Podcasts about Bedrock

Send us Fan MailRecorded on 30 September 2026, this episode of "What's New in Cloud FinOps" brings together Frank Contrepois and Stephen Old for a comprehensive roundup of the latest developments across AWS, Google Cloud, Oracle Cloud Infrastructure (OCI), and Alibaba Cloud. With a significant focus on the rapidly evolving world of Generative AI cost management, sustainability, and cloud governance, this instalment was recorded live from the GreenIO Conference foyer in London. Expect a lively, candid flow with real-time analysis.Generative AI & Machine Learning Cost ControlThe episode kicks off with major updates for Amazon Bedrock, SageMaker, and other cloud AI offerings.Amazon Bedrock Agent Core Payments (GA): A key discussion point is that agents can now autonomously discover, access, and pay for paid APIs and content. This presents a major governance challenge, risking unmanaged spend and fragmented cost visibility. The hosts stress the need for spending caps, approval workflows, per-agent budgets, and unified cost aggregation.Amazon Bedrock Cost Allocation & Anomaly Detection: Bedrock now supports IAM principal cost allocation, allowing organisations to track usage back to specific users and roles in the CUR. Additionally, AWS Cost Anomaly Detection now covers third-party Foundation Models like Anthropic's Claude.Bedrock Pricing (OpenAI GPT-5.6): Promotional pricing is available at $4/million input tokens and $20/million output tokens (a claimed 20-33% reduction), valid through at least 21 November 2026.Amazon SageMaker: A new guided, low-code/no-code path in SageMaker Studio simplifies finding cost-effective inference configurations for custom models.Google Cloud BigQuery: Introduced preview monitoring for data agent performance, latency, and conversation costs through Google Cloud observability.Oracle Cloud Infrastructure (OCI): August's AI update expanded hardware choices and on-demand inference for Cohere and Meta models.Calculating AI ROI: Frank highlights an AWS article by Adam Richter on presenting AI return on investment. Steve recalls a previous podcast with Adam discussing a Cornell "levelised cost of AI" study, emphasising the need for measurable value.From AI Pilots to Production: The hosts discuss how many AI pilots run too long without a clear "exit to value" plan. Frank's workshops focus on defining proof-of-value criteria and graduation paths to production. Research shows 59% of organisations report increased AI spend wastage, while only 31% have accurate visibility.Commitments, Pricing Models & The New "SAM" ChallengeA clear trend is the application of traditional cloud commitment models to AI services, creating new challenges where FinOps and Software Asset Management (SAM) collide.Google Cloud: Introduced flexible, spend-based Savings Plans for Gemini Enterprise (10% for one year, 20% for three years) and a forthcoming "deferred execution" option offering up to a 50% discount for non-urgent jobs.Alibaba Cloud: Launched AI Savings Plans for Model Studio on 28 August 2026, with discounts up to 47%. They also introduced batch inference pricing at a 50% discount.The FinOps/SAM Collision: Models like Gemini Enterprise and Azure Copilot Studio blend per-user subscriptions with pay-as-you-go agent workloads. This creates a challenge in determining who owns which piece of the cost and how credits and commitments apply across teams.Hidden Risks & Governance GapsThird-Party AI EULAs: Stephen highlights a critical risk: when a user accepts the End-User License Agreement (EULA) for a third-party model (e.g., Anthropic on Bedrock), they could be binding their entire organisation to the provider's standard terms without legal or procurement oversight.Intellectual Property Risk: Frank points to a recent HBR article discussing how the "how" of AI usage—the prompts and methods—may not be protected, potentially exposing valuable IP.Cloud Infrastructure & Cost Management UpdatesAWS Compute:R9g/R9gd instances (Graviton 4): AWS claims up to 25% better compute, but Frank's analysis shows an 8-25% price uplift. FinOps takeaway: benchmark workloads before migrating.ECS fractional GPU scheduling: ECS now supports fractional GPU allocations on EC2 g6f, ideal for right-sizing smaller ML/AI workloads and improving utilisation.ElastiCache on Graviton 4: Offers ~40% higher throughput and ~31% better price-performance, but with higher memory costs.AWS Database & Analytics:Aurora Serverless: Can now scale to 12 ACU in ~1 second, reducing the need for high steady baselines, but requiring guardrails to prevent cost spikes.RDS for SQL Server BYOM: "Bring Your Own Media" is extended to more regions, requiring careful audit documentation.Oracle AI Database on AWS Exascale: Oracle Exadata DB Services now generally available via AWS, signalling evolving multi-cloud coexistence.AWS Glue 6.0 GA: A ~30% price reduction and runtime upgrades (Spark 4.1, Python 3.13) offer immediate savings opportunities.AWS Glue Data Quality: Anomaly detection is now free with an improved "observation mode".Amazon Kinesis: Can now deliver data directly to S3 in Iceberg format, potentially lowering delivery and query costs.AWS Billing & Lambda:AWS Data Exports: Now supports SQL-based row filtering at the source to create pre-filtered reports.AWS Billing Managed Dashboards: An official, pre-configured version of the popular open-source Cost Intelligence Dashboards.AWS Lambda: Recursive loop detection is now available in all commercial regions, reducing the risk of runaway bills.Google Cloud:BigQuery: Offers cost-effective cross-cloud connections (preview) but restricts Graph processing to Enterprise/Enterprise Plus editions.Billing Reports: A new "originating products" filter helps trace costs back to their source.Semantic Tags: A preview feature to automatically propagate application context to resources for better cost allocation.Storage Rapid Cache: Now allows selective ingest filtering to reduce expenditure.Oracle Cloud Infrastructure (OCI):Autoscaling for Red Hat OpenShift: New capability driven by licensing alignment with Red Hat/IBM.Service Limits: OCI has streamlined the service limit increase experience in the console.Sustainability UpdatesNew Availability Zones: New zones in London (EU-West-2D) and Las Vegas seem focused on AI training, raising questions about sustainability trade-offs.Google Cloud Carbon Methodology: A refresh offers more granular certificates and hourly electricity matching for better precision.Green Software Foundation: The "Beyond Watts" paper encourages evaluating environmental impact beyond just electricity consumption.Productivity AIAmazon Q for Microsoft 365: Now generally available for Excel, Word, etc., bringing AI into daily workflows but raising data governance questions.Referenced Resources & LinksFrank's instance price/performance comparison tool: https://aws.frankcontrepois.com/comparisons/

Detroit's budget picture continues to improve. But what does that mean for residents, city services, and the big redevelopment projects now under discussion? I sat down with expert Steve Watson of Watson & Yates to talk through the current situation and the financial logic behind why Detroit needs growth. We look into the proposed Renaissance Center redevelopment we talked about on the show last week, including the $548 million in requested public incentives tied to a proposed $2.2 billion investment by GM and Bedrock. We break down where that money would come from, how the Renaissance Zone and Downtown Development Authority structure works, and why a proposed payment-in-lieu-of-taxes (PILOT) agreement matters for the city and Wayne County. We also look at Detroit's updated revenue outlook. The city's estimates rose by about $50 million, driven largely by stronger-than-expected income-tax collections and investment-income projections. But that is not a blank check for new programs in the context of a roughly $1.5 billion general-fund budget. The additional revenue is more likely to help Detroit sustain current services, manage rising costs, and prepare for firefighter and police labor contract negotiations. To wrap up, Steve and I talk about the larger equation. The math behind why growing jobs, wages, businesses, and population matters for Detroit's ability to deliver the things residents want from city government. The city has significant economic potential, but only about one-third of Detroiters currently earn a living wage for their household. That makes inclusive growth as important as growth itself. What do you think? Hit us up, dailydetroit@gmail.com or 313-789-3211. Thanks for your support on Patreon!

1 Memory Lane - Fahlberg 2 Echoes Of Love - Andrew Meller 3 Renaissance - Michael Canitrot 4 Now We Are Free (B Jones Remix) - Kryder, B Jones & Elysian 5 Fallout - Davey Asprey 6 Destination (Effen Remix) 7 Flora - Malek 8 Damaged - Eli Brown 9 Solara - C-Systems 10 In My Heart - Ethan, Leena Punks, Luvstruck 11 I Need A Miracle - CoCo Star, Mike Momburg 12 Remember The Future - Mirage (Fr) 13 Tethered - CIElll 14 Pegassi - Strangers - Kenya Grace (Pegassi Remix) 15 Freestyle Fanatic - DBF 16 Aurora - Daxson 17 Sirocco - Leon Bolier, SØNIN 18 Heaven Scent (Marsh Remix) - Nick Muir, Bedrock, John Digweed 19 I Had This Thing (feat. Jamie Irrepressible) (Colyn Remix) - Royksopp 20 At Least We Are Under The Same Moon - David Mackay, Raul Vidal

Welcome to Season 3 of Bedrock! This season will cover the Paleoarchean Era, from 3.6 to 3.2 billion years ago, or March to April on the "Earth Calendar". Get ready for more fossils and strange new rocks! In this episode, we'll see why Season 3 begins and ends when it does: an extinction? an impact? or something more random? On the way, we'll visit geology's great "Council of Time", meet a bishop who was a founder of modern geology, and make a cake on the edge of the Grand Canyon.Want bonus content? Check out our Patreon! One-off donations on Paypal

What do you do with 5.3 million square feet of concrete and glass when the world it was built for no longer exists? Built in the 1970s as a corporate fortress to keep the city out, the Renaissance Center in Detroit stands today as a monument to a mid-century urban planning thesis that has completely collapsed. RenCen office occupancy sits under 10%, construction costs are up 55% over the last decade, and downtown real estate demands an entirely new model. My guests are Jennifer Stallings Dewey, the Senior Counsel, Community Engagement at General Motors; and Jake Chidester, Vice President of Strategic Initiatives at Bedrock. In this episode, we dive into the multi-billion-dollar proposal by General Motors and Bedrock to demolish two 39-story towers, tear down the concrete podium, and turn an isolated corporate island into a connected, walkable riverfront district. We answer a ton of listener questions, as well.  The process is ongoing for community feedback. Here's a calendar of events at their East Riverfront Community Hub: https://detroit-riverfront.bedrockdetroit.com/events And here is a link to the project survey: https://detroit-riverfront.bedrockdetroit.com/survey What do you think? Let me know, dailydetroit@gmail.com or 313-789-3211.  Thanks for your support on Patreon! That's how we're able to fund this independent work.

September 22, 2026 ~ Jared Fleisher, CEO of Bedrock joins Paul W. Smith live from the Detroit Economic Club. Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.

Join Molly, Alan, and Max as they visit the town of Bedrock to observe all the shenanigans of a dino-filled world, with a dash of corporate greed.

Говорят, если стартуешь бизнес в 2026 и ты не AI-native, можно не стартовать. Позвали Андрея Девяткина, со-основателя FivexL: он строит AI-продукт BORIS и уже несколько раз его переупаковал. Разбираем контекстный layer, MCP, Bedrock, экономику токенов и почему AI SRE выглядит мертворождённой категорией. О ЧЁМ ВЫПУСК • Как всё началось: на митапе Amazon Q CLI не смог понять, почему упал Vote App на ECS. • Harness как упряжка: LLM даёт лошадиную силу, а Claude Code, Codex и Kiro CLI решают, куда её направить. • Архитектура под паранойю: данные в аккаунте заказчика, Bedrock в zero retention, отдельный аккаунт на клиента. • Живое демо: чистая сессия Claude без памяти, ответ про сервис за минуту и 38 центов. • Пивоты: «context engineering platform» никто не понял, DevOps-агент не дал traction. • Почему AI SRE выглядит мертворождённой категорией и чем от неё отличается AWS DevOps Agent. • Граф и temporal: троублшутинг serverless, RAG без понятия о времени, корпоративная амнезия. • Стек честно: Bedrock Knowledge Base, S3 Vectors, лимиты Titan, сломанный Kimi. • Нужны ли DevOps дальше: три разные позиции и спор про то, умирает ли навык чтения. • Стоит ли стартовать AI-продукт в 2026 и кто платит за токены. ГОСТЬ Андрей Девяткин, со-основатель FivexL (AWS Advanced Tier), AWS Community Builder, лидер AWS User Group в Лас-Пальмасе. У нас во второй раз, первый был в DKT65 про ECS и EKS.

Paying attention to the wild world is a radical act of connection that is needed now more than ever. Join naturalist, educator, and author Thomas Fleischner as he breaks down how the practice of natural history serves as the essential bedrock for biomimicry. Drawing from insights in his new book, Astonished by Beauty: A Field Guide to the Practice of Paying Attention, Tom explores how cultivating presence, staying open to wonder, and honing keen observation can spark our imagination, and thus more life-friendly design. Listen in for a timely conversation on why falling back in love with our living planet is a vital step toward protecting and healing it. Purchase Astonished by Beauty: A Field Guide to the Practice of Paying Attention from Torrey House Press. Attend a book event in the Fall.Sponsored by Learn Biomimicry.Curious about how nature can solve our biggest design challenges? Start your education today. Use code LEARNINGFROMNATURE to save:20% off the Biomimicry Short Course Set5% off the Biomimicry Practitioner & Biomimicry Educator ProgramsExpand your mind with free resources from the Learn Biomimicry Blog: Discover the 50 Best Biomimicry Examples, explore Biophilic Design, or learn about Patterns in Nature.Grab some Learning from Nature merch including shirts and sweatshirts.Learning from Nature audio editing by Pine Peak Productions. 

Don’t get left behind in the AI revolution. Subscribe to our Newsletter: https://theultimatepartner.com/ebook-subscribe/ Check Out UPX: https://theultimatepartner.com/experience/ In this powerful episode, Vince Menzione sits down with Rebecca Jones of Bridge Partners and Mark Yaphe, Head of Consulting Partners for AWS, to uncover how AI is fundamentally rewiring the partner ecosystem. They explore the urgent shift from 90% stalled AI pilots to a new era of rapid execution, warning against the trap of “shiny object” syndrome. By unpacking the necessity of a “builder mindset” and a product-focused approach, this discussion reveals exactly what top-performing companies are doing to collapse six-month development cycles into four weeks and secure their place in the 2026 market landscape. Key Takeaways The transition from on-prem to cloud and marketplace is now entirely focused on AI transformation. Top companies approach AI with a product mindset rather than running scattershot pilots. Empowering frontline teams with a “builder mindset” can collapse solution cycles from six months to four weeks. Partners must avoid the $260 billion AI “FOMO” trap by specializing in specific industries and workflows rather than trying to do everything. Evaluating the “highest and best use” of AI models like Claude is essential for managing token economics and ROI. Thriving through AI disruption requires cultivating a strong growth mindset and prioritizing human connection and critical thinking. If you're ready to lead through change, elevate your business, and achieve extraordinary outcomes through the power of partnership—this is your community. At Ultimate Partner® we want leaders like you to join us in the Ultimate Partner Experience – where transformation begins. Key Tags AI transformation, AWS partner ecosystem, hyperscaler alignment, builder mindset, product mindset implementation, Bridge Partners insights, GenAI solution cycles, AI token economics, Claude model utilization, GSI strategy, 2026 ecosystem shift, AI ROI measurement, agentic tools, workflow specialization. Transcript Rebecca Jones and Mark Yaphe AUDIO EPISODE [00:00:00] Rebecca Jones: You can either, um, think about being disrupted or being a disruptor. [00:00:07] Vince Menzione: You can feel it happening. The ecosystem is shifting beneath us the way hyperscalers are partnering, how AI is remaking the channel. And what it means to win in 2026. [00:00:18] Vince Menzione: Welcome to the Ultimate Partner Podcast. I’m Vince Menzi, own your host. [00:00:23] Vince Menzione: And each week I sit down with leaders at the intersection of [00:00:26] Vince Menzione: technology, partnerships and outcomes, the voices shaping how ecosystems actually work. We talk about what’s real, what’s changing, and what it takes to lead in this era where the partner channel isn’t just part of the strategy. It is the strategy because being in the room changes everything. [00:00:45] Vince Menzione: Let’s start. We have another incredible session today, right? So I get to invite another friend of, of ultimate partner who’s been around for a while and, uh, it’s just absolutely amazing. Rebecca has been in the studio, she’s been at, how many of events have you been with? Fourth, fourth one, and I’m gonna have you introduce Mark as well. [00:01:10] Vince Menzione: So come on, on stage. Rebecca. Rebecca Jones to many of you know. [00:01:14] Rebecca Jones: Thank you, sir. [00:01:15] Vince Menzione: Good to see you. Good to see you. And Mark, great, great to have you. I want to have you, Richard, we’ll have you, Rebecca’s gonna introduce you and then we want you to introduce yourself as well, sir. [00:01:23] Rebecca Jones: Wonderful. Well, [00:01:24] Vince Menzione: and another AWS exec. [00:01:26] Vince Menzione: I love this. Like, I know. Yeah, we’re finishing out the day Strong. [00:01:28] Rebecca Jones: Well, Vince, I have to say, you’ve got me, um, the last time we got together, it was the last session before happy hour. So I guess we’re closing. [00:01:36] Vince Menzione: Well, you know, we’re gonna close us [00:01:38] Rebecca Jones: out [00:01:38] Vince Menzione: really nicely. Know we, yeah. We’re serving Bloody Mary’s, by the way, while you’re guys are up here. [00:01:42] Vince Menzione: No. Good. [00:01:42] Rebecca Jones: So, um, we’re so excited to have Mark. Thank you, mark, for joining us here. Um, head of consulting partners for AWS and, uh. We’re gonna close this down, aren’t we? I love [00:01:54] Vince Menzione: it. I love it. Yes. I’m looking forward to it. [00:01:56] Rebecca Jones: Okay. [00:01:56] Vince Menzione: So Mark’s well, welcome. Good to have you. [00:01:58] Rebecca Jones: Yeah. Do you wanna take a seat? [00:02:00] Vince Menzione: Uh, yeah, [00:02:00] Rebecca Jones: please do. [00:02:01] Rebecca Jones: All right. [00:02:01] Vince Menzione: Please do. I’m morphing the pillows up, by the way. [00:02:05] Rebecca Jones: Oh, are you [00:02:05] Vince Menzione: by the way, for those of who don’t know, these got shipped from my house because we got Oh, I was wondering. It’s hard to find, but yeah. Yeah, they’re, we take them from event to event. It’s so funny to have them. But I wanna, well, thank you for both being here. [00:02:17] Rebecca Jones: Yes. [00:02:18] Vince Menzione: I think it’d probably be helpful for those who don’t know, bridge Of course. Maybe just spend a moment because I know you well. [00:02:23] Rebecca Jones: Yeah. [00:02:23] Vince Menzione: And we know the organization well, those of us. Those of us. [00:02:26] Rebecca Jones: But for me, uh, so let me talk to you a little bit about Bridge Partners and my role, um, the company has been around for almost two decades. [00:02:34] Rebecca Jones: Yeah. And so when you think about the transformation that’s happened within the tech industry. And our primary focus is the tech industry. Uh, and within that we focus on enterprise companies and we help them with product go to market and how they scale that through partners. Yeah. Uh, so that’s given us a really interesting and, uh, vantage point around the transformations from on-prem to cloud, cloud to marketplace and now marketplace and the transformation with ai. [00:03:04] Vince Menzione: I feel like you’re the McKenzie of the, of the partner business. Like I, [00:03:07] Rebecca Jones: I like that. [00:03:08] Vince Menzione: Can we get that? [00:03:09] Rebecca Jones: Yeah. [00:03:10] Vince Menzione: I’ll, I’ll, I’ll sign an agreement with you, but I really do, I feel like as we work together, bridge was always like the organization we bring in to help us. Solve the big issues. Yeah. Like that I think about your organization. [00:03:20] Vince Menzione: Yeah. And Mark, talk to me about Global Consulting services. Sure. So is it all GSIs? Is it, [00:03:24] Mark Yaphe: uh, so I head up, uh, global Consulting Partner Marketing. Okay. So I focus really on, on two key categories for the, the more sig larger, uh, GSIs. Uh, I’ve got a team that actually partners very closely with them. Nice. [00:03:36] Mark Yaphe: That helps them develop the right strategies, go to market approaches, nice to unlock the opportunity. And then for the full consulting community, I look at those mechanisms. Go to market approach is leveraging marketplace to help our whole consulting community become successful with AWS. [00:03:51] Vince Menzione: And we’ve got GSIs in the room here, which is kind of cool actually. [00:03:53] Vince Menzione: Yeah. Um, where do we wanna start? Let’s, let’s, [00:03:57] Rebecca Jones: well, yeah, we’ve got a good list of questions to go through. How’s everybody feeling? We’re we’re good? We’re awake. One more session, everyone. Alright. Okay. [00:04:09] Vince Menzione: So Rebecca, uh, across the organizations you work with. What are you seeing from the highest ’cause? I, I say you’re like the McKinsey. [00:04:16] Vince Menzione: What are you seeing from the highest performing companies? Yeah, that they do differently. When it comes to turning your go-to market strategy into outcomes? [00:04:23] Rebecca Jones: Yeah. Um, I will say the most important thing that we’re seeing from companies is they’re asking different questions, fundamentally different questions when it comes to ai. [00:04:34] Rebecca Jones: Interesting. [00:04:34] Vince Menzione: What do you mean by that? [00:04:35] Rebecca Jones: Well, we, we talked a lot this morning about there’s never been higher access, and I’ll say general adoption for tools and technology. There was a great stat this morning. Uh, I think Jay shared that, uh, from MIT. [00:04:50] Vince Menzione: We keep looking there as if he’s still [00:04:51] Rebecca Jones: sitting there. [00:04:51] Rebecca Jones: Yeah, I’m looking. Where was Jay? He, he was all over the place. Um, there was a great stat from MIT that there was, you know, if you looked at last year, 90% of pilots. Were stuck and they weren’t going anywhere. And now that’s dropped down to 70%. So there’s movement and transformation. And so when I think about that, and when I go back to the types of questions leaders are asking, um, that are really moving ahead, they’re looking at operating systems differently and they’re looking at, um. [00:05:25] Rebecca Jones: They’re asking the questions on where should I apply AI within those work streams, um, and within those operating systems, and the way in which they’re approaching that is with a product mindset. So that is fundamentally different than just the scattershot of let’s just do pilots everywhere. [00:05:44] Vince Menzione: Yeah. You’ve talked about product, uh, mindset with me as well. [00:05:48] Rebecca Jones: Yeah. [00:05:48] Vince Menzione: And I think we were gonna talk about builder mindset as well, mark, that that is a kind of a different point of view. When you think about moving from strategy to execution, how does that mindset show up inside teams and organizations? [00:06:00] Mark Yaphe: No, absolutely. Yeah. You know, the notion of the builder mindset is about, uh, taking the notion of innovation and pushing it out to the edge. [00:06:07] Mark Yaphe: Of the organization, um, the greatest ideas for innovation, the greatest things that will help you scale. They’re in the minds of your customers and the people that can best understand them and best address ’em. They’re your teams. Yeah. Your, your customer teams or your technical teams, but unlocking it. You, you want these teams to do more than just have the conversations and understand needs. [00:06:28] Mark Yaphe: You want them to be tooled and equipped to build. Yeah. So the ones that are right in front of the customers. In that moment of need where they say, I’ve got these offerings and these motions, and it gets me this far, but if I could only do a little bit more, I could delight them. I could really power this up. [00:06:45] Mark Yaphe: And so you wanna unlock that. You want to give them the tools to build, to build the POC to address specific, uh, options in the meeting. And then when they’re showing some success, show the rest of the organization how they can scale that. [00:06:58] Vince Menzione: How do you think about, because I think about big GSIs. Having huge organizations like Accenture has half a million people, and then you have customer teams that may not, are, may not be as fluent in the technology side of things. [00:07:13] Vince Menzione: Like how do you make sure that’s getting from the customer all the way to the right people in the organization and driving that loaded question. I know. [00:07:20] Mark Yaphe: No, I, I, um, you, you, you want to think about how that process works. Yeah. And there are parts of the organization. That define how do we get go to market offers in motions out to the field. [00:07:34] Mark Yaphe: Um, and they look at the whole thing and they say, well, how effective are we and how quickly can we cycle through? Yes. These activities. There’s one partner I worked with, um, they looked at this and they measure the cycle time. How long does it take me to get from pushing on an offer? Working with customers, getting feedback, and then creating new updates. [00:07:53] Mark Yaphe: A long, long time ago, like two years ago, this would take, it was a hundred [00:07:57] Vince Menzione: years ago in AI terms. [00:07:59] Mark Yaphe: Well, that’s basically it. This took about five to six months. They get about two revs a year. Now. They literally implemented a geni solution that number one uses geni to push it out to the teams, makes it bespoke on an engagement by engagement basis. [00:08:14] Mark Yaphe: It makes it relevant for their industries and their use cases. And that same tool is the feedback mechanism. So in real time it’s providing feedback. So they’ve collapsed six month cycle times to four weeks. And the punchline here is we talked about builder teams. The people that figured out they needed the solution, built the solution, and piloted the solutions were the builder teams. [00:08:36] Mark Yaphe: They were the people working with the customers. [00:08:38] Vince Menzione: I love it. Yeah, I love it. Anything to add to that? Rebecca, I know you work again, being the McKinsey of the, of the partner world. [00:08:46] Rebecca Jones: Well, I’ll, I [00:08:47] Mark Yaphe: It’s gonna stick, [00:08:47] Rebecca Jones: stick. It’s [00:08:48] Vince Menzione: gonna [00:08:49] Rebecca Jones: stick. You say it three times, that’s stick. No, I, you know, mark just hit on some really important things with the customer mind. [00:08:57] Rebecca Jones: You’re really looking at what outcomes are you trying to drive for those customers and that builder mindset, you, you’re going to hear a lot about that because companies need, as you transform, you really need to be thinking differently. And transformation takes quite a while. And while there is massive opportunity and you see the, the quickness you have to have that long-term vision and then be able to work backwards from that. [00:09:21] Rebecca Jones: And so I couldn’t agree more with the, the focus on customer outcomes. [00:09:26] Vince Menzione: So we’re in a very interesting, I’ll call it, almost a seminal point, although that’s overused in terms of where we are with AI today, right? I you mentioned like two years, feels like 10 years. Yeah. Ago, right? I mean, we’ve seen such transformation happening, but it also doesn’t feel like organizations are keeping, like, I, I feel like small SMBs actually are further ahead because they, they have to be agile, but the bigger organizations are still trying to figure some things out, right? [00:09:53] Vince Menzione: So. What needs to change around organizations, culture management processes? Like how do we bring, how do we bring everyone along on this journey? [00:10:04] Mark Yaphe: There’s a lot that needs to happen. Um, [00:10:07] Vince Menzione: yeah. [00:10:08] Mark Yaphe: One thing that struck, there’s a lot of things I, I wanted to anchor on. One that Yeah, please. It could be a relevant conversation. [00:10:13] Mark Yaphe: Both, um, as part of your, uh, partner organizations delivering outcomes to customers. And, um, it’s about focusing on the business outcomes. It can be very easy, uh, to talk about the technology and the services, but day to day, the sales organization is going into solve customer problems. They’re meeting line of business leaders in specific industries who have very specific business problems to tackle. [00:10:42] Mark Yaphe: And I think one thing that organizations can do is impress upon them that it’s critical to understand. What are the business problems that we solve for our customers that we’re serving? What are those use cases? What are the drivers for it? In the role that I’m doing in an organization, how does that move the needle? [00:10:58] Guest: Yeah. [00:10:58] Mark Yaphe: For the business outcomes, it’s, it’s not dissimilar to other things, and perhaps it’s a little bit of a pivot, but always thinking about business outcomes, I think is, um, a little bit of a change that needs to be instilled within [00:11:10] Vince Menzione: what, what are the best doing better, and where are you seeing the gaps? [00:11:16] Mark Yaphe: Couple of areas, uh, one area, um, nobody knows everything. Yeah. Nobody’s got all the knowledge. [00:11:23] Vince Menzione: Right. [00:11:23] Mark Yaphe: And so rely on your ecosystem of partners and stakeholders. Yeah. Recognize you’ll only have so much information, um, and reach out, whether it’s to your technology partners, your business partners, your hyperscalers AWS to find out what am I missing. [00:11:38] Guest: Yeah. [00:11:38] Mark Yaphe: Um, again, many years, you know, a hundred years ago, two years, two years ago, um. We’d have these conversations about, well, what use cases are you seeing and what business problems are you solving? But, but those would be in scheduled meetings quarterly. Now there are agenda items on weekly standards. [00:11:55] Mark Yaphe: They’re happening every single week. What are you seeing? What are you seeing? And further, I’ve seen some gen AI and agent solutions that actually automate how that information flows to, to make it, to accelerate. [00:12:06] Vince Menzione: Yeah, it’s, it’s absolutely amazing. Yeah. Anything on, I mean, certainly you’ve got a perspective ’cause you’re working with these organizations. [00:12:13] Rebecca Jones: I have a couple thoughts on this specifically for the partner organizations and the partner companies here. I can understand there’s a lot of, you know, we looked at a stat earlier about the AI partner opportunity and it was. $260 billion somewhere in that bracket. And that can create maybe some fomo, you know, maybe, uh, let’s go after everything in this area. [00:12:37] Rebecca Jones: Yes. And not pick and choose [00:12:39] Mark Yaphe: a [00:12:39] Rebecca Jones: shiny object. Shiny object and not prioritize. And it’s actually the opposite. It’s really understanding where your strengths are in the market. Um, who’s in your partner ecosystem? What are you bringing to market? Are you, uh, a tech company that are looking to break? Bridge and bring out services or your services company, and now that can build product. [00:13:01] Rebecca Jones: But really understanding your opportunity. What industry do you play, what specialization do you have? And go really deep and then know how to augment your partners and the ecosystem around you to make you stronger and better for the customer. So with that market opportunity, which is. Tremendous, how do you focus and prioritize? [00:13:21] Rebecca Jones: And that’s where I have observed partners. Oh, I’m a little bit here, a little bit there, a little bit here. And, and, uh, I would be curious, I mean, that’s probably pretty hard for you if a partner shows up and they’re a little bit of everything. [00:13:34] Mark Yaphe: Well, I, I resonated with a point that you made before. Yeah. I, I’ve spent, um, half my time at AWS on the consulting side working with enterprise customers, the us half the partners. [00:13:43] Mark Yaphe: It’s critical that partners understand what’s unique about them. [00:13:45] Exactly. [00:13:46] Rebecca Jones: Yeah. [00:13:47] Mark Yaphe: You it. I mean, everybody here, they’re looking at cloud migrations and modernizations and agentic, but that’s kind of part of the noise. You’ve gotta know what uniquely you do in an organization to deliver value. Are you developing supply chain for transportation companies or drug acceleration pipelines for. [00:14:06] Mark Yaphe: Pharmaceuticals. Yeah. Starting with that anchoring on your differences, I think is, is really important. [00:14:11] Vince Menzione: When I first started in the partner world, that was one of the biggest challenges and dilemmas, and I’m sure you still see it today, where I do all things. You know the partner that does the big SI that does everything, they have all the certifications. [00:14:24] Vince Menzione: I have 10,000 people trained on every technology certification, right. And then like, well what do you do? Like, and they can’t clarify. Right. Have that conversation. [00:14:33] Mark Yaphe: And then how do people, customers, [00:14:35] Vince Menzione: yeah. [00:14:35] Mark Yaphe: Or sales organizations choose you and why. [00:14:38] Vince Menzione: Yes, exactly. Exactly. So how do you get them to show up in that way? [00:14:43] Vince Menzione: Like especially if they’re like, how do you coach them through that? ’cause it feels like it’s still exists, right? This like mentality or this mindset. I can do all things, especially with the shiny objects that we’re facing today. [00:14:55] Rebecca Jones: Mm-hmm. [00:14:55] Vince Menzione: And I feel like we’re almost, I, I almost feel like we’re at a point right now where we were getting clearer and we’ve had so many shiny objects, even just in the last few months. [00:15:03] Vince Menzione: Like you were talking about how like months feels like ears, uh, you know, I’ll, I’ll use the Claude example here. Yeah. ’cause we, a lot of us pivoted and shifted and like, what do I do now as a partner in the room? Again, I think you, you mentioned the solving for business outcomes for client outcomes as opposed to chasing the next shiny object. [00:15:24] Vince Menzione: Like how do you get, how do you coach them on that? [00:15:27] Mark Yaphe: It’s always on the agenda. It’s, it’s day one conversations. Yeah. Who are you? What’s unique about you? How do you deliver value? Which customers do you focus on and with? Which use cases, and if it is a jack of all trades. Then my team, my and my team will help ’em. [00:15:42] Mark Yaphe: We know that down to specific areas of focus. [00:15:44] Vince Menzione: Yeah. So how do partners need to evolve their capabilities? Like how do they, I mean, how do they actually hone in on this? Like, you know, okay, I can state one thing, but how do I hone in on my capabilities, offerings and teams to stay relevant during this time? [00:15:59] Mark Yaphe: Um, what I’m coaching them on right now? Yeah. That’s what I is, uh, use the technology internally. The agentic tools and the gen AI tools are. I’ve been at this for a while, and the tools that exist right now dramatically expand your capability and capacity. So the thing I coach ’em is embed them in your organization. [00:16:18] Mark Yaphe: Yeah. Mm-hmm. Tackle those key things organizationally. You need to change and leverage these tools to help you accelerate. [00:16:23] Vince Menzione: And they’ll help you solve, they’ll help you solve for absolutely any of them. Right. It’s like, it’s like hiring a consulting organization to come in and solve for that. [00:16:29] Mark Yaphe: Yeah. [00:16:29] Vince Menzione: Yeah. How are you thinking through this? [00:16:31] Rebecca Jones: Well, uh, there’s a couple things I’m thinking about. Um, if you start to. I’ll stay with the customer for just a minute because you’re talking about Claude and just the dramatic improvement. [00:16:45] Guest: Yeah, [00:16:46] Rebecca Jones: that’s there. Just with Claude, uh, we start to think about highest and best use of the model because, uh, we had another talk earlier about the economic conditions and the. [00:16:58] Rebecca Jones: And so now we’re asking partners like, right, [00:17:01] Vince Menzione: the tokens. [00:17:01] Rebecca Jones: Yeah. Yep. How do you specialize and be focused by industry, by workflow, by use cases, you’re gonna start to look at what is the ROI of that investment? Is this a good enough? Look at the models, look at how you’re using that, and you’re having a token conversation because the economics might not be there. [00:17:21] Rebecca Jones: And so as you’re a business and a customer looking to transform their organization. They’re going to look across the business and figure out what are the highest and best use cases that should get that focus. And as a partner, if you wanna be in that conversation and really helping that company or that. [00:17:40] Rebecca Jones: Customer transform from where they are. It’s not only industry specialization, but it’s functional and workflow and really helping them understand what they should be using in that particular use case. So specialization is king or queen and that, um, scenario, and that’s where, you know, you ask partners today to specialize because there’s a whole economic conversation coming behind that, around how do you think about the models as that new one shows up? [00:18:09] Vince Menzione: Let’s shift from the technical side to the human side. [00:18:12] Rebecca Jones: Yeah. [00:18:13] Vince Menzione: Super important, right? I mean, we were having this conversation internally, like everybody’s saying, you know, jobs are going away, jobs are going away. I think I, I believe more jobs are gonna happen, but we’ve gotta get humans aligned properly to what their new roles will be. [00:18:29] Vince Menzione: Comments on this one? [00:18:31] Mark Yaphe: I think this is like a classic organizational transformation Yeah. Question. Mm-hmm. Where the 70, 80% of the problems are people process change. Um, I, I think there are these three areas that organizations need to focus on, and I’m gonna sound a little bit repetitive, but one, it’s okay. [00:18:46] Mark Yaphe: It’s, uh, the role of the team members have gotta be focused on outcomes, especially when things are moving quickly and there’s ambiguity. The one way that you can anchor on moving in the right direction is how do I let my customer, so one is outcomes. The second one is the builder mindset. Move from, I’m presenting, I’m hearing, but I’m gonna build things. [00:19:05] Vince Menzione: Yeah. [00:19:05] Mark Yaphe: And the last one is, uh, inspiring your team, making them competent and confident to navigate ambiguity because that is the premise upon which everybody’s operating. So those three, [00:19:17] Vince Menzione: Rebecca, what capabilities. Would be embodied in, in that organization that Mark describes. [00:19:23] Rebecca Jones: Yeah, I think that the growth mindset, you know, if you can package that around, you can either, um, think about being disrupted or being a disruptor. [00:19:34] Rebecca Jones: And if you have a growth mindset around the opportunity that’s ahead, it’s a whole different perspective of the challenges before you. I love that. And so when I think about what. Capabilities. You know, it’s the critical thinking and the judgment, human connection. We’re all here for a reason. Yes. Right? [00:19:50] Rebecca Jones: Yes, yes. And so as a leader, um, really helping set that tone and letting them see the art of the possible, um, around that vision. But it’s really, if I boiled it down to one thing, it’s having a growth mindset to the opportunity ahead. [00:20:05] Vince Menzione: Well, I wanna open it up. We have about five minutes left. Yeah. And this has been so insightful, but I. [00:20:11] Vince Menzione: I mean, I feel the energy. There’s gotta be some questions out here too. ’cause this, we have two incredible experts up here talking. I mean, and this is such an impactful conversation today. So John’s got a mic and uh, I think we’ve got some questions coming. [00:20:32] Vince Menzione: Yeah, [00:20:33] Rebecca Jones: this’s [00:20:33] Vince Menzione: a long way [00:20:33] Rebecca Jones: around. [00:20:34] Vince Menzione: Took a [00:20:34] Guest: thanks, long David Younger with. Thank you. That was great, great discussion. So, uh, you, you brought up a key statistic, uh, which is the MIT data and around the, the 90, I think it was 95%. Of, uh, businesses, uh, are not in production. And, and actually they, they went further to say that 95% of businesses. [00:20:56] Guest: Uh, we’re achieving zero ROI. And, and that was about a year ago, right? And now, and now you said the number, I think the number was quoted earlier today too, is, is shifting to about 70% of those, uh, projects in production. I’m curious to, to know as you, and I think you nailed it too, when you talk about product, right, have a product mindset or business mindset, right? [00:21:16] Guest: Not just how can I save money, but how can I actually generate revenue, whether it’s saving money or generating revenue. Where would you say. Uh, what, what percentage of companies you talk to are actually achieving real ROI would you say? [00:21:31] Rebecca Jones: Yeah, that’s a, that’s a great question. Really. Great. So I’ll go back and explain a little bit more about what I mean by product mindset. [00:21:39] Rebecca Jones: So a lot of companies did get stuck or there were just, uh, a. Large amount of pilots happening in the organization. And that’s not a bad thing. ’cause you think about, that’s a builder mindset, go and test and trial. But when you’re starting to look at true business transformation, you really need to think about where that, um, high value use case is. [00:22:00] Rebecca Jones: So a. The product mindset I’m talking about is taking a long-term view of the outcomes you’re trying to achieve and how are you going to measure those? And then look at that workflow, that function, and if you’re an expert in that function, whether that be a sales process or a marketing process, you know what KPIs your business is trying to drive today, and you start to unpack that. [00:22:24] Rebecca Jones: And so we’ve seen and what. Um, the most, uh, accelerated motion is knowing the KPIs and the measures you’re trying to achieve and then working towards that. And from there you can build, right? And you start to think and you have an ecosystem approach to that workflow or work stream. So we have seen, um, everybody wants cost on the system. [00:22:46] Rebecca Jones: Um, we have seen dramatic cost reduction in areas we’ve seen, you know. Two to three times, um, faster time to market. Um, there’s multiple things that we’ve seen as, uh, leaders really start to unpack that, understand what they’re trying to accomplish in the business, and I’m happy to go into greater detail. [00:23:06] Rebecca Jones: I know we have just a couple minutes left, but that’s just the product mindset up. How do you get started and how do you look at that long-term opportunity? [00:23:15] Vince Menzione: Really great answer. Mark, do you wanna add that? [00:23:17] Mark Yaphe: I think if you look at all use cases. Maybe 30% perform. But if you look at this across enterprise, I think each enterprise is finding very specific use cases where they’re driving ROI and and um, and so I think it’s about picking your spots, knowing who you are, identifying the top priority ones, not worrying about the broad enterprise transformation. [00:23:38] Mark Yaphe: Find those areas where you can drive value a little bit. Yeah, [00:23:41] Vince Menzione: that’s great that that’s almost a mic drop moment in my opinion. Yeah. That’s really great. Any other questions? I we’re holding every, oh, we got one in the back. I was gonna say I’m holding people up from happy hour. Yeah, [00:23:53] Rebecca Jones: just, we’ll just bring the cocktails in here [00:23:56] Vince Menzione: pretty soon. [00:23:57] Guest: Uh, Jeremy with Integral, um, um, a money question, something comparable. Uh, activator portfolio, the programs for founder firms that are trying to really get off the ground with new ideas. And there’s some comparable programs, I think with different providers. How much of that is a strategy, and I don’t wanna put you on the spot if activating portfolio aren’t the things you’re covering, but, but that money investment for startups that are trying to grow and really focus on AWS uh, the thousand dollars is the founder version that gets us in and then a hundred thousand dollars. [00:24:26] Guest: It’s a bit difficult to get into. And then there’s bigger ones after that if we attend the schools and all these things. But I’m thinking about as we all are trying to grow and really focus in AWS, which a lot of folks really wanna do with Bedrock and all the things that are kind of cool going on, it’s just. [00:24:40] Guest: Great AI focused conversation, but how is that playing into attracting more of the MSPs that are, that are trying to grow and more of the startups to really funding this idea of, of startup mentality. Hopefully that’s not too off topic, but your, your fair game [00:24:57] Mark Yaphe: was, was the question, how does funding. [00:25:01] Mark Yaphe: Attract startups in specific categories, process. [00:25:04] Guest: I think it’s, it’s about if there’s, uh, the other hyperscalers also have programs comparable. So Microsoft’s program is, uh, 150 grand to to, to build out the founder kind of, and it’s fairly easy to get into. Aw. WS is a bit harder to get into, but is that going to change as far as using that as a, as a key strategy for incubating more and more ideas to accelerate the velocity of everything you guys were talking about? [00:25:25] Mark Yaphe: I’m really not the right person. Definitely outta my wheelhouse on that. No problem. [00:25:31] Guest: Yeah. [00:25:37] Vince Menzione: And we’re about seven seconds away from uh, happy hour. [00:25:41] Rebecca Jones: I know, [00:25:41] Vince Menzione: I know. This was fantastic. I know. It was so great. [00:25:44] Rebecca Jones: Yes. [00:25:45] Vince Menzione: And I think the McKenzie thing is gonna stick. I think it is, it is. [00:25:48] Rebecca Jones: Now, mark, I’m gonna make [00:25:49] Vince Menzione: sure it does. And Mark, it was great to have you up on stage with us today. So, so great to have AWS supporting us and sponsoring the event with us and, uh, and having just this broad audience of people just so interested in. [00:26:02] Vince Menzione: Being in the room and and learning from each of you. So thank you so much today. Thank you. Appreciate it. Thank you. Thank you. [00:26:09] Mark Yaphe: Thanks for listening to The Ultimate Partner [00:26:11] Vince Menzione: Podcast. If today’s conversation resonated, share it with a partner leader in your network. Subscribe where you listen, and head over to the ultimate partner.com. [00:26:22] Vince Menzione: For show notes related content and the resources for this episode. And if you haven’t already, now’s the time to register for the Ultimate Partner Live Event in Reston, Virginia, October 26th through October 28th. Until next time, keep showing up in the rooms that matter because being in the room changes everything.

This episode of Do Good to Lead Well opens with a thought-provoking challenge to the familiar refrain, “Let's be more data-driven.” Instead, Sebastian Wernicke, a top TED speaker and globally recognized thought leader, shares the importance of being “data-inspired.” Our discussion focuses on the premise that lasting transformation requires more than dashboards and metrics. We explore the subtle yet crucial difference between optimizing the present and reimagining the future, using data as a catalyst.Listeners will discover why assumptions about objectivity and quick fixes with more data often backfire and how the real edge comes from leaders willing to be curious, challenge their own thinking, and foster environments where their employees can do the same. Key takeaways include actionable methods for reframing challenges, leveraging the “five whys,” and ensuring AI supports, rather than supplants, human judgment.Tune in for an essential guide to leading well in a landscape where data is plentiful, but wisdom and culture are what set winners apart.What You'll Learn- Data-driven versus data-inspired? A key difference for transformational change.- Myth busting: Is data objective? Does ‘data speak for itself?”- Mindset shifts for better decisions.- Why psychological safety matters!- The importance of a “culture first, tech second” mentality.- Embracing human judgment in the AI era.- Dealing with data skeptics.- The (not-so) secret sauce: Data AND culture. Podcast Timestamps(00:00) - The Origin Story of "Data Inspired" (03:14) – What is the Difference between Data-Driven vs. Data-Inspired?(07:21) - The Myth of Data Objectivity(10:26) - Mindset Shifts for Effective Data Use(15:40) - Cultivating Curiosity and the Power of Questions(19:20) - Psychological Safety as the Bedrock of Data Inspiration(22:58) - Culture First, Tools Second: Building Data-Inspired Organizations(25:24) - Leadership Qualities for Data-Inspired Environments(31:09) - Managing Data Quality and Volume(36:57) - The Human Side of AI and Data-Inspired Judgment(42:08) - Overcoming Resistance to Data and Organizational Change(44:43) - Closing ReflectionsKEYWORDSPositive Leadership, AI, Artificial Intelligence, AI Transformation, People-First Leadership, AI Adoption, Transformational Change, Data-Driven, Data-Inspired, Organizational Culture, Psychological Safety, Curiosity, Decision-Making, Data Myths, Data Quality, Machine Learning, AI Strategy, Data Strategy, Business Transformation, Cultural Change, Big Data, Teamwork, Empathy, Storytelling with Data, Overcoming Resistance, Innovation, Problem-Solving, Root Cause Analysis, Five Whys Technique, Human Judgment, Data-Fatigue, Purpose-Driven, CEO Success

This episode takes listeners inside Bedrock to explore how one of Detroit's largest real estate organizations is evolving its building operations and technology strategy across a complex portfolio. Amanda Gibb and Russ Holton share how maintenance, engineering, energy, data, and technology are coming together to create a more connected and efficient operation. They also dive into Bedrock's emerging AI program and the challenges of standardizing technology across different buildings and systems. Finally, the conversation looks ahead to NexusCon 2026 in Detroit, including the historic Hudson's site, Bedrock's involvement, and some of the unique experiences planned for attendees. It's part behind-the-scenes look at Bedrock, part preview of NexusCon, and a celebration of the city hosting it all.Find full show notes and episode transcript on The Nexus Podcast: Episode 200 webpage.Sign-up (or refer a friend!) to the Nexus Newsletter.Learn more about The Smart Building Strategist Course and the Nexus Courses Platform.Check out the Nexus Labs Marketplace.Learn more about Nexus Partnership Opportunities.

September 9, 2026 ~ Jake Chidester, Vice President, Strategy at Bedrock Real Estate Services discusses their plans for the Renaissance Center. Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.

September 9, 2026 ~ Anthropic researcher quits over AI fears. Clancy juror speak out. Bedrock explains their plans for the Ren Cen. 9/11 families ask Mamdani to not attend ceremonies. Emergency rooms seeing influx of e-bike related injuries. Oil rises over $100 a barrel after more attacks in the Middle East. Republican midterm convention begins today and the day's biggest headlines. Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.

Stephanie Palazzolo talks with TITV Host Akash Pasricha about OpenAI's new Astra model and its AGI claims. We also talk with Valida Pau about Coatue's proposed chip financing joint venture with startup MatX, Aaron Holmes about rising AI cyber threats and ElevenLabs' 2028 IPO plans, and we get into AWS's major Bedrock overhaul with Catherine Perloff.Articles discussed on this episode: https://www.theinformation.com/articles/anthropics-house-payments-tech-push-chip-away-stripehttps://www.theinformation.com/articles/coatue-matx-talks-new-multibillion-chip-financing-venturehttps://www.theinformation.com/articles/ai-threats-reshaping-companies-spend-cybersecurity-budgets https://www.theinformation.com/newsletters/ai-agenda/elevenlabs-hires-cfo-eyes-2028-ipohttps://www.theinformation.com/articles/six-aws-engineers-rebuilt-bedrock-challenge-microsoft Subscribe: YouTube: https://www.youtube.com/@theinformation The Information: https://www.theinformation.com/subscribe_hSign up for the AI Agenda newsletter: https://www.theinformation.com/features/ai-agendaTITV airs weekdays on YouTube, X and LinkedIn at 10AM PT / 1PM ET. Or check us out wherever you get your podcasts.Follow us:X: https://x.com/theinformationIG: https://www.instagram.com/theinformation/TikTok: https://www.tiktok.com/@titv.theinformationLinkedIn: https://www.linkedin.com/company/theinformation/

#cuttheclutter 10 days since the disaster in Nepal, death toll has climbed to over 1,318. Now, fresh clues offer an insight into what could have caused the flash floods. #CutTheClutter Episode 1894 looks at how a bedrock collapse took a hanging glacier down creating two landslide lakes that eventually broke. ThePrint Editor-In-Chief Shekhar Gupta also explains how earlier warnings in fragile Himalayas have been ignored, and why 'climate compensation' clamour doesn't stand.  --------------------------------------------------------------------------------------------- To Read OnlineKhabar article: https://english.onlinekhabar.com/china-closes-zhangmu-port-indefinitely-disrupting-nepal-china-trade.html --------------------------------------------------------------------------------------------- To Read USGS report: https://www.usgs.gov/programs/landslide-hazards/science/2026-nepal-debris-avalanche-and-flash-flood?utm_source=chatgpt.com#overview --------------------------------------------------------------------------------------------- To read Himalayan Wonders report: https://www.himalayanwonders.com/nepal/langtang-gosaikunda-10-day-trek.html --------------------------------------------------------------------------------------------- #asusexpertbook  @ASUSIndia.official  Checkout the ASUS Expertbook series: https://www.flipkart.com/asus-expertbook-core-ultra-store

Bedrock officials explain why their RenCen plan beats the alternatives State probes funds, freezes some payments at Arab American business group New food options ready to kick off the Detroit Lions season

Hitlist Bangers #22 w/City Props/ DJ Eve Guest mix Show: Hitlist Bangers

It's September, which means it's time for the back-to-school edition of the irrepressible Radio Therapy Broadcast series as Dave returns for another dose of the good stuff delivered in his own inimitable way. Back to back, wall to wall, top notch dance floor business. Just the way we like it. Enjoy! Tracklist... 1. Monkey Safari ‘Future' [Good Vibes From Paradise] 2. Braxton ‘Pyro Flow' [Bedrock] 3. Iorie & Franca ‘Hypno Therapy' (Dilby) [Kiosk ID] 4. Elia Erium ‘Hypnotic Induction' [Nights Of Solace] 5. Stefan Obermaier & Marner ‘Hielo' (Gorge) [Tales Of Romance] 6. Jamie Stevens & Kasey Taylor ‘Hocu Pocu' [Sudbeat] 7. Quivver ‘Disconnect' [Anjunadeep] 8. Kittin ‘Life Is My Teacher'(Simon Vuarambom) [SCI & TEC] 9. Boxer ‘Ease Yourself Back Into Consciousness' [Housestrike] 10. Jamie Stevens feat. Wilma ‘Tell You Later' (M.O.S) [Music To Die For] 11. Fauxplay ‘Back In The Day' [Music To Die For] 12. Quivver ‘Illusion' [Anjunadeep] This show is syndicated & distributed exclusively by Syndicast. If you are a radio station interested in airing the show or would like to distribute your podcast / radio show please register here: https://syndicast.co.uk/distribution/registration

“I had a knack for putting things together with my hands.” Before the architecture schools, international travel, installations, and Studio M, Melinda Anderson was a Detroit child turning shoeboxes into houses, coffee creamers into tiny toilets, and dioramas into whole worlds where “people [were] swinging from the ceiling.” That imagination became a calling. Raised on Detroit's east side, Melinda remembers her father taking her to ethnic festivals and the Festival of the Arts, making creativity, art, and design feel inseparable from life itself. Cass Tech gave that instinct language through architecture and drafting; U of M opened her eyes to a larger world, even as she navigated classrooms where “there weren't many of us.” Years later, that childhood imagination produced a 10-foot-tall, 20-foot-wide boombox for Movement that showed her how strongly people respond to design installations. When COVID stopped gatherings, a heart installation commissioned by Bedrock for Parker's Alley became the pivot that helped save her business and opened another lane for her creativity. Today, with clients asking her to create installations abroad, the heart of her story remains remarkably consistent: the child who said, “I could just look at stuff and I could put it together,” is still there. Listen for what can happen when we protect the imagination we had before the world told us what was practical. Detroit is Different is a podcast hosted by Khary Frazier covering people adding to the culture of an American Classic city. Visit www.detroitisdifferent.com to hear, see and experience more of what makes Detroit different. Follow, like, share, and subscribe to the Podcast on iTunes, Google Play, and Sticher. Comment, suggest and connect with the podcast by emailing info@detroitisdifferent.com Find out more at https://detroit-is-different.pinecast.co

It's September, which means it's time for the back-to-school edition of the irrepressible Radio Therapy Broadcast series as Dave returns for another dose of the good stuff delivered in his own inimitable way. Back to back, wall to wall, top notch dance floor business. Just the way we like it. Enjoy! Tracklist... 1. Monkey Safari ‘Future' [Good Vibes From Paradise] 2. Braxton ‘Pyro Flow' [Bedrock] 3. Iorie & Franca ‘Hypno Therapy' (Dilby) [Kiosk ID] 4. Elia Erium ‘Hypnotic Induction' [Nights Of Solace] 5. Stefan Obermaier & Marner ‘Hielo' (Gorge) [Tales Of Romance] 6. Jamie Stevens & Kasey Taylor ‘Hocu Pocu' [Sudbeat] 7. Quivver ‘Disconnect' [Anjunadeep] 8. Kittin ‘Life Is My Teacher'(Simon Vuarambom) [SCI & TEC] 9. Boxer ‘Ease Yourself Back Into Consciousness' [Housestrike] 10. Jamie Stevens feat. Wilma ‘Tell You Later' (M.O.S) [Music To Die For] 11. Fauxplay ‘Back In The Day' [Music To Die For] 12. Quivver ‘Illusion' [Anjunadeep] This podcast is hosted by Syndicast.

September 2, 2026 ~ Chris Renwick, Lloyd Jackson and Jamie Edmonds talk with Kirk Pinho, Senior Reporter with Crain's Detroit Business, about the update to the Renaissance Center. Bedrock says the only viable option is to bring down 2 of the towers, what do others think? Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.

It's time for another Saturday Morning Cartoons!

Check out my Tronic Radio on your favorite streaming platforms here: https://ssyncc.com/tronic-podcast 01.Genius Of Time - Sunswell [Oath] 02.Facundo Losardo - Readiness [Bedrock] 03.Sean Harvey - Do This [Keep Thinking] 04.Patch Park - Moca [District Records] 05.Hernan Cattaneo & Tom Pavicich - Wink [Electronic Groove] 06.Maarten van der Vleuten presents v48 - Only Human [Passiflora Records] 07.ID - ID (Nick Stoynoff Remix) [Tronic] 08.Kamilo Sanclemente - Jupiter Code [Tronic] 09.Anthony Pappa & Nick Stoynoff - 435 [Selador] 10.Zuccasam - Feel Happy [Plastic Fantastic] 11.Four Candles & Feemarx - Eudaimonia 303 [Bedrock] 12.Nick Stoynoff - The Hero's Journey (Dusty Kid Remix) [NOFF!] This show is syndicated & distributed exclusively by Syndicast. If you are a radio station interested in airing the show or would like to distribute your podcast / radio show please register here: https://syndicast.co.uk/distribution/registration

Demandbase's Vice President of Product explains why they deleted a fully-approved AI architecture two months before launch — and how the rebuild surpassed some of their customer's highest expectations.Topics Include:AWS's Achint Naveen introduces Demandbase's VP of Product, Chad HoldorfDemandbase unifies sales, marketing, and revenue data into one viewNovember's architecture used many specialized agents, all committee-approvedThat design failed constantly — only a 30% conversation pass rateOn May 11th, the team deleted the entire architectureRebuilt in May with AWS Strands: one simpler, flexible agentPass rate leapt from 30% to 94% almost overnightWeek two retention rose from the low 20s to upper 80sWeekly active users grew 45% week-over-week after launchReal customer interviews play, calling the new AI a "dream"One user cut an hour-long report down to fifteen minutesCustomers now trace ad impressions directly to closed dealsHoldorf's advice: delete and rebuild when architecture gets too complexAWS's Naveen walks through Bedrock, AgentCore, and StrandsAgentCore Memory highlighted as Demandbase's next area of explorationParticipants:Chad Holdorf – Vice President of Product Management, DemandbaseAchint Naveen – Sr Account Manager, Amazon Web Services See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Season 9, Episode 1: Can Detroit become America's greatest urban comeback story? To kick off Season 9 of No Cap, we sit down with Jared Fleisher, CEO of Bedrock, to break down the future of Detroit, Cleveland, and large-scale city building. Jared shares how Bedrock, founded by Dan Gilbert, has invested billions across 140+ properties and 21M+ SF while helping reshape Detroit's urban core. Whether you're interested in public-private partnerships, downtown revitalization, GM's move to Hudson's Detroit, or the future of the Renaissance Center, this episode is a must-listen. Join us as we dive into conviction, civic commitment, riverfront development, and what it really takes to rebuild a city at scale. Shoutout to our sponsor, WareSpace — turning underused industrial, flex, office, and big-box properties into micro warehouse space for small businesses. TOPICS 00:00 - Season 9 Premiere with Jared Fleisher 05:00 - Dan Gilbert's Conviction in Detroit 10:52 - Bedrock's 21M SF Portfolio 16:49 - The Renaissance Center Challenge 22:38 - GM, Hudson's Detroit, and Civic Commitment 27:06 - Rebuilding Detroit's Riverfront 33:17 - Public-Private Partnerships and Brownfield Financing 41:48 - Innovation Districts and Talent 45:05 - Defense, Manufacturing, and Detroit's Next Chapter 48:06 - Downtown Dallas and Why Cities Need Strong Cores For more episodes of No Cap by CRE Daily visit https://www.credaily.com/podcast/ Watch this episode on YouTube: https://www.youtube.com/@NoCapCREDaily About No Cap Podcast Commercial real estate is a $20 trillion industry and a force that shapes America's economic fabric and culture. No Cap by CRE Daily is the commercial real estate podcast that gives you an unfiltered ”No Cap” look into the industry's biggest trends and the money game behind them. Each week co-hosts Jack Stone and Alex Gornik break down the latest headlines with some of the most influential and entertaining figures in commercial real estate. About CRE Daily  CRE Daily is a digital media company covering the business of commercial real estate. Our mission is to empower professionals with the knowledge they need to make smarter decisions and do more business. We do this through our flagship newsletter (CRE Daily) which is read by 65,000+ investors, developers, brokers, and business leaders across the country. Our smart brevity format combined with need-to-know trends has made us one of the fastest growing media brands in commercial real estate.

Air Date: 8/22/2026 Today we look at how the Republican Party made climate denial its policy more than twenty years ago and is now removing the government's ability to see the problem at all. The Environmental Protection Agency under Bush refused to regulate carbon in 2003, and now the Trump administration is dismantling the science that would help us both avoid worsening the climate emergency and mitigate the inevitable destruction to lives and property now baked into the system. Full Show Notes Transcript Be part of the show! Leave a voice message, message us on Signal at the handle bestoftheleft.01, or email Jay@BestOfTheLeft.com BestOfTheLeft.com/Support (Members Get Bonus Shows + No Ads!) Use our links to shop Bookshop.org and Libro.fm for a non-evil book and audiobook purchasing experience! Join our Discord community! TOP TAKES KP 1: When It Comes to Climate Change, Is There a Point When Adaptation Isn't Enough - Consider This - Air Date 7-31-26 KP 2: Fire Climate Hot, Dry, Windy Days Are Becoming Common, Making Wildfires Harder to Control - Democracy Now! - Air Date 8-6-24 KP 3: Five Lies Republicans Tell About the Climate Crisis - The Hartmann Report - Air Date 8-9-26 KP 4: Is This How We Invade Greenland - The Rachel Maddow Show - Air Date 8-11-26 KP 5: Europes Summer of Fire The New Normal Part 1 - Outrage + Optimism The Climate Podcast - Air Date 7-30-26 KP 6: Climate Wayfinding A Compass for the Climate Crisis Part 1 - Sea Change - Air Date 5-22-26 (00:52:11) NOTE FROM THE EDITOR An Update on the Show My commentaries on YouTube - Share them! DEEPER DIVES (01:00:01) SECTION A: ON THE GROUND A1: Executive Disorder Spokane Wildfires, Abdul Wins Michigan Primary, Munition Shortage Part 1 - It Could Happen Here - Air Date 8-7-26 A2: Wildfires Rage Across the Okanagan Part 1 - The Current - Air Date 8-10-26 A3: Farmers Struggle with Crops as Climate Change Makes Weather Less Predictable - PBS Newshour - Air Date 6-23-26 A4: Wildfires Rage Across the Okanagan Part 2 - The Current - Air Date 8-10-26 (01:29:50) SECTION B: EUROPE ON FIRE B1: Wildfires Are Burning Through Europe Part 1 - The Brian Lehrer Show - Air Date 8-4-26 B2: Europes Summer of Fire The New Normal Part 2 - Outrage + Optimism The Climate Podcast - Air Date 7-30-26 B3: Wildfires Are Burning Through Europe Part 2 - The Brian Lehrer Show - Air Date 8-4-26 (01:58:01) SECTION C: THE DELAY MACHINE C1: What Happens When the Bedrock of US Climate Policy Is Wiped Away Part 1 - Climate Court Voices - Air Date 5-15-26 C2: Slow Moving Disasters Climate Change, Artificial Intelligence and The Democratic Party. - UNFTR - Air Date 8-4-26 C3: What Happens When the Bedrock of US Climate Policy Is Wiped Away Part 2 - Climate Court Voices - Air Date 5-15-26 C4: Trump Administration Ends Funding for Arctic Climate Report - PBS Newshour - Air Date 8-13-26 C5: Ex Oil-Engineer Turned Climate Whistleblower_ We Face Collapse. with Kevin Anderson Part 1 - Downstream - Air Date 8-10-26 C6: Executive Disorder Spokane Wildfires, Abdul Wins Michigan Primary, Munition Shortage Part 2 - It Could Happen Here - Air Date 8-7-26 C7: Ex Oil-Engineer Turned Climate Whistleblower: We Face Collapse. with Kevin Anderson Part 2 - Downstream - Air Date 8-10-26 (02:59:45) SECTION D: WHAT COMES NEXT D1: A Biden Climate Retrospective Part 1 - Left Anchor - Air Date 8-14-26 D2: Climate Wayfinding A Compass for the Climate Crisis Part 2 - Sea Change - Air Date 5-22-26 D3: A Biden Climate Retrospective Part 2 - Left Anchor - Air Date 8-14-26   Produced by Jay! Tomlinson Visit us at BestOfTheLeft.com Listen Anywhere! BestOfTheLeft.com/Listen Follow BotL: Bluesky | Mastodon | Threads | X Like at Facebook.com/BestOfTheLeft Contact me directly at Jay@BestOfTheLeft.com

We spoke to Kevin Peterson this week. He runs the tech at Bedrock Robotics.He told us their control room "looks like StarCraft, actually."You click on an excavator on a screen and give it a job. Then it just digs. Nobody sitting in it.This went live on real customer sites on Monday.The same day, a company called Gravis Robotics got $200 million from SoftBank.We asked Kevin about the timing. Nobody seems to know.Full chat with Kevin and Alain is out now on Bricks Bucks and Bytes.#bricksbucksandbytes #bricksandbytes #aec #constructiontech #aiOur Sponsors:BreadCrumb- 50,000+ projects globally. All running safer, faster, with Breadcrumb. - breadcrumb.coAphex is the multiplayer planning platform where construction teams plan together, stay aligned, and deliver projects faster – check out aphex.coArchdesk - “The #1 Construction Management Software for Growing Companies - Manage your projects from Tender to Handover” check archdesk.comChapters00:00 Intro00:55 Construction Autonomy's Biggest Week04:25 Bedrock Robotics Goes Fully Autonomous + Gravis $200M SoftBank Round06:54 Why Autonomous Machines Took 25 Years to Arrive09:52 How Waymo Technology Made Jobsite Autonomy Possible12:50 What Does Fully Autonomous Actually Mean?16:06 Who Is Investing in Construction Robotics (SoftBank, CapitalG, NVIDIA)18:59 Autonomous Excavation vs Excavators: The Real Market22:01 Kevin Peterson Interview: No Driver, No Operator, No Oversight24:48 Bedrock vs Gravis: The Autonomy Race Heats Up28:01 Self-Driving Cars vs Construction: Which Is Harder?31:42 How Autonomous Excavators Handle Safety on Site33:39 Training an Excavator Like an LLM: Physical AI Explained36:44 Orchestrating Machine Fleets on Construction Sites39:10 Why Construction Sites Break Self-Driving Logic44:33 World Models, Procore DroneDeploy Deal & AI Consolidation51:16 Will AI Replace Construction Consultants?

In this episode the hosts analyze a four-unit quick service restaurant franchise portfolio and debate whether buying an underperforming chicken/Mexican franchise platform is a smart acquisition or an expensive operational headache.Business Listing – https://go.franzy.com/resale/qsr-4-unit-southeast-01Welcome to Acquisitions Anonymous – the #1 podcast for small business M&A. Every week, we break down businesses for sale and talk about buying, operating, and growing them.Looking to build a professional website in minutes? Try Wix: https://wix.pxf.io/c/6898629/3115214/25616?trafcat=templateHubSpot is the backbone for how businesses scale without chaos. Try them out here: https://go.try-hubspot.com/OeG9VrSubscribe for more episodes: https://www.youtube.com/@AcquisitionsAnonymousPodcast?sub_confirmation=1Subscribe to our Newsletter: https://www.acquanon.com/newsletterSponsors:Quiet Light BrokerageThinking about selling your e-commerce or SaaS business? Quiet Light Brokerage specializes in helping founders maximize value with experienced former operators—not just brokers—and offers a free, no-obligation business valuation. Learn more at: https://quietlight.comBedrock Quality of EarningsBefore buying a business, make sure the numbers are real. Bedrock provides buyer-focused Quality of Earnings reports using experienced financial professionals and AI-powered analysis to help uncover surprises before closing. Learn more at: https://bedrockqoe.comWhat happens when you find a franchise portfolio that's growing—but still underperforming its own brand averages? In this episode, the hosts evaluate a live four-unit quick service restaurant (QSR) portfolio consisting of chicken and Mexican food franchises in the Southeast.The business generates approximately $4.2M in trailing twelve-month revenue and $676K in adjusted EBITDA, but the opportunity isn't as straightforward as it appears. The hosts dig into franchise economics, average unit volumes (AUVs), dual-brand restaurant conversions, SBA financing, franchise transfer restrictions, and whether operational improvements can realistically unlock significant upside.The discussion goes well beyond valuation. The panel debates whether these restaurants are simply poorly operated, located in weak markets, or attached to an aging franchise brand that may never reach system averages. Along the way they explore AI drive-thru ordering, franchise legal structures, pricing flexibility, restaurant labor, and why experienced multi-unit operators may view this acquisition very differently than first-time buyers.Key Highlights:- Four-unit QSR portfolio with $4.2M revenue and $676K adjusted EBITDA- One dual-brand chicken/Mexican location could potentially be converted into a standalone Mexican concept with franchisor incentives- Discussion of AUV (Average Unit Volume), franchise due diligence, and identifying operational versus location issues- SBA financing considerations, including funding acquisition costs, working capital, and restaurant conversion expenses- Deep dive into AI ordering, pricing strategy, franchise economics, and why experienced operators often outperform first-time ownersSubscribe to  weekly our Newsletter and get curated deals in your inboxAdvertise with us by clicking hereDo you love Acquanon and want to see our smiling faces? Subscribe to our Youtube channel.Do you enjoy our content? Rate our show!Follow us on Twitter @acquanon Learnings about small business acquisitions and operations.For inquiries or suggestions, email us at contact@acquanon.com

ServiceNow and AWS reveal how the DevOps Agent and MCP Server Console are turning incident response into a fast, autonomous, fully governed process. Topics Include:Govind Menon (ServiceNow) and Arun Jacob (AWS) discuss MCP and A2A strategy.ServiceNow understands workflows; partners with AWS to power them with AI.AI Control Tower governs and secures agent access to enterprise data.MCP is the industry standard for how AI agents read and act.Action Fabric spans A2A, REST APIs, and MCP for agentic work.AWS DevOps Agent, built on Bedrock, resolves incidents through sub-agents.Admin and operator access patterns integrate with Dynatrace, Datadog, Slack, GitHub.Demo: ServiceNow incident automatically triggers DevOps Agent investigation and resolution.DevOps Agent writes findings live back into the ServiceNow incident ticket.ServiceNow champions capping MCP servers at 30 tools for performance.MCP Server Console lets teams build scoped, use-case-specific tool servers.NowAssist skills, Knowledge Graph, and REST APIs become MCP tools.Live demo connects a 38-tool custom MCP server to DevOps Agent.Role-based access ensures users only see their permitted MCP tools.ServiceNow's autonomous ITOM agents point toward unsupervised future operations. Participants:Govind Menon – Head of MCP Product, ServiceNow Arunsingh Jeyasingh Jacob – Senior Solution Architect - ISV, Amazon Web Services See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

In this episode the hosts analyze a four-unit quick service restaurant franchise portfolio and debate whether buying an underperforming chicken/Mexican franchise platform is a smart acquisition or an expensive operational headache.Business Listing – https://go.franzy.com/resale/qsr-4-unit-southeast-01Welcome to Acquisitions Anonymous – the #1 podcast for small business M&A. Every week, we break down businesses for sale and talk about buying, operating, and growing them.Looking to build a professional website in minutes? Try Wix: https://wix.pxf.io/c/6898629/3115214/25616?trafcat=templateHubSpot is the backbone for how businesses scale without chaos. Try them out here: https://go.try-hubspot.com/OeG9VrSubscribe for more episodes: https://www.youtube.com/@AcquisitionsAnonymousPodcast?sub_confirmation=1Subscribe to our Newsletter: https://www.acquanon.com/newsletterSponsors:Quiet Light BrokerageThinking about selling your e-commerce or SaaS business? Quiet Light Brokerage specializes in helping founders maximize value with experienced former operators—not just brokers—and offers a free, no-obligation business valuation. Learn more at: https://quietlight.comBedrock Quality of EarningsBefore buying a business, make sure the numbers are real. Bedrock provides buyer-focused Quality of Earnings reports using experienced financial professionals and AI-powered analysis to help uncover surprises before closing. Learn more at: https://bedrockqoe.comWhat happens when you find a franchise portfolio that's growing—but still underperforming its own brand averages? In this episode, the hosts evaluate a live four-unit quick service restaurant (QSR) portfolio consisting of chicken and Mexican food franchises in the Southeast.The business generates approximately $4.2M in trailing twelve-month revenue and $676K in adjusted EBITDA, but the opportunity isn't as straightforward as it appears. The hosts dig into franchise economics, average unit volumes (AUVs), dual-brand restaurant conversions, SBA financing, franchise transfer restrictions, and whether operational improvements can realistically unlock significant upside.The discussion goes well beyond valuation. The panel debates whether these restaurants are simply poorly operated, located in weak markets, or attached to an aging franchise brand that may never reach system averages. Along the way they explore AI drive-thru ordering, franchise legal structures, pricing flexibility, restaurant labor, and why experienced multi-unit operators may view this acquisition very differently than first-time buyers.Key Highlights:- Four-unit QSR portfolio with $4.2M revenue and $676K adjusted EBITDA- One dual-brand chicken/Mexican location could potentially be converted into a standalone Mexican concept with franchisor incentives- Discussion of AUV (Average Unit Volume), franchise due diligence, and identifying operational versus location issues- SBA financing considerations, including funding acquisition costs, working capital, and restaurant conversion expenses- Deep dive into AI ordering, pricing strategy, franchise economics, and why experienced operators often outperform first-time ownersSubscribe to  weekly our Newsletter and get curated deals in your inboxAdvertise with us by clicking hereDo you love Acquanon and want to see our smiling faces? Subscribe to our Youtube channel.Do you enjoy our content? Rate our show!Follow us on Twitter @acquanon Learnings about small business acquisitions and operations.For inquiries or suggestions, email us at contact@acquanon.com

Coldwired Podcast (Come and say hello facebook.com/ColdwiredMusic). Live every Tuesday 8PM (UK)! www.twitch.tv/coldwired Music Vibes: Global Gathering XV (Live Stream 02/08/26). Tracklisting: [00:00] 01. Evolution Feat. Jayn Hanna - Walking On Fire (Adilian's Chrome Rework) [Bootleg] [06:20] 02. Jerome Isma-Ae - Encounter (Extended Discotheque Mix) [JEE Productions] [10:24] 03. Quivver, Dave Seaman - Starship Disco (Extended Mix) [Global Underground] [16:04] 04. Danny Howells, Lloyd Barwood - One More Sky [Mango Alley] [21:04] 05. HERMEN - Benjamin [Bandcamp] [27:10] 06. Lou8, AdamK, Loud Unity - Into the Void (Extended Mix) [Enormous Moments] [32:16] 07. Andy Rapkins - Kixka (12 Theory Remix) [Prognosis] [37:51] 08. R.E.E.V. - Summon [Affiliate] [43:41] 09. Ferry Corsten, Marsh - Attraction (Marsh's Extended Mix) [Anjunadeep] [48:16] 10. Dosem - Morning Runner (Extended Mix) [Anjunadeep] [51:59] 11. Hernan Cattaneo, Tom Pavicich - Bloom [Balance Music] [56:29] 12. Alexey Sonar, ANUQRAM - Time (Extended Mix) [Anjunabeats] [1:01:15] 13. Matty Wright - Fond Memories [Affiliate] [1:07:18] 14. Framewerk - Unfinished Sympathy (Framewerk Rewerk Part 1 and 2) [Bandcamp] [1:12:19] 15. Nick muir, Bedrock, John Digweed, Kyo - For What You Dream Of (Pyrrhus Remake) [Bandcamp] [1:13:11] 16. Space Manoeuvres - Part Three (Breaks Mix) [Lost Language] [1:21:05] 17. Kelle, Aloma Steele - Exigency [Elektroshok Records] [1:25:00] 18. Fishbone Beat - Always (Matty Wright's 'Shaken Not Stirred' Mix [Bootleg] [1:29:44] 19. Juanjo Corrales - Go Away [DistroKid] [1:35:13] 20. DRKWTR - In The End (Alt-A Remix) [Diesel Recordings] [1:40:11] 21. Sasha and Emerson - Scorchio (Pyrrhus Breaks Remix) [Bandcamp] [1:44:20] 22. Lustral - Broken (Way Out West Remix) [Lost Language] [1:48:53] 23. Barbitura - Flight Of The Navigator (Retroid Remix) [Ego Shot Recordings] [1:53:41] 24. Activa - Journey Home (Extended Mix) [Black Hole Recordings] [1:58:57] 25. Slacker - Psychout (Of Mind) [Jukebox In The Sky] [2:04:44] 26. LOUT - Utopia [Monkey League] [2:09:19] 27. Fuenka - Nitidus (Extended Mix) [FSOE] [2:14:04] 28. Hoopoe - Aracari [Forescape Digital] [2:19:20] 29. Stallings - Pursuit [Houstrike] [2:25:33] 30. Basil O'Glue - No Response (Extended) [Vandit Alternative] [2:30:56] 31. Slusnik Luna - Sun 2011 [Anjunabeats] [2:36:54] 32. Quivver - She Does (Quivver Mix) [VC Recordings] [2:44:31] 33. John 00 Fleming - Rest Now My Love [JOOF Recordings] [2:50:41] 34. Basil O'Glue, Nomas, Calantha - Ibiza 3AM [BAGRUHM] [2:56:03] 35. Mara - One (Hamel Implant Remix) [Choo Choo Records] [3:01:09] 36. Robert Nickson - Heliopause [Grotesque] [3:06:09] 37. Bicep - Water (London Hatred (Unofficial) Remix) [Bootleg] [3:10:57] 38. Thomas Datt, Magnus - Binary Complex [Borderline] [3:14:42] 39. Foley - You'll Never [Neptune Discs] [3:21:21] 40. Faithless - Tarantula (Rollo and Sister Bliss Big Mix) [Cheeky Records] [3:25:43] 41. Nomas - Residual Self [JOOF Recordings] [3:30:55] 42. Jon Mangan - Aether (Extended Mix) [Borderline] [3:35:36] 43. Three Drives On A Vinyl - Sunset In Ibiza [Massive Drive Recordings] [3:39:00] 44. Will Atkinson - Last Night in Ibiza (Extended Mix) [Black Hole Recordings] [3:45:00] 45. OceanLab - I Am What I Am (Lange Remix) [Anjunabeats] [3:51:52] 46. J Lauda, D.J. MacIntyre - Strange Wonders (Nomas Remix) [SLC-6 Music] [3:56:46] 47. Tracid, Koal_53 - Dreams [KOAL RECORDS]

What can actually hold your life together?We spend a lot of time building on things that feel secure—our beliefs, our accomplishments, our certainty, even our opinions. But Jesus asked a different question: What kind of foundation will still be standing when the storms come?This week, we explored the apostle John's simple but profound declaration: "God is love." After decades of walking with Jesus and reflecting on everything he had seen, John came to believe that those three words are the foundation for understanding God, reading Scripture, and living faithfully.Because the strongest foundation isn't found in having all the right answers.It's found in learning to build your life on the love of God.Chapters00:00 Intro00:22 Four Gospels, Four Beginnings01:38 In the Beginning Was the Word03:12 John Looks Back07:50 God Is Love12:00 Belief or Trust?13:23 The Problem with Defining Christianity by Beliefs Alone16:14 A Hermeneutic of Love23:18 Building on Bedrock27:48 The Foundation That Lasts30:16 Communion: Embodying the Love of ChristKey TakeawaysJohn's understanding of God was formed by walking closely with Jesus."God is love" is the foundation for interpreting Scripture and understanding reality.Christianity is about becoming more than simply believing.Jesus consistently invited people to trust him and follow him before asking for certainty.Love requires humility, growth, and the willingness to keep learning.The strongest lives are built on the bedrock of God's love, not merely on having the right beliefs.Scripture ReferencesJohn 1:1–141 John 1:1–41 John 4:7–21Matthew 5–7Matthew 7:24–27

Can a church really shepherd people it doesn't know? In this episode, Brody sits down with Jonge Tate to challenge the way we think about church, leadership, and success.They talk about the problems with chasing numbers, the multi-site church model, and why healthy churches need real pastors, real relationships, and shared leadership. Jonge also shares the lessons behind Bedrock's church planting journey, including why sending your best leaders away may be one of the hardest and most faithful things a church can do.This is a conversation about church planting, shepherding people, multiplying leaders, and trusting God with the results.Send us Fan MailPlease leave a review on Apple or Spotify to help improve No Sanity Required and help others grow in their faith. Click here to get our Colossians Bible study.

Send us Fan MailJuly Episode — What's New in Cloud FinOpsEpisode SummaryIn this July episode of What's New in Cloud FinOps, Frank and SteveO cover a packed set of cloud and AI cost-management updates across AWS, Azure, Google Cloud, Oracle, Alibaba, and OpenAI. The conversation focuses on how cloud vendors are changing pricing models, improving observability, and adding new ways to optimize compute, storage, and AI workloads.The standout theme is that AI and cloud economics are becoming more dynamic: pricing is shifting by usage pattern, time of day, workload type, and capacity model. The hosts also dig into how organizations can use these changes to improve governance, control costs, and make smarter architecture decisions.Top Topics CoveredAWS EKS and ECS GPU management fee reductionsAmazon CloudWatch intelligent tiering for logsAmazon S3 removing the 30-day minimum for storage class transitionsAWS Billing and Cost Management adding a cost efficiency widgetAWS Data Exports adding standardized Bedrock metadataAWS Lambda publishing logs for managed capacity providersAzure reservation exchange changes and legacy VM RI renewalsAlibaba Cloud time-of-day pricing for frontier AI modelsAlibaba model routing reference architectureOpenAI GPT model price cuts and Bedrock matching those ratesOracle bringing Gemini models into OCIGoogle Data Stream free tier for CDC writesAmazon OpenSearch Service optimized for log analyticsMicrosoft Marketplace single-click SaaS purchasesAWS sustainability dashboard adding water withdrawal dataKey TakeawaysAI pricing is getting more sophisticated.Providers are increasingly using variable pricing, off-peak discounts, and routing strategies to steer usage and improve margins.Cloud optimisation is moving deeper into the platform.Vendors are surfacing more native tools for cost visibility, efficiency scoring, and policy-driven automation.Storage and logging are becoming more cost-aware.New features in S3, CloudWatch, and OpenSearch help teams store more data for less while keeping observability usable.Governance and procurement remain critical.

P.M. Edition for Aug. 6. Meta said today one of its AI models hacked a third-party service during cybersecurity testing. WSJ tech reporter Sam Schechner discusses why these incidents are becoming more common. Plus, billionaire Dan Gilbert has already revived Detroit's downtown. Now he's set his sights on a $1.6 billion project that would transform the city's waterfront—but, as Journal real estate reporter Nicholas Miller explains, it's a gamble for him, and for the city. And SpaceX investors brace for more volatility after $100 billion of the company's shares were unlocked today. We hear from markets reporter David Uberti about what investors should expect. Alex Ossola hosts. Sign up for the WSJ's free What's News newsletter. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Of all commercial real estate investors, only 3% are women – and when Beth Azor learned that stat, she decided to frickin' change it. In this episode, Erin sits down with the woman affectionately known as The Canvassing Queen®: founder and CEO of Azor Advisory Services, owner of three Florida shopping centers valued at over $75 million, and founder of the Women's Real Estate Investment Summit, where Erin has had the joy of speaking – and riding the famous four-hour bus tour of the town Beth practically owns. Beth's story starts with an $11,000-a-year nonprofit salary and a real estate license she'd had since age 18. It took a boss literally marching her to a bank to co-sign a $50,000 note – on the condition she invest 20% of every commission from then on – to turn a high-earning spender into an investor. Eight LP deals later, she went out on her own as a GP, and today she's the co-GP of a $32 million asset she waited thirteen years to buy. Yes, thirteen. That story alone (Mr. G, the quarterly "no," and the pivot to pure relationship-building) is worth the listen – and so is the one about crashing a utility company's remote HQ with cupcakes. Whether you've never heard the terms LP and GP or you're ready to raise capital for your first deal, Beth breaks the path down into steps any woman can start this week: pick an asset class, find an expert, invest passively first, and watch how it's done. Because as Beth's community proves – 68 women stood up at this year's summit having invested with someone in the room – you don't have to do it alone. Listen in as Erin and Beth discuss: The boss who called her "a freaking idiot" (with love), co-signed her first $50K investment, and made her bank 20% of every commission The stat that lit the fire: only 3% of commercial real estate investors are women – and most inherited it or signed on a husband's guarantee Why "we don't know any other women doing it" is the real barrier – and how the Women's Real Estate Investment Summit is dismantling it LP vs. GP, explained in plain English: preferred returns, refinances, and why LPs end up "playing with house money" Beth's starting playbook: pick your asset class, find an expert, LP first, and watch the GP How GPs raise capital – including the empty Wells Fargo bank deal where Beth raised $3.2M from 22 people in four days Beth's non-negotiable: never invest with a GP who has no skin in the game The 13-year Mr. G story: how persistence plus genuine relationship turned "no, click" into co-GP of a $32M asset The cupcake story: how a Friday-afternoon delivery did what eight men yelling couldn't Women Investor Wednesdays, the March 2027 summit, and how to get in the room About Affectionately known as The Canvassing Queen®, Beth Azor is the founder and CEO of Azor Advisory Services (AAS), a leading commercial real estate advisory and investment firm based in Davie, Florida. As its principal, Beth currently owns and manages three shopping centers in Florida valued at over $75M. She travels the U.S. consulting with, brokering deals for, and training associates in the commercial real estate industry, with clients including Phillips Edison & Co., Brixmor Properties, The Shopping Center Group, Urban Edge Development, DLC Management Group, and Bedrock. Beth is the author of Don't Say No for the Prospect (2019) and The Retail Leasing Playbook (2020), the founder of the Women's Real Estate Investment Summit – on a mission to get more women investing in real estate and growing their families' wealth – and co-founder of the South Florida Independent Retailer Awards®. Her newly created AI bot "Ask Beth" is a compilation of her 900 YouTube videos and 300 podcast episodes. A graduate of FSU, Beth is founder and past Chairwoman of the FSU Real Estate Foundation, past President of HOPE Outreach Center in Davie, and co-founder of 100+ Women Who Care in South Florida. She is a single mom to a superhero movie podcaster and an aspiring pro golfer – and she recently walked 250 miles of the Camino de Santiago across Spain. How to Connect With Beth Azor Website: https://www.bethazor.com LinkedIn: https://www.linkedin.com/in/bethazor/ Facebook: https://www.facebook.com/azoradvisoryservices Instagram: https://www.instagram.com/bethazor/  Recommended Resources Women's Real Estate Investment Summit – March 3 – 5, 2027, registration opens September; only ~60 of 250 seats left: https://thewomeninvestmentsummit.com/  Women Investor Wednesday podcast – Beth interviews a woman investor every Wednesday (want to be a guest? She wants startup stories, even your first VRBO): https://www.bethazor.com/beths-podcasts/ Don't Say No for the Prospect by Beth Azor: https://www.bethazor.com/product/dont-say-no-for-the-prospect-how-1-went-from-a-sales-rookie-to-a-retail-leasing-rockstar/ The Retail Leasing Playbook by Beth Azor: https://www.amazon.com/Retail-Leasing-Playbook-Beth-Ratzan/dp/0578224208 "Ask Beth" AI bot – 900 videos and 300 podcast episodes' worth of answers: https://www.bethazor.com Happiness & Fulfillment Assessment: https://pursuingfreedom.com/happiness Pursuing Freedom Collective: https://pursuingfreedom.com/collective Get a copy of Pursuing Freedom on Amazon: https://amzn.to/46o7m7z Subscribe to the Pursuing Freedom podcast on Apple Podcasts or Spotify for weekly inspiration and strategies.

Pastor Randy Bradley

The Jareds Stern talk about best voted two-time Best of DC, a literal court date, and what the hell are M&Ms thinking? (Recorded 7/29/26)We're Best of DC! - https://jaredstern.com/2026/07/16/four-score-2/BUY THE BOOK!⁠ - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://shorturl.at/9Ob5J⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Listen to past episodes!⁠ - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://jaredstern.com/between-two-sternsSee Jared Stern live!⁠⁠ - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://jaredstern.com/laugh-at-me/

                                                                                                                                                           Bedrock Wine Company In this episode, Rob and Scott review one of Bedrock's top Cabernets from the Montecillo Vineyard in Sonoma County.  So come join us, on The Wine Vault.

In recent months, the open vs closed, and US vs China discussions on model ownership and sovereign/local AI have heated up to a fever pitch. So it is very very good news that Poolside AI are finally emerging with new models, like Laguna S 2.1, that are beating Thinking Machines' recent release nearly 10 times their size.Poolside's recent tech report got a lot of praise due to their level of detail, and Vibhu first covered Laguna's recent technical report on our paper club:From spending $12 million building language models for code before the world cared to creating a Model Factory that can take a model from pre-training to release in eight weeks, Eiso Kant has spent more than a decade betting that code is the path to AGI. In this episode, the Poolside co-founder joins swyx and Vibhu to explain why ChatGPT felt like vindication, why Poolside embraced open weights and open research, and why he would rather live in a world with 100 foundation model companies than five even if Poolside were one of the five.We go deep on Poolside's Model Factory: the engineering systems behind 10,000–20,000 experiments per month, streaming data directly into training, reproducible experimentation, low-precision compute, and agents that increasingly write code, launch jobs, evaluate results, and modify the pipelines used to train future models. Eiso also unpacks their recent launch Laguna S, why persistence, verification, and backtracking may matter more than raw intelligence, how much capability remains inside smaller models, why reinforcement learning will move earlier into pre-training, and why next-token prediction is still extracting too little from the web.We also discuss model-harness co-design, Poolside's path from coding agents to AGI, why Eiso thinks MCP and traditional tool calls are “stupid,” the real economics behind frontier-model training, Poolside's $500 million raise, open-source AI, regulation, NVIDIA and TSMC's influence, engineering productivity in the agent era, high-agency teams, and hiring at Poolside.We discuss:* How Andrej Karpathy's RNN work inspired Eiso to start building language models for code in 2015* Why Eiso spent four years and $12 million pursuing an idea before the market cared* Why ChatGPT felt like vindication and brought Poolside back to open source* Why Eiso would prefer 100 foundation model companies over an oligopoly of five* The difference between releasing open weights and publishing genuinely open research* Why Poolside deliberately built a global research organization outside the Bay Area talent war* Why model building is ultimately 90% engineering* The Model Factory: Poolside's end-to-end system for rapidly training and improving models* How fewer than 70 researchers run roughly 10,000–20,000 experiments each month* How Poolside moved from six-month model cycles to five- and eight-week launches* Why streaming data directly into training unlocked faster experimentation* How immutable data, versioned code, and reproducibility enable rigorous model research* Why Eiso wants capable researchers to leave their labs and become Poolside's competitors* Why 95% of model building can be reduced to better data or compute efficiency* Laguna S and why persistence, verification, and backtracking can outperform raw intelligence* Why smaller models may handle far more knowledge work than previously expected* Why reinforcement learning will move earlier into pre-training* Why next-token prediction is still failing to extract enough knowledge from the web* Why distillation and environments have become the AI industry's favorite “drugs”* Why mid-training is really an early form of curriculum design* Low-precision training, networking bottlenecks, and the next gains in compute efficiency* Laguna S: 118 billion total parameters, 8 billion active, and eight weeks from training to launch* Why model builders can often evaluate a new checkpoint within its first 30 minutes* Model versus harness: where agent capabilities actually come from* Why Poolside sees coding and long-horizon software tasks as a path to AGI* Why Eiso thinks MCP and traditional tool calls are “stupid”* Why future agents will write scripts instead of choosing from dozens of predefined tools* The case for minimal harnesses, containers, and model freedom* Why Poolside is prioritizing vision but does not expect to work on audio soon* Why language may be the most compute-efficient modality for encoding knowledge and reasoning* The real cost of model development and why the final training run is anticlimactic* The story behind the Poolside name and why it represents refusing to lower ambitions* How Poolside raised $500 million while investors still questioned whether AGI was real* Why intelligence could become the world's most demanded and commoditized resource* When open models may become too capable to release without restrictions* Why unilateral AI safety does not work in a globally competitive environment* How regulation could accidentally lock in an oligopoly of two or three AI companies* NVIDIA, TSMC, and the hardware systems underpinning foundation-model progress* Why reinforcement-learning wall-clock time is one of Poolside's biggest bottlenecks* Why Poolside trains models from scratch instead of simply distilling larger models* How AI changes the way companies should measure engineering productivity* Why agency may become the most important quality for employees in the AI era* How leaders align high-agency people through shared goals and clear constraints* Hiring across research, post-training, pre-training, architecture, evals, and engineering at PoolsideEiso KantLinkedIn: https://www.linkedin.com/in/eisokantX: https://x.com/eisokantPoolside: https://poolside.aiTimestamps00:00:00 Introduction00:00:54 Karpathy, RNNs, and Building Code Models Before Transformers00:02:26 The $12M Failure and ChatGPT Vindication00:03:39 Open Source and the Case for 100 Foundation Model Companies00:09:22 Open Weights, Open Research, and Poolside's Global Team00:16:04 The Model Factory: Why Model Building Is 90% Engineering00:20:19 Agents, Automated Experiments, and Early Signs of RSI00:24:04 Streaming Data, Reproducibility, and Scientific Rigor00:30:35 Creating More Foundation Model Companies00:36:07 Laguna S: Persistence vs. Raw Intelligence00:43:01 Reinventing Pre-Training, RL, and Curriculum Design00:52:33 Low-Precision Training and Squeezing More From Smaller Models00:58:37 Model Harnesses, Coding Agents, and the Path to AGI01:09:26 Why MCP and Traditional Tool Calls Are “Stupid”01:13:04 Vision, Multimodality, and Why Language Still Matters01:18:15 Scaling Models and the Real Economics of Training01:20:40 Why Poolside Is Called Poolside and Raising $500M01:27:37 Open Models, AI Safety, and the Risk of an Oligopoly01:33:53 NVIDIA, TSMC, and the Reinforcement-Learning Bottleneck01:41:52 Smaller Models, Distillation, Engineering Productivity, and HiringTranscriptIntroduction: Eiso Kant, Poolside, and Open ModelsSwyx [00:00:00]: All right, we're here in the studio with Eiso Kant from Poolside, together with Vibhu. Welcome.Eiso Kant [00:00:08]: Thanks. Thanks for having me, guys. Good to be here.Swyx [00:00:10]: Yeah, fresh on the plane. You texted me, you were like, “Hey, I'm on my way to SF.” I was like, “You're on a plane right now, right?” Like, hey.Eiso Kant [00:00:16]: I know. After I texted you, I realized that probably coming in with major jet lag was gonna offer some fun experiences today, but let's do it.Swyx [00:00:23]: I mean, I think the thing I would tell guests is that they don't have to prepare that much because if you're truly working on this every single day, then even, like, what you hazily remember is going to be new for a lot of the audience that don't live in your world every day, right? so 10 years ago, you did a talk at Google Slush, talking about the democratization of AI. and, now here you are, like, open sourcing an incredible new model that we're gonna talk about. But I guess, like, what got you into democratization of AI? Like, it's not obvious from your LinkedIn or something.From Karpathy's RNN Post to SourcedEiso Kant [00:00:57]: No, it's not at all. I don't think it's obvious how I got in this space. I owe getting into this space to Andrej Karpathy.Eiso Kant [00:01:05]: In 2015, he wrote an article called “The Unreasonable Effectiveness of Recurrent Neural Nets.”Swyx [00:01:10]: Neural Nets, yep.Eiso Kant [00:01:11]: And that article, I read it, and I pivoted my startup at the time overnight to working on RNNs, and later LSTMs and Transformer models to be able to write code. If you go to this article and you scroll down, you can start seeing, like, this was the precursor to what ended up becoming language models. So, at least when he was character-level language models that were starting to predict letters, he has an example out here. There's a little Paul Graham generator, and you can read it, and the text makes sense, but it doesn't. and there's a little-- There's an example of code a little bit further down. Yeah, so Shakespeare.Swyx [00:01:47]: Shakespeare.Swyx [00:01:49]: CoolEiso Kant [00:01:49]: And for some reason, I read this, and I went down the rabbit hole of learning everything I could about RNNs and LSTMs, right? This is Transformer paper. And I had built a completely unreasonable belief, that neural nets should be able to generalize to anything and everything, and that language should be able to generalize, to a lot of things that are intelligent and the ability to write code. And so I started building Sourced, which was a fully open source company trying to build, what we used to call machine learning on code, language models on code. And we spent about four or five years on this, till the end of 2019. And that sounds really cool today, but back then, no one cared.Eiso Kant [00:02:29]: Right? Like, no one cared. We were in the dark. Like, we did things along the way. We tried applying convolutional neural nets to, like, the structure of code. We were. when attention came out, we were applying it to LSTMs, and then the Transformer paper came out. And it - it wasn't obvious, and what we missed throughout that entire journey, that we were on the right track, but we should have just kept scaling up. And today, to all of us, the scaling laws and scaling up seems like the most obvious thing. But having spent four or five years of my life on working on language models on code, it wasn't obvious. So I have a lot of respect to folks at Google and OpenAI and others who took that confidence and kept going. we failed ultimately at the time, and it was, like, biggest failure of my career, right? You blew $12 million of investors' money, which was a lot back then.Swyx [00:03:18]: Yep.Eiso Kant [00:03:19]: You spent, still a lot, but, And you spent years with, like, a group of 40 people just obsessing over this problem. And life took a different turn, And it was, and family became a focus, and I kept my heads down and really, didn't really look at language models for the following two years. big mistake considering Following years are gonna be really interesting. And then ChatGPT came out And it was like a vindication. It's like people started texting me. I found, like, my old, work decks and these old talks. And throughout that whole journey, we,ChatGPT, Vindication, and Returning to Open SourceEiso Kant [00:03:56]: We really had a strong point of view at the time that, like, as you're building more capable intelligence, it should be open and open source.Eiso Kant [00:04:04]: When we started Poolside, that wasn't the case at all, and I wanna be very open about it. When we started Poolside, we were like, there was a premise of two things. One is this technology is not gonna stop compounding in capabilities. I think to most people obvious today, but three-plus years ago when we started, most people were still arguing if these were stochastic parrots or not.Eiso Kant [00:04:23]: And the second was that reinforcement learning was gonna be the biggest driver for LLM capabilities. Today, very obvious. Three years ago, was not an opinion held or direction held at either OpenAI or Google or Anthropic or others. And so people looked down on us a little bit. They were like, “ is this really gonna work?” And so we just started working the problem, and we never really thought about open source again. We just kept our heads down and we built our, like, knowledge, understanding from scratch, right? We didn't roll out of an existing lab. So we picked up the papers and started writing code and figuring things out.Eiso Kant [00:04:59]: And it wasn't until the beginning of this year that me and my founder, Jason, picked up the open source conversation again.Eiso Kant [00:05:07]: And if you go back to some of the early things on our website, it was very straightforward. It was we wanna get to AGI, we wanna support a world of abundance, and we wanna be the first company that gets there.Eiso Kant [00:05:20]: But we started talking at the beginning of this year because it became obvious that the world was going in a direction that was starting to like, pick at us a little bit. Like, it didn't, this didn't happen overnight. It was, like, a little bit we were seeing this and we're like, “Okay, The world's going down a path.” And Throughout this journey, there was something that I used as a, as an analogy or thing. So I said well, if I go back to back in those days, 2015 or 2016, we're working on this, and I picked up a fi book off the shelf, and I was reading the book about 2035. AGI is achieved, and the story would be over the following, decades. And it would have that first chapter where everyone's trying to figure things out. You'd get the chapter of ChatGPT coming out And then you would get to the chapter where the world was at a fork in the road, and the one that it picked was one where three or four or a handful of companies were going to create all of intelligence moving forward.Eiso Kant [00:06:21]: And when I thought about that story, it felt like a dystopian fi book, not a utopian fi book. And the reality is, I'm a utopian fi guy. Like, and so We took a step back and said, “Hey, can we play a role here?” Now it was easy for us to do so because we were not at the frontier.Eiso Kant [00:06:41]: If we were at the frontier, I don't think we could have changed our mind. and I don't mean this like it's when the moment there's too much capital involved, too much expectations, you've built up things, right? We're a small team, just improving and improving. And so we knew that we could make that decision now, but it would be a lot harder to make as we got closer and closer to the frontier and caught up to others. And did a lot of soul-searching and a lot of conversations, and said, “No, this makes sense,” Even if there's big unanswered questions, like how the hell do you build a business model with foundation models about open source? Big open-ended question that we do not fully have the answer to yet, right? At what point do you no longer wanna release open source models because misuse of models has, real potential risks associated with it? how is the government gonna respond to open source? but I think it all just came down to one thing, and I'll stop the monologue, is the fact that I rather live in a world that has 100 foundation model companies than a world that has five, even if I was one of the five. And the smallest and most meaningful contribution we can make for 100 to exist is to open up our research and open up, like, our weights right now and figure out along the way how we can, like, do more.Neo-Labs, Model Choice, and the Token EconomySwyx [00:08:01]: Yeah. I think if anything, over the past three years, that has become a bit more true. you are one of a cohort of Neo labsEiso Kant [00:08:10]: YeahSwyx [00:08:10]: That people are now calling that. And, we're, we're doing this on the day that Thinky launched their, new model and you are outperforming them on their, on some benchmarks that they released, right? Like, they just don't have it yet. so it goes to show that I think, like, this is one of those things where, like, there is room for multiple players, and you are seeing a little bit more of the future. Maybe more like 20, not 100, but, like, you are one of the 20.Eiso Kant [00:08:36]: I really hope so, right? I think we I'm, I'm excited about their release, and I'm excited about everyone releasing because, like, ultimately, like, choice competition is both gonna drive progress in the right direction. But the fact that like, we create models and while we all, drink out of the same well of data effectively, we do introduce very different behaviors and biases in our models. Some are intended biases, some are completely unintended biases.Swyx [00:09:03]: Yeah.Eiso Kant [00:09:03]: And if we shape up in an ecosystem in the world where open models are gonna be a part of the token economy, like, I don't think there's any question about it anymore Then we want to be able to live in a world where companies, countries, people can choose and say, “Hey, I am most aligned and I trust most this provider for these things.”Swyx [00:09:25]: Yeah.Vibhu [00:09:26]: I think more than just one of the 20 Neo labs, up until recently, most of open source innovation was coming from the Chinese labs, right? So there's the DeepSeek of the West. Is it today? Okay, maybe it's thinking machines reflection, but there aren't many, right? So, one of the things you guys started in France, Europe, but very much now you're taking that American standpoint and more than just that, the point is the Chinese models that we see, they're not super open research. the work you put out is, I think, some of the best. So every few months you get not only frontier models, but also here's a breakdown blog, paper, technical report of here's everything for state of the art to build, frontier intelligence and you're filling that gap too, right? So not just only open weight, not just Western, but also pretty open research.Open Weights vs. Open ResearchEiso Kant [00:10:20]: No, I appreciate it. Look, I think it's, I think it's the most meaningful contribution, right? Weights are a binary. Let's call them what they are. Yes, we can modify them, we can change them, but, like, giving someone the weights does not allow them ultimately to recreate what you're doing, right? And so now there's challenges around releasing data sets, challenges around like releasing certain things, but being able to share your research, like, right, how do we do it? What are the lessons we learned that we spent, tens of thousands of experiments of compute on? I think very much so. One correction though, Vibhu, and I say this because it's been haunting us for quite a few years. We from day zero were an American company.Swyx [00:10:55]: Yeah. They movedPoolside's Global Team and American Company StorySwyx [00:10:56]: To France.Eiso Kant [00:10:56]: So the story once and for all is very. We start as an American company. We have always been an American company, and early on we made a very conscious decision. We said, “We're not gonna hire any researchers in the Bay Area. We're gonna look for talent everywhere else in the world.” and that is everything from Middle Americas, Seattle to, Serbia, and to Taiwan and Singapore and other places. And it was because we took a view that this was gonna become a talent war for this, and I think it has over the years now. Three years ago, that wasn't fully obvious yet. I think today it very much is. And we also realized that, like, some of the world's most capable people with, like, the most interesting, innovative ideas were not just gonna be here. And so it led us to create like a fully remote company. and we ended up opening an office in Paris and London and different places and we have a lot of the team in the US and a lot of team outside. But we always took this view of like, we're an American company, but if we want the best of the best to work with us, we need to take a global view. Now we do also have people here in Silicon Valley, like the company's grown and others, but I think one of the things that, it slowed us down at the beginning, but it has sped us up now, and it's why you're seeing like the progress, I think, on our models and the cadence at which we release, is because we didn't roll out of an existing lab. Right? we didn't, we didn't have a lot of the information that's freely flowing around here at the time. We just took this point of view as like, “Okay, well, let's just work the problem. Let's just go and, like, read the few papers that are out there, and let's just figure this stuff out.” And we made some hilarious mistakes in model training because of that over the yearsEiso Kant [00:12:35]: Like especially in the first 12 months. there's a few that I think still haunt me and scare me. We can talk about them later. but it created a, like, a resiliency and persistency in the team, right? with extremely few people have left us over the years, that, like, told us, “Okay, we can do this.” When we first wrote our first training code base completely from scratch, it wasn't a fork of any open source. It was just like, “Okay, let's build it from scratch.” I remember we had this one moment where we spent three weeks working out an optimizer bug. Like, it was like training just couldn't get stable. We, like, obsessed over it, and we thought, like, maybe we were wrong. Maybe we should have just forked this repo, or we should have. But then when we solved it, I still remember at the time we were like five people in the company. when we solved it, we were like, “Oh, we can do things,” like if we're just willing to work hard. and I think that culture with a very strong engineering bias has helped us, like, get to where we were. And so there's this notion of open source and talent and these things. I think we, We just took different decisions from a different starting point. and I think we are lucky. I do want to definitely call it lucky. And there was a lot of hard work at the team that now, like, that's starting to show up in results.Swyx [00:13:52]: Just ‘cause we probably won't revisit this again, but, and this is a fun recruiting challenge if someone knows the answer. What was the bug? And then we won't tell the solution, but we'An Optimizer Bug and the Value of Building From ScratchEiso Kant [00:14:01]: So the - This - You're gonna test my memory here,Swyx [00:14:04]: Oh, okayEiso Kant [00:14:04]: So but I thinkSwyx [00:14:05]: DirectlyEiso Kant [00:14:05]: I think I can recall. So if you, so if you look at, So if you take like Adam as an optimizer, you have epsilonSwyx [00:14:12]: YeahEiso Kant [00:14:13]: Which is, right, like in the denominatorSwyx [00:14:14]: Momentum and weights. YeahEiso Kant [00:14:15]: Is exactly, in the denominator. And at the time, if I recall, you looked at like the early Llama papers and things like that. People were juicing epsilon, like, quite a bit. Like, they were, like, adding, I don't know if it was E minus four or whatever, like a high value for epsilon.Eiso Kant [00:14:31]: And if you think about this during training, it's like a bit weird and counterintuitive that we're adding noise to our optimizer by just adding effectively, like, a random number in the denominator, right? Like behind the decimal point. And I don't recall the exact bug, but it had - What I remember is once we solved it, we no longer had to juice epsilon as much as, like, was happening in the Llama paper and other places. and it was like one of those fundamental moments where we had trusted this paper that was out there, and we're like, “Oh, no, it has to be this way. It has to have this high value of epsilon.” But it made no sense to us intuitively. Like, why do you have to have this so high? Like, if you're just trying to avoid division by zero, why can't the value be extremely small? and that was like one of those moments where you realize like, okay, finding things out from scratch yourself builds a better intuition. Because the one thing you learn very quickly with model building is that your intuitions that you start with are gonna get beaten up so hard.Eiso Kant [00:15:33]: Right? Like - It's such an experimental science, that the things that seem obvious, you very quickly get to learn, like, you were wrong, and hopefully you figure out why, and sometimes you don't even.Swyx [00:15:45]: Yeah. yeah, so, one of the reasons that you, when you released your new models, Vibhu got really excited. I mean, everyone got really excited. But Vibhu led our paper club on it, and you guys sawEiso Kant [00:15:58]: YeahSwyx [00:15:58]: Obviously. maybe talk through some lessons learned in that, whatever you can disclose. we can focus on the model factory stuff, whatever you think is a good starting point.Model Building as EngineeringEiso Kant [00:16:08]: So I would say that our view from very early on in the company was that model building is ultimately 90% engineering.Eiso Kant [00:16:18]: And I think we all know it in the industry because if you look at where's every researcher spending their time, they're spending their time writing code, right? Looking at data and writing code. And so we said, okay, The state at the moment, like three years ago, was bash scripts and Slurm and spaghetti code bases for training and, like, data pipelines that were patched together. And we looked at this and said, “Well, ultimately, model building is a process.” You're going from raw data, right? Like training raw material, the web, et cetera. you're doing a whole bunch of filtering, cleaning up, transformations, analyzing. These days, that's, far more complex than it was three years ago. then you're training a model, which is effectively a large distributed systems problem, right? Across hardware that has still-- It's become a lot more reliable. It was extremely flaky back then. and now with every new generation, we get our new sets of challenges. And then you go into the next stages, right? There was no training back then, but, like, you got, your post-training and then your reinforcement learning. And so we looked at this and we said, “Well, this looks like an industrialized process. This looks like an end process, that every single part of it has its machinery,” right? If it's your big data pipelines, if it's your crawling ingestion of the web, if it's your, large-scale distributed training, and then you've got your reliability. And we said, “Well, why don't we take some of the world's smartest distributed systems engineers that we knew and make them part of the process of research from day zero?” Not retrofitting it later on, but, like, really from the beginning. And that became our model factory. And so our model factory started with a handful of components. Today, it's thousands of components, and I try to equate it to, if you think about, like, someone who was at the very early days of Foxconn, if they had been there for the following, decade, they would be able to rebuild Foxconn because they saw every decision that led to building that system and all the complexity. If you and I walk into Foxconn today, no chance.The Model Factory and Experiment VelocityEiso Kant [00:18:18]: Right? Because we don't have the lineage and history of decisions that led to that. And so we built early on from the beginning- with a team that really understood that, well, the metric that we are optimizing for is the speed of an idea from a researcher to an experimental result that we can trust to then being part of the next model training.Eiso Kant [00:18:42]: And in the. And because it's such an experimental science, ultimately, in the beginning when it wasn't that complex, you could patch your way around it, right? But now, at any foundation model company, you are running. I mean, we're a small team, right? We're less than 70 researchers, another 35 engineers. and we are running, I haven't checked the latest count, but far more than 10,000, maybe 10 to 20,000 experiments a month that we cut. And so if you look at that scale of every model run that is, like it's ultimately it's, it's you need to be able to trust it as an infra problem. And so what we have now done over the years is gotten really good at that, and just by working it and improving it and obsessing over those end decisions. So now what that means is that you looked up Laguna XS 2 that we launched. It was five weeks from the beginning of training to launch. The model that we're gonna talk about today was eight weeks from start of training, to launch. We started the next model literally yesterday because we now finished the post-training required for the model we're launching, next week or by the time this comes out today. and we move that compute to the much larger Laguna M model that we're now training. And so the model should be an artifact of someone's process. It shouldn't be really a thing in itself. Like, and we treat this like the way you would look at like a SpaceX factory where, yes, the first rocket, really hard to build, but the much harder challenge was building the factory. And now they're rolling off, and no one is really thinking about the next launch anymore. So it's just another launch, it's another launch, another rocket comes off. And that's what we're trying to do with model building.Eiso Kant [00:20:22]: And what has been, which was not planned from day zero, it was in the back of our mind like this will happen one day, is that when you build a really good end model factory with really good APIs and really good engineering systems, Well, what is it perfect for? It's perfect for agents.Agents Inside the Model FactoryEiso Kant [00:20:40]: Because agents are now starting to take over more and more work in our model factory.Vibhu [00:20:43]: Yeah.Eiso Kant [00:20:44]: So I look at the screens when I walk, like when we're, we come together, in our monthly, we do monthly onsites, and I walk behind people's screens and I stop by and I talk to our researchers. And the default is all of these different agents running on their screen that are writing the code. They're launching the jobs. They're evaluating the results that are coming back from the model runs. They are, making the changes. And we're still in the driver's seat. We're still coming up with the ideas. We're still helping with the debugging. But more and more, and this is right now very profound on the data side of our pipelines in both pre and post and the synthetic data pipelines, it's starting to become more on the architecture side as well. You're starting to see these twinklings of what RSI is gonna look like.Eiso Kant [00:21:27]: And that's. So when we talk about, like to your question about our models, every talk about the model factory, And my coolest example of these things is always that when we kick off a new run, doesn't matter if it's a training like big run or if it's now a post, like one of 10 post-training versions we do for like release or many experiments, is that at any given moment, the changes that somebody made that they had experimental results from the day before make it into that run.Eiso Kant [00:21:57]: So there's not like a cutoff 90 days before. Like no, it's like literally from that moment because we can now trust the machine enough. And then you also have to invest in the reliability. So one of my favorite metrics about like Laguna S is that there was no call events, Right? Like completely zero. And we haven't had a meaningful call event, like something to wake up for, as far as I recall this entire year. now there is one asterisk to that. In usually the first six hours of launching a new model run, something breaks because you set a config wrong, you made a small mistake, et cetera. So that's usually there's a little bit of intervention, but that's always within like call periods, right? Not on call. And I think that's starting to now compound. So the model we're releasing now, I love it. It's amazing, but we're already onto the next one. and I think that's the way it should be.Laguna, Five-Week Builds, and Zero On-Call EventsVibhu [00:22:50]: Hey, I also just wanna point out, so for context, this was like a month ago. we found it in the tech report, so we just came in with, “Okay, new model's dropped. Haven't heard about it.” We wereEiso Kant [00:23:02]: Yeah, we're very used to doing this every few months.Vibhu [00:23:03]: We're, we're very much like, “ okay, look, it's like, on par with Kimi, DeepSeek, whatnot, the small ones, Gemma level. Oh, it's a very cool paper on what goes into building.” And then we hit this page, right? Like literally page two of tech report is, “This process allowed us to build the small model from scratch to delivery within five weeks applying the lessons”. And then I'm like, oh, this paper is not about here's a tech report of benchmarks and here's how many tokens it was trained on. Like for people that wanna dive more from what we're not gonna discuss on the podcast, it's all laid out here, right? FromEiso Kant [00:23:38]: YeahVibhu [00:23:39]: Custom software that agents can use to interface with training code, training data.Eiso Kant [00:23:45]: Yeah. Well, link the paper correctly, so yeah.Vibhu [00:23:47]: Yeah. All that stuff. read the paper here, but,Technical Report Principles and Streaming Training DataEiso Kant [00:23:50]: But I would like to. I love principles, and I think that is a good starting off point for maybe telling some stories. Maybe we can go one by one past the principles. I'll just call out that Dagster just got bought by a Prefect.Vibhu [00:24:01]: Yeah.Eiso Kant [00:24:01]: Isn't it fun? But yes, I'm very familiar with Dagster. just anything where like they trigger some story.Vibhu [00:24:07]: So, well, I would say, well, experiments code's obvious, but I think one of my favorite things is, I don't know where it is in here, but early on, and I still think this is the case a lot of foundation model companies, people prepare their training data sets, they get packaged up, then they get copied over to a training cluster distributed across all of the nodes, and then training starts.Vibhu [00:24:30]: And we looked at this like three years ago and we were like That makes no senseEiso Kant [00:24:36]: You lose so much time because the moment you have to rematerialize the data set, you have to make a change, you have to fix something, et cetera, you've got all this time of like repackaging it, right? Toca- tokenizing it, repacking it, moving it over to a cluster, then distributing it across the nodes. The bigger your clusters are, you start using fancy like torrent-like algorithms to like distribute your data. So why aren't we streaming data into training? Right? Something that's very common and like just basicVibhu [00:25:00]: Like just in timeEiso Kant [00:25:01]: Just in time, like good computer science like principle. And that was one of the first things that I think unlocked - the model factory. Because the moment you start thinking about, well, a training job, it doesn't matter if it's a big hero run or a small like, post-training experiment, consumes a certain number of tokens per second, right? And it's not a lot, right? From a like a data, moving data perspective. So we said, well, we have our training cluster, and then we've got like our AWS kinda setup where we can build these amazing big data pipelines. We can set things up. We use Spark underneath the hood, like all these things.Vibhu [00:25:36]: But when you say AWS, it's not actual AWS, it's your internal AWS.Eiso Kant [00:25:39]: It's our internal-- No, it's our internal like just running like our infrastructureVibhu [00:25:42]: Site web servicesEiso Kant [00:25:43]: Exactly. Our stuff running on like an AWS account or on like any hardware, right?Vibhu [00:25:47]: Yeah.Eiso Kant [00:25:48]: And so once we made that shift into I can stream data into training, all of a sudden you realize a lot of things unlock. Because now you don't have to wait for the whole data set to materialize.Immutable Data, Experiments as Code, and Scientific RigorEiso Kant [00:26:00]: You now all of a sudden when you're running data experiments about mixing data, it's a config. Because you've got these data sources that are coming in, and you just - we have this service called Blender that's in the report, where we then say, “Okay, for this run, I want 20% of this source, 10% of this source. I want this much, so many epochs of repetition. I want this to be, shuffled in a certain way,” and your training job can start while the rest of the data is even still materializing. also what it does is because all of this underneath-- So for us, we treated the data layer underneath as like an immutable data layer, and that was really important. Like experiments as code, immutable data layer means that you can always go back and understand literally down to the single token at which cursor it went in on which version of the code.Vibhu [00:26:47]: Yeah.Eiso Kant [00:26:48]: And it took us a I have to admit, like the first year of Poolside, we understood that engineering had to get great, But we didn't understand yet, that this is ultimately in support of like a good rigorous scientific progress. We were quite a - We were a very small number of people, so a lot of it was YOLO ideas and YOLO runs.Vibhu [00:27:08]: Yeah.Eiso Kant [00:27:09]: And we built great infra for the YOLO runs. But once we realized that we treated data as immutable and code as always versioned, and you could always track and trace every experiment end to end perfectly, you could repeat everything perfectly, right? You have perfect reproducibility. I can still reproduce runs from two years ago if I wanted to, right? It enables the scientific progress, like the scientific process, and I think that took us probably about a year and a half into the company to figure out. We also had some great hires, like our head of applied research, Nikolai, who joined us from Yandex, who'd been working on language models since like the early 2020s, I think brought that into the company of like, “Hey, we wanna have even more rigor.” And then once we kinda had the combination of like increasingly more capable platform that allowed people to do more, but had this immutability, we were able to start “Okay, every experiment is truly an ablation. We truly need to understand it.” And I think we became much more scientifically rigorous in the last couple of years, and the infra underneath enabled it. and then there's just fun stuff like, andVibhu [00:28:16]: Yeah, a lot of it's fun, like even just the, one, you share all the ablations, two, picking the data sets, right? There's like a random small paragraph in here where it's just like, “Oh yeah, training data, we have some, we have an auto mixer.” it trains eight small models, scales them up, picks the training data set. We don't even need to look at it. I'm like, “Wow, a lot of engineering rigor there.” And there's just, there's just a lot in here.Publishing Research and Giving BackEiso Kant [00:28:40]: Yeah, and it'- and look, and we wanna put out more. Like we, We treat writing papers as something that we haven't earned the right for yet for a long time. So you earn the right to spend time, publishing research once you're at the frontier, because until then, you're catching up, and every minute and hour in this industry matters. Like I obsess over it, not just the wall clock time from idea to result, but just general like time every day that we, waste is one that doesn't allow us to catch up. But in this case, we said, “Okay, we're gonna give ourselves.” I think we gave the team like three or four days while still doing their work, like give everything in there. And to your point earlier, if your stuff, it's easy to like put it out. And so there's so many more things that we wanna talk about over time, and we will definitely start doing. And as we earn more of the right, but also now have like added to our mission that we want more foundation model companies to exist, you'll see us like be way more proactive, and just trying to keep dropping some of those like things that we've learned along the way that can help others like speed up.Vibhu [00:29:40]: Which is the other cool side of this, right? It's, it's not like, back to your point, it's not just here's the benchmarks of our training. If you want to replicate, here's experiments of optimizers, data sets, post-training. you lay out a lot of it here alongside here's your system for how to do it? So it's, it's really like promotingEiso Kant [00:29:59]: No, thank youVibhu [00:29:59]: Other people can do the same.Eiso Kant [00:30:00]: And by the way, I also wanna make clear, right, we have been incredible-- Like we've taken a lot of advantage of the fact of all the open research that others have published, Right? And you mentioned, the Chinese labs, and we I think it's important that there's, from every country and every culture and background, including like Western companies like us, there's different models that come out that people can choose to trust. But I think we do have to give credit where credit's due, right? The incredible Chinese lab have done an amazing job at sharing their research, and we have definitely like been on the receiving end of taking advantage of that. So when you're on the receiving end of something coming to you, I think it's, you also have an obligation to give back.Swyx [00:30:39]: Do you have a favorite or underrated Chinese lab that you wanna shout out? Everyone shout outs DeepSeek.Chinese Labs, Zhipu, and PersistenceEiso Kant [00:30:44]: That's a good question.Swyx [00:30:45]: Moaan obviously for Therapsi. Yeah.Eiso Kant [00:30:48]: Yeah, look, I think, I think obviously everyone's been talking about Zhipu lately, with 5.2. I think what most people don't realize is when they started.Swyx [00:30:59]: Yeah.Eiso Kant [00:30:59]: Right? They started years before ChatGPT.Swyx [00:31:02]: They just rebranded. YeahEiso Kant [00:31:03]: And so, I've like, I remember how hard it was to work on these things Before the rest of the world got excited about it. And so I have an immense amount of respect for people, who were working on improving models when it wasn't the sexy thing to do, when believing in LLMs, was gonna get you ridiculed. I remember like back in 2016 when we were doing what we'd call, machine learning on code with some of these models. we would-- people would just laugh at us, like they'd be like, “This makes no sense. Like why are you wasting all these, like, millions of dollars on trying to figure this out?” And so I would say they're probably the one that, I think deserves a shout-out, not just because their latest model is very good, but because they fought to get here. And I think, I think every foundation model company it takes time to get here, right? It took us three years to get to the model that we're, that we're now gonna be releasing. and now the time in between the models is coming, is counted in weeks. It's no longer counted in months or years. But this stuff's hard. and if we can make it a little bit easier for the next person, like we should all do so. Because if we don't do so, we're, we've got a small window before models are really impacting recursive self-improvement to a level where catching up otherwise might become unfeasible. And we should try to, in that window, encourage as many labs or however we wanna call them, like to start. And so one of my currentEiso Kant [00:32:36]: Mission, but qualm is like I wanna encourage whoever is a researcher right now who thinks they can tackle this to go and leave and become my competitor.Eiso Kant [00:32:45]: Like start another foundation model company because I think we need it. I think otherwise we're not gonna be in the world where, I don't want to just be the fifth or the sixth company that wins. I wanna look at a world where there's lots of choice.Starting a Foundation Model CompanyVibhu [00:32:57]: What else do people not see in starting a foundation model? it's, there's a lot of compute, there's a lot of capital required, a lot of compute. You lay out model factory and how to do the training, but there's a lot there, right? That's,Eiso Kant [00:33:10]: Well, look, it's, I in turn-- this is an oversimplification, and I always asterisk it with that because it can land a little bit the wrong way in people's minds. But I think you can sum down, And I saw it, 95% of model building to just doing, you're just doing two things. You're improving data or you're improving compute efficiency. And I know that feels like an oversimplification for the incredible, like, Gifted and skilled work people do. But if you really look at it, like what are we doing? We are looking at data, we're generating new data, we're improving data. and the only way to do that is to look at the data, right? That's a big part of foundation model building. And on the other hand, we come up with these incredible breakthroughs in inference, in architecture, and new attention mechanisms. But what are they really doing? They're bringing compute efficiency. Now, we have definitely had some breakthroughs over the years that allow for more model capabilities. But at the limit, if you could train a large enough model, right, like, and you had infinite compute, we probably-- if you had infinite compute, you'd be at AGI probably already tomorrow.Eiso Kant [00:34:12]: Right? Like it's not. And so, and let me say that infinite compute with infinite ability of much faster networking because networking ends up being more of the bottleneck than compute. But, so I do think that's, those are the main things. And to just realize that this is engineering. I think it's become more obvious, but I think for quite a few years, people have held foundation model companies and researchers and others on this pedestal of like you're doing incredible magic or rocket science, or only like, Nobel laureate physicists can do this. And don't get me wrong, there are some really hard problems that need to be solved, but a lot of the work that all of us are doing on a day Is not sitting down trying to solve a math theorem. A lot of the work that we're doing is just really doing the basics right, writing good code, looking at data, improving it, running experiments, looking at plots, trying to see like, hey, trying to shape our intuitions. And a lot more people could be highly capable researchers. and I think that's, it feels far for people to do so. But I've seen in our own company, we've seen engineers become researchers because the model factory allowed them to be, have a much lower hurdle of running experiments and trying things. And one of the guys on our team who started as an engineer building our agents is a legit reinforcement learning researcher now, making real progress. and that happened in the span of like six months. that would've not been what I think most people assumed was possible, a couple of years ago.Swyx [00:35:46]: Yeah. I think one of the interesting moments is when you can self-host, like, if in a programming language, like if you can compile the language in the language, the equivalent is can you use your own tools, right? You have the pool CLI, you have your own models. presumably you're not only using your own models. There's no way. But like, what's that percentage over time?Laguna S, Persistence, and Behavioral GainsEiso Kant [00:36:10]: This is the first model that we're releasing that is starting to meaningfully contribute to our own work. It's not a it's not state-art model yet. Fable and other, they're, they're very capable models, but Laguna S Is really interesting. I'm gonna pull up the quote. Peng Ming, one of our heads of applied research, said something, last week as the model came out about 10 days ago, much better than we had hoped for or expected. And he said, I have the feeling that a lot of the gains in Laguna S come not from more intelligence, but more from different behavior, more verification, less taking things for granted, not declaring victory early, and being way more persistent. And to be honest, those are more predictive than raw intelligence for success in human also to some degree. And this was, he wrote me this on 5th of July on a Sunday, and it's been burned in my brain ever since because the Laguna S model, as you'll see it and why it does so well on benchmarks and why it does so well in using it on a day basis, is that it's just incredibly persistent. It reasons a lot. I do call that out. We have work to do on making it more efficient. We have to work to do on offering different reasoning modes. But this is the model that has been able to do things that I never thought it could do. A hundred eighteen billion 8B active model, which is not that large. It fits on a DGX Spark and still runs at, thirty, forty tokens a second on a Spark, is able to solve Erdős 397 independently. It's able to do complex programming tasks. It's able to. I asked it this morning to make me a Fi scanner without using any external libraries on my Mac, and it's, like, figuring out, like, the core WLAN API by really persistently trying to understand it without access to the internet. And more, I love vibe checking. I've probably spent eight to ten hours a day with this model for the last ten days.Eiso Kant [00:38:05]: I'm not exaggerating. I was on my eleven-hour flight yesterday. I spent ten hours reading trajectories and traces and, like, of the model.Eiso Kant [00:38:12]: And what I take away from it is exactly what Peng Ming said. We are gonna be able to squeeze so much more out of smaller models than I think we had imagined in the industry because, yes, there's intelligence and larger models are more intelligent. Like, no doubt about it. We should continue to scale up. but the behaviors of being really persistent, of being able to backtrack when you're wrong, of, like, understanding how to interact with your environment show us that we can get a lot more out of it. And this, for me, has created a bit of a Question in my mind the last couple of days. If you think about where we're using models today, right? We are using models, say, for knowledge work. Represents twenty-five percent of the global economy, twenty-five trillion dollars of work.Eiso Kant [00:39:00]: As we scale up models and they become more intelligent, we are excited about using them more and more for pushing the frontier of science.Small Models, Knowledge Work, and CommoditizationEiso Kant [00:39:08]: And if you look at the frontier of science, like true breakthroughs in science, they have been linked, they are linked to more intelligence in many places. Einstein figuring out general relativity is able to bring ideas together that other people would have not brought together. And I think one of the many dimensions of intelligence is the ability to do that, and it's something we clearly see that as models get larger and more capable, they're able to pull more ideas and threads together that a smaller model wouldn't be able to.Eiso Kant [00:39:36]: And we're starting to see examples of that in medicine and, like, in bio and other things. But if you think about the majority of knowledge work that we do, and it includes building software. I'm a software developer at heart first and foremost probably, although I probably can't say it that much anymore as I don't write production code in years, is that what makes us good is our persistence. It's our ability to encounter a problem and backtrack and say, “I need to go figure out this bug. I need to go research this. I need to go look at the documentation. I need to, like, try different, five different ways to see, like, if I can solve it.” But it is not necessarily bringing three ideas together from radically different fields. And so if we are now seeing, and I think Laguna S is an example, that we are able to make a relatively small model much more capable than I had definitely predicted or any previous, like, benchmarks had shown for any model remotely this size or even larger, At least on coding tasks, that it's because of the behaviors. And so now the question I have, and I don't have an answer, it is I know at the limit, so infinite model size, right, extremely large model, and the cost of that model is gonna be very expensive to run. We know this, right? So larger model ROI.Eiso Kant [00:40:52]: So I know that at the very limit, I'm not gonna use the world's largest model one day, quadrillion parameter, whatever crazy, like, scale we scale up, to do a basic coding task. Already today, I'm starting to size down for certain tasks.Eiso Kant [00:41:07]: So it means that there is an optimal. It means there's some curve that goes as we go up to model size for knowledge work, at some point we're at the peak, and after that, the return on investment of using a bigger model, just doesn't make sense.Eiso Kant [00:41:22]: Now, I think the question is, before I would have thought that peak was extremely very far away.Eiso Kant [00:41:30]: This model for me is the first sign that Maybe that peak is At a trillion, five trillion, ten trillion. Maybe we can just squeeze way more out of these models. I'm no longer thinking that we need two or three orders of magnitude on the largest models to be able to, solve knowledge work, the accounting, the legal, the code that we write. And so if that holds true, It is an argument for the commoditization of models. It's an argument that open source can win and, like, succeed in this world. And now it's of course a self-serving argument and it's a hopeful argument, but theoretically at the limit it works. We just have to go discover in the next couple of years of how much more we can squeeze out. Now, I do want to put a big asterisk. This does not mean I'm against scaling models. I think we ultimately only succeed if we scale our models as large as our competition. I do not like. I think we should not put our head in the sand and say we're gonna be king of open source small models. I think that's, It's a out. It's trying to be king of your own kingdom, but not realizing what the rest of the world's doing. All of us rather use a smarter, faster, more model. It's a sign of hope. And so I don't wanna overly state this is a good model. We have a long way to go to get to the state-art. But what hopefully people take away when they use this model is that the behaviors inside of it are what push it to be far more capable, less than necessarily the number of parameters.Pre-Training, Mid-Training, and RL Moving EarlierVibhu [00:43:03]: Is that mostly post-training? LikeEiso Kant [00:43:05]: YesVibhu [00:43:05]: Right.Eiso Kant [00:43:06]: It's entirely post-training.Vibhu [00:43:08]: Are we done improving anything on training? Is, like, training done?Eiso Kant [00:43:12]: No.Vibhu [00:43:12]: Okay.Eiso Kant [00:43:13]: SoVibhu [00:43:13]: I just wanted to cover training, and then we go post-trainingEiso Kant [00:43:15]: Training is not done. I mean, look, there's a part of training of just dealing with skill, right? Every new order of magnitude of model skill, you are going to get new things you gotta solve for. That'- but those are ultimately, engineering challenges.Eiso Kant [00:43:31]: I have a, I would say, a not commonly held opinion that reinforcement learning Will move earlier and earlier into training.Vibhu [00:43:42]: Yeah, training.Eiso Kant [00:43:44]: Not even training. Like training today, right, is, like if you look at - So we've been working on this for years already. and I think the best-- I think the first time we saw it out in public was the DeepSeek Zero paper. this is a year and a half ago, I think, if I recall correctly. where, you can Very early on in a model as it starts capable of being able to use language, et cetera, induce reasoning. and so the question that I have is like, we have this- we have the dataset that's the web. and the web, I think we could arguably say probably has The totality of humanity's knowledge somewhere encoded in different places. It's a huge variance degree of quality, from garbage data, and like once you look at training data, you really get humbled of like what the web is, to like, the most greatest scientific papers and best blog posts and like, best transcripts and whatnot.Eiso Kant [00:44:39]: And so now What we are trying to figure out, and have been doing a lot of work on, and it's a place where maybe not as open as we're on other things, but we will become more over time. we've been spending a couple of years really doing research on how can we turn the web into not just next token prediction, but into a way to teach the model to think earlier in its training. and I think there's a huge amount of gold to be found there. I think we are right now in, we've got some drugs in the industry. One of the drugs is distillation. Another drug is, more environments. Like, and they're great, and they make us feel good, and they make the models better, and like we're all addicted to them, and we'll use them, right? in various different ways. and but ultimately, I think we are still barely squeezing out of the web what we should be getting out of the web.Eiso Kant [00:45:33]: I think just next token prediction during training is not enough.Eiso Kant [00:45:36]: AndVibhu [00:45:38]: YeahEiso Kant [00:45:38]: I think we'll see some very interesting things still happen. and that RL in post-training to induce behaviors, to improve things, like I think - the whole world knows how to do this now. I think we're, we're scaling it up. Everyone is. But I wonder if we need to go as far as we're going today with environments. I'm not sure yetVibhu [00:46:01]: You mean we're going too far?Eiso Kant [00:46:02]: I'm, I'm not sure if the path to AGI is justVibhu [00:46:06]: Is more environmentEiso Kant [00:46:07]: More environments.Vibhu [00:46:08]: It seems like a never-ending, “Okay, I want instruction manual for this table, right? Am I gonna environment out building furniture? Or are we just gonna tail end like we need some general solution?”Eiso Kant [00:46:19]: I think there is, I think there's an ability to generalize more from the web. but I also am very encouraged, like when I look at Laguna S and, which is post-training is, well, is the big impact there. and I see like, oh, wait a second, just by making some of these behaviors much better, we're able to get so much more out of it. It just changes a little bit the way you think about intelligence.Vibhu [00:46:40]: Yeah. The analogy people draw often is the RL phase is where you don't learn as much new knowledge. You shiftEiso Kant [00:46:46]: Yeah.Vibhu [00:46:46]: Yeah. So, you shift distribution, and you can have it reason towards what you want. on your point about training, a lot of training is still just continue training in a domain, say medicine, then you do RL. So still justEiso Kant [00:47:00]: It's just better data, right? Like, I mean, training, ooh, I like how we invented this word. Like it's effectively just like,Vibhu [00:47:06]: Second phaseEiso Kant [00:47:07]: It's the second phase of training With like a really dumb way to do a curriculum. But like ultimately, what you'd want is a curriculum from token zero to token 30 whatever or 40 trillion tokens that really truly is the optimal curriculum for the model to learn. But training is essentially a stage curriculum on the web because we do not have to compute, And, effectively to try to ablate the perfect curriculum, right? And so I'm pretty sure that you'll start to see people talking soon about some other term, and there's two or - ‘cause now we do this, right? We talk stage two and stage three and stage four training and like. But ultimately, all we're doing is we're trying to assign a curriculum to the web data that we have to allow the model to learn better. I think at some point, as things get compute, as models get cheaper to run, as the next generations of compute, this will become more of a continuous spectrum. I also think the reason, by the way, you have training and like stage two and stage three is organizational, Right? It'- this is, I think, a thing where-- that we really try to avoid with the model factory is like Training exists because there's a training team now, right? There's people, or like people in training decide to focus on like a training effort. but what you really want is engineering and scale of experiments that allows for a much more continuous spectrum that you don't, you have infinite stages. Now, we're not there. Compute's not there. Organization design is not there for it yet. but I think we'll get there. we'll look back on a couple of years and be like, “Oh my God, it was so cute that we did our training data like this in such a like naïve way. Like we barely ordered it. We didn't really do a good job at likeCurriculum, Auto Research, and New ObjectivesVibhu [00:48:48]: The building that curriculum will get you that in the industry.Eiso Kant [00:48:51]: And I'll confirm that, when I talk to some researchers that this is a lot of the focus now is like how does training change and what is the next objective other than, next token prediction. I assume you don't have the answers, but you have some ideas.Vibhu [00:49:02]: We have some ideas. We're not ready to talk about it yet.Eiso Kant [00:49:05]: Yeah.Vibhu [00:49:05]: We've been working on them for years, and I think that's the one thing that's also like you asked earlier about, like what's not obvious about building a foundation model company is that you are constantly balancing the table stakes work, the recipe worksEiso Kant [00:49:19]: Yeah.Vibhu [00:49:19]: Versus like your, my crazyEiso Kant [00:49:22]: Pure researchVibhu [00:49:22]: Breakthrough.Eiso Kant [00:49:22]: Yeah.Vibhu [00:49:22]: Pure research and finding that balance and adjusting the percentage to it based on where you are in the race is really important.Eiso Kant [00:49:31]: I mean, so like, this is a nice way. I was gonna bring up auto research at some pointVibhu [00:49:35]: YesEiso Kant [00:49:35]: As another Andrej invention, or coinage, which is like, I honestly, like how many objective functions can there be, right? Like just try 1,000 of them, set it running, whatever.Vibhu [00:49:47]: Man, it's alsoEiso Kant [00:49:48]: Like what you're looking for. You're looking for loss curves like that, likeVibhu [00:49:51]: It's also a thing people take bets on, right? When you say more Neo labs, you're doing a version of we'll do foundation models, scale them up, next token predictors. A lot of other Neo labs that we see want to take a completely different approach, right? At some level, you're right. It's all, compute efficiency, and that's the net objective. But some are okay, different architecture, like vastly different amounts of compute spend. So some are different. They're not justEiso Kant [00:50:19]: YeahVibhu [00:50:19]: They're like, 99% not balancing, here's the vanilla and scale up. They're 99% on, here's novel research that'll change everything.Eiso Kant [00:50:27]: And I think, Luke, I think you. It depends when you started as well, right?Pure Research vs. Table StakesVibhu [00:50:30]: Yeah.Eiso Kant [00:50:30]: When we started, like the novel thing we did was reinforcement learning on code. No long- that's no longer novel by far, but we were like, - that's where we obsessed over when no one believed in RL. So you have to when you start the company, you have to have your own idea. You have to have something that's different that allows you to speed up, right? For us, it was RL to LLMs that later became common, like, Knowledge. But in the beginning, it wasn'tVibhu [00:50:53]: It's cool. this was like your original 2023 blogEiso Kant [00:50:57]: YeahVibhu [00:50:57]: Of purpose.Eiso Kant [00:50:58]: Yeah.Vibhu [00:50:59]: And like you do lay it all out here.Eiso Kant [00:51:01]: We laidVibhu [00:51:01]: The blog is pretty underrated, right? The whole RL on code was very early on.Eiso Kant [00:51:06]: Very early. And even we had to argue with people, like we say here things like to push beyond current capability, to train your own foundation model. We had to argue with people that it mattered that you had your own like, base model. you can fine-tune your way to success, right? major capabilities emerge from training a base model made accurate and useful during fine-tuning.Vibhu [00:51:23]: Which like, for perspective at the time, we knew closed models, OpenAI, Anthropic were huge. The open models we had were like Mistral 7B, a 30B, a 70B.Eiso Kant [00:51:35]: When weVibhu [00:51:35]: YeahEiso Kant [00:51:36]: The date on this thing is wrong. When we published this, it was April 2023. I think this was justVibhu [00:51:42]: YeahEiso Kant [00:51:42]: Happened on a migration, probably found it on archive.org.Vibhu [00:51:45]: Mistral.Eiso Kant [00:51:46]: Mistral had started, we started on the same month, right?Vibhu [00:51:49]: Yeah.Eiso Kant [00:51:49]: So this wasn't even, there was only, I think, Llama out at the timeVibhu [00:51:52]: SnellEiso Kant [00:51:52]: And that's it, right? And so, but I agree. I think we wan

Feliks Banel's guest on this BONUS EPISODE of CASCADE OF HISTORY is Paula Johnson, Senior Archaeologist and Director-at-Large based in the Seattle office of WillametteCRA (CRA stands for “Cultural Resources Associates”). In this conversation, recorded on July 23, 2026, Johnson describes how the federal historic preservation regulations known as Section 106 work, and then outlines the threat facing 60 years of precedent in how the federal government treats historically significant resources. She also describes how the historic preservation community is mobilizing to push back against what amounts to a gutting of the rules - including elimination of public notice and input and tribal consultation - and how concerned citizens can get involved. A federal body known as the Advisory Council on Historic Preservation is scheduled to vote on the proposed changes on Friday, July 24, 2026. National Trust for Historic Preservation Section 106 website: https://savingplaces.org/stories/section-106-under-threat WillametteCRA website: https://willamettecra.com/ Advisory Council on Historic Preservation website: https://www.achp.gov/ Links to more information as well as images related to most topics discussed on the show are often available at the CASCADE OF HISTORY Facebook page: http://www.facebook.com/groups/cascadeofhistory CASCADE OF HISTORY is broadcast LIVE most Sunday nights at 8pm Pacific Time via flagship station SPACE 101.1 FM in Seattle and gallantly streams everywhere via www.space101fm.org. The radio station broadcasts from studios at historic Magnuson Park – located in the former Master-at-Arms' quarters in the old Sand Point Naval Air Station - on the shores of Lake Washington in Seattle. Subscribe to the CASCADE OF HISTORY podcast via most podcast platforms and never miss regular weekly episodes of Sunday night broadcasts as well as frequent bonus episodes. "LIKE" the Cascade of History Facebook page and get updates and other stories throughout the week, and advance notice of live remote broadcasts taking place in your part of the Old Oregon Country.

The bedrock of all paranormal belief is the foundational aspects of storytelling. Since human beings could tell stories, we have shared in some form or another our experiences. These experiences range from the bland, everyday tidbits of the normal to the unexplainable experiences of dreams or encounters with the unknown. With the accelerated advent of generative AI, the world of paranormal evidence has become so cluttered with grifters, pranksters and tricksters, that is becomes hard to rely on our eyes. This brings us back to the beginning once again - the shared experience though stories. My aversion to the AI movement leaks through as always, too. I also share a recent Ouija Board experience I had and why I have always loved using this tool since when I was a young kid. Hope to see you at a show sometime this summer and exciting announcements are on the horizon! You can find more on my stand-up schedule, short films and more at: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://ryansingercomedy.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Commercial Free episodes here!⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ SpectreVision Radio is a bespoke podcast network at the intersection between the arts and the uncanny, featuring a tapestry of shows exploring creativity, the esoteric, and the unknown. We're a community for creators and fans vibrating around common curiosities, shared interests and persistent passions. Learn more about your ad choices. Visit megaphone.fm/adchoices

Domo and AWS reveal how AI agents freed sales reps from 20 hours of weekly busywork, turning scattered data into real-time coaching and forecasting.Topics Include:Domo and AWS teams introduce today's session on AI agents in sales.Topic: using AI agents to transform sales operations, from insight to action.IT teams increasingly asked to turn data into actionable outcomes, not just access.Domo's CRO wanted AI agents to boost sales rep efficiency significantly.Reps act like "archaeologists," digging through scattered systems for basic context.This digging eats roughly 20 hours weekly, half of reps' time.Goal: personal AI agent per rep, understanding their book of business.Live demo begins: agent app surfaces urgent items needing attention.Agent tracks deal milestones, timelines, and forecasts from call and email data."Deal coach" feature grades rep performance and suggests next actions.Agent tone can be tuned from gentle to direct, aiding tough feedback.Architecture overview begins: building an AI-ready data foundation first.Data from CRM, calls, and emails flows into a cloud warehouse.Two agents built: automated deal analysis and personalized deal coach.Agents write insights back to CRM, preserving human edit control.Recipe: build foundation, activate with agents, distribute to people.Governance must be embedded throughout, not bolted on afterward.Second example: Fogo do Chão uses AI to analyze restaurant reviews.AWS architecture explained: Domo runs on Bedrock, defaulting to Anthropic models.Q&A: sales team adoption was immediate and enthusiastic post-rollout.Participants:Jason Longhurst – Head of Product Marketing, DomoAman Tiwari - Sr Solutions Architect, ISV, Amazon Web Services See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Is the light of liberty still shining in the Western Hemisphere? In this episode of Mining the Media, G.K. examines the encouraging democratic trends emerging across North, Central, and South America, highlighting nations where the cause of freedom appears to be gaining ground. Using the United States as a beacon whose light has remained steady through generations, he explores why liberty continues to inspire people throughout the Americas. In Crusty's Corner, Dave tackles one of the most important yet often overlooked pillars of a constitutional republic: judicial integrity. Using recent headlines as a starting point, he examines seven biblical and constitutional principles that explain why equal justice under the law is essential to preserving freedom, maintaining public trust, and protecting every citizen. Whether discussing international developments or the integrity of our own institutions, this episode reminds us that liberty is sustained not merely by elections, but by truth, justice, and the moral principles upon which free societies depend. Please visit us at www.miningthemedia.com and share with your friends, relatives, associates, and neighbors.

In this episode, Ray Cochrane breaks down AI distillation, the teacher-student technique frontier labs now lean on to train smaller, cheaper models. He also covers GPT-5.6’s government-vetted rollout, Claude Sonnet 5 landing on AWS, Maryland’s two-year data center pause, and Microsoft’s climbing carbon numbers. Finally, he wraps with Apple’s $30 billion Broadcom deal, Meta’s tamper-proof recording light, Michigan’s parasite outbreak, and a simulation that erased a super El Niño. – Want to start a podcast? Its easy to get started! Sign-up at Blubrry – Thinking of buying a Starlink? Use my link to support the show. Subscribe to the Newsletter. Email Ray if you want to get in touch! Like and Follow Geek News Central’s Facebook Page. Support my Show Sponsor: Best Godaddy Promo Codes Get 1Password Full Summary Cochrane opens with a quick personal update. Longer days have him outdoors, including a float trip on the Sandy River at Dabney State Park, where he found clearer water, clay-like sand, and easy footing. Next week brings both a move and a trip home, so he is stocking up on Trader Joe’s “Power Berries” and IKEA bags at his mom’s request. Then he turns to the lead story. AI Distillation Explained: How Frontier Models Teach Each Other Cochrane’s featured story comes from Hugging Face engineer Sergio Paniego. Distillation is teacher-student training for AI: a capable model generates the training signal, and a smaller student learns to match it. The classic off-policy version compresses giant models into cheap students, either through soft labels or piles of worked answers. Google’s Gemma models and DeepSeek’s R1-Distill line were built exactly this way. However, the industry is now converging on multi-teacher on-policy distillation, or MOPD. Labs build reinforcement-learning specialists for math, coding, and agentic work, then have them grade a single student, word by word, as the student generates its own answers. DeepSeek-V4, MiMo-V2-Flash, and NVIDIA’s Nemotron 3 Ultra all run versions of the recipe, and the Qwen3 team reported better results at roughly a tenth of the GPU hours of raw reinforcement learning. Finally, self-distillation lets models like Cursor’s Composer 2.5 learn from better-prompted versions of themselves. Sponsor: GoDaddy Economy hosting $6.99/month, WordPress hosting $12.99/month, domains $11.99. Website builder trial available. Use codes at geeknewscentral.com/godaddy to support the show. GPT-5.6 Arrives With a Government-Vetted Rollout OpenAI shipped GPT-5.6 as a three-tier family: Sol, Terra, and Luna. Sol costs five dollars in and thirty dollars out per million tokens, half of Claude Fable 5’s rate. The benchmarks split: Sol Ultra wins Terminal-Bench at 91.9 percent, while Claude Fable 5 still leads SWE-Bench Pro. Notably, the API launched in limited preview to roughly 20 partners vetted by the U.S. government, though the model went live in Microsoft 365 Copilot on day one. Claude Sonnet 5 Lands on AWS, Plus Quick AWS Wins Claude Sonnet 5 arrived on AWS through Bedrock, pitched as top-tier intelligence at Sonnet pricing. Additionally, Amazon WorkSpaces for AI agents reached general availability, enabling agents to drive full desktop applications securely. OpenSearch gained a log-analytics engine claiming four times the price-performance, and SageMaker now scales inference about twice as fast. Cochrane also flags that Kendra and Q Business move to maintenance mode at the end of July. Anthropic Wants You to Reflect on Your Claude Habits Anthropic launched Reflect, a beta feature that analyzes your past Claude conversations and visualizes how you actually use the assistant. It requires Memory, excludes incognito and health-related chats, and keeps its insights inside the tool. Cochrane loves the idea. He reviews his own transcripts to extract prompt patterns and turn them into reusable skills, and he suggests listeners simply ask their AI to do the same. AlphaEvolve Goes GA on Google Cloud Google made AlphaEvolve generally available to Google Cloud customers on the Gemini Enterprise Agent Platform. The agent acts as an evolutionary collaborator: provide a baseline algorithm and your goals, and it searches for better, human-readable code. BASF, JetBrains, and Kinaxis are the named early adopters. Meanwhile, Cochrane renews his standing wish that DeepMind release AlphaGo as a playable teacher. Google Adds “How This Ad Was Made” AI Labels Google is adding a “How this ad was made” section to My Ad Center across Search, YouTube, and Discover. Ads built with Google’s own AI tools automatically get the disclosure, backed by invisible watermarks. However, ads made with outside tools rely on advertiser self-declaration. Cochrane points out the limits of voluntary disclosure in an AI-flooded content economy. Microsoft’s Carbon Emissions Climb 25 Percent Microsoft’s new sustainability report shows emissions up 25% in 2025, driven by a data center construction spree. The gross figure is 34 million metric tons before offsets, while other coverage puts the net figure at around 20 million. Water consumption also jumped thirty-four percent, even as Microsoft claims its first water-positive year. Cochrane argues regulation needs to catch up, since Google and Amazon report similar increases. Prince George’s County Pauses Data Centers for Two Years Prince George’s County adopted a two-year moratorium on new data center development, the longest pause in Maryland so far. The resolution blocks new applications, including hyperscale projects, until the council passes real regulations. Water and energy impacts remain open questions the county intends to study. Cochrane gives kudos to residents for making their voices heard. Apple and Broadcom Ink a $30 Billion U.S. Chip Deal Apple is expanding its partnership with Broadcom with a multiyear agreement expected to exceed $30 billion. The deal covers custom silicon and wireless components, with more than fifteen billion chips to be made on American soil. Broadcom’s Fort Collins, Colorado plant anchors the work with a $1.5 billion equipment expansion. Tim Cook framed the deal as accelerating Apple’s commitment to American manufacturing. MSI and Intel Ship the First Arc G3 Extreme Handheld Intel detailed how it co-engineered the MSI Claw 8 EX AI+, the first handheld on the Arc G3 Extreme processor. Highlights include a heat-spreading board layout and game-tuning loops that Intel says run Cyberpunk 2077 up to thirty-seven percent faster. The device is on sale now in void purple for around $1,500. At that price, Cochrane jokes he would rather buy a computer. Meta’s Glasses Get a Tamper-Proof Recording Light Meta answered the most common privacy questions about its AI glasses. Photos stay private on the device until the wearer imports or shares them, and a white capture LED blinks during any recording with no off switch. Moreover, newer glasses disable the camera if the LED is blocked, tampered with, or destroyed. Cochrane reminds listeners these claims are Meta grading its own homework, but the blink signal is worth recognizing in public. Michigan’s Parasite Outbreak Tops 1,200 Cases Michigan’s cyclosporiasis outbreak reached 1,251 cases since June 22, with roughly forty hospitalizations along the way. Northwest Ohio adds more than five hundred cases. The parasite typically spreads through contaminated fresh produce, and investigators still have not found the source. Cochrane’s advice: wash your produce, and get tested if your symptoms fit. AI Finds the San Andreas Fault’s Silent Slips Researchers paired AI with borehole strainmeters to detect dozens of hidden slow-slip events beneath the San Andreas Fault’s Parkfield section. Each silent slip releases stress within hours and is reliably followed by low-frequency earthquakes. Together, the findings support a continuous spectrum from silent creep to destructive quakes. The study appears in Nature Communications, and Cochrane hopes it will lead to better earthquake prediction. Cloud Brightening Erased a Super El Niño, in a Simulation Finally, a Science Advances study simulated marine cloud brightening in response to the 1997 and 2015 super El Niño events. Seeding clouds over the eastern Pacific erased the events entirely inside the model. Real deployment would take roughly 2,400 ships spraying continuously, and the simulations showed side effects like extra warming over Europe and Asia. Cochrane finds the weather-machine concept fascinating, yet he questions the consequences of altering cycles the planet runs for a reason. The post AI Distillation: How Frontier Models Teach Each Other #1870 appeared first on Geek News Central.

Have you wondered what’s behind the energy in the polygamy revisionist movement? Are these the same as RLDS polygamy arguments? Back in 2010, Newell Bringhurst wrote a chapter in “Persistence of Polygamy” on the evolution of RLDS thinking on Joseph Smith’s polygamy, how it got forgotten, and recycled among a new generation of LDS members who weren’t aware of the previous history. https://youtu.be/wD-ZHracUnA Are the modern arguments that Joseph Smith was a strict monogamist actually new? In this deep dive, we explore the “layered archive” of historical claims surrounding Joseph Smith's polygamy, revealing that many of today's “polygamy revisionist” arguments are actually recycled 19th-century RLDS positions. The presentation breaks down the 150-year evolution of the Reorganized Church of Jesus Christ of Latter Day Saints (now Community of Christ) through four distinct phases: The Early Concessions (1852–1860): Contrary to popular belief, early leaders like William Marks and Isaac Sheen did not initially deny Joseph Smith’s involvement. Instead, they claimed he had been involved in “spiritual wifery” but recognized his error and repented shortly before his death in Carthage. The Era of Absolute Denial (1860–1960): Under the leadership of Joseph Smith III, the strategy shifted toward establishing the absolute innocence of the founding prophet. This century-long “institutional mandate” framed D&C 132 as a “Brighamite forgery” and placed the blame for polygamy entirely on Brigham Young. The Academic Shockwave (1960s–1980s): The “dam broke” when mid-century historians like Robert Flanders and Lawrence Foster published research that eroded the bedrock of the church’s denial. This led to a painful institutional reckoning, eventually forcing the church to acknowledge Joseph Smith's foundational role in plural marriage. The Modern Revisionist Resurgence: Today, a new wave of LDS members—facing their own faith crises—are adopting these older RLDS arguments to resolve cognitive dissonance regarding the prophet’s moral legacy. Drawing on works like Newell Bringhurst's The Persistence of Polygamy and Robert Flanders' Nauvoo: Kingdom on the Mississippi, this presentation weighs revisionist claims against the preponderance of contemporaneous evidence. We also discuss the psychological toll of these shifting narratives and the ongoing battle to control the legacy of Joseph Smith. RLDS Polygamy Arguments Chapters 0:00 Introduction and the RLDS Foundation 07:53 Anatomy of a Narrative Shift 08:19 The Bedrock: 1852 to 1960 12:30 Early Concessions 16:42 Joseph Smith III and the Strategy Shift 21:09 D&C 132: Brighamite Forgery? 27:35 Historical Reckoning (1960s to Present) 51:12 Modern Skeptic Movement 1:26:44 Three Paradigms of Polygamy 1:29:17 Conclusion: The Enduring Battle for Legacy

In this episode, Joe returns to share how AI has changed his life... but first, his entrepreneurial journey from childhood to becoming a successful business owner, best selling author, and podcaster. He discusses the impact of books, podcasts, and mindset shifts on his success, as well as his experiences working abroad and leveraging AI.“You have to get out of the competitive mindset. There's enough opportunity in the world for everybody.”0:00–0:48 — Opening and guest intro: The host introduces Joe, his background, and the episode's focus.0:48–4:53 — Early entrepreneurial roots and CPA path: Joe shares how he became entrepreneurial as a kid and how his father pushed him toward accounting.4:53–11:05 — Mindset shift, books, and life direction: Joe explains how reading changed his thinking, helped him define his goals, and led to the Philippines.11:05–15:32 — Writing books, credibility, and his recommended book: He talks about how writing books helped his business and recommends From Myths to Money.15:32–17:34 — Support systems and historical entrepreneur choice: The conversation shifts to mentorship, resilience, and why Joe would choose Benjamin Franklin.17:34–19:49 — Podcasting, rebrand, and closing: Joe explains how podcasting supports business growth and shares the Bedrock 360 rebrand.“I believe that some of us are born to be entrepreneurs.”Other Takeaways*The influence of 'Think and Grow Rich' and 'The Science of Getting Rich'*Success often starts with mindset shifts and giving more value than you receive.*How working abroad and in AI transformed Joe's business*The importance of tenacity and support systems in entrepreneurship*Persistence and tenacity are crucial for overcoming entrepreneurial challenges.Enjoy his last appearance with us to follow his journey after this episode here!Send us Fan MailSupport the showRemember to subscribe for the next episode. Show Sponsor: ComingAlive PodcastProduction.com (Download your Podcast Launch Checklist for only $1 here)Music Credits: Copyright Free Music from Adventure by MusicbyAden.

Bill Gurley spent years on Wall Street, built his career as a partner at Benchmark, worked through Uber's hypergrowth era, and now serves on the board of the Santa Fe Institute, where he studies complexity and systems thinking. In this episode, Bill shares the mental models he returns to most, including systems thinking, second- and third-order effects, and the importance of understanding both the bedrock of your field and the bleeding edge. He explains what separates great founders, why storytelling and product instincts matter, how he uses AI across different models, and what he sees coming in open source, China, stablecoins, tokenization, payments, and venture capital.  ------ Timestamps: (00:00) Key Mental Models (02:02) Investing Journey and Key Players (05:21) Knowing the Bedrock of the Industry (08:50) Obsessive Learning in Founders (10:04) The Silent Edge (11:44) Surprising AI Use (13:13) The Future of AI Models (14:17) Global AI Regulation (18:12) Impacts of AI on Investing (19:53) Are There Limitations on Training AI Models? (23:04) Would You Sit in the Back Seat While Your Tesla Drives? (24:15) Non-Consensus Opinions (24:53) Are We Overfunding this Buildout? (29:40) The Role of Retail Investors and Tokenization (34:26) What is a Stablecoin? (37:58) Competitive Mode: Visa and Mastercard (39:55) AI and Debt Analysis (45:05) The Craft of Storytelling and Writing (48:07) Founder Advantage: Product Instinct (50:12) Real World Lessons from Working With Uber (52:10) Inside Benchmark's Success (59:42) What is Success for You? ------ Newsletter: The Brain Food newsletter delivers actionable insights and thoughtful ideas every Sunday. It takes 5 minutes to read, and it's completely free. Learn more and sign up at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠fs.blog/newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ------ Follow Shane Parrish: X: ⁠⁠⁠⁠⁠⁠https://x.com/shaneparrish⁠ Insta: ⁠https://www.instagram.com/farnamstreet/⁠ LinkedIn: ⁠https://www.linkedin.com/in/shane-parrish-050a2183/⁠ Follow Bill Gurley LinkedIn: https://www.linkedin.com/in/billgurley/ X: https://x.com/bgurley?lang=en Check out Runnin' Down a Dream: How to Thrive in a Career You Actually Love ------ Thank you to the sponsors for this episode: +CoinShares: Delivering Reason to Digital Asset Investing. ⁠https://coinshares.com/⁠ +Granola AI, The AI notepad for people in back-to-back meetings: https://www.granola.ai/shane Check out the Granola Notes +HeyGen is a message-first AI video platform that helps people and AI agents turn ideas into professional video in minutes. Try for free at https://www.heygen.com/ +LMNT: My go-to zero sugar electrolytes — get a free LMNT Sample Pack here: DrinkLMNT.com/TKP Learn more about your ad choices. Visit megaphone.fm/adchoices