Podcasts about Harbor

Marshall Harris and Mark Grote were joined by Chicago Sports Network analyst Clay Harbor, who explained why he believes the Bears should look to trade receiver DJ Moore and cut linebacker Tremaine Edmunds this offseason.

Marshall Harris and Mark Grote were joined by Chicago Sports Network analyst Clay Harbor, who detailed why the Bears should pursue defensive end Trey Hendrickson in free agency this offseason.

In the third hour, Marshall Harris and Mark Grote discussed a variety of sports topics in the 5 On It segment. After that, Chicago Sports Network analyst Clay Harbor joined the show to preview the Bears' offseason. Harbor shared why a key player or two could be departing this offseason.

We're more anxious, overwhelmed, and exhausted than ever. Fortunately, what our Heavenly Father offers us isn't just psychological change but spiritual restoration. Join us in our "What If" sermon series as we explore this powerful question: What if God was at the center of all my thoughts, reactions, and decisions?If you're new to Harbor, have questions, or are looking to get connected in anyway, head to harborchurch.com/connect!

We're more anxious, overwhelmed, and exhausted than ever. Fortunately, what our Heavenly Father offers us isn't just psychological change but spiritual restoration. Join us in our "What If" sermon series to hear Pastor Ron Sears' take on this powerful question: What if God was at the center of all my thoughts, reactions, and decisions?If you're new to Harbor, have questions, or are looking to get connected in anyway, head to harborchurch.com/connect!

Speaker: Abi Oosting

In the final hour, Mike Mulligan and David Haugh were joined by Chicago Sports Network analyst Clay Harbor to reflect on the Bears' successful 2025 season and to discuss what lies ahead for them in the offseason.

Mike Mulligan and David Haugh were joined by Chicago Sports Network analyst Clay Harbor to reflect on the Bears' successful 2025 season.

Lindsay Hadley is the Managing Director of Harbor Fund, the first venture capital-shaped 501(c)3 investing in films and television that change culture for good. She's the founding executive director behind Global Citizen, brought the first $17 million to the organization, and has spent her career galvanizing A-list celebrities, billionaire philanthropists, and major corporations around causes that matter. Her journey from witnessing extreme poverty in post-Soviet Russia to raising $12 million in her first year at Harbor Fund is a masterclass in applied faith and relentless purpose. In this conversation, Lindsay reveals how growing up in a conservative Mormon community where women were expected to be stay-at-home mothers shaped her resistance to traditional career paths—and how becoming the primary breadwinner created painful but necessary conversations in her marriage. You'll discover the inflection point when she sold her dream home for double what she built it for, moved to Hawaii, and completely reimagined her relationship with money and mental health. Lindsay shares why she left the "eat what you kill" consulting treadmill to build residual income, how she's now matching philanthropists with Hollywood's elite to fund purpose-driven storytelling, and why changing one person's world matters as much as changing the world. Key Topics: How witnessing extreme poverty in post-Soviet Russia shaped a lifelong paradigm about money and privilege Navigating the painful cognitive dissonance of being the primary breadwinner in a traditional marriage The financial inflection point of selling a home for double and rethinking wealth strategy at 40 Moving from "eat what you kill" consulting to building residual income streams Why the most powerful engine in the world is Hollywood—and how to hijack it for good Creating the first venture capital-shaped nonprofit investing in films that change culture Building a $100 million fund to become top 1% of independent film financing Why dangerous love and being fully known matters more than any professional legacy Connect with Lindsay online: Website: https://www.capitafinancialnetwork.com/team/lindsay-hadley LinkedIn: https://www.linkedin.com/in/lindsay-hadley-6796a748/ Instagram: https://www.instagram.com/lindsayshadley/?hl=en   Find more from Syama Bunten: Instagram: @syama.co, @gettingrichpod Join Syama's Substack: https://thewealthcatalystwithsyama.substack.com/ Website: https://wealthcatalyst.com Download Syama's Free Resources: https://wealthcatalyst.com/resources Wealth Catalyst Summit: https://wealthcatalyst.com/summits Speaking: https://syamabunten.com Big Delta Capital: www.bigdeltacapital.com

In the Summer of 1976, the new owner of Magic Harbor Amusement Park in Myrtle Beach was doing his best to turn the park around. He was starting to experience success with an uptick in visitors and coming close to turning a profit when a masked intruder ruined it all.

In this episode, Joe Crane sits down with Ryan Hogan, a Navy veteran who transitioned from enlisted aircrewman to Surface Warfare Officer while building a career as an entrepreneur. With 15 years of active duty experience and a tenure in the Reserves, Ryan discusses the "trial-by-fire" lessons learned from early ventures like WarWear and Run For Your Lives, emphasizing the unique challenges of managing a business while serving on active duty. The conversation centers on Ryan's success as the co-founder of Hunt A Killer, the high-growth mystery game he eventually sold. He credits much of his scaling success to the Entrepreneurial Operating System (EOS) and peer-to-peer learning through Vistage, which helped him transition from a founder-led startup to a systems-driven organization. Following the sale, Ryan launched Talent Harbor to fix the inefficiencies he encountered in the hiring industry. He introduces the "Recruiting as a Service" (RaaS) model, which replaces traditional high-commission headhunting with a transparent, flat-fee monthly rate. By treating recruiting as a core operational competency rather than a one-off task, Ryan is now helping other founders build more efficient systems for finding and retaining top-tier talent. Episode Resources: Talent Harbor Ryan Hogan - LinkedIn   About Our Guest Prior to founding Talent Harbor, Ryan Hogan co-founded Hunt A Killer, a subscription-based interactive murder mystery experience. In 2019, Hunt A Killer was named by Fast Company as one of the World's Most Innovative Companies. In 2020, Inc Magazine named it the fastest-growing CPG company. Ryan started his career enlisting in the U.S. Navy as an MH-53E aircrewman, and transitioned to officer where he served as a Surface Warfare Officer onboard various warships. Along the way, Ryan founded WarWear and Run For Your Lives, honing the entrepreneurial skills that he would use in Hunt A Killer, and now Talent Harbor.   About Our Sponsors Navy Federal Credit Union   Navy Federal Credit Union offers exclusive benefits to all of their members. All Veterans, Active Duty and their families can become members. Have you been saving up for the season of cheer and joy that is just around the corner? With Navy Federal Credit Union's cashRewards and cashRewards Plus cards, you could earn a $250 cash bonus when you spend $2,500 in the first 90 days. Offer ends 1/1/26. You could earn up to 2% unlimited cash back with the cashRewards and cashRewards Plus cards. With Navy Federal, members have access to financial advice and money management and 24/7 access to award-winning service. Whether you're a Veteran of the Army, Marine Corps, Navy, Air Force, Space Force or Coast Guard, you and your family can become members. Join now at Navy Federal Credit Union. At Navy Federal, our members are the mission.      Join the conversation on Facebook! Check out Veteran on the Move on Facebook to connect with our guests and other listeners. A place where you can network with other like-minded veterans who are transitioning to entrepreneurship and get updates on people, programs and resources to help you in YOUR transition to entrepreneurship.   Want to be our next guest? Send us an email at interview@veteranonthemove.com.  Did you love this episode? Leave us a 5-star rating and review!  Download Joe Crane's Top 7 Paths to Freedom or get it on your mobile device. Text VETERAN to 38470. Veteran On the Move podcast has published 500 episodes. Our listeners have the opportunity to hear in-depth interviews conducted by host Joe Crane. The podcast features people, programs, and resources to assist veterans in their transition to entrepreneurship.  As a result, Veteran On the Move has over 7,000,000 verified downloads through Stitcher Radio, SoundCloud, iTunes and RSS Feed Syndication making it one of the most popular Military Entrepreneur Shows on the Internet Today.

Time is your most limited resource and how you spend it reveals who you trust. In this message, Pastor Josh challenges us to stop wearing “busy” like a badge and start seeing rest as a spiritual discipline, not a luxury. If you've been running on fumes, this is your reminder that real strength comes when your time and your trust is in God's hands.If you're new to Harbor or want to get connected in any way click this link to get your New Here gift, find upcoming events or get involved!https://harborchurch.com/connect

Pastor Ryan explores an image in scripture that we believe will shape us as we enter this next season as a church family.

This week, we revisit one of the most compelling and unsettling UFO cases on record: the Shag Harbor Incident. Often called Canada's Roswell, but with one crucial difference — the government openly admitted it didn't know what happened. On October 4, 1967, airline crews, fishermen, and first responders all witnessed a strange craft descend into the waters off Nova Scotia, leaving behind no wreckage, no survivors, but a thick, sulfur-smelling foam that defied explanation. Every aircraft was accounted for, and the Royal Canadian Air Force officially classified the event as a “UFO Report,” not a meteor or misidentified plane. But the mystery deepens when decades of research uncover a buried military history tied to underwater encounters and secret NATO exercises. With fresh context from our recent USO investigation, and groundbreaking research by eyewitness and investigator Chris Styles, this episode explores the possibility that Shag Harbor wasn't just a crash, but a coordinated recovery involving technology not of this world.Visit our website for a lot more information on this episode.

Speaker: Tim Wilson

Teagan Tugboat learns to appreciate her home with the help of her friend, Izzy Icebreaker.

A talk by Jaune Evans and Neal Shorstein about “Not to Harbor Ill-Will” part of Kannon Do Precepts Series. This talk was given on January 7, 2026. .

Ann visits with Casey Tusa, Education Coordinator for Harbor Home Health and Hospice, about older loved ones and the care they deserve. We get some great music from Japheth Singleton, via the Zack Owen Show, and a new Act Locally Waco with Elizabeth Riley. Learn more about your ad choices. Visit megaphone.fm/adchoices

Mike Mulligan and David Haugh were joined by Chicago Sports Network analyst Clay Harbor to preview the Bears-Rams matchup Sunday at Soldier Field in the NFC divisional round.

In the third hour, Mike Mulligan and David Haugh were joined by Chicago Sports Network analyst Clay Harbor to preview the Bears-Rams matchup Sunday at Soldier Field in the NFC divisional round. Later, Blackhawks legend Chris Chelios joined the show to discuss the team's recent performance. He also previewed the Bears-Rams game.

What if the biggest barrier between you and the life God has for you is your own disobedience? In this message, Pastor Josh challenges us to move from sentiment to substance, because surrender isn't the same as obedience. We may feel surrendered, but until we actually obey, we're still holding back. If Jesus isn't Lord of all, He isn't Lord at all.If you're new to Harbor or want to get connected in any way click this link to get your New Here gift, find upcoming events or get involved!https://harborchurch.com/connect

A new report declares 2025 as one of the costliest years when it comes to climate disasters. Environmental groups are cautiously optimistic about the environmental commitments for the proposed Everett Soccer Stadium. And will YOU add shellfish harvested from Boston Harbor to your dinner table? It's our environmental news roundtable!

Fairport Harbor Light Station, photo by. Jeremy D'Entremont. Fairport Harbor, on the south side of Lake Erie at the mouth of the Grand River, is considered part of the Cleveland, Ohio, metropolitan area. The first lighthouse in the harbor was a 56-foot brick tower built in 1825. The lighthouse that stands today is a 60-foot stone tower that began service in 1871. After a new lighthouse was built on a breakwater in the harbor in 1925, the government planned to destroy the old lighthouse. A number of organizations in the area objected, and the lighthouse was spared. In 1945, the Fairport Harbor Historical Society established a marine museum inside the old keeper's house. Museum highlights include a Fresnel lens and the infamous "ghost cat" story. Marty O'Gara and John Ollila by the third-order Fresnel lens from Fairport Harbor Lighthouse, now on display in the museum in the keeper's house. Photo by Jeremy D'Entremont. Our guests today are Marty O'Gara and John Ollila. John is the historian for the lighthouse and a trustee of the Fairport Harbor Historical Society. Marty is the president of the Fairport Harbor Historical Society.

Speaker: Ben Peters

We have the latest on the Ashley Tisdale and Hilary Duff Drama including Hilary Duff's Husband Matthew Koma coming at Ashley. David Harbour quits movie to focus on his own mental health after a very bumpy last year and what is going on with Louis Tomlinson and Zayn Malik?Plus Dax Shepard is giving an energy to Cher that NO OF US are comfortable with.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

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

Mike Mulligan and David Haugh were joined by Chicago Sports Network analyst Clay Harbor to preview the Bears-Packers matchup Saturday in the wild-card round of the NFL playoffs.

Hometown Radio 01/06/26 3p: Harbor Commissioner Richard Scangarello discusses what's happening in Port San Luis

What if the best year of your life doesn't come from striving harder, but from surrendering deeper? In this message, Pastor Josh unpacks what it means to fully trust God's plan, release control, and let Him lead. You don't find God's will by discovering yourself; you find it by surrendering yourself.If you're new to Harbor or want to get connected in any way click this link to get your New Here gift, find upcoming events or get involved!https://harborchurch.com/connect

Meet Clay Johnston, MD, PhD, MPH, Cofounder & Chief Medical Officer, Harbor Health. They discuss why so many smart care redesign efforts end up as…yet another white paper. Clay talks about his “been there, rebuilt that” experience: you can absolutely reengineer pathways to reduce unnecessary procedures, shift work to the right team members, use tech for follow-ups, and improve outcomes. The problem is that in classic fee-for-service, the system often pays handsomely for the stuff you're trying to avoid (unnecessary utilization) and barely pays for what actually keeps people well (coaching, check-ins, prevention, navigation). To stream our Station live 24/7 visit www.HealthcareNOWRadio.com or ask your Smart Device to “….Play Healthcare NOW Radio”. Find all of our network podcasts on your favorite podcast platforms and be sure to subscribe and like us. Learn more at www.healthcarenowradio.com/listen

As The Harbor celebrates 28 years of God's faithfulness, this Vision Sunday calls us back to our foundation. From the story of Mustard Seed Mountain to Jesus' closing words in the Sermon on the Mount, we're reminded that knowing the right foundation without building our lives upon it will never lead to fruit. Rooted in Matthew 7:24–29, this message challenges us to move beyond merely hearing Jesus' words and instead follow Him fully. As we step into 2026 with a renewed focus on Foundation, our prayer is that we would stand firm on Christ, produce lasting fruit, and live lives that point others to His authority and glory.Download your Vision 2026 Prayer Card by clicking here.Message Notes: https://notes.subsplash.com/fill-in/view?page=SyDFKER7beSmall Group Discussion Questions: https://storage2.snappages.site/PJBKS3/assets/files/14Firm-Foundation.pdfFind us on:YouTube: YouTube.com/TheHarborInstagram: Instagram.com/TheHarbor_lifeFacebook: Facebook.com/TheHarbordotlifeWebsite: https://www.TheHarbor.lifeWatch/listen on The Harbor AppNew episode every week!

Speaker: Jeremy Kreuze

In 1910, a powerful winter storm trapped a passenger train deep in Nevada's snowy mountains for days. Kids, families, and train workers had to work together, stay brave, and get creative to survive one of the snowiest disasters in American history. Grab your coat—this is a true story where teamwork beat the blizzard!⁠⁠⁠Birthday Shout Out Form⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Use Code MKP for Harbor & Sprout⁠⁠⁠⁠⁠⁠Become a ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Patron⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Or a Subscriber on Spotify!⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

Clay Harbor: Bears 'can score on anybody' full 1119 Mon, 29 Dec 2025 15:51:43 +0000 WlvoJ8PjupFqkFPUd5vqg98cmTYa48SG nfl,chicago bears,sports Mully & Haugh Show nfl,chicago bears,sports Clay Harbor: Bears 'can score on anybody' Mike Mulligan and David Haugh lead you into your work day by discussing the biggest sports storylines in Chicago and beyond. Along with breaking down the latest on the Bears, Blackhawks, Bulls, Cubs and White Sox, Mully & Haugh routinely interview the top beat writers in the city as well as team executives, coaches and players. Recurring guests include Bears receiver DJ Moore, Tribune reporter Brad Biggs, former Bears coach Dave Wannstedt, Pro Football Talk founder Mike Florio, Cubs president of baseball operations Jed Hoyer and Cubs pitching coach Tommy Hottovy.Catch the Mully & Haugh Show live Monday through Friday (5 a.m.- 10 a.m. CT) on 670 The Score, the exclusive audio home of the Cubs and the Bulls, or on the Audacy app. For more, follow the show on X @mullyhaugh. © 2025 Audacy, Inc. Sports False https://player.amperwavepodcasting.com?feed-link=ht

Clay Harbor: Bears 'can score on anybody' (Hour 4) full 2013 Mon, 29 Dec 2025 16:25:00 +0000 KhHe7gRUVIGLQBjPdak4G4Kqb8VbUZn3 sports Mully & Haugh Show sports Clay Harbor: Bears 'can score on anybody' (Hour 4) Mike Mulligan and David Haugh lead you into your work day by discussing the biggest sports storylines in Chicago and beyond. Along with breaking down the latest on the Bears, Blackhawks, Bulls, Cubs and White Sox, Mully & Haugh routinely interview the top beat writers in the city as well as team executives, coaches and players. Recurring guests include Bears receiver DJ Moore, Tribune reporter Brad Biggs, former Bears coach Dave Wannstedt, Pro Football Talk founder Mike Florio, Cubs president of baseball operations Jed Hoyer and Cubs pitching coach Tommy Hottovy.Catch the Mully & Haugh Show live Monday through Friday (5 a.m.- 10 a.m. CT) on 670 The Score, the exclusive audio home of the Cubs and the Bulls, or on the Audacy app. For more, follow the show on X @mullyhaugh. © 2025 Audacy, Inc. Sports False https://player.amperwavepodcasting.com?fee

Clay Harbor explains why tight end coaches become good head coaches (Hour 2) full 2608 Thu, 25 Dec 2025 05:14:00 +0000 8xVem1D5N7lGCqyAYwlR12X1DYlKV7UU sports Spiegel & Holmes Show sports Clay Harbor explains why tight end coaches become good head coaches (Hour 2) Matt Spiegel and Laurence Holmes bring you Chicago sports talk with great opinions, guests and fun. Join Spiegel and Holmes as they discuss the Bears, Blackhawks, Bulls, Cubs and White Sox and delve into the biggest sports storylines of the day. Recurring guests include Bears cornerback Jaylon Johnson, former Bears coach Dave Wannstedt, former Bears center Olin Kreutz, Cubs manager Craig Counsell, Cubs second baseman Nico Hoerner and MLB Network personality Jon Morosi. Catch the show live Monday through Friday (2 p.m. - 6 p.m. CT) on 670 The Score, the exclusive audio home of the Cubs and the Bulls, or on the Audacy app. © 2025 Audacy, Inc. Sports False https://player.a

Professor Toby Wilkinson. Cleopatra VII aligned with Julius Caesar to secure her throne, using her intellect and charisma to win his support. During Caesar's defense against Egyptian forces, he burned ships in the harbor, an inferno that accidentally spread to and destroyed the Great Library of Alexandria. 1892 CAIRO

Clay Harbor turns the page early to San Francisco full 789 Wed, 24 Dec 2025 23:11:37 +0000 0OKh41UVKoKeEaNWRjKAqqCVMHBMmB7I nfl,chicago bears,sports Spiegel & Holmes Show nfl,chicago bears,sports Clay Harbor turns the page early to San Francisco Matt Spiegel and Laurence Holmes bring you Chicago sports talk with great opinions, guests and fun. Join Spiegel and Holmes as they discuss the Bears, Blackhawks, Bulls, Cubs and White Sox and delve into the biggest sports storylines of the day. Recurring guests include Bears cornerback Jaylon Johnson, former Bears coach Dave Wannstedt, former Bears center Olin Kreutz, Cubs manager Craig Counsell, Cubs second baseman Nico Hoerner and MLB Network personality Jon Morosi. Catch the show live Monday through Friday (2 p.m. - 6 p.m. CT) on 670 The Score, the exclusive audio home of the Cubs and the Bulls, or on the Audacy app. © 2025 Audacy, Inc. Sports False https://player.amperwavepodcasting.com?feed

Clay Harbor relives the story of breaking his wrist on 'The Bachelorette' full 551 Wed, 24 Dec 2025 22:14:05 +0000 tcP6QZgdPpzR0Cborvrx5nM62kUnT0R3 sports Spiegel & Holmes Show sports Clay Harbor relives the story of breaking his wrist on 'The Bachelorette' Matt Spiegel and Laurence Holmes bring you Chicago sports talk with great opinions, guests and fun. Join Spiegel and Holmes as they discuss the Bears, Blackhawks, Bulls, Cubs and White Sox and delve into the biggest sports storylines of the day. Recurring guests include Bears cornerback Jaylon Johnson, former Bears coach Dave Wannstedt, former Bears center Olin Kreutz, Cubs manager Craig Counsell, Cubs second baseman Nico Hoerner and MLB Network personality Jon Morosi. Catch the show live Monday through Friday (2 p.m. - 6 p.m. CT) on 670 The Score, the exclusive audio home of the Cubs and the Bulls, or on the Audacy app. © 2025 Audacy, Inc. Sports False https://player.ampe

Gabe Ramirez welcomed on Laurence Holmes and Clay Harbor for the daily transition segment.

On a frozen battlefield during World War I, something unbelievable happened — soldiers put down their weapons, sang Christmas songs, and met as friends. This true story proves that even in the middle of war, kindness and peace can win, if only for one magical night.⁠⁠⁠Birthday Shout Out Form⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Use Code MKP for Harbor & Sprout⁠⁠⁠⁠⁠⁠Become a ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Patron⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Or a Subscriber on Spotify!⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

Not to shake up your nativity scene, but the wisemen were late. Fortunately, all are welcome in the presence of Jesus, regardless of their timing. In this Christmas message, Pastor Josh reminds us that God isn't checking our timing, He's looking at our heart. Whether you're early, late, broken, or doubting, you still belong. God meets you where you are, and late worship is still powerful worship.If you're new to Harbor or want to get connected in any way click this link to get your New Here gift, find upcoming events or get involved!https://harborchurch.com/connect

Mike Mulligan and David Haugh were joined by Chicago Sports Network analyst Clay Harbor to preview the Bears-Packers game Saturday at Soldier Field.

In the final hour, Mike Mulligan and David Haugh were joined by Chicago Sports Network analyst Clay Harbor to preview the Bears-Packers game Saturday at Soldier Field.

In the second hour, Marshall Harris, Mark Grote and Clay Harbor were joined by Mike Florio of Pro Football Talk to discuss the Bears' bright future and the latest NFL storylines. After that, Harris, Grote and Harbor discussed what the Bears can learn from their loss to the Packers on Dec. 7 as the teams prepare to meet again this Saturday at Soldier Field.

What began as a weekly pop-up inside the now-shuttered Fort Defiance slowly evolved into one of Brooklyn's most imaginative cocktail destinations. The Sunken Harbor Club charted its own unlikely course—thanks in part to a shortage of decent drinks near the Downtown Brooklyn courthouses (yes, really), and the sudden availability of one of New York's most visionary bartenders.Adam sits down with St. John Frizell, Ben Schneider, and Garret Richard to unpack the twists of fate, creative risks, and behind-the-scenes hustle that turned a temporary idea into one of the most exciting bars in New York and beyond.Follow us: https://www.instagram.com/buildoutpodcastSunken Harbor Club: https://www.instagram.com/sunkenharborclubnycGarret Richard: https://www.instagram.com/garretjrichardSt. John Frizell: https://www.instagram.com/stjohnfrizellBen Schneider: https://www.instagram.com/benisclovisVinePair: https://www.instagram.com/vinepairHosted by VinePair Co-Founder: https://www.instagram.com/adamteeterProduced and edited by: https://www.instagram.com/dolldoctor Hosted on Acast. See acast.com/privacy for more information.

Mike Mulligan and David Haugh welcomed on Marshall Harris, Mark Grote and Clay Harbor for the daily transition segment.

In this episode of Behind The Numbers With Dave Bookbinder, I'm joined by Kristof Gleich, President and Chief Investment Officer at Harbor Capital Advisors, for a deep dive into the human capital factor and its impact on business value and investment performance. Kristof explains how Harbor's partnership with Irrational Capital led to the development of the HAPI ETFs and walks through the seven subfactors that make up the human capital score: organizational effectiveness, innovation, direct management, alignment, engagement, emotional connection, and extrinsic rewards. We get into the data behind the factor, including the use of large-scale employee sentiment surveys and proprietary analytics, the index construction process that identifies the top 150 companies, and the annual reconstitution methodology. Kristof also shares performance insights – from Morningstar recognition to how HAPI has compared with the S&P 500. We also talk about why this factor has the potential to generate real alpha and how investors, private equity firms, and valuation professionals are beginning to incorporate human capital metrics into underwriting and deal analysis. If you're interested in how people truly drive enterprise value, how human capital data can shape portfolios, and what this means for investors, advisors, and dealmakers, this episode offers practical, data-driven insights you can use. About Our Guest: Kristof Gleich is the president and CIO of Harbor Capital Advisors, Inc. Kristof oversees all Investment, Distribution & Marketing and Executive Office functions at Harbor. He provides insight while helping lead Harbor's strategic growth plan. Prior to joining Harbor, Kristof was a managing director and global head of manager selection at JP Morgan Chase & Co. He received a B.S. in Physics from University of Bristol. Kristof is a CFA® charterholder and is FINRA Series 7 and 63 licensed. About the Host: Dave Bookbinder is known as an expert in business valuation and he is the person that business owners and entrepreneurs reach out to when they need to know what their most important assets are worth. Known as a collaborative adviser, Dave has served thousands of client companies of all sizes and industries.  Dave is the author of two #1 best-selling books about the impact of human capital (PEOPLE!) on the valuation of a business enterprise called The NEW ROI: Return On Individuals & The NEW ROI: Going Behind The Numbers.  He's on a mission to change the conversation about how the accounting world recognizes the value of people's contributions to a business enterprise, and to quantify what every CEO on the planet claims: “Our people are this company's most valuable asset.” Dave's book, A Valuation Toolbox for Business Owners and Their Advisors: Things Every Business Owner Should Know, was recognized as a top new release in Business and Valuation and is designed to provide practical insights and tools to help understand what really drives business value, how to prepare for an exit, and just make better decisions. He's also the host of the highly rated Behind The Numbers With Dave Bookbinder business podcast which is enjoyed in more than 100 countries.

Relax to the gentle sound of rain falling over a quiet harbor — soft waves lapping, and a peaceful coastal atmosphere. A calming soundscape perfect for sleep, reflection, or simply unwinding.Want access to an ad-free, 8-hour version of this episode? Try Deep Sleep Sounds Premium free for 7 days: https://sleepsounds.supercast.com/.Create a mix of your favorite sounds by downloading the Deep Sleep Sounds App at: https://deepsleepsounds.onelink.me/U0RY/app.Having an issue with Deep Sleep Sounds or want to ask us a question? Check out our Frequently Asked Questions. Our AppsRedeem exclusive, unlimited access to premium content for 1 month FREE in our mobile apps built by the Slumber Studios team:Slumber App: slumber.fm/deepsleepsounds Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Want to be a top notch candidate for hosting alien life? Then there's a few key requirements you should be aware of: Ideally, you're a large object like a moon or a planet; scientists suspect you also have an atmosphere and water; plus, you should orbit your star from a nice mid-range distance — in the "Goldilocks Zone" of habitability. Until recently, you would be competing against TRAPPIST-1 e. It's a planet outside of our solar system. TRAPPIST-1 e is also only 40 light years away, rocky and the same size as Earth, which prompted researchers to investigate whether it also has an atmosphere — and the potential for alien life. A team of researchers has been investigating TRAPPIST-1 e to learn more about its potential. Their answers, recently published in the Astrophysical Journal Letters, say a lot not just about this exoplanet, but about how scientists should refocus their hunt for alien life.Interested in more space science? Email us your question at shortwave@npr.org.Listen to every episode of Short Wave sponsor-free and support our work at NPR by signing up for Short Wave+ at plus.npr.org/shortwave.Learn more about sponsor message choices: podcastchoices.com/adchoicesNPR Privacy Policy