Podcasts about Roon

In this episode Ray and Lunchbox get you ready for the Winter storm that is taking over America. Ray think's Lunchbox's plan of going to find snow is an awful idea and since Ray is from the North he will tell you the best place to be for the storm. Plus the cameras are now working so we went LIVE on YOUTUBE!!!! Ray has plans to meet up with another dude in a hotel room to drink bourbon and play video games all weekend and we read some emails from Sore Losers Nation. See omnystudio.com/listener for privacy information.

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

Soulful House and Afro House taken from my weekly show on Soulful House Music Collage on Twitch. The show airs every Saturday morning on Twitch.TV/SoulfulHouseMusicCollage from 10am-12pm EST

Soulful House and Afro House taken from my weekly Twitch show which airs every Saturday morning on Twitch.TV/SoulfulHouseMusicCollage from 10am-12pm EST

De nieuwe aflevering van De Mobiliteitsprofessionals staat nu online! In aflevering 114 is Michel van Roon van Go Remarketing Solutions te gast. Ze duiken diep in de gezondheid van accu's, wat cruciaal is voor de actieradius van EV's. Michel vertelt alles over hoe de accu's gemeten worden, waarom dit belangrijk is en hoe de restwaarde bepaalt wordt.

Eine Krankenversicherung ist in Deutschland Pflicht. Trotzdem sind viele nicht abgesichert. Allein in Berlin haben etwa 60.000 keinen Zugang zur Gesundheitsversorgung. Die Dunkelziffer dürfte noch höher sein. Dennoch wird ihnen geholfen. Roon, Anastasija www.deutschlandfunk.de, Das Wochenendjournal

Roon, anastasija www.deutschlandfunk.de, Deutschland heute

Soulful House and Afro House taken from my weekly Twitch.TV show which airs every Saturday morning on Twitch.TV/SoulfulHouseMusicCollage from 10am-12pm EST

Soulful House and Afro House taken from my weekly Twitch.TV show, which airs every Saturday morning on Twitch.TV/SoulfulHouseMusicCollage from 10am-12pm EST

Soulful House and Afro House taken from my weekly Twitch.TV show, which airs live every Saturday morning on Twitch.TV/SoulfulHouseMusicCollage from 10am-12pm EST

Soulful House and Afro House taken from my weekly Twitch.TV show which airs every Saturday morning on Twitch.TV/SoulfulHouseMusicCollage from 10am-12pm EST

Brendan is joined by Dr. Dave Roon from Oregon State University to discuss his work on modelling the effects of wildfire on fish and aquatic habitats in the Pacific North West. Dr. Roon and his coauthors have been using foodweb models to understand how changing fire disturbance regimes could impact aquatic life with an emphasis on fish.  Tune in to learn how fire can negatively and positively impact fish and their habitats.   You can read their recently published article "Linking Fire, Food Webs, and Fish in Stream Ecosystems", available via Open Access, here! Remember to lead with curiosity!   Get in touch with us! The Fisheries Podcast is on Facebook, Twitter, Instagram, Threads, and Bluesky: @FisheriesPod  Become a Patron of the show: https://www.patreon.com/FisheriesPodcast Buy podcast shirts, hoodies, stickers, and more: https://teespring.com/stores/the-fisheries-podcast-fan-shop Thanks as always to Andrew Gialanella for the fantastic intro/outro music. The Fisheries Podcast is a completely independent podcast, not affiliated with a larger organization or entity. Reference to any specific product or entity does not constitute an endorsement or recommendation by the podcast. The views expressed by guests are their own and their appearance on the program does not imply an endorsement of them or any entity they represent. Views and opinions expressed by the hosts are those of that individual and do not necessarily reflect the view of any entity with those individuals are affiliated in other capacities (such as employers).

Soulful House and Afro House taken from my weekly Twitch.TV show, which airs live every Saturday morning on Twitch.TV/SoulfulHouseMusicCollage from 10am-12pm EST

In this second part of a two-part series, Brain & Life co-host Dr. Katy Peters sits down with author and journalist Tom Zeller Jr. to delve into his personal journey with cluster headaches. They explore treatment options and the cultural stigma surrounding cluster headaches. Dr. Peters is then joined by Dr. Stephanie Nahas, professor of neurology at Thomas Jefferson University and Program Director for the Headache Medicine Fellowship at the Jefferson Headache Center of Thomas Jefferson University. Dr. Nahas the importance of advocacy and community support when it comes to cluster headaches.   Additional Resources Tom Zeller Jr. Clusterbusters - The Cluster Headache Advocacy Group Finding Relief for Cluster Headaches Headache on the Hill: Advocating for Migraine Patients Nationwide   Other Brain & Life Podcast Episodes on These Topics Broadcast Journalist Deborah Roberts on Living with Migraine Mulling over Migraines with Photographer Bill Wadman Apps and Self-Advocacy with Roon's Dr. Rohan Ramakrishna   We want to hear from you! Have a question or want to hear a topic featured on the Brain & Life Podcast? ·       Record a voicemail at 612-928-6206 ·       Email us at BLpodcast@brainandlife.org   Social Media:   Guests: Tom Zeller Jr. @tomzellerjr; Dr. Stephanie Nahas @stephanienahasgeiger Hosts: Dr. Daniel Correa @neurodrcorrea; Dr. Katy Peters @KatyPetersMDPhD  

In this first part of a two-part series, Brain & Life co-host Dr. Katy Peters sits down with author and journalist Tom Zeller Jr. to delve into his personal journey with cluster headaches. They explore the severity of these headaches and discuss Tom's diagnosis journey and his experiences living  with this challenging condition. Dr. Peters is then joined by Dr. Stephanie Nahas, professor of neurology at Thomas Jefferson University and Program Director for the Headache Medicine Fellowship at the Jefferson Headache Center of Thomas Jefferson University. Dr. Nahas explains the nature and symptoms of cluster headaches and the challenges in diagnosing and treating them. Tune in next week for part two to hear about the importance of advocacy and community support.   Additional Resources Tom Zeller Jr. Clusterbusters - The Cluster Headache Advocacy Group Finding Relief for Cluster Headaches Headache on the Hill: Advocating for Migraine Patients Nationwide   Other Brain & Life Podcast Episodes on These Topics Broadcast Journalist Deborah Roberts on Living with Migraine Mulling over Migraines with Photographer Bill Wadman Apps and Self-Advocacy with Roon's Dr. Rohan Ramakrishna We want to hear from you! Have a question or want to hear a topic featured on the Brain & Life Podcast? ·       Record a voicemail at 612-928-6206 ·       Email us at BLpodcast@brainandlife.org   Social Media:   Guests: Tom Zeller Jr. @tomzellerjr; Dr. Stephanie Nahas @stephanienahasgeiger Hosts: Dr. Daniel Correa @neurodrcorrea; Dr. Katy Peters @KatyPetersMDPhD

In this episode of Drum History, we take an in-depth look at the legendary gear of Steve Gadd, one of the most recorded and influential drummers of all time. Our returning guest, Paul Wells (Juilliard jazz faculty, Curtis Stigers, Vince Giordano & The Nighthawks), joins host Bart van der Zee for another ultra-detailed “gear episode” exploring the drums that shaped Gadd's sound across his incredible career. Part 1 covers from Childhood to 1989. From his early Gretsch and Rogers kits to the iconic Yamaha setup that became synonymous with his style, we trace how each era of his gear evolved — and how innovations like his tom configurations, hi-hat preferences, and snare choices influenced generations of drummers. Paul also shares insights from his exclusive interview with Steve Gadd, arranged with help from John DeChristopher (Live From My Drum Room), and stories from Artie Smith, Steve's longtime drum tech. Links and resources:

Soulful House and Afro House taken from my live weekly Twitch.TV show which airs every Saturday morning on Twitch.TV/SoulfulHouseMusicCollage from 10am-12pm EST. This is I got into a zone and played an extra 45 minutes, so I hope that you'll enjoy. Any comments or feedback, feel free to drop me a message here. For playlists or booking inquiries , email- Kevvydreds@aol.com

Hello everyone, Jim here. We're taking a brief two-week break from new episodes to spotlight a couple of golden oldies from the Infinite Loops archive. Years later, these remain some of my favorite conversations. We'll be back soon with fresh episodes, but in the meantime, enjoy this trip back to November 2023, when we welcomed the one and only Roon. _________________ AI researcher, memelord extraordinaire, and techno-optimist Roon joins the show to discuss coming up with the shape rotator vs. wordcel meme, what an AGI world could become, and why Tenet is Christopher Nolan's best movie. Important Links: Roon's Twitter Roon's Substack AGI Futures Show Notes: Shape Rotators Vs. Wordcels Why AGI is Possible AI in Science Fiction AGI Future #1: Neuralink Third Impact AGI Future #2: Simulation Theory AGI Future #3: Dumb Matter AGI Future #4: Balrog Awakened AGI Future #5: Ultra Kessler Syndrome AGI Future #6: The Tragedy of Taiwan AGI Future #7: For Dust Thou Art AGI Future #8: CEV Super Intelligence Why Tenet is Christopher Nolan's Best Movie Roon as Emperor of the World MORE! Books Mentioned: The Lucifer Principle: A Scientific Expedition into the Forces of History; by Howard Bloom The Genius of the Beast: A Radical Re-Vision of Capitalism; by Howard Bloom The God Problem: How a Godless Cosmos Creates; by Howard Bloom William Blake vs the World; by John Higgs The Hitchhiker's Guide to the Galaxy; by Douglas Adams  

Soulful House and Afro House taken from my weekly Twitch.TV show which airs every Saturday morning on Twitch.TV/SoulfulHouseMusicCollage from 10am-12pm ESTFor playlists/booking inquires -Email: Kevvydreds@aol.com

Today, Ali is joined by Dr. Amanda Adeleye, the founding partner and Medical Director of CCRM Fertility of Chicago -- and an REI who isn't afraid to tackle some of the biggest myths and misconceptions around fertility. Ali and Dr. Adeleye dig into some of her patients FAQs, from the stereotype that Black-identifying people are “hyper fertile,” to what you should tell your doctor if you have a history of substance use or abuse. They talk about how they are both featured experts who answer questions on Roon.com -- a free, trusted resource built by doctors that helps you navigate your health journey with expert videos. They also bust myths about male factor infertility and whether long-term birth control use contributes to infertility. Spoiler alert: Dr. Adeleye sets the record straight.Follow on IG: @amanda.j.adeleye_md and @roonwomenshealthFor more, click: www.roon.comTOPICS COVERED IN THIS EPISODE: TTC; ART; IVF; male factor infertility; substance use and abuse; fertility stereotypes for Black-identifying people; birth controlEPISODE SPONSORS: BEAUTIFUL BIRD AND WORK OF ARTAli's Children's Book Series about IVF, IUI and Family Building Through Assisted Reproductive Technology https://www.infertileafgroup.com/booksThe latest book in the Work of ART series, “Beautiful Bird” tells the story of three parents, one incredible boy and a family built with love—and a little bit of science.Pre-orders are available now! The first 150 copies will be Personalized, Signed and Numbered! Don't miss out on this limited edition! Tap the link in bio and stories to order your copy today.When Helen decides to have a baby on her own, she welcomes Jack Bird into the world through IUI with the help of her friend, Aaron. But when Jack is born and needs extra care in the NICU, Aaron and his partner, Blake, fall in love with Jack, too. Together, the three join forces to raise Jack, proving that family isn't about how you start—it's about how you grow.Order yours now at https://www.infertileafgroup.com/booksFor bulk orders of 10 or more books at 20% off, go to https://www.infertileafgroup.com/bulk-order-requestFERTILITY RALLYIG: @fertilityrallywww.fertilityrally.comNo one should go through infertility alone. Join the Worst Club with the Best Members at fertilityrally.com. We offer 5 to 6 support groups per week, three private Facebook groups, tons of curated IRL and virtual events, and an entire community of more than 500 women available to support you, no matter where you are in your journey.Join today at link in bio on IG @fertilityrally or at www.fertilityrally.com/membershipSupport this podcast at — https://redcircle.com/infertile-af/donationsAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacySupport this podcast at — https://redcircle.com/infertile-af/donationsAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Soulful House and Afro House taken from my weekly Twitch.TV show, which airs every Saturday morning from 10am-12pm EST on Twitch.TV/SoulfulHouseMusicCollage

Soulful House and Afro House taken from my weekly Twitch.TV show which airs on Twitch.TV/SoulfulHouseMusicCollage every Saturday morning from 10am-12pm EST

¿Te gusta que te den oral pero no siempre lo disfrutas? ¿Te da pena pedir lo que te gusta? ¿No sabes por dónde empezar si quieres aprender a dar un cunnilingus que se recuerde toda la semana?En este episodio de Podcasmo Múltiple, me acompañan Nancy y Nelly para hablar sin pelos en la lengua (o sí

In this conversation, fertility expert, Dr. Amanda Adeleye, MD, is sharing the fertility knowledge you need to feel empowered in your Kids or Childfree decision. This includes... Common fertility myths and misconceptions  The role of age in fertility, including when your fertility starts to decline  How likely it really is to become pregnant in your mid-30's, at 40, and beyond Fertility preservation options — including egg and embryo freezing – and who they're suited for Why understanding personal goals is crucial in making informed family planning decisions As mentioned on the show: Find Amanda online at ccrmivf.com/locations/us/il/chicago/amanda-adeleye-md/ She's on Instagram at instagram.com/amanda.j.adeleye_md Get details on free health education platform, Roon, at roon.com Roon is also on Instagram at instagram.com/roonwomenshealth Find Amanda on Roon at roon.com/expert/DrAmandaAdeleye About Amanda: Dr. Amanda Adeleye, MD, is a reproductive endocrinologist and infertility (REI) specialist. She provides fertility services including IUI, IVF and fertility preservation at the newly opened fertility clinic, CCRM Chicago, where she is the founding partner and Medical Director. She received her Medical Degree from Columbia University, completed her residency at New York Presbyterian - Columbia University, and did her fellowship at the University of California, San Francisco. Dr. Adeleye is committed to improving the educational resources available to all patients to inform them on their fertility journey. As part of this work, she helped to create Roon, a free, trusted health platform founded by doctors to replace unreliable internet searches with expert medical guidance. _ Get details on our upcoming Kids or Childfree Support Series here: kidsorchildfree.com/kids-or-childfree-support-series Check out our free resources here, or at kidsorchildfree.com/free-resources And don't forget to subscribe, rate, and review The Kids or Childfree Podcast if you love what you're hearing! You can leave a rating and review on Apple Podcasts, or a rating on Spotify. Find us online at www.kidsorchildfree.com. Instagram: www.instagram.com/kidsorchildfree

Soulful House and Afro House taken from my weekly Twitch.TV show, which airs every Saturday morning on Twitch.TV/SoulfulHouseMusicCollage from 10am-12pm EST

A new platform that can help us as endometriosis patients, and I wanted to share it with you all today to learn more about Roon. This is not a paid advertisement. I attended a webinar and fell in love with this app! Roon is a trustworthy, free tool for people living with endometriosis, PCOS, infertility, and many other health conditions. You can use it today by going to: roon.com/TheCycleYou can also follow Roon on Instagram and TikTok: @roonwomenshealth My guest is here to tell us all about it:Alexa Tovsen, PA-C, MPHAlexa Tovsen is the Medical Content Lead at Roon, where she is committed to delivering accurate, accessible, and stigma-free health information. She is deeply passionate about advancing health equity and expanding access to reproductive and sexual healthcare, which she actively supports through her work as a Physician Assistant at Planned Parenthood in Boston. She received her Master's in Public Health from the Harvard T.H. Chan School of Public Health.Follow for clips of the show or DM us: Instagram: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.instagram.com/thecycle.endopodcast/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠TikTok: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.tiktok.com/@thecycleendopodcast⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Thank you for listening to and supporting this podcast. We need awareness about this disease. If you want to be on the podcast or have feedback, please reach out via my website, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.melissaboudreau.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Thank you SO much for your support and time.

Vikram Bhaskran, CEO and Co-Founder, and Dr. Rohan Ramakrishna, Chief Medical Officer and Co-Founder of Roon, have taken on the mission to provide trustworthy and personalized medical information to patients and caregivers. Leveraging technology and the expertise of medical professionals, Roon has created a comprehensive resource for navigating medical challenges. They emphasize the importance of addressing misinformation and providing accurate, science-based information. The platform is not intended to replace doctors, but to supplement relevant information and enable better communication with healthcare providers and improve patient outcomes. Vikram explains, "Our mission is to be the best place online for anyone navigating any health condition. I started the company really through my own journey as a caregiver to my dad, who had ALS. And in that journey, I had two insights. One is that the biggest tech companies fail us. And in this moment of crisis, most people navigating any health condition will turn to "Doctor Google" and Facebook groups, which can be an overwhelming experience. And so I felt we could do better as someone coming from the tech world. I was at Pinterest before this, and my second insight was that there's a finite number of doctors. And so the experience of health today for really anyone is that you spend a ton of time in waiting rooms. You spend a ton of time waiting for someone to answer your health questions. And so Roon was born out of those two insights. And our goal is to be the best online platform to scale the world's best medical experts and their knowledge." Rohan elaborates, "The doctors are from more than 70 different academic medical institutions, and the number is growing. They represent all the experts who have expertise to share alongside the health journeys we've launched. So as of today, we've launched brain cancer, ALS, dementia, Fertility and Family Building, PCOS, endometriosis, menopause, and we are soon to launch several other conditions related to women's health, including gynecological health. Our experts span the doctors you would expect, such as oncologists, neurologists, and OBGYNs, among others, but also social workers, physical therapists, occupational therapists, speech therapists, and legal and financial counselors - really anyone who has real expertise that they can lend to the experience of a journey. So much of dementia care, for example, is not driven by your neurologist, but by the experience of caring for someone who needs help, whether it's at a memory care facility or a skilled nursing facility, dealing with issues that doctors typically don't have a great answer for."  #Roon #HealthcareInformation #MedAI #HealthcareOutcomes #DigitalHealth roon.com Download the transcript here

Vikram Bhaskran, CEO and Co-Founder, and Dr. Rohan Ramakrishna, Chief Medical Officer and Co-Founder of Roon, have taken on the mission to provide trustworthy and personalized medical information to patients and caregivers. Leveraging technology and the expertise of medical professionals, Roon has created a comprehensive resource for navigating medical challenges. They emphasize the importance of addressing misinformation and providing accurate, science-based information. The platform is not intended to replace doctors, but to supplement relevant information and enable better communication with healthcare providers and improve patient outcomes. Vikram explains, "Our mission is to be the best place online for anyone navigating any health condition. I started the company really through my own journey as a caregiver to my dad, who had ALS. And in that journey, I had two insights. One is that the biggest tech companies fail us. And in this moment of crisis, most people navigating any health condition will turn to "Doctor Google" and Facebook groups, which can be an overwhelming experience. And so I felt we could do better as someone coming from the tech world. I was at Pinterest before this, and my second insight was that there's a finite number of doctors. And so the experience of health today for really anyone is that you spend a ton of time in waiting rooms. You spend a ton of time waiting for someone to answer your health questions. And so Roon was born out of those two insights. And our goal is to be the best online platform to scale the world's best medical experts and their knowledge." Rohan elaborates, "The doctors are from more than 70 different academic medical institutions, and the number is growing. They represent all the experts who have expertise to share alongside the health journeys we've launched. So as of today, we've launched brain cancer, ALS, dementia, Fertility and Family Building, PCOS, endometriosis, menopause, and we are soon to launch several other conditions related to women's health, including gynecological health. Our experts span the doctors you would expect, such as oncologists, neurologists, and OBGYNs, among others, but also social workers, physical therapists, occupational therapists, speech therapists, and legal and financial counselors - really anyone who has real expertise that they can lend to the experience of a journey. So much of dementia care, for example, is not driven by your neurologist, but by the experience of caring for someone who needs help, whether it's at a memory care facility or a skilled nursing facility, dealing with issues that doctors typically don't have a great answer for."  #Roon #HealthcareInformation #MedAI #HealthcareOutcomes #DigitalHealth roon.com Listen to the podcast here

When faced with an ALS diagnosis, finding trustworthy information shouldn't add to your burden. This episode introduces a groundbreaking solution born from one son's love for his father.Vikram Bhaskaran takes us through the painful journey that sparked innovation – watching his father battle ALS in India while struggling to access reliable information and expertise. The stark contrast between his Silicon Valley tech job, where brilliant minds created seamless user experiences, and the "dark ages" of health information access, drove him to action. The result? ROON.Roon addresses the three dimensions of living with ALS – medical knowledge, practical daily concerns, and the emotional/existential questions that arise. Through short, digestible videos, users can find answers to questions they might never get to ask during brief clinical appointments.This episode offers a masterclass in turning personal tragedy into purpose. Beyond highlighting a valuable resource for the ALS community, it demonstrates how technology, when designed with genuine empathy, can create what Vikram beautifully describes as "a doctor friend who has your back."Download Roon to experience this sanctuary of knowledge, where the burden of searching for reliable information is lifted, and a community of experts and fellow patients are ready to help.  Thanks for sharing with a friend. Hugs, LorriFollow and see what's coming next: Instagram, Facebook, Twitter, TikTok, LinkedIn.

Send us a textGary celebrates the podcast's second anniversary with plenty more great music from the world of bagpipes.PlaylistAssynt with Assynt House from Where From HereNational Youth Pipe Band of Scotland (Development Band) with P/M Sandy Spence, Cabar Feidh, Paddy's Leather Breeches and Butterfingers from ThunderstruckJohn Mulhearn with Roon the Barras and Rip Them Up from The Pipe FactoryOban High School Pipe Band with Donald Maclean's Farewell to Oban, Maggie Cameron and Alec C MacGregor from World Pipe Band Championships 2016, private recordingGordon and Shona Mooney with Jimmy Allan, Geld Him Lasses, Coffee and Tea and Skint o Siller from Reclaimed: Pipe Music and Song from the Scottish BordersCristina Pato and Rosa Cedron with Heicho de Dar from SoasDr Angus MacDonald with Dh'ith na Coin na Maragan, Reel of Tulloch, Muilleann Dubh and Cailleach Liadh Ratharsair from Maidean Dubh' an DonaisSupport the show

Can you have follow-up if you never talked about it in the past? Your DVDs may self-destruct! Did anyone say "flac" this episode? Andrew is getting sleep because of this one neat trick! A peek inside the show? Yeah, maybe? A special mini guest! Follow Up 00:00:00 Your Friendly Neighborhood Spider-Man (https://www.themoviedb.org/tv/138503-your-friendly-neighborhood-spider-man)

Ventitreesima puntata della settima stagione di J-TACTICS, la rubrica di radiomegliodiniente.com, dedicata alla vecchia signora bianconera.Focus sulla sfida che valeva il 3° posto in classifica, Juventus-Atalanta vale un posto sul podio nella classifica di Serie A.La vecchia signora bianconera subisce una sconfitta epica, subendo 4 gol a zero in casa come non accadeva dal lontano '67, in un derby della Mole ed è fuori non solo dal podio ma anche (forse per i più ottimisti) da un'ipotetica lotta scudetto.L'Atalanta a Torino si è imposta per 4-0.Un successo clamoroso, su tutta la linea da parte della squadra di Gasperini che ha dominato in lungo e in largo allo Stadium fagocitando una inerme Juventus.Gol di Retegui, Zappacosta, de Roon e Lookman.L'Atalanta sin da subito dà l'idea di poter fare malissimo.Gli spazi sono praterie e Lookman corre come un cavallo purosangue.Gli orobici passano in vantaggio al 27′ su calcio di rigore.Il mani di McKennie è netto, anche se qualche discussione in merito potrebbe sorgere dato che il giocatore americano viene nell'atto di saltare, palesemente sbilanciato in modo tale da dover necessariamente allargare il braccio in modo anomalo.Il VAR conferma il penalty.Dal dischetto si presenta Mateo Retegui che non sbaglia, 1-0 e gol numero 22 in campionato.La Juve è in narcolessi, l'Atalanta continua a fare la partita e nel recupero ha due occasioni in una. Lookman è sfortunato, centra il palo, poi batte a botta sicura e si trova di fronte un Di Gregorio strepitoso.All'intervallo piovono fischi per i bianconeri.Nella Juventus Koopmeiners prende il posto di Yildiz.La ripresa comincia e subito raddoppia l'Atalanta.Lookman viene imbeccato, avanza e calcia, Di Gregorgio respinge, arriva de Roon e insacca il 2-0.Motta prova a dare una scossa, manda in campo Mbangula, Alberto Costa e Kalulu.L'Atalanta accelera e chiude i giochi dopo il 60 esimo quando Zappacosta riceve da Kolasinac e insacca, 3-0.Poi tocca Vlahovic completare il disastro dando il là a una ripartenza dell'Atalanta, quella che porta al 4-0 di Lookman.Partita finita, umiliazione totale per la Juve, che nell'era moderna non aveva mai perso in modo così secco in casa in Serie A.Thiago Motta, o chi per lui, ora deve difendere il quarto posto, ultimo obiettivo rimasto ai bianconeri.Di questo e altro parleremo in questa puntata!Diteci la vostra!Ecco i link dei nostri social:CANALE TELEGRAM:https://t.me/+TYOn7FZAQwet7MAtINSTAGRAM:https://instagram.com/jtactics_?igshid=YmMyMTA2M2Y=TWITTER:https://twitter.com/RadioMDN?t=woKQltSFRUTw9qibbRZaJA&s=09

#787 | United beat Rangers in the Europa League with a last minute goal from Captain Bruno Fernandes. How many clutch moments can one man produce. There's action in the transfer market, with both Antony (loan) and Alejandro Garnacho (£??) potentially leaving the club. Ed and Tom bemoan the state of the club and - sort of - enjoy United's performance against Rangers.If you are interested in supporting the show and accessing exclusive bonus episodes, check out our Patreon page or subscribe on Apple Podcasts Subscriptions. We do a bonus show and a tactical review every week for backers.No Question About That is available on YouTube, Apple, Spotify, Amazon and all podcast apps. Hit that subscribe button, leave a rating and write a review. Hosted on Acast. See acast.com/privacy for more information.

Our Social Media Pages, follow us and engage with the Pill-grim community! Instagram Twitter  YouTube TikTok LinkedIn  And now for this week's prescription: On this week's dose, we begin (1:33) with Roon, a digital health startup aiming to replace “Dr. Google” with trusted doctor-driven video content, fresh off a $15M Series A. Next (7:54), we spotlight Slip Robotics, the Atlanta-based innovator transforming logistics with its SlipBot Automated Loading Robot, and their $28M Series B. Finally (16:18), we turn to Boom Supersonic, the aerospace pioneer securing over $100M to fund "Symphony," its first supersonic jet engine prototype. Sources: https://www.roon.com/ https://techcrunch.com/2024/11/26/roon-raises-15m-to-replace-dr-google-with-real-doctors-sharing-videos-about-illness-treatments/ https://techpadi.africa/2024/11/nyc-based-health-guidance-platform-roon-secures-15m-in-series-a-funding/ https://www.wect.com/2019/06/24/study-finds-us-citizens-turn-google-before-their-doctor/#:~:text=WILMINGTON%2C%20N.C.%20(WECT)%20%2D,before%20going%20to%20their%20doctor https://techfundingnews.com/roon-doses-up-on-15m-funding-to-replace-dr-google-with-expert-healthcare-answers/ https://blog.roon.com/post/roons-next-chapter-one-step-closer-to-a-doctor-in-your-pocket-for-health https://www.sliprobotics.com/ https://techcrunch.com/2024/12/17/slip-robotics-snags-28m-for-its-bots-that-can-load-a-truck-in-five-minutes/ https://www.ourcrowd.com/startup/slip-robotics?utm_source=chatgpt.com https://boomsupersonic.com/  https://viewfromthewing.com/rejected-by-the-industry-boom-supersonic-raises-100m-to-prove-critics-wrong-but-at-a-cost/  Music Credit: Chapter One by Cole Bauer and Dean Keeton https://www.instagram.com/deankeeton/?hl=en

This episode is a recap of episodes 6 through 11 with special guest, Lauren van Roon, a corporate recruiter. Together, they discuss the core transferable skills shared by past guests Mike Stephen, Amanda DiFederici, Mike Shuman, Brenda Snyder, and Paul Chanan. Themes such as storytelling, curiosity, effective communication, performance under pressure, empathy, and self-awareness are explored. Lauren provides practical advice on how these skills can be applied in career development and job interviews. Don't miss key takeaways, strategies for skill development, and insights into how these transferable skills can enhance your professional journey. Lauren van Roon: ⁠https://www.linkedin.com/in/laurenvr1/⁠ Instagram: ⁠https://www.instagram.com/cacklemedia/⁠ TikTok: ⁠https://www.tiktok.com/@cacklemedia⁠ YouTube: ⁠https://www.youtube.com/@CackleMedia⁠ LinkedIn: ⁠https://www.linkedin.com/company/cacklemedia/⁠ 00:00 Introduction and Episode Recap Format 00:25 Introducing Lauren van Roon 01:21 Theme 1: Defining Transferable Skills 01:25 Episode 6: Mike Stephen - Storytelling and Curiosity 08:59 Episode 7: Amanda DiFederici - Clarity in Communication 11:38 Episode 8: Mike Shuman - Performance Under Pressure 17:03 Episode 9: Brenda Snyder - Problem Solving Through Empathy 19:49 Episode 11: Paul Chanan - Self Awareness and Decision Making 24:32 Theme 2: Strategies for Skill Development 25:00 Theme 3: Common Themes and Unique Perspectives 25:10 Theme 4: Key Takeaways for Listeners 28:48 Conclusion and Final Thoughts

In this episode of The Cognitive Revolution, Nathan shares a fascinating cross-post from Doom Debates featuring a conversation between Liron Shapira and roon, an influential Twitter Anon from OpenAI's technical staff. They explore crucial insights into how OpenAI's team views AI's future, including discussions on AGI development, alignment challenges, and extinction risks. Join us for this thought-provoking analysis of AI safety and the mindset of those building transformative AI systems. Help shape our show by taking our quick listener survey at https://bit.ly/TurpentinePulse SPONSORS: GiveWell: GiveWell has spent over 17 years researching global health and philanthropy to identify the highest-impact giving opportunities. Over 125,000 donors have contributed more than $2 billion, saving over 200,000 lives through evidence-backed recommendations. First-time donors can have their contributions matched up to $100 before year-end. Visit https://GiveWell.org, select podcast, and enter Cognitive Revolution at checkout to make a difference today. SelectQuote: Finding the right life insurance shouldn't be another task you put off. SelectQuote compares top-rated policies to get you the best coverage at the right price. Even in our AI-driven world, protecting your family's future remains essential. Get your personalized quote at https://selectquote.com/cognitive Oracle Cloud Infrastructure (OCI): Oracle's next-generation cloud platform delivers blazing-fast AI and ML performance with 50% less for compute and 80% less for outbound networking compared to other cloud providers13. OCI powers industry leaders with secure infrastructure and application development capabilities. New U.S. customers can get their cloud bill cut in half by switching to OCI before December 31, 2024 at https://oracle.com/cognitive Weights & Biases RAG++: Advanced training for building production-ready RAG applications. Learn from experts to overcome LLM challenges, evaluate systematically, and integrate advanced features. Includes free Cohere credits. Visit https://wandb.me/cr to start the RAG++ course today. CHAPTERS: CHAPTERS: (00:00:00) About the Episode (00:07:18) Introducing roon (00:09:13) roon's Background (00:16:40) roon the Person (Part 1) (00:21:56) Sponsors: GiveWell | SelectQuote (00:24:45) roon the Person (Part 2) (00:26:43) Excitement in AI (00:31:59) Creativity in AI (00:40:18) Sponsors: Oracle Cloud Infrastructure (OCI) | Weights & Biases RAG++ (00:42:36) roon's P(Doom) (00:52:25) AI Risk & Regulation (00:53:51) AI Timelines (01:01:20) Aligned by Default? (01:09:16) Training vs Production (01:14:30) Open Source AI Risk (01:26:25) Goal-Oriented AI (01:34:29) Pause AI? (01:39:46) Dogecoin & Wrap Up (01:41:06) Outro & Call to Action (01:56:38) Outro SOCIAL LINKS: Website: https://www.cognitiverevolution.ai Twitter (Podcast): https://x.com/cogrev_podcast Twitter (Nathan): https://x.com/labenz LinkedIn: https://www.linkedin.com/in/nathanlabenz/ Youtube: https://www.youtube.com/@CognitiveRevolutionPodcast Apple: https://podcasts.apple.com/de/podcast...

In this episode, Brain & Life Podcast co-host Dr. Katy Peters answers your questions. She discusses managing the holidays with a neurologic condition, how weather can affect your brain, self-advocacy, and more. Thank you for submitting your questions and sharing your comments about the Brain & Life Podcast! We invite you to participate in our listener survey! By participating in the brief survey, you will have the opportunity to enter your name and email address for a chance to win one of five $100 Amazon gift cards. Unfortunately, for this episode we had a few audio issues that could not be fixed, but we are excited to bring you this discussion and answer your questions   Additional Resources Six Ways to Maximize Joy and Make Life Easier Around the Holidays Apps and Self-Advocacy with Roon's Dr. Rohan Ramakrishna Anyone Can Become a Patient Advocate Country Singer Drake White's Rare Brain Condition and Healing Journey   We want to hear from you! Have a question or want to hear a topic featured on the Brain & Life Podcast? ·       Record a voicemail at 612-928-6206 ·       Email us at BLpodcast@brainandlife.org   Social Media:   Hosts: Dr. Daniel Correa @neurodrcorrea; Dr. Katy Peters @KatyPetersMDPhD

It's time for The Truth!Atalanta are currently top of Serie A as we close in on the halfway point of the season. After winning the Europa League last season, the glass ceiling for the club from Bergamo has been well and truly smashed, but can they go one further this year, beat out some giants of the game and claim their first Scudetto?We examine just how a club from a small city north-east of Milan has turned the Italian football landscape upside down, upsetting the traditional order of Italy's biggest clubs and managed to put together an incredible run of continental qualification under Gian Piero Gasperini, all whilst making a gargantuan profit in the meantime. This iteration of Gasperini's disruptors has the potential to be their greatest ever, but can they maintain such blistering league form against the depth of Inzaghi's Inter and a Conte Napoli side that don't have European football to contend with? How has Gasperini turned a bunch of players unwanted elsewhere into one of the best units in the game? And is this finally the time where promise becomes domestic silverware, changing the established order forever? The Truth is somewhere in the middle... And remember, if you'd like more from the Rank Squad, including extra podcasts every Monday and Friday (including our weekly Postbox taking a look at the whole weekend of football) and access to our brilliant Discord community, then why not join us here on Patreon?

In this episode of the Brain & Life podcast, Dr. Daniel Correa and Dr. Katy Peters answer some of your questions. Then, Dr. Peters is joined by Dr. Rohan Ramakrishna, professor of Neurosurgery at Weill Cornell Medical College and co-founder of an application called Roon. Roon taps into accurate health information, particularly in the areas of ALS, dementia, and brain tumors, and connects patients and caregivers with the doctors and communities they're looking for. Dr. Ramakrishna explains how this app came to be, who it's helping, and what's coming next.  We invite you to participate in our listener survey! By participating in the brief survey, you will have the opportunity to enter your name and email address for a chance to win one of five $100 Amazon gift cards. Additional Resources Roon- Answers from Medical Professionals How to Be Your Own Best Advocate How Patient Organizations Engage Communities During the Pandemic Caregivers Share How They Keep Loved Ones Engaged at Home   Other Brain & Life Episodes on this Topic Building Healthy Digital Habits with Dr. Faye Begeti Influencer Chan Plante on Coping with Misdiagnosis and Finding a Community We Are Brave Together with Jessica Patay   We want to hear from you! Have a question or want to hear a topic featured on the Brain & Life Podcast? ·       Record a voicemail at 612-928-6206 ·       Email us at BLpodcast@brainandlife.org   Social Media:   Guests: Roon App @roon.care Hosts: Dr. Daniel Correa @neurodrcorrea; Dr. Katy Peters @KatyPetersMDPhD

Fashion boutique owner Bri of Reb & Roon joins the podcast today to share how she left a job that made her feel unfulfilled and pursued her dream of opening her own boutique! Bri shares advice for starting a new business, why the "do it scared" motto has helped her seize new opportunities, and how fashion can help YOU express yourself! This was such a fun conversation! Hope you enjoyed! Follow Bri's boutique on Instagram here. Shop Reb & Roon's website here.

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: AI #74: GPT-4o Mini Me and Llama 3, published by Zvi on July 26, 2024 on LessWrong. We got two big model releases this week. GPT-4o Mini is covered here. Llama 3.1-405B (and 70B and 8B) is mostly covered in yesterday's post, this has some follow up. Table of Contents 1. Introduction. 2. Table of Contents. 3. Language Models Offer Mundane Utility. All your coding are belong to us. 4. Language Models Don't Offer Mundane Utility. Math is hard. Can be expensive. 5. GPT-4o Mini Me. You complete me at lower than usual cost. 6. Additional Llama-3.1 Notes. Pricing information, and more rhetoric. 7. Fun With Image Generation. If you're confused why artists are so upset. 8. Deepfaketown and Botpocalypse Soon. Not surprises. 9. They Took Our Jobs. Layoffs at Activision and across gaming. 10. In Other AI News. New benchmarks, new chip variants, and more. 11. The Art of the Jailbreak. Pliny remains undefeated. 12. Quiet Speculations. Where will the utility be coming from? 13. The Quest for Sane Regulations. Public opinion continues to be consistent. 14. Openly Evil AI. Some Senators have good questions. 15. The Week in Audio. Dwarkesh in reverse, and lots of other stuff. Odd Lots too. 16. Rhetorical Innovation. What are corporations exactly? 17. Aligning a Smarter Than Human Intelligence is Difficult. So are evals. 18. People Are Worried About AI Killing Everyone. Roon warns you to beware. 19. The Sacred Timeline. Hype? 20. Other People Are Not As Worried About AI Killing Everyone. Older Joe Rogan. 21. The Lighter Side. It's on. Language Models Offer Mundane Utility Coding is seriously much faster now, and this is the slowest it will ever be. Roon: pov: you are ten months from working for claude sonnet the new technical founder. Garry Tan: Underrated trend. It's happening. Sully: 50% of our code base was written entirely by LLMs expect this to be ~80% by next year With sonnet we're shipping so fast, it feels like we tripled headcount overnight Not using Claude 3.5 to code? Expect to be crushed by teams who do (us). Not only coding, either. Jimmy (QTing Tan): It can also do hardware related things quite well too, and legal, and logistics (planning) and compliance even. I've been able to put off hiring for months. When I run out of sonnet usage I patch in gpt-4o, it's obviously and notably worse which I why I rarely use it as a primary anymore. Claude 3.5 Sonnet becomes the first AI to crush the Lem Test to 'write an impossible poem.' Laugh all you want, this is actually great. Kache: dude hahahahahah i used so many tokens today on just formatting json logs near: the just stop oil people are gonna come and spray paint you now Compared to how much carbon a human coder would have used? Huge improvement. Language Models Don't Offer Mundane Utility IMO problems are still mostly too hard. The linked one, which GPT-4, GPT-4o and Claude 3.5 Sonnet failed on, seems unusually easy? Although a math Olympiad solver does, predictably given the contests we've seen. [EDIT: I didn't read this properly, but a reader points out this is the floor symbol, which means what I thought was an obvious proof doesn't actually answer the question, although it happens to get the right answer. Reader says the answers provided would actually also get 0/7, order has been restored]. Figure out what song Aella was talking about here. Found the obvious wrong answer. Grok offers to tell you 'more about this account.' I haven't seen the button yet, probably it is still experimental. Our price cheap. Llama 3.1-405B was a steal in terms of compute costs. Seconds: "AI is expensive" its not even half the cost of a middling marvel movie. Teortaxes: Pretty insane that the cost of producing llama-3-405B, this behemoth, is like 40% of *Ant-Man and the Wasp: Quantumania* movie at most If I were Zuck, I'd have open sourced a $...

Intel will in Magdeburg die größte Chipfabrik Europas bauen. Um das Unternehmen mit genügend Wasser zu versorgen, gibt es einen Plan – doch der gefällt nicht jedem. Droht ein Streit ums Wasser ähnlich wie bei Tesla in Brandenburg? Roon, Anastasija; Ottersbach, Niklas

Das Wasser wird knapp – auch in Berlin und Brandenburg. Mancherorts sind neue Bauprojekte nicht mehr möglich. Der Klimawandel und das Ende des Braunkohleabbaus werden zu noch mehr Trockenheit führen. Ist Wasser aus Elbe oder Ostsee eine Lösung? Roon, Anastasija; Jeske, Ann-Kathrin; Moritz, Alexander; Silke, Hasselmann; Scha www.deutschlandfunkkultur.de, Die Reportage

Das Geschäft mit Tiefengrundwasser ist lukrativ – vor allem in Bayern, zum Beispiel beim Bierbrauen. Die Richtlinien zum Schutz von Wasser werden aber strenger. Ist Bier gebraut mit Wasser aus der Tiefe noch zeitgemäß? Roon, Anastasija; Watzke, Michael www.deutschlandfunk.de, Hintergrund

Today on the show, I welcome Vikram Bhaskaran and Dr. Rohan Ramakrishna (Chief of Neurological Surgery at Weill Cornell Medicine) live in studio to talk about Roon, their intriguing new cancer navigation platform for patients and caregivers. Roon is personal to Vikram as his whole career shifted from Pinterest executive to caring for his Father when he was diagnosed with ALS. Roon claims to be "the most supportive place online for people navigating complex health conditions, starting with Glioblastoma." so we power-test how that holds up against unmet patient needs. Rohan is only the second neurosurgeon I've had on the show, so, as a brain cancer survivor, I naturally asked him all sorts of pointed questions like, "What's it like to touch someone's brain?" These guys are the real deal with core compassion for empathy in medicine and helping patients in need access what they never knew they needed. Enjoy the show.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this heartfelt episode of the Ask a Matchmaker podcast, host Matchmaker Maria sits down with Katie Brandt, a dedicated caregiving advocate. Katie shares her deeply personal journey of balancing marriage, motherhood, and the immense responsibilities of caregiving for her husband diagnosed with frontotemporal dementia at 29 and later her father with Alzheimer's. Together, they discuss the emotional, physical, and financial challenges caregivers face, the impact on dating and relationships, and the importance of finding community support. Whether you're a caregiver or someone navigating the dating world while caring for a loved one, this episode offers valuable insights and hope. Tune in for a candid conversation about love, loss, and resilience. Connect with Katie at www.KatieBrandt.org and learn more about services provided through her consulting company, Katie Brandt Advocacy. Interested in learning more about FTD? Go to the Mass General Hospital Frontotemporal Disorders Unit or the Association for Frontotemporal Degeneration (AFTD).  There are many resources available at low or no-cost to help build connections, skills and support along the journey of living life as a caregiver. As you begin your role as a caregiver, consider reading about what to expect by going to the Rosalynn Carter Institute, the National Alliance for Caregiving or AARP. Find a certified elder law attorney through the National Academy of Elder Law Attorneys and find out if there is any legal or financial planning that you can do to ensure you are able to support your loved ones through every stage of care. Did you know that the Alzheimer's Association provides support for people living with all types of dementia? They have a Helpline that is open 24 hours a day, 365 days a year. Call 800.272.3900  to receive free emotional support, education and connections with local resources for loved ones living with dementia. Download the free Roon app and have access to experts (including Katie!) in the palm of your hand that can provide valuable information about caregiving for loved ones with a variety of medical conditions. Caring for a veteran? Go to the Veteran Administration to find out about medical care, services and equipment available for veterans. Need services to help care for someone at home? Go to www.eldercare.acl.gov and put your zip code into the Eldercare Locator to discover services and supports in your local community.   *******Stay Connected!******** @matchmakermaria (Follow!) @askamatchmaker (Follow the pod!) Make sure to subscribe and sign up for notifications for fantastic dating and relationship advice brought to you by Maria Avgitidis!  

To kick off brain cancer awareness month 2024, I sit down with Roon app creators Rohan Ramakrishna & Vikram Bhaskaran in episode 1 of Season 4 of The Game On Glio Podcast. We dive into the importance of real time information and the need for advocacy when it comes to serious diseases like Glioblastoma. My guests also explain why they felt the need to create an app like Roon. The Roon app offers people an opportunity to get extensive information from actual clinicians and doctors, to search for questions they didn't get to ask with their own personal doctors, and to hear from mental health experts, patients, caregivers, and holistic practitioners.    Season Sponsors: GammaTile Therapy Imvax Inc.   Episode Sponsor: Roon.com        

In episode #293 of The Hormone P.U.Z.Z.L.E Podcast, our guest Leslie Schrock, talks about Debunking Fertility Myths from Preconception to Pregnancy and Beyond. More about Leslie: Leslie Schrock is an author and angel investor working at the convergence of health and technology. Her breakout hit, Bumpin': The Modern Guide to Pregnancy mixes the latest clinical research with practical advice for working families. Her second book, Fertility Rules, takes the same approach to male and female fertility. Leslie is an investor in Caribou, Roon, Perchwell, Legacy, Kinfield, and dbt Labs (and others), and an advisor to Maven, Alife, Origin, and Reverance. She is also on Gameto's bioethics board and the board of advisors at the Moody School of Communication at her alma mater, The University of Texas at Austin. She was named one of Fast Company's Most Creative People in Business, and her work has been featured on CNBC, NPR, Time, GQ, Fortune, Entrepreneur, Wired, The Economist, and The New York Times. Thank you for listening! This episode is made possible by Puzzle Brew's Fertility Tea: https://hormonepuzzlesociety.com/fertility-tea Follow Jessica on Instagram: @‌leslieschrock Follow Dr. Kela on Instagram: @‌kela_healthcoach Get your FREE Fertility Meal Plan: https://hormonepuzzlesociety.com/ FTC Affiliate Disclaimer: The disclosure that follows is intended to fully comply with the Federal Trade Commission's policy of the United States that requires to be transparent about any and all affiliate relations the Company may have on this show. You should assume that some of the product mentions and discount codes given are "affiliate links", a link with a special tracking code This means that if you use one of these codes and purchase the item, the Company may receive an affiliate commission. This is a legitimate way to monetize and pay for the operation of the Website, podcast, and operations and the Company gladly reveals its affiliate relationships to you. The price of the item is the same whether it is an affiliate link or not. Regardless, the Company only recommends products or services the Company believes will add value to its users. The Hormone Puzzle Society and Dr. Kela will receive up to 30% affiliate commission depending on the product that is sponsored on the show. For sponsorship opportunities, email HPS Media at media@hormonepuzzlesociety.com

This episode features Vikram Bhaskaran, CEO, & Dr. Rohan Ramakrishna, CMO, Co-Founders of Roon. Here, they discuss their backgrounds & what led them to found Roon - a guide for health conditions currently focused on Glioblastoma Multiforme, ALS and Dementia, the array of different types of medical professionals that contribute to the platform, and much more.