Podcasts about Elo

January 30, 2026 Today we look at the top songs debuting on the Billboard chart this week back in 1986, 1996, 2006, & 2016. Dustin, Jason, & Tyler welcome back our friend Casey. We discuss 12 songs from this week in music history, including 3 Top 10s! We have new songs from David Bowie, Yellowcard, ELO, Pearl Jam, and more. Want to be cool like us and watch the music videos for all the songs? Then here's a convenient playlist that has them all in order of discussion.

Billabong Presents... ATS with Smivvy & Deadly! It’s been the busiest surf week since Elo got his farewell kick up the coit with X3 World Champ John Florence giving the Woz a severe case of blue balls, J-Bay gets the fuh-lick, Rags goes to the big show and Goat trades nubile princesses for burnt out South Tweed units on mobility scooters. The Pipe Chang fires up, and the Swellians take over the asylum for a huge Ask us a Question Special. Slurp Slurp Slurp away! Up the financial revolution that's got young Aussies Backs Presents... (Sign up now for a $20 kick in from us using the code "UTFS20" Yeeeeeeew!)See omnystudio.com/listener for privacy information.

Too long have we waited to turn to the next chapter of the lifeforce that is THE Electric Light Orchestra! The Imbalanced guys discuss this period of the band, with all of the incredible, breakthrough success they had in 1976, 1977 & then in 1979! Mainly the discussion is about the music, and the way this period unfolded, but there's also some discussion of the coming and goings of members, and how One Of Us kept it straight. Dig in to find our first episode about ELO right here!!! Learn more about your ad choices. Visit megaphone.fm/adchoices

Too long have we waited to turn to the next chapter of the lifeforce that is THE Electric Light Orchestra! The Imbalanced guys discuss this period of the band, with all of the incredible, breakthrough success they had in 1976, 1977 & then in 1979! Mainly the discussion is about the music, and the way this period unfolded, but there's also some discussion of the coming and goings of members, and how One Of Us kept it straight. Dig in to find our first episode about ELO right here!!! Learn more about your ad choices. Visit megaphone.fm/adchoices

Comment conserver et exposer les restes humains ? Cette interrogation est au cœur de l'exposition Momies au Musée de l'Homme qui présente 9 corps momifiés exceptionnels issus de ses collections. Que nous disent ces momies de notre rapport à la mort et à la vie ? Partageons des questions troublantes sur notre rapport à la mort et donc à la vie... Pourquoi et comment donner à voir des corps préservés, des défunts momifiés, étudiés et conservés dans la très vaste collection de restes humains du Muséum ? Une interrogation éthique qui est au cœur même de l'exposition Momies, conçue pour les 10 ans de la réouverture du Musée de l'Homme. Les chercheurs du Muséum ont choisi d'ouvrir leur collection au public dans le plus grand respect de ces défunts momifiés, en les réhumanisant, en leur redonnant une histoire et un parcours de vie, en les présentant non pas comme des objets mais bien comme des témoins, des archives biologiques fragiles et précieuses de notre humanité et du rapport à la disparition et à la mémoire. In memento mori, un rappel puissant de notre finitude d'autant plus dans notre monde occidental qui ne veut plus voir la mort, ni les morts... Émission autour de l'exposition Momies qui se tient au Musée de l'Homme à Paris jusqu'au 25 mai avec : Eloïse Quétel, conservatrice-restauratrice des restes humains et matériaux organiques, responsable des collections médicales (Sorbonne Université) Pascal Sellier, anthropologue Nicolas Delestre, spécialiste de l'étude des conservations de la dépouille humaine, directeur des centres de formation d'Assistance et Formations Internationales de Thanatopraxie et Thanatoplastie (AFITT). Musiques diffusées dans l'émission Roberto & the Moods - Tard, trop tard Sébastien Tellier - Portés par le vent.

Comment conserver et exposer les restes humains ? Cette interrogation est au cœur de l'exposition Momies au Musée de l'Homme qui présente 9 corps momifiés exceptionnels issus de ses collections. Que nous disent ces momies de notre rapport à la mort et à la vie ? Partageons des questions troublantes sur notre rapport à la mort et donc à la vie... Pourquoi et comment donner à voir des corps préservés, des défunts momifiés, étudiés et conservés dans la très vaste collection de restes humains du Muséum ? Une interrogation éthique qui est au cœur même de l'exposition Momies, conçue pour les 10 ans de la réouverture du Musée de l'Homme. Les chercheurs du Muséum ont choisi d'ouvrir leur collection au public dans le plus grand respect de ces défunts momifiés, en les réhumanisant, en leur redonnant une histoire et un parcours de vie, en les présentant non pas comme des objets mais bien comme des témoins, des archives biologiques fragiles et précieuses de notre humanité et du rapport à la disparition et à la mémoire. In memento mori, un rappel puissant de notre finitude d'autant plus dans notre monde occidental qui ne veut plus voir la mort, ni les morts... Émission autour de l'exposition Momies qui se tient au Musée de l'Homme à Paris jusqu'au 25 mai avec : Eloïse Quétel, conservatrice-restauratrice des restes humains et matériaux organiques, responsable des collections médicales (Sorbonne Université) Pascal Sellier, anthropologue Nicolas Delestre, spécialiste de l'étude des conservations de la dépouille humaine, directeur des centres de formation d'Assistance et Formations Internationales de Thanatopraxie et Thanatoplastie (AFITT). Musiques diffusées dans l'émission Roberto & the Moods - Tard, trop tard Sébastien Tellier - Portés par le vent.

Why does one love rock 'n roll so dearly? Well, of course, the quality of a given favorite song -- its bass line, the vocals, the guitar solo, etc. -- connects with you(r ears) and makes you love it. But there's more to it than that: The real ground of one's love for a particular song is *Where you were when you first heard it. * And by that I mean: Where you were emotionally when you first heard it. The actual song itself -- superb as it may be -- is made a thousand times more powerful by where you were in experience -- and especially in emotional experience -- when you first heard it. The song itself, in other words, is secondary to the placement of your psycho-dynamic soul when it was first playing in the background of your life. I cannot overstate this truth (of experience): It is not the song itself -- nor, for that matter, the play or the movie or the poem or the painting, even -- which carried "The Weight" (The Band, '68). It was, rather, the contact which the song made with your innermost person, whether you were being loved and accepted at the time, or repulsed and rejected. Therein lies the power of art. (Tell me if this isn't true.) Amazing response recently to an excerpt I played on the cast of an ELO single. It just seemed to blow up one's audience with empathy and exclamatory rejoicings. Note, finally, that there is an explicitly Christian anchorage here: The union we wish so much to feel with another person is the embodiment, in felt experience, of the union we need with God -- that belovedness I talk about so much. I can almost say that a memorable rock song is for many persons the bearer of Christ's One Way Love. Oh, and for the record, the paragraph I read at the conclusion of this episode is from James Hilton's stirring novel from 1934, entitled "Without Armor". LUV U.

Happy New Year! You may have noticed that in 2025 we had moved toward YouTube as our primary podcasting platform. As we'll explain in the next State of Latent Space post, we'll be doubling down on Substack again and improving the experience for the over 100,000 of you who look out for our emails and website updates!We first mentioned Artificial Analysis in 2024, when it was still a side project in a Sydney basement. They then were one of the few Nat Friedman and Daniel Gross' AIGrant companies to raise a full seed round from them and have now become the independent gold standard for AI benchmarking—trusted by developers, enterprises, and every major lab to navigate the exploding landscape of models, providers, and capabilities.We have chatted with both Clementine Fourrier of HuggingFace's OpenLLM Leaderboard and (the freshly valued at $1.7B) Anastasios Angelopoulos of LMArena on their approaches to LLM evals and trendspotting, but Artificial Analysis have staked out an enduring and important place in the toolkit of the modern AI Engineer by doing the best job of independently running the most comprehensive set of evals across the widest range of open and closed models, and charting their progress for broad industry analyst use.George Cameron and Micah-Hill Smith have spent two years building Artificial Analysis into the platform that answers the questions no one else will: Which model is actually best for your use case? What are the real speed-cost trade-offs? And how open is “open” really?We discuss:* The origin story: built as a side project in 2023 while Micah was building a legal AI assistant, launched publicly in January 2024, and went viral after Swyx's retweet* Why they run evals themselves: labs prompt models differently, cherry-pick chain-of-thought examples (Google Gemini 1.0 Ultra used 32-shot prompts to beat GPT-4 on MMLU), and self-report inflated numbers* The mystery shopper policy: they register accounts not on their own domain and run intelligence + performance benchmarks incognito to prevent labs from serving different models on private endpoints* How they make money: enterprise benchmarking insights subscription (standardized reports on model deployment, serverless vs. managed vs. leasing chips) and private custom benchmarking for AI companies (no one pays to be on the public leaderboard)* The Intelligence Index (V3): synthesizes 10 eval datasets (MMLU, GPQA, agentic benchmarks, long-context reasoning) into a single score, with 95% confidence intervals via repeated runs* Omissions Index (hallucination rate): scores models from -100 to +100 (penalizing incorrect answers, rewarding ”I don't know”), and Claude models lead with the lowest hallucination rates despite not always being the smartest* GDP Val AA: their version of OpenAI's GDP-bench (44 white-collar tasks with spreadsheets, PDFs, PowerPoints), run through their Stirrup agent harness (up to 100 turns, code execution, web search, file system), graded by Gemini 3 Pro as an LLM judge (tested extensively, no self-preference bias)* The Openness Index: scores models 0-18 on transparency of pre-training data, post-training data, methodology, training code, and licensing (AI2 OLMo 2 leads, followed by Nous Hermes and NVIDIA Nemotron)* The smiling curve of AI costs: GPT-4-level intelligence is 100-1000x cheaper than at launch (thanks to smaller models like Amazon Nova), but frontier reasoning models in agentic workflows cost more than ever (sparsity, long context, multi-turn agents)* Why sparsity might go way lower than 5%: GPT-4.5 is ~5% active, Gemini models might be ~3%, and Omissions Index accuracy correlates with total parameters (not active), suggesting massive sparse models are the future* Token efficiency vs. turn efficiency: GPT-5 costs more per token but solves Tau-bench in fewer turns (cheaper overall), and models are getting better at using more tokens only when needed (5.1 Codex has tighter token distributions)* V4 of the Intelligence Index coming soon: adding GDP Val AA, Critical Point, hallucination rate, and dropping some saturated benchmarks (human-eval-style coding is now trivial for small models)Links to Artificial Analysis* Website: https://artificialanalysis.ai* George Cameron on X: https://x.com/georgecameron* Micah-Hill Smith on X: https://x.com/micahhsmithFull Episode on YouTubeTimestamps* 00:00 Introduction: Full Circle Moment and Artificial Analysis Origins* 01:19 Business Model: Independence and Revenue Streams* 04:33 Origin Story: From Legal AI to Benchmarking Need* 16:22 AI Grant and Moving to San Francisco* 19:21 Intelligence Index Evolution: From V1 to V3* 11:47 Benchmarking Challenges: Variance, Contamination, and Methodology* 13:52 Mystery Shopper Policy and Maintaining Independence* 28:01 New Benchmarks: Omissions Index for Hallucination Detection* 33:36 Critical Point: Hard Physics Problems and Research-Level Reasoning* 23:01 GDP Val AA: Agentic Benchmark for Real Work Tasks* 50:19 Stirrup Agent Harness: Open Source Agentic Framework* 52:43 Openness Index: Measuring Model Transparency Beyond Licenses* 58:25 The Smiling Curve: Cost Falling While Spend Rising* 1:02:32 Hardware Efficiency: Blackwell Gains and Sparsity Limits* 1:06:23 Reasoning Models and Token Efficiency: The Spectrum Emerges* 1:11:00 Multimodal Benchmarking: Image, Video, and Speech Arenas* 1:15:05 Looking Ahead: Intelligence Index V4 and Future Directions* 1:16:50 Closing: The Insatiable Demand for IntelligenceTranscriptMicah [00:00:06]: This is kind of a full circle moment for us in a way, because the first time artificial analysis got mentioned on a podcast was you and Alessio on Latent Space. Amazing.swyx [00:00:17]: Which was January 2024. I don't even remember doing that, but yeah, it was very influential to me. Yeah, I'm looking at AI News for Jan 17, or Jan 16, 2024. I said, this gem of a models and host comparison site was just launched. And then I put in a few screenshots, and I said, it's an independent third party. It clearly outlines the quality versus throughput trade-off, and it breaks out by model and hosting provider. I did give you s**t for missing fireworks, and how do you have a model benchmarking thing without fireworks? But you had together, you had perplexity, and I think we just started chatting there. Welcome, George and Micah, to Latent Space. I've been following your progress. Congrats on... It's been an amazing year. You guys have really come together to be the presumptive new gardener of AI, right? Which is something that...George [00:01:09]: Yeah, but you can't pay us for better results.swyx [00:01:12]: Yes, exactly.George [00:01:13]: Very important.Micah [00:01:14]: Start off with a spicy take.swyx [00:01:18]: Okay, how do I pay you?Micah [00:01:20]: Let's get right into that.swyx [00:01:21]: How do you make money?Micah [00:01:24]: Well, very happy to talk about that. So it's been a big journey the last couple of years. Artificial analysis is going to be two years old in January 2026. Which is pretty soon now. We first run the website for free, obviously, and give away a ton of data to help developers and companies navigate AI and make decisions about models, providers, technologies across the AI stack for building stuff. We're very committed to doing that and tend to keep doing that. We have, along the way, built a business that is working out pretty sustainably. We've got just over 20 people now and two main customer groups. So we want to be... We want to be who enterprise look to for data and insights on AI, so we want to help them with their decisions about models and technologies for building stuff. And then on the other side, we do private benchmarking for companies throughout the AI stack who build AI stuff. So no one pays to be on the website. We've been very clear about that from the very start because there's no use doing what we do unless it's independent AI benchmarking. Yeah. But turns out a bunch of our stuff can be pretty useful to companies building AI stuff.swyx [00:02:38]: And is it like, I am a Fortune 500, I need advisors on objective analysis, and I call you guys and you pull up a custom report for me, you come into my office and give me a workshop? What kind of engagement is that?George [00:02:53]: So we have a benchmarking and insight subscription, which looks like standardized reports that cover key topics or key challenges enterprises face when looking to understand AI and choose between all the technologies. And so, for instance, one of the report is a model deployment report, how to think about choosing between serverless inference, managed deployment solutions, or leasing chips. And running inference yourself is an example kind of decision that big enterprises face, and it's hard to reason through, like this AI stuff is really new to everybody. And so we try and help with our reports and insight subscription. Companies navigate that. We also do custom private benchmarking. And so that's very different from the public benchmarking that we publicize, and there's no commercial model around that. For private benchmarking, we'll at times create benchmarks, run benchmarks to specs that enterprises want. And we'll also do that sometimes for AI companies who have built things, and we help them understand what they've built with private benchmarking. Yeah. So that's a piece mainly that we've developed through trying to support everybody publicly with our public benchmarks. Yeah.swyx [00:04:09]: Let's talk about TechStack behind that. But okay, I'm going to rewind all the way to when you guys started this project. You were all the way in Sydney? Yeah. Well, Sydney, Australia for me.Micah [00:04:19]: George was an SF, but he's Australian, but he moved here already. Yeah.swyx [00:04:22]: And I remember I had the Zoom call with you. What was the impetus for starting artificial analysis in the first place? You know, you started with public benchmarks. And so let's start there. We'll go to the private benchmark. Yeah.George [00:04:33]: Why don't we even go back a little bit to like why we, you know, thought that it was needed? Yeah.Micah [00:04:40]: The story kind of begins like in 2022, 2023, like both George and I have been into AI stuff for quite a while. In 2023 specifically, I was trying to build a legal AI research assistant. So it actually worked pretty well for its era, I would say. Yeah. Yeah. So I was finding that the more you go into building something using LLMs, the more each bit of what you're doing ends up being a benchmarking problem. So had like this multistage algorithm thing, trying to figure out what the minimum viable model for each bit was, trying to optimize every bit of it as you build that out, right? Like you're trying to think about accuracy, a bunch of other metrics and performance and cost. And mostly just no one was doing anything to independently evaluate all the models. And certainly not to look at the trade-offs for speed and cost. So we basically set out just to build a thing that developers could look at to see the trade-offs between all of those things measured independently across all the models and providers. Honestly, it was probably meant to be a side project when we first started doing it.swyx [00:05:49]: Like we didn't like get together and say like, Hey, like we're going to stop working on all this stuff. I'm like, this is going to be our main thing. When I first called you, I think you hadn't decided on starting a company yet.Micah [00:05:58]: That's actually true. I don't even think we'd pause like, like George had an acquittance job. I didn't quit working on my legal AI thing. Like it was genuinely a side project.George [00:06:05]: We built it because we needed it as people building in the space and thought, Oh, other people might find it useful too. So we'll buy domain and link it to the Vercel deployment that we had and tweet about it. And, but very quickly it started getting attention. Thank you, Swyx for, I think doing an initial retweet and spotlighting it there. This project that we released. And then very quickly though, it was useful to others, but very quickly it became more useful as the number of models released accelerated. We had Mixtrel 8x7B and it was a key. That's a fun one. Yeah. Like a open source model that really changed the landscape and opened up people's eyes to other serverless inference providers and thinking about speed, thinking about cost. And so that was a key. And so it became more useful quite quickly. Yeah.swyx [00:07:02]: What I love talking to people like you who sit across the ecosystem is, well, I have theories about what people want, but you have data and that's obviously more relevant. But I want to stay on the origin story a little bit more. When you started out, I would say, I think the status quo at the time was every paper would come out and they would report their numbers versus competitor numbers. And that's basically it. And I remember I did the legwork. I think everyone has some knowledge. I think there's some version of Excel sheet or a Google sheet where you just like copy and paste the numbers from every paper and just post it up there. And then sometimes they don't line up because they're independently run. And so your numbers are going to look better than... Your reproductions of other people's numbers are going to look worse because you don't hold their models correctly or whatever the excuse is. I think then Stanford Helm, Percy Liang's project would also have some of these numbers. And I don't know if there's any other source that you can cite. The way that if I were to start artificial analysis at the same time you guys started, I would have used the Luther AI's eval framework harness. Yup.Micah [00:08:06]: Yup. That was some cool stuff. At the end of the day, running these evals, it's like if it's a simple Q&A eval, all you're doing is asking a list of questions and checking if the answers are right, which shouldn't be that crazy. But it turns out there are an enormous number of things that you've got control for. And I mean, back when we started the website. Yeah. Yeah. Like one of the reasons why we realized that we had to run the evals ourselves and couldn't just take rules from the labs was just that they would all prompt the models differently. And when you're competing over a few points, then you can pretty easily get- You can put the answer into the model. Yeah. That in the extreme. And like you get crazy cases like back when I'm Googled a Gemini 1.0 Ultra and needed a number that would say it was better than GPT-4 and like constructed, I think never published like chain of thought examples. 32 of them in every topic in MLU to run it, to get the score, like there are so many things that you- They never shipped Ultra, right? That's the one that never made it up. Not widely. Yeah. Yeah. Yeah. I mean, I'm sure it existed, but yeah. So we were pretty sure that we needed to run them ourselves and just run them in the same way across all the models. Yeah. And we were, we also did certain from the start that you couldn't look at those in isolation. You needed to look at them alongside the cost and performance stuff. Yeah.swyx [00:09:24]: Okay. A couple of technical questions. I mean, so obviously I also thought about this and I didn't do it because of cost. Yep. Did you not worry about costs? Were you funded already? Clearly not, but you know. No. Well, we definitely weren't at the start.Micah [00:09:36]: So like, I mean, we're paying for it personally at the start. There's a lot of money. Well, the numbers weren't nearly as bad a couple of years ago. So we certainly incurred some costs, but we were probably in the order of like hundreds of dollars of spend across all the benchmarking that we were doing. Yeah. So nothing. Yeah. It was like kind of fine. Yeah. Yeah. These days that's gone up an enormous amount for a bunch of reasons that we can talk about. But yeah, it wasn't that bad because you can also remember that like the number of models we were dealing with was hardly any and the complexity of the stuff that we wanted to do to evaluate them was a lot less. Like we were just asking some Q&A type questions and then one specific thing was for a lot of evals initially, we were just like sampling an answer. You know, like, what's the answer for this? Like, we didn't want to go into the answer directly without letting the models think. We weren't even doing chain of thought stuff initially. And that was the most useful way to get some results initially. Yeah.swyx [00:10:33]: And so for people who haven't done this work, literally parsing the responses is a whole thing, right? Like because sometimes the models, the models can answer any way they feel fit and sometimes they actually do have the right answer, but they just returned the wrong format and they will get a zero for that unless you work it into your parser. And that involves more work. And so, I mean, but there's an open question whether you should give it points for not following your instructions on the format.Micah [00:11:00]: It depends what you're looking at, right? Because you can, if you're trying to see whether or not it can solve a particular type of reasoning problem, and you don't want to test it on its ability to do answer formatting at the same time, then you might want to use an LLM as answer extractor approach to make sure that you get the answer out no matter how unanswered. But these days, it's mostly less of a problem. Like, if you instruct a model and give it examples of what the answers should look like, it can get the answers in your format, and then you can do, like, a simple regex.swyx [00:11:28]: Yeah, yeah. And then there's other questions around, I guess, sometimes if you have a multiple choice question, sometimes there's a bias towards the first answer, so you have to randomize the responses. All these nuances, like, once you dig into benchmarks, you're like, I don't know how anyone believes the numbers on all these things. It's so dark magic.Micah [00:11:47]: You've also got, like… You've got, like, the different degrees of variance in different benchmarks, right? Yeah. So, if you run four-question multi-choice on a modern reasoning model at the temperatures suggested by the labs for their own models, the variance that you can see on a four-question multi-choice eval is pretty enormous if you only do a single run of it and it has a small number of questions, especially. So, like, one of the things that we do is run an enormous number of all of our evals when we're developing new ones and doing upgrades to our intelligence index to bring in new things. Yeah. So, that we can dial in the right number of repeats so that we can get to the 95% confidence intervals that we're comfortable with so that when we pull that together, we can be confident in intelligence index to at least as tight as, like, a plus or minus one at a 95% confidence. Yeah.swyx [00:12:32]: And, again, that just adds a straight multiple to the cost. Oh, yeah. Yeah, yeah.George [00:12:37]: So, that's one of many reasons that cost has gone up a lot more than linearly over the last couple of years. We report a cost to run the artificial analysis. We report a cost to run the artificial analysis intelligence index on our website, and currently that's assuming one repeat in terms of how we report it because we want to reflect a bit about the weighting of the index. But our cost is actually a lot higher than what we report there because of the repeats.swyx [00:13:03]: Yeah, yeah, yeah. And probably this is true, but just checking, you don't have any special deals with the labs. They don't discount it. You just pay out of pocket or out of your sort of customer funds. Oh, there is a mix. So, the issue is that sometimes they may give you a special end point, which is… Ah, 100%.Micah [00:13:21]: Yeah, yeah, yeah. Exactly. So, we laser focus, like, on everything we do on having the best independent metrics and making sure that no one can manipulate them in any way. There are quite a lot of processes we've developed over the last couple of years to make that true for, like, the one you bring up, like, right here of the fact that if we're working with a lab, if they're giving us a private endpoint to evaluate a model, that it is totally possible. That what's sitting behind that black box is not the same as they serve on a public endpoint. We're very aware of that. We have what we call a mystery shopper policy. And so, and we're totally transparent with all the labs we work with about this, that we will register accounts not on our own domain and run both intelligence evals and performance benchmarks… Yeah, that's the job. …without them being able to identify it. And no one's ever had a problem with that. Because, like, a thing that turns out to actually be quite a good… …good factor in the industry is that they all want to believe that none of their competitors could manipulate what we're doing either.swyx [00:14:23]: That's true. I never thought about that. I've been in the database data industry prior, and there's a lot of shenanigans around benchmarking, right? So I'm just kind of going through the mental laundry list. Did I miss anything else in this category of shenanigans? Oh, potential shenanigans.Micah [00:14:36]: I mean, okay, the biggest one, like, that I'll bring up, like, is more of a conceptual one, actually, than, like, direct shenanigans. It's that the things that get measured become things that get targeted by labs that they're trying to build, right? Exactly. So that doesn't mean anything that we should really call shenanigans. Like, I'm not talking about training on test set. But if you know that you're going to be great at another particular thing, if you're a researcher, there are a whole bunch of things that you can do to try to get better at that thing that preferably are going to be helpful for a wide range of how actual users want to use the thing that you're building. But will not necessarily work. Will not necessarily do that. So, for instance, the models are exceptional now at answering competition maths problems. There is some relevance of that type of reasoning, that type of work, to, like, how we might use modern coding agents and stuff. But it's clearly not one for one. So the thing that we have to be aware of is that once an eval becomes the thing that everyone's looking at, scores can get better on it without there being a reflection of overall generalized intelligence of these models. Getting better. That has been true for the last couple of years. It'll be true for the next couple of years. There's no silver bullet to defeat that other than building new stuff to stay relevant and measure the capabilities that matter most to real users. Yeah.swyx [00:15:58]: And we'll cover some of the new stuff that you guys are building as well, which is cool. Like, you used to just run other people's evals, but now you're coming up with your own. And I think, obviously, that is a necessary path once you're at the frontier. You've exhausted all the existing evals. I think the next point in history that I have for you is AI Grant that you guys decided to join and move here. What was it like? I think you were in, like, batch two? Batch four. Batch four. Okay.Micah [00:16:26]: I mean, it was great. Nat and Daniel are obviously great. And it's a really cool group of companies that we were in AI Grant alongside. It was really great to get Nat and Daniel on board. Obviously, they've done a whole lot of great work in the space with a lot of leading companies and were extremely aligned. With the mission of what we were trying to do. Like, we're not quite typical of, like, a lot of the other AI startups that they've invested in.swyx [00:16:53]: And they were very much here for the mission of what we want to do. Did they say any advice that really affected you in some way or, like, were one of the events very impactful? That's an interesting question.Micah [00:17:03]: I mean, I remember fondly a bunch of the speakers who came and did fireside chats at AI Grant.swyx [00:17:09]: Which is also, like, a crazy list. Yeah.George [00:17:11]: Oh, totally. Yeah, yeah, yeah. There was something about, you know, speaking to Nat and Daniel about the challenges of working through a startup and just working through the questions that don't have, like, clear answers and how to work through those kind of methodically and just, like, work through the hard decisions. And they've been great mentors to us as we've built artificial analysis. Another benefit for us was that other companies in the batch and other companies in AI Grant are pushing the capabilities. Yeah. And I think that's a big part of what AI can do at this time. And so being in contact with them, making sure that artificial analysis is useful to them has been fantastic for supporting us in working out how should we build out artificial analysis to continue to being useful to those, like, you know, building on AI.swyx [00:17:59]: I think to some extent, I'm mixed opinion on that one because to some extent, your target audience is not people in AI Grants who are obviously at the frontier. Yeah. Do you disagree?Micah [00:18:09]: To some extent. To some extent. But then, so a lot of what the AI Grant companies are doing is taking capabilities coming out of the labs and trying to push the limits of what they can do across the entire stack for building great applications, which actually makes some of them pretty archetypical power users of artificial analysis. Some of the people with the strongest opinions about what we're doing well and what we're not doing well and what they want to see next from us. Yeah. Yeah. Because when you're building any kind of AI application now, chances are you're using a whole bunch of different models. You're maybe switching reasonably frequently for different models and different parts of your application to optimize what you're able to do with them at an accuracy level and to get better speed and cost characteristics. So for many of them, no, they're like not commercial customers of ours, like we don't charge for all our data on the website. Yeah. They are absolutely some of our power users.swyx [00:19:07]: So let's talk about just the evals as well. So you start out from the general like MMU and GPQA stuff. What's next? How do you sort of build up to the overall index? What was in V1 and how did you evolve it? Okay.Micah [00:19:22]: So first, just like background, like we're talking about the artificial analysis intelligence index, which is our synthesis metric that we pulled together currently from 10 different eval data sets to give what? We're pretty much the same as that. Pretty confident is the best single number to look at for how smart the models are. Obviously, it doesn't tell the whole story. That's why we published the whole website of all the charts to dive into every part of it and look at the trade-offs. But best single number. So right now, it's got a bunch of Q&A type data sets that have been very important to the industry, like a couple that you just mentioned. It's also got a couple of agentic data sets. It's got our own long context reasoning data set and some other use case focused stuff. As time goes on. The things that we're most interested in that are going to be important to the capabilities that are becoming more important for AI, what developers are caring about, are going to be first around agentic capabilities. So surprise, surprise. We're all loving our coding agents and how the model is going to perform like that and then do similar things for different types of work are really important to us. The linking to use cases to economically valuable use cases are extremely important to us. And then we've got some of the. Yeah. These things that the models still struggle with, like working really well over long contexts that are not going to go away as specific capabilities and use cases that we need to keep evaluating.swyx [00:20:46]: But I guess one thing I was driving was like the V1 versus the V2 and how bad it was over time.Micah [00:20:53]: Like how we've changed the index to where we are.swyx [00:20:55]: And I think that reflects on the change in the industry. Right. So that's a nice way to tell that story.Micah [00:21:00]: Well, V1 would be completely saturated right now. Almost every model coming out because doing things like writing the Python functions and human evil is now pretty trivial. It's easy to forget, actually, I think how much progress has been made in the last two years. Like we obviously play the game constantly of like the today's version versus last week's version and the week before and all of the small changes in the horse race between the current frontier and who has the best like smaller than 10B model like right now this week. Right. And that's very important to a lot of developers and people and especially in this particular city of San Francisco. But when you zoom out a couple of years ago, literally most of what we were doing to evaluate the models then would all be 100% solved by even pretty small models today. And that's been one of the key things, by the way, that's driven down the cost of intelligence at every tier of intelligence. We can talk about more in a bit. So V1, V2, V3, we made things harder. We covered a wider range of use cases. And we tried to get closer to things developers care about as opposed to like just the Q&A type stuff that MMLU and GPQA represented. Yeah.swyx [00:22:12]: I don't know if you have anything to add there. Or we could just go right into showing people the benchmark and like looking around and asking questions about it. Yeah.Micah [00:22:21]: Let's do it. Okay. This would be a pretty good way to chat about a few of the new things we've launched recently. Yeah.George [00:22:26]: And I think a little bit about the direction that we want to take it. And we want to push benchmarks. Currently, the intelligence index and evals focus a lot on kind of raw intelligence. But we kind of want to diversify how we think about intelligence. And we can talk about it. But kind of new evals that we've kind of built and partnered on focus on topics like hallucination. And we've got a lot of topics that I think are not covered by the current eval set that should be. And so we want to bring that forth. But before we get into that.swyx [00:23:01]: And so for listeners, just as a timestamp, right now, number one is Gemini 3 Pro High. Then followed by Cloud Opus at 70. Just 5.1 high. You don't have 5.2 yet. And Kimi K2 Thinking. Wow. Still hanging in there. So those are the top four. That will date this podcast quickly. Yeah. Yeah. I mean, I love it. I love it. No, no. 100%. Look back this time next year and go, how cute. Yep.George [00:23:25]: Totally. A quick view of that is, okay, there's a lot. I love it. I love this chart. Yeah.Micah [00:23:30]: This is such a favorite, right? Yeah. And almost every talk that George or I give at conferences and stuff, we always put this one up first to just talk about situating where we are in this moment in history. This, I think, is the visual version of what I was saying before about the zooming out and remembering how much progress there's been. If we go back to just over a year ago, before 01, before Cloud Sonnet 3.5, we didn't have reasoning models or coding agents as a thing. And the game was very, very different. If we go back even a little bit before then, we're in the era where, when you look at this chart, open AI was untouchable for well over a year. And, I mean, you would remember that time period well of there being very open questions about whether or not AI was going to be competitive, like full stop, whether or not open AI would just run away with it, whether we would have a few frontier labs and no one else would really be able to do anything other than consume their APIs. I am quite happy overall that the world that we have ended up in is one where... Multi-model. Absolutely. And strictly more competitive every quarter over the last few years. Yeah. This year has been insane. Yeah.George [00:24:42]: You can see it. This chart with everything added is hard to read currently. There's so many dots on it, but I think it reflects a little bit what we felt, like how crazy it's been.swyx [00:24:54]: Why 14 as the default? Is that a manual choice? Because you've got service now in there that are less traditional names. Yeah.George [00:25:01]: It's models that we're kind of highlighting by default in our charts, in our intelligence index. Okay.swyx [00:25:07]: You just have a manually curated list of stuff.George [00:25:10]: Yeah, that's right. But something that I actually don't think every artificial analysis user knows is that you can customize our charts and choose what models are highlighted. Yeah. And so if we take off a few names, it gets a little easier to read.swyx [00:25:25]: Yeah, yeah. A little easier to read. Totally. Yeah. But I love that you can see the all one jump. Look at that. September 2024. And the DeepSeek jump. Yeah.George [00:25:34]: Which got close to OpenAI's leadership. They were so close. I think, yeah, we remember that moment. Around this time last year, actually.Micah [00:25:44]: Yeah, yeah, yeah. I agree. Yeah, well, a couple of weeks. It was Boxing Day in New Zealand when DeepSeek v3 came out. And we'd been tracking DeepSeek and a bunch of the other global players that were less known over the second half of 2024 and had run evals on the earlier ones and stuff. I very distinctly remember Boxing Day in New Zealand, because I was with family for Christmas and stuff, running the evals and getting back result by result on DeepSeek v3. So this was the first of their v3 architecture, the 671b MOE.Micah [00:26:19]: And we were very, very impressed. That was the moment where we were sure that DeepSeek was no longer just one of many players, but had jumped up to be a thing. The world really noticed when they followed that up with the RL working on top of v3 and R1 succeeding a few weeks later. But the groundwork for that absolutely was laid with just extremely strong base model, completely open weights that we had as the best open weights model. So, yeah, that's the thing that you really see in the game. But I think that we got a lot of good feedback on Boxing Day. us on Boxing Day last year.George [00:26:48]: Boxing Day is the day after Christmas for those not familiar.George [00:26:54]: I'm from Singapore.swyx [00:26:55]: A lot of us remember Boxing Day for a different reason, for the tsunami that happened. Oh, of course. Yeah, but that was a long time ago. So yeah. So this is the rough pitch of AAQI. Is it A-A-Q-I or A-A-I-I? I-I. Okay. Good memory, though.Micah [00:27:11]: I don't know. I'm not used to it. Once upon a time, we did call it Quality Index, and we would talk about quality, performance, and price, but we changed it to intelligence.George [00:27:20]: There's been a few naming changes. We added hardware benchmarking to the site, and so benchmarks at a kind of system level. And so then we changed our throughput metric to, we now call it output speed, and thenswyx [00:27:32]: throughput makes sense at a system level, so we took that name. Take me through more charts. What should people know? Obviously, the way you look at the site is probably different than how a beginner might look at it.Micah [00:27:42]: Yeah, that's fair. There's a lot of fun stuff to dive into. Maybe so we can hit past all the, like, we have lots and lots of emails and stuff. The interesting ones to talk about today that would be great to bring up are a few of our recent things, I think, that probably not many people will be familiar with yet. So first one of those is our omniscience index. So this one is a little bit different to most of the intelligence evils that we've run. We built it specifically to look at the embedded knowledge in the models and to test hallucination by looking at when the model doesn't know the answer, so not able to get it correct, what's its probability of saying, I don't know, or giving an incorrect answer. So the metric that we use for omniscience goes from negative 100 to positive 100. Because we're simply taking off a point if you give an incorrect answer to the question. We're pretty convinced that this is an example of where it makes most sense to do that, because it's strictly more helpful to say, I don't know, instead of giving a wrong answer to factual knowledge question. And one of our goals is to shift the incentive that evils create for models and the labs creating them to get higher scores. And almost every evil across all of AI up until this point, it's been graded by simple percentage correct as the main metric, the main thing that gets hyped. And so you should take a shot at everything. There's no incentive to say, I don't know. So we did that for this one here.swyx [00:29:22]: I think there's a general field of calibration as well, like the confidence in your answer versus the rightness of the answer. Yeah, we completely agree. Yeah. Yeah.George [00:29:31]: On that. And one reason that we didn't do that is because. Or put that into this index is that we think that the, the way to do that is not to ask the models how confident they are.swyx [00:29:43]: I don't know. Maybe it might be though. You put it like a JSON field, say, say confidence and maybe it spits out something. Yeah. You know, we have done a few evils podcasts over the, over the years. And when we did one with Clementine of hugging face, who maintains the open source leaderboard, and this was one of her top requests, which is some kind of hallucination slash lack of confidence calibration thing. And so, Hey, this is one of them.Micah [00:30:05]: And I mean, like anything that we do, it's not a perfect metric or the whole story of everything that you think about as hallucination. But yeah, it's pretty useful and has some interesting results. Like one of the things that we saw in the hallucination rate is that anthropics Claude models at the, the, the very left-hand side here with the lowest hallucination rates out of the models that we've evaluated amnesty is on. That is an interesting fact. I think it probably correlates with a lot of the previously, not really measured vibes stuff that people like about some of the Claude models. Is the dataset public or what's is it, is there a held out set? There's a hell of a set for this one. So we, we have published a public test set, but we we've only published 10% of it. The reason is that for this one here specifically, it would be very, very easy to like have data contamination because it is just factual knowledge questions. We would. We'll update it at a time to also prevent that, but with yeah, kept most of it held out so that we can keep it reliable for a long time. It leads us to a bunch of really cool things, including breakdown quite granularly by topic. And so we've got some of that disclosed on the website publicly right now, and there's lots more coming in terms of our ability to break out very specific topics. Yeah.swyx [00:31:23]: I would be interested. Let's, let's dwell a little bit on this hallucination one. I noticed that Haiku hallucinates less than Sonnet hallucinates less than Opus. And yeah. Would that be the other way around in a normal capability environments? I don't know. What's, what do you make of that?George [00:31:37]: One interesting aspect is that we've found that there's not really a, not a strong correlation between intelligence and hallucination, right? That's to say that the smarter the models are in a general sense, isn't correlated with their ability to, when they don't know something, say that they don't know. It's interesting that Gemini three pro preview was a big leap over here. Gemini 2.5. Flash and, and, and 2.5 pro, but, and if I add pro quickly here.swyx [00:32:07]: I bet pro's really good. Uh, actually no, I meant, I meant, uh, the GPT pros.George [00:32:12]: Oh yeah.swyx [00:32:13]: Cause GPT pros are rumored. We don't know for a fact that it's like eight runs and then with the LM judge on top. Yeah.George [00:32:20]: So we saw a big jump in, this is accuracy. So this is just percent that they get, uh, correct and Gemini three pro knew a lot more than the other models. And so big jump in accuracy. But relatively no change between the Google Gemini models, between releases. And the hallucination rate. Exactly. And so it's likely due to just kind of different post-training recipe, between the, the Claude models. Yeah.Micah [00:32:45]: Um, there's, there's driven this. Yeah. You can, uh, you can partially blame us and how we define intelligence having until now not defined hallucination as a negative in the way that we think about intelligence.swyx [00:32:56]: And so that's what we're changing. Uh, I know many smart people who are confidently incorrect.George [00:33:02]: Uh, look, look at that. That, that, that is very humans. Very true. And there's times and a place for that. I think our view is that hallucination rate makes sense in this context where it's around knowledge, but in many cases, people want the models to hallucinate, to have a go. Often that's the case in coding or when you're trying to generate newer ideas. One eval that we added to artificial analysis is, is, is critical point and it's really hard, uh, physics problems. Okay.swyx [00:33:32]: And is it sort of like a human eval type or something different or like a frontier math type?George [00:33:37]: It's not dissimilar to frontier frontier math. So these are kind of research questions that kind of academics in the physics physics world would be able to answer, but models really struggled to answer. So the top score here is not 9%.swyx [00:33:51]: And when the people that, that created this like Minway and, and, and actually off via who was kind of behind sweep and what organization is this? Oh, is this, it's Princeton.George [00:34:01]: Kind of range of academics from, from, uh, different academic institutions, really smart people. They talked about how they turn the models up in terms of the temperature as high temperature as they can, where they're trying to explore kind of new ideas in physics as a, as a thought partner, just because they, they want the models to hallucinate. Um, yeah, sometimes it's something new. Yeah, exactly.swyx [00:34:21]: Um, so not right in every situation, but, um, I think it makes sense, you know, to test hallucination in scenarios where it makes sense. Also, the obvious question is, uh, this is one of. Many that there is there, every lab has a system card that shows some kind of hallucination number, and you've chosen to not, uh, endorse that and you've made your own. And I think that's a, that's a choice. Um, totally in some sense, the rest of artificial analysis is public benchmarks that other people can independently rerun. You provide it as a service here. You have to fight the, well, who are we to, to like do this? And your, your answer is that we have a lot of customers and, you know, but like, I guess, how do you converge the individual?Micah [00:35:08]: I mean, I think, I think for hallucinations specifically, there are a bunch of different things that you might care about reasonably, and that you'd measure quite differently, like we've called this a amnesty and solutionation rate, not trying to declare the, like, it's humanity's last hallucination. You could, uh, you could have some interesting naming conventions and all this stuff. Um, the biggest picture answer to that. It's something that I actually wanted to mention. Just as George was explaining, critical point as well is, so as we go forward, we are building evals internally. We're partnering with academia and partnering with AI companies to build great evals. We have pretty strong views on, in various ways for different parts of the AI stack, where there are things that are not being measured well, or things that developers care about that should be measured more and better. And we intend to be doing that. We're not obsessed necessarily with that. Everything we do, we have to do entirely within our own team. Critical point. As a cool example of where we were a launch partner for it, working with academia, we've got some partnerships coming up with a couple of leading companies. Those ones, obviously we have to be careful with on some of the independent stuff, but with the right disclosure, like we're completely comfortable with that. A lot of the labs have released great data sets in the past that we've used to great success independently. And so it's between all of those techniques, we're going to be releasing more stuff in the future. Cool.swyx [00:36:26]: Let's cover the last couple. And then we'll, I want to talk about your trends analysis stuff, you know? Totally.Micah [00:36:31]: So that actually, I have one like little factoid on omniscience. If you go back up to accuracy on omniscience, an interesting thing about this accuracy metric is that it tracks more closely than anything else that we measure. The total parameter count of models makes a lot of sense intuitively, right? Because this is a knowledge eval. This is the pure knowledge metric. We're not looking at the index and the hallucination rate stuff that we think is much more about how the models are trained. This is just what facts did they recall? And yeah, it tracks parameter count extremely closely. Okay.swyx [00:37:05]: What's the rumored size of GPT-3 Pro? And to be clear, not confirmed for any official source, just rumors. But rumors do fly around. Rumors. I get, I hear all sorts of numbers. I don't know what to trust.Micah [00:37:17]: So if you, if you draw the line on omniscience accuracy versus total parameters, we've got all the open ways models, you can squint and see that likely the leading frontier models right now are quite a lot bigger than the ones that we're seeing right now. And the one trillion parameters that the open weights models cap out at, and the ones that we're looking at here, there's an interesting extra data point that Elon Musk revealed recently about XAI that for three trillion parameters for GROK 3 and 4, 6 trillion for GROK 5, but that's not out yet. Take those together, have a look. You might reasonably form a view that there's a pretty good chance that Gemini 3 Pro is bigger than that, that it could be in the 5 to 10 trillion parameters. To be clear, I have absolutely no idea, but just based on this chart, like that's where you would, you would land if you have a look at it. Yeah.swyx [00:38:07]: And to some extent, I actually kind of discourage people from guessing too much because what does it really matter? Like as long as they can serve it as a sustainable cost, that's about it. Like, yeah, totally.George [00:38:17]: They've also got different incentives in play compared to like open weights models who are thinking to supporting others in self-deployment for the labs who are doing inference at scale. It's I think less about total parameters in many cases. When thinking about inference costs and more around number of active parameters. And so there's a bit of an incentive towards larger sparser models. Agreed.Micah [00:38:38]: Understood. Yeah. Great. I mean, obviously if you're a developer or company using these things, not exactly as you say, it doesn't matter. You should be looking at all the different ways that we measure intelligence. You should be looking at cost to run index number and the different ways of thinking about token efficiency and cost efficiency based on the list prices, because that's all it matters.swyx [00:38:56]: It's not as good for the content creator rumor mill where I can say. Oh, GPT-4 is this small circle. Look at GPT-5 is this big circle. And then there used to be a thing for a while. Yeah.Micah [00:39:07]: But that is like on its own, actually a very interesting one, right? That is it just purely that chances are the last couple of years haven't seen a dramatic scaling up in the total size of these models. And so there's a lot of room to go up properly in total size of the models, especially with the upcoming hardware generations. Yes.swyx [00:39:29]: So, you know. Taking off my shitposting face for a minute. Yes. Yes. At the same time, I do feel like, you know, especially coming back from Europe, people do feel like Ilya is probably right that the paradigm is doesn't have many more orders of magnitude to scale out more. And therefore we need to start exploring at least a different path. GDPVal, I think it's like only like a month or so old. I was also very positive when it first came out. I actually talked to Tejo, who was the lead researcher on that. Oh, cool. And you have your own version.George [00:39:59]: It's a fantastic. It's a fantastic data set. Yeah.swyx [00:40:01]: And maybe it will recap for people who are still out of it. It's like 44 tasks based on some kind of GDP cutoff that's like meant to represent broad white collar work that is not just coding. Yeah.Micah [00:40:12]: Each of the tasks have a whole bunch of detailed instructions, some input files for a lot of them. It's within the 44 is divided into like two hundred and twenty two to five, maybe subtasks that are the level of that we run through the agenda. And yeah, they're really interesting. I will say that it doesn't. It doesn't necessarily capture like all the stuff that people do at work. No avail is perfect is always going to be more things to look at, largely because in order to make the tasks well enough to find that you can run them, they need to only have a handful of input files and very specific instructions for that task. And so I think the easiest way to think about them are that they're like quite hard take home exam tasks that you might do in an interview process.swyx [00:40:56]: Yeah, for listeners, it is not no longer like a long prompt. It is like, well, here's a zip file with like a spreadsheet or a PowerPoint deck or a PDF and go nuts and answer this question.George [00:41:06]: OpenAI released a great data set and they released a good paper which looks at performance across the different web chat bots on the data set. It's a great paper, encourage people to read it. What we've done is taken that data set and turned it into an eval that can be run on any model. So we created a reference agentic harness that can run. Run the models on the data set, and then we developed evaluator approach to compare outputs. That's kind of AI enabled, so it uses Gemini 3 Pro Preview to compare results, which we tested pretty comprehensively to ensure that it's aligned to human preferences. One data point there is that even as an evaluator, Gemini 3 Pro, interestingly, doesn't do actually that well. So that's kind of a good example of what we've done in GDPVal AA.swyx [00:42:01]: Yeah, the thing that you have to watch out for with LLM judge is self-preference that models usually prefer their own output, and in this case, it was not. Totally.Micah [00:42:08]: I think the way that we're thinking about the places where it makes sense to use an LLM as judge approach now, like quite different to some of the early LLM as judge stuff a couple of years ago, because some of that and MTV was a great project that was a good example of some of this a while ago was about judging conversations and like a lot of style type stuff. Here, we've got the task that the grader and grading model is doing is quite different to the task of taking the test. When you're taking the test, you've got all of the agentic tools you're working with, the code interpreter and web search, the file system to go through many, many turns to try to create the documents. Then on the other side, when we're grading it, we're running it through a pipeline to extract visual and text versions of the files and be able to provide that to Gemini, and we're providing the criteria for the task and getting it to pick which one more effectively meets the criteria of the task. Yeah. So we've got the task out of two potential outcomes. It turns out that we proved that it's just very, very good at getting that right, matched with human preference a lot of the time, because I think it's got the raw intelligence, but it's combined with the correct representation of the outputs, the fact that the outputs were created with an agentic task that is quite different to the way the grading model works, and we're comparing it against criteria, not just kind of zero shot trying to ask the model to pick which one is better.swyx [00:43:26]: Got it. Why is this an ELO? And not a percentage, like GDP-VAL?George [00:43:31]: So the outputs look like documents, and there's video outputs or audio outputs from some of the tasks. It has to make a video? Yeah, for some of the tasks. Some of the tasks.swyx [00:43:43]: What task is that?George [00:43:45]: I mean, it's in the data set. Like be a YouTuber? It's a marketing video.Micah [00:43:49]: Oh, wow. What? Like model has to go find clips on the internet and try to put it together. The models are not that good at doing that one, for now, to be clear. It's pretty hard to do that with a code editor. I mean, the computer stuff doesn't work quite well enough and so on and so on, but yeah.George [00:44:02]: And so there's no kind of ground truth, necessarily, to compare against, to work out percentage correct. It's hard to come up with correct or incorrect there. And so it's on a relative basis. And so we use an ELO approach to compare outputs from each of the models between the task.swyx [00:44:23]: You know what you should do? You should pay a contractor, a human, to do the same task. And then give it an ELO and then so you have, you have human there. It's just, I think what's helpful about GDPVal, the OpenAI one, is that 50% is meant to be normal human and maybe Domain Expert is higher than that, but 50% was the bar for like, well, if you've crossed 50, you are superhuman. Yeah.Micah [00:44:47]: So we like, haven't grounded this score in that exactly. I agree that it can be helpful, but we wanted to generalize this to a very large number. It's one of the reasons that presenting it as ELO is quite helpful and allows us to add models and it'll stay relevant for quite a long time. I also think it, it can be tricky looking at these exact tasks compared to the human performance, because the way that you would go about it as a human is quite different to how the models would go about it. Yeah.swyx [00:45:15]: I also liked that you included Lama 4 Maverick in there. Is that like just one last, like...Micah [00:45:20]: Well, no, no, no, no, no, no, it is the, it is the best model released by Meta. And... So it makes it into the homepage default set, still for now.George [00:45:31]: Other inclusion that's quite interesting is we also ran it across the latest versions of the web chatbots. And so we have...swyx [00:45:39]: Oh, that's right.George [00:45:40]: Oh, sorry.swyx [00:45:41]: I, yeah, I completely missed that. Okay.George [00:45:43]: No, not at all. So that, which has a checkered pattern. So that is their harness, not yours, is what you're saying. Exactly. And what's really interesting is that if you compare, for instance, Claude 4.5 Opus using the Claude web chatbot, it performs worse than the model in our agentic harness. And so in every case, the model performs better in our agentic harness than its web chatbot counterpart, the harness that they created.swyx [00:46:13]: Oh, my backwards explanation for that would be that, well, it's meant for consumer use cases and here you're pushing it for something.Micah [00:46:19]: The constraints are different and the amount of freedom that you can give the model is different. Also, you like have a cost goal. We let the models work as long as they want, basically. Yeah. Do you copy paste manually into the chatbot? Yeah. Yeah. That's, that was how we got the chatbot reference. We're not going to be keeping those updated at like quite the same scale as hundreds of models.swyx [00:46:38]: Well, so I don't know, talk to a browser base. They'll, they'll automate it for you. You know, like I have thought about like, well, we should turn these chatbot versions into an API because they are legitimately different agents in themselves. Yes. Right. Yeah.Micah [00:46:53]: And that's grown a huge amount of the last year, right? Like the tools. The tools that are available have actually diverged in my opinion, a fair bit across the major chatbot apps and the amount of data sources that you can connect them to have gone up a lot, meaning that your experience and the way you're using the model is more different than ever.swyx [00:47:10]: What tools and what data connections come to mind when you say what's interesting, what's notable work that people have done?Micah [00:47:15]: Oh, okay. So my favorite example on this is that until very recently, I would argue that it was basically impossible to get an LLM to draft an email for me in any useful way. Because most times that you're sending an email, you're not just writing something for the sake of writing it. Chances are context required is a whole bunch of historical emails. Maybe it's notes that you've made, maybe it's meeting notes, maybe it's, um, pulling something from your, um, any of like wherever you at work store stuff. So for me, like Google drive, one drive, um, in our super base databases, if we need to do some analysis or some data or something, preferably model can be plugged into all of those things and can go do some useful work based on it. The things that like I find most impressive currently that I am somewhat surprised work really well in late 2025, uh, that I can have models use super base MCP to query read only, of course, run a whole bunch of SQL queries to do pretty significant data analysis. And. And make charts and stuff and can read my Gmail and my notion. And okay. You actually use that. That's good. That's, that's, that's good. Is that a cloud thing? To various degrees of order, but chat GPD and Claude right now, I would say that this stuff like barely works in fairness right now. Like.George [00:48:33]: Because people are actually going to try this after they hear it. If you get an email from Micah, odds are it wasn't written by a chatbot.Micah [00:48:38]: So, yeah, I think it is true that I have never actually sent anyone an email drafted by a chatbot. Yet.swyx [00:48:46]: Um, and so you can, you can feel it right. And yeah, this time, this time next year, we'll come back and see where it's going. Totally. Um, super base shout out another famous Kiwi. Uh, I don't know if you've, you've any conversations with him about anything in particular on AI building and AI infra.George [00:49:03]: We have had, uh, Twitter DMS, um, with, with him because we're quite big, uh, super base users and power users. And we probably do some things more manually than we should in. In, in super base support line because you're, you're a little bit being super friendly. One extra, um, point regarding, um, GDP Val AA is that on the basis of the overperformance of the models compared to the chatbots turns out, we realized that, oh, like our reference harness that we built actually white works quite well on like gen generalist agentic tasks. This proves it in a sense. And so the agent harness is very. Minimalist. I think it follows some of the ideas that are in Claude code and we, all that we give it is context management capabilities, a web search, web browsing, uh, tool, uh, code execution, uh, environment. Anything else?Micah [00:50:02]: I mean, we can equip it with more tools, but like by default, yeah, that's it. We, we, we give it for GDP, a tool to, uh, view an image specifically, um, because the models, you know, can just use a terminal to pull stuff in text form into context. But to pull visual stuff into context, we had to give them a custom tool, but yeah, exactly. Um, you, you can explain an expert. No.George [00:50:21]: So it's, it, we turned out that we created a good generalist agentic harness. And so we, um, released that on, on GitHub yesterday. It's called stirrup. So if people want to check it out and, and it's a great, um, you know, base for, you know, generalist, uh, building a generalist agent for more specific tasks.Micah [00:50:39]: I'd say the best way to use it is get clone and then have your favorite coding. Agent make changes to it, to do whatever you want, because it's not that many lines of code and the coding agents can work with it. Super well.swyx [00:50:51]: Well, that's nice for the community to explore and share and hack on it. I think maybe in, in, in other similar environments, the terminal bench guys have done, uh, sort of the Harbor. Uh, and so it's, it's a, it's a bundle of, well, we need our minimal harness, which for them is terminus and we also need the RL environments or Docker deployment thing to, to run independently. So I don't know if you've looked at it. I don't know if you've looked at the harbor at all, is that, is that like a, a standard that people want to adopt?George [00:51:19]: Yeah, we've looked at it from a evals perspective and we love terminal bench and, and host benchmarks of, of, of terminal mention on artificial analysis. Um, we've looked at it from a, from a coding agent perspective, but could see it being a great, um, basis for any kind of agents. I think where we're getting to is that these models have gotten smart enough. They've gotten better, better tools that they can perform better when just given a minimalist. Set of tools and, and let them run, let the model control the, the agentic workflow rather than using another framework that's a bit more built out that tries to dictate the, dictate the flow. Awesome.swyx [00:51:56]: Let's cover the openness index and then let's go into the report stuff. Uh, so that's the, that's the last of the proprietary art numbers, I guess. I don't know how you sort of classify all these. Yeah.Micah [00:52:07]: Or call it, call it, let's call it the last of like the, the three new things that we're talking about from like the last few weeks. Um, cause I mean, there's a, we do a mix of stuff that. Where we're using open source, where we open source and what we do and, um, proprietary stuff that we don't always open source, like long context reasoning data set last year, we did open source. Um, and then all of the work on performance benchmarks across the site, some of them, we looking to open source, but some of them, like we're constantly iterating on and so on and so on and so on. So there's a huge mix, I would say, just of like stuff that is open source and not across the side. So that's a LCR for people. Yeah, yeah, yeah, yeah.swyx [00:52:41]: Uh, but let's, let's, let's talk about open.Micah [00:52:42]: Let's talk about openness index. This. Here is call it like a new way to think about how open models are. We, for a long time, have tracked where the models are open weights and what the licenses on them are. And that's like pretty useful. That tells you what you're allowed to do with the weights of a model, but there is this whole other dimension to how open models are. That is pretty important that we haven't tracked until now. And that's how much is disclosed about how it was made. So transparency about data, pre-training data and post-training data. And whether you're allowed to use that data and transparency about methodology and training code. So basically, those are the components. We bring them together to score an openness index for models so that you can in one place get this full picture of how open models are.swyx [00:53:32]: I feel like I've seen a couple other people try to do this, but they're not maintained. I do think this does matter. I don't know what the numbers mean apart from is there a max number? Is this out of 20?George [00:53:44]: It's out of 18 currently, and so we've got an openness index page, but essentially these are points, you get points for being more open across these different categories and the maximum you can achieve is 18. So AI2 with their extremely open OMO3 32B think model is the leader in a sense.swyx [00:54:04]: It's hooking face.George [00:54:05]: Oh, with their smaller model. It's coming soon. I think we need to run, we need to get the intelligence benchmarks right to get it on the site.swyx [00:54:12]: You can't have it open in the next. We can not include hooking face. We love hooking face. We'll have that, we'll have that up very soon. I mean, you know, the refined web and all that stuff. It's, it's amazing. Or is it called fine web? Fine web. Fine web.Micah [00:54:23]: Yeah, yeah, no, totally. Yep. One of the reasons this is cool, right, is that if you're trying to understand the holistic picture of the models and what you can do with all the stuff the company's contributing, this gives you that picture. And so we are going to keep it up to date alongside all the models that we do intelligence index on, on the site. And it's just an extra view to understand.swyx [00:54:43]: Can you scroll down to this? The, the, the, the trade-offs chart. Yeah, yeah. That one. Yeah. This, this really matters, right? Obviously, because you can b

Now that 2026 is officially here, Eric and Victor are still in a celebratory mood. It's not only the first In The Circle episode of the new year, it's also the historic 1,000th episode.The guys are joined by one of their most frequent guests, LSU head coach Beth Torina, who turns the tables on E-Lo during the interview (you'll have to hear it for yourself). Plus, they dive into how the podcast began, who has appeared the most, and some of the show's most memorable milestones.Sit back, relax, and enjoy Episode No. 1,000 of In The Circle, powered by SixFour3.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Aujourd'hui dans Passages, on vous propose de découvrir À Mille Lieux, un podcast Gîtes de France®.

The tornado blows back into Laredo with new commentary from Mike Hudson, plus a snippet of a song that samples the ELO song, plus plus Vinnie Corbitt's self-recorded mash-up of "Laredo Tornado" and "Dark City." Donate to the podcast through Patreon... https://www.patreon.com/ELOPod Or PayPal eloftmpodcast@gmail.com P.O. Box 1932 Superior, AZ 85173.

National bacon day. Entertainment from 1999. Soviet Union formed, 1st color tv's went on sale, One of worst building fire's in US history in Chicago. Todays birthdays - Bo Diddley, Del Shannon, Michael Nesmith, Davy Jones, Jeff Lynne, Suzy Bogguss, Tracey Ullman, Tyrese Gibson. Dawn Wells died.(2024)Intro - Pour some sugar on me - Def Leppard       http://defleppard.com/I love bacon - The Hungry Food BandSmooth - Santana   Rob ThomasBreathe - Faith HillBirthdays - In da club - 50 Cent     http://50cent.com/Bo Diddley - Bo DiddleyRunaway - Del ShannonHey hey were the Monkees - The MonkeesDay dream believer - The MonkeesDon't bring me down - ELOHey Cinderella - Suzy BoggussThey don't know - Tracey UllmanHow you gonna act like that - TyreseExit - In my dreams - Dokken      http://dokken.net/cooolmedia.com

Citoen vient de dévoiler un concept en association avec la marque Decathlon. Son nom : Elo, pourrait préfigurer la voiture de demain. Il faut vous imaginer un modèle au style cubique qui fait penser aux petites voitures japonaises ou aux monospaces des années 2000. Malgré ses 4,10 m de long, c'est à dire la taille d'une citadine type Citroën C3, ce véhicule peut embarquer jusqu'à 6 personnes à bord...Hébergé par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.

(00:00-19:48) Having spritzers at Union Hall. Should we all go to the Billikens game on Saturday? Did we scare of Bob Thomas? Love to see a Mizzou/SLU basketball series. Kegel exercises? Tim hears what he wants to hear. Is it Mr. Soccer or Sir Soccer? Iggy's coat yesterday. Martin's having trouble finding the tweet. ProdJoe's landing strip is active.(19:56-39:34) Jackson loves ELO. Putting out an APB for Lu Lu to call back in. Middle class luxury brands. LuLu sweatpants. The Corrs. TALK SPORTS!!! The Cardinals' three biggest remaining needs per John Denton. Besides not hitting or pitching well, what's wrong with the Cardinals? Doug's down in the mouth. They're just doing movie quotes in the YouTube chat. STOP TALKING SPORTS!!!!(39:44-51:39) We've got some audio of Doug's favorite coach Dan Hurley and he's working blue. Phillip Rivers audio talking about his current weight vs his old playing weight. Big time media chuckles. Why does Dough fat shame so much? Still waiting on more concrete details on the Sherrone Moore story. Hottest coaches in college football. Whatever Ms. Sally's got cooking.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Send us a textHere in Episode 251 of the No Name Music Cast, it is Joy's turn to pick the topic and she chooses to talk about her 2025 Spotify Wrapped!We cover artists she listened to such as Queen, ELO, Elton John and The Dropkick Murphys to name only a few.We also cover mosh pits, Warped Tour, Guardians of the Galaxy and Jukeboxes!Support the showEmail the show: nonamemusiccast@gmail.com Instagram: https://www.instagram.com/nonamemusiccastpodcast/ https://nonamemusiccast.com/

In hour 1 of The Mark Reardon Show, Mark is joined by the Reardon Roundtable which is made up of Jane Dueker, Jeff Rainford and Tim Fitch. They debate and discuss some of the latest trending political news stories. In hour 2, Sue hosts, "Sue's News" where she discusses the latest trending entertainment news, this day in history, the random fact of the day and more. Mark is then joined by Paul Hall, with Common Guy's Film Reviews. He discusses the latest trending movies and shows to watch. He's later joined by Brian Kilmeade, a Fox and Friends Co-Host and the Host of One Nation with Brian Kilmeade and The Brian Kilmeade Show. They discuss the latest trending political news and more. In hour 3, Mark is joined by Brian Kilmeade, a Fox and Friends Co-Host and the Host of One Nation with Brian Kilmeade and The Brian Kilmeade Show. They discuss the latest trending political news and more. He's later joined by Jeff Faulkner, a Local Musician that performs in "Mr Blue Sky", an ELO tribute band. They discuss his band and also go through Mark's top 10 most listened to songs of the year. They wrap up the show with the Audio Cut of the Day.

This episode dives headfirst into the hilarious chaos of our NFC North rivals, tearing apart Bears fans' delusional takes, the Vikings' quarterback nightmare, and the Lions' epic collapse from contenders to complainers. With unfiltered banter and savage breakdowns, we laugh at their misery while celebrating the Packers' rise—because nothing hits harder than watching enemies implode. Shredding Bears fans for their MVP fantasies about Caleb Williams and ignoring real metrics like ELO and DVOA that prove the Packers' superiority Exposing the Vikings' regrets over ditching Sam Darnold for bust JJ McCarthy, who's dead last in EPA and dragging the team to rock bottom Mocking Lions fans' meltdown after Thanksgiving loss, from "same old Lions" cries to calls for firing Dan Campbell and Brad Holmes Highlighting Jordan Love's Player of the Week honors and MVP odds while rivals' dreams fade into despair This episode is brought to you by PrizePicks! Use code PACKDADDY and visit https://prizepicks.onelink.me/LME0/PACKDADDY to get started with America's #1 fantasy sports app. Drop your hottest takes in the comments—do Bears fans really deserve this much smoke, or are Vikings the biggest joke? Smash that subscribe button, leave a review, and let's keep the rival roasting going strong. Catch the next After Dark for more unfiltered Packers truth. To advertise on this podcast please email: ad-sales@libsyn.com Or go to: https://advertising.libsyn.com/packernetpodcast Help keep the show growing and check out everything I'm building across the Packers and NFL world: Support: Patreon: www.patreon.com/pack_daddy Venmo: @Packernetpodcast CashApp: $packpod Projects: Grade NFL Players ➜ fanfocus-teamgrades.lovable.app Packers Hub ➜ packersgames.com Create NFL Draft Big Boards ➜ nfldraftgrades.com Watch Draft Prospects ➜ draftflix.com Screen Record ➜ pause-play-capture.lovable.app Global Economics Hub ➜ global-economic-insight-hub.lovable.app

This episode dives headfirst into the hilarious chaos of our NFC North rivals, tearing apart Bears fans' delusional takes, the Vikings' quarterback nightmare, and the Lions' epic collapse from contenders to complainers. With unfiltered banter and savage breakdowns, we laugh at their misery while celebrating the Packers' rise—because nothing hits harder than watching enemies implode. Shredding Bears fans for their MVP fantasies about Caleb Williams and ignoring real metrics like ELO and DVOA that prove the Packers' superiority Exposing the Vikings' regrets over ditching Sam Darnold for bust JJ McCarthy, who's dead last in EPA and dragging the team to rock bottom Mocking Lions fans' meltdown after Thanksgiving loss, from "same old Lions" cries to calls for firing Dan Campbell and Brad Holmes Highlighting Jordan Love's Player of the Week honors and MVP odds while rivals' dreams fade into despair This episode is brought to you by PrizePicks! Use code PACKDADDY and visit https://prizepicks.onelink.me/LME0/PACKDADDY to get started with America's #1 fantasy sports app. Drop your hottest takes in the comments—do Bears fans really deserve this much smoke, or are Vikings the biggest joke? Smash that subscribe button, leave a review, and let's keep the rival roasting going strong. Catch the next After Dark for more unfiltered Packers truth. To advertise on this podcast please email: ad-sales@libsyn.com Or go to: https://advertising.libsyn.com/packernetpodcast Help keep the show growing and check out everything I'm building across the Packers and NFL world: Support: Patreon: www.patreon.com/pack_daddy Venmo: @Packernetpodcast CashApp: $packpod Projects: Grade NFL Players ➜ fanfocus-teamgrades.lovable.app Packers Hub ➜ packersgames.com Create NFL Draft Big Boards ➜ nfldraftgrades.com Watch Draft Prospects ➜ draftflix.com Screen Record ➜ pause-play-capture.lovable.app Global Economics Hub ➜ global-economic-insight-hub.lovable.app

In hour 3, Mark is joined by Brian Kilmeade, a Fox and Friends Co-Host and the Host of One Nation with Brian Kilmeade and The Brian Kilmeade Show. They discuss the latest trending political news and more. He's later joined by Jeff Faulkner, a Local Musician that performs in "Mr Blue Sky", an ELO tribute band. They discuss his band and also go through Mark's top 10 most listened to songs of the year. They wrap up the show with the Audio Cut of the Day.

In this segment, Mark is joined by Jeff Faulkner, a Local Musician that performs in "Mr Blue Sky", an ELO tribute band. They discuss his band and also go through Mark's top 10 most listened to songs of the year.

EPISODE #119 - From 1988 to 1991, Matt Bissonette played bass in David Lee Roth's solo band. His tenure included the Skyscraper World Tour as well as the recording of the “A Little Ain't Enough” album. In this interview, Bissonette discusses his experience in full detail as well as playing with Joe Satriani, Elton John, ELO. his brother Gregg Bissonette and making his new jazz album, “Common Road.” The Daves also serve up a stacked VH News segment that includes a review of Mammoth's new album, “The End.” Plus, a new mailbag segment completes this November episode. Download the podcast for free on Spreaker, iHeartRadio, Spotify, Google podcasts, Amazon Music, Podvine or iTunes. Connect with the Daves on Twitter: @ddunchained, Facebook: Dave & Dave Unchained – A Van Halen podcast, Instagram: ddunchainedpodcast or via email: ddunchainedpodcast@gmail.com

I hear Jeff Lynne has recently been sidelined by a mysterious infection, and had to cancel some performances. We wish the revered music producer and founder of ELO good health and a quick recovery. Today, Bill and Rich, The Splendid Bohemians would like to celebrate this versatile Rock stylist by playing two cuts separated by decades, from two vastly different incarnations, but somehow identifiable as his, if only by the unique sensibility in which these cuts are marinated: Imposters of Life's Magazine by The Idle Race, and Tweeter and the Monkey Man by The Traveling Wilburys.There are certain tropes that signify this creator's hand: catchy musical themes, thick layers of horns and strings and witty Beatle-esque harmonies - (he even got to work with the resurrected voice of John Lennon when producing the Beatle's ghostly “reunion”tracks Free As a Bird, and Real Love. Like so many of his generation, Jeff came up under the spell of the mop tops, and even this early recording by the Idle Race has that 1967 Psychedelic flavor, pre-dating by a year, but somehow reminiscent of his future Willburys bandmate, George Harrison's song Savoy Truffle. IMPOSTERS OF LIFE'S MAGAZINEThe personnel changes of the several Birmingham beat groups in the mid-sixties are too numerous to recite here, but one group, The Nightriders are notable because they took on a teenaged Lynne as guitarist in '66 - and changed their name to The Idle Race (a more timely handle). Roy Wood of the Move, Jeff's friend and future partner in the formation of ELO, helped the IR get signed, and influential DJ John Peel was an early and strong promoter of the group. Jeff's tenure with IR was short lived however, and in 1970 he founded (with Wood) the legendary Electric Light Orchestra.This early composition has all the hallmarks that would later define Jeff's work: witty, yet Romantic lyrics; hard driving rhythms, but with plenty of surprisingly lush filigree. The ambition of the work, with its startlingly different movements, is impressive for such a young talent - but, at the core is Jeff's unmistakable ear for the “hook” - marking him as one of the most reliable masters of Pop.TWEETER AND THE MONKEY MANTweeter and the Monkey Man, from 22 years later - demonstrates how far the musician had come professionally.  Here Jeff is, arguably the lowest man on the totem pole of The Traveling Wilburys, a Super Group's Super Group (with a Beatle, George Harrison, future Nobel Laureate Bob Dylan, Tom Petty, and Roy - fucking - Orbison…., yet his mark is unmistakeable. This tune, supposedly written by Bob Dylan, who spittingly delivers it like a parody of Springsteen's New Jersey, drenched in a Sopranos sauce - and it's just plain fun. But, the chorus, penned by Jeff with George Harrison, lifts the track to the existential level of a rock opera, giving it “the hook,” the drama and the flair. The layers on this musical cake are delicious - there's George's sly slide work; the Lady Madonna horns, the timpani booms counterpointed by a tinkling piano riff; the building of the strings and horns and oohing and aaahing harmonies… they suck me in every time. Good stuff!

DISCLAIMER: This episode is NOT a condonement or endorsement of generative AI, nor is the thumbnail made by AI. All of us at Rock of Ages are anti-AI because it lacks any sort of creativity; it can only steal what it sees/knows, and it's destructive to the environment. The episode only exists to mock AI "content", putting it in its place as laughable and unserious efforts.So, in the last episode we did, I read a random internet comment relating the album Time by ELO's sound to the popular Disney show Star vs the Forces of Evil. I couldn't hear it that much, but it did get me thinking what a cross between the two would look like. And because one of the albums themes was the threats of AI, I thought what would the dumb machine think it would look like. So I put in a prompt, and it churned out what I can only call 'anti-content'. And this bonus episode is a dramatic reading of it! Yaaaaayyyy....Originally recorded April 13, 2025.

Critically panned at release, this synth heavy effort by Jeff Lynne and his Beatles tribute band has since been looked back as a very progressive, influential, and flat-out amazing record. And the theater kids agree. It's time for ELO's "Time"!Originally recorded April 13, 2025.

Lara Železnik s partnerjem Gašperjem Koscem in argentinsko dogo Elo živi v ribiškem in surferskem mestu Tofino v Kanadi, na otoku Vancouver. V mestecu ni semaforjev, pozimi ima manj kot 3000 prebivalcev, s turisti pa poleti ta številka naraste na približno 30.000. Obiskovalci prihajajo deskat na vodi, opazovat orke in deževni gozd, zelo blizu so tudi črni medvedi in pume, ki kdaj zaidejo celo v mesto. Dom sta Slovenca našla na 13-metrski ladji. Poleg poznavanja tokov in vetrov Pacifika je velik izziv pranje perila, saj se morata za to odpraviti v mestno pralnico. Prednosti pa so izleti, nekaj ur plovbe stran lahko najdeta “zasebne” naravne vroče vrelce.

Programmation consacrée aux nouveautés musicales et aux chansons gold. Dans la séquence Génération Consciente, Eva Kouassi, fondatrice de Ekklesia, une association qui œuvre depuis 2020 auprès des jeunes défavorisés. Elle présente la première soirée caritative de l'association qui aura lieu au CGR (Paris 19ème) le 1er décembre dans le but de récolter des fonds pour la construction de médiathèques dans les zones rurales de Côte-d'Ivoire. Et Ibrahim Hassani, fondateur de Impakty, une marque de sport engagée. A l'occasion de la CAN 2025, au Maroc, la marque a créé un ballon Africa United qui porte 30 drapeaux de pays africains. Pour visionner les clips, cliquez sur les titres des chansons :  Jungeli, Imen ES, Alonzo, Abou Debeing & Lossa - Petit génie Werrason - La vie est compliquée Kocee - PDG Josey - Sexy drill Keros-N - Fwansé Kemmler - Si un jour tu pars Major Lazer feat America Foster - Peppa pot Eloïsha - Plus jamais ça Makhalba Malecheck - Mr Malecheck Biz Ice - Maykalambasse Aya Nakamura - No stress Magnum Band - Paka Pala Magnum Band - Ashadei Magnum Band - Expérience Retrouvez la playlist officielle de RFI Musique.

Programmation consacrée aux nouveautés musicales et aux chansons gold. Dans la séquence Génération Consciente, Eva Kouassi, fondatrice de Ekklesia, une association qui œuvre depuis 2020 auprès des jeunes défavorisés. Elle présente la première soirée caritative de l'association qui aura lieu au CGR (Paris 19ème) le 1er décembre dans le but de récolter des fonds pour la construction de médiathèques dans les zones rurales de Côte-d'Ivoire. Et Ibrahim Hassani, fondateur de Impakty, une marque de sport engagée. A l'occasion de la CAN 2025, au Maroc, la marque a créé un ballon Africa United qui porte 30 drapeaux de pays africains. Pour visionner les clips, cliquez sur les titres des chansons :  Jungeli, Imen ES, Alonzo, Abou Debeing & Lossa - Petit génie Werrason - La vie est compliquée Kocee - PDG Josey - Sexy drill Keros-N - Fwansé Kemmler - Si un jour tu pars Major Lazer feat America Foster - Peppa pot Eloïsha - Plus jamais ça Makhalba Malecheck - Mr Malecheck Biz Ice - Maykalambasse Aya Nakamura - No stress Magnum Band - Paka Pala Magnum Band - Ashadei Magnum Band - Expérience Retrouvez la playlist officielle de RFI Musique.

¡Bienvenidos a un episodio más de Permanencia Involuntaria! En este episodio platicamos sobre El último Samurai en pie, El show de Chuy y Elo, El multiverso de Chuy, El final de temporada de The Morning Show, vistazo de 5 noches en Freddy's 2, Wicked 2 reseña, Las cómplices en VIX, y el cuarto episodio de it: Welcome to Derry.Conduce; Fausto Ponce.Colaboradores: Carlos Andrés Mendiola y Daniel VillamilCreado y conducido por Fausto Ponce.Conviértete en un seguidor de este podcast: https://www.spreaker.com/podcast/permanencia-involuntaria--2789464/support.Permanencia Involuntaria es creado y conducido por Fausto Ponce. Permanencia Involuntaria está disponible en Spreaker, Youtube, iVoox, Amazon Music, Spotify, Apple podcasts y más.  Permanencia Involuntaria es un proyecto que forma parte de la revista digital Alta Fidelidad Magazine. 

durée : 00:10:13 - Graciane Finzi : Fräulein Else - Ramina Abdulla-Zadè, Eloïse Cenac Morthe - Composée en 2013 sur un livret de Heinz Schwarzinger d'après la nouvelle d'Arthur Schnitzler, Fräulein Else est une œuvre pour voix et quatuor à cordes. Vous aimez ce podcast ? Pour écouter tous les autres épisodes sans limite, rendez-vous sur Radio France.

Google just released Gemini 3, and I tested it with 3 real business use cases: prepping for a $200K sales call, building an interactive sales dashboard, and creating a complete website from scratch—all in minutes.In this video, I show you LIVE examples of:✅ Gemini Agent prepping an entire sales call (research, pitch angles, objection handling, follow-ups)✅ Dynamic dashboards with real-time calculations and interactive sliders✅ Full website generation with 921 lines of working code in under 60 seconds✅ Custom image generation and prompt engineeringThis isn't theory—I'm screen recording everything as it happens so you can see the actual speed and quality.⏱️ TIMESTAMPS:0:00 - Intro: Why Gemini 3 is a Big Deal1:15 - Benchmark Breakdown (Why This Matters)2:27 - Use Case #1: $200K Sales Call Prep4:46 - Visual Layout & Interactive Infographic Demo7:03 - Use Case #2: Building a Website from Scratch (Live Code)9:27 - Use Case #3: Interactive Q4 Sales Dashboard11:51 - Testing the Prompt Generator Live12:45 - Final Thoughts & Why This Changes Everything

Dicen que la ignorancia es atrevida, así que no sorprende que los colegas se dedique a pontificar sobre temas de los que no han tenido ni la profesionalidad ni la decencia de informarse. Los datos están ahí, al alcance de todos, pero eso significaría trabajar y, lo que es aún peor, si conocen la realidad quedaría aún más en evidencia su mala fe. Y qué felices viven en la ignorancia voluntaria para no tener ni un atisbo de remordimientos de conciencia. Min. 01 Seg. 49 – Intro Min. 08 Seg. 12 - Una empresa de activos comerciales Min. 15 Seg. 13 - No lo entenderían ni con un croquis Min. 24 Seg. 30 - Está claro, el estadio les arruinó Min. 33 Seg. 57 - Gafas para ver el mundo de color de rosa Min. 40 Seg. 52 – La situación económica no es tan idílica Min. 48 Seg. 04 - Un parón que sentó fatal Min. 56 Seg. 33 - Algo que quieren que olvidemos Min. 64 Seg. 25 - Despedida Duane Betts & Chuck Leavell - Rain (Macon, GA 28/02/2025) Bloque (Madrid 15/10/1994) Abelardo y Eloísa Descubre el sentido terrible de la vida Caracosida Maldito traidor Undécimo poder Rockin' In The Free World Radar Love Savoy El verdadero silencio Ni un día más Rey de la noche Duane Betts, Dereck Trucks & Lamar Williams Jr. - Revival (Macon, GA 28/02/2025)

Mondays are for In The Circle, powered by SixFour3. We wrap up our trip around the Mountain West with a visit to San Diego State. The defending tournament champions feature a young but talented team heading into 2026. Head coach Stacey Nuveman Deniz shares her thoughts on the roster, the Mountain West landscape, and her busy summer with the AUSL. Afterwards, ELo and Victor finally share their thoughts on the D1Softball All-Quarter Century Team and the Top Power 4 Pitchers.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Highlights of what's new in streaming for the week of November 8, 2025. Netflix Countdown: Jake vs. Tank (Nov. 8) Marines (Nov. 10) Sesame Street, volume 1 (aka season 56) (Nov. 10) Being Eddie (Nov. 12) Dynamite Kiss, season 1 (Nov. 12) Eloá the Hostage: Live on TV (Nov. 12) A Merry Little Ex-Mas (Nov. 12) Mrs. Playmen, season 1 (Nov. 12) Selling the O.C., season 4 (Nov. 12) The Beast in Me (Nov. 13) Delhi Crime, season 3 (Nov. 13) Had I Not Seen the Sun, part 1 (Nov. 13) Last Samurai Standing, season 1 (Nov. 13) Tee Yai: Born To Be Bad (Nov. 13) Unicorn Academy, chapter 4 (Nov. 13) The Crystal Cuckoo, season 1 (Nov. 13) In Your Dreams (Nov. 14) Jake Paul vs. Tank Davis (Nov 14) Lefter: The Story of the Ordinarius (Nov. 14) Nouvelle Vague (Nov. 14) Disney+ 2025 Rock & Roll Hall of Fame Induction Ceremony (Nov. 8 at 8 p.m. EST) The Secret Lives of Mormon Wives, season 3 (Nov. 13) LEGO Marvel Avengers: Strange Tails (Nov. 14) A Very Jonas Christmas Movie (Nov. 15) HBO Max Eddington (Nov. 14) The Seduction (Nov. 14) Paramount+ My Nightmare Stalker: The Eva LaRue Story (Nov. 13) Peacock Tiffany Haddish Goes Off (Nov. 13) Prime Video Playdate (Nov. 12) Malice, season 1 (Nov. 14) Apple TV+ Palm Royale, season 2 (Nov. 12) Come See Me in the Good Light (Nov. 14) Hallmark+ Christmas Above the Clouds (Nov. 8) A Keller Christmas Vacation (Nov. 8)

Today, I finally get to talk about the Electric Light Orchestra as author, actor and iconic supermodel Paulina Porizkova joins me to talk about the classic ELO album 'Out of the Blue'. Paulina takes us back to moving to Paris as a 15 year old at the beginning of her career, and how this album soundtracked this chapter of her life, coming to the record as a lover of classical music, why ELO aren't thought of as an albums artist, why the album's theme of stepping out on your own resonated with her, their experiences seeing Jeff Lynne and ELO live in recent years and more.

Welcome back Elo Solarii... It's not been that long, but let's be real, there's been much to say. Sometimes, we need more time to get it all out. Sometimes, our bladders get in the way. :) Check out Elo at Youth Gone Wild in New Richmond, WI. Thank you again, Elo! You honor us with your story. We'll be keeping in touch as time moves ahead.  God bless!    ****If you liked The Apprenticeship Diaries (T.A.D.), please follow us, rate, and review us! Also, get our webpage to climb on the search engine by visiting it HERE. If you would like to donate to the show, we greatly appreciate the support. Click here to throw us a little love.

The crew is back, and this time we're talking Tavern Brawl team management where we share some advice and lessons learned on how to help your squad run smoothly across a long season.Next up, it's been quite a while since the last balance patch, and one upside of that fact is that we have substantial sample sizes to work with. So that means it's time for a Meta Mashup!Speaking of data, Filtrophobe unveils a vibe-coded ELO system using Hero Helper stats, complete with four different ranking interpretations depending on which game types are being factored in.And with upcoming nuptials on the horizon, Tuff and Dubz take a moment to deliver some advice to the husband-to-be.All this and more on the latest episode of Sparks & Rec. Thanks for listening! WWYD: 10:01Meta Mash-up: 28:09Tavern Brawl Team Management Advice: 1:07:14ELO Four Ways: 1:35:52Advice: 1:54:45Community Round-Up: 1:57:54Taps, Scraps, and Good-byes: 2:05:45Hero Realms is a fantasy-themed expandable deckbuilding game from Wise Wizard Games.Hosts: Chris "DblDubz" Walberg, Cooper "Filtrophobe" Fitzpatrick, and John "Tuff" LabellaProducer: Chris WalbergHero Helper: https://hero-helper.com/Realms Rising: https://www.realmsrising.comYou can find the WWYD screenshots for this episode here: https://www.realmsrising.com/podcast/episode-86-arena-updates-and-nut-draw-finalePatreon: https://patreon.com/sparksandrecHyperGeometric Calculator: https://aetherhub.com/Apps/HyperGeometricCommunity Tournaments & Events Primer (+ signup links): https://www.realmsrising.com/community-events/Realms Rising Discord: https://discord.gg/8pTxKqzFDcContact S&R: contact@sparks-and-recreation.comSupport Sparks & Rec: https://hero-helper.com/support-usSparks & Recreation Website: https://www.realmsrising.com/sparks-and-recreation/Thank you so much to Level 12 Hero Sarah T., Warden Slayer, as well as Level 7 Hero Nudeltulpe!Specific songs used in this episode were:Intro/Outro Music: "Uplifting Orchestra Pack" by GoodBunny. (Under the Music Standard License)Licensed under Creative Commons BY Attribution 4.0 License Hosted on Acast. See acast.com/privacy for more information.

Satrancı “kazanmak”tan öte bir düşünme deneyimi olarak anlatan Hasgüleç; taşların anlamlarından ELO sistemine, kadınlar kategorisinden hile tartışmalarına, 4 kişilik satranç ve Chess960 gibi varyantlardan Türkiye'de yükselen genç ustalara kadar merak edilen her şeyi konuşuyor. Hem oyuna yeni başlayacaklara hem de yıllardır oynayanlara “neden bu kadar zevkli?” sorusunun samimi bir cevabını veriyor.Bu bölümde konuşulan bazı konular:

Send us a textSome conversations ask you to sit up a little straighter. This one asks you to relax your shoulders, tell the truth, and feel what you've been carrying. We dive into the messy overlap of trauma and grief in first responder and military cultures, where silence is rewarded and honesty is too often punished, and we share a different path built on authenticity, peer support, and practical skills.Blythe Landry joins us to map the line between privacy and secrecy, and why crossing it keeps people sick. We talk about ethical self-disclosure—when a helper shares only to serve the client—and how human presence beats formal scripts and stiff suits for building trust. You'll hear why fit-for-duty vibes can re-trigger rank-based fear, why plain language matters, and how showing up as a person first invites others to do the same. We also confront the system costs of looking away: moved abusers, muted reports, moral injury, and the downstream mix of suicide risk, substance use, gambling, overwork, and other behavioral addictions that masquerade as coping.Grief work sits at the center. Acute grief isn't a two-week arc; it softens when people gain tools, witness, and meaning. We break down how trauma shapes worldview and therefore grief, and why evidence-based skills plus an honest community can turn pain into purpose without sugarcoating the loss. Blythe shares a trauma-informed grief coaching track designed for grievers and peer supporters—exactly the kind of culture-fit training that spreads healing inside agencies that need it most.If you serve, love someone who serves, or lead a team where the unspoken rule is “suck it up,” this conversation offers a better rule: say what's true, get support, and refuse secrecy. Subscribe, share this with a teammate, and leave a review with one insight you'll bring back to your crew. Your words might be the reason someone reaches out.Reach Blythe through her website at https://www.blythelandry.com/Freed.ai: We'll Do Your SOAP Notes!Freed AI converts conversations into SOAP note.Use code Steve50 for $50 off the 1st month!Disclaimer: This post contains affiliate links. If you make a purchase, I may receive a commission at no extra cost to you.Support the showYouTube Channel For The Podcast

Welcome back to this last piece (for now) with Ilia!... Ilia Sterling of "Youth Gone Wild" in New Richmond, WI. She and her mentor Elo, are living together, doing an apprenticeship together (her as the apprentice) and partners. This is a very rare, 2 part Diary Entry with someone who can affirm the message, "It's never too late!" Thank you Ilia! You honor us with your story. We'll be keeping in touch as time moves ahead.  God bless!    ****If you liked The Apprenticeship Diaries (T.A.D.), please follow us, rate, and review us! Also, get our webpage to climb on the search engine by visiting it HERE. If you would like to donate to the show, we greatly appreciate the support. Click here to throw us a little love.

Hoy escuchamos: Eloísa de Castro- Emilia Pardo Bazán rerum natura, Charlotte Wessels- Backup plan, Entrevista La Fuga: La Fuga- Flores de mentira, La Fuga- En mi pecho. The Axe Project- Silent earth, Phenomy- Phantasmagoria, Delalma- Mañana vuelve a oscurecer.Escuchar audio

The week before last, we had Elo and now, We have Ilia!... Ilia Sterling who is Elo Solari's partner and tattoo apprentice. They both work at "Youth Gone Wild" in New Richmond, WI.  They live together, work together and have a mentor/mentee relationship, on top... But before all of that, we learn Ilia's story, in this 2 part Diary Entry, and what led her to tattooing and Elo. :) Thank you Ilia! This was a great time and you have a very unique story that I'm honored to hear and share. God bless!    ****If you liked The Apprenticeship Diaries (T.A.D.), please follow us, rate, and review us! Also, get our webpage to climb on the search engine by visiting it HERE. If you would like to donate to the show, we greatly appreciate the support. Click here to throw us a little love.

The new ZA game brings an event to GO, Halloween is coming and so is Poltchageist, Anicor and Caleb compare ELO for their Maroon 5 bet, be on the lookout for the Christmas EP from the Battle Catz, and is Florges a powerhouse for this upcoming weekend...? Get The Battle Catz Podcast merchandise here: https://the-battle-catz-podcast-shop.fourthwall.com/ Where to find us! YouTube - https://youtube.com/@thebattlecatzpodcast X - https://twitter.com/BattleCatzPod Caleb Peng YouTube - https://youtube.com/calebpeng  X - https://twitter.com/CalebPeng Twitch - https://twitch.tv/calebpeng  HurricaneKaz X - https://x.com/thehurricanekaz Steve YouTube - https://www.youtube.com/PvPSteve X - https://x.com/PvPSteve1 Twitch - https://twitch.tv/PvPSteve7 Podcast - https://www.youtube.com/@GdayBattlers Twastell X - https://x.com/pogoTwastell Anicor X - https://x.com/AnicorXIII 0:00:00 - Intro & In Game Events 0:25:49 - GO Battle League 0:55:57 - Championship Series 1:13:54 - YouTube Comments

Le titre est une collaboration avec Edday, artiste incontournable du bouyon aux Antilles, et Jixels, maître du clavier. On découvre aussi des extraits des derniers EP de Mightyyout et Madyboy, le nouveau single de Vania Ice et Juste Shani, un des nouveaux talents du rap français. Retrouvez également le blind test musical de Couleurs Tropicales avec nos invitées Eloïsha Iza et Man zèle Cléo Playlist (dans l'ordre de l'émission) Pour visionner les clips, cliquez sur les titres des chansons.  Mightyyout feat. Popcaan – Ibadi Vania Ice - Vas Y Krys feat. Edday x Jixels  - POUKWA Madyboy - Viv'li Juste Shani - FOMO Le Karmapa - La Duchesse  Thembi Seete - Gebhu Issa Bagayogo - Gnangran  Carlos Puebla - Hasta siempre Mozarf - 99 Bucks  ► Retrouvez la playlist officielle de RFI Musique.

This week on Transmissions, we're toasting harvest season with John Stirratt and Pat Sansone of The Autumn Defense, who release their first album in a decade this week. It's called Here and Nowhere, out October 10 on Yep Roc Records. You might know John and Pat from their work in Wilco; Stirratt is a founding member, and Sansone joined in 2004. But the duo's work in the Autumn Defense stretches all the way back to 1999, when they formed the Laurel Canyon-style folk rock band in New Orleans.  Here and Nowhere features everything you like about the band; sterling vocals, beautiful ‘70s style orchestration, replete with shades of the baroque pop that Sansone plays on Baroque Down Palace, his radio show on WYXR. Think Todd Rundgren, Bread, Carole King, and even ELO at their most rustic. It's a tender, funny, and warming record. We discuss the new record in the hour that follows, along with detours into other projects, some Wilco talk, and an extended reflection on the legacy of Big Star—a band that's more than just influential to these two—as they actually play the Big Star catalog with drummer Jody Stephens live these days. Let's dive in with this all new episode of Tranmissions. We're brought to you by ⁠Aquarium Drunkard⁠, an independent music media crew headed by Justin Gage. Over at Aquarium Drunkard, you'll gain access to 20 years of music writing, playlist, essays, mixtapes, radio special, podcasts, videos and more.

Since this is a rerun, would it be dreaming of 8000? New commentary from Mike Hudson, and snippets of the songs that sample this ELO song. Donate to the podcast through Patreon... https://www.patreon.com/ELOPod Or PayPal eloftmpodcast@gmail.com P.O. Box 1932 Superior, AZ 85173.

Dans notre sélection musicale du jour, Badja Tina, composé par l'artiste bissau-guinéenne Fattú Djakité comme un «cri de liberté» des jeunes filles pour dénoncer les mariages d'enfants forcés. On écoute aussi, entre autres, les nouveaux singles de Josey, Ferre Gola et Pépé Oleka avant de repartir en 1976 dans la séquence Gold avec Love's In Need Of Love Today de Stevie Wonder, un appel à nourrir l'amour dans une mise en garde contre l'indifférence collective.  Pour visionner les clips, cliquez sur les titres des chansons :  Josey - 6 devient 9 Daphne - Stand by me Ferre Gola - Ngebu Ngebu Bad Bunny ft. Chuwi - WELTiTA  King Combs feat. North West & Jaas - Lonely Roads Fattú Djakité - Badja Tina Team Paiya, l'oiseau rare - Tui Tui Pépé Oleka - Bábá Dre-A feat. Fanicko – Pas toucher Eloïsha IZA - Rouge à lèvres India Arie - Steady Love Stevie Wonder - Love's In Need Of Love Today Lokua Kanza - Le Bonheur  ► Retrouvez la playlist officielle de RFI Musique.

The crew jumps back to 1976 to take a tip with ELO and their album A New World Record.  Rock On!ELO on the Midnight Special:https://www.youtube.com/@themidnightspecialtvshow/search?query=electric%20lightTheme music "Trance" by The Steepwater Band.  Check tour dates at steepwaterband.com and follow them @steepwaterbandWebsite: https://ridiculousrockrecordreviews.buzzsprout.comContact us! e-mail: ridiculousrockrecords@gmail.comFacebook: https://www.facebook.com/R4podcastTwitter/X: @r4podcasterInstagram: https://www.instagram.com/r4podcaster/

The boys discuss Doom Bots, Elo-nomics, Dumbing down the game, Trivia, emails and more on episode 706 of Leaguecast! Email us - mail@leaguecastpodcast.com   Support us - https://www.patreon.com/leaguecast  Tweet us - https://twitter.com/leaguecast   Facebook - https://www.facebook.com/Leaguecast/   Join Our Discord - https://discord.gg/leaguecast

Nano Banana is no longer a mystery.Google officially released Gemini 2.5 Flash Image on Tuesday (AKA Nano Banana), revealing it was the company behind the buzzy AI image model that had the internet talking. But... what does it actually do? And how can you put it to work for you? Find out in our newish weekly segment, AI at Work on Wednesdays.Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Thoughts on this? Join the convo and connect with other AI leaders on LinkedIn.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:Gemini 2.5 Flash Image (Nano Banana) RevealBenchmark Scores: Gemini 2.5 Flash Image vs. CompetitionMultimodal Model Capabilities ExplainedCharacter Consistency in AI Image GenerationAdvanced Image Editing: Removal and Object ControlIntegration with Google AI Studio and APIReal-World Business Use Cases for Gemini 2.5Live Demos: Headshots, Mockups, and InfographicsGemini 2.5 Flash Image Pricing and LimitsIterative Prompting for AI Image CreationTimestamps:00:00 "AI Highlights: Google's Gemini 2.5"06:17 "Nano Banana AI Features"09:58 "Revolutionizing Photo Editing Tools"12:31 "Nano Banana: Effortless Video Updating"14:39 "Impressions on Nano Banana"19:24 AI Growth Strategies Unlocked20:58 Turning Selfie into Professional Headshot24:48 AI-Enhanced Headshots and Team Photos29:51 "3D AI Logo Mockups"32:22 Improved Logo Design Review35:41 Photoshop Shortcut Critique38:50 Deconstructive Design with Logos44:01 "Transform Diagrams Into Presentations"46:12 "Refining AI for Jaw-Dropping Results"Keywords:Gemini 2.5, Gemini 2.5 Flash Image, Nano Banana, Google AI, Google DeepMind, AI image generation, multimodal model, AI photo editing, image manipulation, text-to-image model, image editing AI, large language model, character consistency, AI headshot generator, real estate image editing, product mockup generator, smart image blending, style transfer AI, Google AI Studio, LM Arena, Elo score, AI watermarks, synthID fingerprint, Photoshop alternative, AI-powered design, generative AI, API integration, Adobe integration, AI for business, visual content creation, creative AI tools, professional image editing, iterative prompting, interior design AI, infographic generator, training material visuals, A/B test variations, marketing asset creation, production scaling, image benchmark, AI output watermark, cost-effective AI images, scalable AI infrastructure, prompt-based editing, natural language image editing, OpenAI GPT-4o image, benchmarking leader, visSend Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Ready for ROI on GenAI? Go to youreverydayai.com/partner

Watch on Philo! - Philo.tv/DTHWe're in Lagos. There's no snow on the ground despite the fact that it's Christmastime. We meet Fiyin who is excited about the holidays. Why? Because this is the year that she tells her best friend, Elo, that she loves him! It's time! Spoiler alert, it's not time...for them. Elo tells Fiyin that he is proposing to his girlfriend he's been dating for 8 months that I guess Fiyin didn't know about. Fiyin wastes no time hatching a plan to ruin his relationship with her. We also be Fiyin mom, Gbemi. She literally bumps into her ex, Zach. He's a hot shot deal maker who begins to shoot his shot relentlessly despite the fact that she has a man friend- Toye. They all go to this birthday party and Filo's cousin, Ivie, meets this guitarist named Ajani. The sparks are flying and they agree to go out sometime. At this party, Zach and Gbeni end up kissssinggggggg. UH OH!!!We meet Elo's girlfriend and she's phenomenal. She basically brought the covid vaccine to Nigeria. She's a hero. IS that gonna stop Fiyin? Nah. Zach keeps shooting his shot, bringing a bunch of gifts to Gbeni. She finally comes clean and is like Zach is my ex-fiance. He is like I love you and want to be with you but you should take some space.Ivie and Ajani go out on a date and it's clear that they're from very different lifestyles but it works for them. There is this side storyline for Elo where his mom doesn't want to celebrate Christmas because it doesn't feel the same without Elo's sister who was killed a few years ago. Ultimately, she comes around to it and surprises Elo by decorating the house. Gbeni tells Toye that she wants to spend the rest of her life with him and they decide to get married on Christmas.After the wedding, Fiyin decides to shoot her shot, tell Elo how she feels, and kiss him. Obviously his girlfriend sees them. He tells Fiyin that she's so selfish. She realizes she goofed so she goes to make it right and they end up making it right. And Ivie tells Ajani to visit her in London sometime.