Podcasts about V3

Guffy Wright is an elite insurance producer. He has a multi-million dollar (commission) Book of Business, but only after figuring out how to remove the hurdles of Friction, overcome Stagnation, achieve maximum Velocity, and then Accelerate month after month. In this episode host Charles Specht walks alongside Guffy as they discuss how producers can get out of their own way, find and dominate a profitable Micro-Niche, overcome the limiting beliefs, and accelerate toward Book growth with a steady velocity. Key Topics: Guffy Wright on writing Attach High after his father's passing Removing friction means attaching to the client's best interest Stagnation vs. cruise control: knowing the difference matters Why failure beats stagnation, since failure creates clarity Three types of friction: not clear, not safe, not aligned Trust gets you in the room, alignment moves the decision The V3 framework: value, vulnerability, and validation Micro-niching builds the expertise needed for real alignment Velocity: directional movement once friction is removed Stagnation cost Guffy seven figures, personally and professionally Reach out to  Guffy Wright Charles Specht Visit: Permission Network Chief Sales Officer Permission Producer School Produced by PodSquad.fm

TRACK LIST1. RISE - K. ALEXI SHELBY2. THE PRECHER - MR. V3. SO LONG - 2FOX FT. LIAM BAILEY & BB JAMES (MANOO RMX)4. MESSAGE IN OUR MUSIC- COREY HOLME FEA OJAYS5. BETCHA' - DJ MARK BRICKMAN6. WARNING - RHEMI FEA LYNN LOCKAMY 7. HEAD NOD - DJ ERV8. TAKE IT EASY - MIKE DUNN(BLACKBALL EZEE MIX)9. DANCE IS MY THERAPY - GARY ADAMS, COREY HOLMES LARRY LABIRT10. WAT IF (SHINO ZONED OUT PIECE)

Skyways has spent nearly a decade working on a problem most of the drone industry largely ignored: logistics. While billions poured into drones built to see targets or strike them, Skyways focused on something less flashy but arguably just as important: moving critical supplies without putting people in harm's way. That bet is starting to look increasingly prescient. The company has worked with the U.S. military since its earliest days and is now building its V3 aircraft to carry 100 pounds of payload up to 1,000 nautical miles. The longer-term ambition is much bigger: use defense to mature autonomous aviation, build a global logistics network, and eventually prove the technology required to carry people. Skyways' Chief Strategy Officer Isaac Roberts joins Valley of Depth from the company's Austin factory to unpack why autonomous logistics could become one of the biggest markets in drones, how the war in Ukraine has accelerated the entire industry, and why the FAA may be the biggest obstacle standing between today's cargo drones and autonomous aviation at scale. We also cover: Why logistics became Skyways' wedge into the Department of Defense How Skyways beat Boeing and Shield AI in military competitions Why the company believes defense is the proving ground, but commercial logistics is the much larger opportunity Why the FAA is holding back commercial drone adoption in the U.S. How Skyways plans to turn aircraft sales into a high-utilization “flight-as-a-service” network What separates real drone companies from the hype in aerospace • Chapters • 00:00 – Trailer 00:48 – Skyways factory floor 01:08 – America, French, and Texas flags 02:16 – The problem Skyways is trying to solve 03:49 – The logistics problem 09:05 – Companies Skyways competes with 11:45 – How Skyways beat out Boeing 13:18 – Will Skyways ever transport people? 15:56 – Why focus on cargo over kinetic systems? 18:35 – How has Ukraine changed the nature of drone warfare? 22:58 – Skyhook 25:34 – Where is the FAA a blocker? 28:40 – Defense contract traction 29:34 – Direct competitors in their contracts 30:28 – Is Isaac worried about Anduril competing with them? 34:06 – Current economics for the v3 36:00 – Hype and BS 37:52 – How to get to ground truth with tech 39:41 – Funding and next phase of growth 40:35 – How is Skyways structurally different than other defense companies? 41:52 – How Isaac got to Skyways 43:29 – Rocky moments at Skyways 44:27 – The FAA unlock for the industry 50:38 – What keeps Isaac up at night? 52:37 – What Mo and Isaac do for fun   • Show notes • Footage courtesy of Skyways Skyways' website — https://www.skyways.com/ Skyways' socials — https://x.com/skyways Mo's socials — https://x.com/itsmoislam Valley of Depth archive — Listen: https://pod.payloadspace.com/   • About us • Valley of Depth is a podcast about the technologies reshaping industry, national security, and the global balance of power. Brought to you by Arkaea Media, the team behind Payload, Tectonic, Ignition, and Decoding Bio, the show covers space, defense, energy, biotech, and the technologies shaping what comes next. We sit down with founders, investors, government officials, scientists, and military leaders to understand what they're building, why it matters, and where these industries are headed. From new companies and breakthrough technologies to capital, policy, and geopolitics, Valley of Depth goes deep on the forces shaping the future. Payload: www.payloadspace.com Tectonic: www.tectonicdefense.com Ignition: www.ignition-news.com Decoding Bio: www.decodingbio.com

Niet alleen buitenlandse webwinkels als AliExpress en Amazon kleuren buiten de lijntjes, ook het Nederlandse Bol scherpt zijn platform aan. De webwinkel heeft de Autoriteit Consument & Markt toezeggingen gedaan over duidelijkere prijzen, betere klantenservice en eerlijkere concurrentie tussen verkopers. Verder blijven Chinese techbedrijven nieuwe, efficiënte AI-modellen lanceren. Joe van Burik vertelt erover in deze Tech Update. De ACM stelde geen wetsovertreding vast, maar maakte zich na een breed onderzoek zorgen over de werking van het platform voor zowel consumenten als verkopers. Bol beloofde onder meer de prijsweergave aan te passen. Nu staat onder de daadwerkelijke prijs vaak een doorgestreepte 'vanaf'-prijs met een besparingspercentage, elementen die kopers over de streep moeten trekken. Daarnaast wordt de klantenservice aangepast: waar bezoekers nu snel naar de chatbot worden geleid, moeten de opties om te mailen of te bellen beter vindbaar worden. Ook wordt het makkelijker om illegale inhoud te melden. Eerlijkere concurrentie voor verkopers Voor zakelijke verkopers belooft Bol snellere communicatie en klachtenafhandeling en duidelijkere algemene voorwaarden. De concurrentie moet eerlijker worden: Bol past aan hoe het bepaalt welk aanbod als eerste prominent in beeld komt, het zogeheten koopblok, en zegt toe verzamelde platformdata niet te gebruiken om zichzelf als verkoper te bevoordelen. Van dat laatste is Amazon wel eens beschuldigd, hetzelfde Amazon dat vandaag als vijfde bedrijf ooit meer dan drie biljoen dollar waard werd. Het gaat vooralsnog om toezeggingen: het ontwerpbesluit ligt van 3 augustus tot en met 13 september ter inzage, waarna de ACM een definitief besluit neemt. Chinese modellen: minder krachtig, maar efficiënter en goedkoper In China verschijnt de laatste weken het ene na het andere AI-model. Anders dan de Amerikaanse modellen van OpenAI en Anthropic, die vooral hun kracht laten zien, zetten de Chinese bedrijven in op efficiëntie: hun modellen kosten minder energie en geld, en zijn vaak te downloaden en lokaal te draaien in plaats van via een duur abonnement. Het jongste nieuws komt van Alibaba en DeepSeek, met respectievelijk Qwen3.8-Max en V4-Flash, allebei zogenoemde open-weights-modellen waarvan techneuten deels kunnen inzien hoe ze reageren. DeepSeek zorgde anderhalf jaar terug met zijn V3-model voor een vergelijkbare schokgolf en wil naar verluidt naar de beurs. Alibaba staat daar al genoteerd en zag zijn aandeel vandaag met zo'n zeven procent stijgen, omdat het nieuwste model volgens een AI-vergelijkingssite alleen onderdoet voor Fable 5, het topmodel van Anthropic. Bol doet toezeggingen aan ACM voor betere bescherming consumenten en verkopers Bol en ACM maken afspraken over meer transparantie op het platform Amazon overschrijdt als vijfde bedrijf ooit de grens van drie biljoen dollar Alibaba presenteert open-weights-model Qwen3.8-Max met 2,4 biljoen parameters Chinese labs zetten met open weights en lagere prijzen druk op VS Over de maker:Joe van Burik volgt en duidt de belangrijkste ontwikkelingen in tech, met scherpte, vlotheid en de nodige humor. Je hoort hem dagelijks op BNR Nieuwsradio over het belangrijkste technieuws, van AI tot cybersecurity en social media tot quantumcomputers. Ook interviewt hij in De Grote Tech Show samen met Ben van der Burg leiders in digitale innovatie. In het bijzonder volgt Joe al twee decennia de wereld van videogames, nu voor zijn podcast All in the Game.See omnystudio.com/listener for privacy information.

There are roughly 100x more people who use code than who can write code. As code that “just works” becomes easier to generate, this group may be the biggest prize of all — if you can get the agentic interface right.A key trend we have been tracking over at AINews is the absolute explosion in Codex usage this year, with MAU now up >10x from Jan 2026. Less than two weeks after their July 9th launch, OpenAI said ChatGPT Work and Codex had reached 10M users combined (as we cover in the pod, Codex now powers ChatGPT Work, so all ChatGPT Work users are now users of the Codex harness, even if they aren't traditional engineers) — showing the early innings of what happens when you graduate from coding agents to knowledge work agents:We've been calling out how coding agents are “breaking containment” to do everything else this year to power every other part of knowledge work - and it started with the org chart, with a major reorg last month that amounted to two of Codex's most prominent leaders, Greg and Tibo, taking responsibility over product and ChatGPT specifically, completing a “Superapp” consolidation cycle first discussed in March.With these updates Codex is no longer just a coding tool. In June, OpenAI said knowledge workers already accounting for roughly 20% of Codex's user base and growing more than 3x as quickly as developers. A product dedicated for knowledge workers was being pulled out of the Codex team.However, knowledge work has a different set of problems and environments than coding. For decades, knowledge work has been scattered across different primitives like documents for writing, spreadsheets for analysis, slide decks for communication, and specialized applications for everything else. ChatGPT Work now enables users to work across every primitive with agents. Instead of opening an application and manually operating its features, the user can describe an outcome and collaborates with an agent that can assemble the tools, context, and artifact needed to reach it.From building no-code products at Airtable to leading Productivity Engineering at OpenAI, Akshay Nathan has spent much of his career trying to make the power of software accessible to people who do not write code. In this episode, Akshay joins swyx and Vibhu to unpack the launch of ChatGPT Work, why Codex unexpectedly took off among non-developers inside OpenAI, and the company's broader plan to bring useful agents from software engineers to knowledge workers and eventually everyone.We go deep on the shared agent harness behind Codex and ChatGPT Work, why OpenAI brought the experiences together without making them identical, and how persistent computers, artifacts, Sites, plugins, memory, and sub-agents are changing what people can delegate to AI. Akshay explains why some teams are replacing decks and spreadsheets with interactive websites, how agents can gather context across code, Slack, documents, and local files, and what OpenAI learned from personal-agent products like OpenClaw.Side note: also don't miss Abhihek's sandbox track keynote at AIE, which now powers a lot of the sandboxing for ChatGPT Work… and yes was also broken by an unreleased OpenAI model in the recent HuggingFace incident.Akshay also reflects on how AI is transforming product development itself: why more people will become generalists with a specialty, why ideas and taste become the bottlenecks when almost anyone can build, why LLMs still struggle to generate genuinely grounded new ideas, and why teams must distinguish increased motion from actual progress.We discuss:* Why Codex unexpectedly took off among non-developers inside OpenAI* Why employees felt like using Codex gave them a new superpower* The product insight that led OpenAI to build ChatGPT Work* Why Codex and ChatGPT Work share the same underlying agent harness* How their UX, Git visibility, artifacts, and sandboxing defaults differ* Why OpenAI merged its agent experiences instead of building separate products* How AI is blurring the boundaries between engineering, design, strategy, and operations* Why OpenAI wants the default model configuration to work for most users* When power users should use deeper reasoning, Ultra, or multi-agent modes* Artifacts, agentic spreadsheets, and creating high-fidelity work products* Why interactive Sites may replace decks and spreadsheets* The challenge of designing a simple interface for an agent that can build almost anything* Why users should retry tasks that models could not handle three or six months ago* How AI can gather context for performance reviews without replacing human judgment* The OpenAI automation that turns internal Slack and document activity into memes* What reaching ten million ChatGPT Work and Codex users means for the product* How OpenClaw inspired persistent environments, scheduled tasks, and personal agents* Using ChatGPT for financial planning, budgeting, workouts, meals, and household management* The design tradeoffs behind sub-agents and how much of their work users should see* ChatGPT memory, Chronicle, and long-term context* Why AI may make more people generalists with deep specialties* Why ideas and taste become more important when almost anyone can build* Why LLMs still struggle with the instruction “bring me new ideas”* Measuring productivity through quality at-bats instead of commits, tokens, or pull requests* The critical difference between AI-generated motion and meaningful progressAkshay Nathan* LinkedIn: https://www.linkedin.com/in/akshaynathan/* X: https://x.com/akshaynathan_Timestamps00:00:00 Introduction and Bringing the Power of Code to Everyone00:01:33 Joining OpenAI and Preserving a Startup Culture00:02:40 What OpenAI Learned from Enterprise AI Adoption00:05:28 Why OpenAI Built ChatGPT Work00:07:17 Codex vs. ChatGPT Work and the Shared Agent Harness00:12:07 Why OpenAI Merged Its Agent Experiences00:16:24 Models, Reasoning Levels, and Choosing the Right Default00:20:26 Artifacts, Agentic Spreadsheets, and Model–Product Collaboration00:24:22 Why Sites Could Replace Decks and Spreadsheets00:30:08 Designing an Agent That Can Build Almost Anything00:34:28 From Developer Agents to Knowledge Work—and Everyone00:36:07 Power-User Advice and AI-Assisted Performance Reviews00:40:41 OpenAI's Internal AI Memes and the Ten-Million-User Launch00:44:39 OpenClaw, Personal Agents, and ChatGPT as an Operating System00:50:24 Sub-Agents, Ultra Mode, and How Much Control Users Need00:54:39 ChatGPT Memory, Personalization, and Chronicle01:00:19 How AI Is Reshaping Product Development and Tech Roles01:03:15 Ideas, Taste, and Why LLMs Struggle to Generate New Ideas01:04:42 Measuring Productivity, Quality At-Bats, and Motion vs. ProgressTranscriptIntroduction: Akshay Nathan, ChatGPT Work, and the No-Code ArcSwyx [00:00:00]: We're here in the studio with Akshay from OpenAI. Welcome.Akshay Nathan [00:00:07]: Thank you.Swyx [00:00:08]: And with our trusty co-host, Vibhu. So you recently launched ChatGPT Work. You lead Core Product Engineering. It's been a long journey, into all this. I find it very interesting that you started with no code or low code, with Walrus and Airtable. And to some extent, ChatGPT Work is like the super app of super apps of, well, here is the ultimate no code. You just write a prompt.Akshay Nathan [00:00:32]: Yeah. It's funny how things come, full circle. I think for a long time in my career, I started my career working consumer fintech, but then after that, like, there's this hypothesis that, the things that we were able to do with code, like, as engineers, like, if we could bring that to many more people in a more, accessible way, then that would be truly magical. We were working on a startup. It's funny, like, before LLMs, before vision LLMs, on how to do automated testing with AI. It was just kinda jank, back then, but doing what we can, and then worked at Airtable for a while on the same thesis that, like, if we can bring a database or the primitives behind a database to people, that'd be really useful to them. But once LLMs came onto the scene, it became clear that, this was the missing piece, like, the missing technology required to, like, bring the magic of code to everyone without them having to know what's going on underneath the hood. And so, like, I think this launch and a lot of the stuff that we've been up to is, like, the manifestation of that.From Walrus and Airtable to OpenAIVibhu [00:01:33]: How was stuff when you joined? So you joined OpenAI 2023. Now we've got, so much more stuff, so ChatGPT, Codex app, ChatGPT Work. Have things changed?Joining OpenAI and What Hasn't ChangedAkshay Nathan [00:01:44]: I think the more interesting thing is how things haven't changed. Like, one, I joined I remember when I joined, it was, like, five hundred people. One thing I was worried about was, like, I was looking for something, more early stage and, like, was it gonna feel startup enough? And I joined, and I was like, “This feels even more startup-y than I could ever imagine.” And, like, that really hasn't changed even till now. I think the, like, level of, like, bottoms-up ambition and, like, the ability of anyone to, like, do anything or have an idea and ship it is really cool. But on the, like, mission side, I think what was really compelling to me is this mission of, bringing frontier intelligence to everyone. Like, building AGI and then bringing it to everyone. And, I think acknowledging back then that, like, that vision is gonna, not be a linear progression. Like, we're probably gonna, like, try different products and have different things that succeed and don't. But the vision has stayed the same, and the mission has stayed the same, and we're starting to see the pieces, fall together, and that's really cool.Enterprise Lessons: No One-Size-Fits-All AISwyx [00:02:40]: You worked on Enterprise. What A lot of people never touch ChatGPT Enterprise. What is something that you learned from there that you're bringing into your work now?Akshay Nathan [00:02:52]: I think how there's no one-size-fits-all solution in Enterprise. I remember in the early days of ChatGPT Enterprise, like, when we talked to customers and, like, everyone. That was, like, when I think it was a year after ChatGPT was released, and everyone was so excited to bring, AI into their enterprise. And, there were all these teams being stood up. It was, like, the AI deployment team with, like, these enormous budgets. And if you asked anyone, like, what were they excited about? Like, what were they excited about solving? Like, at first, you'd get, like, kinda like the baseline answers of, like, “Yeah, we have all this context and data and all this stuff.” But then if you ask them, like, “What was, like, a discrete use case that, like, they want AI to enable in their workplace?” You get such a different, like, variance, like, explosion of, different types of answers. And it's interesting, like, you using, like, these models and these products, you have this box, and you can say anything to it, which is the magic. But it'on the flip side, it also means that, like, you don't know what to do with it. And in Enterprise, I think a big part of that is, like, meeting the users where they are, like, what use case were they trying to solve, and then teaching them how they can use AI to, like, gain leverage there.Swyx [00:03:56]: Do you meaningfully differentiate that from forward-deployed engineering?Akshay Nathan [00:04:01]: I think there is the go-to-market side of it and then there is the product side of it. I think you need someone on the product side. And I think, like, however good we get at FDE motion, like, I think at the end of the day, if we have a user who's, like, looking at their computer or looking at their phone, like, it's our job in the product to, like, be enabling them and showing them where to go. So we're really excited about that.Vibhu [00:04:24]: Do you think there's been changes, over the past three years of adoption? So there have been, step function changes. You have reasoning models and whatnot. Is there still the same problems of Enterprise has black box, don't know what to do with it, or have things changed?Adoption, Agents, and the Next 10x MarketAkshay Nathan [00:04:39]: We're seeing now that, like, there's this huge uptake, right? Everyone is extremely excited about it. It feels like, many people are, millions, hundreds of millions of people are using ChatGPT. They understand, like, how generally to work with AI. But then, like, every time, like, a new capability gets unlocked, so now, like, we're seeing with agents, like, there is probably a contingent of, like, early adopters still who, truly get it, who are like, “ we you can do anything. You just have to make sure the right context is there, it's connected to the right tools, and that you are supervising it, but, like, anything is possible.” But then there's, like, this, like, 10x or 100x bigger market where, like, they don't yet get that, or they don't yet see that. And so I think that's the next stage here. So to answer your question, like, I think the adoption is there and growing fast, but I think the opportunity is, like, far bigger than that. That's where we wanna play, especially with ChatGPT Work.ChatGPT Work, Codex, and the Super App MergeSwyx [00:05:27]: Yeah. well, let's, let's skip ahead to ChatGPT Work. only, like, a month ago or so, announced. what was the decision process that led into it? there was this, overall merging of the super app. Is that what we're officially calling it? you deprecated the browser as well. Just, summarize your last, like, couple months of working on this thing.Akshay Nathan [00:05:50]: Yeah. It feels like forever now, but it's only been a few months. I think maybe the one, impetus that, like- Is most salient is when we release Codex, or even internally had Codex, like, it was really surprising to us, I think we recently put out some stats on this, that there was this, like, real inflection of, like, adoption among non-developers at OpenAI. And, I, through this product development process, like, would go to, like, these UXR sessions to talk to people internally. And the thing that stuck out to me is, like, one, like, you go talk to, like, strategic finance or marketing or whatever, and they're all using Codex for, their use cases. That part's cool, but the thing that really stuck out to me is how proud people were that they were using Codex. Like, how, likeSwyx [00:06:34]: It's like, “I'm not supposed to be using it, but I am.”Akshay Nathan [00:06:36]: It was that. It was, like, that they were, early to this, like, new thing, but it was also this thing of, like, they felt like they had a superpower, right? And, what we recognized then is that, like, the power of Codex, the power of agents, like, we already had this massive distribution base of people who have, come to know and love ChatGPT. Like, how do we show that to them? Like, how do we bring it to them? Which is, like, a hard product problem, and it's, like, a tricky thing, right? There's many ways you can go about it. And so that's what we called the Merge and the Super App over time, and ultimately launched it in ChatGPT Work, is how do we do that? But it came from that initial realization that, like, the power was not only for developers, like, much earlier than probably even we thought. Like, it could be extended to everyone.Swyx [00:07:17]: How do you see the products differently? So, like, who is it for, right? So Codex started out even CLI, then app. Now there's a merge of ChatGPT Codex and ChatGPT Work, so is it the opening for the average user, for enterprise, for work? How do you position it?Akshay Nathan [00:07:36]: I think we want to get it to position it for if you're doing work-related things, for lack of a better word, right?Who ChatGPT Work Is ForAkshay Nathan [00:07:42]: I think productivity is what, like, the pillar that I support. Like, that's the name of the team. And the reason for that, the reason we call it productivity and not, like, enterprise or, like, work or something like that, is because there's also personal productivity, right? And, like, I think ChatGPT Work is I've seen people do things in their personal lives that you wouldn't classify as, like, work technically, but, like, these agents are, super capable for. Like, one recent example that someone posted about, on our Slack is, like, someone had, like, a missed package, like they didn't receive it, and then they got, like, the picture of it, from Amazon or whoever the courier was, and they, like, asked ChatGPT Work to, like, find out where that package is. And, like, the agent, is extremely tenacious and, like, took the image and, like, looked at a bunch of, like, listings around their neighborhood and figured out exactly the apartment complex in which the package was, like, gave them some information. And so, like, I think there's all these things that, like, you, work-related or productivity-related things, I think that's what we want the product to be. You asked about Codex. I think we think Codex is, a durable brand, but we have a principle that, like, the user we don't want a user to get stuck in a tab or an experience where they don't get the power of the product. And so, like, everything that you can do, in the Codex portion of the product on desktop, you can do in ChatGPT Work and vice versa. But we made some opinionated product decisions on, like, how much of the Git state, if you're in a Git repo, do we wanna expose to the end user? Or how much do we wanna make the experience of seeing the agents thinking, like, diff forward so that you get exposed to the diffs out of the box. And then, like, on the safety side, like, how do we wanna think about, like, sandboxing and making sure that we have the right defaults in one state versus the other? So, there's, like, some opinions that go behind that, but we do want We don't want the user to need to choose which experience they're in.Swyx [00:09:26]: That is a good goal for AGI, right? Like, people don't want, like, to hide to choose what version of AGI they want. They just want the AGI to decide for them. can I get an answer or, like It's not super clear to me. Is the Codex harness and the ChatGPT Work harness the same? Is it just UI affordances, or are there prompt level or even deeper differences?Shared Harness, Different UX: Codex vs. WorkAkshay Nathan [00:09:49]: So the harness is the same. The harness is shared. on In both of the products, we made improvements to the harness to make it good for knowledge work, especially as it relates to plug-ins or computer use or artifacts. You get that power regardless of which experience you're in. On the UX side, there's opinionated takes that we have when you're in Codex mode, what the UX should be how the UX should behave, and some stuff around the sandbox like I mentioned, but the underlying harness and capabilities should be the same.Swyx [00:10:16]: I'm just kinda curious. Maybe we can, -- Is there a query that we can run that would look different in the two modes?Akshay Nathan [00:10:23]: Yeah. I tried to create, like ask it to create, like, a retirement calculator spreadsheet or something, in both modes. And then in Codex mode, you might have to be in a repo for this, but you'll see, like, the diffs of, like, the sheet that it's creating and stuff like that, and the file edits. But in Work you won't be able to see that.Swyx [00:10:42]: I think that's, that's super clear. And then also the other thing I wanted to dive into was your, the productivity team. what else is there? first of all, what are the top-level teams other than productivity? Isn't productivity everything?Productivity Teams and Core ChatAkshay Nathan [00:10:55]: SoSwyx [00:10:55]: Science?Akshay Nathan [00:10:55]: We have a team focused on ChatGPT. Like, the core chat experience, for consumer, which is like, not, I think all productivity. Like, there'People are using ChatGPT every day for search to, figure out how to write messages to loved ones, to think about, how to, like, learn a new topic, et cetera. And so there's so much more inside to create images. And there's so much more in chat that, the hundreds of millions of users are using that warrants, like, a very dedicated effort. And there's teams focused on enterprise and infrastructure and API and stuff like that, so.Swyx [00:11:33]: I will bring it up.Retirement Calculator Demo and Git-First UXSwyx [00:11:34]: Yeah. So I have them both running. This is ChatGPT Work. There's a Codex version here. I picked “Five Little Ducks” song, so this will take a while.Akshay Nathan [00:11:43]: Huh.Swyx [00:11:43]: I think we'll just keep it in the background and, as they finish, we'll look into some of the differences.Akshay Nathan [00:11:48]: Yeah. But immediately, I think if you flip back to the Codex version you'll see that,Swyx [00:11:53]: That it assumesAkshay Nathan [00:11:54]: Like theSwyx [00:11:54]: It assumes Git. Yeah. Yeah.Akshay Nathan [00:11:56]: The, like, dynamic island assumes that you're in a Git repo. And you might miss some stuff because some of it is, like, in the actual chain of thought with those changes and how we display that, but yeah.Swyx [00:12:07]: Is there an unintuitive like, is there a thing that you wanted to ship and then you got feedback, and you were like, “No, let's not do it?” Like, what's the thinking behind that?Why Merge the ExperiencesAkshay Nathan [00:12:14]: In, ChatGPT Work?Akshay Nathan [00:12:17]: I think one direction we could have gone with this is, like, keeping the experiences, like, completely separate. So it's like, whySwyx [00:12:22]: Different apps.Akshay Nathan [00:12:23]: Exactly, like different apps or even in the same app, like different, completely different experiences. Like, why merge it all? Like, what is. Codex, people love. Like, why bring these products together? And I think the intuition here is that, like, all of our jobs are, like, changing dramatically with AI. Like, for, like, every few months, like, I feel like I wake up, and I'm, like, doing a completely different thing than I was doing a few months ago. And my hypothesis here is that, or I should say our hypothesis is that, like, part of what we're, we're building, this technology is giving people leverage. Like, the things, maybe it's the more mundane parts of your job or parts that, like, if you were able to automate, you'd be able to share more ideas faster or whatever, like, you're able to do now. And because of that, like, that might blur the lines between someone who's, like, only writing code or creating strategy docs or, planning events or, helping with marketing or doing podcasts or whatever, right? And so, like, these things are gonna get blurred over time. And so, like, trying to draw a hard boundary based on, like, the who you are is gonna be, is gonna be tough. And, like, we should enable users to choose, but we shouldn't box them in. And so a lot of the work that went in here, like, keeping the primitives the same, like for example, plugins are, like, unified across, this product and ChatGPT and the cloud, was because of that. It's this thesis that, like, eventually things are gonna come together and we don't wanna be Like, we wanna be prescriptive about when to be in either experience, but we don't want to box anyone in.Swyx [00:13:45]: I wonder if there's users who are very tuned to the old ChatGPT harness that is effectively now replaced by the Codex harness. I can't imagine what that was, but maybe they're more the more conversational side. Can you compare and contrast the two harnesses? ‘Cause only you've seen it.Akshay Nathan [00:14:02]: Yeah. I think ChatGPT, the existing harness, like, still exists today. Like, it exists in this app,Harness Engineering: ChatGPT vs. CodexSwyx [00:14:08]: The classic, right?Akshay Nathan [00:14:09]: TheVibhu [00:14:09]: You just start a new chat, and you don't go under Work, right?Akshay Nathan [00:14:13]: Yeah. If you startVibhu [00:14:13]: SoAkshay Nathan [00:14:14]: A new chat and go to chat, then you're, you're talking to ChatGPT with the instant model.Vibhu [00:14:16]: Oh, we can technically do another. But on instant.Swyx [00:14:21]: Yeah. So this one's not gonna code or it's gonna be in line. It's on a in line in a sandbox.Akshay Nathan [00:14:26]: It'llVibhu [00:14:27]: Oh, that's coolAkshay Nathan [00:14:27]: We try to push you to go to Work if you're creating a spreadsheet. Yeah, but this isSwyx [00:14:30]: And this is a router decision? Sorry. Is it a router decision?Akshay Nathan [00:14:34]: This is the decision that, the model is making, and then, like it sees that you're able to. or you're trying to do something that would be better served in Work mode. But I think your question was like, what are the advantages of, like, the chat, like ChatGPT chat harness?Swyx [00:14:48]: It's more broadly, like, I wanna, do an oral history of harness engineering. Right? the ChatGPT harness lasted us from, let's call it the ‘01 era, until now, and now it's being replaced by the Codex harness effectively. And they're, they're overlapping somewhat, but I'm curious what changed if there is.Akshay Nathan [00:15:10]: My perspective on this is, like, there's, there's, there's there's like a constant process of, like, divergence, convergence, divergence, convergence. And in chat, like, many of the use cases I was talking about before, like, search or learning, I think we're, we're really optimizing for latency and optimizing for personality and, like, different things that, over time, like the product The reason people love ChatGPT is because we've been optimizing for those things and working on them for so long. Codex, what we learned was that, like, if you give the agent access to this infinitely flexible environment as a computer, it can do really powerful things. And so when we think about, like, okay, well, for knowledge work, like, what is which mode should we choose? It was like it felt more natural to us to bring that to this, like, computer environment and, maybe abstract some of the details of this computer away from users who might not be used to that, but, like, give them that same power. But ultimately, I think that we want the power in all places, right? We wanna meet people where they are. So I'm sure there'll be work down the road in order to get things to be, equivalently capable in all scenarios. But it's just a question of, like, what we've been focusing on the product on historically and what we're focusing on now.Models, Defaults, and the Reasoning SliderVibhu [00:16:24]: I think alongside that, outside of just harness and when to use Codex, ChatGPT, or Work, there's also the new models you've released, right? any guidance there? So people love to min-max what to use, like only use Terra on high reasoning versus, for this, you wanna use Sol here, ignore all theseAkshay Nathan [00:16:44]: There's 32 options.Vibhu [00:16:46]: But, that being said, for people that are expanding, so, productivity trying stuff for work that don't have the breakdown of what all this is what's, what's the advice, right?Akshay Nathan [00:16:59]: Well, I think before the advice, like the first thing is, like, none of this would be possible without these models. Like, the, I think you asked earlier, like, what was, like, the inspiration for work and, like, early on, like I mentioned, like, what we were seeing with Codex, but that was also because the models were getting infinitely more capable. That's happening again. I think it's like another step function jump now. And to answer the question on advice, like we want this default to be the best possible. Like, we wanna be opinionated about the default, and so we've we've chosen a default that we think is gonna be the best for everyone. And, we have for power users options under the hood. We could One could argue that there might be too many right now, and we're, working on simplifying it. But you can extend, the reasoning level, and you can change between the different model classes if you need to, but the default should be the best for most use cases. So my advice to most people would be to stick to that. And then, if you reach a situation in which you think that you could, you wanna try, a different configuration, if you're not seeing either the efficiency on the cost side or the quality on the intelligence side, then you can change the defaults and see if you can get something better. But we think that the default should be good enough.Swyx [00:18:09]: I have, I'm just gonna run something by you since you have way more experience than me. I've recently been doing Sol Lite but with goal, with the idea that the goal augments the reasoning effort, but with more terminations and turns.Swyx [00:18:24]: Is that a good way to think about it as opposed to Sol Ultra or Sol, Extra High?Akshay Nathan [00:18:29]: Yeah. It's hard to say becauseSwyx [00:18:31]: Yeah. It's like an interaction effect.Akshay Nathan [00:18:33]: exactly. It's like there's a preference on, for you as an individual, like how do you like to collaborate with the models? Like how many of those like terminations, as you call them, do you want where, you can steer or make sure that it's doing the right thing?Akshay Nathan [00:18:46]: I think generally people should try whatever works for them. I think that like using Ultra or the like multi-agent setups are best for like when you have like tasks that are either incredibly complicated, like open explorations or very paralyzable. I think even for tasks using goal, I think is best for tasks that you'll be able to make consistent progress in a way that's verifiable over time. But I think for most tasks, they don't fall into either of those buckets. And so like at least when they're starting, and so that's why I think the best first step is like trying it with the default configuration and then seeing like where you wanna go from there.Swyx [00:19:29]: Right. You guys worked on a slider, which is super helpful for reducing the amount of panic.Vibhu [00:19:36]: It's nice on mobile at least. There's a nice slider there.Swyx [00:19:38]: It's nicer.Vibhu [00:19:39]: I haven't tried it.Swyx [00:19:40]: So you have the advanced view there, but if you click advanced view. Yeah.Vibhu [00:19:44]: Ooh, it's just a nice slider. Yeah.Swyx [00:19:46]: Very pretty, very colorful.Akshay Nathan [00:19:48]: Yeah. The idea was here was like reduce it to like one dimension even though there's multiple dimensions, right? Try to project it onto a single dimension for the user. Like, something from that represents like, speed and efficiency on one side and then like quality and thoroughness on the other side.Artifacts, Spreadsheets, and the Work LaunchSwyx [00:20:04]: I am just puzzled that it uses Sol so much, like the lowerVibhu [00:20:07]: NoSwyx [00:20:07]: Grounds I would've usedVibhu [00:20:08]: I think the slider, if I'm not mistaken, isSwyx [00:20:09]: Terra.Vibhu [00:20:10]: Oh, it is.Swyx [00:20:11]: Yeah. See? So they preset Terra to only be the light one. But like I think a lot of people would more people should use Terra. One, because Sol keeps running out of capacity.Vibhu [00:20:22]: I'm the reason. Here's ten minutes of ourSwyx [00:20:24]: There you goVibhu [00:20:25]: Retirement calculator.Swyx [00:20:26]: Oh, that's the Excel thing working for you.Vibhu [00:20:28]: This is,Swyx [00:20:28]: Oh my God. Look at thatVibhu [00:20:28]: This is work, and then Codex is still cooking, so we'll get back into it. I think it'll be interesting to see the thought process, the reasoning, and also, this is eight minutes on work. Codex is still cooking.Swyx [00:20:41]: Yeah. And by the way, so I've, do Gabriel Chua? He's part of the OpenAI Singapore team. He showed me this, and I was like pretty shocked that this looks like Excel. It edits Excel files. You never paid an Excel license, right? Like, but somehow this is like workable and it's agentic Excel.Akshay Nathan [00:21:01]: Yeah. one of the big like pushes that we made for this launch was like artifacts, right?Akshay Nathan [00:21:05]: Like both on the model side, like I think if you compare this with GPT-5.5 and GPT-5.4 before that, you'll see that there's been pretty dramatic improvements in the quality of these artifacts and then also on the product side.Vibhu [00:21:16]: The UX side is also crazy, like hosted sites and whatnot. No longer needing to host your own little webpage, like itSwyx [00:21:23]: Oh, I have a story about that. I can do, a separate thing. I'll need to take the visuals here, but we-we'll, we'll cut to that later. Was there co-training, because you were moving making this big move and you launched GPT-5.6 on the same day as ChatGPT Work? Was there influence between the model training teams and the harness teams, or did they did the launch dates just happen to line up the same day?Akshay Nathan [00:21:46]: I think the we collaborate heavily with the research teams, and I think that's like one of the most magical parts of the job, like the most fun parts of the job. But yeah, just using artifacts as an example. Like, a lot of what you're seeing, like underneath the hood, there's a lot of work that went into making sure that like, we had the right infra to be able to train the models to get better at this. And then on the product side, like had the right experience for users to be able to collaborate with the model on an artifact like this. In fact, like this whole viewer, like the intuition here is that like, it's not necessarily that you wouldn't need an Excel license. This is stage one, right? Like, this is probably not what you meant when you're like making a retirement calculator.Vibhu [00:22:24]: Yeah, you can iterate very easily. Yeah.Akshay Nathan [00:22:24]: You wanna iterate and like when you're seeing it, and if this thing is high fidelity to like what you would see in or what your coworkers would see if you were to send this to Sean, like that I think makes it so easier and makes you trust the product in terms of iteration.Vibhu [00:22:39]: When you say coworkers would see, do you see a multiplayer, multi-team collaboration with artifacts? Any things you guys think about that?Multiplayer Artifacts and CollaborationSwyx [00:22:46]: You can already share it, right?Akshay Nathan [00:22:48]: Yeah. It's inter It's something that, we're actively thinking about. one thing that, we've noticed internally without talking too much about the roadmap is that like there's many times when someone will ping me about something, and I will ask ChatGPT Work the question, and then I'll ping them back the answer.Akshay Nathan [00:23:04]: And then I'll be thinking likeVibhu [00:23:04]: Like the simplest would be, the three of us are just all on one hosted.Akshay Nathan [00:23:07]: Exactly. And I'll think about like was I required in this loop or and then maybe it was, rephrase like what they were asking or pulled from certain context or whatever. But like, when I gave them back the answer, that process was also lossy, right? Like I gave them just like my interpretation of what ChatGPT Work cooked up. But like underneath the hood, there's so much context like in the rollout and stuff that could be interesting.Vibhu [00:23:28]: Yeah, it'sSwyx [00:23:28]: So like the answer was preemptively respond to every inbound request?Akshay Nathan [00:23:33]: No, it was just like literally like this is what I do sometimes as my job.Swyx [00:23:36]: I know you copy-paste and then you're just a message forwarding serviceAkshay Nathan [00:23:39]: Yeah. Yeah, exactlySwyx [00:23:39]: From AI to AI.Vibhu [00:23:40]: But I think it's interesting, right? It helps people understand the capability of what you can ask and delegate that oftentimes people don't realize until they try or someone shows you, and then you're like, “Oh, okay. Okay, I see.”Swyx [00:23:52]: I think it's als there's also like a, light security issue, where like you're the permissions layer. Like yes, I could query everything that you query, and I could get an automated response, but maybe I'm not supposed to see it. And that there's no way I would know because I'm not supposed to know what I don't know.Akshay Nathan [00:24:07]: Especially as like, with ChatGPT Work, we're, we're asking you to connect your plug-ins and, it's pulling from your local files and stuff like that. Like the amount of context that the agent has access to is like- Deeply personal and like that's something I think we need to preserve, so that'll be definitely a challenge.Swyx [00:24:22]: There's Excel, there's PowerPoint, there's Docs, the, grand trio of work. What other formats of work do you think about? like you worked on Airtable. Is there a future where there's like OpenAI Airtable? Like what does that look like if you ever ended up doing it?Akshay Nathan [00:24:41]: It's a really good question. I think,Formats of Work: Sites as Knowledge ArtifactsAkshay Nathan [00:24:43]: one that you didn't bring up was Sites, and I think that wasSwyx [00:24:46]: SitesAkshay Nathan [00:24:46]: A core part of this launch. There's one side of Sites that I think people commonly talk about, especially on Twitter and stuff or X, of like, this like prototyping tool. And like we saw that happen with this launch even. The model slider that you guys were referencing earlier, like that was developed almost fully in a Site. Like, the collaboration between design and engineering and product on that was like on a site where we play with, the affordance and figure out how it feels and all of that. But the other aspect that I think is a little bit less talked about is like Sites as like an artifact for knowledge work. I was talking to someone the other day who's on like our corporate finance team, and like we were mentioning how like now when they have these reports that they're, they're working on as a team month to month, historically those things were in slide decks and in spreadsheets, and now they're just in Sites. And like Sites is the mechanism that they collaborate across the team. And the reason is ‘cause it's like, it's like somewhat higher bandwidth. Like, at these tools like PowerPoint and Excel are like infinitely flexible, but at some point you reach the boundary of like either as a human you may not know how to use some feature or something, or the product itself doesn't support it. But with a site you can do anything. You ask for anything and you can get that. once people see that magic, I think it's been really valuable.Swyx [00:26:02]: Yeah, let me show you my case study. this involves all the hot topics including ChatGPT Work, but also GPT-5.6 token billionaires and token maxing and Sites and auto research. I'm a fan of this game called Strata. It's, it's like a little board game that youSites, Auto Research, and Research DashboardsSwyx [00:26:17]: That you play with, physical blocks, that come on top of it like that. So over the weekend I took like thirty photos and just threw into ChatGPT. one point seven billion tokens later, out comes this site with a fully playable thingAkshay Nathan [00:26:32]: WowSwyx [00:26:32]: With 3D, block placement and everything. Because it requires physical blocks and I needed friends to train on it so they can get better, so I can play against them. But also, I could also, do things like train an AI on it and that's, thatAkshay Nathan [00:26:45]: That's your auto researchSwyx [00:26:46]: That gets into auto research. So, you want to train your own AIs, and then make sure they self-play against, each other. I need to set both AIs. So this is AI versus AI, and they're, they're gonna self-play. the AIs start out bad and then you want to define a loss function and get good. I wasn't gonna supervise all this. I was at, I was down in San Mateo, attending a conference. What I ended up doing was, auto researching and on this and creating benchmarks and that there was just way too many parameters for me to read. So I started asking it for a site, and it's created this lab, panel. Where is there a, is there a shortcut for a site that is created?Akshay Nathan [00:27:28]: You should be able to go in the sidebar to Sites, top of the sidebar. The left sidebar.Swyx [00:27:33]: This one? Oh, left?Akshay Nathan [00:27:35]: Yeah. Just scroll all the way to the top.Swyx [00:27:36]: Oh. Oh, it says Sites. Oh, there you go. Yeah.Akshay Nathan [00:27:39]: Ooh.Swyx [00:27:40]: So it create, it creates the sites. I don't, I don't think this is, it is exactly what I wanted, but let me show you what it popped up, right? Like I think as a research artifact, it is very important to communicate, exactly, what is being done. Outputs this thing which I eventually started publishing. So I moved it off of Sites because I wanted more, database and infrastructure than Sites afforded me. But this is like a research output that you can start to mess with and like try to think about like what hyperparameters are you tuning for training AIs. And like I was trying to make like scaling laws and everything and doing all sorts of like game optimization stuff. And the fact that you can just throw this up as a research artifact, like I no longer need to read ChatGPT output. I read Site output. But then there's also a huge sprawl. Like look at how long this thing is. There's so many numbers. It is pretty overwhelming, so then I have to start pruning it from there. But, it's an interesting transition from Markdown effectively that you're putting out to, you're putting out a whole functional site.Akshay Nathan [00:28:41]: I think Markdown just isn't that optimal for people to read, right? Might as well just write HTML website and I don't know. I think you can do a lot with customizing this, right? You have your skills that explain what you want. Like I noticed they're quite verbose. I don't need a lot of this information.Swyx [00:28:57]: It's very verbose.Akshay Nathan [00:28:58]: So and then the nice thing of having a site side by side is, you just iterate on what you want and what you don't, right?Swyx [00:29:05]: Yeah. I don't know if, any that triggers any stories for you of how it's run internally. Am I doing this right?Akshay Nathan [00:29:11]: Yeah. I think that this is like a workflow that we're seeing like all different types of teams use, where like the canonical artifact that was previously a deck or something is now becoming a site. And like with a site you, because it's just HTML, you can like. It's infinitely flexible. And so, if you want to give more prominence to a certain thing that like in a slide deck would, feel like it was buried, like you can do that. You can have it be like the hero image, right? And so I think that like, people are starting to see that. There's more work to be done to make these things like much more easier, easy to collaborate on. You mentioned that they're very, they're long and verbose, could be broken up. I'm sure that there's still something to do there.Swyx [00:29:53]: They're super long. Yeah.Akshay Nathan [00:29:54]: Yeah. But I think we're starting to see that like there is this aspect of this is a really interesting, format, for people to use, that's like much more flexible than what they ever had before.Swyx [00:30:07]: I think your job also comes becomes meta. You're not designing the products. You're designing a product to make products, and I'm curious how you manage that.Designing a Product That Makes ProductsAkshay Nathan [00:30:18]: I think one thing that we've been Like when we look at the UX, like that we've been thinking a lot about is how can we balance like simplicity with capability? Like if we're designing a product, like you said, that like is made to make up build other things, right? You can build so many different things. But we can't put that all in front of you because you'll get overwhelmed.Vibhu [00:30:41]: Yes.Akshay Nathan [00:30:41]: And so we had similar problem or similar challenges even Chat-with ChatGPT, but especially now, like when there's so much that can be done, I think the balance that we're constantly trying to strike is like, how can we give the user enough of a UI surface where, they can be expressive, they can tell the agent what they need, they can verify that it's using the right tools, it's pulling from the right sources, et cetera, but then it gets out of the way. And then how can we build the right system such that we can show them instead of telling them what can be done? Because so much of this is gonna be like, how do they discover the next use case and the next one after that if they really want to be super powered by the AI.Games, Private Evals, and Show-Don'TellVibhu [00:31:19]: Yeah. It's interesting. I feel like everyone also just has a different way to do it, right? I made a similar version of this same game. I didn't take any pictures of board or rule game. I threw in at goal eighteen minutes, fifty-three seconds later, a lot of tokens later, I've got a similar version. not with all the auto research and whatnot, butAkshay Nathan [00:31:39]: You gotta do all the latest trends.Vibhu [00:31:40]: And yeah, I did it with, did it with Codex, not Work, but it's interesting, right?Akshay Nathan [00:31:45]: Yeah. And this is GPT Image generating the pro avatars. Very good for game design. LikeVibhu [00:31:51]: AndAkshay Nathan [00:31:52]: A lot of game designers were like really into GPT Image for assets.Vibhu [00:31:54]: I will say like the broader takeaway probably is the reason that we do this is more so just to test the tools, right? Like, this was also a test for GPT-5.6 came out. I had done the game on GPT-5.5, right? The ability for me to no longer need it to. I had to feed it the rules. It's, it's a pretty niche game. It couldn't find how to do this on its own.Akshay Nathan [00:32:15]: Oh, yeah.Vibhu [00:32:15]: GPT-5.6Akshay Nathan [00:32:16]: It is out-of-distribution, which is why I was also very keen on testing the GPT-5.6 capability.Vibhu [00:32:21]: But, this is just as work comes out, as new things come out, these are just our side ways to test things, right?Akshay Nathan [00:32:27]: Yeah. It's some private eval. That is not this private.Vibhu [00:32:31]: But also valuable because now you can send this to your friends and I learned about this game through seeing this.Akshay Nathan [00:32:36]: It's a hard game. He's very good.Vibhu [00:32:39]: It's good to when no one is competing with you. But yes, it's a classic RL problem of like self-play, bootstrapping your game AI. yeah, you see how easily work becomes personal and personal becomes work because the thing I do for personal, it directly informs people I work with because I showed it to them. They were like, “Oh, you can do that with GPT?” Which like I imagine is the growth strategy.Akshay Nathan [00:33:02]: Yeah. The show not tell is a big piece that, I think we've we're not still not fully cracked of like, showing people all the things that they can do with the product versus like trying to teach that to them through like, articles or onboarding or whatever.Akshay Nathan [00:33:18]: So meeting them in the moment.Vibhu [00:33:19]: It's a career risk for me, because I used to be in developer relations, right? Where your job is to show, and then you're like, “What do you mean? You don't, you don't need.” your job is to tell. And then. But the product people are like, “Well, we don't need you if our product is intuitive enough.” SoAkshay Nathan [00:33:37]: Yeah. that's the magic of the models. So you can tailor the telling or the showing to like specifically what the user needs, like what they care about, what they've done in the past, exactly where they are on the adoption journey. So I think that's like gonna be a super big opportunity.Vibhu [00:33:50]: Seems easier and easier now to tailor custom showing, right? People have different use cases. As much as you said you don't wanna segment different people into different buckets, right? It's also not that hard to for people that are in different categories. But the question, is you said your team is more broadly on. What was the term you used? Productivity?From Developers to Knowledge Work to EveryoneAkshay Nathan [00:34:12]: Productivity.Vibhu [00:34:12]: Productivity. So howAkshay Nathan [00:34:12]: Which is now work.Vibhu [00:34:14]: Is it work? Is there another distribution that we're not hitting? Is there a group of people that will have something different than ChatGPT, Codex or Work? Is there more that the mass isn't targeting?Akshay Nathan [00:34:28]: I see it as like a sequencing, like. The vision is like bring useful agents to everyone. We started with like developers. Like developers historically are like early adopters that are willing to put up with more friction, set things up, et cetera. Like that's where, Codex started. I think the next opportunity is like what we call general knowledge work, all the other functions around developers. I think when you go from developers to this segment, like there's inherent challenges with like, this show not tell thing that we're talking about, making the product more understandable, bringing in new capabilities that matter more for this cohort than matter for developers, things like artifacts, things like computer use, et cetera. And then I think like the same learnings, like similarly how we took the learnings from developers and brought it to, general knowledge work, the next stage will be like taking the learnings from general knowledge work and bringing it to everyone no matter what they're doing in their lives. And we're already seeing that a little bit. Like this game example that you have is, something that's like on the border of like fun and personal life to, your professional life. I use ChatGPT Work full-time at home for everything, like for whatever I'm doing. I used it the other day to come up with a meal plan and like, save that on the like computer environment that it has and something that I can continue going back to. Like is everyone doing that yet? Probably not because the thing says work on it, but eventually, we wanna get people there.Vibhu [00:35:51]: ChatGPT life.Akshay Nathan [00:35:52]: Yeah, exactly. ChatGPT cooking. But I think there's a lot of, there's a lot of opportunity there, but I see it as like, we're, we're built we built a foundation in software engineering, and we're gonna take the same learnings that we take from software engineering to knowledge work to everyone.Vibhu [00:36:07]: Do you have any power user advice? I feel like, there's a group of people that will live it, use it for everything, stay on it twenty four-seven. And then there's a bit of a gap between that crew and people that, okay, I use it for work. I use it occasionally. Sometimes I type questions. any advice, any learnings, anything you recommend or just, takeaways that you've found that help bridge that gap?Power User Advice: Push the Frontier of ImaginationAkshay Nathan [00:36:30]: I think a couple things that I've seen is like, one, that it really helps to broaden your imagination of what's possible, and this has been a learning even for me. Like, the technology has progressed so fast that, something that, like, even three months ago, like, no way the models can do this. Like, now it's like, wow, it's like it can. Like,Swyx [00:36:52]: Give an exampleAkshay Nathan [00:36:52]: We're going through right now our, like, review cycle internally, and, people always talked about this as, like, a thing that the models are good at and like, there's a cliché of like: Okay, like, no one wants to be writing reviews and, like, we just use AI to do it. But in all seriousnessSwyx [00:37:09]: And it can evaluate it as well.Akshay Nathan [00:37:10]: Yeah, exactly. In all seriousness, before it was, like, just, like, slop and, like, I think it was helpful, but, not super productive. Now I've found that, like, the model can do a much better job than me, especially in this environment of, like, pulling context on, like, what people are up to, how they've like the things that they've done to make a difference, highlighting like, wins that they've had that, like, I might may not even have seen. It has access to, like, everything, right? Like the code, like, things that they've caught, reviews, Slack, everything. And so it's, like, incredibly powerful in that domain and, like, just like six months ago, the last time we did this cycle, like, I didn't even I tried using it, but it was not at all helpful. And this time it's been, like, incredibly helpful and, like, so I think continuing to push the frontier of imagination of what's possible, even if you tried something before, I think is maybe the my biggest piece of advice. The other, thing is, like, the more you put in, especially in this environment where, like, the model has access to everything on your computer or in ChatGPT Work, like you can create, artifacts over time and save them in your library and, like, the model will continue having access to those. Like, the more information you give it about whatever domain you're in, whether it's your life or your work, the more valuable it becomes, and it'll become valuable in, like, ways that might surprise you. Like, it might pull from context in a way that, may be proactive and that you might not even have thought about. But it needs to have access to those, to that those tools or that context first.Reviews, Agentic Search, and Context GatheringSwyx [00:38:27]: One thing I just wanna talk about the review stuff because I'm still that's a very sensitive thing and you're, you're a founder, you've managed people, you've hired people. As manager myself, I'm very reticent to put out any LLM-generated things especially when it comes to people, ‘cause it feels like you don't care.Swyx [00:38:46]: Presumably at OpenAI, people are more open to being eval rated by GPT. But are there any unofficial rules around this? Like, what's the etiquette?Akshay Nathan [00:38:57]: Oh, I think the etiquette is that, like, I would never write something via, like, well, solely via AI and, like, present it as, like, a review for someone. What I was talking about is more, like, gathering context. That's the place where it's incredibly helpful.Swyx [00:39:08]: So it's just search.Akshay Nathan [00:39:09]: Yeah, exactly.Swyx [00:39:09]: It's agentic search. Yeah.Akshay Nathan [00:39:10]: It's like agentic search, but, that you can tailor and steer much more capably than you could before, ‘cause, like, the thing is it's all there's a flywheel happening, right? Because of Codex, people are able to do, and because of ChatGPT, people are able to do so much more now than ever before. And if you're able to do so much more, it's easy to miss things as well. And so, like, I think we need to use these same tools to keep up with all the impact that people are having and understand, where we can be helpful.Swyx [00:39:39]: I think the thing, like, I run a small company, so easy to search, but at the scale of OpenAI with the amount of messages that you guys put in Slack, do you think that it misses things?Remembering What Humans MissAkshay Nathan [00:39:50]: Probably, but I think that I also miss things.Swyx [00:39:52]: Like, it doesn't matter, right?Vibhu [00:39:53]: I think sometimes it'sSwyx [00:39:53]: Like it's, as it needs to be human-levelAkshay Nathan [00:39:54]: It's all relative, right? Yeah.Vibhu [00:39:56]: Sometimes it's nice when it finds things you wouldn't, right? Like right now, my Codex system prompts, they're set up in such a way that every project I have has a secret- separate, notes MD, and it just writes learnings to there. And then the global one can pull from all these. So sometimes it'll be like: Oh, there's this project you did like four months ago. Here's a note that we had, and it randomly pulls it back into context that I would never do, I haven't thought about.Vibhu [00:40:20]: And I'm like, okay, this is quite superhuman, right? Like, stuff that would. And, it'll save like hours on chunking of stuff or find something that's already been done. I'm like, as much as it might miss stuff, I would too, but it's very useful when it finds stuff. And I have like a very, non-super engineered solution to this. It's just marked down files that get pulled whenever they want.Akshay Nathan [00:40:41]: Yeah. I have a funny anecdote about this. Like, recently gearing up to this launch, the team has been, really cooking on it for a couple months, and over that time, like there's so much conversation and chatter going on in Slack and Docs and elsewhere. And, one of the members of the team set up this, scheduled tasks, like automation to like look at everything that's going on and, like, come up with the best memes and then post it in one of our shared channels. And like, there are two cool things about this. Like, the first is, like, I think the models are, over time, like starting to become like funny.Swyx [00:41:13]: Funny. Nice.Akshay Nathan [00:41:13]: Whereas like, a year ago, like that was not at all the case. The second is, it was what you were saying, like they find things that in surprising ways that you may not have thought of and like create connections that you may not have thought of. And that really helps with like the meme generation because then you can see something that, genuinely surprises you and, is funny in that way. So yeah, that's like not like the most productive, use of this the technology, but it does it does uncover this, like this capability that's emerging, which is just like to find information that you otherwise would not know of.Launch Momentum and the 10 Million User MilestoneSwyx [00:41:43]: Talking about the launch, I think, I have pretty much said this is the most successful launch in a long time. I think even more successful personally than 5.0, and they're announcing ten million users. Does it feel different? You've been through a lot of launches.Akshay Nathan [00:41:58]: I think it feels like a culmination. Well, I think two things. One, it feels like a culmination, like I was mentioning earlier, like this like vision mission that we've been on for a long time. Like I said, we saw the magic of Codex internally, and then we're like extremely excited to bring this to many more people and to see it working, to like see us reach, the distribution goal, numbers that you mentioned, like I think that's like huge and super exciting. The flip side of that is like, there's so much more to do too. Like, that's also really exciting. Like, ChatGPT as a whole, like the this product that, everyone almost equates to AI and like loves, has hundreds of millions of users. And so like ten million is really cool, but like we need to get this to everyone. Like, we need everyone to feel this magic. And so that's the next step from here. But yeah, I think extremely pumped about how it's going so far and the opportunities.Swyx [00:42:46]: Awesome. I did want to also Because I've, I've, I've been tracking the number closely, it transitioned at some point from just Codex users to Codex plus ChatGPT Work, because they're same harness. The whole point is that you don't, you can't, count them separately. Do you have roughly a billion, ChatGPT users? Why did it just jump to one billion right away? Like, isn't that the default on ChatGPT or no?Codex, ChatGPT Work, and the Developer BrandAkshay Nathan [00:43:11]: We don't default you into ChatGPT Work if you're on ChatGPTSwyx [00:43:14]: If you're free. YeahAkshay Nathan [00:43:15]: It's also only available to paid users right now. And I think there's like a process of, educating users of what is the value of this product, having them try it, learning from their feedback, and making it better over time. But the goal is to, get as many of the people who love ChatGPT today to like feel the power of ChatGPT Work. But I think it'll be a journey.Swyx [00:43:36]: Yeah. And Codex will still be alive as a brand for the foreseeable future. And we'll just toggle between them as needed for UI stuff.Akshay Nathan [00:43:44]: Yeah, I think it's even stronger point than that. Like, I think we fully intend to like, treat developer. Like, developers have been, a core market for us for so long, and like there's, there's so much more that we can do to make Codex great specifically for, software development, and we'll continue to do that. This doesn't take away from that at all. If anything, it should increase the utility of something like Codex, because now you can move seamlessly between writing a diff to creating an artifact or, doing a search over your factor.Swyx [00:44:11]: I do wonder how much this terminology leaks to the non-technical user. Like, do they have to learn to say artifact if I want artifact? Or.Akshay Nathan [00:44:20]: It's funny, like we call it artifacts internally ‘cause that's what the teams call it.Swyx [00:44:23]: It's nice. Yeah.Akshay Nathan [00:44:23]: But like externally, like no one says that, no one calls it an artifact. But I think that people like often, like describe things, whatever they're used to, right? So if, ChatGPT Work is good at creating slides, they'll say ChatGPT Work is good at creating slides, and that's what we want.OpenClaw, Personal OS, and Persistent ComputersSwyx [00:44:38]: One big Another, it's July of twenty-six. One big thing that also happens in, for OpenAI was OpenClaw, and that's I think a lot of people's first time really maxing a agent for personal stuff, but also crossing over to work in essence same way. As far as I understand, OpenClaw is still independent, but did you go through your own OpenClaw moments? Were there any lessons you took from OpenClaw to Codex or back? Whatever.Akshay Nathan [00:45:06]: I think there's a lot of inspiration. I did go through my own OpenClaw moment. I,Swyx [00:45:10]: Yeah, tell the storyAkshay Nathan [00:45:10]: Me and my wife like set up an OpenClaw to like try to manage everything in our house. Not that there's like a ton, but it was like quite useful. We gave it a calendar. It started, creating events for us and stuff. At some point, the laptop that we were running on, it died and never got a chance to pick it back up. But there was a lot of inspiration there, like, in ChatGPT Work, in web and mobile, like you get access to this like persistent computer environment where, you can store files, and those files stay around between sessions. And the idea is to be able to enable use cases like this. one of the members of our team uses ChatGPT Work for what they used OpenClaw from before, and then feel like it has like completely transitioned, which is like, workout planning and like meal tracking. which again, it's like a work-related thing, right? It's like not work necessarily, but it's like in personal productivity space. But it has all the same primitives. So it has scheduled tasks. It has the ability to store files on a file system. It has the ability to like reference those things over time. And so you start to see the same types of use cases emerge, which has been really cool.Swyx [00:46:14]: Is there a point that ChatGPT Work completely replaces OpenClaw? they're independent, so.Akshay Nathan [00:46:20]: Yeah, I'm, I'm not close to it, so I can't speak to the OpenClaw roadmap, but I don't think so. I think that there's gonna be, there's always a need for like this like incredible, like open source technology that team has built. And I think that we can draw inspiration, in the product and, ChatGPT, I think many more people have like heard about and used ChatGPT than have used OpenClaw. And if we can take the magic from OpenClaw and bring it to them, I think that'll be a success. I think that like one thing on the ChatGPT Work side that we feel strongly about is that like the core experience is that you come to this product and you have a conversation, start a session, whatever you wanna call it, with this agent. And the magic of the product is that you can do anything in that moment. And we would like to create a product where you don't have to click a button or to go to a different place, whatever, and you can get whatever functionality exists in, your finances app or where or any other product like in this one place. And so that's the goal. It's like it we want an extensible system with plugins where you can connect to the tools that you need in order to be able to accomplish like a financial task, where you can, if you're doing like science work, like we have an ability to like extend the system in such that you can like write the tech and it performs well. There'll always be like products that we support that are best in class at those things, but we want as much of the magic as possible in that core experience.Swyx [00:47:45]: Yeah. Do you think that you can do everything you used to do with Wealthfront in ChatGPT Finance?Finance, Data Access, and Centralized ContextAkshay Nathan [00:47:50]: I tried it. like ChatGPT doesn't yet custody, cash and assets for me. So that part, no, not yet. But I, there was like a whole component of like retirement planning and, like financial planning and budgeting and stuff that, we were looking into when I was there. And like with the finances plugin, like that's all possible with ChatGPT today. So, I feel

En este programa hacemos un repaso a algunas noticias de la actualidad commodoriana y a los lanzamientos de las últimas semanas, y vemos la revista Commodore Computing International Vol.6 N.12 de 1988. Todo esto lo veremos con el equipo habitual formado por David Asenjo (https://twitter.com/darro99), Toni Bianchetti (https://twitter.com/seuck), Narciso Quintana "Narcisound" (https://twitter.com/narcisound), Jonatan Jiménez (https://twitter.com/jsabreman) y Paco Herrera (https://twitter.com/pacoblog64). Las noticias comentadas son: - Noticias del Callback de Commodore: https://commodore.net/why-a-flip-phone/; https://www.generationamiga.com/2026/07/04/after-the-laughter-callback-8020-nears-10000-units-and-proves-critics-wrong/ - Primer vistazo al TheA1200 y nuevos datos de Retro Games Limited: https://www.youtube.com/watch?v=yNTpvojtI0g ; https://www.youtube.com/watch?v=EfYy-Mg6QFU&t=38s ; https://amigatronics.com/2026/07/11/the-a1200-retro-games-despeja-dudas-frecuentes/ - Actualización de la disputa entre Hyperion y Amiga Corporation: https://www.amiga-news.de/en/news/AN-2026-07-00060-EN.html - Preorder del Apidya Special disponible.: https://iningames.com/products/apidya-special-special-edition-playstation-5-limited - Amigame Jam 9: https://itch.io/jam/amigamejam - Core de Amiga 500 para MEGA65: https://github.com/sy2002/AExp - Conmutador de columnas 40/80 para PET/CBM 8296: http://cbmsteve.ca/8296-4080/index.html - Cartucho IDUN para C64/128: https://github.com/idun-project/idun-cartridge - Disponible la planimetría de la fuente de alimentación del Amiga 2000: https://github.com/andreamazzai/A2000_Rev4_PSU - Nueva política de envíos a la UE de la Royal Mail (RU), las aduanas se pagan por adelantado. - Banda sonora de Return to Blacktooth a la venta: https://thalamusdigital.itch.io/return-to-blacktooth-album Actualizaciones: - Commodore PET D64 Image Maker v1.2: https://milasoft64.itch.io/commodore-pet-d64-image-maker - Sevgi Engine v0.194: https://github.com/alpyre/Sevgi_Engine - AmigaGPT v3: https://github.com/sacredbanana/AmigaGPT - Amiberry v8.2.2: https://amiberry.com - RedPill v.0.9.71: https://aminet.net/package/dev/misc/REDPILLGameCreator - Cartridge programming tool V3.2: https://martin-piper.itch.io/c64-cartridge-programming-tool-v32 Los juegos y programas nuevos comentados son: - Mort & Phil (Amiga Factory, Amiga): https://amiga-factory.itch.io/mortandphil - Infec2tation (Monte Boyd, C64): https://monteboyd.itch.io/infe2tation - Raiden (Imagitec Design, Amiga): https://www.gamesthatwerent.com/2026/06/raiden/ - Commando (jotd666, Amiga): https://jotd666.itch.io/commando - Galamiga (Mainake Project, Amiga): https://aminet.net/package/game/shoot/galamiga - Jet Set Willy (Kweepa, VIC20): https://sleepingelephant.com/ipw-web/bulletin/bb/viewtopic.php?t=11574 - Ouroboros (Aardvark Soup, C64): https://aardvark-soup.itch.io/ouroboros64 - Rock-Ballons (orac81, C64/C16/VIC-20): https://orac81.itch.io/rock-balloons - Penultimate Fantasy (Endurion, C64): https://endurion.itch.io/penultimate-fantasy-c64-cartridge - Sabre Wulf (gekka, VIC20): https://www.sleepingelephant.com/ipw-web/bulletin/bb/viewtopic.php?t=11578 - Ninjanimal (Titus, Amiga, C64): https://www.gamesthatwerent.com/2026/07/ninjanimal/ - Daimyo (Windigo Productions, C64): https://windigoproductions.itch.io/daimyo - Comchinko (dmortalwombat, C64): https://drmortalwombat.itch.io/comchinko - Cromo Shock (Christian Cubillos, C64): https://cristiancubillos.itch.io/cromo-shock - Space Station Elysium (descarga en cuarentena) (EmperoR Studios, C64): https://emperorstudios.itch.io/elysium - Princess Amelia (BARKAMI, C64): https://barkami.itch.io/princess-amelia - Disco Volante (Megastyle, C64 (Windows)): https://megastyle.itch.io/disco-volante - Atic Atac (gekka, VIC20): https://sleepingelephant.com/ipw-web/bulletin/bb/viewtopic.php?t=11547 ; https://sleepingelephant.com/ipw-web/bulletin/bb/viewtopic.php?p=126461#p126461 - Rust'n'Steel (natthrafn, C64): https://natthrafn.itch.io/rustnsteel-reboot - Ramizes (tukinem, Amiga): https://tukinem.itch.io/ramizes-amiga - Castle Smash (Milasoft, C64/PET): https://milasoft64.itch.io/castle-smash - SERK (Emmonks, C64): https://emmonks.itch.io/serk - Owarix (orac81, PET, VIC-20, plus/4): https://www.lemon64.com/forum/viewtopic.php?t=89674 - Graphic Designer (lifeschool@lemonamiga, Amiga): https://lifeschool22.itch.io/graphic-designer-amiga - Rally X reloaded (beamrider, VIC20): https://sleepingelephant.com/ipw-web/bulletin/bb/viewtopic.php?t=11573 - Runaway (cronosoft, VIC20): https://cronosoft.fwscart.com/product/vic-3kram-runaway - Gravity Snake (Naufr4g0, C64): https://naufr4g0.itch.io/gravity-snake-c64 - Hang the DJ (Moonknight313, C64): https://moonknight313.itch.io/hang-the-dj - LightTreeFM (dsavioni, Amiga): https://dsavioni.itch.io/lighttreefm - The Pawn Guild of Thieves Myth EasyFlash (EmperoR Studios, C64): https://emperorstudios.itch.io/the-pawn-guild-of-thieves-myth-easyflash - CoverMaker: Easy Cassette J-Card Creator (funkyretro, Güindous): https://flunky-retro.itch.io/covermaker - Cyber-Run: Project Black Ice (Christian Cubillos, C64): https://cristiancubillos.itch.io/cyber-run - AmiWeatherForecasts16 (emarti, Murat Ozdemir, Amiga): https://aminet.net/package/comm/misc/AmiWeatherForeCasts16 - Busted Boulders (Richard Goedeken, C128): https://demozoo.org/productions/390765/ - Teletext (fputmango, C64U/C64): https://github.com/fputmango/Teletext64U/tree/main - Holiday (Dan Waddington, Amiga): https://aminet.net/package/game/role/Holiday

Astronomy Daily S05E139 — Monday, 13 July 2026   Starship could fly again as soon as Wednesday — carrying its first-ever real payload. Japan quietly joins the reusable rocket club just a day after China. SpaceX asks regulators for a jaw-dropping 100,000 satellites. Physicists may have heard the accumulated whispers of every star that ever exploded. Isar Aerospace signs a $150 million deal to launch from Canada. And a ravenous black hole just 1.8 billion light-years away is giving astronomers — including teams from CSIRO and the University of Sydney — a window into the dawn of time.   In this episode •         Starship Flight 13: Booster 20's 33-engine static fire complete; launch NET Wednesday (AEST); first deployment of 20 Starlink V3 satellites; in-space Raptor relight and Indian Ocean splashdown planned. •         JAXA's RV-X reusable rocket completes its first hop at Noshiro — about 40 seconds, 10–11 metres up, landing upright — one day after China's Long March 10B sea recovery. •         SpaceX files with the FCC for a 100,000-satellite Gen3 constellation in very low Earth orbit, pitched at multi-gigabit AI-era connectivity — and entirely dependent on Starship. •         Super-Kamiokande reports the first indication of the Diffuse Supernova Neutrino Background from nearly 5,000 days of data — the accumulated neutrinos of every core-collapse supernova in cosmic history. •         Isar Aerospace signs a 10-year deal with Maritime Launch Services for a dedicated complex at Spaceport Nova Scotia — first orbital launches targeted for 2028, up to 40 per year by 2029. •         Galaxy SDSS J110546.07+145202.4: a lightweight, ferociously fast-growing black hole behaving like the early universe's titans, shining 20-fold brighter in radio for eight-plus years — with CSIRO's ATCA among the follow-up telescopes. •         Skywatching: New Moon Tuesday evening (7:44pm AEST / 9:44pm NZST); prime Milky Way core viewing; Venus brilliant in the west; Mars near Aldebaran pre-dawn; Comet 10P/Tempel 2 favours southern observers.   Sources & further reading •         Space.com — Starship Flight 13 static fire & launch outlook; Starlink Gen3 filing; Isar/MLS deal; ravenous black hole; supernova neutrino whispers •         AP / Japan Times / RTÉ — JAXA RV-X first test flight •         Max Planck Institute for Radio Astronomy press release; Komossa et al., The Astrophysical Journal (2026) •         Tohoku University / phys.org — Super-Kamiokande DSNB indication (Neutrino 2026 conference) •         CBC / The Globe and Mail / SpaceQ — Isar Aerospace & Maritime Launch Services agreement details •         NASA JPL What's Up July 2026; EarthSky; Space.com night sky guide — skywatchingBecome a supporter of this podcast: https://www.spreaker.com/podcast/astronomy-daily-the-latest-space-news--5648921/support.Sponsor Details:Ensure your online privacy by using NordVPN. To get our special listener deal and save a lot of money, visit www.bitesz.com/nordvpn. You'll be glad you did!Become a supporter of Astronomy Daily by joining our Supporters Club. Commercial free episodes daily are only a click way... Click HereThis episode includes AI-generated content.

Are you looking for a reliable trail shoe that can handle technical terrain without feeling like a sponge? In today's video, we dive deep into the Dynafit Ultra 100 V3 to see how this German-engineered shoe performs after 50 miles on the trail.

Empezamos con la noticia del nuevo KYMCO CV-X45 y parece que, cuando venga, será un rival justo en medio de los Zontes 368G, Honda ADV350 y el Honda X-ADV. ¿Qué os parecen estos scooter “trail”? La noticia deportiva del fin de semana fueron las 8 Horas de Suzuka, una carrera que todavía es quizás la más importante en Japón. Este año ha sido un poco pasada por agua en todos los sentidos… pero Honda logró su quinta victoria consecutiva, y uno de sus pilotos Takumi Takahashi conseguía su ¡octava! y quinta consecutiva… Por cierto, en las 8H Honda suele aprovechar para desvelar algún modelo “clave”, antiguamente lo hizo con sus deportivas, pero… no, no vimos la V3 “turbo”. Sí la CB400 Super Four con e-clutch que parece será barata, y la dejaban probar a la gente sobre rodillos. La que sí han confirmado que seguirá es la Kawasaki ZX-25R, una deportiva como le gustan a Mariano… La semana pasada contamos la celebración del 80 aniversario de la Vespa en Roma… y este fin de semana le tocó a Ducati por su centenario con el famoso “World Ducati Weekend” (fin de semana mundial de Ducati). Hubo carrera pero esta vez Marc no tiró a nadie para subir al podio… y Bulega se vengó arrasando. Este episodio está patrocinado por Yamaha y su renovada gama YZ de motocross 2027. La gran protagonista es la nueva YZ 250 F, una moto completamente rediseñada con importantes mejoras en el motor, el chasis y las suspensiones… para ofrecer más rendimiento, mayor control y una conducción aún más eficaz en competición. Junto a ella, Yamaha también ha actualizado modelos como la YZ 85 y ha renovado toda su familia de competición off-road. Si quieres conocer todos los detalles de la nueva gama YZ 2027, visita la web yamaha. Recuerda que puedes enviarnos todas tus dudas o sugerencias al correo electrónico redaccion@moto1pro.com o bien dejar tus comentarios en Ivoox, Youube o en la red de podcast que utilices.

In this episode, Ray Cochrane unpacks how two colliding black holes revealed a whirlpool in spacetime, a direct detection of frame dragging hidden in the cleanest gravitational-wave signal ever recorded. Additional stories cover the James Webb Space Telescope, counting 16.5 million stars in the Cigar Galaxy, SpaceX rolling out Starship V3, deadly back-to-back earthquakes in Venezuela, GitHub fighting a California law that could break open source, and Meta engineering a battery narrow enough to live in a pair of glasses. – Want to start a podcast? It’s 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 personal update before the night’s lead story. He recently graduated with a Bachelor of Science in Computer Science from Portland State University, celebrated with family in town, and launched a new site at rayc.world. That site links to a final-project study he built on collaborative filtering using podcasting data, hosted at cohort.rayc.world and drawn from OP3 analytics. He also plans to return to the show’s classic twice-weekly cadence on Mondays and Thursdays. From there, he goes deep on a new black hole discovery, then pivots through space, earth science, climate, biotech, open source, cloud infrastructure, and consumer hardware. Colliding Black Holes Reveal a Whirlpool in Spacetime Two black holes spiraled together, merged, and sent a gravitational wave rippling across the universe. Researcher Neil Lu and colleagues at the Australian National University found the fingerprint of frame dragging buried in GW250114, the cleanest signal LIGO has ever recorded. Frame dragging means a spinning black hole drags spacetime around with it, like a spoon turning in honey, except the honey is reality itself. Remarkably, the wave changed the distance between your nose and your ear as it passed, by far less than the width of a single atom. Sponsor: GoDaddy Economy hosting is $6.99/month, WordPress hosting is $12.99/month, and domains are $11.99. Website builder trial available. Use codes at geeknewscentral.com/godaddy to support the show. Webb Counts the Stars in the Cigar Galaxy NASA released a striking new James Webb Space Telescope view of Messier 82, the edge-on galaxy nicknamed the Cigar Galaxy. Because Webb sees in infrared, it peers straight through the dust that normally hides the galaxy’s interior. Combined with archival Hubble data, the image resolves roughly 16.5 million individual stars. M82 is a starburst galaxy, meaning it forms stars at a furious rate, a frenzy likely triggered when it merged with a neighbor. SpaceX Rolls Out Starship V3 SpaceX officially introduced Starship V3, the third generation of the largest rocket ever built. The vehicle now flies on the Raptor 3 engine, pushing liftoff thrust to around 20 million pounds and making it the most powerful rocket ever flown. More importantly, V3 is designed to carry over 100 metric tons to low Earth orbit while staying fully reusable, roughly triple the previous version. SpaceX also added in-orbit refueling hardware, the capability that finally makes operational Moon and Mars missions realistic. The Asteroid Barrage That Kept Earth From Forming Continents A team led by Curtin University and the Queensland University of Technology argues that relentless asteroid impacts shaped the very young Earth. During the Hadean, more than four billion years ago, the planet was struck far more often than it is today. Each impact dumped heat deep into the interior, repeatedly melting and reworking the crust. Consequently, stable continents formed much later than calmer models assumed, painting a picture of a hotter, weaker, more chaotic early Earth. Back-to-Back Earthquakes Devastate Northern Venezuela Northern Venezuela was struck by two major earthquakes on June 24, a magnitude 7.2 foreshock followed by a magnitude 7.5 mainshock. Both hit only about six miles underground, so the shallow shaking delivered its full force at the surface. Tragically, at least 164 people died, and the region sits along the tangled boundary where the Caribbean and South American plates grind past each other. These were the largest quakes to hit the area since a magnitude 7.7 event near Caracas in 1900. The ‘Guerrilla Solar’ Era Has Arrived A quiet energy shift, nicknamed “guerrilla solar,” is spreading across Europe. These small plug-in panels deliver power to a home’s wiring via a standard wall outlet, with no electrician or permit required. Germany now counts roughly a million of these systems. However, the U.S. payoff remains modest, with savings estimates of around $15 per month against a $500 to $1,500 setup cost. Why a Broken-Up Forest Stores Less Carbon Researchers quantified what foresters long suspected: an intact forest stores far more carbon than the same acreage split into fragments. A hectare inside a large, continuous forest proved about 38 percent more productive than an isolated one. The culprit is edge effects, the extra wind, heat, and direct sun that stress trees at a forest’s boundary. Because a large forest maintains a large protected core while fragments are nearly all edge, planting trees together matters for carbon storage. Edited Human Embryos Reveal a Surprise Researchers used base editing, a precise cousin of CRISPR that rewrites a single DNA letter without cutting the strand, in human embryos. They discovered that a protein called NANOG plays a role in early human development that it does not play in mice. In humans, switching it off still let cells form that seed the placenta and yolk sac. The finding argues that understanding human development requires studying human embryos directly, which reignites a thorny ethical debate. GitHub Fights a California Law That Could Break Open Source GitHub joined Black Forest Labs, Hugging Face, and Mozilla to push for fixes to California’s AI Transparency Act. As written, the bill could force revocation of an open-source license when a downstream user fails to meet certain obligations, which clashes with the permanent, irrevocable promise of open source. Cochrane pointed to curl and its longtime maintainer, Daniel Stenberg, warning that the rule could destabilize the supply chain on which the whole tech world runs. Instead, the coalition points to the EU’s AI Act transparency code as a saner model. Rust Opens Its Maintainers Fund The Rust Foundation launched a Maintainers Fund to pay the people who keep the language’s ecosystem healthy. Backed by RFC 3931, it establishes a funding team and a new Maintainer-in-Residence program for the often thankless work on the compiler, standard library, Cargo, and Clippy. Individuals can donate through GitHub Sponsors, while companies can sponsor there or contact the foundation directly. Cochrane urged any business that depends on open source to invest in the projects it actually uses. AWS Gives Lambda Its Own Isolated Sandboxes AWS introduced MicroVMs inside Lambda, its serverless platform. Each session runs in a dedicated micro virtual machine with no shared kernel and up to eight hours of total runtime. The feature exists for the AI era, in which applications increasingly run code written by an AI agent rather than by the developer. Use cases include AI coding assistants, data analytics platforms, vulnerability scanners, and game servers running user-supplied scripts. Meta Engineers a Battery Narrow Enough for Glasses Meta built custom steel-can battery cells as narrow as seven millimeters to fit the temple arms of smart glasses like the Ray-Ban Meta and Oakley Meta Vanguards. These cells power cameras, speakers, and AI features in a space most engineers would call impossible. To prevent brownouts, Meta swapped wound electrodes for precisely die-cut stacked layers that lower electrical resistance. Now the company is spreading the technology across multiple vendors and eyeing other wearables. Polestar Gets Locked Out of the US Market Starting in 2027, Polestar will not be able to sell its new models in the United States. A federal Connected Vehicle Rule bars cars containing certain Chinese or Russian software or hardware on national security grounds. The painful irony is that Polestar moved production of the Polestar 3 to South Carolina specifically to dodge tariffs on Chinese-built EVs. Because the rule targets the technology’s origin rather than its assembly location, the company is shut out anyway. Retroid’s Pocket Nova Packs Serious Power for $229 Retroid returned with a new retro handheld, the Pocket Nova, starting at $229 with a step-up model around $269. It features a 4.5-inch AMOLED screen in a 4:3 aspect ratio, a shape well suited to classic games. On paper, it should handle GameCube- and PlayStation 2-era titles, though that remains an early expectation rather than a benchmarked promise. Retroid has earned a strong reputation for high-quality, genuinely portable consoles. Cochrane signs off with the usual ecosystem mentions: GNC Insider at geeknewscentral.com/insider, the show newsletter, email at geeknews@gmail.com, and modern podcast app recommendations at podcastapps.com. The post Colliding Black Holes Reveal a Whirlpool in Spacetime #1866 appeared first on Geek News Central.

Jonny and Phil unpack Warlord's new Armies of the Commonwealth book, a standalone V3 supplement covering Australia, Canada, East Africa Command, India, New Zealand and South Africa. They explain how each nation's special rules and unique units work, and how the generic Commonwealth roster ties it all together. Highlights include Australian infiltrators and fanatical defenders, Canadian stubbornness and vehicle upgrades, East Africa's local-area tactics and camel riders, India's Gurkha and mountain options, New Zealand's steadfast infantry and Maoris, plus South Africa's vehicle bonuses. The hosts discuss playstyles, modeling tips and tournament implications in a breezy, tactical overview. Want to support the channel? Why not use one of our affiliate links: Warlord Games: https://r.warlordgames.com/aff/?TABLETOPTOMMIES Firestorm Games: https://www.firestormgames.co.uk/wargames-miniatures/bolt-action?aff=64a025ee621f1 Wayland Games: https://affiliates.waylandgames.co.uk/1240.html Warlord Games: https://r.warlordgames.com/aff/?TABLETOPTOMMIES   You can also support our endeavour to produce Bolt Action content on Patreon: https://www.patreon.com/TabletopTommies Or you can support these two mugs by buying a fancy mug: https://tabletoptommies.co.uk/collection/new/

IPO price. $135Retail. Process. Allocation. ConfirmationGavin Baker on 4th largest cloud ahead of Oracle. Jensen likes to give GPu's to people that can use themBrad Gerstner on how “smart” people lose money. Price target lower by $20.

In this episode of LAB the Podcast, Zach sits down with V3's LAB Initiative Director, Christina Kruse, to discuss the ongoing fight against human trafficking and how the LAB Initiative is helping bring hope, awareness, and tangible support to survivors. Together, they reflect on the unveiling of the first Freedom Is Beautiful commissioned artwork, the growing partnership with the SAFE Alliance Tampa Bay, and the impact of Freedom Roast Coffee in funding anti-trafficking efforts throughout Tampa Bay.  Christina shares stories from survivors who encountered the artwork, explains how beauty can help restore the human spirit, and highlights the practical ways individuals, churches, and businesses can join the movement. From murals and merchandise to coffee subscriptions and community partnerships, this conversation explores how ordinary choices can create extraordinary impact.If you've ever wondered how art, faith, and justice intersect, this episode offers a compelling vision of what it means to lead with beauty in a broken world.Thank you for joining the conversation and embodying the life and beauty of the gospel. Don't forget to like, subscribe, and follow LAB the Podcast. Learn more about the LAB Initiative: https://vuvivo.com/lab-initiativeOrder Freedom Roast: https://www.buddybrew.com/products/life-beauty-blend-lab-podcast-x-bbc-collaborationSupport / Sponsor: https://vuvivo.com/supportFor More Videos, Subscribe: @VUVIVOV3 | https://www.youtube.com/@VUVIVOV3Follow: @labthepodcast | @vuvivo_v3 | @zachjelliott | @buddybrewcoffee Like:  https://www.facebook.com/vuvivo.v3#LABPodcast #FreedomIsBeautiful #HumanTraffickingAwareness #LeadWithBeauty #VUVIVO #BuddyBrew #FreedomRoast #SAFEAlliance #FaithAndCulture #HumanDignity #ChristianPodcast #TampaBay #ArtForGood #EndHumanTrafficking #CommonGoodSupport the show

On the KMOJ Morning Show, Malik Rucker, founding Executive Director of V3 Sports, joined Freddie Bell for the "Community Builders Series" to discuss his North Minneapolis roots and the experiences that inspired his mission to invest in the community through health, wellness, and opportunity. A fifth-generation Northsider, Rucker reflected on the vision behind V3 Sports and how the organization has grown from a one-person operation into a major nonprofit serving families through fitness, aquatics, youth programming, and education. He explained that V3's mission extends far beyond athletics, focusing on building healthier communities, creating access, and strengthening economic empowerment through intentional partnerships with Northside businesses. Rucker also shared the pride he feels seeing the completed first phase of the V3 Center in action and looked ahead to Phase II, which will bring an Olympic-sized pool and expanded programming to further serve the community. 

Send us Fan MailThis message centers on the profound truth that the mystery of God's redemptive plan—formerly hidden but now revealed—is the unity of Jew and Gentile in one body, the Church, through faith in Christ. Drawing from Ephesians 3, it emphasizes that this inclusion was not part of the Old Testament revelation but was made known by the Holy Spirit to the apostles and prophets, fulfilling God's promise to Abraham that all nations would be blessed through his seed, Jesus Christ. The preacher underscores Paul's identity as a prisoner of Christ for the Gentiles, not as a victim of circumstance, but as one divinely positioned to proclaim this inclusive gospel, demonstrating that God uses every situation—especially suffering and imprisonment—for His greater purpose. The message calls believers to embrace their identity in Christ, recognizing that salvation, adoption, and spiritual blessings are gifts of grace, not earned by works, and that true maturity comes through enduring trials with faith, allowing patience to have its perfect work. Ultimately, the sermon calls for humility, gratitude, and worship, reminding listeners that all glory belongs to God, who empowers believers through Christ to live faithfully and share the gospel with all peoples.Eph 3:1 For this reason I, Paul, the prisoner of Christ Jesus for you Gentiles-- 2 if indeed you have heard of the dispensation of the grace of God which was given to me for you, 3 how that by revelation He made known to me the mystery (as I have briefly written already, 4 by which, when you read, you may understand my knowledge in the mystery of Christ), 5 which in other ages was not made known to the sons of men, as it has now been revealed by the Spirit to His holy apostles and prophets: 6 that the Gentiles should be fellow heirs, of the same body, and partakers of His promise in Christ through the gospel, 7 of which I became a minister according to the gift of the grace of God given to me by the effective working of His power. A.   A MYSTERY ONCE HIDDEN, NOW REVEALED (1-7)1. Interrupting himself, Paul makes mention of His StatusHow Did it Effect Paul v1,2,7V1 Paul was a Prisoner of Christ 2Ti 1:11 to which I was appointed a preacher, an apostle, and a teacher of the Gentiles.12 For this reason I also suffer these things; nevertheless I am not ashamed, for I know whom I have believed and am persuaded that He is able to keep what I have committed to Him until that Day.Ac 9:10-16A.   V2 Paul was a Steward of the Mystery- He was entrusted with a great taskB.   V3-5 The Mystery is that Jews and Gentiles would be joined in the ChurchHow Did it Effect the GentilesA.   V6 A new relationship1)    Fellow Heirs of the Inheritance…sums up Eph 3:11-222)    of the same body 3)    and partakers of His promise in Christ through the gospel,Gen 12:3 I will bless those who bless you, And I will curse him who curses you; And in you all the families of the earth shall be blessed."Galatians 3:28-29 There is neither Jew nor Greek, there is neither slave nor free, there is neither male nor female; for you are all one in Christ Jesus. 29 And if you are Christ's, then you are Abraham's seed, and heirs according to the promise.A.   V7 New PowerPaul's role as a minister to the Gentiles of this "mystery" was a gift from God (7)         a. A gift of God's grace         b. A gift given to him by the effective working of God's power To Live the Christian Life – Overcome Sin, suffering and temptation to be able to walk in righteousness

SpaceTime Series 29 Episode 66 *Starship undertakes its 12th test flight The world's largest and most powerful rocket, the SpaceX super heavy Starship has undertaken its 12th test flight with mixed results. *Massive rocket explosion at Cape Canaveral Blue Origin's latest New Glenn rocket has exploded in a spectacular ball of flame and fire during a static hot fire test at the Cape Canaveral Space Force base in Florida. *How Earth recycles the continents A new study claims Earth's crust and mantle have been mixing together for billions of years continuously reworking the planet's continents deep beneath the surface. *The Science Report A new study shows that dentists have been drilling teeth to treat cavities for almost 60,000 years. Warnings that even moderate increases in temperatures heightens the likelihood of koala deaths. One in six kids now experiencing some form of online sexual exploitation and abuse. Alex on Tech: Rokid's new smart glasses.Become a supporter of this podcast: https://www.spreaker.com/podcast/spacetime-with-stuart-gary--2458531/support.

The Space Show Presents Open Lines Discussion Today, Sunday, 5-3-26Quick Summary:This meeting focused on open discussion topics in space exploration and national security. Bob shared speculation about a potential SpaceX acquisition of 200+ square miles of land in Louisiana for data centers and manufacturing facilities, though this remained unconfirmed. The group extensively discussed the Artemis 3 mission delay, with participants debating the challenges of SLS rocket assembly versus SpaceX's Starship development approach. Ajay raised significant concerns about Russia's nuclear-powered missile program, specifically the Burevestnik missile tested in October 2025, which he described as difficult to detect and potentially dangerous. The conversation also touched on nuclear power applications for data centers and military bases, with Dr. Ajay mentioning new small modular reactor companies emerging in the market. The discussion concluded with debate about defense strategies against such nuclear capabilities and the current state of hypersonic weapons development.Detailed Summary:Bob discussed a speculative story about SpaceX potentially acquiring a 200-square-mile piece of land in Louisiana, which could be used for data centers, satellite manufacturing, and Starship production. He noted that this would allow SpaceX to shift operations away from California. The conversation concluded with a mention of Artemis 3's delay and a brief reference to Robert's recent article about the potential Louisiana land acquisition.David announced that Robert would be scheduled for a show on May 26th at 6 PM, and discussed upcoming shows including Dr. Eligar Sadeh returning on Tuesday to discuss Astropolitics journal reviewing opportunities. The group briefly discussed unconfirmed news about Elon Musk's salary and potential Mars colonization plans, though Bob repeated that much of this information was speculative. David also mentioned upcoming shows including an ISDC episode with Rod Pyle and Aggi Kobrin on May 12th.Bob shared unconfirmed rumors that SpaceX may be acquiring approximately 136,000 acres of coastal Louisiana marshland near Pecan Island for potential data centers and manufacturing facilities. The discussion explored the strategic benefits of this location, including proximity to intercoastal waterways, power infrastructure, and natural gas facilities, though participants noted concerns about launch debris dispersion and local community impact. The group acknowledged this was speculative information pending official confirmation from SpaceX.The group discussed the delay of the Artemis III mission, with Bob explaining that both Blue Origin and SpaceX requested additional time to prepare their landers for an Earth-orbiting test mission. Robert noted that this delay would impact the scheduling of subsequent Artemis missions in 2028, as SLS rockets can only be assembled one at a time using a single mobile launcher. The discussion compared SLS and Starship assembly processes, with Joe highlighting how SLS involves numerous complex steps due to its design requirements, while Starship's assembly is more streamlined. Bob concluded that Jared Isaacman's goal is to demonstrate SLS's limitations over the next two years, potentially paving the way for Starship and New Glenn rockets to replace SLS in the future.The group discussed the competitive dynamics between SLS and Starship programs, with different perspectives on NASA's intentions. Phil and Joe had a different view, suggesting NASA believed SLS could beat Starship if it increased production rates faster. The discussion also covered technical aspects of Starship's design, with Ajay raising concerns about the high dry weight requiring multiple refueling trips to the moon, while Marshall and others highlighted the importance of SpaceX's new launch facilities in enabling frequent launches.The group discussed different approaches to refueling a lunar mission depot, with Ajay presenting a plan involving expendable tankers while Phil and Bob described a reusable tanker concept aligned with SpaceX's philosophy. Ajay cited NASA and Aerospace Corporation analyses suggesting 10-16 refueling launches would be needed with expendable tankers, though the group noted these estimates were based on V2 configurations rather than the more efficient V3. Bob defended SpaceX's approach, emphasizing the company's focus on reusability and rapid launch capabilities, while acknowledging that current payload limitations might require temporary use of expendable vehicles if development timelines don't meet requirements by mid-2027.The group discussed SpaceX's Starship program and its potential, with Ajay cautioning against extrapolating success from Falcon 9 to other projects. David interrupted the Starship-focused discussion to broaden the conversation, particularly wanting Ajay to share insights about a new Russian nuclear-powered missile system that can fly at low altitudes and evade detection. Ajay explained that this missile system, demonstrated on October 21, poses a significant threat as it cannot be detected by current defense systems and could potentially remain airborne for extended periods. When asked about countermeasures, Ajay indicated he had provided suggestions to defense departments but could not share details in the open forum.Ajay discussed his work on hypersonic and nuclear power applications, highlighting his experience since 1990 and recent developments in nuclear power plants. He mentioned new companies like ILO Atomics and Astra working on 10-megawatt power plants for data centers, which could be factory-built within a year. Ajay also shared his conversations with senators about the Burevestnik missile and his meeting with Jared at Mar-a-Lago, where he inquired about the Falcon Heavy idea. Marshall raised concerns about the time required for permits for nuclear power plants, to which Ajay responded that recent executive orders have reduced the timeline to 3-6 months.The discussion focused on nuclear power applications, particularly small modular reactors and micro-reactors. Ajay explained his work on a 25-megawatt thermal power plant design and discussed the military's micro-reactor program, noting that molten salt reactors would be more suitable than pressurized water reactors for energy applications. The conversation also addressed hypersonic missile technology, with Ajay clarifying that current U.S. hypersonic programs use rocket-boosted systems with limited range, distinguishable from the nuclear-powered hypersonic missiles discussed in the context of Russian weapons. John Hunt suggested that developing such nuclear-powered systems might not be a priority for the U.S. given existing deterrent capabilities and potential public opposition.The group discussed Russia's nuclear-powered missile development, specifically the Burevestnik missile tested on October 21, 2025, which flew for 15 hours at subsonic speeds and demonstrated capabilities to evade missile defenses. Ajay emphasized the danger of these nuclear-capable missiles, noting their ability to approach from any direction and their challenging detection due to flying at low altitudes. cautioned that Russia's technical competence with high-tech projects should be viewed with skepticism, though acknowledged the need to address these developments. The discussion concluded with Dr. Ajay expressing skepticism about fusion energy timelines and advocating for Generation 4 nuclear reactors, particularly molten salt reactors using thorium or uranium-233.The group discussed thorium reactors and fusion technology. Ajay explained that China copied thorium reactor technology from Oak Ridge National Lab in the 1960s, but development was halted due to lack of plutonium production, despite its potential for clean energy. The discussion covered fusion for space applications, with Ajay expressing skepticism about the feasibility of Pulsar Fusion's proposed system due to the high energy requirements and weight constraints for space travel. The conversation also touched on the challenges of space-based data centers, with participants questioning the practicality of using space for cooling purposes given existing technical limitations.The group discussed space-based data centers and energy transmission methods. Joe explained that Overview Energy, backed by Meta, is exploring using infrared lasers to transmit energy from space to ground-based solar farms. Bob highlighted that while space data centers may not be economically viable, they could drive significant launch demand and benefit the aerospace industry. The discussion also touched on the massive capital expenditure plans of major tech companies, with Joe noting that approximately $750 billion in capital expenses could potentially include space-based data center projects, creating new opportunities for rocket companies.The group discussed the challenges of cooling data centers in space, with Ajay explaining that radiating heat into space requires large radiators due to the lack of convection and conduction in vacuum. Joe noted that operating chips at higher temperatures could reduce the size of radiators, but this would negatively impact performance. The discussion also covered nuclear propulsion options for space travel, with Ajay expressing skepticism about the feasibility of implementing nuclear electric propulsion for the planned Mars mission within the proposed timeline. The group agreed that nuclear thermal propulsion, while more efficient, would require significant development time and testing. (Summary provided by Zoom AI).Special thanks to our sponsors:American Institute of Aeronautics and Astronautics, Helix Space in Luxembourg, Celestis Memorial Spaceflights, Astrox Corporation, Dr. Haym Benaroya of Rutgers University, The Space Settlement Progress Blog by John Jossy, The Atlantis Project, and Artless EntertainmentWe use Zoom phone numbers for program participation.For real time program participation, email Dr. Space at: drspace@thespaceshow.com for instructions and access.The Space Show is a non-profit 501C3 through its parent, One Giant Leap Foundation, Inc. To donate via Pay Pal, use:To donate with Zelle, use the email address: david@onegiantleapfoundation.org.If you prefer donating with a check, please make the check payable to One Giant Leap Foundation and mail to:One Giant Leap Foundation, 11035 Lavender Hill Drive Ste. 160-306 Las Vegas, NV 89135Upcoming Programs:No Program for Friday, May 29, 2026 | Friday 29 May 2026 930AM PTGuests: Dr. David LivingstonNo program today, Friday, May 26, 2026Broadcast 4596: Zoom: Open Lines Discussion | Sunday 31 May 2026 1200PM PTGuests: Dr. David LivingstonZoom: Open Lines Discussion. Email DrSpace prior to air time for Zoom phone number access. Get full access to The Space Show-One Giant Leap Foundation at doctorspace.substack.com/subscribe

Episode: S05E112 — Tuesday, 26 May 2026 Hosts: Anna & Avery Network: Bitesz.com Podcast Network Website: astronomydaily.io  |  Social: @AstroDailyPod   Story Summaries 1. NASA Unveils Ambitious Moon Base Plan As this episode was recorded, NASA Administrator Jared Isaacman was preparing to announce a landmark plan for a permanent human outpost at the lunar south pole by 2036. The programme carries a price tag of approximately $30 billion across a seven-year foundational phase, relies on nuclear power systems, leverages lunar water ice for fuel and life support, and effectively retires the Gateway orbital station concept. Commercial partners will supply rovers and habitat modules. Phase one targets around two dozen lunar launches, including Artemis IV, by 2028. Full details will be covered in tomorrow's episode. 2. Starship V3 Flight 12 — Engine Drama, Historic Debut SpaceX launched the first Starship V3 rocket on Friday, 22 May 2026, from brand-new Pad 2 at Starbase, Texas. Ship 39 reached space and completed a controlled splashdown in the Indian Ocean despite losing one of its six vacuum Raptor engines during ascent. The flight computer compensated by extending burns on the remaining five. The Super Heavy booster was lost in the Gulf of Mexico after a failed boostback burn. The FAA has opened a review. SpaceX declared most pre-planned test objectives met. 3. JWST Maps First Daily Weather Cycle on a Distant World Published in Science on 21 May 2026. Researchers from Johns Hopkins and Arizona State Universities used Webb's NIRISS instrument to observe WASP-94Ab — a hot Jupiter 690 light-years away — and detected the first daily cloud cycle ever recorded on another planet. Thick magnesium silicate clouds form each morning, then completely clear by evening. The finding also corrected a decade of skewed atmospheric composition data. 4. NASA's Fermi Telescope Solves 20-Year Supernova Mystery An international team led by Fabio Acero used NASA's Fermi Gamma-ray Space Telescope to confirm the first definitive gamma-ray detection from a superluminous supernova — SN 2017egm. The data confirms a newly formed magnetar as the power source behind these extraordinarily bright explosions. Published in Astronomy & Astrophysics, 2026. 5. Most Rocky Exoplanets May Lack Earth-Like Metallic Cores A new paper submitted to the Astrophysical Journal challenges the long-held assumption that dense metallic cores are standard features of rocky planets. Researchers argue that most rocky exoplanets may have formed without Earth-style metallic cores — meaning no global magnetic field, with significant implications for atmospheric retention and habitability. 6. The Soviet Rover That Went Silent — and Came Back Lunokhod 1 was the world's first remote-controlled rover on another world (1970). After traversing 10.5 km of Mare Imbrium, contact was lost in 1971. For nearly 40 years its exact position was unknown — until NASA's Lunar Reconnaissance Orbiter identified it in 2010. The APOLLO project then fired laser pulses and received ~2,000 photons back from its French-built retroreflector — four times stronger than expected. It remains an active contributor to lunar science today.   Sources & Further Reading •       NASA Moon Base announcement: nasa.gov/2026-news-releases •       Starship Flight 12 updates: space.com •       WASP-94Ab paper: Science, 21 May 2026 — DOI via Johns Hopkins Hub •       Fermi supernova paper: Astronomy & Astrophysics, 2026 — DOI: 10.1051/0004-6361/202558547 •       Exoplanet cores paper: submitted to Astrophysical Journal, May 2026Become a supporter of this podcast: https://www.spreaker.com/podcast/astronomy-daily-space-news-updates--5648921/support.Sponsor Details:Ensure your online privacy by using NordVPN. To get our special listener deal and save a lot of money, visit www.bitesz.com/nordvpn. You'll be glad you did!Become a supporter of Astronomy Daily by joining our Supporters Club. Commercial free episodes daily are only a click way... Click HereThis episode includes AI-generated content.

「NordVPN X M觀點」: https://nordvpn.com/miula 專屬優惠碼「miula」 透過專屬優惠連結購買兩年方案加贈4個月好禮，還有30天內退款保證，完全零風險！ #NordVPN --- EP306. 星艦 V3 首飛成功、Anthropic 獲利傳聞、艾克曼看好微軟 | M觀點 --- (00:40) EP306 預告 (03:59) 業配時間：NordVPN (06:46) 開場閒聊：台股又創新高了 (13:35) 第一個話題：星艦 V3 首飛成功 (33:57) 第二個話題：Anthropic 獲利傳聞 (49:50) 第三個話題：艾克曼看好微軟 --- M觀點資訊 --- 科技巨頭解碼: https://bit.ly/3koflbU M觀點 Telegram - https://t.me/miulaviewpoint M觀點 IG - https://www.instagram.com/miulaviewpoint/ M觀點Podcast - https://bit.ly/34fV7so M報: https://bit.ly/345gBbA M觀點YouTube頻道訂閱 https://bit.ly/2nxHnp9 M觀點粉絲團 https://www.facebook.com/miulaperspective/ 任何合作邀約請洽 miula@outlook.com -- Hosting provided by SoundOn

Sponsor Link:You've heard us talking about them now it's time to check out the special money saving deal from our sponsor NordVPN. CLICK HEREYour weekly roundup of the biggest stories from across the cosmos — two fresh stories plus the best of the past seven days from Astronomy Daily.   In This Episode •          Starship V3 Flight 12: SpaceX launches its redesigned megarocket for the first time — an historic milestone with some drama along the way •          New Glenn Cleared to Fly: Blue Origin completes its NG-3 failure investigation — the FAA approves the report and the rocket is back in action •          First Direct Image of the Cosmic Web: A 3-million-light-year filament photographed in unprecedented detail by ESO's Very Large Telescope •          Dark Matter Fingerprint? MIT researchers find a gravitational wave signal that may carry the first direct imprint of dark matter •          Roman Space Telescope: NASA's next great observatory is targeting September 2026 launch — eight months ahead of schedule •          AI Space Chip: NASA tests a radiation-hardened chip that could give future spacecraft genuine autonomous decision-making   Story Sources & Further Reading Starship V3 / Flight 12: Space.com, Universe Today, Spaceflight Now, Next Spaceflight New Glenn / Blue Origin: SpaceNews (May 22, 2026), Space.com, TechCrunch Cosmic Web Image: Nature Astronomy — Tornotti et al.; ESO/VLT press release; Mirage News (May 16, 2026) Dark Matter / Gravitational Waves: Physical Review Letters — Aurrekoetxea et al.; ScienceDaily, Universe Today (May 19, 2026) Roman Space Telescope: NASA.gov, Scientific American, ScienceDaily (May 18, 2026) NASA AI Space Chip: ScienceDaily, NASA (May 15, 2026)   About Astronomy Daily Astronomy Daily delivers the latest space and astronomy news every weekday, plus a Weekend Wrap on Saturdays. Hosted by Anna and Avery, and produced by the Bitesz.com Podcast Network. Website: astronomydaily.io  |  Social: @AstroDailyPodBecome a supporter of this podcast: https://www.spreaker.com/podcast/astronomy-daily-space-news-updates--5648921/support.Sponsor Details:Ensure your online privacy by using NordVPN. To get our special listener deal and save a lot of money, visit www.bitesz.com/nordvpn. You'll be glad you did!Become a supporter of Astronomy Daily by joining our Supporters Club. Commercial free episodes daily are only a click way... Click HereThis episode includes AI-generated content.

Sponsor Link:To check out our special money saving deal and upgrade your online security with NordVPN - Click HereSpaceX came agonisingly close to launching the most powerful rocket ever built in its newest configuration — but a technical halt at T-minus 40 seconds sent Starship V3's debut back to the drawing board, with another attempt window opening this evening (6:30–8:00 p.m. EDT). Anna and Avery dig into what went wrong, what makes V3 different, and the stunning announcement buried in the webcast: a named commander for humanity's first crewed Mars flyby mission. Plus: JWST rewrites exoplanet atmospheric science with unexpected ice clouds on a distant super-Jupiter, researchers map a mysterious magnetic 'flip' inside the Milky Way, and Rocket Lab launches from New Zealand.Links & Further Reading •       Starship Flight 12 live updates: space.com •       SpaceX launch webcast (tonight): spacex.com •       Chun Wang Mars announcement: universetoday.com •       JWST Epsilon Indi Ab paper (ApJL): doi.org/10.3847/2041-8213/ae5823 •       Milky Way magnetic field study (ApJ): doi.org/10.3847/1538-4357/ae28d1 •       Rocket Lab Viva La Strix mission: rocketlabusa.com •       Astronomy Daily website: astronomydaily.ioBecome a supporter of this podcast: https://www.spreaker.com/podcast/astronomy-daily-space-news-updates--5648921/support.Sponsor Details:Ensure your online privacy by using NordVPN. To get our special listener deal and save a lot of money, visit www.bitesz.com/nordvpn. You'll be glad you did!Become a supporter of Astronomy Daily by joining our Supporters Club. Commercial free episodes daily are only a click way... Click HereThis episode includes AI-generated content.

LIVE: SpaceX Starship Launch & UFO NewsStarship Flight 12 (IFT-12) is the maiden flight of Starship Version 3 (Block 3), using Booster 19 (B19) and Ship 39 (S39). It marks the first launch from Starbase's new Orbital Launch Pad 2 (Pad B) and debuts major redesigns for full rapid reusability.The flight is a suborbital test (transatmospheric trajectory), with the booster splashing down in the Gulf of Mexico and the ship in the Indian Ocean. Launch targeted for May 21, 2026, around 5:30–7:00 PM CDT (window shifted to ~6:00 PM CT).Key Highlights and Special FeaturesFirst V3 Vehicles: Significant upgrades to Starship, Super Heavy, and Raptor 3 engines (clean-sheet propulsion changes, increased tank volume, new startup methods, larger grid fins on booster). These incorporate lessons from prior flights for higher performance, reliability, and eventual 100+ ton payloads to orbit.New Launch Pad (Pad 2): First use of the redesigned pad with upgraded propellant farms (more capacity and faster pumps) and improved tower chopsticks (electromechanical actuators for speed/reliability).Heavy Payload Demo: Deploys 22 Starlink simulators (~44 tonnes total mass, a record for Starship tests). Includes 20 standard simulators + 2 specially modified ones to scan Starship's heat shield during flight and transmit imagery (testing future tile inspection for return-to-launch-site missions). Some tiles were painted white to simulate damage.In-Space and Reentry Tests:Single Raptor engine relight in space.Controlled reentry with banking maneuver (simulating future Starbase return trajectory).Intentional stress test on rear flaps.One heat shield tile was intentionally removed to measure the effects on adjacent tiles.Booster Objectives: Full launch, ascent, hot-staging separation, boostback burn, and landing burn — but no tower catch attempt (conservative water landing as it's the first V3 flight).Raptor 3 Power: 33 engines on the booster delivering massive thrust (over 9,000 metric tons), with improved reliability shown in static fires.This flight focuses on proving the redesigned architecture in real conditions rather than attempting catches or full orbits yet. It's a big iterative step toward operational reusability, orbital refueling, and missions like Artemis or Mars.AttributionSpielberg on The Late Show via UAP James@UAPJames on Xhttps://x.com/UAPJames/status/2057061621818683797?s=20Avi Loeb on Neil DeGrasse Tyson via Red Panda Koala @RedPandaKoala on Xhttps://x.com/RedPandaKoala/status/2056986929795871046?s=20Starship Flight 12 Launch via SpaceX Broadcasthttps://www.spacex.com/launches/starship-flight-12Become a supporter of this podcast: https://www.spreaker.com/podcast/the-tempest-universe--4712510/support.Please follow the #podcast on YouTube: https://www.youtube.com/@TheTempestUniversePodcast?sub_confirmation=1

LIVE: SpaceX Starship Launch & UFO News Starship Flight 12 (IFT-12) is the maiden flight of Starship Version 3 (Block 3), using Booster 19 (B19) and Ship 39 (S39). It marks the first launch from Starbase's new Orbital Launch Pad 2 (Pad B) and debuts major redesigns for full rapid reusability. The flight is a suborbital test (transatmospheric trajectory), with the booster splashing down in the Gulf of Mexico and the ship in the Indian Ocean. Launch targeted for May 21, 2026, around 5:30–7:00 PM CDT (window shifted to ~6:00 PM CT).Key Highlights and Special Features First V3 Vehicles: Significant upgrades to Starship, Super Heavy, and Raptor 3 engines (clean-sheet propulsion changes, increased tank volume, new startup methods, larger grid fins on booster). These incorporate lessons from prior flights for higher performance, reliability, and eventual 100+ ton payloads to orbit. New Launch Pad (Pad 2): First use of the redesigned pad with upgraded propellant farms (more capacity and faster pumps) and improved tower chopsticks (electromechanical actuators for speed/reliability). Heavy Payload Demo: Deploys 22 Starlink simulators (~44 tonnes total mass, a record for Starship tests). Includes 20 standard simulators + 2 specially modified ones to scan Starship's heat shield during flight and transmit imagery (testing future tile inspection for return-to-launch-site missions). Some tiles were painted white to simulate damage.In-Space and Reentry Tests: Single Raptor engine relight in space.Controlled reentry with banking maneuver (simulating future Starbase return trajectory).Intentional stress test on rear flaps. One heat shield tile was intentionally removed to measure the effects on adjacent tiles. Booster Objectives: Full launch, ascent, hot-staging separation, boostback burn, and landing burn — but no tower catch attempt (conservative water landing as it's the first V3 flight).Raptor 3 Power: 33 engines on the booster delivering massive thrust (over 9,000 metric tons), with improved reliability shown in static fires. This flight focuses on proving the redesigned architecture in real conditions rather than attempting catches or full orbits yet. It's a big iterative step toward operational reusability, orbital refueling, and missions like Artemis or Mars. Attribution Spielberg on The Late Show via UAP James@UAPJames on X https://x.com/UAPJames/status/2057061621818683797?s=20 Avi Loeb on Neil DeGrasse Tyson via Red Panda Koala @RedPandaKoala on X https://x.com/RedPandaKoala/status/2056986929795871046?s=20 Starship Flight 12 Launch via SpaceX Broadcast https://www.spacex.com/launches/starship-flight-12 Become a supporter of this podcast: https://www.spreaker.com/podcast/the-tempest-universe--4712510/support. Please follow the #podcast on YouTube: https://www.youtube.com/@TheTempestUniversePodcast?sub_confirmation=1

Sponsor Link:To secure your online life and save money into the bargain, check out our NordVPN offer - Click HereStarship V3 is on the pad and tonight's the night — Flight 12 launches the most powerful rocket ever built. Plus: Webb solves a decades-old Neptune mystery, why space debris is quietly corrupting climate science, new doubts cast on DESI's dark energy results, a smarter route to the Moon, and why the galaxy may be full of hellish Venus-twins rather than Earths. All that on Astronomy Daily for Thursday, May 21, 2026.Become a supporter of this podcast: https://www.spreaker.com/podcast/astronomy-daily-space-news-updates--5648921/support.Sponsor Details:Ensure your online privacy by using NordVPN. To get our special listener deal and save a lot of money, visit www.bitesz.com/nordvpn. You'll be glad you did!Become a supporter of Astronomy Daily by joining our Supporters Club. Commercial free episodes daily are only a click way... Click HereThis episode includes AI-generated content.

Sponsor Link:To check out our great NordVPN money saving deal - Click HereAstronomy Daily • S05E107 • Wednesday 21 May 2026 Starship V3 is on the pad and counting down for Thursday's debut launch — we bring you the full update including technical objectives, the Artemis stakes, and a sober note about a worker fatality at Starbase. Plus: a NIST proposal to build GPS for the Moon using lasers inside permanently frozen polar craters; space station startup Vast enters the satellite market; JWST finally has an explanation for the universe's impossibly large early black holes; the Roman Space Telescope locks in a September 2026 launch; and interstellar comet 3I/ATLAS gives up two remarkable new secrets — alien water thirty times richer in heavy hydrogen than anything in our solar system, and pre-discovery images that show it was spotted before anyone knew it was there.   Stories This Episode •       STORY 1 — Starship V3 Flight 12: Launch window opens Thursday 21 May at 6:30 PM EDT (8:30 AM AEST Friday 22 May). Splashdown of upper stage in Indian Ocean off Western Australia ~65 min after liftoff. First flight of Starship V3, first use of Starbase Pad 2. Key objectives: Raptor 3 engines, heat shield imaging by modified Starlink sats, 22 dummy Starlink deployments, Raptor relight in space. Worker fatality at Starbase 15 May under OSHA investigation. •       STORY 2 — Lunar GPS via NIST: Proposal to place ultrastable silicon optical cavity lasers in permanently shadowed craters near lunar south pole (~16K, near-perfect vacuum). Could enable lunar GPS network, atomic timekeeping on Moon, precise satellite ranging, gravitational wave detection. •       STORY 3 — Vast Corporation: Space station builder announces new line of high-power satellites, expanding beyond Haven-1 into commercial satellite manufacturing. Announced 19 May 2026. •       STORY 4 — JWST Black Holes: New arXiv paper proposes 'episodic super-Eddington accretion' in gas-rich dark matter-dominated early galaxies explains overmassive black holes found by JWST. Identifies them as 'missing link' between heavy seeds and luminous quasars. •       STORY 5 — Roman Space Telescope: Launch now confirmed as early as September 2026 — 8 months ahead of schedule, under budget. 100x Hubble's field of view, 1,000x survey speed. Targets dark energy, dark matter, exoplanets. Coronagraph for direct exoplanet imaging. •       STORY 6 — 3I/ATLAS: Pre-discovery images found in Rubin Observatory data from 21 June–2 July 2025, over a week before official ATLAS discovery. Water deuterium ratio at least 30x higher than any solar system comet (ALMA/U of Michigan/Nature Astronomy). Comet estimated ~12 billion years old.   Key Links •       SpaceX Starship Flight 12 livestream: spacex.com •       Flight 12 timeline (Space.com): space.com/space-exploration/launches-spacecraft/what-time-is-spacex-starship-v3-launch-starship-flight-12-timeline •       Starbase worker death (Space.com): space.com/space-exploration/launches-spacecraft/worker-dies-at-spacexs-starbase-in-leadup-to-starship-v3-megarocket-launch •       Lunar laser GPS (NIST): nist.gov/news-events/news/2026/05/shooting-moon-ultrastable-lasers-dark-craters-could-enable-lunar-navigation •       Vast satellite announcement: space.com (19 May 2026) •       Roman Space Telescope launch update: nasa.gov •       3I/ATLAS pre-discovery images: space.com/astronomy/comets •       3I/ATLAS water chemistry (ALMA): almaobservatory.orgBecome a supporter of this podcast: https://www.spreaker.com/podcast/astronomy-daily-space-news-updates--5648921/support.Sponsor Details:Ensure your online privacy by using NordVPN. To get our special listener deal and save a lot of money, visit www.bitesz.com/nordvpn. You'll be glad you did!Become a supporter of Astronomy Daily by joining our Supporters Club. Commercial free episodes daily are only a click way... Click HereThis episode includes AI-generated content.

Sponsor Link:To get our special money saving deal from NordVPN - Click Here A launch day episode packed with big science and bigger rockets. Today we cover the real-time launch of the ESA/China SMILE space weather satellite, SpaceX's Starship V3 sitting on its brand-new pad (and why it's now heading for Thursday), a UCL study warning that megaconstellation launches may be accidentally conducting an 'unregulated geoengineering experiment' in our upper atmosphere, the world's first space-based neutrino detector operating in orbit, extraordinary evidence in Antarctic ice that Earth is collecting material from a dead star, and a clever new cosmic mapper called TIME that studies the ancient universe using a single spectral line.Become a supporter of this podcast: https://www.spreaker.com/podcast/astronomy-daily-space-news-updates--5648921/support.Sponsor Details:Ensure your online privacy by using NordVPN. To get our special listener deal and save a lot of money, visit www.bitesz.com/nordvpn. You'll be glad you did!Become a supporter of Astronomy Daily by joining our Supporters Club. Commercial free episodes daily are only a click way... Click HereThis episode includes AI-generated content.

Sponsor Link:To grab our special money saving NordVPN deal - Click HereIn today's episode, Anna and Avery cover a blue whale-sized asteroid making a close pass of Earth today, the imminent debut of SpaceX's most powerful rocket yet, NASA's Psyche spacecraft successfully completing its Mars gravity assist, fresh science arriving at the ISS, a new physics paper challenging the simulation hypothesis at its foundations, and Congress pushing back hard against proposed cuts to NASA's science budget.   Story 1 — Asteroid 2026 JH2 Newly discovered asteroid 2026 JH2 (first spotted 10 May 2026) makes a close Earth flyby today at ~90,000 km — within the orbital radius of many satellites. Estimated size: up to ~35 metres (blue whale-sized). Zero impact risk confirmed. Observable with binoculars at peak magnitude ~11.5. Live stream available via the Virtual Telescope Project. Orbital period: 3.7 years between Earth and Jupiter.   Story 2 — Starship V3 / Flight 12 SpaceX targets May 19, 2026 for the debut of Starship Version 3 (Flight 12) from Pad 2 at Starbase, Texas. Launch window opens 6:30 PM EDT. Key upgrades: Raptor 3 engines (250 tf SL thrust, up from 230 tf), three larger grid fins, new integrated hot-stage design, updated propellant systems. No tower catch on this flight; booster splashes in Gulf of Mexico. Upper stage (Ship 39) targets Indian Ocean after 65 minutes. Payload: 22 Starlink simulator satellites. Critical step toward Artemis lunar landings.   Story 3 — NASA Psyche Mars Flyby On 15 May 2026 at 3:28 PM EDT, Psyche completed its Mars gravity assist at 4,500 km altitude travelling at 12,333 mph. Passed inside the orbits of both Martian moons. Confirmed by Doppler shift monitoring. Mission: en route to metal-rich asteroid 16 Psyche (arrival July 2029). Thousands of Mars observations gathered for science calibration.   Story 4 — SpaceX CRS-34 SpaceX's 34th Dragon cargo mission docked at ISS at 6:37 AM EDT on 17 May 2026, delivering ~6,500 lb of cargo for Expedition 74. Science payloads include: microgravity simulator validation study, wood-based bone scaffold (osteoporosis research), red blood cell/spleen spaceflight study. Dragon will return to Earth mid-June splashing down off California coast.   Story 5 — Simulation Hypothesis Paper Paper: ‘Non-algorithmic physics and the limits of the simulation hypothesis', published in the Journal of Holography Applications in Physics. Authors: Mir Faizal (UBC Okanagan), Lawrence Krauss, Arshid Shabir, Francesco Marino. Core argument: using Gödel's incompleteness theorems, the team argues any theory of quantum gravity would be non-algorithmic — containing truths no computation can capture. Since any simulation requires algorithms, reality cannot be fully simulated. Note: this is a theoretical paper, not an experimental result. The authors acknowledge no complete quantum gravity theory currently exists.   Story 6 — NASA FY2027 Budget House Appropriations Committee approved $24.438 billion for NASA in FY2027 — matching FY2026 and rejecting the White House's proposed $18.8 billion (a 23% cut). The proposal would have cut the Science Mission Directorate by 46%, terminating 50+ missions. Committee protects science, Habitable Worlds Observatory, and STEM education funding. Bill still needs Senate passage and reconciliation.   Skywatching TONIGHT — Moon-Venus conjunction: look west after sunset for the crescent Moon close to brilliant Venus. Earthshine visible on dark lunar limb. Southern Hemisphere: look west-northwest, best in first hour after sunset. Blue Moon on 31 May (second full Moon of the month).  Become a supporter of this podcast: https://www.spreaker.com/podcast/astronomy-daily-space-news-updates--5648921/support.Sponsor Details:Ensure your online privacy by using NordVPN. To get our special listener deal and save a lot of money, visit www.bitesz.com/nordvpn. You'll be glad you did!Become a supporter of Astronomy Daily by joining our Supporters Club. Commercial free episodes daily are only a click way... Click HereThis episode includes AI-generated content.

I spoke to Scoopy Trooples, the pseudonymous co-founder of Alchemix, about self-repaying loans, the wreckage of DeFi scams, and what ethical crypto finance could actually look like.We dig into how Alchemix works, allowing your debt pays itself off over time and what's new in V3. We also get into the broader rot in the space: meme coin pump-and-dumps, celebrity rug pulls, prediction market gambling, and how DeFi summer's promise curdled into financial nihilism. Scoopy is one of the rare crypto founders willing to say the quiet part loud,  that most of what gets built in this space is zero-sum at best and predatory at worst, and has been trying to build something different since day one.This episode is sponsored by NYM, the world's most private VPN. Unlike traditional VPNs, Nym uses a decentralized mixnet to scramble your internet data — hiding who you're talking to, when, and how often. You can switch between full mixnet mode for maximum anonymity, or a faster VPN mode for everyday use.Use the code blockchainsocialist when signing up and get an extra month!If you liked the podcast be sure to give it a review on your preferred podcast platform. If you find content like this important consider donating to my Patreon starting at just $3 per month. It takes quite a lot of my time and resources so any amount helps. Follow me on Twitter (@TBSocialist) or Mastodon (@theblockchainsocialist@social.coop) and join the r/CryptoLeftists subreddit. Support the showICYMI I've written a book about, no surprise, blockchains through a left political framework! The title is Blockchain Radicals: How Capitalism Ruined Crypto and How to Fix It and is being published through Repeater Books, the publishing house started by Mark Fisher who's work influenced me a lot in my thinking. The book is officially published and you use this linktree to find where you can purchase the book based on your region / country.

This episode reviews the eight cranial bones for board exam prep, focusing on their role in protecting the brain, the function of sutures as immovable joints for growth and strength, and the cushioning effect of cerebrospinal fluid. It highlights key bones and functions, including the frontal (decision-making), parietal (sensory processing), temporal (hearing, balance, memory), and occipital (vision, foramen magnum). The sphenoid is emphasized as a central hub with important trigeminal nerve pathways (V1, V2, V3), while the ethmoid is noted for its role in the nasal cavity and olfaction.  #1 dental hygiene boards review: 

Scoopy Trooples is the Founder of Alchemix.Many DeFi protocols from 2021 are gone. But Alchemix never quit.In this episode, Scoopy walks through the full Alchemix V3 redesign: how the new Mix-Yield Token (MYT) creates vaulted yields users can borrow against at 90% LTV, with zero interest, looping up to 10x, and how fixed-term redemptions unlock an entirely new yield primitive. Alchemix was the first protocol to use yield-bearing collateral for loans. V3 looks to be the long overdue evolution of the self-repaying loan, and we think this version can scale to be even bigger than V1.------

Quanto vai custar GTA 6? CEO fala sobre preço e promete valor justo, justo pra quem? Brasil é campeão e recebe mais de 1 bilhão de ligações abusivas por mês. RTX 5070 ganha versão de 12 GB: é o fim do sofrimento nos notebooks? Aleluia! YouTube libera Picture-in-Picture de graça para todos os usuários. Sony finalmente explica DRM de 30 dias do PlayStation: 'verificação temporária'. Visa testa IA agêntica que paga suas contas 'sozinha'. Google investe bilhões na Anthropic, Cade e União Europeia pressionam Google, OpenAI pede desculpas, China barra venda de startup de IA, Intel cresce com IA. E ainda as novidades com nossos apresentadores direto da Gamescom 2026.

The Bank of England expects stock markets around the world to fall as share prices are not reflecting the many risks facing the world economy, so says the Bank of England Deputy Governor and head of financial stability Sarah Breeden. It is unusual for a senior figure at the Bank to be so forthright on market movements. Breeden, who is also the Bank's head of financial stability, declined to say when she expected markets to fall or by how much, but pointed to a number of factors that markets seemed complacent about. The Chinese artificial intelligence company, DeepSeek, has released a preview version of its long-awaited new model, allowing users to test its capabilities and features. DeepSeek introduced its first open source model last year, causing turmoil in western tech markets because of its matching capabilities with American rivals and low cost. The firm says its V3 model achieves strong performance against others. And one of the world's biggest car shows has been showcasing the very latest electric car technology. The timing of the Beijing show is pivotal - as the Iran war pushes up fuel prices, global demand for EVs is accelerating.Presenter: Leanna Byrne Senior producer: Craig Henderson

Is your corn crop ready for the V3 milestone? In this episode, we sit down with Scott Dickey, Regional Agronomy Manager at Beck's Hybrids, to break down the critical management decisions facing corn growers right now. If your budget includes a top-dress application, you can't afford to miss this discussion on timing and nutrient balance. In this episode, we cover: The V3 Sweet Spot: Why Beck's PFR (Practical Farm Research) proves that V3 is the ultimate timing for side-dress Nitrogen applications. The Sulfur Ratio: Why 25–30 lbs of Sulfur is the "magic number" for your crop's health and nitrogen efficiency. Post-Freeze Checkup: Assessing the "cosmetic bruising" from the recent cold spell—should you be worried? (Hint: Scott has some reassuring news). Budgeting for Yield: Making your inputs work harder for you this season. Guest: Scott Dickey, Regional Agronomy Manager at Beck's Hybrids YouTube Video Timestamps 0:00 – Introduction: Agronomy Moment with Wendell Koehn 0:52 – Corn Management & Early Season Field Reports 1:25 – Assessing Cold Weather Bruising: Is it Cosmetic? 1:55 – The V3 Milestone: PFR Proven Side-Dress Timing 3:20 – Nitrogen Efficiency: Units per Bushel Targets 4:40 – Managing Risk: Late-Season Top Dressing Options 6:22 – Sulfur Strategy: Pre-Plant vs. Top Dress Rates 6:55 – Final Advice: Pre-Planning and Product Availability 8:50 – "Tailgate Talk" Bloopers & Outtakes Topic: Corn Management & PFR Proven Side-Dress Strategies #CornFarming #Agronomy #BecksHybrids #NitrogenManagement #V3Corn #FarmTips #cropyieldpotential TOP Ag Services is a Beck's Hybrids seed dealer as well as a franchise partner for Sweetwater Technologies. We provide Hybrid Corn Seed, Soybean Seed, and Wheat Seed. Beck's has access to the best genetics and trait technologies from suppliers worldwide. Through Sweetwater Technologies we have access to industry standard name brand herbicides, insecticides, fungicides, and many others! We have access to biological stress mitigators, biological fertility foliar, and many other products in the category of crop protection and stress prevention. Through our business associates Dirks Bros, we offer fertilizer, soil sampling, and a whole suite of crop nutrition solutions. We are the first to market with the best products & provide the latest, most accurate agronomic information through proven research. If you need agronomic assistance or want to be added to these updates, feel free to reach out via the messaging feature or contact us at topagservices.com/contact or call us at 417-684-5301 to be connected with someone who can help you. All information here is for informational purposes only. It is not a recommendation for your farm. You should not act or refrain from acting on the basis of any content included in this presentation without seeking other professional advice. The contents of this presentation contain general information and may not reflect current agronomic or developments or address your situation. We (Wendell Koehn and all of his affiliates, guests, or assistants) disclaim all liability for actions you take or fail to take based on any content in this presentation.

SpaceX just hit the brakes. Flight 12, the first launch of the Starship V3, is officially pushed to May. While Elon claims it is a 4 to 6 week tweak, there is more going on with the V3 hardware than just a schedule shift. We are breaking down the specific bottlenecks holding up the most powerful rocket ever built.The Raptor 3 Risk: The new shroudless engines are supposed to be more efficient, but rumors of cooling issues during static fires are heating up.The Stretch Problem: V3 is significantly taller than its predecessors. We look at whether the structural welds can actually handle the increased propellant mass.Heat Shield 3.0: After the near-misses of Flight 11, did SpaceX finally solve the tile-loss issue, or is that what is causing the May delay?The $2 Trillion Pressure: With the SpaceX IPO rumors swirling, a failure on the maiden V3 flight is not an option. Is this a technical delay or a strategic one?The transition from V2 to V3 is the biggest hardware jump in Starship history. If they do not get this right in May, the entire moon manifest slides. Listen to find out what is actually happening at Starbase.

Gospel of Moses?; For the Levites - called out; Redirecting Israel; Rebuilding the Temple?; Lively stones; Covetous practices; Abortions in Egypt?; Bondage of Egypt; Standing armies; William the conqueror; Law of Nations; No treaties; Covenants/Leagues; Power with the people?; Making agreements; History repeating itself; Arts of the Temple; No social security trust fund; Ear-tickling preachers; Replacement theology; Jesus, king of Judea; Rejecting Jesus?; Binding people together; Golden calf?; "Idolatry"; One purse; Changing word meanings; Altars of charity; Corban?; Burnt offerings?; Committing to loving neighbors; The story of Leviticus; Lev 7:1 Likewise…; vav+zayin-aleph-tav; Continuing from Lev 6; Two witnesses; Sophistry; Shearing sheep; Zoroaster?; Learning the past to not repeat it; Jews accepting Jesus; "Satan"; Parables; Leaving all behind to follow Jesus; "law" - tav-vav-resh-tav; Trespass offering; Status of offering; Sprinkling blood?; V3 fat of the rump; What if you didn't have sheep?; Offering value; Poker hand?; Kingdom business or doing your own thing; Anarchists?; Perfect law of liberty; Caring for neighbors; Kidneys?; Liver?; Why cryptic language?; Postponing the kingdom; Mystery Babylon; Getting scales off your eyes; Being doer's of the Word; Dry bones; Ministering to congregations; Election day?; Government corruption; "Holy"; Cougars; Offerings to take care of others; Giving wisely; Thanksgiving; "frying"; Why anoint with oil?; Spirit in the offering; Who are the priests?; DOers of the Word; v14 - heave offering; tav-vav-dalet-tav (Thanksgiving); Worldly ways; vs Righteousness; Two types of governments; Loving your enemy; Charity vs force; Tax-funded churches?; Straying from Christ's plan; Advocating repentance; Are you in?

In this episode, Chris Cochrane dives into Apple’s $599 MacBook Neo – the cheapest Mac laptop ever made – and whether it spells trouble for Chromebook makers. He also covers Samsung’s CEO blaming AI for rising phone prices, Framework raising RAM prices for the third time in three months, Meta unveiling four custom AI chips, NVIDIA’s GTC 2026 conference preview, a billion-dollar bet against large language models, Microsoft’s game-changing Project Helix Xbox with native Steam support, Windows 11’s new Xbox Mode, and SpaceX gearing up for a critical Starship Flight 12 test. – 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 Chris 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 Apple MacBook Neo The lead story covers Apple’s MacBook Neo. It launched at $599 and marks the cheapest Mac laptop ever made. The device runs on the A18 Pro chip from the iPhone 16 Pro. Cochrane notes a solid market for students, casual users, and anyone who needs a reliable home laptop. However, he advises photographers and videographers to invest in a MacBook Air or Pro instead. The real question remains whether this kills Chromebook sales in education. Samsung CEO Blames AI for Price Hikes Cochrane tackles Samsung’s Galaxy S26 price increases. CEO TM Roh blamed AI infrastructure demand for the hikes. Meanwhile, DDR4 DRAM prices surged sevenfold in a single year. Cochrane points out the irony. Samsung manufactures memory chips, shifted production toward AI data centers, and now cites that same shortage to justify higher consumer prices. He calls the situation “a little shady” but appreciates the transparency. Framework RAM Prices Up Again The RAM crisis extends beyond phones. Framework raised RAM prices for the third consecutive time in three months. Cochrane reinforces advice from a recent episode. He urges listeners to buy now before prices climb further. Analysts project peak prices by mid-2026. The shortage could last through late 2027. 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. Meta Unveils Four Custom AI Chips Cochrane reports on Meta’s four new MTIA chip generations. The company aims to reduce its dependence on NVIDIA by building custom silicon. The MTIA 300 is already in production. New generations will ship every six months through 2027. The chips are built on open-source RISC-V architecture and manufactured by TSMC. NVIDIA GTC 2026 Preview NVIDIA’s GTC conference starts Monday in San Jose. Jensen Huang promises “chips the world has never seen.” Rumored architectures include Rubin Ultra and Feynman. The keynote streams free at nvidia.com on Monday at 11am Pacific. Cochrane notes that while companies like Meta are building chips to escape NVIDIA, competition will eventually catch up. Yann LeCun’s AMI Labs Raises $1.03 Billion Former Meta AI chief Yann LeCun raised $1.03 billion for AMI Labs at a $3.5 billion valuation. It marks the largest European seed round in history for a company just four months old. LeCun is building “world models” that learn from physical reality rather than text. Backers include Jeff Bezos, NVIDIA, and Samsung. Cochrane notes both approaches to AI can coexist. Microsoft Project Helix Microsoft revealed Project Helix at GDC 2026. For the first time, an Xbox will natively support Steam and GOG. Cochrane sees it as both desperate and inevitable. The only reason to buy from the Xbox store would be exclusives. He notes this is a breath of fresh air after months of talk that the Xbox era was ending. Dev kits ship in 2027 with a consumer launch likely late 2027 or 2028. Windows 11 Xbox Mode Microsoft is rolling out Xbox Mode to all Windows 11 PCs in April. The full-screen controller-optimized interface works with Steam, Epic, and Battle.net. Cochrane sees it as the first half of Microsoft’s two-phase gaming strategy. Xbox Mode trains users now. Project Helix delivers dedicated hardware later. He asks whether Sony and Nintendo will follow in Xbox’s footsteps. SpaceX Starship Flight 12 SpaceX announced stacking complete for the next Super Heavy booster at Starbase. Flight 12 targets April and debuts V3 hardware with Raptor 3 engines. Orbital refueling remains the critical unknown for NASA’s Artemis III moon landing. SpaceX has a track record of delivering eventually, just never on Elon’s original timeline. The post Is the MacBook Neo a Chromebook Killer? #1860 appeared first on Geek News Central.

In this episode of LAB the Podcast, Zach Elliott sits down with Michael Barna, Tampa native, commercial real estate executive, and board member of VU VI VO (V3).Michael shares about his journey of faith, his life in business, and why he and his wife Bryn are passionate about supporting the work of V3. Together they discuss the importance of partnership behind the scenes, the role of beauty in expressing the gospel, and the growing vision of projects like “The Vision of Jesus” series.This conversation offers a glimpse into the people who help make the mission to express the life and beauty of the gospel.The Vision is Jesus Visual Series: https://vuvivo.com/the-vision-is-jesusSehnsucht Symphony | Listen: https://vuvivo.com/Support / Sponsor: https://vuvivo.com/supportFor More Videos, Subscribe: @VUVIVOV3 | https://www.youtube.com/@VUVIVOV3Follow: @labthepodcast | @vuvivo_v3 | @zachjelliottThank you for joining the conversation and embodying the life and beauty of the gospel. Don't forget to like, subscribe, and follow LAB the Podcast.Support the show

Tonight on The Late Night Vision Show we're reviewing the AGM Rattler V3 25-384 thermal rifle scope. This is a compact and affordable option built for hog and predator hunters. With a 384 resolution sensor, 2.5x base magnification, larger higher resolution display screen, and improved thermal sensor, the new Rattle V3 brings several updates over the previous Rattler V2 models. We discuss image quality, real world hunting performance, and whether the V3 25-384 is the right thermal for your needs.

Welcome to Episode 60 of Astronomy Daily Season Five! In today's episode, Anna and Avery cover six major stories from the world of space and astronomy — including a neutron star collision in an unprecedented location, the latest Artemis II news, and a cosmic mystery solved after decades.   Stories covered in this episode:   1. NASA Discovers Neutron Star Crash in Unexpected Location A fleet of NASA telescopes — including Chandra, Fermi, Swift, and Hubble — has detected a neutron star merger inside a tiny galaxy buried in a vast stream of gas, 4.7 billion light-years away. It's the first time this type of collision has been spotted in such an environment, and it may explain why gamma-ray bursts sometimes appear outside any galaxy — and how precious metals like gold and platinum ended up in distant stellar regions. Published in The Astrophysical Journal Letters.   2. Artemis II Flight Readiness Review NASA will host a Flight Readiness Review press conference on Thursday 12 March at Kennedy Space Center, covering progress toward the first crewed Artemis mission. The rocket is currently back in the Vehicle Assembly Building following a helium issue, with rollout to the launchpad expected around 19 March and a launch target of no earlier than 1 April 2026.   3. Firefly Alpha 'Stairway to Seven' Scrubbed Again Firefly Aerospace's Alpha rocket — attempting its return to flight after a 10-month grounding — has been scrubbed three times in 10 days. The latest scrub occurred on 10 March during fluid loading after off-nominal readings. A new launch date will be confirmed following engineering review. This mission is the final Block I Alpha flight, with the upgraded Block II debuting on Flight 8.   4. DART Mission Reveals 'Cosmic Snowball Fight' Between Asteroids Researchers at the University of Maryland have found the first direct visual proof of material transfer between two asteroids — fan-shaped streaks on the surface of asteroid moon Dimorphos, left by debris thrown off its parent asteroid Didymos at just 30.7 cm/s. The discovery provides visual confirmation of the YORP effect and has implications for planetary defence modelling. ESA's Hera mission arrives at Didymos in December 2026. Published in The Planetary Science Journal.   5. Starship Flight 12 — About Four Weeks Away SpaceX is approximately four weeks from the launch of Starship Flight 12, which will be the first flight of the upgraded V3 configuration — the most powerful version of the already record-breaking vehicle. Engineers have completed propellant system tests on Ship 39 at Starbase, Texas, and preflight preparations are continuing.   6. Giant Cosmic Sheet Discovered Around the Milky Way Astronomers from the University of Groningen, publishing in Nature Astronomy, have used advanced computer simulations to discover that the matter surrounding our Local Group is arranged in a vast, flat sheet — dominated by dark matter — stretching tens of millions of light-years across. This structure, flanked by enormous empty voids, explains why nearby galaxies are moving away from us rather than being pulled inward. It's the first detailed map of dark matter distribution in our cosmic neighbourhood.     Astronomy Daily is part of the Bitesz.com Podcast Network. Website: astronomydaily.io | Social: @AstroDailyPod on all major platformsBecome a supporter of this podcast: https://www.spreaker.com/podcast/astronomy-daily-space-news-updates--5648921/support.Sponsor Details:Ensure your online privacy by using NordVPN. To get our special listener deal and save a lot of money, visit www.bitesz.com/nordvpn. You'll be glad you did!Become a supporter of Astronomy Daily by joining our Supporters Club. Commercial free episodes daily are only a click way... Click HereThis episode includes AI-generated content.

Tonight on The Late Night Vision Show we review the AGM Rattler V3 35-640 LRF thermal rifle scope. This scope features a 2.5x base magnification, a 640 resolution sensor and a LRF built directly into the lens. With an onboard ballistic calculator and an American Defense Manufacturing QD mount included, the V3 brings serious capability to predator and hog hunters. We break down image quality, real world performance and ID range and discuss whether this new Rattler upgrade is a scope you should consider.

幻冬舎の暗号資産（仮想通貨）/ブロックチェーンなどWeb3領域の専門メディア「あたらしい経済 https://www.neweconomy.jp/ 」がおくる、Podcast番組です。 ーーーーー 【番組スポンサー】 この番組は、暗号資産取引におけるフルラインナップサービスを提供する「SBI VCトレード」のスポンサーでお届けします。 ーーーーー SBI VCトレードは、「暗号資産もSBI」のスローガンのもと、国内最大級のインターネット総合金融グループであるSBIグループの総合力を生かし、暗号資産取引におけるフルラインナップサービスを提供しております。暗号資産交換業者・第一種金融商品取引業者・電子決済手段等取引業者として高いセキュリティ体制のもと、暗号資産の売買にとどまらない暗号資産運用サービスや法人向けサービスの展開、さらにステーブルコインのユーエスディーシー（USDC）を国内で初めて取り扱っております。 ーーーーー SBI VCトレード公式サイト：https://account.sbivc.co.jp/signup?hc_ak=1RNML.3.M06AS ーーーーー 【紹介したニュース】 ・SBI、シンガポールの暗号資産取引所Coinhako買収の意向表明 ・トランプ大統領関連ブランド、クロノスのETFとビットコイン・イーサリアムのETF2本をSECに申請 ・X、株式・暗号資産データを表示する「スマート・キャッシュタグ」提供へ ・イーサリアム財団、トマシュ・スタンチャク共同エグゼクティブディレクターが退任へ ・エスプレッソ、ネイティブトークン「ESP」のエアドロ請求とステーキングを開始 ・Nyx、イーサリアムにおけるDEX取引の「公開・非公開ルート」格差を実証 ・WBTC、ハイパーレーンでイーサリアムとソラナ接続の設定追加か ・ライトニングラボ、AIエージェントが支払える決済基盤を公開。L402活用で ・ビットコイン量子耐性提案「BIP 360」、公式リポジトリに統合 ・アーベV3がマントルでローンチ、バイビットも連携 【あたらしい経済関連リンク】 ニュースの詳細や、アーカイブやその他の記事はこちらから https://www.neweconomy.jp/

Thursday, February 5, 2026 - Week 6 Happy #RareDisease & #BlackHistory Month!   #NaturalHistory means how this disease progresses.  Reminder: We have only been at this for 17 years, first patients were identified via Hamdan, 2009. https://pubmed.ncbi.nlm.nih.gov/19196676/   Retrospective Digital NHS: cureSYNGAP1.org/Citizen (Growing list of tools available to families, for free)   Prospective Multi-disciplinary Multi-site NHS: ProMMiS cureSYNGAP1.org/ProMMiS   Reminder, only possible by CS1 support for non-CHOP sites and travel plus huge gift to Penn. https://www.chop.edu/news/25-million-gift-penn-medicine-and-children-s-hospital-philadelphia-establishes-center-epilepsy   Potential for being a control arm in the future.   Protocol: https://www.linkedin.com/posts/curesyngap1_syngap1-stxbp1-dee-activity-7425223573134327808-SVEQ & early data: https://pubmed.ncbi.nlm.nih.gov/40119723/   Join the ~160 families who have enjoyed excellent clinical care and contributed tot he future of SYNGAP1.  Today, a 4 month old is going! CHOP: 119 new, V2- 67, V3- 32, V4- 10, V5- 4 CHCO: 37 new, V2- 7 Stanford: 8 new, V2- 2 Total: 164 (double counting one family who goes to multiple sites)   Survey English: https://curesyngap1.org/SurveyProMMiS Spanish: https://curesyngap1.org/encuestaProMMiS   94 Responses to survey, so far: Why not? Did not receive an invitation, Too far to travel, Too expensive Barriers: Logistics, Cost, Time off, Behaviors, Insurance   ETC. Pubmed 2026 is at 6!  But will soon be 7 with the McKee paper! https://pubmed.ncbi.nlm.nih.gov/?term=syngap1&filter=years.2026-2026&sort=date   Biorepository needs more samples.  Check out the list and map here https://docs.google.com/presentation/d/1IjaHILXj7AlBDlbTJgvYrkBS_0bnI8VCnTIiPXJ7JGM/edit?usp=sharing and contribute blood.  The data and research we do with these samples is invaluable.   May 28, San Francisco, CA: cureSYNGAP1.org/SF26   SOCIAL MATTERS 4,668 LinkedIn.  https://www.linkedin.com/company/curesyngap1/ 1,520 YouTube.  https://www.youtube.com/@CureSYNGAP1 11.2k Twitter https://twitter.com/cureSYNGAP1 45k Insta https://www.instagram.com/curesyngap1/   $CAMP stock is at $3.59 on 5 Feb. ‘26 https://www.google.com/finance/beta/quote/CAMP:NASDAQ   Like and subscribe to this podcast wherever you listen.  https://curesyngap1.org/podcasts/syngap10/ Episode 198 of #Syngap10 #CureSYNGAP1 #Podcast

Statisícová demonstrace na Staroměstském a Václavském náměstí nebyla jen důsledkem textových zpráv ministra zahraničí Macinky, které prezident Pavel označil za vydírání. Šlo maximálně o poslední kapku, kterou přetekl pohár trpělivosti části české veřejnosti s Babišovou vládou. O co šlo? Už řadu týdnů je část společnosti znejistěna z toho, kam se ubírá český stát. Začalo to reorientací zahraniční politiky a orientací na autoritativní režimy Slovenska a Maďarska. Zatímco část západních zemí a Polsko uvažuje o užší integraci bezpečnostní politiky, mluví český premiér stále o spolupráci s Maďary a Slováky ve V3. Jejich proputinovská politika nás ovšem tahá na Balkán, kde Češi nemají žádné zájmy. Stále víc se taky orientace těchto zemí na autoritativní režimy začíná projevovat v utužování vnitřní politiky. V Česku se to například demonstruje pokusem postátnit Českou televizi. V jistém smyslu je to spor mezi reprezentanty českých a uherských politických tradic. Nebo ještě jinak - mezi těmi, kteří chtějí stát posunout k autoritativním metodám řízení. Je na každém, co si vybere. Pražská demonstrace byla hlasitým NE těch, co nechtějí systém omezené demokracie.

In this episode of LAB the Podcast, Zach Elliott sits down with artist and designer Mauricio Vega, founder of the SuperVivo Art Collective and the first commissioned artist for V3's Freedom Is Beautiful project.Mauricio shares his life journey—from growing up under communism in Cuba, navigating scarcity and censorship,  to discovering faith, creativity, and calling in the midst of political and spiritual tension. This conversation introduces the very first Freedom Is Beautiful art commission—an original piece created in collaboration with SuperVivo that launches an ongoing annual series. Together, we talk about the power of beauty to confront darkness, and why leading with beauty matters in the fight against human trafficking.Support / Sponsor: https://vuvivo.com/supportFor More Videos, Subscribe:   @VUVIVOV3 | https://www.youtube.com/@VUVIVOV3Follow: @labthepodcast | @vuvivo_v3 | @zachjelliottShop V3 Conservatory Collective: https://v3conservatorycollective.myshopify.com/collectionsOrder Freedom Roast: https://www.buddybrew.com/products/life-beauty-blend-lab-podcast-x-bbc-collaboration#LABthePodcast #FreedomIsBeautiful #MauricioVega #SuperVivo #ArtAndFaith #ChristianArtists #FaithAndCulture #LeadingWithBeauty #ArtAsResistance #CreativeFaith #BeautyAndJustice #ChristianImagination #HumanTraffickingAwareness #LABInitiative #V3 #FaithInAction #CulturalEngagement #ArtForGoodSupport the show

This episode is a little different. I'm diving into tennis gear, everything except the racquet and shoes, with the founder of one of my favorite brands, ADV. I use their bags, dampeners, and even sweat bands.Lavie Sak has a tech background, plays tennis, and used to coach as well. We explore how his company lets players lead the design of its products. From dampeners to grips to their popular bags, ADV innovates as well as any company in tennis. Lavie shares the messy first prototype, the tough cuts, and why ADV chose quality over mass pricing while partnering with pros who give real feedback.How ADV got startedDampener testing across 27 racquets and sound profilesTennis grips - how to choose between dry and tackyDesigning the ADV Pro bag and prioritizing featuresHow they develop an idea into a finished productFeedback loops that shaped V2 and V3 of the bagWhy ADV makes two backpacksCurated training kit components and use casesPricing tradeoffs, materials, and longevityDoubles tips on serve variety and aiming middlePartnerships with Sem Verbeek, JP Smith, and Zus TennisI use the ADV Pro for travel and the Flex bag locally around Fort Worth.Links:Shop ADV TennisLearn more about ADV & follow:ADV Tennis - InstagramADV Tennis - YouTubeADV Tennis - Facebook ----- **Join the #1 Doubles Strategy Newsletter for Club Tennis Players** New doubles strategy lessons weekly straight to your inbox **Become a Tennis Tribe Member**Tennis Tribe Members get access to premium video lessons, a monthly member-only webinar, doubles strategy Ebooks & Courses, exclusive discounts on tennis gear, and more. Learn More & Sign Up Here **Other Free Doubles Content** Serve Strategy Cheatsheet Return Strategy Cheatsheet Serve Strategy 101 - Video Course

Mark Omo and James Rowley spoke with us about safecracking, security, and the ethics of doing a bad job. Mark and James gave an excellent talk on the development of their safecracking tools at DEF CON 33: Cash, Drugs, and Guns: Why Your Safes Aren't Safe. It included a section of interaction involving the lock maker's lawyers bullying them and how the Electronic Frontier Foundation (EFF) has a Coders' Rights Project to support security research. As mentioned in the show, the US Cyber Trust Mark baseline has a very straightforward checklist; NISTIR 8259 is the overall standard, NISTIR 8259A is the technical checklist, NISTIR 8259B is the non-technical (process/maintenance) checklist. Roughly the process is NISTIR 8259 -> Plan/Guidance; NISTIR 8259A -> Build; NISTIR 8259B -> Support. We discussed ETSI EN 303 645 V3.1.3 (2024-09) Cyber Security for Consumer Internet of Things: Baseline Requirement and the EU's CRA: Cyber Resilience Act which requires manufacturers to implement security by design, have security by default, provide free security updates, and protect confidentiality. See more here: How to prepare for the Cyber Resilience Act (CRA): A guide for manufacturers. We didn't mention Ghidra in the show specifically, but it is a tool for reverse engineering software: given a binary image, what was the code? Some of the safecracking was helped by the lock maker using the same processor in the PS4 which has many people looking to crack it. See fail0verflow :: PS4 Aux Hax 1: Intro & Aeolia for an introduction.  Mark and James have presented multiple times at Hardwear.io, a series of conferences and webinars about security (not wearables). Some related highlights: 2024: Breaking Into Chips By Reading The Datasheet is about the exploit developed for the older lock version on the safes discussed in the show. USA 2025: Extracting Protected Flash With STM32-TraceRip is about STM32 exploits.

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

Drew and Rory start with eyeball horror, Stranger Things hype, and the idea of AI-powered contact lenses before stumbling straight into the real mind-melt: Midjourney, Grok Imagine, Mystic 3, and Flux all colliding in one episode. They roast their own prompts, trigger an accidental NSF-DoubleU moment live inside Grok, argue about “flux face,” and still somehow manage to pull out real, practical tips for people trying to make better AI images without losing their minds.Across an hour of chaos, they unpack Midjourney v8's subtle shifts, hidden personalization signals, Style Explorer tricks, Smart Search shortcuts, Grok's Sora-style infinite feed, Mystic 3's scary-good skin detail, and why Midjourney still owns lo-fi, lived-in, “shot-on-a-phone” energy. If you care about composition, cinematic ratios, editorial portraits, food realism, or just want to hear two people dunk on Flux and node editors while actually teaching you something, this one hits.Listeners will come away knowing how to use stills archive for composition, when to skip upscales for more analog realism, how Grok Imagine's image + video workflow really behaves, and where Mystic 3 can replace Midjourney in a serious portrait or product stack.--⏱️ Midjourney Fast Hour0:00 Intro, eyeballs, and a Friday brain check2:05 Contact lens horror stories, Mission Impossible, Black Mirror eyes3:07 Stranger Things Season 5 hype and binge vs weekly TV4:51 Movies, biopics, sports docs, and couch season setting in6:23 Cowboys documentary, sports pipelines, and TV as passive story feed7:00 AI overload, nobody keeping up, and why this pod exists8:30 Midjourney profiles, Style Creator, and new personalization talk9:29 Like/dislike buttons as hidden training data and 7:3 aspect ratio love10:35 Stills Archive, cinematic framing, and cleaner compositions12:00 Style Explorer vs old-school SREF and what quietly vanished13:16 Three under-the-radar Midjourney Smart Search + right-click + Option-upscale tweaks15:35 V8, fewer wall-of-text prompts, and a move toward visual controls18:12 First look at Grok Imagine's interface and infinite scroll feel19:35 Sora-style endless bottom feed, variants, and “make video” in Grok22:51 Cinematic looks, color grading, and Grok as “idea and curate” engine24:19 Live NSFW surprise inside Grok Imagine and instant rating change25:23 Finding Grok history, stills, and video exports with sound26:31 Who actually gets Grok video and Drew's first real reaction to using it27:38 Mystic 3 enters the chat and upscaling less for analog vibes29:02 Why “too sharp” screams AI and how grain + smart detail saves realism30:18 Outpainting, editing, and why Midjourney still wins surgical compositing35:01 Mystic 3 V3 screen-share and first impressions35:45 Editorial portraits, skin detail, eyelashes, and hands that finally look human37:26 Mystic 3 model comparisons: Zen, State-of-the-Art, and weird description blur39:16 Zooming all the way into pores, fingerprints, and micro skin texture43:44 Cocktail and food prompts where Mystic falls behind Midjourney50:05 Nano Banana 2 rumors, native 4K wishes, and how Midjourney might respond50:58 Why Midjourney still rules lo-fi, disposable camera, and Polaroid-style shots52:16 Grok Imagine vs Flux vs Midjourney for lived-in Y2K flash photos53:39 Flux face, direct flash tests, and “go flux yourself” is born55:30 Nodes, Grok workflows, and why scrolling is faster than wiring graphs56:01 Why Midjourney is avoiding node-based interfaces on purpose57:05 Final sendoff: go flux yourself and get out of here

In this episode of LAB the Podcast, poet and V3 artist Wendy Kieffer joins us for a conversation inspired by “The Patience of Ordinary Things” by Pat Schneider. From cups that hold tea to floors that receive our feet, Wendy and Zach reflect on the quiet love hidden in the simplest moments — and how seeing with fresh eyes can awaken gratitude, imagination, and joy.Take a pause from the hurry, pour a warm cup of tea, and join us as we reflect on patience, presence, and the extraordinary grace of ordinary thingsThank you for joining the conversation and embodying the life and beauty of the gospel. Don't forget to like, subscribe, and follow LAB the Podcast. Support / Sponsor: https://vuvivo.com/supportFor More Videos, Subscribe: @VUVIVOV3 | https://www.youtube.com/@VUVIVOV3Follow: @labthepodcast | @vuvivo_v3 | @zachjelliott | @wendy.kiefferLike:  https://www.facebook.com/vuvivo.v3Order Alchemy of Praise: https://www.amazon.com/dp/1944470220?ref=cm_sw_r_cp_ud_dp_3NCER8Y41NXPRQ5QE469&ref_=cm_sw_r_cp_ud_dp_3NCER8Y41NXPRQ5QE469&social_share=cm_sw_r_cp_ud_dp_3NCER8Y41NXPRQ5QE469&skipTwisterOG=2Support the show