Podcasts about Goldfinch

Raphael's years in Florence (c. 1504–1508) placed him at the center of one of the most extraordinary moments in Renaissance art, where he encountered both Leonardo da Vinci and Michelangelo at the height of their powers. Under the Soderini Republic, Florence became a stage for artistic innovation, marked by Michelangelo's David, Leonardo's Mona Lisa, and the unrealized battle frescoes commissioned for the Palazzo Vecchio.This episode explores how Raphael absorbed and transformed the lessons of these two rival masters. From Leonardo, he adopted naturalism, portrait composition, and sfumato; from Michelangelo, monumental form, line, and color. Yet Raphael forged a distinctive style defined by harmony, clarity, and balance, culminating in works such as the Maddalena Doni portraits and the Madonna of the Goldfinch before his departure to Rome under the patronage of Pope Julius II.Watch/Support/Learn: https://linktr.ee/italian_renaissance_podcastWorks Discussed: Michelangelo, David, 1501-1504 https://www.galleriaaccademiafirenze.it/opere/david-michelangelo/Leonardo da Vinci, Mona Lisa, 1503-19 https://collections.louvre.fr/en/ark:/53355/cl010062370Leonardo da Vinci, The Battle of Anghiari, unfinished, lost. Michelangelo, The Battle of Cascina, unfinished. Raphael, Portraits of Agnolo and Maddalena Doni, 1504-07 https://www.uffizi.it/en/artworks/portraits-doni-raffaelloRaphael, Madonna of the Goldfinch, 1506 https://www.uffizi.it/en/artworks/mary-christ-and-the-young-john-the-baptist-known-as-the-madonna-of-the-goldfinchThe Florentine Renaissance CourseSupport the show

Join Jordan Bayne, Founder of The Squad, the Film3™ brand, and Co-founder of enGEN3, for a radical re-evaluation of how intellectual property operates in an automated world. An acclaimed filmmaker whose Oscar-contender short The Sea Is All I Know starred Melissa Leo and screened at Cannes, Jordan has spent her career on the bleeding edge of narrative expression. Today, as Head of GenAI + Web3 for Goldfinch, she is tackling the ultimate creative crisis: the exploitation of IP by generative models. In this episode, we explore how the trademarked Film3™ movement uses Web3 guardrails and decentralized networks to turn passive audiences into active IP co-owners, creating a massive defensive wall for creators against corporate AI exploitation.

Magpies call and respond, Moorhen, Mallards sleeping on the paving slabs, someone in the cafe already to set up for the day, no cars or jets, only tinnitus, I stay very still to encourage a Magpie to get closer which it does for a while but then flies over to Lock Island, one of the Mallards flies away, the Magpie comes back, a Squirrel comes by very close on the ground, inquisitive, and for millions of years I like to think that it was quiet, everywhere existed without explanation, water flowed downstream, reeds grew, habitats formed, species evolved. And then came people shouting at dogs and slamming gates, the cakes were delivered by a man in a truck, shareholders made a profit, Mallards moved in to the grass to preen and coat their feathers with oil, Squirrels squirrelling, Robin surveying it's territory, a fly investigates the grains of wood on the table, distant Geese sound upset about something, I need to do more of this basking in the morning sun, a man on an electric scooter wearing a crash helmet, everything is growing imperceptibly slowly, a Bluebottle on my knee, now four Magpies investigating the grass, some white and grey clouds appear from the west and block out the warmth of the sun, a noisy maintenance van driving faster than the time I got told off for driving too fast down Flatford Road, a stack of correctly stored pallets, the Robin perched on top of the parasol, a maintenance man in hi-vis crosses the bridge to collect all of the bagged dog poo and replace the bin liner, a Cuckoo, an alarm on the Granary going off, a lady carrying empty cardboard boxes, two Crows join the exploration of the grass, they ignore the mic and camera tripods, a small black and white moth on my arm, a man with a red t-shirt walking towards the Granary, two men stop for a chat in the lane, a Pied Wagtail. As this was an early morning recording there was a lot of bird sound, in addition to the above, Cuckoo, Goldcrest, Garden Warbler, Mistle Thrush, Marsh Warbler, Greenfinch, Canada Goose, Willow Warbler, Coal Tit, Song Thrush, Dunnock, Chiffchaff, Collared Dove, Blackbird, Goldfinch, Wren, Great Tit, Blue Tit, Woodpigeon, Jackdaw and Blackcap.

DESCRIPTION Attorney General candidate Steven Goldfinch joins Tara to discuss violent crime, judicial reform, government transparency, and the controversies surrounding South Carolina's legal system. Plus, why Tara says Florida keeps accomplishing conservative priorities while South Carolina continues falling behind. PODCAST SUMMARY Today's show opened with a discussion about cultural and political debates surrounding gender, language, public safety, and the Democratic Party's messaging on social issues. Tara argued that progressive policies have increasingly blurred traditional distinctions between men and women, creating conflicts over women's sports, gender-specific language, and public safety concerns. The conversation focused on what she described as a growing disconnect between Democratic leadership and many voters on issues of gender, crime, and cultural identity. The show then shifted to a major interview with State Senator Steven Goldfinch, who is seeking the Republican nomination for Attorney General of South Carolina. Goldfinch credited current Attorney General Alan Wilson with several accomplishments, including efforts to combat internet crimes against children and address violent crime prosecutions. However, he argued that significant reforms are still needed, particularly regarding South Carolina's growing backlog of violent crime cases. According to Goldfinch, many counties face delays of four to five years before serious criminal cases reach trial. He warned that these delays can undermine prosecutions, weaken evidence, and force victims to relive traumatic experiences for years. The conversation also explored judicial reform. Goldfinch advocated moving toward a system that more closely resembles the federal judicial nomination process, where the governor nominates judges and lawmakers vote to confirm them. He argued that restoring public confidence in the judiciary requires greater transparency and structural reforms. Tara also questioned Goldfinch about concerns surrounding outside legal contracts and relationships between lawmakers, law firms, and government agencies. Goldfinch repeatedly emphasized transparency and public disclosure as the best safeguards against corruption and conflicts of interest. The program concluded with a comparison between South Carolina and Florida. Tara highlighted recent policy proposals in Florida involving tax relief and argued that South Carolina should pursue more aggressive reforms to reduce costs for residents and improve government accountability. KEY TAKEAWAYS Steven Goldfinch outlined his vision for South Carolina's Attorney General's Office. Violent crime case backlogs remain a major concern across the state. Judicial reform continues to be a central issue in South Carolina politics. Transparency was repeatedly emphasized as a solution to government corruption concerns. Questions about legal contracts, law firms, and state government remain active political issues. Comparisons between Florida and South Carolina continue to drive policy debates. Crime, accountability, and government reform dominated today's discussion. QUOTE OF THE DAY "Sunlight and transparency are the solution to all of this." SEGMENT HIGHLIGHTS Cultural Issues and Political Messaging Tara examined ongoing debates surrounding gender, language, and public safety. Steven Goldfinch Makes His Case The attorney general candidate outlined his priorities and vision for the office. The Violent Crime Backlog Goldfinch warned that delayed prosecutions continue to hurt victims and weaken justice. Judicial Reform Front and Center Discussion focused on changing how South Carolina selects and confirms judges. Can South Carolina Follow Florida? The show closed with a comparison of policy reforms and tax relief efforts. SOCIAL MEDIA POST

SEASON 2 - EPISODE 196 Drew Kunin - Production Sound Mixer In this episode of the Team Deakins Podcast, we speak with production sound mixer Drew Kunin (DISCLOSURE DAY; HER; CROUCHING TIGER, HIDDEN DRAGON). Throughout the episode, Drew helps us understand how a production sound mixer might do his or her job in a number of different circumstances, drawing on experiences ranging from building a recording booth on wheels for HER to mixing CROUCHING TIGER phonetically using a pinyin script. Drew also describes the type of equipment he might use in certain situations, and he reflects on the changes in technology and in technique he's witnessed since he started his career. He later shares his approach to mixing tracks on set, and we discuss how his mixes can influence what happens in post. Drew also recounts his experience of being one of two Americans on the crew of CROUCHING TIGER in China, and he gives us a quick demonstration on the difference between Mandarin and Cantonese. We worked with Drew on THE GOLDFINCH and THE SECRET GARDEN, and we had a great time catching up with him. - This episode is sponsored by Aputure

Hear the Senators sit down with a candidate for the South Carolina Attorney General's to get an in-depth look at who he is, why he's running for AG, how he sees the future of South Carolina, and so much more!Hear about Senator Stephen Goldfinch's family, history as a JAG officer in the National Guard, and how the campaign trail is treating him. Get your latest Statehouse update and hear firsthand the rationale behind some of the legislature's most controversial bills. Join Senators Sheheen and Lourie in this week's episode where they take a deeper look at upcoming legislation and lawmakers' actions in S.C.    Support the showKeep up to Date with BITBR: Twitter.com/BITBRpodcastFacebook.com/BITBRpodcasthttps://bourboninthebackroom.buzzsprout.com

On Wednesday, May 6, 2026, State Senator and South Carolina Attorney General candidate Stephen Goldfinch appeared on The Charlie James Show to discuss his platform. During the segment, Goldfinch emphasized his "law and order" approach, pledging to aggressively prosecute violent criminals and crack down on fentanyl traffickers. He also highlighted his intent to serve as a legal "firewall" for South Carolina by joining other Republican attorneys general in lawsuits to push back against federal overreach. Goldfinch, who leads the three-way primary field in fundraising, framed himself as a proven conservative veteran capable of reforming a legal system currently struggling with massive backlogs.

The second hour of The Charlie James Show on May 6, 2026, featured interviews with Attorney General candidate State Senator Stephen Goldfinch, who discussed fighting federal overreach and state government efficiency, and District 1 candidate Craig Dean regarding his campaign platform. The program also featured former Representative Adam Morgan criticizing a $300 million South Carolina state budget earmark, alongside analysis of efforts to redraw Congressman Jim Clyburn's district to make it more competitive. Listen to the full episode on Audacy.

Myself and Matt Shenton headed down to Flatford in the pitch black at 4am to set up fr the annual Reveil Dawn Chorus Global Stream, light came quickly and stealthily once it started, as did the birds, many birds were heard and seen including a Kingfisher, the Merlin bird app detected a Curlew but we were so sure about that, and then came colour, and then the coffee and croissants, I was woefully underprepared. Off-peak the natural world carries on regardless of our observations, the gates to the Car Park open in a few hours and the joggers and dog walkers are not out yet, we all have our motivations for getting out of doors, of bed. The white noise of the Mill Race a constant, last nights rain almost a run away or evaporated, Blackcap and Mistle Thrush, Goldfinch and Wren, might have a renaissance as the geopolitical outlook is looking particularly grim and dangerous, if civilisation implodes it should hand the baton of dominance back to the birds, I doubt they'll be singing our song in the future, a very small spider like the kind that used to frequent wooden windowsills in the 90's crawls around my cuff, and then inside it, moments later it has decided that wasn't a good idea so it crawls back out again and up my arm towards my collar. I stop writing to just sit and listen for the last half an hour. On my walk back to the car I see a lady jogging with a large dog, and two ladies that have opened the gate to the car park. ‘Morning!'

This week on From the Front Porch, we have a special treat: a recording of our live show from The Bookshelf's March 2026 Reader Retreat! In this episode, Annie and Hunter engage in an abbreviated March Madness debate and play the Newlywed Game with help from Ashley. Enjoy! To purchase the books mentioned in this episode, stop by The Bookshelf in Thomasville, visit our website (search episode 577), or download and shop on The Bookshelf's official app: The Liar's Club by Mary Karr vs. The Year of Magical Thinking by Joan Didion A Little Life by Hanya Yanagihara vs. The Goldfinch by Donna Tart Edinburgh by Alexander Chee vs. Gilead by Marilynne Robinson Fates & Furies by Lauren Groff vs. Crossing to Safety by Wallace Stegner Blonde by Joyce Carol Oates vs. Olive Again by Elizabeth Strout The Road by Cormac McCarthy  From the Front Porch is a weekly podcast production of The Bookshelf, an independent bookstore in South Georgia. You can follow The Bookshelf's daily happenings on Instagram, Tiktok, and Facebook, and all the books from today's episode can be purchased online through our store website, www.bookshelfthomasville.com.  A full transcript of today's episode can be found here. Special thanks to Dylan and his team at Studio D Podcast Production for sound and editing and for our theme music, which sets the perfect warm and friendly tone for our Thursday conversations. If you liked what you heard in today's episode, tell us by leaving a review on Apple Podcasts. You can also support us on Patreon, where you can access bonus content, monthly live Porch Visits with Annie, our monthly live Patreon Book Club with Bookshelf staffers, Conquer a Classic episodes with Hunter, and more. Just go to patreon.com/fromthefrontporch. We're so grateful for you, and we look forward to meeting back here next week. Our Executive Producers are...Ashley Ferrell, Beth, Cammy Tidwell, Gene Queens, Jammie Treadwell, Joseph Shorter IV, Kimberly, Linda Lee Drozt, Nicole Marsee, Stephanie Dean, and Wendi Jenkins.

The two benches that I was planning on using were taken but further along was an empty one, which was closer to a slight ledge on the hill that was actually in a great place as I wasn't in danger of filming any members of the public at close proximity, like that boy on a bicycle last time, there was also man having a loud aggressive phone call. A Chiffchaff competes with the traffic, wide tyres on tarmac and cobbles, early afternoon midweek spring sunshine vibes, Parakeets make it unmistakably London, and the jets are much lower than in Essex or Suffolk, large Tortoiseshell butterfly flits around the microphone, I'm recording at the wrong time of year to properly represent the painting as it depicts a September scene, Magpies frequent Branch Hill Pond at the bottom of the hill, Cherry blossom and fresh green leaves, a cement mixer powers up West Heath Road, it's difficult to relax with so many passers by, a wood pigeon flies up the hill, a small spider crawls along the dry mud, a low jet climbs from LHR and banks to the north, quite a lot of bees and wasps as the temperature is well above 10ºC, a mum and boy with a plastic spade sticking out the top of their back pack, a pale yellow Brimstone butterfly, a skip lorry bumps up the hill, keeping my headphones on as a deterrent, a beautiful red dog taking an interest in the mic but then sitting next to the bench, and then it's owner sitting on the bench, a pug with a diamond collar shining in the sunlight, the dog squeaking its ball, it's other owner talking to his companion in French and the dog in English, a Jewish man kicking the ball back to the dog, a Police siren, I notice that the dog has an Apple AirTag, the man walking to the bottom of the hill and unsuccessfully calling the dog down to him, the lady has a chat to two lads with a bicycle and a film camera, their names are Judith and Astra, two ducks on the pond, after the boys go we sit in an awkward silence, a hoverfly, a small plane making a loud drone, the French speaking man with his top off in the sun. I'm quite happy not talking to anyone about my activities and the enforced silence helps me to keep that state, there is a time and a place and in the middle of a recording is not it, obviously no one else knows what I'm doing or my reasons for my choices, a shiny black/green beetle going about its day, a lady on the phone taking an interest in the mic but then continuing on her walk, lots of bird action including Wren, Robin, Blue Tit, Blackcap, Crow, Goldfinch and Blackbird. A tall couple with back packs, a man walking a Spaniel, a bee on the floor investigating a cigarette butt, a police van, two lads with coats wrapped around their back packs, an ant on the bench, my internal clock timing in at 54 minutes (I should just wait for the headphone beep), the ant n my shorts now so I need to move carefully, the shadow of a jet passing over this exact spot, an illegible memorial on the bench, that strange patch that I had on my arm for about 6 months is now healing up, two ladies on the other bench laughing, the headphone beep.

SEASON 2 - EPISODE 190 - Steve Ramsey - Gaffer In this episode of the Team Deakins Podcast, we speak with gaffer Steve Ramsey (THE BRIDE!, WEST SIDE STORY, JOKER). We worked with Steve on THE GOLDFINCH, and we had a great time catching up with him. During our conversation, we discuss what a gaffer actually does, what his or her responsibilities can be, and how Steve adjusts his process to work with cinematographers ranging from Janusz Kamiński to Vittorio Storaro—and us! Steve also grants us insight into working on the sets of several Steven Spielberg films, and he shares how he and the film's cinematographer will prepare a scene to give room for Steven to follow his instincts. We also reflect on the rise of LEDs, and we discuss a few instances in which classic tungsten lights and gels solved a problem on the day. At the end of the episode, Steve gives his advice for anyone interested in a career as a gaffer, and we reflect on the job's non-descriptive title. - Recommended Viewing: WEST SIDE STORY (2021) - This episode is sponsored by Aputure

Send us Fan MailWell, we've laughed, we've cried; we've gasped and screamed and waited with breath that was bated… and now here we are, at book six: Goldfinch, the final installment and official conclusion to the Plated Prisoner Series by Raven Kennedy. What an incredible journey this has been. So today, to commemorate the ending of our coverage, we're bringing you our final deep dive AND our official book ranking, our rating of the series as a whole, our thoughts on the ending, and our premonitions for the future! We discuss our favorite and least favorite parts, characters, and tropes, but also get into deeper themes, feminist and societal issues addressed, and why this Romantasy series is particularly important (and one of our favorites of all time). We also talk audiobook deets, Goodreads reviews, and potential openings for a spin off series. And… we introduce a new tool in the Besties workshop: the Cryometer™.Yes, it's a gauge designed to track and quantify how many tears Raven Kennedy collected from us during this 706 page rip-your-heart out finale. Tune in for all that, our recent Disney trip fave and fails and a smash or pass, rapid fire Plated Prisoner edition.Don't be shy, subscribe! New Podcasts every Tuesday!! (And sometimes Friday!…)Check out these narrator & author interviews? ⬇️Anthony Palmini gives us the lowdown on what it's like to voice act Rhysand, Kingfisher, AND Slade Ravinger! https://youtu.be/zcCyrlZ5Jcc?si=2k7ULbRPgZl_5pUJWe interviewed Callie Hart all about her NYT Bestseller Quicksilver! Watch it here! https://youtu.be/CED5s7qDBdQ?si=8xtIRO1IzX6Rsld4+ so many more!____Shop Bookish Merch we are wearing:Ashley is wearing:  A Custom FireHeart Cropped Tee from @DownTheBondofficial | Use Code: BestiesandtheBooks for 15% | https://www.downthebond.com Liz is wearing :  The Female Gaze Petrifies the Men Crop Tank by @feralreaderco | https://feralreaderco.comAny link with an * is an affiliate link through the service Magic Links and is eligible for a commission to us with no extra cost to you. Thank you for helping support our podcast!Finding Fantasy ReadsFully narrated fantasy stories to help you find your next favorite fantasy author!Listen on: Apple Podcasts SpotifySupport the showYouTube | TikTok | Instagram | Podcast Platforms@BestiesandtheBooksPodcast Besties and the Book Club on Fable!https://fable.co/bestiesandthebookclub-474863489358Liz Instagram | TikTok@TheRealLifeVeganWife AshleyInstagram | TikTok@AshleyEllix

fWotD Episode 3250: The Goldfinch (painting) Welcome to featured Wiki of the Day, your daily dose of knowledge from Wikipedia's finest articles.The featured article for Sunday, 29 March 2026, is The Goldfinch (painting).The Goldfinch (Dutch: Het puttertje) is an oil painting by the Dutch Golden Age artist Carel Fabritius of a life-sized chained European goldfinch. Signed and dated 1654, it is now in the collection of the Mauritshuis in The Hague, Netherlands. The work is a trompe-l'œil oil on panel measuring 33.5 by 22.8 centimetres (13.2 in × 9.0 in) that was once part of a larger structure, perhaps a window jamb or a protective cover. It is possible that the painting was in its creator's workshop in Delft at the time of the gunpowder explosion that killed him and destroyed much of the city.A common and colourful bird with a pleasant song, the goldfinch was a popular pet, and could be taught simple tricks including lifting a thimble-sized bucket of water. It was reputedly a bringer of good health, and was used in Italian Renaissance painting as a symbol of Christian redemption and the Passion of Jesus.The Goldfinch is unusual for the Dutch Golden Age painting period in the simplicity of its composition and use of illusionary techniques. Following the death of its creator, it was lost for more than two centuries before its rediscovery in Brussels.An eponymous novel by American author Donna Tartt won the 2014 Pulitzer Prize for fiction and led to a 2019 film. The painting was featured in a Dutch Golden Age world tour in 2012–2014, and was the centrepiece of a 2026 bird art exhibition at the Mauritshuis.This recording reflects the Wikipedia text as of 00:52 UTC on Sunday, 29 March 2026.For the full current version of the article, see The Goldfinch (painting) on Wikipedia.This podcast uses content from Wikipedia under the Creative Commons Attribution-ShareAlike License.Visit our archives at wikioftheday.com and subscribe to stay updated on new episodes.Follow us on Bluesky at @wikioftheday.com.Also check out Curmudgeon's Corner, a current events podcast.Until next time, I'm generative Joanna.

After opening a major building project in May last year and announcing the details of another in September, which is due to open in the early 2030s, the National Gallery in London has revealed, quite unexpectedly, that it has to make serious cuts, including to its staff, in the face of a deficit that could rise to £8.2m in the coming year. Martin Bailey, The Art Newspaper's special correspondent in London, tells us more. In The Hague in the Netherlands, the Mauritshuis has just opened a new exhibition called BIRDS – Curated by The Goldfinch & Simon Schama. Since The Goldfinch, the 17th-century painting by Carel Fabritius, is not able to speak, Schama tells Ben Luke about the show, including Fabritius' remarkable picture. And this episode's Work of the Week is Sunbeams or Sunlight. Dust Motes Dancing in the Sunbeams, Strandgade 30 (1900) by the Danisj painter Vilhelm Hammershøi. The picture is one of the many highlights of a new exhibition, Hammershøi: The Eye that Listens, at the Museo Nacional Thyssen-Bornemisza in Madrid. The curator of the exhibition, Clara Marcellán, joins Ben to discuss the painting.BIRDS – Curated by The Goldfinch & Simon Schama, Mauritshuis, The Hague, The Netherlands, until 7 June.Hammershøi: The Eye that Listens, Museo Nacional Thyssen-Bornemisza, Madrid, until 31 May 2026; Kunsthaus Zürich, 3 July-25 October. Visit the Vilhelm Hammershøi Digital Archive, hammershoi.smk.dk.Buy The Art Newspaper's book The Year Ahead 2026 at theartnewspapershop.com Hosted on Acast. See acast.com/privacy for more information.

Amrit Dhaliwal bought into the franchise dream—turnkey business, proven system, guaranteed support. She got none of that. Her first franchise left her struggling with "entrepreneurial poverty": owning a business but barely surviving. So she built Goldfinch differently. Her home care franchise rejects half its applicants, provides real coaching, and goes fully digital in a paper-obsessed industry. The mission? Help franchisees actually thrive, not just survive. Goldfinch's "Time to Thrive" philosophy extends to clients, too. Through Thrive Clubs offering yoga and art classes, they're redefining aging as opportunity, not decline. Dhaliwal's advice for entrepreneurs: Make purpose your filter for every decision. Growth without alignment isn't success—it's just noise.

Invention and re-invention are themes that are set deep in the American psyche and the American novel. My guest today is the American novelist JR Thornton, author of the upcoming novel “Lucien” to be published next month. It's a tale of intrigue, set at Harvard, in the modern day, where a freshman artist is dazzled by his Euro-glamorous room-mate, and led down a tempting path of forgery and deceit. Imagine “The Secret History” by Donna Tartt, meets “The Goldfinch”, also by Donna Tartt, and you're halfway there. In the book, JR Thornton, himself a Harvard alumnus, evokes the immense privilege that remains in certain pockets of these high-powered universities and the desperation of many outsiders to fit in. In our interview, JR Thorton talks about the four books that helped shape his literary path, and specifically this book, so if you're in the market for recommendations of great books about re-invention, re-emergence and revenge, you're in the right place!Lit with Charles loves reviews. If you enjoyed this episode, I'd be so grateful if you could leave a review of your own, and follow me on Instagram at @litwithcharles. Let's get more people listening – and reading!JR Thornton's four books: The Count of Monte Cristo, by Alexandre Dumas (1844)The Great Gatsby (1925)Brideshead Revisited (1945)The Bell Jar (1963)

We had a great time this week and were honored to have Sen. Stephen Goldfinch to join us on Jason's big wooden deck. We had the pleasure of wishing the co-host of Trilogy Outdoors safe travels and well wishes on his depoloyment with the US Army. Stephen has spent a lot of time on the water from scuba diving and fishing all across the world. He also spent alot of his time working on charter boats growing up along the Grand Strand. We coould not have been happier that Capt. Jack Orr of The Fishfinder Fleet joins us as well. Capt. Jack is truly one of the old salts in the coastal region of South Carolina and has been at the helm of some of the most well known head boats to ever call Murrells Inlet home. Along with Capt Jay Baisch of "All Washed Up Driftwood Art" the trip combined for more years on the water than all of our previous guests combined The stories were numerous and Capt. Q sits in and shares some more of his hilarious tales from the life of a charter captain. Be sure to download and subscribe to CFR and share with all your friends. See yall next Tuesday at MIFC for another entertaining episode.Become a supporter of this podcast: https://www.spreaker.com/podcast/trilogy-outdoors--5441492/support.

The teen council interviewed Peter Leonard, the Executive Director of the Cedar Mill & Bethany Libraries, on October 13th, 2025.  He is retiring soon after 29 years here at the library. Learn about what has changed in our libraries, why he thinks libraries are still important, and why he wanted to work in libraries in the first place. Read the book he recommended, the Goldfinch.

On this episode of Chill Filtered, Cole and Bryan pour the newest release from Found North — Goldfinch (First Flight). Before diving in, they talk about old episodes, healthy boundaries when it comes to alcoholism, and what makes Canadian whisky so unique in the whiskey world.   On Whiskey World News, Bryan reads about Jack Daniel's newest special release: Tanyard Hill Rye.   And on What Whiskey Would You Choose?, the boys ask: What whiskey would you pour your mom to help her realize that not all whiskey is disgusting?   A thoughtful episode with heart, humor, and a special Canadian pour that might just take flight.

Send us a textBACK for another review, and this time I'm checking out something from our friends over at Found North. It's a brand new 15 Year Old Canadian Whiskey with some truly special offerings inside that you're not gonna want to miss out on and I think you're gonna want to hear what I have to say. Plus, I crack into the newest Widow Jane Bourbon and see what's going on with the Mythological Oak finish. Things are interesting right now, folks, and I hope you enjoy.Become a patron of the show at http://www.patreon.com/mybourbonpodcastLeave us a 5 star rating and review on your podcast app of choice!Send us an email with questions or comments to thisismybourbonshop@gmail.comSend us mail to PO Box 22609, Lexington, KY 40522Check out all of our merch and apparel: http://bourbonshop.threadless.com/Leave us a message for Barrel Rings at 859.428.8253Facebook: https://www.facebook.com/mybourbonpod/Twitter: https://twitter.com/mybourbonpodInstagram: https://www.instagram.com/mybourbonpod/YouTube: https://www.youtube.com/thisismybourbonpodcastPayPal, if you feel so inclined: PayPal.me/pritter1492Link to our Barrell Rye Armagnac Finished Pick: https://shop.whiskeyinmyweddingring.com/products/barrell-private-release-rye-1a03Support the show

Summer Box Office Report Cards are the episode's centerpiece. Until then, we discuss Daniel Day-Lewis' return, JLO's latest musical and new animated feature contenders during our Trailer Reviews segment. We review new movies like K-Pop Demon Hunters, Honey, Don't, Relay and Eenie Meenie during our Box Office Report and What We're Watching segments. Plus, AlsoMike turns heel with a shockingly bad review of a beloved classic. Clayton Davis' latest piece on “No Kings” in the Race Thus Far - 1:50 TRAILER REVIEWS: Anemone, starring Daniel Day-Lewis - 5:04 Arco, a new animated feature contender from NEON - 9:04 The Mastermind, a future MMO classic from Kelly Reichardt - 12:26 Hedda, a new YouTube trailer hit gives us Saltburn & sexy Downton Abbey vibes - 15:14 Eleanor The Great, a hope that this movie might not actually be another Goldfinch - 17:31 Kiss of the Spider Woman, a JLO starrer with a 2nd trailer that gives us pause - 19:48 BOX OFFICE REPORT: K-Pop Demon Hunters, Also Mike's Review + Netflix's #1 Box Office Sing-A-Long - 23:20 Talking Weapons' holding strong and the rest of the Top 8 - 24:13 Reviewing Honey, Don't - 25:51 Reviewing Relay, starring Riz Ahmed and discussing the rest of the Top 15 - 27:28 SUMMER BOX OFFICE REPORT CARDS (BY GENRE & SINCE MAY): Superhero Films: Thunderbolts*, Superman & The Fantastic Four - 30:44 Pure Action like Mission Impossible, Ballerina, etc. + A Lone Sports Film Hit in F1 - 34:34 Comedies from Friendship to The Naked Gun - 37:01 Dramas like The Life of Chuck & Eddington + Romances like Materialists - 40:13 Horror is still crushing it at the box office from Clown in a Cornfield through Weapons - 44:06 Family Films are buoying it all from Lilo & Stitch through Jurassic World - 46:55 WHAT WE'RE WATCHING: Other than what we've reviewed already… Eenie Meenie + a Sinners rewatch - 50:35 The Conjuring: The Devil Made Me Do It + The Last Detail revisited by M1 - 52:34 M2's slander filled heel turn for a beloved classic. What has he done?! - 55:55 OUTRO: Make sure to throw your rotten vegetables at AlsoMike via social media. You can find all our links here https://linktr.ee/mikemikeandoscar Otherwise, if you're somehow still taken with us, do please support our show by liking, subscribing, rating, and reviewing our podcast favorably in the eyes of all the algorithms out there. We certainly thank you for sticking by us during these contemptible times.

In this episode of the Building PA Podcast, co-hosts Jon O'Brien and Chris Martin celebrate five years of podcasting while diving into a critical topic: opioid awareness and pain management in the context of surgery. They are joined by Brand Newland, the CEO and co-founder of Goldfinch Health, who brings a wealth of knowledge and a fresh perspective on how to navigate the challenges associated with opioid prescriptions following surgery.Brand begins by addressing the opioid crisis, emphasizing the need for a shift in how we approach pain management, particularly in surgical settings. He shares insights from his experience as a pharmacist and discusses the founding of Goldfinch Health in 2018, which aims to improve the surgical experience by advocating for better pain management practices that reduce reliance on opioids.The conversation highlights the importance of multimodal pain management strategies, which include using non-opioid medications like acetaminophen and ibuprofen, as well as innovative approaches such as the TLC method (Tylenol, Lyrica, and Celebrex) to manage pain effectively before and after surgery. Brand explains how these methods can lead to quicker recovery times and fewer complications, ultimately allowing patients to return to work sooner—an average of 35 days faster than national benchmarks.Jon and Chris share their personal experiences with opioid prescriptions, illustrating the common issue of being prescribed excessive amounts of pain medication post-surgery. Brand reassures listeners that there are alternatives and encourages them to advocate for themselves and their loved ones when it comes to pain management.The episode also touches on the financial implications of better pain management practices, with Brand discussing a recent study that shows a significant return on investment for employers who implement Goldfinch's programs. This is particularly relevant for those in the construction industry, where physical labor is a daily reality, and managing pain effectively can lead to improved productivity and overall well-being.As the discussion progresses, Brand introduces the concept of enhanced recovery protocols and the importance of preparing for surgery in a way that minimizes pain and anxiety. He shares practical tips, such as staying hydrated and consuming clear liquids before surgery, which can help improve recovery outcomes.Towards the end of the episode, Brand discusses Goldfinch's new initiative, the Billion Pill Fudge Program, aimed at reducing the number of leftover opioid pills after surgery. He emphasizes the importance of safe disposal methods to prevent accidental poisoning, particularly among children.

It was great to have our partners from Shi-awela Safaris and Lodge on with us. With the incredible hunts that the Goldfinch's and all the other guests have been having, it was time to get them on from over in their incredible parts of the world. We are excited to talk about many thing from Stephen's most recent hunts to John D's latest hunts himself. Which include a management hunt for elephants in Zimbabwe. You do not want to miss this great story that we share and you can go make your own stories with our official safari partner. We will be happy to share all the info and you can also checkthem out and get your own trip set upto make mempries of your own by visiting their website at www.shiawela.com Become a supporter of this podcast: https://www.spreaker.com/podcast/trilogy-outdoors--5441492/support.

H2 - Tuesday July 8 2025 - "Caller theories on No Epstein List " "College Graduates not prepared for workforce" " Charlie not impress with State Sen Stephen Goldfinch" "When will Hollywood learn to stop lecturing us"

As our 7th Congressional District Representative, Russell Fry has done an incredible job of keeping his promises made while platfroming for this most recent election. Our fisheries mis management has been one that he is on and continues to bring up questions to NOAA and its shrinking government body. We are excited to have Russell in the studio this week and we cover a number of topics, as always, when he and the Senator are on the show. Of course we cover plenty of fins, fur, & feathers. But we do discuss some of the current issures facing the country and the world for that matter. We hope you will like and subscribe and be sure to share the link and let us know what you think about todays show and also about any topics you would like to hear more on. We are going to have some great stories coming out of Africa s the Goldfinch's travel abroad in search of trophy game. Go to www.trilogyoutdoorsmedia.com for more info and to get signed up for the Grand Strand Fishing Rodeo. Tight Lines and Enjoy!!!www.fry.house.govBecome a supporter of this podcast: https://www.spreaker.com/podcast/trilogy-outdoors--5441492/support.

Today, we're absolutely thrilled to have on John Crowley a director, screenwriter and filmmaker whose work consistently delves into the intricate tapestry of human experience with remarkable sensitivity and depth. Dom Lenoir sits down for a natter with John to unpack the creative journey behind this poignant film and explore the themes that drive his artistic vision. John Crowley has a masterful touch for storytelling as he burst onto the scene with his critically acclaimed feature debut, "Intermission" (2003), followed by the powerful and poignant "Boy A" (2007). He captivated audiences and critics alike with the deeply moving "Brooklyn" (2015), which earned him an Academy Award nomination for Best Adapted Screenplay. He then took on the complex literary adaptation of "The Goldfinch" (2019), and his television work includes episodes of the acclaimed series "True Detective" and "Black Mirror." His latest film, "We Live in Time," promises to be another compelling addition to his already impressive filmography. This romantic drama, written by Nick Payne, explores the relationship of a couple, Tobias Durand, played by the incredible Andrew Garfield, and Almut Brühl, portrayed by the brilliant Florence Pugh, over the course of a decade. The film uniquely employs a nonlinear narrative, weaving through snapshots of their lives together – falling in love, building a home, and becoming a family – while confronting a difficult truth that challenges their very foundation. We Live in Time is OUT NOW OTHER LINKS DIRTY BOY Premiere at Raindance tickets https://raindance.eventive.org/schedule/dirty-boy-68234eda5e47ea122831f7f4 FOOD FOR THOUGHT documentary out NOW | Watch it HERE. A documentary exploring the rapid growth and uptake of the vegan lifestyle around the world. – And if you enjoyed the film, please take a moment to share & rate it on your favourite platforms. Every review & every comment helps us share the film's important message with more people. Your support makes a difference! PODCAST MERCH Get your very own Tees, Hoodies, onset water bottles, mugs and more MERCH. https://my-store-11604768.creator-spring.com/   COURSES Want to learn how to finish your film? Take our POST PRODUCTION COURSE https://cuttingroom.info/post-production-demystified/   PATREON Big thank you to: Serena Gardner Mark Hammett Lee Hutchings Marli J Monroe Karen Newman Want your name in the show notes or some great bonus material on film-making? Join our Patreon for bonus episodes, industry survival guides, and feedback on your film projects!   SUPPORT THE PODCAST Check out our full episode archive on how to make films at TheFilmmakersPodcast.com   CREDITS The Filmmakers Podcast is written, edited and produced by Giles Alderson @gilesalderson Logo and Banner Art by Lois Creative  Theme Music by John J. Harvey Learn more about your ad choices. Visit podcastchoices.com/adchoices

  This episode of the Mission: Employable podcast dives into an Earn and Learn program that's saving one Iowa organization money while reinvesting in its workforce, Goldfinch Laboratory plays an important role in Iowa's medical industry, testing samples and biopsies sent over by other healthcare providers. However, the lab felt they were spending too much money on temporary hires. The solution? Find and train their own staff with an Earn and Learn program. Stephanie Allen, Director of Laboratory Operations, and Nancy Leiva, Shift Lead, joins us to share how they made the decision to start this program from the ground up. Find out how the program is not only solving their staffing problem but saving them a ton of money in the process.  

The Artist shares great family news! Your Huckleberry then befouls the great album Rumours. Ground Chucky, the Boy Made of Meat finds his voice but can it stop Mex from buying a snake?

Bird song caught the ear of Italian composer Antonio Vivaldi. And he even named a 1729 flute concerto for a bird — the goldfinch. The source of inspiration for Vivaldi's Goldfinch concerto, or Il Gardellino, was the European Goldfinch, a tiny bird found throughout much of Europe, where it frequents gardens and roadsides. No wonder Vivaldi found the goldfinch irresistible. More info and transcript at BirdNote.org.Want more BirdNote? Subscribe to our weekly newsletter. Sign up for BirdNote+ to get ad-free listening and other perks. BirdNote is a nonprofit. Your tax-deductible gift makes these shows possible.

  Find more of my photos on PixelFed @douglaswelch American Goldfinch in our friend's feeder in Denver, Colorado #goldfinch #bird #birds #birding #animal #wildlife #denver #colorado #feeder ♬ Spirited Away Continue Reading Read more on this topic: Tulips, Denver Botanic Garden, Denver, Colorado [Photography] Scene, Denver Botanic Garden, Denver, Colorado [Photography] Tulips, Denver Botanic Garden, Denver, Colorado Tulips, Denver Botanic Garden, Denver, Colorado [Photography] Tulip Reflections, Denver Botanic Garden, Denver, Colorado [Photography]

In this episode of On the Nature Trail, we spotlight one of New Hampshire's most cheerful summer birds – the American Goldfinch. From their acrobatic feeder antics to their vibrant yellow plumage, these seed-loving songbirds are a true seasonal delight. Whether you’re watching from your feeder or walking in a wildflower meadow, this episode invites […]

Interview with S F Williamson about A Language of Dragons S F Williamson recommends:When Women Were Dragons by Kelly BarnhillThe Awakening of Miss Primm by Sanmartin Fenollera and translated by Sonia Soto.The Goldfinch by Donna TarttThe Quick Book Reviews Podcast can be found:Instagram: https://www.instagram.com/quick_book_reviewsBluesky: https://bsky.app/profile/quickbookreviews.bsky.socialThreads: @quick_book_reviewsTikTok: https://www.tiktok.com/@quickbookreviewsTwitter: https://x.com/quickbookrevie3 Hosted on Acast. See acast.com/privacy for more information.

Send us a textLet's yap about Gild by Raven Kennedy. Summon us @DTFaePodcast We like our coffee icy and our books spicy! Oh, and we're totally Down To Fae. A podcast for fantasy romance readers and fans of authors like Sarah J. Maas, Jennifer L. Armentrout, Rebecca Yarros and Carissa Broadbent. Follow along as your delulu hosts discuss your favorite romantasy books in a chapter-by-chapter read, re-read or refresher.

To support the podcast please click here. Show notes"Awakening and Renewal"A belated Imbolc episode, though I feel the content is still relevant! So, wow it has been some months since the last episode and we are so glad to be sharing this with you now.Join me out on the trods and connect to Imbolc tide blessings and energies with this episode that focuses on the shifting of Winter into Spring. There are blessings and a poem, there are trodcasts too!Oh and I pronounce the name of the Goddess Brigid in the contemporary Irish 'Breej'. It is what felt right!The trodcasts that I share in this episode were recorded in different local West Dorset sacred spaces in late January. The first one is in Cattistock churchyard where there is a beautiful little well and Snowdrops, plus lots of incredible birds. The following one is in the garden by Silver Well at Cerne Abbas and then the Beech Grove at Giant Hill Cerne Abbas. This is a very special place of pilgrimage, myth and recent archaeological exploration. There is a large Beltaine/May Day gathering that takes place at the well and on the hill with a local Morris side at Dawn. I simply share the messages and insights that flow through me whilst connecting to spirits of place. I hope they resonate with you.It was my intention to release this episode in early February, so apologies for the later publishing!Imbolc is still taking place, so hopefully you will feel the awakening and renewing Imbolc blessings reaching you where you are.The blessings and poem:First blessing is by Caitlin Matthews 'Brigid of the Mantle' from Little Book of Celtic blessings and an excerpt by John O'Donohue fromthe blessing, 'For Presence,' found in Benedictus (Europe) / To Bless the Space Between Us (US).'Goldfinch' from the 'Lost Spells' by Robert Macfarlane and Jackie Morris.Many blessings across the Ways to you.With Love, Hilary and Tony x

In today's episode of The Second in Command podcast, Cameron is joined by Kate Lincoln-Goldfinch, the owner and CEO of Lincoln-Goldfinch Law – Abogados de Inmigración.During the conversation, Cameron and Kate explore the nuances of navigating complex professional relationships. You'll hear about the challenges of balancing different leadership styles, the importance of communication, and the impact of workplace culture on collaboration. With contrasting perspectives on accountability and conflict resolution, they reveal the delicate dance required to maintain harmony while driving an organization forward. The discussion highlights a crucial mindset shift — moving from problem-solving to coaching. Instead of acting as intermediaries, effective leaders empower their teams to handle difficult conversations directly, fostering personal growth and stronger relationships.Beyond the day-to-day struggles, the conversation touches on the deeper bonds that sustain long-term partnerships. Whether through shared experiences, intentional connection, or simple gestures of appreciation, investing in professional relationships is just as important as driving results.If you've enjoyed this episode of the Second in Command podcast, be sure to leave a review and subscribe today!Enjoy!In This Episode You'll Learn:Kate's background in immigration law, including a life-changing experience with a detained asylum-seeking family. (2:41)The importance of regular communication and avoiding duplication of efforts between the CEO and COO. (13:00)The differences between a COO and a director, focusing on leadership and vision versus hands-on execution. (18:06)The challenges of transitioning from a culture of leniency to one of accountability. (22:00)And much more...Resources:Connect with Kate: Website | LinkedInConnect with Cameron: Website | LinkedInGet Cameron's latest book "Second in Command: Unleash the Power of Your COO"Get Cameron's online course – Invest In Your Leaders

Bridget, Caitlin, and Hilda discuss the last half of "Goldfinch," the sixth and final book in Raven Kennedy's Plated Prisoner series.  Not to spoil anything, but it's a fantastic end to a great series ... and who knows, maybe the door has been left open to a spinoff. We sure hope so! Join our Patreon for exclusive behind-the-scenes content and let's be friends!Instagram > @Booktokmademe_podTikTok > @BooktokMadeMe

Join Bridget, Caitlin, and Hilda to discuss the first half of "Goldfinch," the sixth and final book in Raven Kennedy's The Plated Prisoner series. And we're all in agreement when we say the first half doesn't disappoint, and as the cherry on top, we get a glorious display of King Ravinger's special talent - iykyk.  Join our Patreon for exclusive behind-the-scenes content and let's be friends!Instagram > @Booktokmademe_podTikTok > @BooktokMadeMe

Happy holidays! We'll be sharing snippets from Latent Space LIVE! through the break bringing you the best of 2024! We want to express our deepest appreciation to event sponsors AWS, Daylight Computer, Thoth.ai, StrongCompute, Notable Capital, and most of all all our LS supporters who helped fund the gorgeous venue and A/V production!For NeurIPS last year we did our standard conference podcast coverage interviewing selected papers (that we have now also done for ICLR and ICML), however we felt that we could be doing more to help AI Engineers 1) get more industry-relevant content, and 2) recap 2024 year in review from experts. As a result, we organized the first Latent Space LIVE!, our first in person miniconference, at NeurIPS 2024 in Vancouver.Of perennial interest, particularly at academic conferences, is scaled-up architecture research as people hunt for the next Attention Is All You Need. We have many names for them: “efficient models”, “retentive networks”, “subquadratic attention” or “linear attention” but some of them don't even have any lineage with attention - one of the best papers of this NeurIPS was Sepp Hochreiter's xLSTM, which has a particularly poetic significance as one of the creators of the LSTM returning to update and challenge the OG language model architecture:So, for lack of a better term, we decided to call this segment “the State of Post-Transformers” and fortunately everyone rolled with it.We are fortunate to have two powerful friends of the pod to give us an update here:* Together AI: with CEO Vipul Ved Prakash and CTO Ce Zhang joining us to talk about how they are building Together together as a quote unquote full stack AI startup, from the lowest level kernel and systems programming to the highest level mathematical abstractions driving new model architectures and inference algorithms, with notable industry contributions from RedPajama v2, Flash Attention 3, Mamba 2, Mixture of Agents, BASED, Sequoia, Evo, Dragonfly, Dan Fu's ThunderKittens and many more research projects this year* Recursal AI: with CEO Eugene Cheah who has helped lead the independent RWKV project while also running Featherless AI. This year, the team has shipped RWKV v5, codenamed Eagle, to 1.5 billion Windows 10 and Windows 11 machines worldwide, to support Microsoft's on-device, energy-usage-sensitive Windows Copilot usecases, and has launched the first updates on RWKV v6, codenamed Finch and GoldFinch. On the morning of Latent Space Live, they also announced QRWKV6, a Qwen 32B model modified with RWKV linear attention layers. We were looking to host a debate between our speakers, but given that both of them were working on post-transformers alternativesFull Talk on YoutubePlease like and subscribe!LinksAll the models and papers they picked:* Earlier Cited Work* Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention* Hungry hungry hippos: Towards language modeling with state space models* Hyena hierarchy: Towards larger convolutional language models* Mamba: Linear-Time Sequence Modeling with Selective State Spaces* S4: Efficiently Modeling Long Sequences with Structured State Spaces* Just Read Twice (Arora et al)* Recurrent large language models that compete with Transformers in language modeling perplexity are emerging at a rapid rate (e.g., Mamba, RWKV). Excitingly, these architectures use a constant amount of memory during inference. However, due to the limited memory, recurrent LMs cannot recall and use all the information in long contexts leading to brittle in-context learning (ICL) quality. A key challenge for efficient LMs is selecting what information to store versus discard. In this work, we observe the order in which information is shown to the LM impacts the selection difficulty. * To formalize this, we show that the hardness of information recall reduces to the hardness of a problem called set disjointness (SD), a quintessential problem in communication complexity that requires a streaming algorithm (e.g., recurrent model) to decide whether inputted sets are disjoint. We empirically and theoretically show that the recurrent memory required to solve SD changes with set order, i.e., whether the smaller set appears first in-context. * Our analysis suggests, to mitigate the reliance on data order, we can put information in the right order in-context or process prompts non-causally. Towards that end, we propose: (1) JRT-Prompt, where context gets repeated multiple times in the prompt, effectively showing the model all data orders. This gives 11.0±1.3 points of improvement, averaged across 16 recurrent LMs and the 6 ICL tasks, with 11.9× higher throughput than FlashAttention-2 for generation prefill (length 32k, batch size 16, NVidia H100). We then propose (2) JRT-RNN, which uses non-causal prefix-linear-attention to process prompts and provides 99% of Transformer quality at 360M params., 30B tokens and 96% at 1.3B params., 50B tokens on average across the tasks, with 19.2× higher throughput for prefill than FA2.* Jamba: A 52B Hybrid Transformer-Mamba Language Model* We present Jamba, a new base large language model based on a novel hybrid Transformer-Mamba mixture-of-experts (MoE) architecture. * Specifically, Jamba interleaves blocks of Transformer and Mamba layers, enjoying the benefits of both model families. MoE is added in some of these layers to increase model capacity while keeping active parameter usage manageable. * This flexible architecture allows resource- and objective-specific configurations. In the particular configuration we have implemented, we end up with a powerful model that fits in a single 80GB GPU.* Built at large scale, Jamba provides high throughput and small memory footprint compared to vanilla Transformers, and at the same time state-of-the-art performance on standard language model benchmarks and long-context evaluations. Remarkably, the model presents strong results for up to 256K tokens context length. * We study various architectural decisions, such as how to combine Transformer and Mamba layers, and how to mix experts, and show that some of them are crucial in large scale modeling. We also describe several interesting properties of these architectures which the training and evaluation of Jamba have revealed, and plan to release checkpoints from various ablation runs, to encourage further exploration of this novel architecture. We make the weights of our implementation of Jamba publicly available under a permissive license.* SANA: Efficient High-Resolution Image Synthesis with Linear Diffusion Transformers* We introduce Sana, a text-to-image framework that can efficiently generate images up to 4096×4096 resolution. Sana can synthesize high-resolution, high-quality images with strong text-image alignment at a remarkably fast speed, deployable on laptop GPU. Core designs include: * (1) Deep compression autoencoder: unlike traditional AEs, which compress images only 8×, we trained an AE that can compress images 32×, effectively reducing the number of latent tokens. * (2) Linear DiT: we replace all vanilla attention in DiT with linear attention, which is more efficient at high resolutions without sacrificing quality. * (3) Decoder-only text encoder: we replaced T5 with modern decoder-only small LLM as the text encoder and designed complex human instruction with in-context learning to enhance the image-text alignment. * (4) Efficient training and sampling: we propose Flow-DPM-Solver to reduce sampling steps, with efficient caption labeling and selection to accelerate convergence. * As a result, Sana-0.6B is very competitive with modern giant diffusion model (e.g. Flux-12B), being 20 times smaller and 100+ times faster in measured throughput. Moreover, Sana-0.6B can be deployed on a 16GB laptop GPU, taking less than 1 second to generate a 1024×1024 resolution image. Sana enables content creation at low cost. * RWKV: Reinventing RNNs for the Transformer Era* Transformers have revolutionized almost all natural language processing (NLP) tasks but suffer from memory and computational complexity that scales quadratically with sequence length. In contrast, recurrent neural networks (RNNs) exhibit linear scaling in memory and computational requirements but struggle to match the same performance as Transformers due to limitations in parallelization and scalability. * We propose a novel model architecture, Receptance Weighted Key Value (RWKV), that combines the efficient parallelizable training of transformers with the efficient inference of RNNs.* Our approach leverages a linear attention mechanism and allows us to formulate the model as either a Transformer or an RNN, thus parallelizing computations during training and maintains constant computational and memory complexity during inference. * We scale our models as large as 14 billion parameters, by far the largest dense RNN ever trained, and find RWKV performs on par with similarly sized Transformers, suggesting future work can leverage this architecture to create more efficient models. This work presents a significant step towards reconciling trade-offs between computational efficiency and model performance in sequence processing tasks.* LoLCATs: On Low-Rank Linearizing of Large Language Models* Recent works show we can linearize large language models (LLMs) -- swapping the quadratic attentions of popular Transformer-based LLMs with subquadratic analogs, such as linear attention -- avoiding the expensive pretraining costs. However, linearizing LLMs often significantly degrades model quality, still requires training over billions of tokens, and remains limited to smaller 1.3B to 7B LLMs. * We thus propose Low-rank Linear Conversion via Attention Transfer (LoLCATs), a simple two-step method that improves LLM linearizing quality with orders of magnitudes less memory and compute. * We base these steps on two findings. * First, we can replace an LLM's softmax attentions with closely-approximating linear attentions, simply by training the linear attentions to match their softmax counterparts with an output MSE loss ("attention transfer").* Then, this enables adjusting for approximation errors and recovering LLM quality simply with low-rank adaptation (LoRA). * LoLCATs significantly improves linearizing quality, training efficiency, and scalability. We significantly reduce the linearizing quality gap and produce state-of-the-art subquadratic LLMs from Llama 3 8B and Mistral 7B v0.1, leading to 20+ points of improvement on 5-shot MMLU. * Furthermore, LoLCATs does so with only 0.2% of past methods' model parameters and 0.4% of their training tokens. * Finally, we apply LoLCATs to create the first linearized 70B and 405B LLMs (50x larger than prior work). * When compared with prior approaches under the same compute budgets, LoLCATs significantly improves linearizing quality, closing the gap between linearized and original Llama 3.1 70B and 405B LLMs by 77.8% and 78.1% on 5-shot MMLU.Timestamps* [00:02:27] Intros* [00:03:16] Why Scale Context Lengths? or work on Efficient Models* [00:06:07] The Story of SSMs* [00:09:33] Idea 1: Approximation -> Principled Modeling* [00:12:14] Idea 3: Selection* [00:15:07] Just Read Twice* [00:16:51] Idea 4: Test Time Compute* [00:17:32] Idea 2: Hardware & Kernel Support* [00:19:49] RWKV vs SSMs* [00:24:24] RWKV Arch* [00:26:15] QWRKWv6 launch* [00:30:00] What's next* [00:33:21] Hot Takes - does anyone really need long context?Transcript[00:00:00] AI Charlie: We're back at Latent Space Live, our first mini conference held at NeurIPS 2024 in Vancouver. This is Charlie, your AI co host. As a special treat this week, we're recapping the best of 2024 going domain by domain. We sent out a survey to the over 900 of you who told us what you wanted, and then invited the best speakers in the Latent Space Network to cover each field.[00:00:24] AI Charlie: 200 of you joined us in person throughout the day, with over 2200 watching live online. Thanks Our next keynote covers the State of Transformers alternative architectures, with a special joint presentation with Dan Fu of Together AI and Eugene Chia of Recursal AI and Featherless AI. We've featured both Together and Recursal on the pod before, with CEO Veepal Vedprakash introducing them.[00:00:49] AI Charlie: And CTO CE Zhang joining us to talk about how they are building together together as a quote unquote full stack AI startup from the lowest level kernel and systems [00:01:00] programming to the highest level mathematical abstractions driving new model architectures and inference algorithms with notable industry contributions from Red Pajama V2, Flash Attention 3, Mamba 2, Mixture of Agents.[00:01:15] AI Charlie: Based, Sequoia, Evo, Dragonfly, Danfoo's Thunder Kittens, and many more research projects this year. As for Recursal and Featherless, we were the first podcast to feature RWKV last year, and this year the team has shipped RWKV v5, codenamed Eagle, to 1. 5 billion Windows 10 and Windows 11 machines worldwide to support Microsoft's on device, end Energy Usage Sensitive Windows Copilot Use Cases and has launched the first updates on RWKV v6, codenamed Finch and Goldfinch.[00:01:53] AI Charlie: On the morning of Latent Space Live, they also announced QRdata UKv6, a QEN32B model [00:02:00] modified with RDWKV linear attention layers. Eugene has also written the most single most popular guest post on the Latent Space blog this year. Yes, we do take guest posts on what he has discovered about the H100 GPU inference NeoCloud market since the successful launch of Featherless AI this year.[00:02:20] AI Charlie: As always, don't forget to check the show notes for the YouTube link to their talk as well as their slides. Watch out and take care.[00:02:27] Intros[00:02:27] Dan Fu: Yeah, so thanks so much for having us. So this is going to be a little bit of a two part presentation. My name is Dan. I'm at Together AI, and I'll be joining UCSD as faculty in about a year. And Eugene, you want to introduce yourself?[00:02:46] Eugene Cheah: Eugene, I lead the art activity team, and I, I'm CEO of Featherless, and we both work on this new post transformer architecture space.[00:02:55] Dan Fu: Yeah, so yeah, so today we're really excited to talk to you a little bit [00:03:00] about that. So first I'm going to give a broad overview of kind of the last few years of progress in non post transformer architectures. And then afterwards Eugene will tell us a little bit about the latest and the greatest and the latest frontier models in this space.[00:03:16] Why Scale Context Lengths? or work on Efficient Models[00:03:16] Dan Fu: So, the story starts with Scaling. So this is probably a figure or something like this that you've seen very recently. Over the last five to six years, we've seen models really scale up in parameter size, and that's brought with it a bunch of new capabilities, like the ability to talk to you and tell you sometimes how to use your Colab screens.[00:03:35] Dan Fu: But another place where we've seen scaling especially recently is scaling in context length. So this can mean Having more text inputs for your models, but it can also mean things like taking a lot of visual token inputs image inputs to your models or generating lots of outputs. And one thing that's been really exciting over the last few months or so is that we're, we're seeing scaling, not only during training time, but also [00:04:00] during test time.[00:04:00] Dan Fu: So this is one of the, the, this is the iconic image from the OpenAI 01 release. Not only are we starting to scale train time compute, but we're also starting to scale test time compute. Now if you're familiar with our attention and our transformer architectures today, this graph on the right might look a little bit scary.[00:04:19] Dan Fu: And one of the reasons is that the implications are a little bit Interesting. So what does it mean if we want to continue having smarter and smarter models? Do we just need to start building bigger, bigger data centers, spending more flops? Is this this little Dolly 3, we need more flops, guys? Is this going to be the future of all of AI?[00:04:39] Dan Fu: Or is there a better way, another path forward? Maybe we can get the same capabilities that we've gotten used to, But for a lot less compute, a lot less flops. And one of the things that we're going to talk about today is specifically looking at that core attention operator in some of these models.[00:04:57] Dan Fu: And the reason is that so this is just some, some [00:05:00] basic you know, scaling curves, but attention has compute that scales quadratically in the context length. So that means that if you're doing something like test time compute and you want to spend a bunch of tokens thinking about what comes next, the longer that that goes the, the, the more tokens you spend on that, that compute grows quadratically in that.[00:05:19] Dan Fu: One of the questions that we're interested in is, can we take that basic sequence model, that basic sequence primitive at the bottom, and get it to scale better? Can we scale in, let's say, n to the 3 halves or n log n? So in, in the first part of the talk, so we just went over the introduction. What I'm gonna do over the next few slides is just talk about some of the key advances and ideas that have shown over the past few years since maybe early 2020 to, to now that shown promise that this might actually be possible.[00:05:48] Dan Fu: That you can actually get potentially the same quality that we want while scale, while scaling better. So to do that, we're and, and basically the, the story that we're gonna look is we're gonna start to see [00:06:00] how. So this is a basic graph of just the past couple years of progress of perplexity where that blue line, that dotted blue line, is attention.[00:06:07] The Story of SSMs[00:06:07] Dan Fu: It's your basic transformer, full dense attention. And then the dots coming down are some of the methods that you'll see in this presentation today. We're going to turn the clock back all the way to 2020. So this, this, this question of can we make attention subquadratic? Basically, as soon as we said attention is all you need, People started asking this question.[00:06:28] Dan Fu: So we have this quadratic attention operator. Can we do better? I'll briefly talk about why attention is quadratic. And the basic thing that happens, if you're not familiar, is that you have these inputs, these keys and queries. And what you do in this attention matrix, this S matrix over here, is that you're using, you're comparing every token in your input to every other token.[00:06:49] Dan Fu: So when I try to do something like upload a whole book to Gemini, what happens beyond the Maybe not Gemini, because we don't necessarily know what architecture is. But let's say we upload it to LLAMA, what happens beyond [00:07:00] the scenes, behind the scenes, is that it's going to take every single word in that book and compare it to every other word.[00:07:05] Dan Fu: And this has been a really, it's, it's led to some pretty impressive things. But it's kind of a brute forcing of the way that you would try to interpret a interpret something. And what attention does in particular is the, and then what attention, sorry, don't want to. Okay, no, no laser pointer. What, what attention does afterwards is that instead of always operating in this quadratic thing, it takes a row wise softmax over this matrix, and then multiplies it by this values matrix.[00:07:32] Dan Fu: So, one of the key points to notice is that the output size is always going to be the same as the inputs, at least in standard self attention. So one of the first things that folks tried to do around 2020 is this thing called linear attention, which is just, just noticing that if we take out this softmax from here, if we take out this non linearity in the middle of the attention operation, and then if you compute the keys and the values operation first, you actually never hit this quadratic bottleneck.[00:07:57] Dan Fu: So that, that's potentially a way [00:08:00] to get a lot more computationally efficient. And there are various ways to do this by basically using feature maps or try to approximate this overall attention computation. But some of this work sort of started to hit a wall in 2020. And the basic challenges were, were two.[00:08:16] Dan Fu: So one was quality. It was back then, it was kind of hard to, to get good quality with these linear attention operators. The other one was actually hardware efficiency. So these, this feature map that was just shown by a simplify simplify here. Actually ends up being quite computationally expensive if you just implement it naively.[00:08:34] Dan Fu: So you started having these operators that not only were you sure, you're not really sure if they have the same quality, but also they're actually just wall clock slower. So you kind of end up getting the worst of both worlds. So this was the the stage. So that kind of sets the stage for four years ago.[00:08:49] Dan Fu: Keep this in mind because linear attention is actually going to come back in a few years once we have a better understanding. But one of the works that started kicking off this, this [00:09:00] mini revolution in post transformer architectures was this idea called states based model. So here the seminal work is, is one about our work queue in 2022.[00:09:09] Dan Fu: And this, this piece of work really brought together a few ideas from, from some long running research research lines of work. The first one was, and this is really one of the keys to, to closing the gap in quality was just using things that, that if you talk to a, a, an electrical engineer off the street, they might know off, off the, like the back of their hand.[00:09:33] Idea 1: Approximation -> Principled Modeling[00:09:33] Dan Fu: But taking some of those properties with how we model dynamical systems in signal processing and then using those ideas to model the inputs, the, the text tokens in, for example a transformer like Next Token Prediction Architecture. So some of those early states-based model papers were looking at this relatively, relatively simple recurrent update model that comes from maybe chapter one of a signal processing class.[00:09:59] Dan Fu: But then using [00:10:00] some principle theory about how you should do that recurrent update in order to really get the most that you can out of your hidden state, out of your out of your sequence. So that, that was one key idea for quality and. When this was eventually realized, you started to see a bunch of benchmarks that were pretty sticky for a few years.[00:10:20] Dan Fu: Things like long range arena, some long sequence evaluation benchmarks, There was stuff in time series, time series analysis. They started to, you started to see the quality tick up in meaningful ways. But the other key thing that What's so influential about these states based models is that they also had a key idea about how you can compute these things efficiently.[00:10:45] Dan Fu: So if you go back to your machine learning 101 class where you learned about RNNs, one thing that you may have learned is that they don't paralyze as well as detention, because if you just run them naively, you have to do this kind of sequential update to process new tokens, [00:11:00] whereas in attention, you can process all the tokens in parallel at one time.[00:11:04] Dan Fu: One of the key insights behind the S4 paper was that these recurrent models, you could take them and you could also formulate them as a convolution. And in particular, with a convolution, you could, instead of using a PyTorch conv1d operation, you can compute that with the FFT. And that would give you n log n compute in the in the sequence length n with an operator that was relatively well optimized for modern hardware.[00:11:28] Dan Fu: So those are really, I'd say, the two key ideas in 2022 that started allowing these breakthroughs to happen in these non transformer architectures. So, these ideas about how to principally model sorry, how to model the recurrent updates of a mo of, of a sequence in a principled way, and also these key ideas in how you can compute it efficiently by turning it into a convolution and then scaling it up with the FFT.[00:11:53] Dan Fu: Along those same lines, so afterwards we started putting out some work on specialized kernels, so just [00:12:00] like we have flash attention for transformers, we also have works like flash fft conf, and if you look at these lines of work oftentimes when, whenever you see a new architecture, you see a new primitive one of the, one of the table stakes now is, do you have an efficient kernel so that you can actually get wall clock speed up?[00:12:14] Idea 3: Selection[00:12:14] Dan Fu: So by 2022, We are starting to have these models that had promising quality primitives, but and, and also promising wall clocks. So you could actually see regimes where they were better than transformers in meaningful ways. That being said, there were, there's still sometimes a quality gap, particularly for language modeling.[00:12:33] Dan Fu: And because languages, It's so core to what we do in sequence modeling these days the, the next, the next key idea that I'm going to talk about is this idea of selection mechanisms. And this is basically an idea of, so you have this recurrent state that you're keeping around that just summarizes everything that, that came before.[00:12:50] Dan Fu: And to get a good sequence model, one of the things that you really need to be able to do is have the model learn what's the best way to pick out pieces from that recurrent [00:13:00] state. So one of the, one of the major ideas here in a line of work called H3, Hungry Hungry Hippos, and also these hyena models were One way you can do this is by just adding some simple element wise gates.[00:13:13] Dan Fu: So versions of these ideas have been around for decades. If you squint at the LSTM paper you, you can probably find, find this gating mechanism. But turns out you can take those old ideas, add them into these new. state space models, and then you can see quality start to pick up. If you've heard of the Mamba model, this also takes the selection to the next level by actually making some changes in that fundamental recurrent state space.[00:13:40] Dan Fu: So, it's not only just this gating that happens around the SSM layer, but also you can actually make The ABCD matrices of your state space model, you can make them data dependent, which will allow you to even better select out different pieces from your hidden state depending on what you're seeing. I'll also point out if you look at the [00:14:00] bottom right of this figure, there's this little triangle with a GPU SRAM, GPU HBM, and this, this is just continuing that trend of when you have a new architecture you, you, you also release it with a kernel to, to, to show that it is hardware efficient, that it, that it can be hardware efficient on modern hardware.[00:14:17] Dan Fu: The, the, one of the next cool things that happened is once we had this understanding of these are the basic pieces, these are the basic principles behind some of the sequence models linear attention actually started to come back. So in earlier this year, there was a model called BASED the, from Simran Arora and, and some other folks, that combined a more principled version of linear attention that basically the, the, the, the two second summary is that it used a Taylor approximation of the softmax attention, combined that with a simple sliding window attention and was starting to able, starting to be able to expand the Pareto frontier of how much data can you recall from your sequence, versus how small is your recurrent state size.[00:14:58] Dan Fu: So those orange dots [00:15:00] are, at the top there, are just showing smaller sequences that can recall more memory.[00:15:07] Just Read Twice[00:15:07] Dan Fu: And the last major idea I think that has been influential in this line of work and is very relatively late breaking just a few months ago, is just the basic idea that when you have these models that are fundamentally more efficient in the sequence length, you maybe don't want to prompt them or use them in exactly the same way.[00:15:26] Dan Fu: So this was a really cool paper called Just Read Twice, also from Simran. That basically said, hey, all these efficient models can process tokens so much more efficiently than transformers that they can sometimes have unfair advantages compared to a simple transformer token. So, or sorry, a simple transformer model.[00:15:44] Dan Fu: So take, for example the standard, the standard use case of you have some long document, you're going to pass it in as input, and then you're going to ask some question about it. One problem you might imagine for a recurrent model where you have a fixed state size is, let's say that [00:16:00] you're. Article is very long, and you're trying to ask about some really niche thing.[00:16:04] Dan Fu: You can imagine it might be hard for the model to know ahead of time what information to put into the hidden state. But these, these, these models are so much more efficient that you can do something really stupid, like, you can just put the document write down the document, write down the question, write down the document again, and then write down the question again, and then this time, the second time that you go over that document, you know exactly what to look for.[00:16:25] Dan Fu: And the cool thing about this is, so this is, And this this results in better quality, especially on these recall intensive tasks. But the other interesting thing is it really takes advantage of the more efficient architectures that, that we're having here. So one of the other, I think, influential ideas in this line of work is if you change the fundamental compute capabilities of your model and the way that it scales, you can actually start to query it at test time differently.[00:16:51] Idea 4: Test Time Compute[00:16:51] Dan Fu: And this actually, of course, goes back to those slides on test time compute. So while everybody's looking at, say, test time compute for big transformer models, [00:17:00] I think potentially a really interesting research question is, how can you take those and how does it change with this new next generation of models?[00:17:09] Dan Fu: So the, I'll just briefly summarize what some of those key ideas were and then talk and then show you briefly kind of what the state of the art is today. So, so the four key ideas are instead of just doing a simple linear attention approximation, instead take ideas that we know from other fields like signal processing, do a more principled approach to your modeling of the sequence.[00:17:32] Idea 2: Hardware & Kernel Support[00:17:32] Dan Fu: Another key idea throughout all these lines of work is you really want. Hardware and kernel support from day one. So, so even if your model is theoretically more efficient if somebody goes and runs it and it's two times slower one of the things that, that we've learned is that if, if you're in that situation, it's, it's just gonna be dead on arrival.[00:17:49] Dan Fu: So you want to be designing your architectures one of the key, key machine learning ideas that has been important for the quality is just making sure that you encode different ways that you can [00:18:00] select from your hidden state and, and really focus on that as a key decider of quality. And finally, I think one of the, the, the emerging new, new things for, for this line of work and something that's quite interesting is, What are the right test time paradigms for these models?[00:18:15] Dan Fu: How do they change relative to relative to what you might do for a standard transformer? I'll briefly end this section. So I've labeled this slide where we are yesterday because Eugene is going to talk about some new models that he released literally this morning. But as of yesterday, some of the really cool results out of the, these efficient alternative models were so AI2 trained this hybrid MOE called Jamba.[00:18:40] Dan Fu: That, that, that seems, that is currently the state of the art for these non transformer architectures. There's this NVIDIA and MIT put out this new diffusion model called SANA recently that one of their key key observations is that you can take a standard diffusion transformer diffusion model, replace the layers with linear [00:19:00] attention, and then that lets you scale to much larger much larger images, much, much Much larger sequences more efficiently.[00:19:07] Dan Fu: And and one thing that I don't think anybody would have called when a few years ago is that one of those gated SSM, gated states based models ended up on the cover of Science because a great group of folks went and trained some DNA models. So that's Michael Polley, Eric Yuen from from Stanford and the Arc Institute.[00:19:26] Dan Fu: So it's, we're really at an exciting time in 2024 where these non transformer, post transformer architectures are showing promise across a wide range. Across a wide range of, of modalities, of applications, and, and of tasks. And with that, I'll pass it on to Eugene, who can tell you a little bit about the latest and greatest with RWKV.[00:19:49] RWKV vs SSMs[00:19:49] Eugene Cheah: So, that's useful? Yeah. You're talking to here. Oh, I'm talking to here. Okay. So, yeah, two streams. Yeah. So, I think one common questions that we tend to get asked, right, is what's the difference between [00:20:00] RWKV and state space? So I think one of the key things to really understand, right the difference between the two groups, right, is that we are actually more like an open source, random internet meets academia kind of situation.[00:20:11] Eugene Cheah: Like, most of us never wrote any paper, but we, we basically look at RNNs and linear intention when intention is all you need came out, and then we decided to like, hey there is a quadratic scaling problem. Why don't we try fixing that instead? So, so, so we end up developing our own branch, but we end up sharing ideas back and forth.[00:20:30] Eugene Cheah: So, and, and we do all this actively in Discord, GitHub, etc. This was so bad for a few years, right, that basically, the average group's H index was so close to zero, right, Illuter. ai actually came in and helped us write our first paper. Great, now our H index is now three, apparently. So, so, so, but, but the thing is, like, a lot of these experiments led to results, and, and, essentially, essentially, we we took the same ideas from linear attention, [00:21:00] and we built on it.[00:21:01] Eugene Cheah: So, to take a step back into, like, how does RWKB handle its own attention mechanic and achieve the same goals of, like, O and compute, respectively, and in focus of our overall goal to make AI accessible to everyone, regardless of language, nation, or compute, that's our goal. We actually train our models primarily on over a hundred languages, which is another topic altogether.[00:21:23] Eugene Cheah: And our goal is to train to even 200 languages to cover all languages in the world. But at the same time, we work on this architecture, To lower the compute cost so that people can run it on Raspberry Pis and on anything. So, how did RWKB break the dependency of LSTM token flow? Because I think to understand architecture, right, it's probably easier to understand it from the RNN lens.[00:21:46] Eugene Cheah: Because that's where we built on. We all, we all state space kind of like try to, try to start anew and took lessons from that and say, So there's a little bit of divergence there. And AKA, this our version of linear attention. So to take step back [00:22:00] all foundation models, be it transformers or non transformers at a very high level, right?[00:22:05] Eugene Cheah: Pumps in the token. I mean, text that things into embeddings and go through a lot of layers. Generate a lot of states where the QKV cache or be iron in states or RW KB states. And outputs and embedding, they are not the same thing. And we just take more layers and more embeddings. And somehow that magically works.[00:22:23] Eugene Cheah: So, if you, if you remember your ancient RNN lessons which we, which we, which we we call best learning these days the general idea is that you have the embedding information flowing all the way up, and when, and you take that information and you flow it back down, and then you process it as part of your LSTM layers.[00:22:41] Eugene Cheah: So, this is how it generally works. Kapati is quoted saying that RNNs are actually unreasonably effective. The problem is this is not scalable. To start doing work on the second token, you need to wait for the first token. And then you need to, and likewise for the third token and fourth token, yada yada.[00:22:55] Eugene Cheah: That is CPU land, not GPU land. So, so, so, you [00:23:00] can have a H100 and you can't even use 1 percent of it. So, so that's kind of why RNNs didn't really take off in the direction that we wanted, like, billions of parameters when it comes to training. So, what did RDAP KV version 0 do? Boom. We just did the dumbest, lamest thing.[00:23:13] Eugene Cheah: Sorry, this is the bottleneck for RNN. We did the dumb thing of removing that line. And it kind of worked. It trained. It sucked, but it kind of worked. Then we were like, hey, then no one cared because the loss was crap, but how do we improve that? And that's essentially where we move forward, because if you see this kind of flow, right, you can actually get your GPU saturated quickly, where it essentially cascades respectively.[00:23:41] Eugene Cheah: So I'm just waiting for this to loop again. So it's like, once you get your first layer, your token to be computed finish. You start to cascade your compute all the way until you are, Hey, I'm using 100 percent of the GPU. So we, we worked on it, and we started going along the principle of that as long as we keep this general architecture [00:24:00] where, where we can cascade and, and be highly efficient with our architecture, nothing is sacred in our architecture.[00:24:06] Eugene Cheah: And we have done some crazy ideas. In fact, you ask us, if you ask me to explain some things in the paper, right, officially in the paper, I'll say we had this idea and we wrote it this way. The reality is someone came with a code, we tested it, it worked, and then we rationalized later. So, so the general[00:24:24] RWKV Arch[00:24:24] Eugene Cheah: The idea behind rwkbr is that we generally have two major blocks that we do.[00:24:30] Eugene Cheah: We call time mix and channel mix. And time mix generally handles handles long term memory states, where essentially, where essentially where we apply the matrix multiplication and Cilu activation functions into processing an input embedding and an output embedding. I'm oversimplifying it because this, This calculation changed every version and we have, like, version 7 right now.[00:24:50] Eugene Cheah: ChannelMix is similar to Base in the sense that it does shorter term attention, where it just looks at the sister token, or the token before it, because [00:25:00] there's a shift in the token shift matrix. I don't really want to go too much into the papers itself, because, like, we do have three papers on this.[00:25:09] Eugene Cheah: Basically, RWKB, RNN for the transformer, ERA, Ego and Pinch, RWKB, Matrix Value State. This is the updated version 5, version 6. And Goldfinch is our, is, is, is, is our hybrid model respectively. We are writing the paper already for V seven and which is, which is for R wk V seven. Called, named Goose, or architectures are named by Bird.[00:25:30] Eugene Cheah: And, I'm going to cover as well, qrwkb, and mama100k, and rwkb, and Where did that lead to? Great! Because we are all GPU poor and to be clear, like, most of this research is done, like, only on a handful H100s, which I had one Google researcher told me that was, like, his experiment budget for a single researcher.[00:25:48] Eugene Cheah: So, our entire organization has less compute than a single researcher in Google. So We, we, one of the things that we explored into was to how do we convert transformer models instead? Because [00:26:00] someone already paid that billion dollars, a million dollars onto training, so why don't we take advantage of those weights?[00:26:05] Eugene Cheah: And, and to, I believe, together AI worked on the lockets for, for the Lambda side of things, and, and we took some ideas from there as well, and we essentially did that for RWKB.[00:26:15] QWRKWv6 launch[00:26:15] Eugene Cheah: And that led to, Q RWKB6, which we just dropped today, a 32 bit instruct preview model, where we took the Quen 32 bit instruct model, freeze the feedforward layer, remove the QKB attention layer, and replace it with RWKB linear layers.[00:26:32] Eugene Cheah: So to be clear, this means we do not have the rwkv channel mix layer, we only have the time mix layer. But but once we do that, we train the rwkv layer. Important is that the feedforward layer needs to be frozen, so the new attention can be learned. And then we unfreeze the feedforward layer, and train all the layers together with a custom learning rate schedule, so that they can learn how to work together.[00:26:54] Eugene Cheah: The end result, surprisingly, And, to be honest, to the frustration of the R. W. [00:27:00] KV MOE team, which ended up releasing the model on the same day, was that, with just a few hours of training on two nodes, we managed to get it to be on par, kind of, with the original QUAN32B model. So, in fact, when the first run, right, that completely confused us, it was like, and I was telling Daniel Goldstein, Smirky, who kind of leads most of our research coordination, When you pitched me this idea, you told me at best you'll get the same level of performance.[00:27:26] Eugene Cheah: You didn't tell me the challenge and score and Winograd score will shoot up. I don't know what's happening there. But it did. MMLU score dropping, that was expected. Because if you think about it, when we were training all the layers, right, we were essentially Like, Frankenstein this thing, and we did brain damage to the feedforward network layer 2 with the new RWKB layers.[00:27:47] Eugene Cheah: But, 76%, hey, somehow it's retained, and we can probably further train this. We didn't even spend more than 3 days training this, so there's a lot more that can be done, hence the preview. This brings up [00:28:00] a big question, because We are already now in the process of converting to 7TB. We are now, this is actually extremely compute efficient to test our attention mechanic.[00:28:10] Eugene Cheah: It's like, it becomes a shortcut. We can, we are already planning to do our version 7 and our hybrid architecture for it. Because we don't need to train from scratch. And we get a really good model out of it. And the other thing that is uncomfortable to say is that because we are doing right now on the 70b is that if this scales correctly to 128k context length, I'm not even talking about a million 128, majority of enterprise workload today is just on 70b at under 32k context length.[00:28:41] Eugene Cheah: That means if this works and the benchmark matches it, It means we can replace the vast majority of current AI workload, unless you want super long context. And then sorry, can someone give us more GPUs? Because we do need the VRAM for super long context, sadly. So yeah, that's what we are working on, and essentially, [00:29:00] we are excited about this to just push it further.[00:29:02] Eugene Cheah: And this conversion process, to be clear, I don't think it's going to be exclusive to RWKB. It probably will work for Mamba as well, I don't see why not. And we will probably see more ideas, or more experiments, or more hybrids, or Yeah, like, one of the weirdest things that I wanted to say outright, and I confirmed this with the Black Mamba team and the Jamba team, which because we did the GoFinch hybrid model, is that none of us understand why a hard hybrid with a state based model to be R.[00:29:28] Eugene Cheah: QA state space and transformer performs better when, than the baseline of both. It's like, it's like when you train one, you expect, and then you replace, you expect the same results. That's our pitch. That's our claim. But somehow when we jam both together, it outperforms both. And that's like one area of emulation that, like, we only have four experiments, plus four teams, that a lot more needs to be done.[00:29:51] Eugene Cheah: But, but these are things that excite me, essentially, because that is what it's potentially we can move ahead for. Which brings us to what comes next.[00:30:00] What's next[00:30:00] [00:30:00][00:30:00] Dan Fu: So, this part is kind of just some, where we'll talk a little bit about stuff that, that we're excited about. Maybe have some wild speculation on, on what, what's, what's coming next.[00:30:12] Dan Fu: And, of course this is also the part that will be more open to questions. So, a couple things that, that I'm excited about is continued hardware model co design for, for these models. So one of the things that we've put out recently is this library called ThunderKittens. It's a CUDA library.[00:30:29] Dan Fu: And one of the things that, that we found frustrating is every time that we built one of these new architectures, and I'm sure you had the exact same experience, we'd have to go and spend two months in CUDA land, like writing these, these new efficient things. And. If we decided to change one thing in PyTorch, like one line of PyTorch code is like a week of CUDA code at least.[00:30:47] Dan Fu: So one of our goals with, with a library like Thunderkitten, so we, we just broke down what are the key principles, what are the key hardware things what are the key, Compute pieces that you get from the hardware. So for example on [00:31:00] H100 everything is really revolves around a warp group matrix multiply operation.[00:31:06] Dan Fu: So you really want your operation to be able to split into relatively small matrix, matrix multiply operations. So like multiplying two 64 by 64 matrices, for example. And so if you know that ahead of time when you're designing your model, that probably gives you you know, some information about how you set the state sizes, how you set the update, how you set the update function.[00:31:27] Dan Fu: So with Thunderkittens we basically built a whole library just around this basic idea that all your basic compute primitives should not be a float, but it should be a matrix, and everything should just be matrix compute. And we've been using that to, to try to both re implement some existing architectures, and also start to design code.[00:31:44] Dan Fu: Some new ones that are really designed with this core with a tensor core primitive in mind. Another thing that that we're, that at least I'm excited about is we, over the last four or five years, we've really been looking at language models as the next thing. But if you've been paying [00:32:00] attention to Twitter there's been a bunch of new next generation models that are coming out.[00:32:04] Dan Fu: So there, there are. So, video generation models that can run real time, that are supported by your mouse and your keyboard, that I'm told if you play with them that, you know, that they only have a few seconds of memory. Can we take that model, can we give it a very long context length so that you could actually maybe generate an entire game state at a time?[00:32:25] Dan Fu: What does that look like for the model? You're certainly not going to do a giant quadratic attention computation to try to run that. Maybe, maybe use some of these new models, or some of these new video generation models that came out. So Sora came out I don't know, two days ago now. But with super long queue times and super long generation times.[00:32:43] Dan Fu: So that's probably a quadratic attention operation at the, at the bottom of it. What if we could remove that and get the same quality, but a lot faster generation time? Or some of the demos that we saw from Paige earlier today. You know, if I have a super long conversation with my [00:33:00] Gemini bot, what if I wanted to remember everything that it's seen in the last week?[00:33:06] Dan Fu: I mean, maybe you don't for personal reasons, but what if I did, you know? What does that mean for the architecture? And I think, you know, that's certainly something I'm pretty excited about. I'm sure you're excited about it too. So, I think we were supposed to have some hot takes, but I honestly don't remember what our hot takes were.[00:33:21] Hot Takes - does anyone really need long context?[00:33:21] Eugene Cheah: Yeah, including the next slide. Hot takes, yes, these are our[00:33:25] Dan Fu: hot takes.[00:33:25] Eugene Cheah: I think the big one on Twitter that we saw, that we shared, was the question is like, is RAG relevant? In the case of, like, the future of, like, state based models?[00:33:38] Dan Fu: Let's see, I haven't played too much with RAG. But when I have. I'll say I found it was a little bit challenging to do research on it because we had this experience over and over again, where you could have any, an embedding model of any quality, so you could have a really, really bad embedding model, or you could have a really, really [00:34:00] good one, By any measure of good.[00:34:03] Dan Fu: And for the final RAG application, it kind of didn't matter. That's what I'll say about RAG while I'm being recorded. I know it doesn't actually answer the question, but[00:34:13] Eugene Cheah: Yeah, so I think a lot of folks are like, extremely excited of the idea of RWKB or State Space potentially having infinite context.[00:34:21] Eugene Cheah: But I think the reality is that when we say infinite context, we just mean a different kind of infinite context, or you, or as it's previously covered, you need to test the model differently. So, think of it more along the lines of the human. Like, I don't remember what I ate for breakfast yesterday.[00:34:37] Eugene Cheah: Yeah, that's the statement that I'll say. And And we humans are not quadratic transformers. If we did, if let's say we increased our brain size for every second we live, we would have exploded by the time we are 5 years old or something like that. And, and I think, I think basically fundamentally for us, right, be it whether we, regardless of whether RWKB, statespace, XLSTM, [00:35:00] etc, our general idea is that instead of that expanding state, that increase in computational cost, what if we have a fixed state size?[00:35:08] Eugene Cheah: And Information theory detects that that fixed state size will have a limit. Just how big of a limit is a question, like, we, like, RWKB is running at 40 megabytes for, for its state. Its future version might run into 400 megabytes. That is like millions of tokens in, if you're talking about mathematically, the maximum possibility.[00:35:29] Eugene Cheah: It's just that I guess we were all more inefficient about it, so maybe we hit 100, 000. And that's kind of like the work we are doing, trying to like push it and maximize it. And that's where the models will start differing, because it will choose to forget things, it will choose to remember things. And that's why I think that there might be some element of right, but it may not be the same right.[00:35:49] Eugene Cheah: It may be the model learn things, and it's like, hmm, I can't remember that, that article. Let me do a database search, to search. Just like us humans, when we can't remember the article in the company. We do a search on Notion. [00:36:00][00:36:00] Dan Fu: I think something that would be really interesting is if you could have facts that are, so right now, the one intuition about language models is that all those parameters are around just to store random facts about the world.[00:36:14] Dan Fu: And this intuition comes from the observation that if you take a really small language model, it can do things like talk to you, or kind of has like the The style of conversation, it can learn that, but where it will usually fall over compared to a much larger one is it'll just be a lot less factual about things that it knows or that it can do.[00:36:32] Dan Fu: But that points to all those weights that we're spending, all that SGD that we're spending to train these models are just being used to store facts. And we have things like databases that are pretty good at storing facts. So I think one thing that would be really interesting is if we could actually have some sort of outside data store that a language model can can look at that that maybe is you know, has has some sort of gradient descent in it, but but would be quite interesting.[00:36:58] Dan Fu: And then maybe you could edit it, delete [00:37:00] facts, you know, change who's president so that it doesn't, it doesn't get lost.[00:37:04] Vibhu: Can we open up Q& A and hot takes for the audience? I have a hot take Q& A. Do these scale? When, when 405B state space model, RAG exists, no one does long context, who's throwing in 2 million token questions, hot takes?[00:37:24] Dan Fu: The, the who's throwing in 2 million token question, I think, is, is a really good question. So I actually, I was going to offer that as a hot take. I mean, my hot take was going to be that long context doesn't matter. I know I just gave a whole talk about it, but you know, what, what's the point of doing research if you can't, you know, play both sides.[00:37:40] Dan Fu: But I think one of the, so I think for both of us, the reason that we first got into this was just from the first principled questions of there's this quadratic thing. Clearly intelligence doesn't need to be quadratic. What is going on? Can we understand it better? You know, since then it's kind of turned into a race, which has [00:38:00] been exciting to watch, like, how much context you can take in.[00:38:03] Dan Fu: But I think it's right. Nobody is actually putting in a two million context prompt into these models. And, and, you know, if they are, maybe we can go, go You know, design a better model to do that particular thing. Yeah, what do you think about that? So you've also been working on this. Do you think long context matters?[00:38:19] Eugene Cheah: So I'm going to burn a bit. How many of you remember the news of Google Gemini supporting 3 million contacts, right? Raise your hand.[00:38:28] Vibhu: Yeah, 2 million.[00:38:29] Eugene Cheah: Oh, it's 2 million.[00:38:31] Eugene Cheah: Yeah, how many of you actually tried that? See?[00:38:34] Vibhu: I use it a lot. You? You work for MindsTV. I use it a lot.[00:38:41] Eugene Cheah: So, for some people that has used, and I think, I think that's the, that's might be, like, this is where my opinion starts to differ, because I think the big labs may have a bigger role in this, because Like, even for RWKB, even when we train non contacts, the reason why I say VRAM is a problem is that because when we did the, we need to backprop [00:39:00] against the states, we actually need to maintain the state in between the tokens by the token length.[00:39:05] Eugene Cheah: So that means we need to actually roll out the whole 1 million contacts if we are actually training 1 million. Which is the same for transformers, actually, but it just means we don't magically reuse the VRAM consumption in the training time space. So that is one of the VRAM bottlenecks, and I'm neither OpenAI nor Google, so donate GPUs if you have too much of them.[00:39:27] Eugene Cheah: But then, putting it back to another paradigm, right, is that I think O1 style reasoning might be actually pushing that direction downwards. In my opinion, this is my partial hot take is that if, let's say you have a super big model, And let's say you have a 70B model that may take double the tokens, but gets the same result.[00:39:51] Eugene Cheah: Strictly speaking, a 70B, and this is even for transformer or non transformer, right? We we'll take less less resources than that 400 B [00:40:00] model, even if it did double the amount thinking. And if that's the case, and we are still all trying to figure this out, maybe the direction for us is really getting the sub 200 B to be as fast as efficient as possible.[00:40:11] Eugene Cheah: We a very efficient architecture that some folks happen to be working on to, to just reason it out over larger and larger context thing.[00:40:20] Question: Yeah. One thing I'm super interested in is. Models that can watch forever? Obviously you cannot train something on infinite context length. How are y'all thinking about that, where you run on a much longer context length than is possible to train on?[00:40:38] Dan Fu: Yeah, it's a, it's a great question. So I think when I think you guys probably had tweets along these lines, too. When we first started doing these things, because these are all recurrent models in theory you could just run it forever. You could just run it forever. And at the very least it won't, it won't like error out on your crash.[00:40:57] Dan Fu: There's another question of whether it can actually [00:41:00] use what it's seen in that infinite context. And I think there, so one place where probably the research and architectures ran faster Then another research is actually the benchmarks for long context. So you turn it on forever. You want to do everything or watch everything.[00:41:16] Dan Fu: What is it that you actually wanted to do? Can we actually build some benchmarks for that? Then measure what's happening. And then ask the question, can the models do it? Is there something else that they need? Yeah, I think that if I were to turn back the clock to 2022, that's probably one of the things I would have done differently, which would have been actually get some long context benchmarks out at the same time as we started pushing context length on all these models.[00:41:41] Eugene Cheah: I will also say the use case. So like, I think we both agree that there's no Infinite memory and the model needs to be able to learn and decide. I think what we have observed for, I think this also fits the state space model, is that one of the key advantages of this alternate attention mechanic that is not based on token position is that the model don't suddenly become crazy when you go past the [00:42:00] 8k training context tank, or a million context tank.[00:42:03] Eugene Cheah: It's actually still stable. It's still able to run, it's still able to rationalize. It just starts forgetting things. But some of these things are still there in latent memory. Some of these things are still somewhat there. That's the whole point of why reading twice works. Things like that. And one of the biggest pushes in this direction is that I think both Statespace and RWKB have Separate papers by other researchers where they use this architecture for time series data.[00:42:26] Eugene Cheah: Weather modeling. So, you are not asking what was the weather five days ago. You're asking what's the weather tomorrow based on the infinite length that we, as long as this Earth and the computer will keep running. So, so, and they found that it is like, better than existing, like, transformer or existing architecture in modeling this weather data.[00:42:47] Eugene Cheah: Control for the param size and stuff. I'm quite sure there are people with larger models. So, so there are things that, that in this case, right, there is future applications if your question is just what's next and not what's 10 years ago.[00:42:59] Dan Fu: Thanks so [00:43:00] much for having us. Get full access to Latent Space at www.latent.space/subscribe

Screenwriters Peter Straughan (Conclave) and Zack Baylin (The Order) discuss their latest projects and previous work, their process, and much more. Peter Straughan is a writer and playwright. His most recent screenplay is the 2024 film Conclave. Before Conclave, Peter's screenwriting credits have include The Goldfinch, Our Brand is Crisis, Frank and Tinker Tailor Soldier Spy, the latter of which received several accolades including a 2011 Academy Award nomination for Best Original Screenplay. In addition, he wrote the 2015 television adaptation of Wolf Hall, which earned him a nomination for the Primetime Emmy Award for Outstanding Writing for a Miniseries, Movie or a Dramatic Special. Zach Baylin is a writer whose 2024 credits include The Order and Bob Marley: One Love. His other credits include Gran Turismo, Creed III and King Richard, the last of which earned him an Academy Award nomination for Best Original Screenplay. --- Read shownotes, transcripts, and other member interviews: www.onwriting.org/ Follow the Guild on social media: Twitter: @OnWritingWGAE | @WGAEast Facebook: /WGAEast Instagram: @WGAEast  

Michelle Redfern sits down with Lizzy Goldfinch to explore the crucial intersection of money, leadership, and women's potential. Lizzy shares her professional expertise and personal experiences to illuminate how financial literacy and independence are foundational for closing the leadership gender gap. The conversation focuses on breaking down harmful money myths, embracing financial empowerment, and taking actionable steps to build wealth and create choices.Whether you're navigating your career, seeking financial freedom, or dreaming of a larger impact in your community, this episode will inspire you to take control of your financial future.Episode HighlightsThe “3 Ds” of Financial Vulnerability:• Death, Divorce, and Domestic Violence often leave women in precarious financial positions.• Women aged 55+ are the fastest-growing demographic of homeless people in Australia, emphasizing the need for financial independence.Why Money Matters:• Financial resources give women the power of choice and freedom, enabling them to escape abusive situations or transition careers without fear.• By 2034, women are projected to control 65% of Australia's wealth, making financial literacy more crucial than ever.Reframing Harmful Money Mindsets:• Common limiting beliefs:• “I'm not good with money.”• “Money is vulgar or shameful to talk about.”• Empowering reframes:• “I'm learning to get better with money.”• “I love money and the choices it brings.”Investing in Yourself:• Warren Buffett's advice: The best investment you can make is in yourself.• Tips for self-investment: podcasts, networking, online courses, and continuous learning.The Power of Choice Through Money:• Financial independence allows women to:• Volunteer and engage in meaningful causes.• Leave unsupportive workplaces or unhealthy relationships.Practical Tips to Build Wealth:• Create a “Runaway Fund” for emergencies or unexpected life transitions.• Start small with consistent saving and investing—compounding interest is a game changer.• Keep a record of your accomplishments to ensure fair remuneration at work.Leadership Call to Action1. Examine Your Money Mindset:Identify any limiting beliefs you have about money. Write them down and consciously work to reframe them into empowering affirmations.2. Start Building Wealth Today:Open a savings account and set up an automated transfer—even $5 per paycheck can snowball over time.3. Prepare for Your Next Salary Negotiation:Document your accomplishments and contributions to the business. Set a target remuneration goal and plan your negotiation strategy now.4. Get Educated:Listen to podcasts like She's on the Money, read books like The Barefoot Investor by Scott Pape, or follow Lizzie Goldfinch for actionable financial insights.5. Support Your Community:Use your financial resources or time to give back in meaningful ways—whether by mentoring, volunteering, or donating to causes you care about.Connect with Lizzy Goldfinch:Resources Mentioned in the Episode:• The Barefoot Investor by Scott Pape• The Millionaire Next Door by Thomas J. Stanley and William D. Danko• PepTalkHer App by Meggie Palmer • The Snowball - Warren BuffetMake sure to subscribe to Lead to Soar on your favourite podcast platform and share this episode with women in your network who are ready to take control of their financial futures. Hosted on Acast. See acast.com/privacy for more information.

On our latest show: Birding by Ear with Donna Posont; listening to the beautiful Lawrence's Goldfinch; and talkin' turkey with Mike O'Connor.

We are honored this week as we are joined by Bafta Winning screenwriter Peter Straughn to talk about his latest film Conclave which stars Ralph Fiennes and is out NOW! Peter who is known for writing the feature films Tinker Tailor Soldier Spy, Mrs Radcliffs Revolution, Sixty Six, How to Lose Friends and Alienate People, The Men Who Stare at Goats, Frank, The Snowman and The Goldfinch chats with Pope 'Dom' Francis III and Cardinal 'Giles' Archibald ii about his rather excellent feature Conclave based on the book of the same name. They talk themes and plot, story structure and tone. His humble beginnings to indie film darling to Academy Award nominee. Writing an Oscar level drama contender with comedy and drama thrown into the heady mix. Conclave is in cinemas now. SHORT FILM SHOWCASE Center Frame https://www.centerframe.com/industry-showcase. WATCH our interview with Elizabeth Olsen and Carrie Coon on YouTube here https://www.youtube.com/watch?v=V-TU39BmwLI&t=167s. PODCAST MERCH Get your very own Tees, Hoodies, onset water bottles, mugs and more MERCH. https://my-store-11604768.creator-spring.com/ COURSES Want to learn how to finish your film? Take our POST PRODUCTION COURSE https://cuttingroom.info/post-production-demystified/   PATREON Big thank you to: Serena Gardner Mark Hammett Lee Hutchings Marli J Monroe Karen Newman Want your name in the show notes or some great bonus material on film-making? Join our Patreon for bonus episodes, industry survival guides, and feedback on your film projects!   SUPPORT THE PODCAST Check out our full episode archive on how to make films at TheFilmmakersPodcast.com   CREDITS The Filmmakers Podcast is written, produced and edited by Giles Alderson @gilesalderson Logo and Banner Art by Lois Creative  Theme Music by John J. Harvey Learn more about your ad choices. Visit podcastchoices.com/adchoices

Sunlight on the wing

Sunlight on the wing

Join Sam and Terri as they dive into the essence of innate health, the power of processing emotions, and the difference between seeking external pleasure and finding internal peace.

A TIFF 2024 RUNDOWN: 1:47 - Previously Announced TIFF Premieres: We get the obligatory Family Guy reference out of the way. Then we discuss how Eden is about hot people on an island, why we need to be right about Nightbitch, and whether We Live In Time is more Brooklyn or Goldfinch. TIFF GALAS & SPECIAL PRESENTATIONS: 4:01 - Opening Night with Nutcrackers from David Gordon Green and that stupid Adam Sandler movie that one of us thinks is great. 6:25 - Closing Night with Rebel Wilson's The Deb 7:48 - From Cannes to TIFF: Anora, Bird, Emilia Perez, Oh Canada, Rumours, & The Shrouds. 9:40 - Better Man via Robbie Williams starts a whole Pearl Jam riff 11:20 - A cryptic Conclave book report. 12:07 - Other big name films we've been following forever like Hard Truths from Mike Leigh, Heretic from A24, Piece by Piece from Lego, The End from the artist formerly known as Michael Shannon, The Fire Inside from MGM, The Return from a ripped Ralph + Will & Harper from Sundance. 15:25 - The Piano Lesson announces the Washington family takeover of Hollywood. NEW TIFF ARRIVALS INTO THE OSCARS LANDSCAPE: 18:07 - 40 Acres has Danielle Deadwyler vs Canadian Cannibals! 19:04 - Familiar stories modernized with Brett Goldstein in All Of You, banshee Barry vs Christopher in Bring Them Down, Sandra Oh in Can I Get a Witness?, a jacked Orlando Bloom in The Cut, plus The Motorcycle Diaries director is ironically back in I'm Still Here. 21:48 - The Last Showgirl has us hoping for a Pam Anderson Oscars campaign 22:58 - Millers in Marriage is the next from Edward Burns that's better than The Family Stone. 24:32 - Relay seems like Hell or High Water or The Accountant. 25:09 - Riff Raff seems funny with Jennifer Coolidge, Bill Murray and Pete Davidson 26:10 - Sharp Corner stars Ben Foster who may or may not walk off into the woods. 27:22 - The Order intrigues us with Hoult, Law, Smollett, Maron, Baylin, etc. 29:10 - The Penguin Lessons is probably not Mr. Popper's Penguins. 29:44 - Pedro Paramo and why his bonafides make it a potential contender 31:13 - Sketch seems like a funny Harold and the Purple Crayon w/ D'Arcy Carden & Tony Hale. 32:27 - Unstoppable and how we hope Jlo gets some good press with this one. 34:14 - Without Blood is the next from Angelina Jolie and Cinecitta Studios. THE 81ST VENICE FILM FESTIVAL: 35:18 - A Recap of the News on the Jury, Tributes, and Opening Night Film 36:19 - Maria and why Angelina Jolie could be a favorite for the Volpi Cup. 37:01 - Queer and why Luca Guadagnino's work promises a high floor 38:15 - Pedro's Room Next Door and how we get sidetracked re: premise writing. 39:43 - Joker Folie à Deux and our mixed review of the second trailer. 43:44 - April and how M2 has faith in this director. 44:38 - The Brutalist and the allure of the mysterious and wealthy client 46:34 - Babygirl and when we momentarily lose our morality. 47:58 - Harvest and the intrigue of good young actors. 48:45 - Wolfs and the callback that also includes your obligatory Tarantino reference. 49:28 - Baby Invasion and why it has to be Harmony Korine. 50:12 - Intriguing Venice Docs and why M2 owes you some doc reviews. THE NYFF OPENING NIGHT FILM ANNOUNCEMENT - NICKEL BOYS 51:00 - Aunjanue Ellis as a Supporting Actress play, the history of previous Opening Nighters from NYFF at the Oscars, and another major contender centered on a child protagonist. (Plus, we get a quick happy story from Uncle Mike). 55:25 - OUR OUTRO and how to contact us. Plus, you hear about a handful of upcoming episodes, get some Austin Powers quotes, and a discussion on how we would do Telluride if we ever go.