Podcasts about Critical point

Mego joins the show for some final thoughts on the Patriots win over the Chargers and who was the MVP from that win. Plus, why the offensive line is the most critical part for the Pats when they go up against the Texans.

don't miss George's AIE talk: https://www.youtube.com/watch?v=sRpqPgKeXNk —- From launching a side project in a Sydney basement to becoming 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—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) — Artificial Analysis Website: https://artificialanalysis.ai (https://artificialanalysis.ai ("https://artificialanalysis.ai")) George Cameron on X: https://x.com/grmcameron (https://x.com/grmcameron ("https://x.com/grmcameron")) Micah Hill-Smith on X: https://x.com/_micah_h (https://x.com/_micah_h ("https://x.com/_micah_h")) Chapters 00:00:00 Introduction: Full Circle Moment and Artificial Analysis Origins 00:01:08 Business Model: Independence and Revenue Streams 00:04:00 The Origin Story: From Legal AI to Benchmarking 00:07:00 Early Challenges: Cost, Methodology, and Independence 00:16:13 AI Grant and Moving to San Francisco 00:18:58 Evolution of the Intelligence Index: V1 to V3 00:27:55 New Benchmarks: Hallucination Rate and Omissions Index 00:33:19 Critical Point and Frontier Physics Problems 00:35:56 GDPVAL AA: Agentic Evaluation and Stirrup Harness 00:51:47 The Openness Index: Measuring Model Transparency 00:57:57 The Smiling Curve: Cost of Intelligence Paradox 01:04:00 Hardware Efficiency and Sparsity Trends 01:07:43 Reasoning vs Non-Reasoning: Token Efficiency Matters 01:10:47 Multimodal Benchmarking and Community Requests 01:14:50 Looking Ahead: V4 Intelligence Index and Beyond

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

A Good Omens fanfic by DiminishingReturns. Music: Flying Angels by Sascha Ende (CC-BY 4.0⁠⁠⁠)Cover art used with kind permission from cassieoh_draws (cassieoh)For tags and other details, to leave kudos and comments, please visit the corresponding post on archiveofourown: https://archiveofourown.org/works/76922241!

Critical Point

While the number of traditional pensions has been shrinking for decades, one type of defined benefit (DB) plan is expanding: the cash balance plan. Combining features of DB and 401(k) plans, cash balance plans are particularly well-suited to helping small-business owners and highly paid professionals such as doctors and lawyers save for retirement. On this episode of Critical Point, three Milliman employee benefits experts explain why the number of cash balance plans has recently swelled to 25,000, how plans can be tailored to fit organizations of all sizes, and why this hybrid plan style may help address the looming retirement crisis. 

In Europe, flooding was the most prevalent—and most expensive—climate peril last year, making 2024 the “Year of the Flood.” On this episode of Critical Point, five authors of Milliman's annual Extreme Weather in Europe report assess the toll of flooding in their country and the response from insurers, governing bodies, and communities. They discuss what Italy is doing to protect the insurance industry, how construction in the UK and France may be contributing to flood risk, and why new regulations in Romania and the Benelux region may not do enough to keep pace with climate change. Don't miss the full Extreme Weather in Europe report, as well as the related paper, Flood risk modelling in Europe, both available at Milliman.com.  

Comprehensive coverage of the day's news with a focus on war and peace; social, environmental and economic justice. SF unions, activists call on big tech to pay fair share of taxes, as city faces budget deficit and business lawsuits Russia conducts massive attacks as Ukraine peace process reaches critical point April 24th is UN International Day of Multilateralism and Diplomacy for Peace SF Supervisors consider measure to replace harm-reduction addiction treatment with abstinence-only approach Judge blocks Trump action to cut funding to schools with DEI programs The post Russia attacks Ukraine capital as peace process at critical point; SF considers replacing harm-reduction addiction treatment with abstinence-only approach – April 24, 2025 appeared first on KPFA.

AI has taken the world by storm—but not all insurance companies, or countries, are approaching the technology the same way. On this special episode of Critical Point, experts from Munich Re and Milliman who work across five continents come together for a global conversation exploring how insurers and regulators are approaching AI around the world. Plus, don't miss Part 1 and Part 2 of this series on AI and insurance. You can read the episode transcript on our website.

Oliver McTernan, Director of Forward Thinking, discusses the situation in Gaza after Hamas announced its intention to postpone the release of Israeli hostages.

Talks between Japan's ruling coalition and opposition parties on some key policies are poised to reach a critical juncture as the governing pair aims to enact the state budget for the fiscal year starting in April within the current fiscal 2024, which ends March 31.

The world's nations will finish negotiating a treaty this weekend to address the global plastic pollution crisis. Led by Norway and Rwanda, 66 countries plus the European Union say they want to address the total amount of plastic on Earth by controlling its design, production, consumption and disposal.

The majestic Bighorn Sheep, a symbol of North America's wild beauty, is facing an unprecedented crisis. From thriving populations of nearly 2 million to a mere 80,000 today, these iconic animals are under siege from habitat loss, deadly diseases like Mycoplasma ovipneumoniae from domestic livestock, and a critical need for conservation support. This documentary dives deep into the challenges facing Bighorn Sheep and the efforts of the National Bighorn Sheep Center to secure a future for this species. Join Amanda Verheul, Executive Director of the National Bighorn Sheep Center, as she sheds light on the conservation measures, habitat restoration, and community efforts pivotal to the survival of wild sheep populations. A crucial part of this story is the North American Model of Wildlife Conservation—a set of principles that guides sustainable wildlife management across the continent. By emphasizing science-based practices, public ownership of wildlife, and funding from hunter conservationists, this model has played a significant role in preserving wildlife populations, including bighorns, through initiatives funded by state hunting licenses and special conservation tags. This film explores the power of collaboration—from local communities to nationwide conservation organizations—in safeguarding these herds for generations to come. Discover the impact of hunter contributions and the surprising role of hunting as a conservation tool in this delicate balance of wildlife preservation.

In this episode, Adam and Nina open up with the latest news from the region, including the recent decision by Poland's Prime Minister to stop granting asylum to refugees and the agreement between Italy and Albania to relocate migrants there. They also discuss upcoming elections in Moldova and Georgia. For the main interview, Adam sits down with Tamar Jacoby, an American reporter and the Kyiv-based director of the Progressive Policy Institute's New Ukraine Project. They discuss the current moods in Ukraine, the upcoming US election and its impact on Ukraine and how the West can help right now.Read Tamar's reporting on the Ukrainian drone industry: https://www.progressivepolicy.org/jacoby-for-new-york-post-ai-is-reshaping-drone-warfare-in-russia-and-ukraine-2/Support our podcast! Join us on patreon: www.patreon.com/talkeasterneurope 

Healthcare costs are a hot topic as we approach open enrollment and the presidential election. In this episode of Critical Point, host Deana Bell sits down with Dave Liner and Jason Clarkson, principals at Milliman and co-authors of the Milliman Medical Index (MMI), to discuss the key drivers of 2024's healthcare costs. They dive into the effects of government programs like Medicaid and the Inflation Reduction Act, the rising use of weight loss medications, and how these factors are shaping healthcare costs for Americans on employer-sponsored insurance. Learn how policy changes and market trends could impact your healthcare expenses and decisions in the coming year.You can read the episode transcript on our website.

China's banking system is collapsing, with assets amounting to 340% of its GDP, a stark contrast to the US's 1X GDP. Over one-third of these assets are tied to the struggling Chinese and Hong Kong real estate sectors, leading to a potential financial crisis and insolvency. Get your 2024 Trump Silver Presidential Medal here: https://bit.ly/TrumpSilver Secure & Preserve your future against market & government unpredictability. Visit https://colonialmetalsgroup.com/rtd or call 888-521-2448 to speak with the experts today. Get your questions answered about a Self-Directed IRA. Special offer: You'll receive a safe and up to $10,000 in free silver from Colonial Metals Group. Start your own Silver Standard. Learn about and join the RTD Silver Team to automate your silver accumulation: https://www.rethinkingthedollar.com/silver-team/ ----- The 5 Steps To Monetary Savviness (Unplug From the Matrix) ----- Go to the RTD Website to start your journey: https://www.rethinkingthedollar.com/ Keep Up with the Latest: Subscribe for the latest news and trends to never miss out! Connect with RTD! Your hub for all our social media platforms. Subscribe & never miss out. https://www.rethinkingthedollar.com/social-media Support the RTD Mission: Our goal is to educate through video & written articles a contrarian viewpoint against mainstream lies in order to wake up the masses. Any contribution to the mission helps. Thanks in advance!!! https://www.rethinkingthedollar.com/donate/ DISCLAIMER: The financial and political opinions expressed in this video are those of the guest and not necessarily of "Rethinking the Dollar." Views expressed in this video should not be relied on for making investment decisions or tax advice and do not constitute personalized investment advice. The information shared is for the sole purpose of education and entertainment only. Some links included in description are affiliate links and cost you no additional money if used.

Life insurers have historically invested their portfolios in the relative safety of mortgages and bonds, but economic volatility, sticky inflation, and other headwinds have spurred many insurance companies to diversify into alternatives, a trend explored in a recent Milliman paper. On this episode of Critical Point, three of the paper's authors discuss two alternative asset classes that are particularly attractive to insurers in today's market: private equity and real estate. They talk about unexpected assets like student housing and storage facilities, the synergy of private equity firms investing in insurers and vice versa, and why many insurance companies continue to hold commercial real estate despite post-pandemic office vacancies.You can read the episode transcript on our website.

The Philadelphia Flyers have not won a game against a Metropolitan division opponent since December 19th, head to NYC to faceoff with the Rangers, a team Philly hasn't beaten in almost 2 years. Travis Konecny, Sam Ersson and the rest of the squad is also fending off the surging Washington Capitals, who can supplant Philly with a win on Tuesday night, and a Flyers regulation loss. 7 total goals were scored in the 3rd period alone, and Tyson Foerster's game-tying goal with 3:32 sent the game to overtime. However, the Rangers scored just 36 seconds into OT to win it. Charlie, Bill & JP break down the action. Learn more about your ad choices. Visit podcastchoices.com/adchoices

On the Thursday edition of the DSR Daily, we cover the worsening state of Ukraine's military shortages, NY Attorney General Letitia James threatening to seize Trump's properties if he doesn't pay his fraud penalty, Japan's soaring stoke market, and more. Learn more about your ad choices. Visit megaphone.fm/adchoices

On the Thursday edition of the DSR Daily, we cover the worsening state of Ukraine's military shortages, NY Attorney General Letitia James threatening to seize Trump's properties if he doesn't pay his fraud penalty, Japan's soaring stock market, and more. Learn more about your ad choices. Visit megaphone.fm/adchoices

On the Thursday edition of the DSR Daily, we cover the worsening state of Ukraine's military shortages, NY Attorney General Letitia James threatening to seize Trump's properties if he doesn't pay his fraud penalty, Japan's soaring stock market, and more. Learn more about your ad choices. Visit megaphone.fm/adchoices

AP Washington correspondent Sagar Meghani reports on Russia-Ukraine War.

MrTD FMHTYE Presents - Music Through The Decades1. Jazzanova Feat Thief Soldiers Of House - Lie2. United Future Organization - Listen Love3. Cassius - The Sound Of Violence4. Wet - That's the Game5. Mike Lindup, Dave Lee ZR - Atlantia6. Garys Gang - Makin Music7. Takuya Kusajima - I Love The Piano8. House To House Feat. Kim Mazelle - Taste My Love9. Ian Pooley - What I Do10. Willie Hutch - Slick11. AC Soul Symphony - Six Billion Dollar Man12. Doctor's Cat - Feel The Drive13. Nightmoves - Transdance14. Los Charly's Orchestra, Juan Laya, Jorge Montiel - Vibration15. Coflo - Lux16. Demarkus Lewis - Do You Really Know17. Stardust - Music Sounds Better With You18. Kevin Yost - Wanna Dance19. Jimmy Deer - Look Into My Eyes20. Critical Point, Vikter Duplaix - Messages21. Expanded People - My Freedom22. Harold Brandon - Haunted23. Seal - Violet24. Mr. O - Be Free25. Joi Cardwell, Fred Jorio - I Won't Waste Your Time26. Blaze - Wishing You Were Here27. Fingers Inc - Never No More Lonely28. Barry Manilow - Copacabana29. Blackjoy - Moustache30. Gaucho - Dance Forever31. Deep Sensation - Get Together32. Kerri Chandler - I Know33. Kerri Chandler - Sunshine & Twilight34. Kerri Chandler Feat. Roy Ayers - Vibrations

Titans at critical point between playoff push and a Top 5 draft pick For More A to Z Morning Show coverage follow us here: www.atozsports.com/nashville Podcasts: atozsports.com/podcasts Facebook: https://www.facebook.com/atozsportsnashville  Instagram: https://www.instagram.com/atozsports/  Twitter: https://twitter.com/AtoZSports  TikTok: https://www.tiktok.com/@atozsportsnashville #AtoZSports #TennesseeTitans #NFLFootball #Titans #NFLUpdates #NFLFootball Learn more about your ad choices. Visit megaphone.fm/adchoices

In this inspiring devotional, we delve into the profound journey of Jacob, exploring the transformative power of the Law of Process. Have you ever wondered why some effortlessly achieve success while others face constant challenges? Life is a process, and just like a seed grows into a tree, our personal growth requires patience and perseverance.Jacob's Journey: Uncover the life of Jacob, the "supplanter" born in competition and striving. His desire for God's blessings led him on a transformative journey.Critical Point in Genesis 32: Jacob faces his past, wrestling with a mysterious man until daybreak. This spiritual battle symbolizes inner turmoil and the need for transformation.Renaming to Israel: Witness Jacob's surrender to God's process, marked by a profound transformation as his name changes to Israel, signifying his struggle with God.The Law of Process in Scripture: Explore Romans 8:29, understanding that God predestines us to be conformed to the image of His Son. Transformation involves time, experiences, challenges, and lessons.Embracing Growth in Uncertainty: During waiting periods or adversity, it's crucial to stay faithful, seek guidance, and trust in God's refining process. Remember, discomfort leads to growth and Christ-likeness.

Critical Point

In our second Critical Point episode about AI applications in insurance, we drill down into the topic of machine learning and particularly its evolving uses in healthcare. Milliman Principal and Consulting Actuary Robert Eaton leads a conversation with fellow data science leaders about the models they use, the challenges of data accessibility and quality, and working with regulators to ensure fairness. They also pick sides in the great debate of Team Stochastic Parrot versus Team Sparks AGI. You can read the episode transcript on our website.

Artificial intelligence has been the buzz term of 2023, evolving at a pace unimaginable when Milliman launched this podcast five years ago. For this 50th episode of Critical Point, we gathered a group of our AI experts to discuss how the technology is poised to reshape the insurance value chain, from hiring practices and actuarial modeling to customer service and communication.You can read the episode transcript on our website.

AccuWeather Daily brings you the top trending weather story of the day - every day. Learn more about your ad choices. Visit megaphone.fm/adchoices

Shiny swords, sharpshooting archers, magically-assisted martial arts: all these things are staples of fantasy literature. But how do you do fights right? Guest SL Huang joins us to discuss all the pointy bits! In this episode, we think not just about the technology and technicalities of fighting, but also how combat fits into (or goes against the grain of) social norms. Is your world one where a citizen can routinely be challenged to a duel? Or expects to be punched in the face if they say something rude? Or is physical violence more taboo? How do societal standards and more tangible concerns shape the style of combat? How do ideas of gender and class play into who fights, where, when, and how? We explore all this and more! [Transcript TK] Our Guest: SL Huang is a Hugo-winning and Amazon-bestselling author who justifies an MIT degree by using it to write eccentric mathematical superhero fiction. Huang is the author of the Cas Russell novels from Tor Books, including Zero Sum Game, Null Set, and Critical Point, as well as the new fantasies Burning Roses and The Water Outlaws. In short fiction, Huang's stories have appeared in Analog, F&SF, Nature, and more, including numerous best-of anthologies. Huang is also a Hollywood stunt performer and firearms expert, with credits including “Battlestar Galactica” and “Top Shot.” Find SL Huang online at www.slhuang.com or on Twitter as @sl_huang.

We're back from the 3 day Labor Day weekend and Johnny got us up to speed with the latest on our friend George's medical condition...and while we aren't out of the dark just yet, George's continues to improve greatly!

Insurers are feeling the heat as another record-breaking summer finally cools off. In this episode of Critical Point, Milliman leaders Rich Moyer, Garrett Bradford, and Andi Shah—who have studied the impact of extreme heat from the Middle East to Europe to North Carolina—discussed the ramifications on property, workers' compensation, health, and other types of insurance. They also talk about data, including available sources for measuring heat risk now, and the data they would like to see to help measure the future effects of a warming planet. You can read the episode transcript on our website.

As every level of government appears to spin its wheels on homelessness, the problem is just getting worse. We visit one of the more dangerous encampments in Seattle and speak with neighbors who complain that leaders are doing nothing. PLUS: The Iowa State Fair is this weekend. We

Striking the ball properly is all about the the point of impact. It's nearly impossible to modify our swing mechanics on our own, but when you understand the club face position at contact with the ball, you will become a better ball striker. Martin Chuck, PGA, is also the inventor of the very popular Tour Striker, one of the best (based on listener feedback) and most effective training tools you can ever try. If you've ever seen the infomercial, you know the selling points. In this half hour interview with host Fred Greene, Martin discusses how most amateurs sweep, flip or pick at the ball instead of understanding how to get their hands in the correct position for quality impact. Practicing with a Tour Striker does that better than any tool I've ever tried. Martin also shatters some classic cliches we all repeat but don't understand, like “slow down your backswing”, “swing easy”, and “hit down to make it go up”. Originally published as Golf Smarter #315 on January 10, 2012This show is part of the Spreaker Prime Network, if you are interested in advertising on this podcast, contact us at https://www.spreaker.com/show/3464073/advertisement

On a new episode of the “Amazin' But True” podcast, Jake Brown is joined by Mets'd Up Podcast co-host Marc Luino. They react to the Mets splitting the Subway Series, their Yankee Stadium experience, the Mets trade pieces and who could be the first domino to fall. They break down what the Mets plan should be before Tuesday's trade deadline, the Rosario for Syndergaard trade, the Ronny Mauricio situation, this weekend series against the Nationals, the NL Wild Card race, why they are trying to keep a sliver of hope that the Mets can go on a run to the playoffs and Marc's journey to now over 250K YouTube subscribers. Learn more about your ad choices. Visit megaphone.fm/adchoices

On this special Canada Day edition of The House, we're taking a closer look at the North. It's an integral part of Canada's identity, but for those living there, a lack of housing and high food prices are creating extreme pressures. Northwest Territories Premier Caroline Cochrane explains why she thinks the federal government isn't listening to those living in her territory. Then Nunavut NDP MP Lori Idlout welcomes us to a remote fly-in community off Baffin Island to kick off our “Backbenchers' backyards” summer series. Plus — ITK President Natan Obed explains why Inuit are the “bedrock” of arctic sovereignty and Northern Affairs Minister Dan Vandal defends his government's record in the North, but says fixing the housing crisis will take decades.

World leaders have been meeting in Paris to discuss the need to reform the global financial system to tackle climate change. The summit aims to create a strategy for the next 18 to 24 months, ranging from debt relief to climate finance.

When the SECURE 2.0 Act passed in late 2022, it introduced 92 provisions affecting retirement plans—and created a long to-do list for plan professionals. In this episode of Critical Point, Milliman employee benefits expert Nina Lantz talks with Brandy Cross, our director of defined contribution (DC) plan compliance, about how DC plans are coping. Learn which provisions plan sponsors are addressing first, and why it's OK to wait before making plan changes. Plus, don't miss Nina's companion episode on SECURE 2.0's impact on defined benefit plans.You can read the episode transcript on our website .

More than three years after the coronavirus shut down the world, the Biden administration announced in early 2023 the end of the U.S. National Emergency and the Public Health Emergency, two declarations that had mandated health insurance coverage of COVID-19 tests and vaccines, among other requirements. As these regulations are now rolled back, what does that mean for employer-sponsored health plans? In this episode of Critical Point, Milliman health experts discuss the implications, from whether to continue paying for at-home COVID-19 tests and when to phase out standalone telehealth benefits, to how to communicate any changes with plan members at this significant moment in the pandemic. You can read the episode transcript on our website.

As climate change causes more storms and floods, residents of coastal areas are being forced to move inland—driving up rents and displacing current residents of those regions. Milliman recently studied this issue in a paper called “Climate Displacement in New York City: Making Space for Our Neighbors,” published with Rebuild by Design, a nonprofit that helps communities build resilience. On this episode of Critical Point, two of the study authors discuss the groups most at risk, designing Manhattan to be more like Hong Kong, and how 40% of New Yorkers may be displaced without proactive city planning. You can read the episode transcript on our website.

You can tell your story from any point of view you want. In fact, you can tell it from multiple points of view.So how many points of view should you use? What will most effectively communicate your story to your readers? And how many points of view is too many?That's what this episode is all about. I'm breaking down the strengths and pitfalls of using multiple points of view.You'll learn:How using multiple points of view can enhance the story2 pitfalls of having too many point of view charactersA simple principle to help you find the perfect number of points of view for your novel4 qualities of great point of view charactersAnd more!Plus, I'll share examples of some multiple point of view novels I love—and what makes them work so well.Are you working on a multiple point of view novel? Use the questions in this episode to make sure every single point of view is serving your story well.And be sure to download the worksheet to get all those questions in one place. Grab the worksheet at alicesudlow.com/multipov.Links mentioned in the episode:THE GUEST LIST by Lucy FoleyWONDER by R. J. PalacioEp. 16: The Critical Point of View Mistake to Fix in Your Second DraftDownload the Multi POV Quiz: alicesudlow.com/multipovSupport the showWant more editing tips and resources? Follow me on Instagram and Facebook.And if you're enjoying the podcast, would you mind leaving a rating and review on Apple Podcasts? That helps more writers find these editing resources. And it helps me know what's helpful to you so I can create more episodes you'll love!Loving the show? Show your support with a monthly contribution »

The point of view you choose for your novel shapes the information that you give your readers. And it shapes how your readers receive and interpret that information.Which means your point of view actually shapes the story itself. And that's a really big deal!The challenge is, point of view can be tricky to master. There are nearly unlimited ways you can craft your point of view. And there are just as many point of view mistakes you can make.In this episode, I'm sharing the most important mistake I see writers make in their point of view. Here it is:Being inconsistent.Ever find yourself accidentally switching between first person and third person point of view? Or between past and present? Or between multiple characters' perspectives?Those are inconsistencies. And they'll distract, confuse, and frustrate your readers.Not to worry, though! In this episode, you'll learn:Why point of view inconsistencies matterHow to spot inconsistencies in your own writingWhen in the editing process you should correct point of view inconsistenciesWhat to do if you're not sure you've caught them all (hint: don't panic!)And more!Plus, I've put together a worksheet for you to reference and practice. In it, I'll show you what a consistent point of view looks like, and how to spot an inconsistent point of view shift.Then, challenge yourself to spot all the point of view inconsistencies I've hidden in the practice exercise.Get the worksheet at alicesudlow.com/povworksheet and practice your point of view skills.Then, pull out a scene of your novel and clean up any point of view inconsistencies you find!Links mentioned in the episode:Point of View Worksheet: alicesudlow.com/povworksheetSupport the showWant more editing tips and resources? Follow me on Instagram and Facebook.And if you're enjoying the podcast, would you mind leaving a rating and review on Apple Podcasts? That helps more writers find these editing resources. And it helps me know what's helpful to you so I can create more episodes you'll love!Loving the show? Show your support with a monthly contribution »

We had the pleasure to hang out with Don Fort lead refrigeration trainer for Heatcraft.  Don has been the the HVACR industry for over 40 years and has been training refrigeration professionals for over 25 years. Topics we discuss: Training technicians on CO2 refrigeration Review CO2 Terminology  Troubleshoot tips on CO2 system Heatcraft CO2 training programs Learn more about Heatcraft training - https://www.heatcraftrpd.com/training/ Heatcraft TV Don talked about - https://www.heatcraftrpd.com/resources/heatcraft-tv/ ============================================================== Refrigeration Mentor Website: www.refrigerationmentor.com All Access to Refrigeration Mentor Content:  Learn More Upcoming Compressor Masterclass: Learn More Upcoming Supermarket Learning Program: Learn More Upcoming CO2 Learning Program: Learn More  Free System & Compressor Troubleshooting Guide Subscribe to the Refrigeration Mentors video newsletter and get your Free Compressor Guide

In this episode, Dr. Cohn answers a question from a reader about how to not let important points derail your mindset for the match
. Here's the question of the week: HENRY: Most times when I lose a critical point, I Iose all rhythm and feel for the ball. I will lose all confidence and ability to play at a high level. I will usually drop several games in a row and eventually the match. I have tried many different things to overcome this. I am hoping to find guidance to improve my game. Peak Performance Sports, LLC helps athletes and performers improve mental skills for success in sports. We work with athletes in all sports - junior to professional - via video mental coaching from anywhere in the world. Please contact us for more details. Resources for Athletes, Coach, and Sports Parents Learn about Mental Game Coaching For Athletes Download our a FREE Mental Toughness Report Read our Sports Psychology Articles Check out our ports Psychology Audio ProgramsS Check out Tennis Confidence 2.0 Audio program *Subscribe to The Tennis Psychology Podcast on iTunes *Subscribe to The Tennis Psychology Podcast on Spotify

Our guest this week is Kathy Jones, the managing director and chief fixed-income strategist for the Schwab Center for Financial Research. Prior to joining Schwab in 2011, Jones was a fixed-income strategist at Morgan Stanley. Before that, she served as executive vice president of the debt capital markets division of Prudential Securities. Jones received her bachelor's degree in English literature from Northwestern University and her Master of Business Administration from Northwestern's Kellogg Graduate School of Management.BackgroundBioTwitter handle: @KathyJonesPractical Matters“U.S. Treasurys at ‘Critical Point': Stocks, Bonds Correlation Shifts as Fixed-Income Market Flashes Recession Warning,” by Christine Idzelis, MarketWatch, Jan. 23, 2023.“Don't Give Up on the 60/40 Portfolio, Says Schwab's Jones,” Bloomberg, Aug. 9, 2022.Interest Rates and Bonds“Bond Market Mess Is a Chance to Lock In Higher Yields for Longer,” by John Manganaro, Think Advisor, Sept. 29, 2022.“For Income Investors, Bond Yields Are Looking Attractive,” by Tom Lauricella, Morningstar.com, Sept. 15, 2022.“Has the U.S. Dollar Peaked?” by Kathy Jones, Charles Schwab, Jan. 12, 2023.“Fixed Income Outlook: Bonds Are Back,” by Kathy Jones, Charles Schwab, Dec. 6, 2022.Inflation“Fed Rate Hikes: Why Are Bond Yields Falling?” by Kathy Jones, Charles Schwab, July 6, 2022.“Inflation Is Real Enough to Take Seriously,” by Jeff Sommer, The New York Times, July 28, 2021.“Kathy Jones on Monitoring Inflation Risks,” TD Ameritrade Network.Rates and Recession“Is a Recession Coming Soon? This Bond Market Indicator Is Flashing Red,” by Mallika Mitra, Money, April 11, 2022.“Liftoff: Fed Hikes Rates, Signals More to Come,” by Kathy Jones, VettaFi, March 17, 2022.“Market Perspective: Slowdown or a Recession?” by Liz Ann Sonders, Kathy Jones, and Jeffrey Kleintop, Charles Schwab, Jan. 13, 2023.“The Fed's Policy Tightening Plan: a One-Two Punch,” by Kathy Jones, Schwab Moneywise, Jan. 11, 2022.

Watch on Youtbe - https://youtu.be/6l4U_zKLTwU We had the pleasure to speak with Copeland compressor experts Petr Chadima & Joe Zheng. We discussed many great topics on Copeland CO2 Stream Compressors and you are going to want to take notes because there is a huge amount of knowledge in this episode. Topics Discussed Copeland CO2 Stream portfolio  Motors and oil pump vs flinger High pressure hazards mitigation  High discharge temperature management Compressor mass flow Liquid flood back testing E360 Resource - https://climate.emerson.com/en-ca/tools-resources/e360 ============================================================== Refrigeration Mentor Website: www.refrigerationmentor.com All Access to Refrigeration Mentor Content:  Learn More Upcoming Compressor Masterclass: Learn More Upcoming Supermarket Learning Program: Learn More Upcoming CO2 Learning Program: Learn More  Free System & Compressor Troubleshooting Guide Subscribe to the Refrigeration Mentors video newsletter and get your Free Compressor Guide Youtube Channel: https://www.youtube.com/c/refrigerationmentor Instagram: @refrigerationmentor

Had a great conversation on CO2 Cold Storage Applications with Grady McAdams who is the Northwest Region and Cold Storage Sales Leader at Heatcraft Refrigeration Products. Some of the topics we discussed were: Types of CO2 cold storage applications How Heatcraft designs CO2 system for Cold Storage Discussed Adiabatic Gas Coolers Tips and tricks on designs for low and high ambient locations The latest update on Refrigeration and Regulation Codes for the US - https://www.heatcraftrpd.com/regulatory/regulatory-overview/ Newer division of Heatcraft called Magna Learn More Here  Grady wrote for Food Logistics magazine on CO2: 5 Reasons Why CO2 is Ideal Refrigerant for Food Logistics | Food Logistics ============================================================== Refrigeration Mentor Website: www.refrigerationmentor.com All Access to Refrigeration Mentor Content:  Learn More Upcoming Compressor Masterclass: Learn More Upcoming Supermarket Learning Program: Learn More Free System & Compressor Troubleshooting Guide Subscribe to the Refrigeration Mentors video newsletter and get your Free Compressor Guide Youtube Channel: https://www.youtube.com/c/refrigerationmentor Connect with the Refrigeration Mentor IG: @RefrigerationMentor  

Do the hard things and read the manual. In this episode I will be diving into the steps how to quickly find the specific CO2 rack manuals you are looking for.  We will discuss a few points out of Kysor Warren, System LMP and Hill Phoenix CO2 manuals. =========================================================================== Upcoming CO2 Learning Program: Learn More Would you like to invest in yourself to become better at refrigeration. Check out these upcoming programs at  Refrigeration Mentor Events

We had a great session with Shaun Spencer who is helping grow the refrigeration industry by sharing his extensive knowledge on CO2 systems with technicians from around the globe. We get into some great tips like Safety Tips, Service Tips, Superheat Tips, Training Tips and Where to find CO2 information Tips.  ===================================================================== Upcoming CO2 Learning Program: Learn More Would you like to invest in yourself to become better at refrigeration. Check out these upcoming programs at  Refrigeration Mentor Events      

Albert Breer talks about the issues for the Browns in the first two games, why the players-only meeting for the defense caught his attention, how likely assistant coaching changes could be if struggles continue in certain areas, the matchup with the Steelers on Thursday night, the 49ers' situation with Jimmy Garoppolo now the starter again and more.