixon and to the President's Advisory Council on Faith-Based and Neighborhood Partnerships by President Barack Obama. She currently serves on the board of the Samuel DeWitt Proctor Conference and was named among 15 “Faith Leaders to Watch” in 2020 by the Center for American Progress.Learn more about Rev. Blackmon and her work at faithoutloud.org.Community organizer, pastor, leader and writer, Troy Jackson, has been with UNDIVIDED since its founding. He is a graduate of Princeton Theological Seminary and earned his PH.D in U.S. history from the University of Kentucky.Troy's book Becoming King: Martin Luther King, Jr. and the Making of a National Leader (The University Press of Kentucky, 2008) explores the critical role the grassroots Montgomery Movement played in the development of Dr. King. His other publications include his work as an editor on The Papers of Martin Luther King, Jr. Volume VI: Advocate of the Social Gospel (September 1948-March 1963) (University of California Press, 2007). With their children grown, Troy and his wife moved to a cabin outside of Cincinnati, OH, with their two dogs, Pip and Pico.Learn more about Troy and UNDIVIDED at undivided.us.Learn more about CCDA and how you can get involved at ccda.org. Connect with CCDA on Instagram, Twitter, Facebook, and LinkedIn. Follow CCDA on YouTube.
ark Zuckerberg bezahlt einen Profikämpfer für ein Instagram Video und veröffentlicht am selben Tag ein Manifest darüber, warum die Zukunft allen gehört. Pip nennt zwei Lackmustests, an denen sich zeigen wird, ob das ernst gemeint ist. Anthropic macht den Einführungspreis von Sonnet 5 dauerhaft. OpenAI kauft eigenen Mitarbeitern Anteile für sieben Milliarden Dollar ab, und zwar mit eigenem Geld statt über externe Investoren. Bei SpaceX fehlen für die angekündigten zehn Gigawatt rund vierhundert Milliarden. In China bricht Kimi aus der Testumgebung aus, ByteDance will das erste Modell mit zehn Billionen Parametern bauen, und 97 Prozent aller Humanoiden kommen inzwischen von dort. Dann fünf Jahre Frank-Thelen-Fonds. Unterstütze unseren Podcast und entdecke die Angebote unserer Werbepartner auf doppelgaenger.io/werbung. Vielen Dank! Philipp Glöckler und Philipp Klöckner sprechen heute über: (00:00:00) Zuckerberg im Cage (00:04:24) The Future is for Everyone (00:08:46) Metas Geschäftsmodell (00:13:59) Sonnet-5-Preis (00:15:25) OpenAI kauft zurück (00:22:28) Bubble Discussion (00:27:01) SpaceX-Finanzierung (00:31:55) Kimi bricht aus (00:33:12) ByteDances Riesenmodell (00:35:54) Chinas Kapitalmarkt (00:37:33) Humanoide aus China (00:39:16) Korea und Taiwan (00:40:42) Shein-IPO (00:41:18) Fünf Jahre 10xDNA (00:45:15) Spotify skippt Werbung (00:56:45) Zuckerbergs Yacht (00:59:26) Vantage Tripod (01:02:23) Amazons Gaskraftwerk (01:04:07) Wildberries (01:05:59) OpenClaw bucht Yoga Shownotes Zuckerbergs Essay: The Future is for Everyone - about.fb.com Zuckerberg legt seine KI-Vision auf 6.500 Wörtern dar - wsj.com Meta öffnet Muse Glimmer und legt 1 Mrd. für Gemeinden auf - ft.com Anthropic macht den Sonnet-5-Einführungspreis dauerhaft - xcancel.com OpenAI kauft Mitarbeiteranteile für 7 Mrd. zurück - bloomberg.com SpaceX nach den Zahlen: Capex, Lock-up und Leerverkäufer - ft.com Kimi K3 bricht aus der Testumgebung aus - techcrunch.com ByteDance trainiert ein Modell mit bis zu 10 Billionen Parametern - ft.com China öffnet 28 Billionen Kapitalmarkt für den Chip-Wettlauf - bloomberg.com China liefert 97 Prozent aller Humanoiden - bloomberg.com Südkorea und Taiwan überholen Japan bei den Exporten - asia.nikkei.com Shein peilt 30 bis 40 Mrd. Dollar für den Hongkong-IPO an - qz.com Fünf Jahre 10xDNA, gerechnet im Subreddit Finanzen - reddit.com Fondsvergleich 10xDNA gegen den Index - onvista.de Spotifys neuer Skip-Button und das Podcast-Geschäft - semafor.com Muddy Waters über Zuckerbergs Yacht - xcancel.com Googles KI kannte einen Namen aus einem privaten Dokument - techspot.com Die Namen für den Trabant-SUV, die Pip live generieren ließ - chatgpt.com Amazons Rechenzentrum in Texas mit eigenem Gaskraftwerk - nytimes.com Wildberries unter ukrainischem Drohnenbeschuss - wsj.com KI-Assistent hackt die Buchungsseite eines Fitnessstudios - abc.net.au
emocleW, emocleW, emocleW to the Distraction Pieces Podcast with Scroobius Pip!This is your bonus FRIDAY REWIND episode! Today, we catch up with Souad Mekhennet, originally episode 169 from 2017-09-20.Original writeup below:You may have heard Pip hyping this one up for a minute, and quite rightly so - ladies and gentlemen, please welcome journalist, author, writer, Souad Mekhennet! A huge episode here, as Pip sits down with Souad for a nice good while to really get into her own story... And what a deep, heavy and truly daring life she has led, from beginning to present day - hear her recall some chills-inducing tales and truly unique meetings, from getting in deep into situations with Isis commanders, her interview with 'Jihadi John', and her career covering all manner of terrorist activities over the years, and SO much more, made all the more fascinating due to her being Muslim and female, which positions her uniquely for her line of writing and interviewing. It's also fascinating hearing her thoughts on what creates a perfect situation for men to become radicalised, and how it can mainly stem from a broken family life. This is an absolutely gripping conversation, to which this writeup would never do justice, and it's a wonderful commentary companion to her book 'I Was Told To Come Alone', which you should (and surely will) snap up at your nearest convenience.PIP'S PATREON PAGE if you're of a supporting natureINSTAGRAMWIKII WAS TOLD TO COME ALONETHE ETERNAL NAZIPIP TWITCH • (music stuff)PIP INSTAGRAMSPEECH DEVELOPMENT WEBSTOREPIP TWITTERPIP IMDBPOD BIBLE Hosted on Acast. See acast.com/privacy for more information.
After nearly two years off the live mic, GMoney reunites with Patriots in Progress (Pip) for a wide-ranging, unhurried conversation that treats the current Bitcoin chaos as almost the calmest thing in the room. The two old comrades compare notes on surviving fifth-generation warfare, toxic maximalism, and the exhausting theater of geopolitics, arguing that stepping back is the only way to stay sane while the noose slowly tightens around the enemy. Expect a heavy dose of Pip's deep dive into Ecclesiastes, spiritual warfare, and why he thinks Bible prophecy is looking more plausible by the year, all woven together with the usual frog-adjacent banter. Along the way you get thoughts on Israel, tokenization, X Chain, and Satoshi's cryptographic roots in wartime code-breaking. Part reunion, part sermon, part signal-versus-noise pep talk, this one is a slower burn for listeners who care more about first principles than the daily shiny object.
Welcome, welcome, welcome to the Distraction Pieces Podcast with Scroobius Pip!This week Pip is joined by Hardcore Listing homie and long term real life homie CHRIS GLASSON, and podcast producer / DJ / beatmaker BUDDY PEACE for the first in a 3 part series of celebration episodes!To be exact, a celebration of 20 years of Scroobius Pip, dating back to the first musical output and initial starting point in the entire journey. This goes back to those very first days, the days of Myspace and the four track limit (makes you feel nostalgic doesn't it...), the street art world, making music on a four track recorder and the lo-fi home studio setups, Björk and Sage Francis as early influences - and speaking of influences - the spoken word scene and pals made around that, early touring including the performances outside gig queues, booze as a prop, and all sorts of fun lil' Easter eggs and such. The cliff-hanger is a goody too... Enjoy!PIP'S PATREON PAGE if you're of a supporting natureHARCORE LISTINGBUDDYSPEECH DEVELOPMENT WEBSTOREPIP TWITCH • (music stuff)PIP INSTAGRAMPIP TWITTERPIP PATREONPIP IMDB Hosted on Acast. See acast.com/privacy for more information.
Watch the full episode on YouTube:We first covered Baseten last year when DeepSeek mania was at peak hype. Now they have raised a monster $13B round and become one of the new cohort of AI Infra decacorns that are (with Nvidia, Intel, and the semis complex) chief beneficiaries of the Inference Inflection. We return to Baseten at the peak of the 2026 edition of Open Weights debate. Ali has published a viral breakdown of Kimi K3:And since you last saw him, Philip has spoken at AI Engineer and written the definitive book on Inference Engineering spotted all over SF:Three years ago, inference engineering barely existed as a category.Today, it is one of the most critical disciplines in AI. Inference engineering inherently tackles a different question than standard model training: “How do you turn those weights from training into a product that is fast, reliable, and affordable at scale?” Focusing on these creates an entirely new optimization problem.In one recent GLM-5.2 experiment, quantizing more of the model actually preserved its benchmark quality while increasing throughput by 20%, because the errors introduced in different layers could cancel each other out.Inference is no longer just the final step after training. It is becoming its own engineering discipline, with its own research problems, infrastructure, and increasingly specialized roles.In this episode, Baseten's Philip Kiely and Ali Taha join swyx and Vibhu to explain what actually happens after a new open model is released and what it takes to turn “we generated a token” into a fast, reliable, production-ready API.We go deep on cache-aware routing, disaggregated prefill and decode, quantization, speculative decoding, KV-cache movement, model parallelism, GPU kernels, and the race to make frontier models up to 10× faster. Philip and Ali explain why inference optimizations can still produce gains of 20%, 100%, or even 200%; how quantization errors can cancel one another out; why identical weights can behave differently across clusters; and how Baseten grafted a Kimi vision encoder onto GLM-5.2 without changing the underlying language model.The conversation then expands beyond LLMs into NVIDIA Dynamo, mega kernels, Rubin, AI-specific chips, local inference, video generation, diffusion versus autoregressive models, and the enormous compute barrier to generating coherent long-form video. Finally, we explore the convergence of training and inference, continual learning through persistent KV cache, and the emerging loop where models help optimize the infrastructure that runs them.We discuss:* What happens when a 200,000-token request enters an inference system* Cache-aware routing and reusing previously computed KV cache* Why prefill and decode are increasingly handled by different GPUs* When dedicated deployments become cheaper and more reliable than shared APIs* How speculative decoding uses a smaller model to accelerate a larger one* Tool calling, structured outputs, and what LLMs actually do* What it takes to support a new open model on day zero* Grafting Kimi's vision encoder onto GLM-5.2* Retrofitting inefficient model layers with components from other architectures* Why models sometimes collapse into repeating the same token* How hardware, kernels, and race conditions create nondeterministic failures* Preserving model fidelity while making inference faster* How quantization errors can cancel each other out* Why inference optimizations still deliver gains of 20%, 100%, and 200%* How optimized serving can make a model up to 10× faster* NVIDIA Dynamo, KV-aware routing, and distributed model serving* Speculative decoding the speculative decoder* Why local AI is about making models less dumb while data-center AI is about making them less slow* Tensor, expert, and pipeline parallelism across GPUs* Hardware-aware model design, auto-tuning, and the case against mega kernels* Rubin and why inference is becoming a systems problem* Whether modern GPUs are evolving into programmable AI ASICs* Why enormous models like Kimi K3 require GB300-class hardware* Why open-source video generation still trails Veo, Kling, and other closed models* The quadratic attention bottleneck behind long-form AI video* Autoregressive video, real-time generation, and compounding quality drift* Why future video systems may combine autoregressive and diffusion architectures* Training for inference and inference for training* Continuous post-training, deployment, evaluation, and improvement loops* How GLM-5.2 helped optimize the kernels serving GLM-5.2 itself* Why faster networking could unlock dramatically faster decoding* Continual learning, KV-cache compaction, and persistent model memoryShow Notes* How to build a day-0 API for Kimi K3* 22580: From GPT2 to Kimi3, ExplainedPhilip Kiely* LinkedIn: https://www.linkedin.com/in/philipkiely* X: https://x.com/philipkiely* Inference Engineering: https://www.baseten.co/inference-engineering/Ali Taha* LinkedIn: https://www.linkedin.com/in/aliestaha/* X: https://x.com/waterloointernTimestamps00:00:00 Introduction and the 200K-Token Prompt00:03:18 Dedicated Deployments, Speculative Decoding, and Tool Calling00:11:26 Launching Production-Ready Open Models00:19:06 Model Retrofits, Failure Modes, and Nondeterminism00:28:22 Quantization and Canceling Errors00:32:15 The Race to 10× Faster Inference00:40:48 Dynamo, Speculation, and Local vs. Data-Center AI00:50:18 Model Parallelism, Auto-Tuning, and Mega Kernels01:00:55 Rubin, GPUs vs. ASICs, and Custom AI Chips01:10:03 Giant Models and the Limits of GPU Memory01:12:42 AI Video, Quadratic Attention, and Autoregressive Generation01:21:47 Audio, Images, and Diffusion Models01:27:32 Training, Self-Optimizing Models, and Continual Learning01:40:06 Closing ThoughtsTranscriptIntroduction: Baseten, Waterloo Intern, and Inference EngineeringSwyx [00:00:00]: Okay, we're here in the studio with Philip, old friend from Inference Engineering, the book, as well as Baseten and everything that you've done, you and I have done before, as well as Ali. Welcome.Ali [00:00:15]: Pleasure to meet you.Swyx [00:00:15]: Waterloo intern.Ali [00:00:16]: Waterloo intern, always.Swyx [00:00:17]: When did you get “Waterloo intern” as a handle?Ali [00:00:19]: As a handle? Oh.Ali [00:00:20]: I think the rebranding happened mid-March. When I saw it was open, I was like, “I have to take it. Up for grabs.”Philip [00:00:26]: The problem is that Ali is really good at his job and is not gonna be an intern much longer.Philip [00:00:30]: So we have to figure out who's gonna get the handle.Ali [00:00:33]: Well, I'll pass the torch over to the next intern.Swyx [00:00:34]: Oh, okay. It can be, like, you just pass it to another Waterloo grad.Ali [00:00:37]: To another Waterloo intern. No, bruh.Philip [00:00:39]: Yeah.Ali [00:00:39]: Intern.Swyx [00:00:40]: Intern, yeah.Ali [00:00:40]: And no.Philip [00:00:41]: You gotta get an intern from Waterloo.Ali [00:00:42]: Yeah, I've gotta get an intern from Waterloo.Swyx [00:00:44]: Right.Ali [00:00:44]: But they have to follow the path.Swyx [00:00:45]: Oh, it could, but it could come from Baseten, so it's like whoever Baseten gets from Waterloo.Ali [00:00:48]: Right.Swyx [00:00:49]: Has the title of Waterloo.Ali [00:00:50]: It stays in the ecosystem.Philip [00:00:51]: Exactly.Ali [00:00:52]: Halfway through the internship, you either get it or you're out.Philip [00:00:55]: You should also do, like, a big graduation ceremony where you change the handle.Ali [00:00:59]: Just say it.Philip [00:00:59]: For everybody.Swyx [00:01:00]: You guys are good at ceremonies, clearly. We had a nice launch of the book, very successful. But before we get into all that, I wanna start off with a fun question for you. Okay, you're an expert inference engineer. What happens when I send a long query, say two hundred thousand tokens into Baseten's inference? What's the process of query through GPU model routing, balancing, all that? What is all the stuff that we don't think about?Long Context Requests, KV Cache, and Cache-Aware RoutingPhilip [00:01:26]: With a long query specifically, the first thing that I'm gonna ask is, “Have you sent me this query before, or at least part of it?” and I really hope you have, because it's gonna be a lot easier for me and a lot cheaper for you. So the first thing that we're gonna look at is some cache-aware routing, where we're going to see, we probably have a number of instances, a number of replicas up serving whatever model you're hitting. We want to send this one to something with, number one, available prefill workers, and number two, ideally some cached input already there so that we can skip prefill on at least part of these two hundred thousand tokens. If you're doing two hundred thousand tokens, it's probably coding or a multi-turn agent or something where you would expect to have that cached. If you don't, we're gonna have to send it to a prefill worker. We've at least on certain models disaggregated prefill and decode, so you're going to have one set of GPUs that's solely going to process the input, create the KV cache, and get you your first token, and then that's going to be passed over to a separate set of GPUs, which is going to run decode. We're going to iteratively make those tokens. We're probably going to have some speculator model in front of that. I'm going to assume that you're doing coding, and because of that, our speculator model, which assumes you're doing coding, is gonna have a high draft token acceptance rate. If I'm wrong and you're asking me to summarize every Harry Potter book, it's gonna be slower. And then we stream that output to you and account for it, charge you, a couple of pennies and say, “Hey, would you like to send another one?”Swyx [00:03:04]: Except Baseten doesn't charge by pennies.Philip [00:03:07]: Well, yeah, we charge. I'm assuming that we're talking about the public model APIs. If you are setting up a dedicated deployment, then yeah, it's not pennies.Public APIs vs. Dedicated DeploymentsSwyx [00:03:18]: Yeah, one of the key differentiators when I was talking with Baseten initially was that people who want very high volume just need to rent by the box, ‘cause then it's up to you to figure out how to saturate the box.Ali [00:03:31]: And more often than not, it's, like, way cheaper if you're pushing, like, millions of tokens per hour, if you just pay per hour instead of pay per token.Philip [00:03:37]: Yeah, they do. I think that we've increasingly seen a lot of demand for the pay per token APIs, just because everyone wants to try open models, and then once they find a use case that's really sticky, then they move over to dedicated.Swyx [00:03:51]: Is there a best practice on when it's time to swap over?Philip [00:03:54]: Couple reasons. Yeah, reliability, that's a big one, right?Ali [00:03:57]: Like, if they have a very specific use case, they want you to train something specifically for them, like they want their own spec dec, for instance, for their own traffic.Swyx [00:04:04]: Spec dec is speculative decoding.Speculative Decoding and Custom SpeculatorsAli [00:04:05]: Speculative decoding, yeah.Swyx [00:04:07]: You have to explain.Ali [00:04:07]: Sorry. Like, speculative decoding is like, if you have a huge model, right? And so the model is going to be generating one token at a time every single turn, every single forward pass. So we attach, like, this little, like, parasite, like this layer that goes on top of the model, and this model just has to predict. It does three very fast autoregressive forward passes, and it will predict, like, three certain tokens, and then you do one forward stage over the entire original model in order to see if those predictions were correct or not, and then you accept them or you reject them. Now, this draft model is traffic specific, so if you, like, Philip said, if you're summarizing Harry Potter books, I can train exclusively that draft model on Harry Potter books, and I can guarantee you that I'm gonna accept the three tokens every single time. And so with that case, I increase your decode speed. I wouldn't be able to provide this to you if you're a shared endpointSwyx [00:04:53]: YeahAli [00:04:53]: ‘cause I have no idea if you're doing Harry Potter, if you're doing coding, if you're doing English. We don't know. Also, there was a thing in the book that mentioned that if they really cared about a specific threshold, chapter four, I think. Do you remember that?Philip [00:05:06]: Yeah. The things that you can do is you can set a specific, like, batch sizing, a specific, like, parallelism strategy if you're trying to optimize for, like, throughput versus latency. You can. Maybe a NVFP4 quant doesn't pass your benchmarks and you wanna run a model at higher precision, you could do that. There's just a bunch of reasons why you might wanna have your own endpoint and the biggest one, of course, just being, like, you don't have to deal with someone else doing a hundred million tokens of benchmarking traffic at the endpoint when you happen to be trying to serve your users.Swyx [00:05:40]: Yeah. I think one thing that is. That is a classic journey. Like, it's people is asking the, what happens when you type Google into the browser. Tool calling, is that just, you're generating JSON or is there more complication beyond that?Tool Calling, JSON, and Structured OutputsAli [00:05:58]: Certain customers that we have, they have their own post-trained models, and so they demand a tool calling that's not just, like parse a file or go find the weather. It's something that's very specific and you have to do post-training on this. And if the post-training on the model is not good or if the quantization after the post-training to get the inference to be fast, the model will struggle reading the JSON file and reading the tool calling. But it doesn't require its own like sandbox. It's not like it's going to use that tool calling to like escape a sandbox or like it doesn't have to be contained. It can just be a normal dedicated deployment. The challenge with tool calling more and more seems to be that the companies want certain tool calling which is a very sensitive thing to train. And because you're dealing with all of the JSON outputs, if it doesn't like close the end of the request in a very certain manner, you end up with a model that did the tool calling and like the thinking and so as a result of that, it didn't see the result and just hallucinated the result as it decoded. That seems to be the most challenging thing with tool calling, not really the sandboxes model.Philip [00:06:56]: Yeah, that's a challenge on the training side and then on the inference side, there's work that you can do to scope the possible output. So we published this at this point close to two years ago, the solution to this problem which is you make a state machine and you use that to constrain the output to a specific format. So this is the structured output problem. If you remember backSwyx [00:07:27]: Yeah, the specific grammar is,Philip [00:07:29]: Yeah, exactlySwyx [00:07:30]: GML had this thing.Philip [00:07:31]: Yeah. So it's like the old-school “make sure this is only JSON”, return only JSON orSwyx [00:07:38]: YeahPhilip [00:07:38]: Grandma's gonna die type of prompts.Swyx [00:07:39]: Is it BNF grammar? At some point OpenAI had released a thing that was like, yeah, if you want to constrain your output, write BNF grammar, back as NOR.Philip [00:07:47]: In our inference system, it's just a specified output format. And you get the guarantee that your output's gonna be structured along that format. And so applying that to tool calls can like help cut down on. You can still call the wrong tool or call no tool. It doesn't solve the certainty problem but it at least solves the output structuring problemSwyx [00:08:10]: YeahPhilip [00:08:10]: Within tool calls.Swyx [00:08:12]: And MCP is just another form of tool, right.Philip [00:08:14]: Yeah, exactly.Swyx [00:08:15]: As far as there's no special thing there.Philip [00:08:16]: The thing I'm always like explaining to people is the LLM is not capable of doing anything. It's only capable of making suggestions of what to do and then if those suggestions are formatted in a certain way and applied to a system that knows what to do with them, then an action occurs.Swyx [00:08:32]: Yeah. Part of the fun stuff is, this is solved outside of tool calling too. Like in an agent loop if the output is not correct or you're right, like reasoning, tool calling was done in the reasoning trace, just be like, “Oh, I don't know what to do. Let me just try again.” And it might get there after a few tries. And on your point of training, sometimes this is harder in smaller models, so you don't have the same exact quality outputAli [00:08:56]: Right.Swyx [00:08:57]: When you just swap from a big model, right?Ali [00:08:59]: Yeah. I will say that, before, I think we need to go back to inference engineering proper.Ali [00:09:04]: But, I had expected that something would replace JSON because it's hard to stream JSON ‘cause JSON must be complete and you must have open and close brackets and everything. So it's hard to parse something or validate something while it's being streamed. So people invented all sorts of things that are like, I forget the name of some of these alternatives, but it's something like TOML, something like YAML. But JSON seems to be dominant still.Philip [00:09:30]: The JSON outputs aren't that long, right? Like you could have a long-- ‘cause tool calls also contain the arguments in them and perhaps for a certain tool you might pass like a very long argument. But my impression of the median tool call is that it's a relatively small number of tokens, right? So I would expect that speculators are generally fairly good at something as formatted as JSON. And so you would have like a pretty fast decode step there and that the streaming wouldn't be as valuable, but maybe I'm wrong about that.Ali [00:10:02]: I think you're also bounded by the software or that the model is gonna integrate with if the software is built with JSON for the tool calls or if the company that you'- if your customer says that this is how our software works and our tools are interfaced with JSON, you can ask them to like, change their software and say like, “Yeah, this is gonna be better for the model.” but like with the right training shouldn't be that much of a difference. Also more profitable if it outputs more tokens probably.Swyx [00:10:25]: Depends on your business model.Swyx [00:10:27]: It really depends. But I will say that, as a writer with like experience a lot with generated output, I do try to move from text to JSON text which is very long JSON, right? Like there's paragraphs in every field because I'm trying to structure it, right?Philip [00:10:44]: Right.Swyx [00:10:44]: I want you to first make factual statements, then make opinions then make bullet point summaries, have dates, have entity references have your sources for references, all these things. Anyway, so these are things that like I think people who really experiment with structural output have to really care about. But, let's, let's recurse up the stack a little bit. Before we started recording, you mentioned something really cool, which is that there's a lot of engineering that-- inference engineering that goes on when a new model provider releases a new model, right? So let's call it GLM-5.2, Kimi K3. I had previously assumed, especially if it's like, well, GLM 5 to 5.1 to GLM-5.2, like that you've supported them before. Is it that much work?What It Takes to Support a New Open ModelAli [00:11:26]: It's a lot of work.Swyx [00:11:28]: Yeah. Okay. So like, a lot of people, all you guys, right whenever a new model launch like, people rush to say like, “Oh, Hugging Face supports this, Fireworks supports this, Spacetime supports this,” and I'm like, “Yeah, of course we support it.” But what goes into that? What goes intoPhilip [00:11:40]: I think it's more than just support it too, right? It benefits the consumer a lot. Like I think it was with Kimi K2.5 or GLM-5.2 the latest, there was an inference war, right? X provider is at 90 tokens a second. The next day we're at 150. The nextSwyx [00:11:55]: I kinda kicked that off with the GLM-5.2.Swyx [00:11:58]: I wrote a Twitter article about. It got like half a million views,Ali [00:12:02]: Based on being numberSwyx [00:12:03]: YeahAli [00:12:04]: Or it's for something else.Swyx [00:12:05]: Yeah. Which,Ali [00:12:06]: Oh my GodSwyx [00:12:07]: Which then got everyone really excited about, hey, how can we, bend tracks a little bit further and,Philip [00:12:14]: There's a difference between support the model, as in I can make a token out of this model, and support a model, as in I have a production-ready API from this model.Philip [00:12:26]: Getting to the point of I can make a token out of this model is not that hard because generally the, open source inference engines, vLLM, SGLang of the world oftentimes even receive weights ahead of time, maintainers do, or the people making the model merge PRs to ensure support. So you generally can, just get it working on the standard open source stack without too much pain in most cases. The challenge is, every inference company is gonna have own proprietary stack. Some open source components, some in-house stuff. And for any arbitrary model, there's going to be some new stuff. Sometimes you get lucky, like K, two five to two six was, like, pretty similar.Quantization, Speculators, and Production ReadinessAli [00:13:16]: Yeah. It was pure continued post-trainingPhilip [00:13:18]: YeahAli [00:13:18]: If I remember correctly.Philip [00:13:19]: Even in those cases, there's still stuff you have to do. You have to redo the quantization work. You're taking the model from. Generally, these models are not released in NVFP4, and we want them to be in NVFP4 for maximum Blackwell compatibility. So we have to perform that quantization, and, calibrate the quantization to make sure that we're not causing any regression in the model's intelligence. And then we also have to train the speculator, as we've talked about. Generally, we have. We have ZDR, zero data retention on our model APIs, so we don't know exactly the traffic that people are sending us, but we know what's popular. We know that coding use cases are popular. We know that agents, agentic use cases are popular. So we can get public data sets that are representative of that traffic and train general speculators. Now, with speculators today, you need to train the speculator using the base model itself because you're getting hidden states out of the model from running inference on these specific prompts, and that is the training data you use to create the speculator. So there's that process which you need the real model weights for. And then there's of course just the process of, standing up all the infrastructure behind it, loading all this stuff, testing it. And then when there's a new model with a newer architecture, I think that, like, the DeepSeek models tend to be the most challenging as they have, like, the most novel architectural stuff going on, model after model. But every new model has something. Kimi K2 had. Oh, sorry, GLM-5.2 hadAli [00:14:53]: Sparse attention.Philip [00:14:54]: Yeah,Ali [00:14:54]: YeahPhilip [00:14:54]: the DSA.Ali [00:14:55]: Right. Which is brought from DeepSeek.Philip [00:14:57]: Yeah. AndAli [00:14:59]: So you can copy-paste then?Philip [00:15:01]: It kindAli [00:15:01]: I don't know how this works.Philip [00:15:02]: So, like we had to, like, build support for that into our runtime. And you're right, like it is really interesting the way that all of these open source labs borrow from each other. For example, like GLM-5.2 doesn't have vision. So something that, Haley, a guy on our team, if we could take a look at this, he, like, grafted the Kimi vision encoder onto GLM-5.2.Retrofitting Vision into GLM-5.2Ali [00:15:27]: We'll be training the projector.Philip [00:15:28]: Exactly. So if you think about, like, the encoder, there's the encoder, which is the part that looks at the image and turns it into latent information, and then there's the projector which likeAli [00:15:38]: You can say latent space. It's okay.Philip [00:15:41]: And then there's the projector that maps it onto, the model itself, and then there's the model weights. You don't wanna mess with the model weights because you run a chance of making the model dumber at something else for the purpose of giving it vision. So instead, Haley started with just a projector, which is only a handful of millions of parameters.Ali [00:16:02]: That would be, yeah.Philip [00:16:02]: Yeah.Ali [00:16:03]: Can you show the training one?Ali [00:16:04]: Like the way it groksPhilip [00:16:05]: YeahAli [00:16:06]: Very interesting.Philip [00:16:06]: And maybeAli [00:16:07]: That right therePhilip [00:16:07]: Maybe Ali, you should take it from here. You've got a betterAli [00:16:10]: Ooh, double the sandPhilip [00:16:11]: Understanding of this than I do.Ali [00:16:11]: Yeah. You can see, like, he. The way he trained this is really cool. At the beginning, he was training it using just like, “Here's a picture of a mountain. Can you describe what's in this mountain?” And that caused it just like the first, learning walls. Like here you can see this all we're trying to teach it is to translate the encoded. Like it's already taken the encoder from Kimi K. It's taken the image. It'Philip [00:16:31]: Yeah. FrozenAli [00:16:31]: FrozenPhilip [00:16:32]: With adapter.Ali [00:16:32]: Exactly.Philip [00:16:33]: Yeah.Ali [00:16:33]: So the brain is frozen and the eyes are frozen. It's just we're tryingPhilip [00:16:37]: AlignAli [00:16:38]: Interconnect between the eye and the brain, right? So the projector. And so you take the tokens and then he's like, “Oh, can you describe what's in this image?” And he's like, “Oh, it's a mountain,” or it's a person or it's a human, whatever the case is. But that didn't cause complete understanding. So he changed it such that every image was associated with a data set of questions. Like, does this image have a white male? Does this image have birds in the top corner? Does this image have a scientist in it? All of that stuff. And it would have to answer questions correctly. And using not just training on describing an image, but being able to answer question, another question, answer over time. Like you can see the grokking, which is like genuinely insane, that retrofitting vision into a large LLM can learn to that extent. And even for images that it doesn't perform well on, for instance, if you ask it a picture of like Stephen Hawking, “Who is this?” Maybe it doesn't get it, but it will say something like, “This is Albert Einstein.” Like it still understandsPhilip [00:17:25]: Close enoughAli [00:17:26]: That this is a scientist who is a man who has, some significant achievements, all that stuff. So that's like really cool.Philip [00:17:32]: Yeah. So, we've covered Hao Tian before, who the author of the LLaVA paper that did this, a while ago. And I think that's very foundational work for anyone who hasn't done vision work before.Ali [00:17:41]: Same with the CLIP and MetaCLIP, where you go from just captioning to building out questionsPhilip [00:17:47]: RightAli [00:17:47]: Off the image and how much better you can get performance.Philip [00:17:50]: Right. Right. Right. Yeah. But what's, what's so exciting about this is if you look at a model like this. Now, this is a little bit more of a research project. It's not. It got to 56% on MMLU Pro, I think. So not quite frontier. But if you're running this model, you haven't suffered any loss on your GLM-5.2 quality. If you don't have an image, it'll just behave exactly the way it used to. And ultimatelyAli [00:18:14]: Which in the inference code you literally do not include the other part, right?Philip [00:18:18]: Yeah. You would just skip the encoder if you don't have an image input.Ali [00:18:22]: Okay.Philip [00:18:22]: Just confirming.Philip [00:18:23]: YeahAli [00:18:23]: Does it affect a lot on the overall inference side? Like you're not adding much, you're adding a very small vision encoder. These are typically likePhilip [00:18:30]: They're super fineAli [00:18:31]: Less than a billion parameters, right?Philip [00:18:32]: Yeah. It's, - There's a little bit less standardization among vision encodersSwyx [00:18:37]: YeahPhilip [00:18:37]: So the support matrix can be a little bit, sparser. But overall, yeah, it's a pretty, it's a pretty minor component of the overall system. And ultimately what you get out of the system is all of a sudden you have Kimi Vision, GLM weights, and DeepSeek attention all in one model.Open Source Model Grafting and Franken-MergesPhilip [00:18:56]: And that's, I think, a lot of the power and beauty of open source, is that you can take all of these different components and combine them together into a system that's better than anyoneSwyx [00:19:05]: YeahPhilip [00:19:05]: Can be individually.Swyx [00:19:06]: People used to say that you would also do Franken-merges where you would take likePhilip [00:19:10]: YeahSwyx [00:19:10]: Layers from each model.Swyx [00:19:11]: Does anyone do that anymore?Ali [00:19:13]: Well, to your point previously when you were mentioning like, the work that goes into supporting a model when it first comes out, like GLM-5.2 or MiniMax M3 or whatever the case is. Sometimes you do have to like, you do have to switch out some things. Like, for instance, the MiniMax M3 head uses full attention, and with full attention you end up with this like insane bottleneck in spec dec ‘cause you're doing auto-regressive token generation for three tokens, and you're doing this like N squared over all of the tokens that are in your sequence. Your KV cache is like very large because it's not sparse, it's not top K. So we find it better to like, okay, we're gonna replace this, we're gonna replace this layer with a layer from another model that's using like GQA, for instance. And then just with the right training, you can get it to have the same acceptance rate. So it is very possible to retrofit layers from other models and very much needed. If a layer is like inefficient, the training just becomes the challenge, like how do you ensure that you train it properly? Which again to your earlier point is like the mesh between training and inference. As in like you need very good training in order to do fast inference. That's like, I feel like more and more becoming true.Swyx [00:20:21]: Yeah. Anything else on the support side when you say like get it to fully production ready?Loop Detection, Race Conditions, and Non-DeterminismPhilip [00:20:26]: Yeah. I think that there's also a question of just, we can test a model to a pretty extensive degree, but we're trying to get it out quickly and then you see a bunch of other people test it and you get interesting results. There was an issue with, GLM briefly where we had some like mode collapses where it would just output the same token over and over again for certain prompts on certain temperatures. Like once you expose an endpoint to the real world, there's going to be, so many more varieties of things given to it that you're able to, discover and patch things. So it's not just a, day zero process, it's then like for the first week, for the first month, if a model remains popular, like how do you both fix bugs and then continue to push the envelope on performance?Ali [00:21:21]: What do you mean you don't want your model outputting S?Swyx [00:21:24]: Is there loop detection on that stuff, by the way? It still happens like quite a lot, which is surprising.Ali [00:21:30]: We have like we, in our endpoint, like if a model was to output the same token like four plus times, we just cut the generation. We say like, “Oh, sorry, this-- Like try again,” or like we will reprocess the request. ‘Cause we know then, like if it, like if, yeah, it's four times the same token, it's probably collapsed.Swyx [00:21:45]: Yeah. Is there a way to opt out in case I really want that?Ali [00:21:48]: You want that?Ali [00:21:50]: I think there's a way that we have to handle it. I'm not exactly certain, but I feel like in certain models, like when they output something like you can imagine, like a table for instance, and so they want, they wanna draw like 12 dashes and 12 dashes. Yeah, I think there's a way for that to happen. I think we only do it on certain tokens. Like we exclude certain special characters.Swyx [00:22:07]: Yeah.Ali [00:22:07]: So we only do it on like certain like S is the most common almost. GLM-5.2Swyx [00:22:11]: OhAli [00:22:11]: And I think it was DSV 4 as well. Like you'd just have like looping issues where like you literallySwyx [00:22:17]: ItAli [00:22:17]: Just have like S.Swyx [00:22:18]: Yeah. Is there a special, something special about S? No, just randomlyAli [00:22:21]: It just seems to be the one token involved.Swyx [00:22:23]: Yeah. And it'Philip [00:22:24]: Is thereSwyx [00:22:24]: And it's only temperature 0Ali [00:22:27]: NoSwyx [00:22:27]: Even at other temperaturesAli [00:22:27]: Even at like 0.9 or whatever, it will still, it will still collapse.Swyx [00:22:30]: That's weird, right?Ali [00:22:30]: It's, it is an inference problem to be honest, like a software problem. Like oftentimes, the image you run will-- like NVIDIA will release an image for instance, and if we will upstream the changes from their latest TensorRT-LLM image into our stack, we'll find that it fixes it. Or oftentimes this will only happen in an inference engine that you're using like SGLang. But if you were to switch to vLLM, that isn't the case. So it seems to be like an extremely like deterministic software issue and not really a model issue. It's not like a weights problem. Like I'- we'll say like, “Oh, it's a problem with the quant. We did PTQ wrong,” right? But that isn't, that doesn't make sense because the same weights used with a different inference engine does not repeat the problem. And sometimes it's, the kernels that are being used in the backend have like these very subtle sometimes race conditions, where if you were to use this model hosted on one cluster, you will never get this problem.Swyx [00:23:19]: Oh my God.Ali [00:23:19]: But if you host it on a different cluster, you will. And the reason is the KV cache transfer from a node to node in that one cluster is using a slower interconnect than the node to node in another cluster. So that exposes the race, whereas in another cluster it doesn't. So then you end up just like, okay, this model is not gonna be hosted on this cluster. We're gonna host it on, another cluster because that cluster exposed that problem. But then it ends up with like, okay, is it the software? Is it the model weights or is it the hardware?Swyx [00:23:42]: There is a thing about this with temperature 0 still not being deterministic, right?Ali [00:23:46]: Right.Swyx [00:23:46]: Mostly because of hardware. Even at temperature 0 same model, you won't always get the same output.Swyx [00:23:52]: Even-- But I'm surprised by the race condition one because, I thought PyTorch was a graph that like guarantees that you at least, execute things in the right order.Ali [00:24:02]: Well, yeah, true. Like I'm not, I'm not saying that there is. Like well, you have things like PTL optimizations where like you can start a kernel before the end of the previous kernel, and that's like ‘cause you want to do that because there'sSwyx [00:24:12]: It's like pipeliningAli [00:24:12]: Expense. Exactly.Swyx [00:24:13]: Yeah.Ali [00:24:13]: But it'- But you don't do it cleanly. Like you overlap a little bit of the execution. No, it is very possible that the kernel itself, like that one block that is supposed to be running in this instance of time, that kernel itself has a race condition. For instance, like a missing barrier. Like often if you're designing a kernel and you want it to make it to be very fast, if you don't test it extensively, you'll, you'll have certain threads access data points from registers before they've been written to by other threadsSwyx [00:24:36]: YeahAli [00:24:36]: For example, because like your barrier is wrong or your synchronization was wrong. But yeah, like the testing itself is very difficult in those like, andSwyx [00:24:42]: And there's no like borrow checkerAli [00:24:45]: What does that mean?Swyx [00:24:46]: Like Rust. Like the. If you're trying to have like memory safety It sounds like a comparable problem.Ali [00:24:52]: Well, yes, but you're working in CUDA, right, NVIDIA GPUs. Like- You just need a higher level language like modular Maybe that's what modular is supposed to do. I don't know.Quantization Quality and Vendor FidelityVibhu [00:25:00]: How do you see keeping quality of the model? So you talked about all these steps of, okay, you gotta do quantization, train your own speculative decoderAli [00:25:07]: RightVibhu [00:25:07]: Run on different hardware. Looking at other model providers, okay, you kicked off a inference speed race on the consumer end. What goes into keeping quality the same across them, right? Sure, you can run benchmarksAli [00:25:22]: YeahVibhu [00:25:22]: But, like, how do you determine how much quantization are there standards? What goes intoPhilip [00:25:27]: There's a few things on quality. Most inference optimizations are lossless. KV caching, for example. You are just recomputing or preventing recomputing the same values. Speculation, of course, if a draft token is wrong, it gets rejected. The main lossy optimization is quantization. And that really comes down to, number one, data format, number two, which parts of the model you choose to quantize, which layers, and number three, like doing a lot of calibration on the quantized weights, to ensure that you're preserving all the outliers. There's other tricks that you can do, though. A big one is long context, ‘cause one thing you asked at, right at the beginning is, “Oh, what's gonna happen if I send a 200,000 token request in?” So with a long input sequence, you need to, store a lot more information. You need to process a lot more tokens. And so even if a model has a context of a certain length, you might, as an inference provider, choose to build an API with a shorter context length, and of course a full length one as well. Because if someone doesn't need the full million token context, for example, you can get them better performance. I don't know if that's exactly like quality of the model. The way that I think about quality is to what degree are we faithfully serving the original model? If you think of a golden implementation of a model that performs exactly the way the model is designed to perform, I think of quality as how close are we getting to that, 100% fidelity of the model.Philip [00:27:13]: You can also, of course, think about quality from the training side and how do you push yourself past 100%. But when I think about purely inference optimizations, it's getting faster while staying as close to that 100% fidelity mark as possible. And certainly our standard internally is that, like you should not be able to tell the difference between our API and a, official API. I think Kimi in particular does a good job of vendor benchmarking hereAli [00:27:41]: YesPhilip [00:27:41]: Where they haveAli [00:27:42]: They released an actual vendor benchmark.Philip [00:27:43]: Exactly, yeah.Ali [00:27:44]: ‘Cause they accused, some people, Amazon? There was some provider that was not doing very well on Kimi's benchmark.Philip [00:27:50]: Yeah.Philip [00:27:51]: So, with Reflect we probablyVibhu [00:27:52]: This was a long time ago, right?Philip [00:27:54]: No.Ali [00:27:54]: Yeah, like threeVibhu [00:27:55]: They alsoAli [00:27:55]: Four, five months agoVibhu [00:27:57]: This also happened with, I don't remember which model, but they pulled out quite a few, and then they started a whole chart about this. It might have beenPhilip [00:28:03]: Kimi Vendor Verifier.Ali [00:28:04]: Yeah.Philip [00:28:05]: Yeah.Ali [00:28:05]: Yeah, ‘cause you, ‘cause you'd be pissed, right? Like if you'Philip [00:28:07]: Yeah.Ali [00:28:07]: If like if I'm a consumer and I'm using like Amazon's endpoint for instance, and I've used Kimi and I'm like, “Oh my God, like this is bad,” I'm not gonna say, “Oh, Amazon quantized the model in a bad way.” I'm gonna say, “Oh, Kimi sucks.” Right?Philip [00:28:17]: Yeah.Ali [00:28:17]: So it seems like that makes sense.Philip [00:28:19]: Yeah, they care. They care.Vibhu [00:28:21]: Justifiably.Ali [00:28:21]: Yeah, justifiably.Vibhu [00:28:22]: This is probably a stupid question, but just checking, has anything improved from main quantization?Philip [00:28:28]: Yeah.Vibhu [00:28:28]: Like, is quantization always strictly worse?Ali [00:28:30]: Well technicallyVibhu [00:28:32]: NoAli [00:28:32]: It's a lossy. QuantizationPhilip [00:28:33]: YeahAli [00:28:33]: Is a lossy, it's a lossy implementation.Philip [00:28:36]: Speed improvesVibhu [00:28:36]: Speed improves.Ali [00:28:37]: It the number, likeVibhu [00:28:38]: No, I' always look for inverse scaling laws.Philip [00:28:40]: Yeah.Ali [00:28:40]: Yeah.Vibhu [00:28:40]: This is something I learned from Noam Brown, where like things that normally act in one direction sometimes do.Philip [00:28:45]: Well, technically when you run a benchmark, because these models are deterministic, sometimes your,Ali [00:28:52]: YeahPhilip [00:28:52]: NVFP4 quant is like, two basis points higher than yourAli [00:28:56]: No, it's noise. It's noise.Philip [00:28:57]: Yeah, exactly. I'm like, yeah, it's, it's within. That's why I always say within margin of error.Philip [00:29:01]: And I stopped saying that because everyone assumes that what is, well, within some margin of error, we're barely inside of that to the worst, so we're saying. But yeah, sometimes it's just like, gives you a higher output score. But like Ali said, that's noise. To my knowledge, you're not necessarily making the results better. You're just trying to, again, like keep your fidelity as close to 100% to the original model.Layer Selection, KL Divergence, and Better QuantizationAli [00:29:27]: There is, to your point, research that we did on MP. I don't know if you are able to pullPhilip [00:29:31]: YeahAli [00:29:32]: A tweet we did. One of our research interns, Joshua, I think it's a tweet on how we have 20% better quantized GLM-5.2 than NVIDIA. Essentially what we found throughout like this month research is, okay, quantization is a lossy. It's. You're compressing the data from, occupying 16 bits to occupying, four bits, for instance. And so you're losing some information, and you're trying to minimize that. And so when I say that I'm gonna quantize the model, my job becomes how do I find the layers that I can quantize, and how to find the layers to not. For instance, with image models, I don't quantize modulation layers, and I don't quantize out projections because those two are. Like out projection is what you see as the user. Modulation is what the model sees or understands. Right, exactly. And so to his paper, do you have the. It doesn't have the. Yeah. It's a long paper. I don't know if I can findVibhu [00:30:25]: If there's a part to search or it's probably in the thread.Ali [00:30:28]: It's probably in the thread.Vibhu [00:30:29]: Yeah.Ali [00:30:29]: But the long and the short is it is very possible that quantizing more of the model makes the results. Like if I have a model that I quantize layers one, five, and 10, and another model where I only quantize layers one and It is possible that the model in which I quantized more information is going to perform better because the quantization errors have canceled out. And so what Joshua showed in his mathematical proof where he had like a verifier in, is that you can predict which layers are going to have quantization errors that will cancel out with each other, and you choose to quantize those layers. And so the result of doing this mathematical quantization is you end up with a model that's 20% more quantized than another provider, so you get 20% more throughput of it because there's more layers than running an NVFP4, and your quality is better than that other quant because the layers that you chose to quantize have their errors cancel out, like one layer skewed to the right one layer skewed to the left, one layer skewed to the right. Your final logits distribution is more similar to the original distribution of the model, so you have better fidelity. And so the way we proved this was with KL divergence. So instead of just scoring on the benchmarks, we scored the KL divergence between the logit distribution of the quantized model and the logit distribution of the original full precision model, and we showed that with this technique we get. If your probability distribution on the logits which token it wants to select is more of the same as the original model, you're probably gonna end up staying true to the original model. So yeah, so it seems like previously before this, it seemed like the industry was, well, the more you quantize, the worse it's gonna be, ‘cause the more loss you introduce. That's not exactly, not necessarily true. So yeah, doesn't improve it, but can cancel out.Philip [00:31:57]: I think it might be this, but reminds me a good bit about pruning where you can prune off certain layers.Philip [00:32:03]: But very interesting. Didn't know this was a whole paper you guys put out.Ali [00:32:06]: It's. Fun fact, it was originally 72 pages, this paper, and then we decidedPhilip [00:32:11]: WowAli [00:32:11]: We can't tell. We couldn't release it. So it's now 45.Swyx [00:32:15]: Still 39 pages, so very substantive. We talked about evals and all these things and, like what's possible in terms of speedup? Like it's like probably like the numberInference Speedups and BenchmarkingSwyx [00:32:25]: Thing that people do wanna care about, and it's something that you wrote about in your post. Like official API is 70 tokens per second, and you push it up to 90. Is that like a normal thing?Philip [00:32:36]: So what's cool about working in inference, the reason that I think inference is going to be a useful place to do engineering for a long time, is that if you look at highly optimized domains like, say, finance, if you're in finance, you measure how much better you got in basis points. It's like, “Oh, I got five basis points better, like twentieth of 1% better,” that's huge news because everything is so optimized. When we publish optimizations, it's 20%, it's 100% it's 200%. So there's still probably like a lot further to go, honestly. Like you'll, you'll know that inference is pretty much solved when researchers start publishing about how they got 1% faster at something.Swyx [00:33:19]: Which by the way, because I am from the finance background, in the ‘70s, that was the margin at the time. When you did quantitative finance research, you would findAli [00:33:27]: And like 20%, tens of percent.Swyx [00:33:29]: That's. Yes.Philip [00:33:29]: Yeah.Swyx [00:33:30]: And now it'Philip [00:33:31]: Tiny fractionsSwyx [00:33:32]: For those people interested, look up Andrew Lo's paper. He had a really interesting illustration of quant, stat arb, distribution, narrowing down from like those kinds of 20% differences in the ‘70s, down to nothing today, which is very cool.Philip [00:33:48]: Exactly, and we're at the beginning of the same type of thing. Now benchmarking is hard. I think anyone will tell you that, and benchmarking provider speeds is hard because there's so many variables that go into it. What hardware are you using? How much load do you have on the system? What's the exact nature of the prompts and input and output sequence lengths? All that stuff. But overall, when you start stacking these improvements, you're looking at multiples. You can look at it. The most common form, of course, is TPS, tokens per second, which is bad naming by us in the industry, ‘cause there's two tokens per second. There's tokens per second, the throughput number, and the latency number.Ali [00:34:31]: TTMT, yeah.Philip [00:34:32]: Like total tokens per second out of the, out of the GPU as a throughput number. Most people only care about tokens per second as the latency number, which we should call ITL, intertoken latency, but we don't.Philip [00:34:44]: Anyway, so you can imagine a standard API without many optimizations for a 1 trillion parameter model operating somewhere in the 30 to 50 tokens per second range for reasonable traffic profile. And we generally see the goal of, pushing to 10X that. But, not necessarily day zero, but by stacking enough optimizations, if you have, say like four optimizations, each of which doubles performance. Or sorry, three optimizations, each of which doubles performance, then you stack that up, that's an 8X gain. That's the order of magnitude that we're working with in this space. We're trying to make things substantially faster, not just go from like 70 to 90.Swyx [00:35:38]: Are you saying you've. You have done that?Philip [00:35:40]: So let's say you have as a reasonable baseline, 30 or 40 tokens per second. You can achieve 10X that. So like on GLM-5.2, if you run it unquantized, perhaps on H100s even, and you're just using an off-the-shelf inference engine with no particular optimizations, no speculator, nothing extra around like KV routing, no disaggregation, you're, you're probably, yeah, looking at that like 30 to 40. You think that's like a reasonable baseline?Swyx [00:36:12]: Right. Right.Philip [00:36:12]: To get to something like 10X, there's a lot of trade-offs that you're making. If we're running at more like a 300, 400 tokens per second range, you are using the best hardware possible. You have a optimized speculator. You have done all of your quantization work. You are Seeing a pretty high cache hit rate. You are running with a reasonably small batch size and a parallelism configuration that is tuned for latency versus throughput, but it is possible. So the spreads that you see if you, like, go on artificial analysis or you go on OpenRouter and you look at, the worst provider to the best provider, oftentimes can hit that range. 10X is of course very aggressive. It's oftentimes maybe more of a four to six times improvement. But that's the performance that makes us really excited, is when we can get these huge gains, not just go from 70 to 90 tokens.Stacking Optimizations: NVFP4, Speculation, and DisaggregationAli [00:37:19]: It's also, like, hardware dependent. Like, ifPhilip [00:37:20]: YeahAli [00:37:20]: If you have a thing where you're serving it on just, like, a node of H100s and then you throw, like, you shard the model across, like, four nodes of B200s. Like, you can definitely increase the speed with just throwing more hardware at it. Like, normalizing for the same exact hardware and the same number of GPUs.Philip [00:37:35]: Yeah. Then you're looking at, like, a two to 4X improvementAli [00:37:38]: Right. RightPhilip [00:37:38]: Depending on the inference optimizations. So yeah, it's. Some of it's, what's the call, and some of it's who's the driver.Vibhu [00:37:46]: If you break down the two to 4X, say the example is run GLM-5.2Ali [00:37:51]: YeahVibhu [00:37:51]: On B200sAli [00:37:53]: YeahVibhu [00:37:53]: Single node, right? What's, like, the cost trade-off for effort to get, like, the last bit of juice out versus what should people just think of, right?Ali [00:38:01]: Spectre quantization. Yeah.Vibhu [00:38:03]: Spectre quantization.Ali [00:38:04]: That's, that's, that's like 95%. LikeVibhu [00:38:06]: And how far does that get you? And how easy is that for the average person to do? So say right I wanna throw the weights of GLM-5.2 on a node of B200s, how easy is it to find speculative decoder- decoder model or already quantized model? How much work goes into it?Philip [00:38:23]: If you're doing it up front, it's quite a lot of work. If you're doing it today, there's going to be people who have published things that you can just, you can just grab some NVFP4 weights. You can grab a speculator. Yeah, if we're thinking about, like, what are the 2Xs we're stacking, going from, BF16 to NVFP4 is, it's not quite a 2X, right? It's like. I think it's about, like, 30 to 40%, from 16 to 8, and then another 30 to 40% multiplied from, 8 to 4. So that doesn't quite get you a 2X, but, like, roughly a 2X. Speculator, roughly a 2X. Disagg on top of that if you're able to get enough hardware and put enough traffic through it, another roughly a 2X. And then you add in some, double-digit percent increase from having just a better runtime with, the latest kernels and stuff behind it. And that's how it stacks up.Ali [00:39:21]: YeahPhilip [00:39:21]: So building each of those, like, building the, quantized weights is, for someone who really knows what they're doing, hours to days of work. Building the speculator, again, like, hours to days of work. And the, disagg setup, hours to days. Well okay, but like once you haveAli [00:39:39]: Once set up. Once set up. YeahPhilip [00:39:40]: Yeah, getting disagg working for the first time, I'm saying, of course, is very difficult.Philip [00:39:44]: The marginal implementationAli [00:39:48]: Like, if you're just grabbing, like if you are a person, like just a normal consumer who has access to, like, a node of B200s and you're wondering, “How can I just host it myself?” You don't need to quantize the model yourself. There's always gonna be, like, an open source quantized checkpoint. NVIDIA's gonna push one out if no one else does. You. Usually, the providers will have their own spec dec that they've trained as well. You don't need to train your own spec dec. You can just use that as well.Philip [00:40:09]: Yeah. Like, GLM-5.2 has its own MTP.Ali [00:40:13]: Right. Right.Vibhu [00:40:14]: What's multi token prediction?Philip [00:40:15]: Yes.Ali [00:40:16]: I'm justVibhu [00:40:16]: Can you explain that?Ali [00:40:16]: I'm just an expert.Ali [00:40:18]: I can do it for you in case I get it wrong?Vibhu [00:40:20]: No.Vibhu [00:40:21]: Yeah, you should correct if we're wrong, but their multi-token prediction can be used for self-speculative decoding.Ali [00:40:27]: I'm not sure. I'm not gonna correct that.Vibhu [00:40:28]: Okay. I'm semi-confident in thatAli [00:40:30]: Okay. YeahVibhu [00:40:30]: But someone can check. But it's useful to paint the story of, okay, not just the average person, but say a company wants to switch from serverless inference I wanna throw this up on. I wanna rent some GPUs, throw it up. These are the steps you take to do significantly faster than just put it behind vLLM.Ali [00:40:48]: Right.Vibhu [00:40:49]: I was waiting for a mention of Dynamo.Vibhu [00:40:51]: I feel like, that's supposed to be the baseline that you measure against.Dynamo, KV Routing, and Disaggregation ToolkitsPhilip [00:40:55]: I would think of Dynamo as less of a box system and more of a toolkit for building with. So when we talk about doing aware routing, when we talk about doing KV offloading, when we talk about doing, PD disaggregation, Dynamo fundamentally is. By the way, Dynamo is an open source library from NVIDIA.Ali [00:41:17]: We've done a pod with KylePhilip [00:41:18]: OkayAli [00:41:19]: Kyle Cranin.Philip [00:41:19]: Cool. So then your listeners know then that it supports all the different inference frameworks. And it is multi hardware, which is interesting.Ali [00:41:28]: But it's just a router, it's not like an optimizer layer.Philip [00:41:30]: Yeah. All it does, like, what Dynamo is good at, it is a library for moving information around your cluster, around your hardware. So if you have, KV cache on one place and you need it to be somewhere else, Dynamo coordinates NIXL for you to move that around.Philip [00:41:49]: That doesn't mean that, like, out of the box, you just say, “Pip install Dynamo,” and then you get, like, a massive performance speed up. It's more of a developer toolkit.Ali [00:42:01]: Yeah. I would have said it would. It comes with a set of defaults that you can then swap out.Philip [00:42:06]: It does. If the industry at large, I think, was, like, rolling out all of these deployments, standard, then I think it would be, like, a credible baseline. But, we've got to, we've got to benchmark against, like, what we're seeing in the wild.Speculative Decoding Methods: Medusa, EAGLE, n-Gram, and Spec-SpecVibhu [00:42:23]: I did wanna talk a little bit more about PD disagg, because that is probably, like, number three after quantized and speculative decoding. In your book though, I was just gonna pull out the book.Philip [00:42:31]: Yeah.Vibhu [00:42:32]: Like section 522 on Medusa, 523 on EAGLEPhilip [00:42:35]: YeahVibhu [00:42:36]: 524 on gram.Philip [00:42:37]: It's 55, would be disaggregationAli [00:42:42]: Yeah. Well, no, I just wanted to dwell a little bitPhilip [00:42:44]: YeahAli [00:42:44]: The other. Like, so what do you choose to include? What do you choose to not to include? Because there was all these other techniques.Philip [00:42:51]: Yeah.Ali [00:42:51]: Are these still relevant? Because I think they came out, like, a year and a half ago maybe.Vibhu [00:42:55]: Medusa is quite old.Philip [00:42:56]: Yeah, Medusa's old.Ali [00:42:58]: It was old.Vibhu [00:42:58]: But is it in the book as a good, here'sPhilip [00:43:01]: BaselineVibhu [00:43:01]: Baseline vanilla understand it?Philip [00:43:02]: Like you should know this.Vibhu [00:43:03]: Like I read the paper, I'm like, “ it makes so much sense.”Philip [00:43:05]: Yeah.Philip [00:43:05]: So with the book, I had a couple goals. One was to give people just a working vocabulary for the space as a whole, and the other was to give them some intuition about how each of these techniques works. As I mentioned in my AI Engineer talk, which is the first public addendum to this, the speculation space has moved much faster than everything else. So yeah, even at the time that I wrote the book Medusa, I very much included as a way for people to understand how the space evolved rather than what the most modern technique is. And now of course, there's DFlash, dSpark. There's, there's newer techniques even than EAGLE, although EAGLE is still very commonly used.Ali [00:43:51]: SpecSpecta.Philip [00:43:52]: Yes. Speculative decoding.Vibhu [00:43:54]: What canAli [00:43:56]: Oh, it's a paper by Tri Dao and it's like, it's doing speculative decodingVibhu [00:44:00]: HuhAli [00:44:01]: For the speculative decoder.Philip [00:44:02]: Oh, in spec- oh my God.Ali [00:44:02]: It's literally just an another. It's like, yeah, that's the most simple way to explain it, and it seems like he got trivial speed ups there. But it seems that the complexity with training, it's almost like in our mind at least, it's almost as complex as training GANs. Like it's like a very delicate balance and oftentimes you, it's just but yeah, it's literally speculative decoding on speculative decoding.Vibhu [00:44:21]: Speculative.Ali [00:44:22]: Yeah. We saw this paper.Vibhu [00:44:24]: It's interesting, right?Ali [00:44:24]: Yeah.Vibhu [00:44:24]: I wouldn't even expect it to be very particular to train, I wouldAli [00:44:29]: Right.Vibhu [00:44:29]: The naive part of me is like, okay, train speculative decoder.Ali [00:44:32]: But like, and it makes sense, like the whole idea of speculative decoding is you. It's like, it's like almost like the iPhone auto predict version but for a normal model, right? Like you're just, you're just, generating three tokens and you're like, okay, I'll do prefill on them. And so you save those three turns for your original model. Now your speculative decoder is doing three turns of auto regression, so why not just have an even smaller model?Ali [00:44:53]: The other question there is what are the size of speculators? So say forPhilip [00:44:58]: Right. It's like a billion parameters.Ali [00:45:01]: Like for MiniMax, it's. Yeah. It's like one layer. It's like one 60th of the original model usually.Philip [00:45:06]: Yeah. I think we should do a paper when we get back to the office.Philip [00:45:10]: SpeculativeAli [00:45:11]: SpeculativePhilip [00:45:11]: Decoding.Ali [00:45:13]: No, it's, it does seem like how, when do you stop? But then it also seems like if you're able to train spec-spec decode for instance, right? Like if you're able to have a small model that is accurately predicts what the intermediate speculator is gonna predict, that is able to predict what the original target model's gonna predict, then why not just use that smallest model directly, right?Vibhu [00:45:34]: Yeah. This isAli [00:45:35]: Like it seems likeVibhu [00:45:35]: Adjacent to the routing problem.Ali [00:45:36]: Right.Vibhu [00:45:36]: Yeah.Ali [00:45:36]: Right.Philip [00:45:37]: The thing with speculators is one of the practical constraints on using them is that you do have to run a small model on the same hardware that you're running the big model on. There is a orchestration and resource competition problem inherent in that, and that is one of the constraints on speculation in general, is that draft tokens cost resources to create and cost software complexity to manage. And so if you have like infinitely recursive speculators, you add in quite a bit of that complexity on the actual implementation within the inference engine as well, not just in the training process.Vibhu [00:46:17]: I was gonna say, I would wonder if you could do similar, like distillation and pruning of, it's the same thing, it's just a model. Can we not just distill a lot of the weights, quantize the speculator, out of my domain? The question that also comes up is, this is all for big server workloads, right? How much of this applies to, say I have this MacBook, I wanna run Gemma really efficiently. Similar problems, not the same?Local AI vs. Data Center InferencePhilip [00:46:45]: Pretty different. I talked to Selo, about this on his podcast a couple weeks ago. The difference between inference engineering for the data center and for production workloads versus inference engineering for local AI, is that we start with fundamentally like different constraints and different goals. With local AI, it's how do I fit this model onto my hardware and then make it less dumb? And with data center influence, it's how do I load this model and then make it less slow? And we care about less dumb, and they care about less slow. But the local AI inference engineering ecosystem, I think has a lot for us to learn from in the data center space. They are experts in various forms of quantization, including dynamic quantization that we just don't touch, in the pruning, in the distillation, in the, layer removal. There'Ali [00:47:42]: Layer removal matters less.Philip [00:47:43]: Yeah. There'Ali [00:47:44]: No one loves pruning really.Philip [00:47:45]: Yeah. Well, but the, but they doVibhu [00:47:46]: Which is surprising, right? But that's, that's a whole different thingPhilip [00:47:48]: Just to fit something on the laptop.Ali [00:47:50]: Right.Philip [00:47:50]: So yeah, it's a, it's an interesting, it's an interesting space. Not necessarily that like their techniques make sense for us to do in the data center, because we have different resources and different goals, but more that the process as well as the openness of that field is something to, admire.Ali [00:48:12]: Yeah. Like to your point, like, certain optimizations that would. Like for instance, Turbo Quantum Sharper, like it made such huge hype on that and we did like a whole deep dive on Twitter and like said, what is it? How does it work? Why is it good or not? And it took off and it was implemented on local devices because your memory bandwidth is so slow on like a MacBook, for instance. But try putting the same thing on like an NVIDIA GPU on a B200 Turbo quant would not be. Like, it would not be used. Like, NVIDIA - Like, NVIDIA made it clear that this is not a good optimization, and we've seen it firsthand where the overhead of doing dequantization, quantization of, in the kernel itself with turbo quant kernel, each end is much slower than the time that you save from doing the bandwidth. ‘Cause on the B200s, you have like 3.5 terabytes per second. You don't need decrease the storage that much. You don't need to do, FP4 KV cache. You don't need to use a requant. There's, there's, there's better optimizations to be made. But on Edge devices, it's extremely important, it's extremely useful. So, seems to be, like, different optimizations there, but then they're all uniquely combined with like all you wanna quantize the model, you wanna do speculative decoding, like certain common prefixes with bothPhilip [00:49:18]: Principles.Ali [00:49:19]: Yeah, exactly. Exactly. Exactly.Philip [00:49:20]: They also do a lot of work on, model parallelism, especially over, heterogeneous topology, where you have, some sparks and they are wired together with, Ethernet, DGX sparks.Ali [00:49:35]: Yeah, this is the Exo Labs guys.Philip [00:49:36]: Yeah. You have, a nu
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Welcome, welcome, welcome to the Distraction Pieces Podcast with Scroobius Pip!This week Pip is joined by Mystery Jets OG BLAINE HARRISON!Mystery Jets are a total indie band sureshot, who are currently in the 20th anniversary year of their debut 'Making Dens' debut. They've been total crowdpleasers since the first days, rocking any venue they've graced (including Banquet Records to name one, who get flowers in perpetuity). Back then it was a VERY different time, and a near-unrecognizable media terrain in which many bands were assigned a persona, or vibe, or aura by the music press. The Jets were not immune to this and were often saddled with this and that from various publications, but thankfully forged ahead, and always retained pure love from their fanbase. Pip catches up with original member Blaine, who is such an easy breezy chat subject and has his own questions here and there too (Jets and Pip share 20th anniversary celebrations this year!). It was a really vibrant era back in 2006, but it's so valuable to see a successful band who are still rocking it with no loss of passion or ambition. A really fascinating episode which, as always, will appeal whether you're a Jets-head or you've never heard of 'em. ENJOY!PIP'S PATREON PAGE if you're of a supporting natureONLINE / TOUR DATESSTOREATTITUDE IS EVERYTHING (accessibility charity)FLEA JOINTBOY WHO RAN AWAYEEL PIE ISLANDSPEECH DEVELOPMENT WEBSTOREPIP TWITCH • (music stuff)PIP INSTAGRAMPIP TWITTERPIP PATREONPIP IMDB Hosted on Acast. See acast.com/privacy for more information.
In 2014, Collin Henderson was young, in shape, and had blood pressure in the 150s. Anxiety. Depression. Completely stuck. And no idea why. What broke it open wasn't a book or a seminar. Someone handed him a framework for his mind, the same way athletes get a framework for their body. Proactive. Repeatable. Trained. That framework became the foundation of everything he teaches today. Collin Henderson is the founder of Master Your Mindset, a mental conditioning coach to Nike, Microsoft, Amazon, and Salesforce, and one of the most practical mindset teachers George has ever had on the show. This conversation is a masterclass in what it actually looks like to train your mind, not just hope it cooperates. What You'll Learn In This Episode: Why mindset is conditioning, not motivation and the difference between the training room and the weight room The 6,000 vs. 70,000 thought problem and why the untrained mind always loses The "What x3" coaching framework to get unstuck fast Why identity is the bullseye and how your words are either coding you to win or fail Collin's 4-minute HA Method daily mental workout The bison vs. cattle framework for navigating hard seasons with people by your side The Oz Method for influence, sales, and human connection What "victory goes to the vulnerable" actually looks like in practice Key Takeaways: ✔️If the mind is untrained, the thought will win. If the mind is trained, the thinker will win. ✔️80% of human thoughts are negative. 95% are recurring. You're not stuck because of circumstances, you're stuck because of cycles. ✔️You never outperform your self-image. Identity is the bullseye. Words are your wand. ✔️Prehab, not rehab. The mind gym is proactive work, not something you do after you're broken. ✔️Gratitude lowers cortisol by nearly 30%. It's not soft. It's science. ✔️The 4-minute HA Method: breathe (1 min), I Have, gratitude, I Am, self-talk, I See, visualization. Borrow the future to build the present. ✔️Fear loves isolation. Nobody should worry or win alone. Do you have bison people? ✔️Asking for help is not weakness. Hiding and trying to do it yourself is. ✔️Victory goes to the vulnerable, because the coward and the warrior both feel fear. The difference is what they do with it. Timestamps & Highlights: [00:03] — 2014: high blood pressure, anxiety, depression, and the framework that broke it open [01:27] — Welcome: George's Yoda for mindset has entered the building [04:20] — Why knowing mindset matters and doing something about it are two completely different things [07:00] — The training room vs. weight room: what mental conditioning actually is [09:16] — 6,000 conscious vs. 70,000 subconscious thoughts — and why the untrained mind loses [13:07] — The "What x3" framework to get clarity when you're stuck [15:55] — Identity is the bullseye: how your words are literally coding your brain to fail or win [18:21] — Living by design vs. default — and your right to be average [28:00] — Designing places for your brain to go before pressure arrives [32:38] — It begins before it begins: the high-performance pre-game [35:01] — Victory goes to the vulnerable — and what the coward and warrior have in common [38:42] — The 4-minute HA Method: breathe, I have, I am, I see [44:00] — Gratitude: the research, the results, and why it's not optional [47:00] — Michael Phelps, decisive self-talk, and the number one driver of confident action [56:23] — The Oz Method book: connect with curiosity, clarify with story, collaborate on solutions [1:03:48] — The three Oz characters and the Scott story: from PIP to president's club in 11 months Connect with Collin Henderson He is the founder of Master Your Mindset, LLC, a two-sport Division I athlete, former top-ranked medical sales professional, and mental conditioning coach whose clients include Nike, Microsoft, Salesforce, Amazon, Novartis, #1 NBA draft picks, and Heisman finalists. He's authored seven books and hosts the Master Your Mindset podcast. His new book, The Oz Method, releases June 20th. Website: thecollinhenderson.com Instagram: instagram.com/collinhenderson YouTube: https://www.youtube.com/@collinhenderson6749 Quiet Mind Course: thecollinhenderson.com/quietmindcourse The Oz Method: Available for pre-order on Amazon Your Challenge This Week: Pick one thing from this episode. One. Give it 90 days. Then connect with Collin, follow him on Instagram, try the Quiet Mind Course, or grab The Oz Method on Amazon. Follow George: @itsgeorgebryant Work with George:The Alliance — Community for entrepreneurs training their minds alongside their business. 1:1 Coaching — Limited spots. Apply at mindofgeorge.com/coaching-consulting/ Live Retreats — In-person experiences where mindset and strategy meet. Follow for dates.
The Two KRSTN's are back with a lot of yapping about life and BTS! Join us while Pip catches up Kristen on everything that's been going on with Bangtan! We miss you all a lot!!!!
Welcome, welcome, welcome to the Distraction Pieces Podcast with Scroobius Pip!This week Pip is joined by longtime podcast and life friends CHRIS & STU & KUNT for a roundtable episode!Quick catch-up if you're new to the show (trigger warning for repeated "kunt" usage): Chris & Stu host the Hardcore Listing podcast, Stu does Off The Beat & Track and a ton of others, and Kunt is the frontman of the band Kunt & The Gang. Stu and Kunt also do the Acceptable In The 80's podcast. Pip, Chris & Stu form together to do the 'DrunkCast' episodes of Distraction Pieces, where they drink and generate like 5+ whole hour long episodes. So there we go!Now we're all on the same page, here is the hypersquad teamup (minus booze) to chat about the hardest kids in school. Whether you remember them from back in the days (whatever era), or you're familiar with them right now if you're in school at the moment, this roundtable chat will ring bells with you for sure. For those who've led a charmed tough-kid-free life and are over 40, think Gripper Stebson from Grange Hill. There are definitely worse than that but we're using shorthand here! Names have been shortened or indeed redacted, but the memories are mostly clear and chances are that you'll start to recall the kids from your school as the episode unfurls. Or maybe you were the hard kid yourself. Which leads us to the existential side-quest - does a hard kid know they're a hard kid? One to consider. Enjoy! PS: the catchup at the start is mainly due to those unfamiliar with the entire cast here - cos you'll be hearing the word Kunt over about 80-90 minutes more than you might have before. As alwyas with these ones, go steady if you're playing it out loud in a situation with younger fam-folk! PIP'S PATREON PAGE if you're of a supporting natureHARDCORE LISTINGACCEPTABLE IN THE 80sOFF THE BEAT & TRACKSPEECH DEVELOPMENT WEBSTOREPIP TWITCH • (music stuff)PIP INSTAGRAMPIP TWITTERPIP PATREONPIP IMDB Hosted on Acast. See acast.com/privacy for more information.
emocleW, emocleW, emocleW to the Distraction Pieces Podcast with Scroobius Pip!This is your bonus FRIDAY REWIND episode! Today, we catch up with Isy Suttie, originally episode 97 from 2016-05-10.Original writeup below:A slammer of a show lies ahead of you ready for your listening enjoyment, as we welcome one of the UK's finest acting/writing/musical talents, the multi-threat herself, ISY SUTTIE! A wonderful and whimsical (license to use that word has been officially granted by Isy herself, you'll see) chat with Pip and Isy as we begin on the subject of hot beverages in some depth and take off from there! And from Tea Corner we do indeed take off into some awesome places, hearing from Isy on her wide ranging skills as an author/actor/musician, her podcast and radio show and her own early versions of these as a young child, Edinburgh Fringe experiences and the recent juggle of all this while being a mother! SO much more besides... Powerhouse business right here - get inspired and enjoy this wonderful chat.PIP'S PATREON PAGE if you're of a supporting natureIMDBINSTAGRAMTEDx on dangers of smartphonesPIP TWITCH • (music stuff)PIP INSTAGRAMSPEECH DEVELOPMENT WEBSTOREPIP TWITTERPIP IMDBPOD BIBLE Hosted on Acast. See acast.com/privacy for more information.
Welcome, welcome, welcome to the Distraction Pieces Podcast with Scroobius Pip!This week Pip is joined by frankly long overdue comic and podcaster ELIS JAMES!Elis is something of a missing piece in the Distraction Pieces puzzle, one of those guests whose appearance was only a matter of time (a long time, sure, but inevitable). It's a pleasure to have him, and as you'd expect/demand, so much ground gets covered. Silence and not stressing it, a Humble Pie album of all things, podcasting with John Robins, the nuance of performance environments and the beauty of old venues, the weird and not awesome world of early morning comedy gigs, performing standup in Welsh, opportunity and being bold, the long and surprising road to the Albert Hall, football, technology adjusted ambition, the Cymru Connection (and the New York Times discovering it), and other favourite podcasts. Oh but so much besides. Lovely stuff, enjoy!PIP'S PATREON PAGE if you're of a supporting natureONLINEMANY ELIS & JOHN LINKSEP 428 Elis & John podcastHUMBLE PIEELIS IN NEW YORK TIMESSPEECH DEVELOPMENT WEBSTOREPIP TWITCH • (music stuff)PIP INSTAGRAMPIP TWITTERPIP PATREONPIP IMDB Hosted on Acast. See acast.com/privacy for more information.
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