Podcasts about things we learned

Ian Henderson, founder of MashApp and former Spotify executive, talks with Dmitri about how MashApp enables non-musicians to remix tracks without production experience, and the broader context of technological disruptions in the music industry driven by AI. He explains the challenges in securing licenses from major record labels and his vision for the future of music interactivity. We also hear the news from Dmitri and Tristra.   News Roundup!   50 Million More Americans Are Buying Music Than a Decade Ago   Amazon Makes Last-Minute Bid for TikTok as US Ban Deadline Looms   5 Million Songs and $288m Spent on Catalogs: 8 Things We Learned from Universal Music Group's 2024 Annual Report   Udio, the Startup Sued by Record Companies Last Year, Unveils Tool to Clone ‘sonic identity' of Existing Songs   Inside YouTube's Weird World Of Fake Movie Trailers   The Music Tectonics podcast goes beneath the surface of the music industry to explore how technology is changing the way business gets done. Visit musictectonics.com to find shownotes and a transcript for this episode, and find us on LinkedIn, Twitter, and Instagram. Let us know what you think!    Get Dmitri's Rock Paper Scanner newsletter.  

Rule changes to overtime and kickoffs are just some things that will go into effect for the 2025 NFL Season. Mike Renner and Kyle Long are joined by CBS Sports senior NFL reporter and insider Jonathan Jones to discuss everything he learned from the NFL League Meetings. 0:00 - Intro 3:00 - Should the NFL ban the Tush Push? 9:45 - Latest on Aaron Rodgers 13:45 - Should the NFL reseed teams for the playoffs? 19:00 - Should the NFL expand to 18 games? 26:40 - Will Deshaun Watson be the Browns' quarterback next season? 30:15 - Explaining the new throwback jersey rule 33:20 - Explaining the new kickoff rule 38:20 - NFL Draft talk Pushing the Pile is available for free on the Audacy app as well as Apple Podcasts, Spotify and wherever else you listen to podcasts. Subscribe to our YouTube channel: https://www.youtube.com/@NFLonCBS Download and Follow Pushing the Pile on Spotify: https://open.spotify.com/show/2RFkEgdbFxbPBDU5F5xEjJ?si=1062d40c04e24fd5 Follow our PTP team on Twitter: @mikerenner_, @Ky1eLong, @pushingthepile For more NFL coverage from CBS Sports, visit https://www.cbssports.com/nfl/ To hear more from the CBS Sports Podcast Network, visit https://www.cbssports.com/podcasts/ Visit the betting arena on CBSSports.com for all the latest sportsbook reviews and sportsbook promos. You can listen to Pushing the Pile on your smart speakers! Simply say "Alexa, play the latest episode of the Pushing the Pile podcast" or "Hey Google, play the latest episode of the Pushing the Pile podcast." To learn more about listener data and our privacy practices visit: https://www.audacyinc.com/privacy-policy Learn more about your ad choices. Visit https://podcastchoices.com/adchoices

As we begin to wrap up our second season of working with multiple youth hockey organizations across North America, this week Topher and Jeff talk about the top 10 things they've learned about youth hockey this year. Special thanks to all of the organizations we have worked with in our Youth Hockey Organization Blueprint. If you feel like your organization or team could benefit from working with us, check out the referral link below! BLUEPRINT ORGANIZATION REFERRAL Thank you to our title sponsor IceHockeySystems.com, as well as Train-Heroic, Helios Hockey, and Crossbar! And thank you to our AMAZING LISTENERS; We appreciate every listen, download, comment, rating, and share on your social sites! If you'd like to join our Hockey Think Tank Community, head over to Community.TheHockeyThinkTank.com and check it out! Follow us: IG: @HockeyThinkTank X (Twitter): @HockeyThinkTank TikTok: @HockeyThinkTank Facebook: TheHockeyThinkTank

With the NFL season ending just a few days ago, we thought we would bring our guy, Dawson-- an NFL fanatic-- back on the pod to recap this season. In this episode, Nick, Jacob, and Dawson rank the top 5 things they learned from this NFL season. Enjoy! Follow us on Instagram and X - search for @therankerspodcastYou can email us for anything at therankerspodcast@gmail.com Don't forget to like, rate, and review us on whatever platform you're listening on! 

Onsides/Offsides I Angelo Cataldi, Legendary Philly Sports Radio Host I 5 Things We Learned from the Super Bowl

Note to listeners: Humans of Travel is on hiatus for the winter, with new, full-length episodes resuming Spring 2025. This is a bonus Humans in the Hot Seat Episode, recorded at SmartFlyer's CORE conference in Lake Louise, Alberta, last month. This episode is released in conjunction with a Trade Secrets episode on email marketing, with SmartFlyer's Kayla Douglas. Welcome to Humans in the Hot Seat, a spinoff series of Humans of Travel. This is Emma Weissmann, Executive Editor at TravelAge West, and your host. This week, we are sitting down with Erina Pindar, Managing Partner and COO of luxury travel agency SmartFlyer. Pindar is a veteran of Humans of Travel – her previous episode was recorded in 2020 with former host Valerie Chen. But for her return to the show, we chat all about what it takes to build a successful brand, beyond colors and fonts. How do brands remain authentic, but not "too perfect?" What was Pindar's goals when she rebranded SmartFlyer years ago? Find out this, and more, during our episode. This episode is coming out out in conjunction with a new episode of Trade Secrets, a joint production of TravelAge West and Travel Weekly. On that podcast, we will be interviewing SmartFlyer's Kayla Douglas, director of content, about another unique form of branding: e-marketing. Head on over to the Trade Secrets feed for a listen.Join us next week for an episode with our podcast sponsor, Riverside Luxury Cruises. RESOURCES MENTIONED IN THIS EPISODE SmartFlyerErina Pindar on Humans of Travel in 2020: SmartFlyer's Erina Pindar on How Her Childhood in Asia Shaped Her Worldview, Plus the 411 on Branding Trade Secrets PodcastContact Pindar on Instagram: @theSmartFlyer @erinapindar From TravelAge West: 5 Things We Learned at SmartFlyer's 2025 CORE Conference ABOUT YOUR HOST Emma Weissmann is the Executive Editor of TravelAge West, a print magazine and website for travel advisors based in the Western U.S. She is also the co-host of Trade Secrets, a podcast created with sister publication Travel Weekly. TravelAge West also produces national trade publications Explorer and Family Getaways, as well as events including the Future Leaders in Travel Retreat, Global Travel Marketplace West, the WAVE Awards gala and the Napa Valley Leadership Forum. ABOUT THE SHOW TravelAge West’s award-winning podcast, “Humans of Travel,” features conversations with exceptional people who have compelling stories to tell. Listeners will hear from the travel industry’s notable authorities, high-profile executives, travel advisors and rising stars as they share the highs and lows that make them human.See omnystudio.com/listener for privacy information.

Watch this episode on Youtube youtube.com/@inherskinpodcast   Follow the podcast on Instagram https://www.instagram.com/inherskinpodcast/   Follow the podcast on TikTok https://www.tiktok.com/@inherskin?_t=8iXmc3sEuHS&_r=1   Follow Bri on Instagram https://www.instagram.com/briannafoxmakeup/   Follow Amanda on Instagram https://www.instagram.com/amandadevon/

In this episode of the Buckeye Weekly Podcast, hosts Tony Gerdeman and Tom Orr discuss their key takeaways from rewatching Ohio State's 34-23 win over Notre Dame in the national championship game. The discussion includes analysis of the game plays, in-depth breakdowns of standout performances, and insight into strategic decisions made during the game. They also talk about the effectiveness of Ohio State's offensive line, special teams, and the exceptional performance of players like Jeremiah Smith and Will Howard. 00:00 Introduction and Welcome 00:12 Notre Dame Rewatch: Initial Impressions 01:18 Key Plays and Observations 02:47 Notre Dame's Strategy and Struggles 07:08 Ohio State's Offensive Dominance 12:54 Will Howard's Impact and Legacy 18:09 Broadcast Highlights and Crowd Atmosphere 22:07 Ticket Demand and Crowd Dynamics23:15 Special Teams Blunders 24:06 Field Goal Controversy 30:43 Offensive Line Performance37:24 Run Defense Dominance38:32 Clock Management and Final Thoughts

In this episode of the Buckeye Weekly Podcast, hosts Tony Gerdeman and Tom Orr delve into Notre Dame's performance against Penn State in the Orange Bowl. They analyze key players, moments, and strategies from the game, drawing insights on how these elements might influence the upcoming Ohio State vs Notre Dame national championship game. Key topics include Notre Dame's offensive line struggles, the effectiveness of their short-yardage game, the role of special teams, and potential matchups that could sway the game's outcome. This comprehensive breakdown aims to provide a detailed preview of what Ohio State can expect and how they can possibly capitalize on Notre Dame's vulnerabilities while also dealing with their strengths. Tune in for an in-depth examination of this highly anticipated championship clash. 00:00 Introduction and Opening Banter 01:33 Penn State's Offensive Struggles 04:29 Notre Dame's Offensive Line Woes 13:43 Notre Dame's Running Game Analysis17:08 Special Teams and Turnover Impact 21:44 Nick Singleton's Red Zone Dominance 21:56 Notre Dame's Line of Scrimmage Concerns 22:28 Special Teams and Defensive Struggles 23:03 Christian Gray's Performance and Key Plays 23:46 Notre Dame's Resilience Against Penn State 24:09 Ohio State's Strategy Against Notre Dame 26:45 High Snap Issues for Notre Dame 28:18 Grateful Griddle Sponsorship 28:56 Penn State's Tight Ends and Ohio State's Defense 33:21 Notre Dame's Creative Playmaking 39:07 Ohio State's Talent Advantage 42:20 Final Thoughts and Game Preview

In this episode of the Buckeye Weekly Podcast, hosts Tony Gerdeman and Tom Orr break down their takeaways from rewatching Notre Dame's playoff victory over Georgia in the College Football Playoff quarterfinals. They discuss key points like Notre Dame's strategies, their defensive prowess, and special teams' aggressiveness. The hosts also explore how these factors will impact Ohio State's upcoming national championship game against the Irish. 00:00 Welcome to the Buckeye Weekly Podcast 00:12 Notre Dame vs. Georgia: A Game of Confusion 01:40 Notre Dame's Unimpressive Offensive Stats 03:44 Georgia's Missed Opportunities 07:33 Notre Dame's Defensive Standouts 16:46 Young and Undersized: Notre Dame's Defense 19:37 Georgia's Struggles and Notre Dame's Methodical Offense 21:26 Strategizing the Next Play 21:50 Riley Leonard: The Dual Threat 22:45 Notre Dame's Offensive Challenges34:07 Special Teams: The Game Changer35:50 Keys to Victory for Notre Dame 43:27 Final Thoughts and Sign Off

In this episode of the Buckeye Weekly Podcast, hosts Tony Gerdeman and Tom Orr break down what they learned from rewatching Notre Dame's playoff win over Indiana from last month. They discuss key moments, player performances, and how these insights apply to Ohio State's preparation for the upcoming national championship game against the Irish. Topics include Notre Dame's rushing attack, defensive strengths and weaknesses, special teams play, and how Ohio State can exploit mismatches. 00:00 Introduction and Podcast Welcome 00:13 Revisiting Notre Dame's Playoff Game 00:47 Big Ten's Performance in the Playoffs 03:11 Notre Dame's Rushing Analysis04:16 Jeremiyah Love's Impact06:39 Notre Dame's Offensive Challenges10:08 Riley Leonard's Passing Game17:32 Notre Dame's Path to Victory19:57 Concerns for Notre Dame's Defense21:46 Notre Dame's Young Secondary Faces Challenges 22:39 Comparing Notre Dame's Defense to Previous Opponents 25:14 Key Players and Their Performances34:26 Special Teams and Miscellaneous Observations 40:53 Upcoming Games and Final Thoughts

Shawn Siegele and Colm Kelly recap the NFL Wildcard Round while highlighting the key players, plays, and performances that will impact the future of fantasy football. Players discussed include Jayden Daniels, Derrick Henry, Bucky Irving, James Cook, and more. Timestamps: 0:00 Intro 1:00 Jayden Daniels does it again - Terry McLaurin, Dynami Brown, Mike Evans, Bucky Irving 14:00 Ladd McConkey shines as the Chargers slump to the Texans 26:00 The Eagles beat the Packers in a battle of attrition with some questionable officiating calls 35:00 Keynotes from the Ravens/Steelers and Bills/Broncos 46:00 Outro Subscribe to the RotoViz YouTube Channel here! HOSTS RotoViz Radio Executive Producer Colm Kelly (@OvertimeIreland) RotoViz co-owner Shawn Siegele (@FF_Contrarian) SPONSORS BetterHelp - This episode is brought to you by BetterHelp. Give online therapy a try at betterhelp.com/ROTOVIZ and get on your way to being your best self. Gametime - Download the Gametime app, create an account, and use code ROTOVIZ for $20 off your first purchase. Underdog Fantasy – Get a 100% deposit match on your first deposit up to $100 when you sign up at Underdogfantasy.com using this link or the promo code ROTOVIZ. Listeners of RotoViz Radio can save 10% on a one-year RotoViz subscription by visiting RotoViz.com/podcast or by using the promotional code "rvradio2024" at the time of purchase. SHOW NOTES RotoViz Radio provides the power for RotoViz Overtime. Email: RotoVizRadio@gmail.com @RotoVizOvertime on Twitter Learn more about your ad choices. Visit podcastchoices.com/adchoices

In this episode of the Buckeye Weekly Podcast, hosts Tony Gerdeman and Tom Orr break down what they learned from rewatching Ohio State's 28-14 win over Texas in the Cotton Bowl. They discuss key plays and performances, including standout moments from Jordan Hancock, TreVeyon Henderson, and the Ohio State defensive line. The hosts also talk about coaching decisions, officiating, and look forward to Ohio State's upcoming game against Notre Dame. Tune in for a detailed analysis and much more!00:00 Welcome to the Buckeye Weekly Podcast 00:27 Rewatching The Cotton Bowl 01:58 Jordan Hancock's Stellar Performance 06:57 Quinshon Judkins: The Unsung Hero 12:50 Ohio State's Dominant Defensive Line 19:24 Will Howard's Composure Under Pressure 22:16 Breaking Down the Screenplay 22:39 TreVeyon Henderson's Speed and Impact 23:42 Ohio State Tight Ends: Unsung Heroes 26:19 TreVeyon Henderson's Personal Foul Incident 29:06 Questionable Play Calls and Officiating 38:00 Key Defensive Plays and Reactions 41:00 Looking Ahead to Notre Dame 42:15 Wrapping Up and Final Thoughts

Shawn Siegele and Colm Kelly recap the NFL Wildcard Round while highlighting the key players, plays, and performances that will impact the future of fantasy football. Players discussed include Jayden Daniels, Derrick Henry, Bucky Irving, James Cook, and more. Timestamps: 0:00 Intro 1:00 Jayden Daniels does it again - Terry McLaurin, Dynami Brown, Mike Evans, Bucky Irving 14:00 Ladd McConkey shines as the Chargers slump to the Texans 26:00 The Eagles beat the Packers in a battle of attrition with some questionable officiating calls 35:00 Keynotes from the Ravens/Steelers and Bills/Broncos 46:00 Outro Subscribe to the RotoViz YouTube Channel here! HOSTS RotoViz Radio Executive Producer Colm Kelly (@OvertimeIreland) RotoViz co-owner Shawn Siegele (@FF_Contrarian) SPONSORS BetterHelp - This episode is brought to you by BetterHelp. Give online therapy a try at betterhelp.com/ROTOVIZ and get on your way to being your best self. Gametime - Download the Gametime app, create an account, and use code ROTOVIZ for $20 off your first purchase. Underdog Fantasy – Get a 100% deposit match on your first deposit up to $100 when you sign up at Underdogfantasy.com using this link or the promo code ROTOVIZ. Listeners of RotoViz Radio can save 10% on a one-year RotoViz subscription by visiting RotoViz.com/podcast or by using the promotional code "rvradio2024" at the time of purchase. SHOW NOTES RotoViz Radio provides the power for RotoViz Overtime. Email: RotoVizRadio@gmail.com @RotoVizOvertime on Twitter Learn more about your ad choices. Visit podcastchoices.com/adchoices

Due to overwhelming demand (>15x applications:slots), we are closing CFPs for AI Engineer Summit NYC today. Last call! Thanks, we'll be reaching out to all shortly!The world's top AI blogger and friend of every pod, Simon Willison, dropped a monster 2024 recap: Things we learned about LLMs in 2024. Brian of the excellent TechMeme Ride Home pinged us for a connection and a special crossover episode, our first in 2025. The target audience for this podcast is a tech-literate, but non-technical one. You can see Simon's notes for AI Engineers in his World's Fair Keynote.Timestamp* 00:00 Introduction and Guest Welcome* 01:06 State of AI in 2025* 01:43 Advancements in AI Models* 03:59 Cost Efficiency in AI* 06:16 Challenges and Competition in AI* 17:15 AI Agents and Their Limitations* 26:12 Multimodal AI and Future Prospects* 35:29 Exploring Video Avatar Companies* 36:24 AI Influencers and Their Future* 37:12 Simplifying Content Creation with AI* 38:30 The Importance of Credibility in AI* 41:36 The Future of LLM User Interfaces* 48:58 Local LLMs: A Growing Interest* 01:07:22 AI Wearables: The Next Big Thing* 01:10:16 Wrapping Up and Final ThoughtsTranscript[00:00:00] Introduction and Guest Welcome[00:00:00] Brian: Welcome to the first bonus episode of the Tech Meme Write Home for the year 2025. I'm your host as always, Brian McCullough. Listeners to the pod over the last year know that I have made a habit of quoting from Simon Willison when new stuff happens in AI from his blog. Simon has been, become a go to for many folks in terms of, you know, Analyzing things, criticizing things in the AI space.[00:00:33] Brian: I've wanted to talk to you for a long time, Simon. So thank you for coming on the show. No, it's a privilege to be here. And the person that made this connection happen is our friend Swyx, who has been on the show back, even going back to the, the Twitter Spaces days but also an AI guru in, in their own right Swyx, thanks for coming on the show also.[00:00:54] swyx (2): Thanks. I'm happy to be on and have been a regular listener, so just happy to [00:01:00] contribute as well.[00:01:00] Brian: And a good friend of the pod, as they say. Alright, let's go right into it.[00:01:06] State of AI in 2025[00:01:06] Brian: Simon, I'm going to do the most unfair, broad question first, so let's get it out of the way. The year 2025. Broadly, what is the state of AI as we begin this year?[00:01:20] Brian: Whatever you want to say, I don't want to lead the witness.[00:01:22] Simon: Wow. So many things, right? I mean, the big thing is everything's got really good and fast and cheap. Like, that was the trend throughout all of 2024. The good models got so much cheaper, they got so much faster, they got multimodal, right? The image stuff isn't even a surprise anymore.[00:01:39] Simon: They're growing video, all of that kind of stuff. So that's all really exciting.[00:01:43] Advancements in AI Models[00:01:43] Simon: At the same time, they didn't get massively better than GPT 4, which was a bit of a surprise. So that's sort of one of the open questions is, are we going to see huge, but I kind of feel like that's a bit of a distraction because GPT 4, but way cheaper, much larger context lengths, and it [00:02:00] can do multimodal.[00:02:01] Simon: is better, right? That's a better model, even if it's not.[00:02:05] Brian: What people were expecting or hoping, maybe not expecting is not the right word, but hoping that we would see another step change, right? Right. From like GPT 2 to 3 to 4, we were expecting or hoping that maybe we were going to see the next evolution in that sort of, yeah.[00:02:21] Brian: We[00:02:21] Simon: did see that, but not in the way we expected. We thought the model was just going to get smarter, and instead we got. Massive drops in, drops in price. We got all of these new capabilities. You can talk to the things now, right? They can do simulated audio input, all of that kind of stuff. And so it's kind of, it's interesting to me that the models improved in all of these ways we weren't necessarily expecting.[00:02:43] Simon: I didn't know it would be able to do an impersonation of Santa Claus, like a, you know, Talked to it through my phone and show it what I was seeing by the end of 2024. But yeah, we didn't get that GPT 5 step. And that's one of the big open questions is, is that actually just around the corner and we'll have a bunch of GPT 5 class models drop in the [00:03:00] next few months?[00:03:00] Simon: Or is there a limit?[00:03:03] Brian: If you were a betting man and wanted to put money on it, do you expect to see a phase change, step change in 2025?[00:03:11] Simon: I don't particularly for that, like, the models, but smarter. I think all of the trends we're seeing right now are going to keep on going, especially the inference time compute, right?[00:03:21] Simon: The trick that O1 and O3 are doing, which means that you can solve harder problems, but they cost more and it churns away for longer. I think that's going to happen because that's already proven to work. I don't know. I don't know. Maybe there will be a step change to a GPT 5 level, but honestly, I'd be completely happy if we got what we've got right now.[00:03:41] Simon: But cheaper and faster and more capabilities and longer contexts and so forth. That would be thrilling to me.[00:03:46] Brian: Digging into what you've just said one of the things that, by the way, I hope to link in the show notes to Simon's year end post about what, what things we learned about LLMs in 2024. Look for that in the show notes.[00:03:59] Cost Efficiency in AI[00:03:59] Brian: One of the things that you [00:04:00] did say that you alluded to even right there was that in the last year, you felt like the GPT 4 barrier was broken, like IE. Other models, even open source ones are now regularly matching sort of the state of the art.[00:04:13] Simon: Well, it's interesting, right? So the GPT 4 barrier was a year ago, the best available model was OpenAI's GPT 4 and nobody else had even come close to it.[00:04:22] Simon: And they'd been at the, in the lead for like nine months, right? That thing came out in what, February, March of, of 2023. And for the rest of 2023, nobody else came close. And so at the start of last year, like a year ago, the big question was, Why has nobody beaten them yet? Like, what do they know that the rest of the industry doesn't know?[00:04:40] Simon: And today, that I've counted 18 organizations other than GPT 4 who've put out a model which clearly beats that GPT 4 from a year ago thing. Like, maybe they're not better than GPT 4. 0, but that's, that, that, that barrier got completely smashed. And yeah, a few of those I've run on my laptop, which is wild to me.[00:04:59] Simon: Like, [00:05:00] it was very, very wild. It felt very clear to me a year ago that if you want GPT 4, you need a rack of 40, 000 GPUs just to run the thing. And that turned out not to be true. Like the, the, this is that big trend from last year of the models getting more efficient, cheaper to run, just as capable with smaller weights and so forth.[00:05:20] Simon: And I ran another GPT 4 model on my laptop this morning, right? Microsoft 5. 4 just came out. And that, if you look at the benchmarks, it's definitely, it's up there with GPT 4. 0. It's probably not as good when you actually get into the vibes of the thing, but it, it runs on my, it's a 14 gigabyte download and I can run it on a MacBook Pro.[00:05:38] Simon: Like who saw that coming? The most exciting, like the close of the year on Christmas day, just a few weeks ago, was when DeepSeek dropped their DeepSeek v3 model on Hugging Face without even a readme file. It was just like a giant binary blob that I can't run on my laptop. It's too big. But in all of the benchmarks, it's now by far the best available [00:06:00] open, open weights model.[00:06:01] Simon: Like it's, it's, it's beating the, the metalamas and so forth. And that was trained for five and a half million dollars, which is a tenth of the price that people thought it costs to train these things. So everything's trending smaller and faster and more efficient.[00:06:15] Brian: Well, okay.[00:06:16] Challenges and Competition in AI[00:06:16] Brian: I, I kind of was going to get to that later, but let's, let's combine this with what I was going to ask you next, which is, you know, you're talking, you know, Also in the piece about the LLM prices crashing, which I've even seen in projects that I'm working on, but explain Explain that to a general audience, because we hear all the time that LLMs are eye wateringly expensive to run, but what we're suggesting, and we'll come back to the cheap Chinese LLM, but first of all, for the end user, what you're suggesting is that we're starting to see the cost come down sort of in the traditional technology way of Of costs coming down over time,[00:06:49] Simon: yes, but very aggressively.[00:06:51] Simon: I mean, my favorite thing, the example here is if you look at GPT-3, so open AI's g, PT three, which was the best, a developed model in [00:07:00] 2022 and through most of 20 2023. That, the models that we have today, the OpenAI models are a hundred times cheaper. So there was a 100x drop in price for OpenAI from their best available model, like two and a half years ago to today.[00:07:13] Simon: And[00:07:14] Brian: just to be clear, not to train the model, but for the use of tokens and things. Exactly,[00:07:20] Simon: for running prompts through them. And then When you look at the, the really, the top tier model providers right now, I think, are OpenAI, Anthropic, Google, and Meta. And there are a bunch of others that I could list there as well.[00:07:32] Simon: Mistral are very good. The, the DeepSeq and Quen models have got great. There's a whole bunch of providers serving really good models. But even if you just look at the sort of big brand name providers, they all offer models now that are A fraction of the price of the, the, of the models we were using last year.[00:07:49] Simon: I think I've got some numbers that I threw into my blog entry here. Yeah. Like Gemini 1. 5 flash, that's Google's fast high quality model is [00:08:00] how much is that? It's 0. 075 dollars per million tokens. Like these numbers are getting, So we just do cents per million now,[00:08:09] swyx (2): cents per million,[00:08:10] Simon: cents per million makes, makes a lot more sense.[00:08:12] Simon: Yeah they have one model 1. 5 flash 8B, the absolute cheapest of the Google models, is 27 times cheaper than GPT 3. 5 turbo was a year ago. That's it. And GPT 3. 5 turbo, that was the cheap model, right? Now we've got something 27 times cheaper, and the Google, this Google one can do image recognition, it can do million token context, all of those tricks.[00:08:36] Simon: But it's, it's, it's very, it's, it really is startling how inexpensive some of this stuff has got.[00:08:41] Brian: Now, are we assuming that this, that happening is directly the result of competition? Because again, you know, OpenAI, and probably they're doing this for their own almost political reasons, strategic reasons, keeps saying, we're losing money on everything, even the 200.[00:08:56] Brian: So they probably wouldn't, the prices wouldn't be [00:09:00] coming down if there wasn't intense competition in this space.[00:09:04] Simon: The competition is absolutely part of it, but I have it on good authority from sources I trust that Google Gemini is not operating at a loss. Like, the amount of electricity to run a prompt is less than they charge you.[00:09:16] Simon: And the same thing for Amazon Nova. Like, somebody found an Amazon executive and got them to say, Yeah, we're not losing money on this. I don't know about Anthropic and OpenAI, but clearly that demonstrates it is possible to run these things at these ludicrously low prices and still not be running at a loss if you discount the Army of PhDs and the, the training costs and all of that kind of stuff.[00:09:36] Brian: One, one more for me before I let Swyx jump in here. To, to come back to DeepSeek and this idea that you could train, you know, a cutting edge model for 6 million. I, I was saying on the show, like six months ago, that if we are getting to the point where each new model It would cost a billion, ten billion, a hundred billion to train that.[00:09:54] Brian: At some point it would almost, only nation states would be able to train the new models. Do you [00:10:00] expect what DeepSeek and maybe others are proving to sort of blow that up? Or is there like some sort of a parallel track here that maybe I'm not technically, I don't have the mouse to understand the difference.[00:10:11] Brian: Is the model, are the models going to go, you know, Up to a hundred billion dollars or can we get them down? Sort of like DeepSeek has proven[00:10:18] Simon: so I'm the wrong person to answer that because I don't work in the lab training these models. So I can give you my completely uninformed opinion, which is, I felt like the DeepSeek thing.[00:10:27] Simon: That was a bomb shell. That was an absolute bombshell when they came out and said, Hey, look, we've trained. One of the best available models and it cost us six, five and a half million dollars to do it. I feel, and they, the reason, one of the reasons it's so efficient is that we put all of these export controls in to stop Chinese companies from giant buying GPUs.[00:10:44] Simon: So they've, were forced to be, go as efficient as possible. And yet the fact that they've demonstrated that that's possible to do. I think it does completely tear apart this, this, this mental model we had before that yeah, the training runs just keep on getting more and more expensive and the number of [00:11:00] organizations that can afford to run these training runs keeps on shrinking.[00:11:03] Simon: That, that's been blown out of the water. So yeah, that's, again, this was our Christmas gift. This was the thing they dropped on Christmas day. Yeah, it makes me really optimistic that we can, there are, It feels like there was so much low hanging fruit in terms of the efficiency of both inference and training and we spent a whole bunch of last year exploring that and getting results from it.[00:11:22] Simon: I think there's probably a lot left. I think there's probably, well, I would not be surprised to see even better models trained spending even less money over the next six months.[00:11:31] swyx (2): Yeah. So I, I think there's a unspoken angle here on what exactly the Chinese labs are trying to do because DeepSea made a lot of noise.[00:11:41] swyx (2): so much for joining us for around the fact that they train their model for six million dollars and nobody quite quite believes them. Like it's very, very rare for a lab to trumpet the fact that they're doing it for so cheap. They're not trying to get anyone to buy them. So why [00:12:00] are they doing this? They make it very, very obvious.[00:12:05] swyx (2): Deepseek is about 150 employees. It's an order of magnitude smaller than at least Anthropic and maybe, maybe more so for OpenAI. And so what's, what's the end game here? Are they, are they just trying to show that the Chinese are better than us?[00:12:21] Simon: So Deepseek, it's the arm of a hedge, it's a, it's a quant fund, right?[00:12:25] Simon: It's an algorithmic quant trading thing. So I, I, I would love to get more insight into how that organization works. My assumption from what I've seen is it looks like they're basically just flexing. They're like, hey, look at how utterly brilliant we are with this amazing thing that we've done. And it's, it's working, right?[00:12:43] Simon: They but, and so is that it? Are they, is this just their kind of like, this is, this is why our company is so amazing. Look at this thing that we've done, or? I don't know. I'd, I'd love to get Some insight from, from within that industry as to, as to how that's all playing out.[00:12:57] swyx (2): The, the prevailing theory among the Local Llama [00:13:00] crew and the Twitter crew that I indexed for my newsletter is that there is some amount of copying going on.[00:13:06] swyx (2): It's like Sam Altman you know, tweet, tweeting about how they're being copied. And then also there's this, there, there are other sort of opening eye employees that have said, Stuff that is similar that DeepSeek's rate of progress is how U. S. intelligence estimates the number of foreign spies embedded in top labs.[00:13:22] swyx (2): Because a lot of these ideas do spread around, but they surprisingly have a very high density of them in the DeepSeek v3 technical report. So it's, it's interesting. We don't know how much, how many, how much tokens. I think that, you know, people have run analysis on how often DeepSeek thinks it is cloud or thinks it is opening GPC 4.[00:13:40] swyx (2): Thanks for watching! And we don't, we don't know. We don't know. I think for me, like, yeah, we'll, we'll, we basically will never know as, as external commentators. I think what's interesting is how, where does this go? Is there a logical floor or bottom by my estimations for the same amount of ELO started last year to the end of last year cost went down by a thousand X for the [00:14:00] GPT, for, for GPT 4 intelligence.[00:14:02] swyx (2): Would, do they go down a thousand X this year?[00:14:04] Simon: That's a fascinating question. Yeah.[00:14:06] swyx (2): Is there a Moore's law going on, or did we just get a one off benefit last year for some weird reason?[00:14:14] Simon: My uninformed hunch is low hanging fruit. I feel like up until a year ago, people haven't been focusing on efficiency at all. You know, it was all about, what can we get these weird shaped things to do?[00:14:24] Simon: And now once we've sort of hit that, okay, we know that we can get them to do what GPT 4 can do, When thousands of researchers around the world all focus on, okay, how do we make this more efficient? What are the most important, like, how do we strip out all of the weights that have stuff in that doesn't really matter?[00:14:39] Simon: All of that kind of thing. So yeah, maybe that was it. Maybe 2024 was a freak year of all of the low hanging fruit coming out at once. And we'll actually see a reduction in the, in that rate of improvement in terms of efficiency. I wonder, I mean, I think we'll know for sure in about three months time if that trend's going to continue or not.[00:14:58] swyx (2): I agree. You know, I [00:15:00] think the other thing that you mentioned that DeepSeq v3 was the gift that was given from DeepSeq over Christmas, but I feel like the other thing that might be underrated was DeepSeq R1,[00:15:11] Speaker 4: which is[00:15:13] swyx (2): a reasoning model you can run on your laptop. And I think that's something that a lot of people are looking ahead to this year.[00:15:18] swyx (2): Oh, did they[00:15:18] Simon: release the weights for that one?[00:15:20] swyx (2): Yeah.[00:15:21] Simon: Oh my goodness, I missed that. I've been playing with the quen. So the other great, the other big Chinese AI app is Alibaba's quen. Actually, yeah, I, sorry, R1 is an API available. Yeah. Exactly. When that's really cool. So Alibaba's Quen have released two reasoning models that I've run on my laptop.[00:15:38] Simon: Now there was, the first one was Q, Q, WQ. And then the second one was QVQ because the second one's a vision model. So you can like give it vision puzzles and a prompt that these things, they are so much fun to run. Because they think out loud. It's like the OpenAR 01 sort of hides its thinking process. The Query ones don't.[00:15:59] Simon: They just, they [00:16:00] just churn away. And so you'll give it a problem and it will output literally dozens of paragraphs of text about how it's thinking. My favorite thing that happened with QWQ is I asked it to draw me a pelican on a bicycle in SVG. That's like my standard stupid prompt. And for some reason it thought in Chinese.[00:16:18] Simon: It spat out a whole bunch of like Chinese text onto my terminal on my laptop, and then at the end it gave me quite a good sort of artistic pelican on a bicycle. And I ran it all through Google Translate, and yeah, it was like, it was contemplating the nature of SVG files as a starting point. And the fact that my laptop can think in Chinese now is so delightful.[00:16:40] Simon: It's so much fun watching you do that.[00:16:43] swyx (2): Yeah, I think Andrej Karpathy was saying, you know, we, we know that we have achieved proper reasoning inside of these models when they stop thinking in English, and perhaps the best form of thought is in Chinese. But yeah, for listeners who don't know Simon's blog he always, whenever a new model comes out, you, I don't know how you do it, but [00:17:00] you're always the first to run Pelican Bench on these models.[00:17:02] swyx (2): I just did it for 5.[00:17:05] Simon: Yeah.[00:17:07] swyx (2): So I really appreciate that. You should check it out. These are not theoretical. Simon's blog actually shows them.[00:17:12] Brian: Let me put on the investor hat for a second.[00:17:15] AI Agents and Their Limitations[00:17:15] Brian: Because from the investor side of things, a lot of the, the VCs that I know are really hot on agents, and this is the year of agents, but last year was supposed to be the year of agents as well. Lots of money flowing towards, And Gentic startups.[00:17:32] Brian: But in in your piece that again, we're hopefully going to have linked in the show notes, you sort of suggest there's a fundamental flaw in AI agents as they exist right now. Let me let me quote you. And then I'd love to dive into this. You said, I remain skeptical as to their ability based once again, on the Challenge of gullibility.[00:17:49] Brian: LLMs believe anything you tell them, any systems that attempt to make meaningful decisions on your behalf, will run into the same roadblock. How good is a travel agent, or a digital assistant, or even a research tool, if it [00:18:00] can't distinguish truth from fiction? So, essentially, what you're suggesting is that the state of the art now that allows agents is still, it's still that sort of 90 percent problem, the edge problem, getting to the Or, or, or is there a deeper flaw?[00:18:14] Brian: What are you, what are you saying there?[00:18:16] Simon: So this is the fundamental challenge here and honestly my frustration with agents is mainly around definitions Like any if you ask anyone who says they're working on agents to define agents You will get a subtly different definition from each person But everyone always assumes that their definition is the one true one that everyone else understands So I feel like a lot of these agent conversations, people talking past each other because one person's talking about the, the sort of travel agent idea of something that books things on your behalf.[00:18:41] Simon: Somebody else is talking about LLMs with tools running in a loop with a cron job somewhere and all of these different things. You, you ask academics and they'll laugh at you because they've been debating what agents mean for over 30 years at this point. It's like this, this long running, almost sort of an in joke in that community.[00:18:57] Simon: But if we assume that for this purpose of this conversation, an [00:19:00] agent is something that, Which you can give a job and it goes off and it does that thing for you like, like booking travel or things like that. The fundamental challenge is, it's the reliability thing, which comes from this gullibility problem.[00:19:12] Simon: And a lot of my, my interest in this originally came from when I was thinking about prompt injections as a source of this form of attack against LLM systems where you deliberately lay traps out there for this LLM to stumble across,[00:19:24] Brian: and which I should say you have been banging this drum that no one's gotten any far, at least on solving this, that I'm aware of, right.[00:19:31] Brian: Like that's still an open problem. The two years.[00:19:33] Simon: Yeah. Right. We've been talking about this problem and like, a great illustration of this was Claude so Anthropic released Claude computer use a few months ago. Fantastic demo. You could fire up a Docker container and you could literally tell it to do something and watch it open a web browser and navigate to a webpage and click around and so forth.[00:19:51] Simon: Really, really, really interesting and fun to play with. And then, um. One of the first demos somebody tried was, what if you give it a web page that says download and run this [00:20:00] executable, and it did, and the executable was malware that added it to a botnet. So the, the very first most obvious dumb trick that you could play on this thing just worked, right?[00:20:10] Simon: So that's obviously a really big problem. If I'm going to send something out to book travel on my behalf, I mean, it's hard enough for me to figure out which airlines are trying to scam me and which ones aren't. Do I really trust a language model that believes the literal truth of anything that's presented to it to go out and do those things?[00:20:29] swyx (2): Yeah I definitely think there's, it's interesting to see Anthropic doing this because they used to be the safety arm of OpenAI that split out and said, you know, we're worried about letting this thing out in the wild and here they are enabling computer use for agents. Thanks. The, it feels like things have merged.[00:20:49] swyx (2): You know, I'm, I'm also fairly skeptical about, you know, this always being the, the year of Linux on the desktop. And this is the equivalent of this being the year of agents that people [00:21:00] are not predicting so much as wishfully thinking and hoping and praying for their companies and agents to work.[00:21:05] swyx (2): But I, I feel like things are. Coming along a little bit. It's to me, it's kind of like self driving. I remember in 2014 saying that self driving was just around the corner. And I mean, it kind of is, you know, like in, in, in the Bay area. You[00:21:17] Simon: get in a Waymo and you're like, Oh, this works. Yeah, but it's a slow[00:21:21] swyx (2): cook.[00:21:21] swyx (2): It's a slow cook over the next 10 years. We're going to hammer out these things and the cynical people can just point to all the flaws, but like, there are measurable or concrete progress steps that are being made by these builders.[00:21:33] Simon: There is one form of agent that I believe in. I believe, mostly believe in the research assistant form of agents.[00:21:39] Simon: The thing where you've got a difficult problem and, and I've got like, I'm, I'm on the beta for the, the Google Gemini 1. 5 pro with deep research. I think it's called like these names, these names. Right. But. I've been using that. It's good, right? You can give it a difficult problem and it tells you, okay, I'm going to look at 56 different websites [00:22:00] and it goes away and it dumps everything to its context and it comes up with a report for you.[00:22:04] Simon: And it's not, it won't work against adversarial websites, right? If there are websites with deliberate lies in them, it might well get caught out. Most things don't have that as a problem. And so I've had some answers from that which were genuinely really valuable to me. And that feels to me like, I can see how given existing LLM tech, especially with Google Gemini with its like million token contacts and Google with their crawl of the entire web and their, they've got like search, they've got search and cache, they've got a cache of every page and so forth.[00:22:35] Simon: That makes sense to me. And that what they've got right now, I don't think it's, it's not as good as it can be, obviously, but it's, it's, it's, it's a real useful thing, which they're going to start rolling out. So, you know, Perplexity have been building the same thing for a couple of years. That, that I believe in.[00:22:50] Simon: You know, if you tell me that you're going to have an agent that's a research assistant agent, great. The coding agents I mean, chat gpt code interpreter, Nearly two years [00:23:00] ago, that thing started writing Python code, executing the code, getting errors, rewriting it to fix the errors. That pattern obviously works.[00:23:07] Simon: That works really, really well. So, yeah, coding agents that do that sort of error message loop thing, those are proven to work. And they're going to keep on getting better, and that's going to be great. The research assistant agents are just beginning to get there. The things I'm critical of are the ones where you trust, you trust this thing to go out and act autonomously on your behalf, and make decisions on your behalf, especially involving spending money, like that.[00:23:31] Simon: I don't see that working for a very long time. That feels to me like an AGI level problem.[00:23:37] swyx (2): It's it's funny because I think Stripe actually released an agent toolkit which is one of the, the things I featured that is trying to enable these agents each to have a wallet that they can go and spend and have, basically, it's a virtual card.[00:23:49] swyx (2): It's not that, not that difficult with modern infrastructure. can[00:23:51] Simon: stick a 50 cap on it, then at least it's an honor. Can't lose more than 50.[00:23:56] Brian: You know I don't, I don't know if either of you know Rafat Ali [00:24:00] he runs Skift, which is a, a travel news vertical. And he, he, he constantly laughs at the fact that every agent thing is, we're gonna get rid of booking a, a plane flight for you, you know?[00:24:11] Brian: And, and I would point out that, like, historically, when the web started, the first thing everyone talked about is, You can go online and book a trip, right? So it's funny for each generation of like technological advance. The thing they always want to kill is the travel agent. And now they want to kill the webpage travel agent.[00:24:29] Simon: Like it's like I use Google flight search. It's great, right? If you gave me an agent to do that for me, it would save me, I mean, maybe 15 seconds of typing in my things, but I still want to see what my options are and go, yeah, I'm not flying on that airline, no matter how cheap they are.[00:24:44] swyx (2): Yeah. For listeners, go ahead.[00:24:47] swyx (2): For listeners, I think, you know, I think both of you are pretty positive on NotebookLM. And you know, we, we actually interviewed the NotebookLM creators, and there are actually two internal agents going on internally. The reason it takes so long is because they're running an agent loop [00:25:00] inside that is fairly autonomous, which is kind of interesting.[00:25:01] swyx (2): For one,[00:25:02] Simon: for a definition of agent loop, if you picked that particularly well. For one definition. And you're talking about the podcast side of this, right?[00:25:07] swyx (2): Yeah, the podcast side of things. They have a there's, there's going to be a new version coming out that, that we'll be featuring at our, at our conference.[00:25:14] Simon: That one's fascinating to me. Like NotebookLM, I think it's two products, right? On the one hand, it's actually a very good rag product, right? You dump a bunch of things in, you can run searches, that, that, it does a good job of. And then, and then they added the, the podcast thing. It's a bit of a, it's a total gimmick, right?[00:25:30] Simon: But that gimmick got them attention, because they had a great product that nobody paid any attention to at all. And then you add the unfeasibly good voice synthesis of the podcast. Like, it's just, it's, it's, it's the lesson.[00:25:43] Brian: It's the lesson of mid journey and stuff like that. If you can create something that people can post on socials, you don't have to lift a finger again to do any marketing for what you're doing.[00:25:53] Brian: Let me dig into Notebook LLM just for a second as a podcaster. As a [00:26:00] gimmick, it makes sense, and then obviously, you know, you dig into it, it sort of has problems around the edges. It's like, it does the thing that all sort of LLMs kind of do, where it's like, oh, we want to Wrap up with a conclusion.[00:26:12] Multimodal AI and Future Prospects[00:26:12] Brian: I always call that like the the eighth grade book report paper problem where it has to have an intro and then, you know But that's sort of a thing where because I think you spoke about this again in your piece at the year end About how things are going multimodal and how things are that you didn't expect like, you know vision and especially audio I think So that's another thing where, at least over the last year, there's been progress made that maybe you, you didn't think was coming as quick as it came.[00:26:43] Simon: I don't know. I mean, a year ago, we had one really good vision model. We had GPT 4 vision, was, was, was very impressive. And Google Gemini had just dropped Gemini 1. 0, which had vision, but nobody had really played with it yet. Like Google hadn't. People weren't taking Gemini [00:27:00] seriously at that point. I feel like it was 1.[00:27:02] Simon: 5 Pro when it became apparent that actually they were, they, they got over their hump and they were building really good models. And yeah, and they, to be honest, the video models are mostly still using the same trick. The thing where you divide the video up into one image per second and you dump that all into the context.[00:27:16] Simon: So maybe it shouldn't have been so surprising to us that long context models plus vision meant that the video was, was starting to be solved. Of course, it didn't. Not being, you, what you really want with videos, you want to be able to do the audio and the images at the same time. And I think the models are beginning to do that now.[00:27:33] Simon: Like, originally, Gemini 1. 5 Pro originally ignored the audio. It just did the, the, like, one frame per second video trick. As far as I can tell, the most recent ones are actually doing pure multimodal. But the things that opens up are just extraordinary. Like, the the ChatGPT iPhone app feature that they shipped as one of their 12 days of, of OpenAI, I really can be having a conversation and just turn on my video camera and go, Hey, what kind of tree is [00:28:00] this?[00:28:00] Simon: And so forth. And it works. And for all I know, that's just snapping a like picture once a second and feeding it into the model. The, the, the things that you can do with that as an end user are extraordinary. Like that, that to me, I don't think most people have cottoned onto the fact that you can now stream video directly into a model because it, it's only a few weeks old.[00:28:22] Simon: Wow. That's a, that's a, that's a, that's Big boost in terms of what kinds of things you can do with this stuff. Yeah. For[00:28:30] swyx (2): people who are not that close I think Gemini Flashes free tier allows you to do something like capture a photo, one photo every second or a minute and leave it on 24, seven, and you can prompt it to do whatever.[00:28:45] swyx (2): And so you can effectively have your own camera app or monitoring app that that you just prompt and it detects where it changes. It detects for, you know, alerts or anything like that, or describes your day. You know, and, and, and the fact that this is free I think [00:29:00] it's also leads into the previous point of it being the prices haven't come down a lot.[00:29:05] Simon: And even if you're paying for this stuff, like a thing that I put in my blog entry is I ran a calculation on what it would cost to process 68, 000 photographs in my photo collection, and for each one just generate a caption, and using Gemini 1. 5 Flash 8B, it would cost me 1. 68 to process 68, 000 images, which is, I mean, that, that doesn't make sense.[00:29:28] Simon: None of that makes sense. Like it's, it's a, for one four hundredth of a cent per image to generate captions now. So you can see why feeding in a day's worth of video just isn't even very expensive to process.[00:29:40] swyx (2): Yeah, I'll tell you what is expensive. It's the other direction. So we're here, we're talking about consuming video.[00:29:46] swyx (2): And this year, we also had a lot of progress, like probably one of the most excited, excited, anticipated launches of the year was Sora. We actually got Sora. And less exciting.[00:29:55] Simon: We did, and then VO2, Google's Sora, came out like three [00:30:00] days later and upstaged it. Like, Sora was exciting until VO2 landed, which was just better.[00:30:05] swyx (2): In general, I feel the media, or the social media, has been very unfair to Sora. Because what was released to the world, generally available, was Sora Lite. It's the distilled version of Sora, right? So you're, I did not[00:30:16] Simon: realize that you're absolutely comparing[00:30:18] swyx (2): the, the most cherry picked version of VO two, the one that they published on the marketing page to the, the most embarrassing version of the soa.[00:30:25] swyx (2): So of course it's gonna look bad, so, well, I got[00:30:27] Simon: access to the VO two I'm in the VO two beta and I've been poking around with it and. Getting it to generate pelicans on bicycles and stuff. I would absolutely[00:30:34] swyx (2): believe that[00:30:35] Simon: VL2 is actually better. Is Sora, so is full fat Sora coming soon? Do you know, when, when do we get to play with that one?[00:30:42] Simon: No one's[00:30:43] swyx (2): mentioned anything. I think basically the strategy is let people play around with Sora Lite and get info there. But the, the, keep developing Sora with the Hollywood studios. That's what they actually care about. Gotcha. Like the rest of us. Don't really know what to do with the video anyway. Right.[00:30:59] Simon: I mean, [00:31:00] that's my thing is I realized that for generative images and images and video like images We've had for a few years and I don't feel like they've broken out into the talented artist community yet Like lots of people are having fun with them and doing and producing stuff. That's kind of cool to look at but what I want you know that that movie everything everywhere all at once, right?[00:31:20] Simon: One, one ton of Oscars, utterly amazing film. The VFX team for that were five people, some of whom were watching YouTube videos to figure out what to do. My big question for, for Sora and and and Midjourney and stuff, what happens when a creative team like that starts using these tools? I want the creative geniuses behind everything, everywhere all at once.[00:31:40] Simon: What are they going to be able to do with this stuff in like a few years time? Because that's really exciting to me. That's where you take artists who are at the very peak of their game. Give them these new capabilities and see, see what they can do with them.[00:31:52] swyx (2): I should, I know a little bit here. So it should mention that, that team actually used RunwayML.[00:31:57] swyx (2): So there was, there was,[00:31:57] Simon: yeah.[00:31:59] swyx (2): I don't know how [00:32:00] much I don't. So, you know, it's possible to overstate this, but there are people integrating it. Generated video within their workflow, even pre SORA. Right, because[00:32:09] Brian: it's not, it's not the thing where it's like, okay, tomorrow we'll be able to do a full two hour movie that you prompt with three sentences.[00:32:15] Brian: It is like, for the very first part of, of, you know video effects in film, it's like, if you can get that three second clip, if you can get that 20 second thing that they did in the matrix that blew everyone's minds and took a million dollars or whatever to do, like, it's the, it's the little bits and pieces that they can fill in now that it's probably already there.[00:32:34] swyx (2): Yeah, it's like, I think actually having a layered view of what assets people need and letting AI fill in the low value assets. Right, like the background video, the background music and, you know, sometimes the sound effects. That, that maybe, maybe more palatable maybe also changes the, the way that you evaluate the stuff that's coming out.[00:32:57] swyx (2): Because people tend to, in social media, try to [00:33:00] emphasize foreground stuff, main character stuff. So you really care about consistency, and you, you really are bothered when, like, for example, Sorad. Botch's image generation of a gymnast doing flips, which is horrible. It's horrible. But for background crowds, like, who cares?[00:33:18] Brian: And by the way, again, I was, I was a film major way, way back in the day, like, that's how it started. Like things like Braveheart, where they filmed 10 people on a field, and then the computer could turn it into 1000 people on a field. Like, that's always been the way it's around the margins and in the background that first comes in.[00:33:36] Brian: The[00:33:36] Simon: Lord of the Rings movies were over 20 years ago. Although they have those giant battle sequences, which were very early, like, I mean, you could almost call it a generative AI approach, right? They were using very sophisticated, like, algorithms to model out those different battles and all of that kind of stuff.[00:33:52] Simon: Yeah, I know very little. I know basically nothing about film production, so I try not to commentate on it. But I am fascinated to [00:34:00] see what happens when, when these tools start being used by the real, the people at the top of their game.[00:34:05] swyx (2): I would say like there's a cultural war that is more that being fought here than a technology war.[00:34:11] swyx (2): Most of the Hollywood people are against any form of AI anyway, so they're busy Fighting that battle instead of thinking about how to adopt it and it's, it's very fringe. I participated here in San Francisco, one generative AI video creative hackathon where the AI positive artists actually met with technologists like myself and then we collaborated together to build short films and that was really nice and I think, you know, I'll be hosting some of those in my events going forward.[00:34:38] swyx (2): One thing that I think like I want to leave it. Give people a sense of it's like this is a recap of last year But then sometimes it's useful to walk away as well with like what can we expect in the future? I don't know if you got anything. I would also call out that the Chinese models here have made a lot of progress Hyde Law and Kling and God knows who like who else in the video arena [00:35:00] Also making a lot of progress like surprising him like I think maybe actually Chinese China is surprisingly ahead with regards to Open8 at least, but also just like specific forms of video generation.[00:35:12] Simon: Wouldn't it be interesting if a film industry sprung up in a country that we don't normally think of having a really strong film industry that was using these tools? Like, that would be a fascinating sort of angle on this. Mm hmm. Mm hmm.[00:35:25] swyx (2): Agreed. I, I, I Oh, sorry. Go ahead.[00:35:29] Exploring Video Avatar Companies[00:35:29] swyx (2): Just for people's Just to put it on people's radar as well, Hey Jen, there's like there's a category of video avatar companies that don't specifically, don't specialize in general video.[00:35:41] swyx (2): They only do talking heads, let's just say. And HeyGen sings very well.[00:35:45] Brian: Swyx, you know that that's what I've been using, right? Like, have, have I, yeah, right. So, if you see some of my recent YouTube videos and things like that, where, because the beauty part of the HeyGen thing is, I, I, I don't want to use the robot voice, so [00:36:00] I record the mp3 file for my computer, And then I put that into HeyGen with the avatar that I've trained it on, and all it does is the lip sync.[00:36:09] Brian: So it looks, it's not 100 percent uncanny valley beatable, but it's good enough that if you weren't looking for it, it's just me sitting there doing one of my clips from the show. And, yeah, so, by the way, HeyGen. Shout out to them.[00:36:24] AI Influencers and Their Future[00:36:24] swyx (2): So I would, you know, in terms of like the look ahead going, like, looking, reviewing 2024, looking at trends for 2025, I would, they basically call this out.[00:36:33] swyx (2): Meta tried to introduce AI influencers and failed horribly because they were just bad at it. But at some point that there will be more and more basically AI influencers Not in a way that Simon is but in a way that they are not human.[00:36:50] Simon: Like the few of those that have done well, I always feel like they're doing well because it's a gimmick, right?[00:36:54] Simon: It's a it's it's novel and fun to like Like that, the AI Seinfeld thing [00:37:00] from last year, the Twitch stream, you know, like those, if you're the only one or one of just a few doing that, you'll get, you'll attract an audience because it's an interesting new thing. But I just, I don't know if that's going to be sustainable longer term or not.[00:37:11] Simon: Like,[00:37:12] Simplifying Content Creation with AI[00:37:12] Brian: I'm going to tell you, Because I've had discussions, I can't name the companies or whatever, but, so think about the workflow for this, like, now we all know that on TikTok and Instagram, like, holding up a phone to your face, and doing like, in my car video, or walking, a walk and talk, you know, that's, that's very common, but also, if you want to do a professional sort of talking head video, you still have to sit in front of a camera, you still have to do the lighting, you still have to do the video editing, versus, if you can just record, what I'm saying right now, the last 30 seconds, If you clip that out as an mp3 and you have a good enough avatar, then you can put that avatar in front of Times Square, on a beach, or whatever.[00:37:50] Brian: So, like, again for creators, the reason I think Simon, we're on the verge of something, it, it just, it's not going to, I think it's not, oh, we're going to have [00:38:00] AI avatars take over, it'll be one of those things where it takes another piece of the workflow out and simplifies it. I'm all[00:38:07] Simon: for that. I, I always love this stuff.[00:38:08] Simon: I like tools. Tools that help human beings do more. Do more ambitious things. I'm always in favor of, like, that, that, that's what excites me about this entire field.[00:38:17] swyx (2): Yeah. We're, we're looking into basically creating one for my podcast. We have this guy Charlie, he's Australian. He's, he's not real, but he pre, he opens every show and we are gonna have him present all the shorts.[00:38:29] Simon: Yeah, go ahead.[00:38:30] The Importance of Credibility in AI[00:38:30] Simon: The thing that I keep coming back to is this idea of credibility like in a world that is full of like AI generated everything and so forth It becomes even more important that people find the sources of information that they trust and find people and find Sources that are credible and I feel like that's the one thing that LLMs and AI can never have is credibility, right?[00:38:49] Simon: ChatGPT can never stake its reputation on telling you something useful and interesting because That means nothing, right? It's a matrix multiplication. It depends on who prompted it and so forth. So [00:39:00] I'm always, and this is when I'm blogging as well, I'm always looking for, okay, who are the reliable people who will tell me useful, interesting information who aren't just going to tell me whatever somebody's paying them to tell, tell them, who aren't going to, like, type a one sentence prompt into an LLM and spit out an essay and stick it online.[00:39:16] Simon: And that, that to me, Like, earning that credibility is really important. That's why a lot of my ethics around the way that I publish are based on the idea that I want people to trust me. I want to do things that, that gain credibility in people's eyes so they will come to me for information as a trustworthy source.[00:39:32] Simon: And it's the same for the sources that I'm, I'm consulting as well. So that's something I've, I've been thinking a lot about that sort of credibility focus on this thing for a while now.[00:39:40] swyx (2): Yeah, you can layer or structure credibility or decompose it like so one thing I would put in front of you I'm not saying that you should Agree with this or accept this at all is that you can use AI to generate different Variations and then and you pick you as the final sort of last mile person that you pick The last output and [00:40:00] you put your stamp of credibility behind that like that everything's human reviewed instead of human origin[00:40:04] Simon: Yeah, if you publish something you need to be able to put it on the ground Publishing it.[00:40:08] Simon: You need to say, I will put my name to this. I will attach my credibility to this thing. And if you're willing to do that, then, then that's great.[00:40:16] swyx (2): For creators, this is huge because there's a fundamental asymmetry between starting with a blank slate versus choosing from five different variations.[00:40:23] Brian: Right.[00:40:24] Brian: And also the key thing that you just said is like, if everything that I do, if all of the words were generated by an LLM, if the voice is generated by an LLM. If the video is also generated by the LLM, then I haven't done anything, right? But if, if one or two of those, you take a shortcut, but it's still, I'm willing to sign off on it.[00:40:47] Brian: Like, I feel like that's where I feel like people are coming around to like, this is maybe acceptable, sort of.[00:40:53] Simon: This is where I've been pushing the definition. I love the term slop. Where I've been pushing the definition of slop as AI generated [00:41:00] content that is both unrequested and unreviewed and the unreviewed thing is really important like that's the thing that elevates something from slop to not slop is if A human being has reviewed it and said, you know what, this is actually worth other people's time.[00:41:12] Simon: And again, I'm willing to attach my credibility to it and say, hey, this is worthwhile.[00:41:16] Brian: It's, it's, it's the cura curational, curatorial and editorial part of it that no matter what the tools are to do shortcuts, to do, as, as Swyx is saying choose between different edits or different cuts, but in the end, if there's a curatorial mind, Or editorial mind behind it.[00:41:32] Brian: Let me I want to wedge this in before we start to close.[00:41:36] The Future of LLM User Interfaces[00:41:36] Brian: One of the things coming back to your year end piece that has been a something that I've been banging the drum about is when you're talking about LLMs. Getting harder to use. You said most users are thrown in at the deep end.[00:41:48] Brian: The default LLM chat UI is like taking brand new computer users, dropping them into a Linux terminal and expecting them to figure it all out. I mean, it's, it's literally going back to the command line. The command line was defeated [00:42:00] by the GUI interface. And this is what I've been banging the drum about is like, this cannot be.[00:42:05] Brian: The user interface, what we have now cannot be the end result. Do you see any hints or seeds of a GUI moment for LLM interfaces?[00:42:17] Simon: I mean, it has to happen. It absolutely has to happen. The the, the, the, the usability of these things is turning into a bit of a crisis. And we are at least seeing some really interesting innovation in little directions.[00:42:28] Simon: Just like OpenAI's chat GPT canvas thing that they just launched. That is at least. Going a little bit more interesting than just chat, chats and responses. You know, you can, they're exploring that space where you're collaborating with an LLM. You're both working in the, on the same document. That makes a lot of sense to me.[00:42:44] Simon: Like that, that feels really smart. The one of the best things is still who was it who did the, the UI where you could, they had a drawing UI where you draw an interface and click a button. TL draw would then make it real thing. That was spectacular, [00:43:00] absolutely spectacular, like, alternative vision of how you'd interact with these models.[00:43:05] Simon: Because yeah, the and that's, you know, so I feel like there is so much scope for innovation there and it is beginning to happen. Like, like, I, I feel like most people do understand that we need to do better in terms of interfaces that both help explain what's going on and give people better tools for working with models.[00:43:23] Simon: I was going to say, I want to[00:43:25] Brian: dig a little deeper into this because think of the conceptual idea behind the GUI, which is instead of typing into a command line open word. exe, it's, you, you click an icon, right? So that's abstracting away sort of the, again, the programming stuff that like, you know, it's, it's a, a, a child can tap on an iPad and, and make a program open, right?[00:43:47] Brian: The problem it seems to me right now with how we're interacting with LLMs is it's sort of like you know a dumb robot where it's like you poke it and it goes over here, but no, I want it, I want to go over here so you poke it this way and you can't get it exactly [00:44:00] right, like, what can we abstract away from the From the current, what's going on that, that makes it more fine tuned and easier to get more precise.[00:44:12] Brian: You see what I'm saying?[00:44:13] Simon: Yes. And the this is the other trend that I've been following from the last year, which I think is super interesting. It's the, the prompt driven UI development thing. Basically, this is the pattern where Claude Artifacts was the first thing to do this really well. You type in a prompt and it goes, Oh, I should answer that by writing a custom HTML and JavaScript application for you that does a certain thing.[00:44:35] Simon: And when you think about that take and since then it turns out This is easy, right? Every decent LLM can produce HTML and JavaScript that does something useful. So we've actually got this alternative way of interacting where they can respond to your prompt with an interactive custom interface that you can work with.[00:44:54] Simon: People haven't quite wired those back up again. Like, ideally, I'd want the LLM ask me a [00:45:00] question where it builds me a custom little UI, For that question, and then it gets to see how I interacted with that. I don't know why, but that's like just such a small step from where we are right now. But that feels like such an obvious next step.[00:45:12] Simon: Like an LLM, why should it, why should you just be communicating with, with text when it can build interfaces on the fly that let you select a point on a map or or move like sliders up and down. It's gonna create knobs and dials. I keep saying knobs and dials. right. We can do that. And the LLMs can build, and Claude artifacts will build you a knobs and dials interface.[00:45:34] Simon: But at the moment they haven't closed the loop. When you twiddle those knobs, Claude doesn't see what you were doing. They're going to close that loop. I'm, I'm shocked that they haven't done it yet. So yeah, I think there's so much scope for innovation and there's so much scope for doing interesting stuff with that model where the LLM, anything you can represent in SVG, which is almost everything, can now be part of that ongoing conversation.[00:45:59] swyx (2): Yeah, [00:46:00] I would say the best executed version of this I've seen so far is Bolt where you can literally type in, make a Spotify clone, make an Airbnb clone, and it actually just does that for you zero shot with a nice design.[00:46:14] Simon: There's a benchmark for that now. The LMRena people now have a benchmark that is zero shot app, app generation, because all of the models can do it.[00:46:22] Simon: Like it's, it's, I've started figuring out. I'm building my own version of this for my own project, because I think within six months. I think it'll just be an expected feature. Like if you have a web application, why don't you have a thing where, oh, look, the, you can add a custom, like, so for my dataset data exploration project, I want you to be able to do things like conjure up a dashboard, just via a prompt.[00:46:43] Simon: You say, oh, I need a pie chart and a bar chart and put them next to each other, and then have a form where submitting the form inserts a row into my database table. And this is all suddenly feasible. It's, it's, it's not even particularly difficult to do, which is great. Utterly bizarre that these things are now easy.[00:47:00][00:47:00] swyx (2): I think for a general audience, that is what I would highlight, that software creation is becoming easier and easier. Gemini is now available in Gmail and Google Sheets. I don't write my own Google Sheets formulas anymore, I just tell Gemini to do it. And so I think those are, I almost wanted to basically somewhat disagree with, with your assertion that LMS got harder to use.[00:47:22] swyx (2): Like, yes, we, we expose more capabilities, but they're, they're in minor forms, like using canvas, like web search in, in in chat GPT and like Gemini being in, in Excel sheets or in Google sheets, like, yeah, we're getting, no,[00:47:37] Simon: no, no, no. Those are the things that make it harder, because the problem is that for each of those features, they're amazing.[00:47:43] Simon: If you understand the edges of the feature, if you're like, okay, so in Google, Gemini, Excel formulas, I can get it to do a certain amount of things, but I can't get it to go and read a web. You probably can't get it to read a webpage, right? But you know, there are, there are things that it can do and things that it can't do, which are completely undocumented.[00:47:58] Simon: If you ask it what it [00:48:00] can and can't do, they're terrible at answering questions about that. So like my favorite example is Claude artifacts. You can't build a Claude artifact that can hit an API somewhere else. Because the cause headers on that iframe prevents accessing anything outside of CDNJS. So, good luck learning cause headers as an end user in order to understand why Like, I've seen people saying, oh, this is rubbish.[00:48:26] Simon: I tried building an artifact that would run a prompt and it couldn't because Claude didn't expose an API with cause headers that all of this stuff is so weird and complicated. And yeah, like that, that, the more that with the more tools we add, the more expertise you need to really, To understand the full scope of what you can do.[00:48:44] Simon: And so it's, it's, I wouldn't say it's, it's, it's, it's like, the question really comes down to what does it take to understand the full extent of what's possible? And honestly, that, that's just getting more and more involved over time.[00:48:58] Local LLMs: A Growing Interest[00:48:58] swyx (2): I have one more topic that I, I [00:49:00] think you, you're kind of a champion of and we've touched on it a little bit, which is local LLMs.[00:49:05] swyx (2): And running AI applications on your desktop, I feel like you are an early adopter of many, many things.[00:49:12] Simon: I had an interesting experience with that over the past year. Six months ago, I almost completely lost interest. And the reason is that six months ago, the best local models you could run, There was no point in using them at all, because the best hosted models were so much better.[00:49:26] Simon: Like, there was no point at which I'd choose to run a model on my laptop if I had API access to Cloud 3. 5 SONNET. They just, they weren't even comparable. And that changed, basically, in the past three months, as the local models had this step changing capability, where now I can run some of these local models, and they're not as good as Cloud 3.[00:49:45] Simon: 5 SONNET, but they're not so far away that It's not worth me even using them. The other, the, the, the, the continuing problem is I've only got 64 gigabytes of RAM, and if you run, like, LLAMA370B, it's not going to work. Most of my RAM is gone. So now I have to shut down my Firefox tabs [00:50:00] and, and my Chrome and my VS Code windows in order to run it.[00:50:03] Simon: But it's got me interested again. Like, like the, the efficiency improvements are such that now, if you were to like stick me on a desert island with my laptop, I'd be very productive using those local models. And that's, that's pretty exciting. And if those trends continue, and also, like, I think my next laptop, if when I buy one is going to have twice the amount of RAM, At which point, maybe I can run the, almost the top tier, like open weights models and still be able to use it as a computer as well.[00:50:32] Simon: NVIDIA just announced their 3, 000 128 gigabyte monstrosity. That's pretty good price. You know, that's that's, if you're going to buy it,[00:50:42] swyx (2): custom OS and all.[00:50:46] Simon: If I get a job, if I, if, if, if I have enough of an income that I can justify blowing $3,000 on it, then yes.[00:50:52] swyx (2): Okay, let's do a GoFundMe to get Simon one it.[00:50:54] swyx (2): Come on. You know, you can get a job anytime you want. Is this, this is just purely discretionary .[00:50:59] Simon: I want, [00:51:00] I want a job that pays me to do exactly what I'm doing already and doesn't tell me what else to do. That's, thats the challenge.[00:51:06] swyx (2): I think Ethan Molik does pretty well. Whatever, whatever it is he's doing.[00:51:11] swyx (2): But yeah, basically I was trying to bring in also, you know, not just local models, but Apple intelligence is on every Mac machine. You're, you're, you seem skeptical. It's rubbish.[00:51:21] Simon: Apple intelligence is so bad. It's like, it does one thing well.[00:51:25] swyx (2): Oh yeah, what's that? It summarizes notifications. And sometimes it's humorous.[00:51:29] Brian: Are you sure it does that well? And also, by the way, the other, again, from a sort of a normie point of view. There's no indication from Apple of when to use it. Like, everybody upgrades their thing and it's like, okay, now you have Apple Intelligence, and you never know when to use it ever again.[00:51:47] swyx (2): Oh, yeah, you consult the Apple docs, which is MKBHD.[00:51:49] swyx (2): The[00:51:51] Simon: one thing, the one thing I'll say about Apple Intelligence is, One of the reasons it's so disappointing is that the models are just weak, but now, like, Llama 3b [00:52:00] is Such a good model in a 2 gigabyte file I think give Apple six months and hopefully they'll catch up to the state of the art on the small models And then maybe it'll start being a lot more interesting.[00:52:10] swyx (2): Yeah. Anyway, I like This was year one And and you know just like our first year of iPhone maybe maybe not that much of a hit and then year three They had the App Store so Hey I would say give it some time, and you know, I think Chrome also shipping Gemini Nano I think this year in Chrome, which means that every app, every web app will have for free access to a local model that just ships in the browser, which is kind of interesting.[00:52:38] swyx (2): And then I, I think I also wanted to just open the floor for any, like, you know, any of us what are the apps that, you know, AI applications that we've adopted that have, that we really recommend because these are all, you know, apps that are running on our browser that like, or apps that are running locally that we should be, that, that other people should be trying.[00:52:55] swyx (2): Right? Like, I, I feel like that's, that's one always one thing that is helpful at the start of the [00:53:00] year.[00:53:00] Simon: Okay. So for running local models. My top picks, firstly, on the iPhone, there's this thing called MLC Chat, which works, and it's easy to install, and it runs Llama 3B, and it's so much fun. Like, it's not necessarily a capable enough novel that I use it for real things, but my party trick right now is I get my phone to write a Netflix Christmas movie plot outline where, like, a bunch of Jeweller falls in love with the King of Sweden or whatever.[00:53:25] Simon: And it does a good job and it comes up with pun names for the movies. And that's, that's deeply entertaining. On my laptop, most recently, I've been getting heavy into, into Olama because the Olama team are very, very good at finding the good models and patching them up and making them work well. It gives you an API.[00:53:42] Simon: My little LLM command line tool that has a plugin that talks to Olama, which works really well. So that's my, my Olama is. I think the easiest on ramp to to running models locally, if you want a nice user interface, LMStudio is, I think, the best user interface [00:54:00] thing at that. It's not open source. It's good.[00:54:02] Simon: It's worth playing with. The other one that I've been trying with recently, there's a thing called, what's it called? Open web UI or something. Yeah. The UI is fantastic. It, if you've got Olama running and you fire this thing up, it spots Olama and it gives you an interface onto your Olama models. And t

The Ohio State Buckeyes used two enormous plays, and one incredible 13-play drive to knock off the Texas Longhorns, 28-14 in the 2025 Cotton Bowl to advance to the National Championship Game against Notre Dame. After the game, Ohio State head coach Ryan Day, QB Will Howard, and DE Jack Sawyer discussed the plays that will go down in Buckeye history, as well as how Texas shut down Jeremiah Smith and how the Buckeyes worked around that, what it meant to Howard to finally beat Texas, what it meant to Sawyer to make an iconic play like his strip and score touchdown, and much more. 

Follow The Girl Gainz Podcast on Instagram https://www.instagram.com/thegirlgainzpodcast/Email: girlgainzpodcast@gmail.comWatch episodes on Youtube. Don't forget to subscribe! https://www.youtube.com/@TheGirlGainzPodcastFollow Alexis on Instagram: @the_alexis_nicole Follow Amber on Instagram: @amberpacholokFor posing @posing_with_alexis https://posing-with-alexis.square.site/For Stage Glam: https://amberpbeauty.square.site/book-stage-glamCompetition prep (fit model/bikini/wellness) & lifestyle with Alexis email: onyxcoaching.alexis@gmail.comCompetition prep (fit model/bikini) with Amber www.amberpcoaching.com/bikiniprepCODES:The Shoe Fairy Code: AlexisFree Spirit Outlet: FitKH Customs AlexisA Pro Tan: Alexis Uprising https://iamuprising.com/athletes/alexis-adams/KH Customs Amberp

In this episode of the Buckeye Weekly Podcast, hosts Tony Gerdeman and Tom Orr dive into Texas' thrilling double-overtime win against Arizona State in the Peach Bowl. They break down the key moments that shaped the game, standout performances, and the notable plays from both teams. The hosts also discuss the implications of this game for Ohio State as they prepare to face Texas in the upcoming Cotton Bowl. From Texas' defensive pressure to the inconsistencies in their offensive line and special teams, Tony and Tom cover it all. Tune in for a detailed analysis and what Ohio State fans can expect in the Cotton Bowl showdown. 00:00 Introduction and Welcome 00:10 Texas vs Arizona State Peach Bowl Recap 00:50 Arizona State's Offensive Highlights 02:10 Texas Defensive Standouts 05:58 Special Teams Drama 09:37 Quinn Ewers' Performance Analysis 11:54 Key Players to Watch 16:12 Texas Offensive Line Challenges 20:35 Offensive Line Dynamics 21:23 Texas vs Arizona State: Key Moments24:25 The Targeting Controversy 28:33 Texas Defense and Key Players 29:45 Arizona State's Offensive Struggles 33:03 Texas Offensive Challenges 37:27 Game Analysis and Final Thoughts 40:49 Closing Remarks and Call to Action

In this episode of the Buckeye Weekly Podcast, hosts Tony Gerdeman and Tom Orr dive into Texas' 38-24 win over Clemson in the College Football Playoff. They analyze key performances, including Texas' dynamic run game and defensive standouts, and discuss what this means for Ohio State's upcoming Cotton Bowl matchup against Texas. Additionally, they consider the impact of injuries and key players on both sides, offering insights into the strategies both teams might employ. Don't miss this detailed breakdown to get ready for the Cotton Bowl showdown! 00:00 Welcome to the Buckeye Weekly Podcast 00:10 Recap of the Texas vs. Clemson Game 01:28 Analyzing Texas's Rushing Performance 02:45 Texas's Offensive Inconsistencies 08:07 Texas's Red Zone Struggles 18:15 Texas's Defensive Standouts 34:23 Special Teams and Final Thoughts 35:49 Closing Remarks and What's Next

Join Tony Gerdeman and Tom Orr on this episode of the Buckeye Weekly Podcast as they analyze the SEC Championship game between Georgia and Texas. They discuss key takeaways, including Texas' offensive line struggles, Quinn Ewers' performance under pressure, and Texas' defensive strengths. The episodes explores how this performance by the Longhorns could relate to their matchup next week with the Buckeyes. What are the biggest areas of concern for Ohio State against Texas? Where are the weaknesses? All of this gets discussed and much more. 00:00 Welcome to the Buckeye Weekly Podcast 00:53 Analyzing Texas vs. Georgia SEC Championship Game 01:01 Quinn Ewers' Performance Under Pressure 03:22 Concerns About Texas' Offensive Line 09:25 Texas' Struggles in the Red Zone 10:01 Texas' Inconsistent Running Game 15:02 Potential Impact of Arch Manning22:08 Texas' Defensive Strengths and Weaknesses 34:26 Special Teams and Final Thoughts 36:36 Closing Remarks and Upcoming Content

Join hosts Tony Gerdeman and Tom Orr as they rewatch and analyze the Ohio State Buckeyes' commanding 41-21 victory over the Oregon Ducks in the Rose Bowl. In this episode, they discuss key moments, standout performances, and strategic insights that emerged upon reviewing the broadcast.From Ohio State's offensive exploits to their defensive dominance, and the standout efforts of players like Jeremiah Smith, Cody Simon, and Will Howard, Tony and Tom provide a detailed breakdown of what made this victory special and how it sets up the Buckeyes for their upcoming clash against Texas. Don't miss their expert analysis and takeaways from this memorable game. 00:00 Introduction and Welcome 00:12 Game Overview and Initial Impressions 00:45 Ohio State's Dominant Offense 02:40 Oregon's Defensive Struggles 03:28 Key Plays and Player Performances 04:16 Ohio State's Physicality and Line of Scrimmage Dominance 09:35 Cody Simon's Impactful Performance 20:45 Oregon's Missed Opportunities and Ohio State's Defensive Discipline27:00 The Impact of Transfers on Team Culture 27:58 Reflecting on Oregon's Season and Future 31:10 Special Teams Performance Highlights 33:42 Defensive Strategies and Adjustments 36:15 Quarterback Performance and Offensive Tactics 41:32 Pre-Game Strategies and Coin Toss Decisions 50:10 Looking Ahead to the Texas Matchup

HI GIRLS! Welcome back to another episode! We hope you enjoy! Don't forget to follow us on Instagram and TikTok @literallysamepodcast!! :)

In this episode of the Buckeye Weekly podcast, hosts Tony Gerdeman and Tom Orr analyze Oregon's 45-37 victory over Penn State in the Big Ten Championship Game. They discuss the key players, standout moments, and what Ohio State fans can expect in the Rose Bowl matchup against the Ducks. From offensive breakdowns to defensive struggles, find out how both teams are preparing for one of college football's biggest stages. 00:00 Welcome to the Buckeye Weekly Podcast01:33 Oregon's Offense: Tez Johnson's Impact05:07 Oregon's Defense Struggles Against Penn State 06:13 Penn State's Offensive Dominance13:28 Game Management and Coaching Decisions 18:42 Defensive Line Performance and Concerns 21:04 Ohio State's Potential Against Oregon 40:07 Final Thoughts and Looking Ahead to the Rose Bowl

In this episode of the Buckeye Weekly Podcast, hosts Tony Gerdeman and Tom Orr dive into their observations from Oregon's impressive 49-21 victory over Washington in the Ducks' regular-season finale. They discuss standout performances, key players, and strategic insights that could impact the upcoming Rose Bowl matchup between Ohio State and Oregon. Topics include the dominant play of Oregon's defensive line, the impressive play of Washington's freshman quarterback Demond Williams, and how Oregon's multifaceted offense continues to pose challenges for opponents. Don't miss this comprehensive breakdown and analysis!00:00 Welcome to the Buckeye Weekly Podcast 00:32 Transfer Portal Madness 01:44 Washington vs. Oregon Game Recap 04:06 Oregon's Dominant Defensive Line 11:48 Linebackers and Special Teams Analysis 16:57 Offensive Standouts and Strategy 19:06 Oregon's Offensive Dominance 20:23 Diverse Offensive Threats 23:07 Defensive Highlights and Strategies 26:05 Special Teams and Kicking Game28:06 Washington's Passing Game and Defensive Tactics 33:02 Final Thoughts and Analysis 36:36 Closing Remarks and Subscription Call

In this episode of the Buckeye Weekly Podcast, hosts Tony Gerdeman and Tom Orr dive into Oregon's 16-13 victory over Wisconsin last month as they begin their reviewing and previewing of the Ducks. What did they see and how different was it from the matchup earlier this season with the Buckeyes? They break down key performances, pivotal plays, and coaching decisions while comparing the Oregon Ducks' tactics and strategies to what Buckeye fans saw earlier this season. Tune in for the first of a few game breakdowns of what you might be able to expect from Oregon in next week's Rose Bowl against the Ohio State Buckeyes. 00:00 Introduction and Podcast Kickoff 00:35 Analyzing Oregon's Performance Against Wisconsin 01:46 Wisconsin's Offensive Struggles and Surprises 02:51 Oregon's Defensive Concerns 03:08 Key Players Missing for Oregon03:28 Wisconsin's Rushing Success04:49 Comparing Ohio State and Oregon Defenses 05:58 Wisconsin's Missed Opportunities09:01 Oregon's Passing Attack and Key Receivers 14:29 Dillon Gabriel's Performance and Comparisons 18:54 Dan Lanning's Fourth Down Decisions 19:30 Aggressive Play Calls and Risks 22:44 Wisconsin's Missed Opportunities 24:23 Oregon's Offensive Performance 28:20 Questionable Officiating 35:37 Oregon's Defensive Standouts 38:33 Looking Ahead to Future Games 40:02 Wrapping Up and What's Next

Here's a replay of the original edition of what we learned after rewatching Ohio State's 32-31 loss at Oregon back in October. Obviously things have changed for the Buckeyes since that game, but this will be a good baseline and reminder of where things started as we continue to cover and preview the Rose Bowl. We will have much more on the Ducks and Bucks over the next week.

In this episode of the Buckeye Weekly Podcast, join hosts Tony Gerdeman and Tom Orr as they dive into a detailed rewatch of Ohio State's impressive 42-17 College Football Playoff victory over Tennessee. They discuss standout performances, tactical nuances, and the emotional roller coaster experienced by fans and players.The duo covers a range of topics including the efficiency of Ohio State's offensive line, the impactful play of the defensive unit, the controversial officiating, and how Ohio State controlled the game from start to finish. Don't miss their insights on Ryan Day's coaching decisions, player highlights like TreVeyon Henderson's standout plays, and the team's preparation for their upcoming Rose Bowl clash with Oregon.00:00 Welcome to the Buckeye Weekly Podcast 00:15 Rewatching Ohio State vs. Tennessee 01:02 Broadcast Impressions and Drone Shots 01:45 Tennessee Fans' Reactions 03:55 Camera Angles and Technical Nitpicks 05:22 Nico Iamaleava's Tough Game 10:10 Ohio State's Offensive Line Performance 16:25 Ohio State's Defensive Dominance 19:38 Tight Ends and Offensive Versatility 24:25 Weather and Football Commentary 24:38 Impressive Plays and Mismatches 25:41 TreVeyon Henderson's Performance 28:46 Officiating Controversies 31:42 Carnell Tate's Contributions 33:52 Ohio State's Defensive Highlights 40:25 Defensive Line Rotation 42:19 Near Butt Fumble and Athleticism 44:06 Final Thoughts and Game Analysis

With the first-round matchup between No. 8 seed Ohio State and No. 9 seed Tennessee just days away, there is a lot to talk about before the game. Tennessee head coach Josh Huepel met with the media on Monday to preview the showdown between the Volunteers and Buckeyes, and discuss the threat posed by the Ohio State wide receiving corps, what it means for Tennessee's senior class to have this kind of opportunity, how QB Nico Iamaleava not only grown, but also shown leadership, and much more. 

The Ohio State Buckeyes will have one of the most physical practices of game week later today as they prepare for their first-round College Football Playoff game against the Tennessee Volunteers. OSU defensive coordinator Jim Knowles just met with the media to preview the game, along with a handful of Buckeye defensive stars.Tony Gerdeman and Tom Orr of BuckeyeHuddle.com break down what they learned talking to the Buckeyes today, and how they think the Ohio State defense will try to stop the Volunteers on Saturday.

In this episode of the Buckeye Weekly Podcast, hosts Tony Gerdeman and Tom Orr delve into Tennessee's 36-23 victory over Vanderbilt in the Vols' last regular season game, analyzing key aspects of the Volunteers' performance. They discuss the significance of special teams, penalties, and the depth of Tennessee's defensive line, as well as potential vulnerabilities Ohio State could exploit in the upcoming College Football Playoff game. Tune in to hear their insights on how these elements might impact the Buckeyes' strategy and performance. 00:00 Introduction and Opening Remarks 00:37 Tennessee vs. Vanderbilt Game Recap 01:09 Vanderbilt's Special Teams Performance 04:00 Tennessee's Defensive Line Depth 07:47 Ohio State's Offensive Strategy 10:41 Tennessee's Run Game Analysis 13:42 Tennessee's Fourth Down Decisions 16:55 Nico Iamaleava's Performance 21:12 Tennessee's Defensive Dominance 22:32 Effective Use of Tempo Against Tennessee 23:51 Tennessee's Penalty Problem 24:28 Ohio State's Penalty Advantage 25:34 Tennessee's Inconsistent Discipline 32:49 Tennessee's Vulnerable Defense 34:31 Key Matchups to Watch 35:47 Tennessee's Momentum Swings 38:55 Special Teams Impact 41:05 Final Thoughts and Wrap-Up

In this episode of the Buckeye Weekly Podcast, hosts Tom Orr and Tony Gerdeman dive into Tennessee's 19-14 loss to Arkansas. They discuss how the game unfolded, the performance of key players, and what Ohio State might learn from this matchup. They analyze the Tennessee offensive line's struggles, their approach to tempo, and how Arkansas's defensive strategy could inform Ohio State's game plan. The episode also touches upon the effectiveness of Tennessee's run game and the potential impact of crowd noise. With detailed breakdowns and keen insights, this episode offers a comprehensive look at what to expect when Tennessee faces Ohio State. 00:00 Welcome to the Buckeye Weekly Podcast 01:03 Diving into Tennessee vs. Arkansas Game Analysis 03:51 Tennessee's Offensive Struggles and Key Plays 11:38 Impact of Crowd Noise and Offensive Line Issues 20:01 Tennessee's Wide Receivers and Final Thoughts 22:57 Deep Dive into Nico Iamaleava's Downfield Throws 23:37 Arkansas' Run Game Control and Tennessee's Response 24:54 Dylan Sampson's Impactful Performance 25:23 Ohio State's Defensive Challenges 29:17 Fourth Down Decision Making Analysis 33:23 Tennessee's Game Management and Key Plays 39:55 Final Thoughts and Defensive Standouts 43:41 Closing Remarks and Podcast Information

Welcome to the Buckeye Weekly Podcast with hosts Tom Orr and Tony Gerdeman. In this episode, they dive into their analysis of Tennessee's 31-17 loss this season to Georgia in order to gain insights ahead of the upcoming matchup between the Vols and Ohio State.They discuss key takeaways, performance of players such as Dylan Sampson and Carson Beck, Tennessee's defense, and how the weather and other factors might play a role in the big game. Tune in to get an in-depth look at what to expect and how these teams might strategize for their showdown. 00:00 Introduction and Series Overview 00:10 Tennessee vs. Georgia Game Analysis 01:01 Tennessee's Third Down Conversions and Georgia's Passing Game 02:51 Georgia's Tight Ends and Tennessee's Secondary Struggles 04:25 Comparing Georgia and Ohio State Receivers 06:57 Georgia's Offensive Strategy and Tennessee's Defensive Issues 13:09 Weather Conditions and Their Impact 17:21 Tennessee's Defensive Line and Game Plan 21:56 Incredible Defensive Effort 22:40 Dylan Sampson's Impact 24:21 Tennessee's Offensive Strategy 26:27 Quarterback Performance Analysis 34:58 Special Teams and Coaching Decisions 36:12 Linebackers and Defensive Observations 39:29 Final Thoughts and Upcoming Games

In this episode of the Buckeye Weekly Podcast, hosts Tom Orr and Tony Gerdeman delve into Tennessee's 24-17 win over Alabama from earlier this season. They discuss the key takeaways and performances from the game, including the impact of Tennessee's defensive strategies, the struggles of Alabama's offensive line, and standout players like Dylan Sampson and Nico Iamaleava.The hosts also preview how these insights could inform Ohio State's approach as they prepare to face Tennessee in the upcoming College Football Playoff game at Ohio Stadium.00:00 Introduction and Podcast Setup 00:10 Cramming for the Final Exam: Ohio State vs. Tennessee 01:13 Analyzing Alabama vs. Tennessee Game 02:06 Tennessee's Defensive Performance 04:40 Key Players: Tennessee's Safeties and Corners 09:48 Tennessee's Defensive Line and Ohio State's Strategy 14:04 Tennessee's Defensive Adjustments and Alabama's Struggles 19:15 Ohio State's Game Plan Against Tennessee 20:40 Tennessee's Game Plan and Alabama's Decisions 24:30 Tennessee's Special Teams and Kicking Woes 25:17 Nico Iamaleava's Performance and Tennessee's Receivers 31:04 Tennessee's Running Game and Key Players 38:44 Ohio State's Defensive Strategy 43:04 Concluding Thoughts and Next Steps

In this live episode of the Buckeye Weekly Podcast, Tom Orr and Tony Gerdeman recap what Ryan Day had to say on Wednesday at his signing day press conference. This was the first time the media has spoken with Day since the loss to Michigan, so there was plenty of talk about the Wolverines and what went wrong and why. The second half of the show discusses the recruiting class and the things Day had to say about the newest Buckeyes. All of that and more.

In this episode of the Internet People podcast, cohosts Anna and MJ discuss:

In this episode of the Buckeye Weekly Podcast, hosts Tom Orr and Tony Gerdeman delve into the details of Ohio State's 13-10 defeat against Michigan. They revisit the game, analyzing key missed opportunities, questioning conservative play-calling, and highlighting pivotal moments such as interceptions and ineffective runs. They also discuss the effectiveness of Ohio State's defense and the impact of special teams blunders. Tune in for a comprehensive breakdown and post-game analysis of one of the most significant matchups of the season. 00:00 Introduction and Welcome 00:26 Reflecting on the Ohio State vs. Michigan Game 02:29 Analyzing Ohio State's Offensive Strategy 02:49 First Drive Breakdown 03:26 Coaching Decisions and Play Calls 06:51 First Down Plays Analysis 09:17 Impact of Injuries on Performance 11:23 Missed Opportunities and Key Plays 21:40 Special Teams and Game-Changing Moments 22:39 Kickoff Mistakes and Special Teams Blunders 23:52 Red Zone Struggles and Missed Opportunities 25:43 Questionable Play Calling and Missed Targets 28:59 Conservative Decisions and Missed Chances 36:43 Defensive Highlights and Missed Calls 38:32 Post-Game Reactions and Final Thoughts

The Mark Titus Show Episode 152 | FULL EPISODE - Feast Week Recap Thank you to our sponsors: DraftKings: Download the DraftKings Sportsbook app and use code TITUS.  GAMBLING PROBLEM? CALL 1-800-GAMBLER, (800) 327-5050 or visit gamblinghelplinema.org (MA). Call 877-8-HOPENY/text HOPENY (467369) (NY). Please Gamble Responsibly. 888-789-7777/visit ccpg.org (CT), or visit www.mdgamblinghelp.org (MD). 21+ and present in most states. (18+ DC/KY/NH/WY). Void in ONT/OR/NH. Eligibility restrictions apply. On behalf of Boot Hill Casino & Resort (KS). 1 per new customer. Min. $5 deposit. Min. $5 bet. Max. $150 issued as non-withdrawable Bonus Bets if your bet wins. Bonus Bets expire in 7 days (168 hours). Stake removed from payout. Terms: dkng.co/dk-offer-terms. Ends 1/5/25 at 11:59 PM ET. Sponsored by DK. Lucy: Get LUCY shipped straight to your door. Visit LUCY.CO/MARK and use promo code MARK to get 20% off your first order. Subscribe for another 15% off & shipping's always free! Jack Black Skin Care: For 10% off your order & FREE Shipping, head to https://JackBlack.com/TITUS and use code TITUS. Subscribe to Mostly Sports with Mark Titus and Brandon Walker on Youtube: https://www.youtube.com/@MostlySportsTitusandWalker?sub_confirmation=1 On today's episode Mark and Ohio's Tate dive into the games from the weekend and more. We'll be back with a new episode later this week! Follow the show on all socials: Twitter: https://twitter.com/MarkTitusShow Instagram: https://www.instagram.com/marktitusshow/ Facebook: https://www.facebook.com/MarkTitusShow Tiktok: https://www.tiktok.com/@marktituspod?lang=en Listen on Podcast platforms: Spotify: https://open.spotify.com/show/2TPzE6Oo17yQ0IbLmo5AvY Apple Podcasts: https://podcasts.apple.com/us/podcast/the-mark-titus-show/id1673616227 Follow Mark Titus: Twitter: https://twitter.com/clubtrillion Instagram: https://www.instagram.com/marktheshark34/ If for some reason you see this, subscribe to mostly sports for a hi five. IYKYK.

The No. 2 Ohio State Buckeyes controlled the game in all three phases on Saturday, rolling to a 38-15 win over the No. 5 Indiana Hoosiers. Kevin Noon of BuckeyeHuddle.com joins host Tom Orr to discuss some of their biggest takeaways from the win. 

In this episode of the Buckeye Weekly Podcast, hosts Tony Gerdeman and Tom Orr dive deep into Ohio State's 31-7 win over Northwestern at Wrigley Field. They discuss various aspects such as camera angles, standout performances from players like Carnell Tate, Sonny Styles, and Jack Sawyer, and the overall game strategy.Tony and Tom also break down critical moments in the game, including controversial officiating calls, the effectiveness of Ohio State's blitzing, and the running backs' performances. Tune in for a comprehensive analysis of the game and what it means for Ohio State moving forward. 00:00 Introduction and Welcome 00:11 Rewatching the Ohio State Northwestern Game 01:05 Camera Angles and Broadcast Challenges 04:13 Key Plays and Player Performances 08:54 Officiating Controversies 12:10 Jeremiah Smith's Impressive Run 14:41 Ohio State Safeties Shine 16:29 Carnell Tate's Catching Skills 19:51 Sonny Styles' Versatility 25:58 Running Backs' Strong Performance 28:41 Game Management and Strategy 31:17 Conclusion and Sign Off

Ohio State's much-anticipated appearance in Wrigley Field is now in the books, and the Buckeyes handled business in a 31-7 win. The Wildcats actually led the game midway through the second quarter, before a 28-point avalanche in about 10 minutes of game time turned to the game from a mild concern into a comfortable Buckeye win. Kevin Noon of BuckeyeHuddle.com joins host Tom Orr to discuss some of their big takeaways from the game, including the atmosphere inside Wrigley Field, the Buckeyes' slow start, Ohio State's dynamic duo of running backs, an incredible homecoming for Carnell Tate, and much more. 

Join Tony Gerdeman and Tom Orr as they rewatch and break down Ohio State's 45-0 victory over Purdue. They delve into the standout performances, particularly focusing on offensive line play, the emergence of key players like Donovan Jackson, and the effective use of special teams. They also discuss the defensive showing, including the impact of young players like Eddrick Houston and innovative strategies such as the 'Jack' position. The episode wraps up with an analysis of linebacker performances and a conversation about the ongoing issues with Ohio State's turf. 00:00 Introduction and Welcome 00:32 Rewatching the Ohio State vs. Purdue Game 01:09 Offensive Line Performance 04:10 Quarterback and Receiver Analysis 09:09 Running Back Highlights 13:42 Tight End Development 19:06 Defensive Line and Strategy 26:31 Special Teams Focus 35:24 Linebacker Performance and Conclusion

The Ohio State Buckeyes are now just three wins away from a spot in the Big Ten Championship Game and a potential rematch with No. 1 Oregon. Ryan Day's team handled its business on Saturday, knocking off lowly Purdue, 45-0 in front of more than 103,000 fans inside Ohio Stadium.Kevin Noon of BuckeyeHuddle.com joins host Tom Orr to discuss their biggest takeaways from the game on this episode of the Buckeyes TomOrrow Morning podcast. 

In this episode of the Buckeye Weekly Podcast, hosts Tom Orr and Tony Gerdeman delve into the nail-biting 20-13 victory of Ohio State over Penn State from this past Saturday. They discuss key highlights, including dramatic swings in gameplay, standout performances from Ohio State's offensive line, and the unstoppable defense led by Caleb Downs and Davison Igbinosun.The hosts also explore the crafty play strategies, the intense crowd interactions, and the thrilling on-field moments that kept fans on the edge of their seats. Tune in for an in-depth analysis of Ohio State's critical win. 00:00 Introduction and Welcome 00:12 Game Recap: Dramatic Swings and Key Plays 02:16 Ohio State Crowd and Atmosphere 04:25 Offensive Line Performance Analysis 13:04 Defensive Highlights: Igbinosun and Downs 19:25 Chirping and Trash Talk on the Field 20:52 Celebrations and Traditions 23:05 Creative Play Designs 25:52 Defensive Strategies and Performances 28:11 Penn State's Unconventional Plays 33:46 Fox's Hockey Puck Technology 37:27 Final Thoughts and Reflections

The No. 4 Ohio State Buckeyes picked up their biggest win of the 2024 college football season, knocking off No. 3 Penn State on the road in State College, 20-13. Beyond just the win, there was a lot for Buckeye fans to feel good about during the game, including the play of the offensive line, the rushing attack, the linebackers, the defense allowing zero touchdowns and just six points, and the special teams even adding a 2-for-2 day on field goals. Kevin Noon of BuckeyeHuddle.com joins host Tom Orr to break down the biggest takeaways from the win, and what they could mean for Ohio State moving forward. 

Join Tom Orr and Tony Gerdeman on the latest episode of the Buckeye Weekly Podcast as they recap Ohio State's 21-17 victory over Nebraska. They analyze the offensive line struggles, particularly focusing on Zen Michalski's challenges at left tackle and Donovan Jackson's emerging role.The hosts also explore critical decisions in Ohio State's running game, discuss the impact of injuries on key positions, and highlight standout performances from players like Cody Simon and Caleb Downs. Tune in for a comprehensive breakdown of play-calling issues, special teams' strategy, and insights on what this means for the Buckeyes' upcoming clash against Penn State. 00:00 Introduction and Episode Overview 00:52 Ohio State vs. Nebraska: Offensive Line Struggles 03:43 Analyzing Key Plays and Player Performances 12:21 Cody Simon's Standout Game 15:33 Will Howard's Running Game Decisions 20:16 Analyzing Offensive Struggles 23:00 First Down Performance Breakdown 24:22 Evaluating the Ground Game 25:35 Passing Game Insights 32:25 Special Teams Woes 34:27 Defensive Highlights 39:35 Concluding Thoughts and Call to Action

We've learned a lot over the last 6 years of producing Didn't I Just Feed You — about ourselves, each other, food and the emotional weight that it carries for so many, entrepreneurship, and (so much) more. But these are the 10 most important things that we learned that we hope you'll take with you too. We hope you'll listen and walk away feeling like you can (mostly) stay sane and well fed. XoLINKSMeghan on Substack Meghan on Instagram @meghan_splawnStacie on Substack Stacie on Instagram @staciebillisStacie's siteStacie's cookbook, Make It Easy: 120 Mix-and-Match Recipes to Cook from Scratch -- with Smart Store-Bought Shortcuts When You Need ThemStacie's second cookbook, Winner! Winner! Chicken Dinner: 50 Winning Ways to Cook It Up!Lots of helpful DIJFY playlists on Spotify, including our Must-Listen Episodes, episodes about Feeding Picky Eaters, ones that focus on essential Kitchen Skills, and ones that aim to support your parenting around foodWhat's the Difference Between All the Salts?What Kitchen Tools Will Save Me Time?How Do I Season Food So That It (Actually) Tastes Good?What Are Back Pocket Dinners (and Why Do We Love Them)?The Special Episode on Special BreadMeal Planning For Everyone (get your copy to keep forever before the end of the year!)Our Sponsors:* Check out Quince: https://quince.com/dijfy* See how you can kick your allergies to the curb at getcurex.com. Treatment starts as low as $59 a month, and if you sign up now, you can save 80% off the $49 sign-up fee.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

The Ohio State Buckeyes escaped a scare from the Nebraska Cornhuskers, coming from behind in the fourth quarter to win, 21-17.After the game, Kevin Noon of BuckeyeHuddle.com joined host Tom Orr to discuss four big takeaways, including: How another injury could put the Buckeyes in a bad spot on the offensive lineWhat was different with the defense, and did things improve?Why did Ohio State struggle so badly running the ball?How Jordan Hancock saved the day in the closing moments.  

Ohio State's 21-17 win over Nebraska brought a little more drama than Buckeye fans were probably expecting, but with a trip to Penn State on deck, what was there to learn from the come-from-behind victory?In this episode of the Buckeyes TomOrrow Morning podcast, you'll hear directly from Ohio State head coach Ryan Day and quarterback Will Howard about some of their big takeaways. Plus, a remarkable postgame message from Nebraska head coach Matt Rhule. 

Lily is joined by her little sister, Lexie for a very special conversation reminiscing on their time working at an iconic downtown Annapolis spot. The girls chat about the good, the bad, and the ugly of working in the hospitality industry, share tips for new servers, and laugh about all the memories they made together through the years. Connect with 80/20 on Instagram https://instagram.com/8020pod Connect with Lily https://instagram.com/lilyrakow Submit To 80/20  Advice Column https://forms.gle/L486EGDeX34CEVqX8 For Press & Advertisement Inquires Contact: hello@lilyrakow.com Learn more about your ad choices. Visit megaphone.fm/adchoices

Sam and Andy discuss things they learned during the preseason including (8:26) Steve Kerr reinventing himself and Moses Moody taking a leap.  (20:36) Later they discuss three unanswered questions including wing rotations, (24:13) who's the second option outside of Steph and (26:40) will Kuminga be able to play at the 3? Host: Sam Esfandiari and Andy Liu Producer: Tim Angan Learn more about your ad choices. Visit podcastchoices.com/adchoices