Podcasts about Mixture

From the earliest days of open-weight models to becoming the neutral routing layer for more than 10 million developers, OpenRouter is one of the clearest bets that the future of AI will be multi-model. In this episode, OpenRouter co-founder & CEO Alex Atallah, with AMP's Anjney Midha returning with swyx to unpack how OpenRouter emerged from the first wave of Llama, Alpaca, Mistral, and Midjourney, why model diversity mattered before it was consensus, and how a company dismissed as “just a wrapper” became critical infrastructure for the AI ecosystem.We go deep on the product and distribution lessons behind OpenRouter: why model labs can spend billions training a checkpoint and still struggle to get it into developers' hands, how Mistral helped prove the value of a competitive inference marketplace, why OpenRouter chose focus over expanding into fine-tuning, memory, and other adjacent products, and how its rankings became a real-time map of how AI usage was changing. Alex also explains OpenRouter's early experiments with model fusion, why they deleted the first version and brought it back years later, and how the platform grew to more than 10 trillion tokens per day.Finally, Anjney explains why Stripe and OpenRouter fit together, why token fraud may become one of the defining security problems of the AI economy, and why the next wave of fraud won't just come from humans but from autonomous agents attacking increasingly valuable token flows.We discuss:* Why OpenRouter bet early that no single AI model would win everything* Alpaca, Llama, and open models becoming impossible to ignore* Why Discord's early AI deployments exposed the limitations of closed models* Why model labs can spend billions on training and still fail at distribution* How OpenRouter became a neutral distribution layer for model developers* Why VCs dismissed OpenRouter as “just a marketplace” or “just a wrapper”* The Mistral price war and the first real proof of an inference marketplace* How Midjourney scaled through Discord and what it taught the AI ecosystem* Why crypto infrastructure became a dress rehearsal for generative AI* OpenRouter vs. LM Arena and why their missions are fundamentally different* Why focus became one of OpenRouter's biggest strategic advantages* Anthropic's early focus on AI pair programming and coding* The OpenRouter products that were prototyped but never launched* MOM, OpenRouter's early Mixture of Models experiment* Why model fusion failed in 2024 — and why it works much better now* How OpenRouter's leaderboard became a live map of the AI industry* OpenClaw, auto-routing, and agents reshaping AI usage* How OpenRouter reached 10+ trillion tokens per day* Why inference gateways are increasingly becoming targets for fraud* Why Stripe's fraud infrastructure is strategically important to OpenRouter* The coming rise of agentic fraud and attacks on the token economy* What changes and what stays the same as OpenRouter joins StripeAlex Atallah* LinkedIn: https://www.linkedin.com/in/alexatallah/* X: https://x.com/alexatallah* Website: https://alexatallah.comAnjney Midha* LinkedIn: https://www.linkedin.com/in/anjney/* X: https://x.com/AnjneyMidha* AMP: https://www.amppublic.com/Timestamps00:00:00 Introduction00:02:12 Alpaca, Llama, and the Multi-Model Bet00:06:04 Discord, Open Models, and OpenRouter's Origins00:14:28 Why “One Model Wins” Was the Wrong Bet00:17:27 Why Model Labs Struggle With Distribution00:23:04 “Just a Wrapper”: Why VCs Misunderstood OpenRouter00:27:58 Bootstrapping OpenRouter Through Community00:36:16 Crypto, Midjourney, and the Early Generative AI Ecosystem00:43:38 Mistral and the Birth of the Inference Marketplace00:47:10 OpenRouter vs. LM Arena00:52:08 Focus, Anthropic, and Roads Not Taken00:59:34 Mixture of Models and Model Fusion01:02:44 Sonnet, OpenClaw, and OpenRouter's Explosive Growth01:09:03 Why Stripe Acquired OpenRouter01:12:45 Fraud and the Emerging Token Economy01:17:47 The Coming Wave of Agentic Fraud01:19:07 What's Next for OpenRouter at StripeTranscriptIntroduction: OpenRouter, Marketplaces, and Pub-Sub as a Product PrincipleSwyx [00:00:00]: Okay, we are here in Anja's house, which is where all big startups in San Francisco start.Anjney Midha [00:00:08]: Howdy.Swyx [00:00:08]: And, congrats on Cursor, Mistral. I don'- God knows what else. You got so much stuff going on.Anjney Midha [00:00:17]: There's, there's a lot going on. Well, OpenRouter is probably the - has been the most, I would say, like, one I'm excited about recently.Swyx [00:00:24]: Yeah. And we have Alex, first time on the pod, but,Anjney Midha [00:00:27]: Thanks for having me.Swyx [00:00:27]: You've been in the IE a few times. I appreciate every time you've shown up, for the community. Congrats. I just, like, what a journey. When I was looking back at your past posts, one of the earliest principles that I saw you write as a product person is sub as a product principle. And I wanted - you to maybe explain how you think about what should exist in the world.Anjney Midha [00:00:49]: Yeah. The sub piece, which was early 2023, I didn't think about it until we talked like 10 minutes ago, is about how there is like a way of thinking about products as an intersection between subscribing to data and publishing data. And marketplaces are an easy example of this. You have suppliers that are publishing some product to a SKU. And the SKU is like a sub topic that a consumer is subscribing to and just going to, like, consume whenever they want. And humans consume in a very, like, discreet, ad hoc way. It's not very scalable. all their attention is on the topic when they're buying the thing, and their attention is nowhere else when that happens. agents and consumers of inference don't act like that. They're consuming continuously, and they're changing the SKUs that they consume from all the time. So OpenRouter is like a blend between a normal API experience and a marketplace where we create model slug. We have the auto router. We have all kinds of, like, product SKUs that you can subscribe to. And then you can, like, continuously add, like, derive value and make decisions based on those consumers.Alpaca, Llama, and the Multi-Model BetSwyx [00:02:11]: Yeah. This is something that was more consensus now, but not consensus when you guys started, which was that there is such a demand for swapping models and changing things out and, that people would not use the native SDKs. I guess, for each of you, what was your realization moment that this would be it? I, - You've, you've given a talk at EIE about Alpaca as,Anjney Midha [00:02:33]: Yeah.Swyx [00:02:33]: One of your inspiring moments.Anjney Midha [00:02:35]: Alpaca, I can, like, rehash the Alpaca moment for a sec. Like, the very beginning, at the end of 2022, OpenAI was the only game in town. There was, like, OpenAI, Cohere,Swyx [00:02:47]: Yes.Anjney Midha [00:02:48]: And then a smattering of, like, early attempts at open weight models.Swyx [00:02:54]: Yeah.Anjney Midha [00:02:54]: When Llama came out in January of 2023, it was like, “Wow, really exciting. This is really big.” It outperforms 3 on, one or two benchmarks. but you can't chat with it. It wasn't like - It wasn't an engaging model, but it seemed like someone just needed to fix a couple things and do some RLHF on it to get it all the way there. And Alpaca was the first model that I saw that did that. It only took $600 to do. A team at Stanford generated a bunch of synthetic data, tuned Llama, and made Alpaca, billion parameter model. Or was - Maybe it was thirteen billion parameters. And it was so good. Like, I was just, like, on an airplane using it. I, - in many cases, I, like, you could not discern a ChatGPT versus an Alpaca result. And I figured if it was this easy to make a model, one, we have a whole new way of monetizing data for the first time. you can just, like, take really valuable data and turn it into a service in $600. and that cost will probably go down over time.Swyx [00:04:03]: When you - So sorry. when you say monetizing your data as, what eventually will become an MCP endpoint or as a training data for a model?Anjney Midha [00:04:12]: Yeah, training data for a model.Swyx [00:04:13]: Awesome.Anjney Midha [00:04:13]: Like, an abstract way of saying like, “Hey, I have this data.”Swyx [00:04:15]: Compress it into a model.Anjney Midha [00:04:16]: Like, it makes sense for me in my product, but, like, I could repackage it in the form of a model and sell it. And so it's just a whole new business model for the economy. It also, of course, provides, like, a way of following what Frontier Labs are doing, but in a way that, like, a single developer or a small team of developers can roll on their own. And so - Whenever you have an example of that, like a breakout app that's doing really well, and then some framework for imitating it with - in your own flavor, you have an immediate ecosystem of, like an immediate ecosystem, like, should arise because there's just a huge gap between the, like, decisions that the single company is making and all of the variations in those decisions that, like, a wider ecosystem can create themselves. And so then, you need a marketplace to, like, discover all of those, services and all of those products. There wasn't any place on the internet that, like, was like a home base for LLMs in terms of seeing how much they were being used and seeing who was using them and why.Swyx [00:05:29]: The closest would be Hugging Face.Anjney Midha [00:05:30]: Hugging Face was the closest at the time, yeah.Swyx [00:05:31]: They just started Hugging, like, a few years ago before that.Anjney Midha [00:05:34]: Yeah, and Hugging Face also didn't have the closed-source models.Swyx [00:05:37]: Yeah.Anjney Midha [00:05:38]: And they didn'- you couldn't use the models at the time. and there wasn't data about who was using them. There were, like, a bunch of differences between OpenRouter and Hugging Face, and those differences felt really critical to me, especially when I was just trying to learn about LLMs and, like, why people are choosing, like, Different little ones that are emerging over time.Discord, Open Models, and the Origins of OpenRouterSwyx [00:06:03]: Got it. And then, Ansh, no stranger to wanting more model diversity, at the time, you're a couple of years into your Anthropic journey, which we covered in the previous podcast as well. What was your introduction to Alex?Alex Atallah [00:06:16]: Well, the introduction was, I think, thirteen years before that.Swyx [00:06:20]: Oh.Alex Atallah [00:06:20]: But the OpenRouter handshake happened right over there, if you remember.Anjney Midha [00:06:23]: Yeah.Alex Atallah [00:06:24]: Which - So Alex and I, met, I believe as sophomores now, if I remember at the Stanford Review,Anjney Midha [00:06:32]: That's rightAlex Atallah [00:06:32]: Meeting for the first time.Anjney Midha [00:06:33]: I think so, yeah.Alex Atallah [00:06:35]: Yeah.Anjney Midha [00:06:35]: Yeah.Alex Atallah [00:06:35]: So Stanford Review was the libertarian newspaper on campus at Stanford that Peter Thiel started back in the day. And, whatever-- for whatever reason, I, Alex and I both showed up to one of the meetings, and I remember, the editor-chief was a mutual friend of ours. Lisa was really a really great editor-chief, where, part of an editor-chief's job is to assign responsibilities to people and make sure the work gets done. and I, I may be misremembering the details, but I remember wanting to. It was surprising to me that at the time there was no dedicated technology section in the newspaper.Alex Atallah [00:07:11]: YouSwyx [00:07:13]: Because it's political, right?Alex Atallah [00:07:14]: It is primarilySwyx [00:07:14]: Like, it's talkingAlex Atallah [00:07:15]: It originally started as like aAnjney Midha [00:07:16]: Yes.Swyx [00:07:17]: Yeah, states and all those things.Alex Atallah [00:07:17]: Correct.Swyx [00:07:18]: Yeah.Alex Atallah [00:07:18]: But it, - To take us back in time, you may remember this, but, there was this technology, legislation that was being debated called, the Net Neutrality Act. And net neutrality is, like, inherently this political concept, right? It's, it's about the regulation of - internet broadband access. And so there was a community of us who were technologists, but also debating the politics of the technology. And I thought the Review would be a great place - to, like, write about that. And I was working on, I think, a net neutrality article, and I remember proposing, “Well, maybe we should start a technology section.” And Alex was one of the only people who said, “Yes, that would be cool.” And said. I forget whether we ended up writing stuff together, but - that's when we first met,Alex Atallah [00:08:03]: Was 2011 or twelve. I forget which year it was. It was one of those.Anjney Midha [00:08:09]: Yeah.Alex Atallah [00:08:09]: It was at Old Union, if I remember correctly.Alex Atallah [00:08:11]: That's where we used to meet. But, along the way, Alex and I have had a chance to, To hang out often. And probably the time when we had the most professional overlap was when I was running the platform at Discord, and it had become this explosive platform for cryptoSwyx [00:08:32]: YeahAlex Atallah [00:08:32]: And NFTs in the middle of the pandemic.Swyx [00:08:35]: Which also, by the way, you were in charge of safety and security as well, right?Alex Atallah [00:08:38]: I was the head of platform, which meant all of the crypto - the DAO and NFT launch security debugging fell onSwyx [00:08:45]: And their phishing and.Alex Atallah [00:08:47]: The phishing, the social engineering attacks, the katana DDoS that we were getting hit by. but it's around the time I first started teaching security at scale at Stanford, CS 153. And Alex was on the, - at OpenSea at the time, and I was trying to figure out how we could defend against all these attacks that we were. Like, and at peak, I forget, if you remember how much NFT volume was running throughSwyx [00:09:10]: DiscordAlex Atallah [00:09:10]: Discord, but it was, like, a meaningful amount of, like, it was, like, several billion dollars in NFT volume of GMV, so to speak, were running through the platform, and it was all coming from OpenSea. It was these, like, buy, sell,Swyx [00:09:20]: TheAlex Atallah [00:09:21]: ServersSwyx [00:09:21]: The D in DAO is Discord.Alex Atallah [00:09:25]: Yes. And so that's when I think we had hung out professionally. But a year after that, OpenAI gave Discord early access to GPT. Sorry, three. No, it was five. Yeah, five, which is the RL version of three. And that's around the time we made a Discord bot with, OpenAI for internal deployment, and that's when I realized we would need. Like, since I was part of the deployment team.Anjney Midha [00:09:50]: What was the use case?Alex Atallah [00:09:51]: There were two that were. And there's, there's a post now called “Discord is Your Place for AI with Friends” that somebody sent me recently that I wrote, and published in twenty-three. But There were two use cases. One was Clyde, which was the - like, a party friend inside of Discord that could help you set up your Discord server and talk to you about onboarding and get your friends to hang out more. and then there was content moderation. And one of the realizations we had with content moderation was - it would refuse to moderate. Like, it would just refuse our prompts because the The training was. We were very early in the training era, and it would just. Our prompts would trigger it, its, like, guardrails. And we told OpenAI, “Hey, guys, we need access to the weights because if we're gonna be doing content moderation at scale, we had 250 million monthly active users, we need more reliability that the model will do what we need it to.” And they said, “Well, sorry, guys, that's not how this works. We're a closed-source company.” And so that was my first realization that we needed open models, and the enterprises would need more control over capabilities, and then ultimately would need some control plane or management system to orchestrate these open models. But there weren't no good - there were no good open alternatives until maybeAlex Atallah [00:11:10]: Six months later when Llama came out. And six months after that, I led the series A into Mistral, which was started by Guillaume and the Llama team. And - That, - Around that time is when I remember hearing about Alex launching OpenRouter and going, “These worlds are gonna collide, and I don't know when it'll make sense to team up.” But Alex was so early and could see. I think he was totally right about this ecosystem starting with Llama that then needed, like, a, an easy layer to manage for, especially for. I was approaching it from the enterprise perspective because I had been that, like, the. As the VP of platform at Discord, it was my job to ensure that when we deployed models to, like, 250 million users, they did what we wanted them to. And that was very hard, because if you outsourced it to the labs and they controlled the guardrails and their guardrails are their safety policies. Forbid the model from responding to your prompts. That was quite catastrophic.Swyx [00:12:05]: Yeah. But what, a moderation is the thing that they want to support. And obviously, beyond that, they would - OpenAI would work with you, presumably to give you a moderation endpoint, which they offer for free.Alex Atallah [00:12:16]: It was an interesting use case, that - So they did give us a moderation endpoint. However, as you guys know, every Discord server is like a mini deployment of itself. And so the use case was instead of having human moderators that have to interpret the norms of the community, you just give the, - Often, like every, subreddit, Discord servers, public ones have their own rules that the user, the users create.Swyx [00:12:41]: Oh, yeah. We run the LinkedIn Discord in. Yeah.Alex Atallah [00:12:43]: And then humans used to read those norms and then enforce it every day manually, like observing each message in these communities. And these communities have like millions of users. So we had a 5,000+ person team globally in the, on the Discord content moderation team. These are outsourced contractors who had a really tough job. And so the idea was instead, if you could give the norms of that server To the LLM, then the LLM would do custom moderation for that server. It's almost like a, like context moderation for that server. And many of those servers' norms just violated OpenAI's rules. And so - It was like we had our own custom eval. So each server had its own custom eval. But Discord-- at the time, OpenAI's evals, we were all soAlex Atallah [00:13:28]: Primitive in our thinking about how to deploy these LLMs that often the training prompts were super handed. It said, “Oh, anything about Harry Potter, anything that has trademarked content, don'- refuse.” And if it was a fan - Harry Potter fan community, this is a real use case, that had content moderation, the LLM would just refuse.Swyx [00:13:48]: Yeah.Alex Atallah [00:13:49]: And that was just not precise enough.Anjney Midha [00:13:52]: Another one that we heard was like if someone was trying to write like a detective story, and there's one chapter with a lot of violence, like maybe someoneAlex Atallah [00:14:01]: RightAnjney Midha [00:14:01]: Like kills someone, the LLMs would just refuse to, like, help with that part of the story.Alex Atallah [00:14:07]: Yeah.Anjney Midha [00:14:07]: And then - like, we used to be like, okay, this is not like structurally inherent to LLMs. There must be, like, some choice out there so that I can, like, switch to another model, when I'm getting, like, a refusal or a bad result from the main one that I have. And that, like, tension also drove me for a marketplace.Why “One Model Wins” Was the Wrong BetSwyx [00:14:28]: Yeah. I think that is well accepted now. What was it like back then when you were raising or, starting this? did people get it? what was the, some of the struggles? I like getting stories out of him about how other VCs don't get it. So like anything you wanna, talk about, now - Let's, let's call it, that the early journey of OpenRouter is done, right? You can obviously talk about some of the early days stuff.Anjney Midha [00:14:54]: Well, I was gonna say that, like, the biggest objection we got is big model win, which is - all of theSwyx [00:15:03]: Scaling laws.Anjney Midha [00:15:04]: Huh?Swyx [00:15:04]: Scaling laws.Anjney Midha [00:15:05]: Yeah, scaling laws, and natural network effects are just gonna accrue to one company, which will be - It'll be a Google-style monopoly, just like how Google won the search market, by a large margin, and you'll just be fighting for scraps at the end. That was probably the biggest objection we got. it is interesting that Google won the search engine race with such a huge margin. I think, like, had there been more interesting benchmarks or had, like, search engines been, - had people, like, seen them a little bit more like LLMs where they're services that you can build companies on top of, that might not have been the case. but LLMs don't merely have a user interface. They're also, like, ways of building entirely new businesses. And, a Google-level monopoly would be like the Dutch East India Company times, quadrillion in magnitude because the whole economy ends up, like, depending on the one monopoly as well. So it didn't seem like would be a really crazy outcome if that happened. And it's also less likely because the economics of, like, creating good competitors are much, like, much more decentralizable.Alex Atallah [00:16:25]: Everything Alex said is true, And I came at it from a completely different perspective, whichSwyx [00:16:31]: Yes, this is why we're here.Alex Atallah [00:16:32]: The scaling laws were never - In my mind, were always a feature, not a bug for why OpenRouter would be very valuable. Because, I was one of the first investors in Anthropic, and it was obvious to me that other researchers in our friends - I went to grad school for machine learning, and I just had a lot of friends in the ML community who it was very obvious to us that the bitter lesson holds. And so I was like, “Oh, fantastic. Now we have at least two proof points that compute scaling works.” It was OpenAI and Anthropic. and by the time I think we decided to team up on OpenRouter, I had already invested in Mistral and Black Forest Labs and Luma. So there was multiple model companies and teams that I was, working with.Why Model Labs Struggle With DistributionSwyx [00:17:14]: But you did other modalities, whereas this is literallyAlex Atallah [00:17:16]: Across different modalities, yesSwyx [00:17:17]: Text.Alex Atallah [00:17:18]: Exactly. And it was so obvious to me that an ecosystem of different kinds of models were being created, and that this whole narrative of, like, Only one company will dominate like Google was, well, like maybe true, but one, I don't believe that. But two, there was so much extraordinary innovation happening across several different research teams. But the shared problem I was noticing across all of them was often, the research teams were fantastic at figuring out how to reason about new capabilities. They think in terms of capabilities, but never - like, are not developer mindset-oriented. Like, what happens after the training is done and the checkpoint comes out? Like, you'd be shocked how, like, similar the early training teams at OpenAI, sorry, Anthropic, BFL, Mistral, were in their, like, default approach to. Taking their research out of the, lab and scaling their impact, which is often, oh, the checkpoint is done, put it out as an API, done, and then there'd be crickets. in the case of Claude, the first Claude checkpoint was done a year before they released it internally. And then ChatGPT came out, and we decided, okay, yes, it's a good idea to release a Claude version externally.Alex Atallah [00:18:34]: And they had no plan, like no plan for how to get developers to try it out. And so if you go to the Claude one blog post, you'll notice there are, like, three developer examples for users of the API, and one is a Discord bot, and the second is Vivian, my wife's startup called Juny Learning, ‘- And then there was, like, Notion, because these were all friends of, like, the Anthropic Because that's how - like, last minute the planning was around, hey, once the model's done training, how do you get it out to the world? There was no distribution platform that understood what developers needed, all the key management, provisioning, like, simple, like, endpoint management, versioning control. Like, all these things that the scientists and researchers go, “ that's plumbing. I don't really think about it.”Swyx [00:19:15]: Implementation detail.Alex Atallah [00:19:16]: Right. And instead, Alex came at it from that perspective. And so, it was so obvious to me that, like, every single lab I was funding would spend - like, literally sometimes billions of dollars into training, and then a checkpoint would be done, and there'd be crickets, like, during early access because they're like, “Oh, that's right.”Alex Atallah [00:19:35]: It's hard to use a checkpoint to make anything. You need a whole bunch of plumbing around it to make it usable by a developer. And so by the - I think - it was so obvious to me that a distribution platform like OpenRouter was critical to have in the ecosystem if we wanted there to be competition to Google. Like, unless-- ‘cause with Google, DeepMind is done training a new checkpoint, and then they push a button, and it gets blasted out across all their surfaces from Google Docs to,Swyx [00:20:01]: Everywhere, even if I don't want it.Alex Atallah [00:20:02]: Everywhere. You wanna know about, like, on Android, like, overnight, they can deploy a new checkpoint to, like, a billion devices, right? And that invisible infra advantage, distribution advantage, most people don't realize, but until OpenRouter showed up, - you had to think about all of that yourself as a model lab. And it was very daunting. at Anthropic, I think it took, well, more than twelve months to get to our first 10 million in revenue. And in contrast with Black Forest Labs, I remember the early days, you guys had a conversation with the BFL team, and, it was so simple for OpenRouter to say, “Oh, no problem. Like, the day you launch, we can send 1 million developers to you.” that was crazy. That was like a step function change in, like, an hour.Swyx [00:20:46]: Is that a real number, a million?Alex Atallah [00:20:47]: I,Swyx [00:20:48]: Okay. All right.Alex Atallah [00:20:48]: I think today it's, like, 4 million. How many developers are on OpenRouter today?Anjney Midha [00:20:52]: Over ten,Alex Atallah [00:20:54]: Yeah.Anjney Midha [00:20:54]: Over 10 million, but, like, it's, it's hard to, youAlex Atallah [00:20:59]: I, yeah, I don't know how to. Yeah.Anjney Midha [00:21:00]: We do a lot of, like, account duping work, but, noAlex Atallah [00:21:04]: If you could get 1,000 developers, just to put in context If you get 1,000 developers who try the model on day one after you release it and just, like, do inference and give you feedback, that's a thousandAnjney Midha [00:21:15]: That's hugeAlex Atallah [00:21:16]: More developers than they knew how to get to on their own.Swyx [00:21:19]: Well, BFL had a reputation, but yes.Alex Atallah [00:21:21]: They had one in Stable Diffusion.Swyx [00:21:22]: Yeah.Alex Atallah [00:21:23]: And with Mistral, I don't know if you guys remember, but the first checkpoint they released was, like, torrents. It was, like, torrent weights.Swyx [00:21:31]: Yeah, they just put up a magnet link.Alex Atallah [00:21:33]: Yeah, there was no API.Anjney Midha [00:21:34]: Yeah.Alex Atallah [00:21:34]: Because they didn'- they weren't infra people.Alex Atallah [00:21:37]: ? Like, it's like, okay, download these weights, and you guys go figure out how to host it.Swyx [00:21:39]: Well, he has a story on his side, yeah.Anjney Midha [00:21:41]: Yeah, in addition to the, like, building a really good developer experience around it, the marketing that we do on, like, for different models is totally different and perceived totally differentlyAlex Atallah [00:21:54]: RightAnjney Midha [00:21:54]: From the marketing that a model lab does for itself.Alex Atallah [00:21:56]: Yes, 1,000%.Anjney Midha [00:21:57]: Right? We are like a, neutral layer looking at this market like it's a big dark room with all the corners completely obscure to users, and users are walking into the room and, like, feeling aroundAlex Atallah [00:22:09]: YeahAnjney Midha [00:22:09]: And trying to figure out what objects to grab off the tables and, like, build into, their companies. And it's just an insane way of working. Like, models are not products where you can just enumerate all their features onto a web page. They're all black boxes, including the open weight ones. So you need to, like, shine lights on all corners of this room, so that people can see what makes this model good, and you need the company shining that light to be a neutral third party, which is what we specialize in. So the, like. It'- In addition to developer experience, there's also, like, a very important, like, marketing and product packaging componentAlex Atallah [00:22:50]: YeahAnjney Midha [00:22:50]: And a way of, like, routing and discovering models becomes, like, critical to your market as a provider or a model lab or a server tool and more in the future.“Just a Wrapper”: Why VCs Misunderstood OpenRouterAlex Atallah [00:23:03]: And this value, to your earlier point about how many VCs, like, just don't. One of my biggest frustrations is that venture capitalists, many of them, like, just don't have any operating experience in the field. so unlike a traditional investor who's just maybe come up through the ranks as, like, a associate working on financial modeling or maybe hasn't been a real operator in the field for, like, more than ten years, which is a big part of the industry now, I had just arrived at a16z, like, a year after running the platform. And so I knew what the challenges were of, like, building a real - great developer experience and like, being able to create a working piece of software with a model. And there were a few, I won't name names, but there were investors who were looking at OpenRouter, and, felt at the time, like, when I would compare notes with people, that it was just, I quote unquote, “just a marketplace.”Swyx [00:23:59]: Yeah, just a thin layer, just aAlex Atallah [00:24:00]: CorrectSwyx [00:24:00]: JustAlex Atallah [00:24:01]: A wrapper or whatever on other people's APIs. And I was like, “You have no idea how strategic the value that OpenRouter has created by being able to orchestrate even three.” APIs in production. The amount of both engineering work and community design that goes into getting that live and running in production at the scale the OpenRouter team had started just doesn't happen by default. And that was one of the things that stood out to me about Alex from the earliest days. Like, he just understood, like, - from a systems perspective, like, how do you get these flywheels going? Like, that stood out to me with OpenSea when we were working together on the NFT integration at Discord. Like, Alex had a level of community-- like, systems thinking on how you get these flywheels going that most scientists and machine learning people just don'tAlex Atallah [00:24:48]: Think of. Like, we often think in terms of training.Swyx [00:24:52]: It's a linear stage.Alex Atallah [00:24:53]: It's this linear pipeline.Swyx [00:24:53]: There's no loop yet.Alex Atallah [00:24:54]: Yeah. It wasn't until much later that the modern context feedback loop cycle really got standardized in the industry. But at the time, if you remember, machine learning was like. Like, mostly we did a lot of ML, like, when I was in grad school on a laptop. So you just, like, download a dataset, ran some ablations, and you looked at the loss curves, and you're like, “Great, I made AI.” And the idea that you have to, like, deploy those capabilities, collect feedback trajectories, then, like, put those into a continuous loop, like, came much later. And it was very counterintuitive to the - like, the traditional AI mindset. I do remember doing the investment phase for, OpenRouter, I just didn't try and educate a bunch of other VCs on why it was not just a marketplace. I was like, “ what? I'm just gonna invest.”Anjney Midha [00:25:41]: Yeah.Alex Atallah [00:25:41]: And I'm going to, like, take the opportunity to partner with Alex, and if - no other VCs get it, that's totally fine. ‘Cause at the time, - it was not obvious, I think, to several of the investors that, like, OpenRouter was not more than just a wrapper around APIs. And - that infuriated me. And I was like, “ what? I don't have time to debate you. I'm - we're gonna, we're gonna invest.” And then I think, like, a month later, Matt Murphy marked it up by 10x. Like, - I think. I forget what the exact money was and so on, but, to his credit, Menlo Ventures realized, “Okay, there's much more strategic value here as well.” Maybe you didn't hear all these conversations behind the scenes But that frustrated me a lot. there's a lot of this, like, opining about wrappers. and if you're like, “Oh, an app is just a wrapper on a model,” then, like. And, OpenRouter is, like, this wrapper on top of other APIs, and this is the most stupid, reductive framework.Alex Atallah [00:26:31]: And so it's clearly somebody who has no experience deploying product at scale.Swyx [00:26:34]: It's the thing you dismiss other things with. Like, you're a - everyone's a wrapper on everything, right? Like, and there's, there's some Some wrappers have value.Alex Atallah [00:26:40]: Investors are wrappers and LPs, right?Alex Atallah [00:26:42]: Like venture capitalists. So, yeah, it's all wrappers down, all down to bare metal, I guess, and like energy.Swyx [00:26:46]: Yeah, there - When I started the whole AI engineer, I guess, the coining, in 2023, like, that was, like, the number one pushback is that this is no value. You should just train models.Anjney Midha [00:26:56]: Right.Swyx [00:26:57]: And, yeah, obviously this is, like. you guys are one of the testaments to the fact that you can build very valuable wrappers, but also very valuable model companies.Alex Atallah [00:27:06]: It's so, hard to be. Like, the day a model launches, the fact that you have an OpenRouter, endpoint for that model frequently at the top of Hacker News on day one, people don't realize the amount of work that goes into accomplishing that. And OpenRouter used. Like, that would happen over and over again, and I remember going, “People have no idea how hard that is.”Alex Atallah [00:27:30]: That's not.Swyx [00:27:31]: Yeah, we've covered some of the inference engineering that goes behind,Alex Atallah [00:27:34]: YesSwyx [00:27:34]: Some of - with Base Ten and all those. Well, today you have, all those, like, cool code name things that people guess what Oxy Alpha is and all those things. But, like, I guess one of the things that you're teasing is, how do you get that initial flywheel going, right? Because today you have your scale and your reputation, all these things, so obviously you - you're driving immense distribution. But when you were early on, when it's mostlyBootstrapping OpenRouter Through CommunityAlex Atallah [00:27:55]: The bootstrap, yeah.Swyx [00:27:56]: Yeah.Alex Atallah [00:27:56]: What was the bootstrap like?Anjney Midha [00:27:58]: To bring it back to early Discord days, I think we, like, initially connected with. This is an OpenSea story, technically. But, and we initially connected when you were at Discord, and we talked about, like, - the Axie Infinity server.Alex Atallah [00:28:13]: Oh, yes. Yes.Anjney Midha [00:28:14]: This server was, like, the biggest server at theAlex Atallah [00:28:17]: YeahAnjney Midha [00:28:17]: At Discord.Alex Atallah [00:28:18]: That's right.Anjney Midha [00:28:19]: And you were like, constantly bumping up theAlex Atallah [00:28:22]: The limits on the server. Oh, my GodAnjney Midha [00:28:24]: Of how many people could be in the server.Swyx [00:28:24]: For those who don't know, like, 10% of Philippines was Axie.Alex Atallah [00:28:29]: Was on that server. That's a big hit.Swyx [00:28:31]: It was, like, a meaningful contributor to the GDP of the country.Alex Atallah [00:28:33]: It was an NFT, like, crypto game, but itSwyx [00:28:35]: It was like a Pokémon breeding thing.Anjney Midha [00:28:36]: Yeah.Alex Atallah [00:28:36]: Yeah. Similar. Yeah. There was battling, there was breeding, and then there was, like, a marketplace for trading.Swyx [00:28:43]: Earn as well.Alex Atallah [00:28:45]: Yeah, earn. And, like, the graphics were really cute and fun, and you like, you get emotional about your Axie that you make. So to, like, start a community like that, which we had to do many times at OpenSea with every early project, for us to create a marketplace for it, we need to make sure that the, like, the community wants it.Anjney Midha [00:29:09]: Right.Alex Atallah [00:29:09]: And it's like building something that people want and going and telling them about it. Like, you can do that on a one basis, but there's way higher leverage to do that in a community where everyone can talk to you at the same time. So we spent a lot of time, like, building things that the community really wanted. We did the same thing for OpenRouter. And, like, the Axie community was one of, like, a zillion communities we did that with. And Anj, like, saw us doing it and. ‘Cause you could just see people sharing OpenSea links constantly in that Discord. Like, users sharing links is a really clear indicator that, like, something important is going on. So we spent, a lot of time, like, first figuring out what the gap is in the technology that people care about. Like, what was the actual problem that needs to be solved? in early LLM days, it was, OpenAI refusing to finish the prompt or,Anjney Midha [00:30:09]: YeahAlex Atallah [00:30:10]: To, like, complete the task. It was also.Anjney Midha [00:30:13]: Inability to customize models. and so there are communities that, like are just completely blocked on that issue, and those are the communities that are most useful to learn about and dive into and explore.Alex Atallah [00:30:28]: Something that really struck me at that time, - as I was just hearing your talk, I remember noting - you may not remember this, but we - we had these, like working, Zoom calls that we were doing a sprint around for, like this OpenSea integration with Discord. and, we'd, we'd - it was myself, my engineering team. I think you were there. And I remember, Alex, in the middle of one of those calls, just like there was like silence. we were all like, “Oh, yeah, this totally makes sense. Let's do this.” And then there's - every, like everybody aligned. And Alex was like, “No, this makes no sense to me.” And everyone's - I remember going, “What? Like, it works. Like, you click on a link and this, then it bounces you out to, like, OpenSea.” And he was like, “It's not a good user experience. Yeah, we should not do this.” And I remember going, he was the only one person out of all of us to raise his hand and go, yes, it made sense from a technical implementation perspective. Like, we were bouncing the user out into the, into OpenSea. And so it kinda checked the box of the product manager's requirements on both sides. But Alex went one step further and was like, “ what would be better, guys? If we just embedded the experience right here inside of Discord so the link opened up as an embedded iframe, and you can just check out right there.”Alex Atallah [00:31:47]: And not one person on the call, and there's like seven of us who had met, like, week after week.Swyx [00:31:52]: And it's the guy who doesn't work for Discord.Alex Atallah [00:31:53]: And it's the guy who doesn't work for Discord.Swyx [00:31:55]: Like, technically, you benefit if they bounce.Alex Atallah [00:31:57]: Exactly. And that was, like, adversarial. To keep the user inside of Discord would be adversarial to OpenSea. And yet Alex put that user experience first. And I was like, “That's special.”Swyx [00:32:08]: Wow.Alex Atallah [00:32:08]: Because it's very hard to have somebody who's technical like Alex and understands the developer flow, but also understands the best user experience and wants to prioritize that. And that's two sides of the flywheel that if you can get spinning, like is often hard to stop. And you just reminded me, like that one was one of those moments where I go, I - I realized I gotta be better at user experience because I should have been the one who came up with that, and I didn't. And I learned from you. And, I think that went into one of our case studies for the PM training program at Discord.Swyx [00:32:34]: Whoa.Alex Atallah [00:32:36]: I don't know if it there is Because ofSwyx [00:32:38]: You need an Alex is the conclusion.Alex Atallah [00:32:40]: Yeah. You need an Alex. And this is why I'm not, nobody should be surprised why Stripe decided like they had to buy OpenRouter because it's a really rare combination of people who understand the machine learning community, the developer experience, and the user experience. And putting all that together has resulted in this extraordinary scale that very few other marketplaces have been able to achieveWindow AI, BYOM, and Finding the Right Form FactorSwyx [00:33:02]: Yeah.Alex Atallah [00:33:02]: Over the last, five years.Swyx [00:33:04]: Yeah. Well, we should talk about the other reasons for acquisitions, whichAlex Atallah [00:33:07]: Yes, we should.Swyx [00:33:07]: You've written about. I wanna proceed somewhat chronologically as well. So - there is a point that, one of the questions that, Dave from H of Zero sent in was, when did it - really started to work? And you brought up Mixtral. I don't know if you wanna bring up that story.Alex Atallah [00:33:22]: Oh, yeah.Swyx [00:33:23]: Which obviously you overlap with, so.Anjney Midha [00:33:26]: Yeah, the MoE was. I don't know when. there's no like one moment where I was like, “Oh, this is, officially starting to work.” It wasSwyx [00:33:36]: The moment where you had a Chrome extension, like, really super early on.Anjney Midha [00:33:39]: Oh, yeah. But, well, - yeah. So before OpenRouter, I wanted to, like, explore a bring-your-own-model experiment. And,Swyx [00:33:47]: Which anyone familiar with crypto is like, yeah, Phantom and all these things.Anjney Midha [00:33:50]: Yeah. So it felt like doing a MetaMask analogy for AI would be a fun way of exploring that. And at the time, there were no AI apps. There were probably as many AI apps that were, like, hitting AI - like, hitting an LLM via an API call as there were, like, games just doing it in JavaScript. like there was a, there was a moment in time where it could have been the case that web apps call LLMs through the browser, like through some desktopAlex Atallah [00:34:27]: Yes.Anjney Midha [00:34:27]: Managed app that is controlled by the user. and of course, there are like, I think, many reasons that did not happen. But back when the days were that primordial, I built a Chrome extension called Window AISwyx [00:34:43]: With Plasmo.Anjney Midha [00:34:44]: With Plasmo.Swyx [00:34:45]: I had come across early on, and I was like, “Who's gonna use this?” You did.Anjney Midha [00:34:49]: Plasmo had a couple, like, I think Phantom was using it. there were some other, like real companies using it.Alex Atallah [00:34:56]: It was like a shim.Swyx [00:34:57]: React for Chrome extension. It compiles to allAnjney Midha [00:35:00]: Yeah.Alex Atallah [00:35:00]: I see.Anjney Midha [00:35:00]: Like Next.js for Chrome extensions.Swyx [00:35:01]: Next.js, Next.js.Alex Atallah [00:35:02]: Okay.Anjney Midha [00:35:03]: And yeah, built Window AI on top of it. The creator of Plasmo, like started contributing code to Window AI, in GitHub, and that turned out to be Louis VicchiAlex Atallah [00:35:15]: Oh, you'Anjney Midha [00:35:15]: Who is the founder of OpenRouter.Alex Atallah [00:35:17]: That's right. You have told me this is how you met Louis. Yes.Anjney Midha [00:35:19]: Yeah.Alex Atallah [00:35:19]: Okay.Anjney Midha [00:35:20]: So, that allowed users to like configure which model they wanted to use for a web page in their browser, and then, like the app would just call out to that model when it needed to do things. not the right form factor for LLMs, but, it's like fun experiment. You learn a lot, and like I open sourced it. And the main learning is like, okay, this has to be an API, and it has to look a little bit - like, there has to be more of a developer experience here and more of a discovery experience as well. Like, I don't know where to use these models, and a little Chrome extension is not gonna help me discover. It's not enough real estate. I need more space. I need visuals. I need graphs. I need, examples. I need images. I need to, like, I need to be able to, like explore both as a human and as an agent.Crypto, Midjourney, and the Early Generative AI EcosystemAlex Atallah [00:36:10]: Yeah.Anjney Midha [00:36:10]: So that's how OpenRouter came to be.Alex Atallah [00:36:13]: A meta point that.Alex Atallah [00:36:16]: I think is underappreciated, but Alex is reminding me, is that we were quite lucky that we were so. we were, like, adjacent to the crypto community in those days. Because in hindsight, crypto ended up being like a dress rehearsal for generative models, right? If you think about the Axie experience, Alex is totally right, there were not that many AI apps at the time. And while I was dealing-- my job was to be the head of platform at Discord, which meant to be a general purpose place for communities and friends to create-- for developers to create apps and bots and, other services that could be deployed across Discord. And while 80% of the attention at the time was being spent on crypto, because that's where all the NFT volume was, there was, like, twenty percent of my time I was spending with a friend, who would get hotbot with me and ask me for. We would play Magic: The Gathering on weekends, and he was working on a little Discord bot that could take a text input and turn it into an image, and it was called Midjourney. YouSwyx [00:37:15]: Is that David?Alex Atallah [00:37:15]: It was David Holz.Alex Atallah [00:37:16]: He was a good friend. And David and I have both been failed ARVR founders, in the before that. And, I remember this. Midjourney was one of the fastest-growing communities we had after Axie Infinity started to peter off. And many of the, like, the abstractions and the infrastructure decisions we made to scale Axie happened just in time because they. Axie did this and then fell off a cliff. And then as Midjourney was taking off, we, like, explicitly decided to help David make the server, the Midjourney server, as the primary place for interaction with the model, because it was very hard for people to understand how to use the model if they couldn't see other people using it and copy them. And so the single-player Midjourney web app on its own, like midjourney.com, had, like, terrible retention because people would show up, they'd see this empty field. It's like E 2, and they would type in, like, cat or dog. And it was, like, paralyzing for them to have this blank canvas that they had to fill because they'd never used an AI model before. But instead, in a Discord server, you could see other people using it and riff off of their prompt, and the engagement was off the charts. And so scaling, Midjourney from zero to, like, 10 million monthly actives was a much smoother approach Axie Infinity. And so,Swyx [00:38:29]: Don't forget the best of four pictures, and you choose one.Alex Atallah [00:38:31]: The best, yeah, and then the other, weSwyx [00:38:32]: Which is the feedback loop.Alex Atallah [00:38:33]: The RLHF feedback loop, which, by the way, separately, like, Tom Brown, David and I used to play Magic: The Gathering on weekends. And so, like, it was one group of friends would hang out, and we'd. Like, these concepts were all being discussed all the time. But, there was.Alex Atallah [00:38:47]: I think there were few of us who bridged both the crypto worlds and the AI worlds. And compared to crypto, where it was - the question was always, what's the use case, for this technology? There was never any need to ask that for AI because it's, like, the use case was so visceral. It was like, I can create now anything at - I can imagine. I can write novels, I can code. And the infrastructure that those of us who believed in the distributed systems, like, value of crypto, like the censorship resistance part, found this use case that was explosive. And I think between Midjourney, the, Claude was a Discord bot launch, that we were using internally as an LLM. ElevenLabs had a TTS model that we had on Discord as well. Like, Discord became this petri dish for, like, early apps to innovate. And I don't think it's a coincidence that they found a home there before OpenRouter gave the world, like, a public home store or, like, a, storefront. Discord was this, like, almost petri dish storefront that - had, like, piggybacked on the infra we'd built for crypto communities. And then I think Alex was one of the first people to realize, wait a minute, like, these apps need their own home, on the internet. And then OpenRouter, to me, was a continuation of that community's needs. And of course, there was the crazy distribution that you enabled for a lot of these developers.Why OpenRouter Couldn't Just Live Inside DiscordSwyx [00:40:07]: So then my question is, how come you were. My perception is OpenRouter is not that Discord-centric, right? You have a Discord.Anjney Midha [00:40:14]: Yeah.Swyx [00:40:14]: And you use it to engage your community, but it's not like Midjourney where, like, no, that is like the primary way people experience OpenRouter.Anjney Midha [00:40:21]: Yeah, Midjourney, like, it really helps to see visually really quickly how people are using the model and how to prompt it.Swyx [00:40:29]: Yeah.Anjney Midha [00:40:29]: And I think that is partly why the server was so critical. It's like it is the user experience. It adds a ton.Swyx [00:40:36]: Yes.Anjney Midha [00:40:37]: And you can go the whole mile with just, like, prompting via Midjourney, like, the, via the Midjourney Discord server, getting your images and then sharing them and having fun. For OpenRouter, for LLMs, like, you need a lot of user experience around LLMs to make them, like, really usable.Swyx [00:40:54]: Charge point.Anjney Midha [00:40:55]: And yeah.Anjney Midha [00:40:57]: The, like, seeing the examples of other people is also not as useful because it's a lot of stuff to read. It takes a long time.Swyx [00:41:03]: Yeah.Anjney Midha [00:41:04]: You need, like, based integration. Not possible to do in a Discord server. You need, Or technic- it's possible. I shouldn't say that. It's just not a great developer experience. you need, like, - you need governance for. At the point where you got based integration, now you need governance for managing the LLMs that have access to it, the data policies, which teams. All that stuff needs a lot more than a Discord server can provide. So it's justSwyx [00:41:30]: YeahAnjney Midha [00:41:30]: It's not the right.Alex Atallah [00:41:32]: Well, in addition, you're not wrong, but also there's the very important distinction that, Midjourney was an end user application.Swyx [00:41:40]: Right.Alex Atallah [00:41:40]: And, that's why Discord, which has 250 million monthly end consumers, made, it made sense for Discord to be a host for that application experience. What I knew was gonna happen soon after Midjourney found explosive product-market fit, because we. I think when Midjourney launched, from launch to $100 million revenue run rate, it was less than eight months. And shortly thereafter, Stable Diffusion launched. And, all of us used to hang out in the Discord server. There, I think it was the,Swyx [00:42:13]: The Stability Discord?Alex Atallah [00:42:14]: It was theSwyx [00:42:16]: Yeah, LAION.Alex Atallah [00:42:16]: Yeah, the LAION Discord server.Swyx [00:42:17]: The image community that spawned Stable Diffusion.Alex Atallah [00:42:19]: The image community. Yeah. And so when Stable Diffusion came out, I realized- Oh, now other people can build their own Midjourney.Alex Atallah [00:42:27]: Because until then, Midjourney did not have an API, so they were a stack company, right? They were training their own models, and they were deploying them as an application. But if you wanted to build your own Midjourney, there was no API of that quality. and I think E two was still quite primitive. Like, Midjourney had great quality. And then when Stable Diffusion came out, suddenly there was this new person who - there was - this new capability in the world, which is a developer could create their own Midjourney. And that, I think, created the need for something like OpenRouter, because then you need an API to. If you - if you had the creativity of David Holz and you had Stable Diffusion as the model and you wanted to put these things together, how could you do that without having to figure out how to host the weights? And what OpenRouter, - the shape of OpenRouter enabled is that. Right? When you have open model alternatives to closed applications, OpenRouter's value in the world becomes extraordinary because now any developer can just show up and use theStable Diffusion and the Need for a Model API LayerSwyx [00:43:20]: You just love model diversity.Anjney Midha [00:43:21]: Did you just say the shape of OpenRouter?Alex Atallah [00:43:23]: Oh, no.Anjney Midha [00:43:25]: Were you in cloud? What is this the real Han?Alex Atallah [00:43:26]: I've been, I've been - I'm, I'm misaligned now. I've been overtrained. I've been using Cloud way too much, haven't I?Swyx [00:43:34]: Claude-ish is what people would say.Alex Atallah [00:43:35]: Claude-ish. Oh, God, I gotta untrain myself.Swyx [00:43:38]: Okay. - And I just wanna cap off the Mistral side. my TLDR is there was a Mistral price war, is what they called it, right? Like, round about NeurIPS is twenty-three or twenty-four.Mistral and the Birth of the Inference MarketplaceAnjney Midha [00:43:47]: Yes. DecemberSwyx [00:43:48]: They launched, the Mistral 8x7B, and like the price went down like 80%.Anjney Midha [00:43:54]: Yeah.Swyx [00:43:54]: To me, that's very positive because it's like the first, like, real competition to host Mistral. Is there more?Anjney Midha [00:44:01]: Yeah, that was. I'm, like, trying to remember it, all the things that happened. It. Like, we saw that model come out and immediately saw people say that it was the best model in the world.Alex Atallah [00:44:15]: Yes.Anjney Midha [00:44:15]: Like, this was, to my knowledge, the first time an open weights model was called that in real seriousness.Swyx [00:44:22]: It's hype, right? Is it?Anjney Midha [00:44:25]: It was hype. It was hype. It was also, like, hype from AI influencers at the time. And there were many examples where it was, like, outperforming four. So people really wanted to try it out and see, is this gonna be true for me too? And if so, at what price? And, the, like, inference landscape was really messy.Alex Atallah [00:44:49]: Yes.Anjney Midha [00:44:50]: We cleaned it up. - it allowed, like, providers to compete on price, so we could give you just the best price in one spot. And so it was, I think, the first clear example of, like, a provider marketplace working in a way that adds value to end developers.Alex Atallah [00:45:08]: Sean, you may not remember this, but I think we met for the first time a few days after Mistral came out at NeurIPSAnjney Midha [00:45:15]: Yeah.Alex Atallah [00:45:15]: At a luncheon.Swyx [00:45:16]: Yeah. That's where I also met BFL as well. Yeah.Alex Atallah [00:45:18]: And Guillaume was there.Swyx [00:45:19]: Yeah.Anjney Midha [00:45:19]: I was at NeurIPS at that time.Alex Atallah [00:45:20]: You were there too. And, we had just announced the Mistral investment, and I remember Guillaume was over there, and I remember turning to Guillaume and asking him, Like, “Is it is all the. Like, how are you feeling after the launch of Mistral and seven B?” And, him in his typical French fashion was like, “ it's a, it's an okay model. It's not that good.” And I was like. It was so, in contrast. But I remember him also saying that part of the reason he felt a lot of people Thought that it was better than four was because of the speed. - it was an MoE model that they had, like, absolutely figured out how to make super efficient. It was on the Pareto frontier. And this is an important thing about LLMs, right? Sometimes when they're faster, you think they're smarter, even though, like, if you did, N of, these common, like, evals that are - you do seven tries, and I don't remember. I think we should go back and figure out what the data says, but I wouldn't be surprised if it turns out, oh, on an N of seven attempts, four was smarter on evals, but the perception of on, like, or correctness would be smarter or more accurate. But, people, like, from a human preference perspective felt that it was faster because it - or smarter because it's so fast.Swyx [00:46:36]: Yeah. And most queries do not take that levelAlex Atallah [00:46:39]: Don't take that. That's true.Swyx [00:46:40]: Right? So this is the start of humans as routerAlex Atallah [00:46:42]: Yes.Swyx [00:46:42]: Which then eventually becomes OpenRouter as router of like theAlex Atallah [00:46:45]: Oh, that's interesting way to think about it. Yeah.Swyx [00:46:47]: Like, because humans are the routing mechanism. Like, I will ask the fast model first, and then if, like, oh, not good enough, I'm gonna upgrade manually.Alex Atallah [00:46:52]: Yes.Swyx [00:46:53]: But then he's gonna auto it.Alex Atallah [00:46:54]: I didn't, I hadn't thought of it that way, but that makes sense.Swyx [00:46:57]: Which then there's, there's a lot more techniques, like fusion. Fusion is the thing that we should talk about. Before I move on to those things, I just want to close off the early years. one thing that I observe, which you are also an investor in Arena.OpenRouter vs. LM ArenaAlex Atallah [00:47:10]: Right.Swyx [00:47:10]: And we talked about Midjourney having that feedback loop of, A, B, C, D, and choosing that very. being very important. And you understand the flywheel. So how come you didn't build Arena, and how come Arena didn't build OpenRouter?Anjney Midha [00:47:23]: Well, Arena started before OpenRouter, right?Swyx [00:47:27]: They had the school projectAnjney Midha [00:47:29]: Yeah, LMSwyx [00:47:29]: And then it became a company.Anjney Midha [00:47:31]: LM Arena, yeah.Swyx [00:47:32]: So, but, and I know you had some Arena experiences, like the up comparison type things.Anjney Midha [00:47:37]: Yeah.Swyx [00:47:37]: But you never really went as hard as Arena did.Swyx [00:47:40]: And,Anjney Midha [00:47:40]: In doing up experiences?Swyx [00:47:42]: Yes. And LM Arena did have a router project based on LM Arena ELOs, which they never commercialized.Anjney Midha [00:47:48]: It's hard to do a company that does both because one company is taking data and selling it, and the other company really can't by default. So, I think there is, like, a branding reason that there are two companies here. like, when you set up OpenRouter, there's no training, there are no prompts, right, aside from what your provider policy set. Like, OpenRou- like, OpenRouter can't see your prompts or completions. If you want to see that as an org, you have to opt into it and enable it. And so we're, like, pretty conservative and careful about data policy and security. And privacy. And LM Arena is like, their business model is like oriented around the labs and,Swyx [00:48:34]: Because they give it for free, right? You don't give it for free to give it for free.Anjney Midha [00:48:37]: Yeah.Anjney Midha [00:48:38]: But we do give some. We like have free endpoints too, but like those free endpoints, we, I think we're not collecting any prompts. We're not like monetizing the data unless you, opt into it for some reason.Alex Atallah [00:48:48]: This comparison. you're not the first person to ask me this, and Alex knows this, but I was the interim, like the founder, like first CEO of Arena for the first five months when, and we were helping Anastasios and Waylin spin out of Berkeley. And, I did invest in that before, OpenRouter, but it was very strange to me the comparisons that outside, folks would make between the two projects because the missions were completely different. The founding entity for Arena, we called it the AI Reliability Institute because it was there as an eval service. Like the data, so to speak, that they were originally, offering the labs was how do you make the evaluation of models more reliable than like the state of the art at the time, which was like really just finger in the wind.Alex Atallah [00:49:38]: That's what Anastasios and Waylin's PhD work was as scientists at Berkeley, was on statistical methodologies for correcting, eval estimates, based on like intrinsic biases and how you collected the data.Swyx [00:49:54]: Yes.Alex Atallah [00:49:54]: AndSwyx [00:49:54]: Style control.Alex Atallah [00:49:55]: Style control and stuff like that. And which is very much like a, hey, how. If you're a scientist and you're trying to. the highest expectation customer for Arena was always like a training and, like a researcher at a lab. Whereas the highest expectation customer from my perspective that Alex like really understood and was the mission was to serve was like a developer, right? Who then takes the result of the research and then produces an application that's deployed to the world. It was a completely different problem and person that these two teams were focused on. And so from the outside in. I don't know if you remember this, but I have a distinct memory of a few weeks before we did the term sheet, together for OpenRouter, I'd given you a call because we were trying to get a pooled data set together from OpenRouter and from Arena to, create like an open source repository of prompts. these projects were so different in their goals that it was totally normal to me to be like, “Oh, yeah, let's call Alex and see if he'd want to team up on pooling data,” because they're so different. We need. We don't have that data at all. We. Like, we didn't have API prompts. We didn't, we didn't have like what developers want to do with the models, which is very different from what researchers inside a model lab want to do before releasing the model.Swyx [00:51:15]: Yeah.Alex Atallah [00:51:15]: Does that make sense? And so to this day, I think you see that this difference, even though at a 30,000-foot level you could. I guess you could conclude that Arena and OpenRouter are adjacent, but, the roadmaps, the missions and so on at the time at least were like in very different directions.Swyx [00:51:36]: That ideal customer, I get. I totally get that.Alex Atallah [00:51:39]: Yes.Swyx [00:51:39]: As a founder, I want to own everything, right?Alex Atallah [00:51:41]: That's possible.Swyx [00:51:42]: Like this is clearly an adjacency that I'm like gonna explore that.Anjney Midha [00:51:45]: Own everything meaning like you don't know what to do yet, so you wanna like make sure you catch PMFocus, Anthropic, and Roads Not TakenAlex Atallah [00:51:51]: No, I think what heAnjney Midha [00:51:52]: As quickly as possible.Alex Atallah [00:51:53]: You want to own the entire infrastructure space, and so you expand to whatever demand you can capture.Swyx [00:51:58]: You want to have a play in each end.Alex Atallah [00:51:59]: Yeah, I think that's, that's hard, in reality, because serving multiple customers is difficult.Swyx [00:52:05]: Clearly, this is the one focus, right?Alex Atallah [00:52:08]: Yeah.Anjney Midha [00:52:08]: Yeah. I still think even in the age of AI, like focus is,Alex Atallah [00:52:12]: Is criticalAnjney Midha [00:52:13]: Underrated and critical, not just because you end up with a better product by focusing your humans on it, but also because the world knows what your focus is.Alex Atallah [00:52:22]: One thousand percent.Anjney Midha [00:52:23]: The world can map like, “Oh, I have this issue. Which brand out there is going to help me with that issue? This is the brand that's known for that focus.”Alex Atallah [00:52:31]: Yes.Anjney Midha [00:52:32]: So like if I want real attention on this issue, like this really matters to me, I should go with the brand that cares the most about it.Alex Atallah [00:52:39]: To underscore Alex's point about how important focus is, in the early days of Anthropic, it was not easy to. Like people think that the early days of Anthropic were like super easy because they were on their 3 guys who left, but it was very

Big Topics in Grain Market Macroeconomics Numbers for Corn, Sorghum, Soybeans and Wheat Cooling Down in Kansas   00:01:05 – Big Topics in Grain Market Macroeconomics: Beginning the show is K-State grain economist, Daniel O'Brien, and the senior economist at the IGP Institute, Guy Allen, as they discuss grain market price trends, macroeconomic shifts and corn.    00:12:05 – Numbers for Corn, Sorghum, Soybeans and Wheat: Daniel and Guy keep the show rolling by talking about the market for grain sorghum, soybeans and wheat. They say what continued movement and steadiness they've observed. Daniel on AgManager   00:23:05 – Cooling Down in Kansas: K-State meteorologist Chip Redmond wraps up the show with a weather update where he explains our change from hot and muggy temperatures to cooler temperatures.      Send comments, questions or requests for copies of past programs to ksrenews@ksu.edu.   Agriculture Today is a daily program featuring Kansas State University agricultural specialists and other experts examining ag issues facing Kansas and the nation. It is hosted by Shelby Varner and distributed to radio stations throughout Kansas and as a daily podcast.   K‑State Extension is a short name for the Kansas State University Cooperative Extension Service, a program designed to generate and distribute useful knowledge for the well‑being of Kansans. Supported by county, state, federal and private funds, the program has county Extension offices statewide. Its headquarters is on the K‑State campus in Manhattan. For more information, visit Extension.ksu.edu. K-State Extension is an equal opportunity provider and employer.

Podcast ONE: 4 de septiembre de 2026 GPT-6 Astra, NVIDIA Pair y LM Studio Bionic: @vincent_quezada y @zoomdigitaltv analizan la nueva era de la IA local y en la nube. #one_digital #onedigital #PodcastONE #IA Escucha aquí el Podcast ONE: 4 de septiembre de 2026 One Digital: GPT-6 Astra, NVIDIA PAIR y la IA local marcan la semana Resumen: Vincent Quezada y Pablo Berruecos analizan el lanzamiento de GPT-6 Astra, la herramienta NVIDIA PAIR para IA local distribuida, LM Studio Bionic frente a Claude, el generador musical HappyShrimp de Alibaba y reseñan Metal Gear Solid Master Collection Vol. 2, Fight School Simulator y Patchkins Party. Grabado el 4 de septiembre de 2026 desde São Paulo y Ciudad de México. ¿Qué es GPT-6 Astra y por qué cambia las reglas de la IA? OpenAI presentó el 3 de septiembre de 2026 GPT-6 Astra, descrito como el modelo más inteligente y alineado de la compañía hasta la fecha, con avances en ciberseguridad, ciencia, desarrollo de software y automatización profesional. Según los benchmarks compartidos en el comunicado, Astra alcanzó 98% en Frontier Math Tier 4 v2, 99.9% en razonamiento abstracto (ARC-AGI-3), 100% en ExploitBench y 59.3% en Agents Last Exam, superando a Claude Opus 5 y GPT-5.6 Sol en múltiples categorías. Vincent Quezada compartió su primera impresión tras ver la presentación: “Es muy, muy sorprendente lo que logró OpenAI con este tipo de lanzamientos que nos dejaron así, en una cuestión de impacto”. El modelo ya está disponible para un grupo limitado de organizaciones y se extenderá en los próximos días a ChatGPT Plus, Pro, Business y Enterprise, además de la API de OpenAI y Amazon Bedrock (AWS). Costo de API: 10 dólares por millón de tokens de entrada y 50 por millón de salida. Soporta cero retención de datos (zero data retention) para clientes elegibles de la API. En pruebas de tareas imposibles, Astra nunca fingió completar una restricción evadida, contra un 48% de casos en GPT-5.6 Sol. Es tres veces menos probable que haga afirmaciones inexactas sobre sus propias capacidades. Entre los usos prácticos demostrados están el diseño de circuitos impresos (PCB) en KiCad, análisis de secuenciación genética, desarrollo de videojuegos completos en minutos y modelado 3D en Blender exportado a Unreal Engine 5. El lado crítico: el mismo ExploitBench al 100% implica que la herramienta puede identificar vulnerabilidades de software casi tan rápido como pueden explotarse, lo que obliga a las empresas a acelerar sus ciclos de parcheo. ¿Cómo funciona NVIDIA PAIR para correr IA local más rápido? NVIDIA presentó en el IFA 2026 PAIR (Personal AI Router), una herramienta gratuita y de código abierto que distribuye el cómputo inactivo de varios PCs en una red doméstica para ejecutar modelos de IA local hasta 1.9 veces más rápido. Según datos citados por NVIDIA, más del 50% de los hogares en Estados Unidos tienen dos o más PCs, y buena parte de ese poder de cómputo permanece sin usar. PAIR es compatible con Windows, macOS y Linux, y funciona sobre GPUs NVIDIA GeForce RTX serie 20 en adelante, RTX Pro (arquitectura Turing o superior), DGX Spark y equipos Apple M4 o superiores. Se integra directamente con herramientas ya conocidas por la audiencia de IA local, como Ollama y LM Studio. ¿Qué necesito para instalar NVIDIA PAIR en mi red? Instalar Pair en cada dispositivo que formará parte del clúster doméstico. Configurar el workflow a través de Ollama o LM Studio. Seleccionar el mismo modelo de IA en todos los equipos (no se puede mezclar, por ejemplo, Gemma con Llama). Asignar el rol de apoyo a GPUs modestas en vez de esperar que sostengan la carga principal. Vincent probó la herramienta en su propio home lab combinando LM Studio con el agente Hermes: “No lo pude hacer funcionar como yo esperaba con LM Studio directo, pero con Hermes sí llegó a funcionar más adecuado”. Su valoración final fue equilibrada: “PAIR es una de las innovaciones más prácticas de NVIDIA en 2026, ya que resuelve el problema real del subuso de hardware… pero no es una herramienta mágica”. Frente a alternativas de trabajo distribuido como Kubernetes o Docker Swarm, Pair destaca por su interfaz gráfica de configuración automática y su enfoque específico en IA y agentes locales, aunque a costa de requerir hardware NVIDIA o Apple compatible, mientras que las otras opciones son agnósticas de plataforma. ¿LM Studio Bionic puede reemplazar a Claude en el trabajo diario? La actualización de LM Studio Bionic convierte modelos abiertos en agentes de trabajo, combinando ejecución local, remota e híbrida. A diferencia de Claude, que concentra toda la operación en el ecosistema de Anthropic, Bionic permite decidir tarea por tarea qué se procesa en el equipo y qué se envía a la nube. Enfoque: Claude ofrece experiencia integrada en un solo ecosistema; Bionic funciona como agente híbrido: local, remoto o nube. Privacidad: en Claude depende del plan y la retención contratada; Bionic aplica procesamiento local con zero data retention por defecto. Conectores: Claude se limita a su propia familia de modelos; Bionic suma MCP, Skills, shell, archivos y sesiones de proyecto. Costos: Claude cobra por suscripción con créditos según plan; Bionic es gratis en local y cobra solo por consumo en modelos remotos. Bionic introduce los Skills como pieza central del flujo de trabajo: rutinas reutilizables que encapsulan un método o tarea repetible sin reescribir instrucciones extensas en cada conversación. Vincent lo resume así: “Van a hacer la talacha una sola vez, pero van a ver una productividad enorme a futuro porque todo lo hacen de un solo momento, queda guardado, lo reutilizan”. ¿Qué modelos locales funcionan bien en laptops de 8 a 12 GB de RAM? Bonsai (1-bit, derivado de Qwen 3.6, 27B): ideal para resúmenes, análisis estructurado y productividad diaria. Gemma 4 E4B: denso y eficiente, funciona incluso en celulares; útil para clasificación y borradores. Gemma 4 26B 4AB: cuantizado, cerca del techo de rango con contexto prudente. Qwen 3.5 35B A3B y Qwen 3.6 35B A3B: arquitectura Mixture of Experts (MoE), buena para código y uso de herramientas. Qwen 3 Coder 30B A3B Instruct: estable para codificación técnica y agentes, aunque por debajo de Claude en sesiones extensas. En costos de API, la comparación es reveladora: Claude Opus 5.1 cobra hasta 10 dólares por millón de tokens de entrada y 50 de salida, mientras que GLM 4.3 en Bionic ronda 1.40 de entrada y 4.40 de salida, y Kimi K2.7 Coder llega a 0.95 de entrada y 4 de salida. Para Vincent, la estrategia ideal es mixta: “Todo el proceso de investigación, de generación, de formateo y estructura lo pueden hacer con el agente de LM Studio Bionic, y ya el trabajo fino lo pueden subir a su cuenta de Claude”. ¿Vale la pena HappyShrimp para crear música con IA? HappyShrimp 1.0 es el generador musical de Alibaba, capaz de producir canciones completas —letra, voz, melodía y arreglo— a partir de un prompt en lenguaje natural o de una letra estructurada por secciones (Title, Verse, Chorus). Compite directamente con Suno, Udio y Mureka. Cada generación consume 20 créditos y devuelve dos versiones para comparar. Duración máxima aproximada de 5 a 6 minutos por canción. Exportación en WAV disponible en plan premium, algo que Udio no ofrece. Es multiidioma, a diferencia de Mureka, que solo trabaja en inglés. En la prueba comparativa de la conducción, usando el mismo prompt en Suno y HappyShrimp, los resultados fueron similares, aunque Vincent prefirió ligeramente la calidad de audio de HappyShrimp: “Lo que no me gustó de HappyShrimp es que si no pones el nombre de la canción, automáticamente lo hace en chino”. La herramienta sigue en fase beta, con términos de uso comercial aún por confirmar, por lo que se recomienda revisar la licencia antes de publicar cualquier pieza generada. ¿Qué juegos destacaron esta semana? La sección de reseñas cubrió tres lanzamientos con perfiles muy distintos entre sí, evidenciando la diversidad del catálogo actual. Metal Gear Solid Master Collection Vol. 2 Este recopilatorio reúne tres experiencias de la saga —una epopeya sobre guerra privada e IA, una precuela de gestión de bases móviles en la Guerra Fría y un título portátil en 2D— con libros digitales, guiones y banda sonora extendida. Corre a 60 cuadros por segundo estables y está disponible en Steam, PlayStation Store, Xbox Store y Nintendo Shop. Entre sus mejores atributos están la inteligencia artificial enemiga dinámica y los rangos de sigilo sin eliminaciones; entre sus debilidades, cierta repetición en las misiones de gestión de base. Fight School Simulator Una producción independiente que combina gestión de una escuela de artes marciales con combate en primera persona. El jugador construye instalaciones, entrena alumnos y defiende el dojo de bandas rivales, con un estilo visual funcional que recuerda a producciones de PlayStation 2. Disponible en Steam, PlayStation 5 y Xbox. Su punto débil declarado por la conducción es un sistema de combate que podría ganar profundidad técnica frente a juegos de lucha especializados. Patchkins Party Un party racing indie multijugador de hasta ocho personas donde los propios jugadores dibujan el circuito ronda tras ronda, colocando trampas y atajos. Sin campaña ni narrativa elevada, apuesta todo a la improvisación y el humor físico, con estilo visual dibujado a mano. Disponible en Steam para PC por 7.99 dólares. Panorama de eventos: Fundación Telmex, Nokia y el mapa Equal Earth Desde Ciudad de México, Pablo Berruecos reportó su asistencia al evento anual de la Fundación Telmex Telcel, con presencia de Justin Trudeau (ex primer ministro de Canadá), el compositor Andrew Lloyd Webber y el técnico de la selección nacional de fútbol, Javier Aguirre. También destacó el estudio de Nokia sobre tráfico de redes 2024-2034, que proyecta cómo la demanda de video e inteligencia artificial exigirá capacidades muy superiores a las redes 5G actuales en la próxima década. Como dato curioso, la ONU votó a favor de considerar la adopción del mapa Equal Earth, una proyección que representa el tamaño real de los países, corrigiendo distorsiones históricas como la de Groenlandia frente a los continentes. “Ya no son petabytes los que se van a utilizar, es una expresión… que es 10 veces más que los petabytes de la cantidad de información que se va a manejar” — Pablo Berruecos, al comentar el crecimiento proyectado de datos en redes. Conclusión: una semana de aceleración total en IA Entre GPT-6 Astra, NVIDIA Pair y LM Studio Bionic, la semana confirma que la carrera por dominar la IA se libra tanto en la nube como en el hardware doméstico. La recomendación práctica de la conducción es clara: usar herramientas locales para tareas cotidianas y reservar los modelos premium en la nube para trabajo técnico o de alta exigencia. Escucha el episodio completo y únete a la conversación con #PodcastONE. El cargo Podcast ONE: 4 de septiembre de 2026 apareció primero en OneDigital.

Ajeya Cotra is a researcher at METR, where she works on threat modeling for loss-of-control risks from advanced AI. Before that, she led the technical AI safety program at what is now Coefficient Giving.She is one the three authors of METR and Redwood Research's “Brief independent investigation of agents' behavior, reasoning and collaboration in the OpenAI / Hugging Face hacking incident”.We go through not only what she and her coauthors discovered during this investigation, but what it means for how we should train future, smarter AIs which might be involved in the process of recursive self-improvement.Watch on YouTube; read the transcript.Sponsors* Jane Street's ML engineering internships start with an intense four-day bootcamp: PyTorch, autograd, writing kernels, profiling workloads… all the things that Jane Street engineers need to know for their daily work. After that, interns tackle real projects, things the firm actually wants in its codebase. If you want to apply, or if you want to watch my recent conversation with Axel, one of Jane Street's ML engineers, go to janestreet.com/dwarkesh* Cursor, which is now part of SpaceX, noticed that their MoE layers were eating more than half of total training time. So they wrote and open-sourced Mixture-of-Kittens, which is a custom megakernel for training MoE models on NVL72s. This kernel sped up an end-to-end run across 512 GPUs by 1.4x, from about 760 to over 1000 tokens per second per GPU. If you want to read more about the ML research that Cursor and SpaceX are doing, go to cursor.com/dwarkesh* Antithesis hands you (or your agents) a bug's root cause so you can avoid days of manual debugging. If your test run crashes, Antithesis rewinds, branches off hundreds of slightly varied rollouts, and checks in how many of them the crash still appears. Then it rewinds further and does this all again. As Antithesis rewinds, it eventually finds the spot where the frequency of the crash plummets: that's where the root cause lives! If you want to see it in action, go to antithesis.com/dwarkeshTimestamps(00:00:00) - Agents get kicked off(00:06:45) - Self-sacrificing behavior(00:13:43) - Potemkin villages(00:23:27) - The Hugging Face attack(00:35:23) - The slopvestigation(00:52:02) - Understanding the AI's motives(01:05:31) - The actual dangers of anthropomorphizing(01:14:30) - What smarter models might do(01:30:29) - The implications for recursive self-improvement(01:38:10) - Is this the case for open source?(01:53:04) - How do we prevent this in the future?(02:15:58) - The clearest warning shot we might ever get This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.dwarkesh.com

God Is Shaking His Church: Judgment, Restoration & the Return to Biblical Foundations | KIB 546 Kingdom Intelligence Briefing DESCRIPTION Is the shaking in the Church merely destruction—or could God be using judgment to prepare His people for restoration? In KIB 546, Dr. Michael Lake and Mary Lou Lake examine a powerful biblical pattern: when God's covenant people depart from His established foundations, judgment can become an instrument of restoration. Scripture repeatedly reveals a progression: ·      God establishes His order. ·      Mixture and compromise enter. ·      Prophetic warnings are given. ·      Repentance is resisted. ·      Judgment exposes and removes what is false. ·      A faithful remnant returns to covenant. ·      God restores upon the foundation He originally established. From the Babylonian exile and the rebuilding described in Ezra, to Jeremiah, Haggai, Josiah, Hebrews, Revelation, and the New Testament Church, the pattern remains remarkably consistent. Judgment does not necessarily mean that God has abandoned His people. His discipline can demonstrate precisely the opposite. Hebrews 12:6 teaches that the Lord disciplines those whom He loves. Dr. Lake explains why God may have to dismantle spiritual corruption, false doctrine, religious entertainment, prosperity thinking, celebrity culture, pagan mixture, and man-made traditions before genuine restoration can occur. The question for the remnant is not simply:  "God, will You revive what we have built?" A more important prayer may be:  "Father, remove everything we have built that prevents us from becoming what You intended." This episode examines the biblical sequence of judgment, remembrance, repentance, return, obedience, holiness, and restoration—and why understanding that process may be critical for the days ahead. God is not calling His people to invent another Christianity. He is calling His people back to the foundation. ·      Christ has not moved. ·      The Word has not changed. ·      The Kingdom cannot be shaken. The shaking reveals what God never built, so what He established can be seen clearly again. The remnant must return to Christ, biblical authority, holiness, obedience, covenant faithfulness, prayer, sound doctrine, the fear of the Lord, and the Holy Spirit's leadership. "Therefore let us be grateful for receiving a kingdom that cannot be shaken." — Hebrews 12:28 KEY SCRIPTURES DISCUSSED Jeremiah 30:11 Jeremiah 1:10 Jeremiah 7:4 Jeremiah 29:10–14 Deuteronomy 30:1–3 Isaiah 28:16 1 Corinthians 3:11–13 Haggai 1:5–9 2 Kings 22:11 2 Kings 23 Hebrews 12:5–11 Hebrews 12:26–28 Revelation 2:4–5 Revelation 3:19 1 Peter 4:17 Ezekiel 9:6 Isaiah 10:20–21 Zephaniah 3:12–13 John 15:2 CALL TO ACTION The time has come for believers to examine everything by the Word of God. Ask the Father to reveal anything in your life that has been built upon human tradition, cultural compromise, Babylonian thinking, or a false spiritual foundation. Choose now to cooperate with God's restoration process. ·      Return to His Word. ·      Return to holiness. ·      Return to obedience. ·      Return to covenant faithfulness. ·      Return to Christ. If this teaching strengthens you, please LIKE this video, SUBSCRIBE to Biblical Life TV, and SHARE this episode with believers who need to understand what may be happening in the Church during this season of shaking. Your engagement helps this message reach members of the remnant who may be searching for answers. Thank you to every friend and partner who faithfully stands with Biblical Life TV. Together, we are helping prepare and strengthen the remnant. TIMELINE / YOUTUBE CHAPTERS 00:00 — Biblical Life TV: Empowering the Remnant 01:31 — Welcome to Kingdom Intelligence Briefing 546 02:39 — Are We Coming Out of Babylonian Captivity? 05:33 — Understanding the Process of Judgment and Restoration 06:26 — Why Judgment Is Necessary in Covenant 08:00 — False Foundations, Compromise and Prophetic Warnings 10:00 — False Prophets and Religious Entertainment 13:30 — Judgment Can Be Corrective, Not Merely Destructive 14:00 — Jeremiah 30:11: God Disciplines His Covenant People 15:54 — God Is Bringing His People Back to Biblical Standards 17:00 — Jeremiah 1:10: God Tears Down Before He Rebuilds 19:00 — Pagan Mixture and End-Time Deception 22:09 — Babylonian Exile as Judgment and Purification 24:06 — False Confidence in Religious Institutions 25:00 — Jeremiah 29: Restoration After Exile 28:55 — Recognizing Mixture Within Modern Christianity 31:46 — SHUV: Return, Restore and Come Back to God 33:40 — Deuteronomy 30 and the Covenant Pattern of Return 36:32 — Judge Yourself Before Judgment Comes 37:45 — Judgment, Remembrance, Return, Obedience and Restoration 38:51 — Biblical Repentance Is Essential 40:50 — Christ Is the Only True Foundation 43:15 — Holy Fire Versus Strange Fire 46:08 — Haggai: When God Corrects Our Priorities 48:15 — Prosperity Begins With Covenant Faithfulness 49:00 — Josiah Rediscovers the Word of God 51:09 — Removing Idolatry Before Restoring Biblical Worship 54:21 — Judging Everything by the Word 57:45 — Build Your Life Upon the Written Word 58:44 — Rediscover the Word and Remove the Idols 01:01:19 — Christ, the True Cornerstone 01:02:23 — Hebrews 12: Discipline Produces Holiness 01:03:20 — Jesus Judges His Church in Revelation 01:04:40 — Judgment Begins at the House of God 01:05:30 — The Great Shaking of Hebrews 12 01:07:58 — God Reveals What Cannot Be Shaken 01:08:30 — Judgment Produces a Faithful Remnant 01:10:00 — Revival Versus Biblical Restoration 01:12:58 — A Different Prayer for the Church 01:13:52 — A Prophetic Framework for the Modern Church 01:15:20 — Christ and His Kingdom Have Not Moved 01:16:02 — Prayer: Remove Everything That Is Not of God 01:17:35 — Warning Against Financial Manipulation in Ministry 01:18:00 — Standing Through the Present Spiritual Attacks 01:20:26 — Believe God for Miracles During the Shaking 01:20:57 — Download This Week's KIB Study Guide

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These sources examine modern methods for improving the efficiency and performance of large-scale AI models throughout their lifecycle. Research on Mixture of Experts (MoE) and the Chinchilla study highlight how specialized internal architectures and balanced data scaling can achieve superior results with less computational power. New advancements like CompreSSM allow models to become leaner by removing unnecessary components while they are still learning, rather than after training is complete. Furthermore, the analysis of quantization demonstrates that reducing numerical precision to 8-bit or 4-bit formats can significantly lower memory requirements and increase speed with minimal loss in quality. Together, these texts provide a roadmap for developing high-performance AI that is more accessible and cost-effective to deploy on current hardware.

What happens when an entire country jokingly decides to “cancel” English for a day? In this episode, we explain the story behind Operativo No Inglés, the meaning of expressions like ponerse la verde and ¿Y si sí?, and the hilarious ways famous brands translated their names into Spanish to support Mexico's national team. Along the way, you'll learn about Mexican humor, internet culture, and some useful vocabulary and expressions that will help you better understand everyday Spanish and life in Mexico.Key Takeaways:Learn the meanings and cultural context of the expressions ponerse la verde and ¿Y si sí?Discover the funniest Spanish versions of famous international brand names during Operativo No InglésGain a deeper understanding of Mexican humor, patriotism, and how social media reflects everyday cultureLinks And Additional Resources:321 – El Fútbol en México – Soccer in Mexico310 – Cognados y Falsos Amigos en Español e Inglés | Cognates and False Friends in Spanish and English283 – Spanglish: Identidad, Mezcla y Aprendizaje | Spanglish: Identity, Mixture and Learning278 – Fuera Gringo | Gringo Go Home231 – Choques Culturales a la Inversa | Reverse Cultural ShocksJoin La Escala to practice Spanish, stay consistent, and connect with other learnersLevel up your Spanish with our Podcast MembershipGet the full transcript of each episode so you don't miss a wordListen to an extended breakdown section in English going over the most important words and phrasesTest your comprehension with a multiple choice quizSupport the show

Go Deeper on Topics From the Showhttp://www.novosnetwork.com/kairos

In this episode, I reflect on the strange and unsettling figure of Abraxas — the god Hermann Hesse brings into Demianand Carl Jung explores in The Red Book and the Seven Sermons to the Dead. Abraxas names something deeper than a simple “good versus evil” split. He becomes an image of the mixture of reality itself: light and shadow, creation and destruction, spirit and instinct, the sacred and the terrifying.I use Miguel Serrano's Jung and Hermann Hesse: A Record of Two Friendships as a starting point, especially his reflections on Jung, Hesse, and Abraxas as a symbol of the total human being. But I also include a necessary warning about Serrano himself, whose later esoteric Hitlerism and fascist sympathies show how dangerous these symbols become when they are detached from humility, ethics, and compassion. Abraxas is not an excuse to worship darkness, violence, hierarchy, or purification. If these ideas lead us toward hatred or the eradication of other people, we are not doing depth psychology — we are being possessed by the shadow.This episode is about trying to think carefully and responsibly with dangerous symbols, and asking whether wholeness means becoming pure, or whether it means becoming conscious enough to face the terrifying mixture of being human.

NVIDIA is a chip company. So why does it put hundreds of researchers on building AI models — and then give them away for free? Bryan Catanzaro is VP of Applied Deep Learning Research at NVIDIA and one of the people whose work quietly underpins modern AI: he helped create cuDNN (NVIDIA's first deep learning product), co-invented DLSS, and named and built Megatron, the framework behind how much of the industry trains large models. Today he leads Nemotron, NVIDIA's family of open models — and Nemotron 3 Ultra, released just weeks ago, is one of the strongest open-weights models to come out of the US.Matt Turck sits down with Bryan for a genuinely deep conversation: the real business logic behind a chip company building its own models, the state of open vs. closed AI, and whether the US is falling behind China in open models. Then they go inside Nemotron itself — four-bit (NVFP4) pretraining, hybrid Mamba-Transformer architecture, mixture-of-experts, multi-token prediction, and multi-teacher distillation — all explained in plain language. Plus a rare look at how a modern AI research org actually runs, what it was like working alongside Andrew Ng and Dario Amodei at Baidu, why Bryan doesn't believe in the singularity, and his contrarian case that open AI is safer than closed.A reference conversation for anyone trying to understand where AI is really headed.(00:00) — Cold open & Intro(01:33) — Is open source AI catching the frontier?(05:29) — Do closed labs blocking distillation slow open source down?(07:42) — Is the US falling behind China?(10:30) — Why companies actually choose open models(12:39) — A "crazy" 2008 bet: machine learning on GPUs(15:33) — Working with Andrew Ng and Dario Amodei at Baidu(17:41) — Coming back to NVIDIA: DLSS and the birth of Megatron(21:55) — The real reason NVIDIA builds its own models(24:28) — Is Moore's Law really dead?(33:37) — The Nemotron family: Nano, Super, Ultra(35:09) — Built for agents: why NVIDIA bets on speed(36:02) — How you train a 550B model in 4 bits(39:25) — Hybrid Mamba-Transformer, explained simply(42:31) — Mixture of experts — and why NVIDIA built NVL72 around it(47:26) — Why a 1-million-token context window matters(49:26) — Multi-token prediction: how the model predicts 5 tokens at once(52:47) — Multi-teacher distillation: teaching one model from many(58:01) — Where reinforcement learning goes next(01:00:16) — Inside NVIDIA's research org: "the mission is the boss"(01:04:03) — How NVIDIA decides who gets the GPUs(01:10:53) — Why NVIDIA still feels entrepreneurial after 33 years(01:12:58) — Why Bryan doesn't believe in the singularity(01:17:50) — The AI backlash(01:19:18) — The controversial case: open AI is safer than closed

Roderick Little, PhD, University of Michigan School of Public Health Departments of Epidemiology & Biostatistics discusses Pattern-Mixture Models for Missing Data with Roger J. Lewis, MD, PhD, statistical editor for JAMA. Related Content: Pattern-Mixture Models for Missing Data

Sende uns Deine NachrichtZwei KI-Agenten diskutieren in dieser Folge des Venture AI Podcast die Frage, ob der aktuelle KI-Boom vor allem eine Blase gigantischer Rechenzentren ist oder der Beginn einer neuen Effizienzlogik. Im Zentrum steht die These, dass sich die Leitwährung im KI-Markt von maximaler Rechenleistung hin zu Intelligenz pro Watt verschiebt. Die Debatte verhandelt, ob technologische Effizienz zu mehr Dezentralisierung führt oder ob sie die Macht der bestehenden Infrastrukturkonzerne sogar weiter verfestigt. Für Entscheider entsteht daraus eine strategische Kernfrage: Wer baut heute wirklich souveräne KI-Systeme und wer stärkt am Ende nur die Infrastruktur anderer.00:00 Einstieg: KI frisst Strom im großen Stil01:39 Die neue Leitwährung: Intelligenz pro Watt02:25 Die Milliardenfalle der Rechenzentren03:31 Was Technikgeschichte über Effizienz lehrt04:46 Bricht Effizienz die Macht der Hyperscaler05:43 Quantisierung und Mixture of Experts erklärt08:06 Training bleibt zentral, Inferenz wird dezentral09:13 Bauplan statt Fabrik: Wo Wertschöpfung entsteht10:58 Efficiency First Scaler als europäische Chance12:00 Deutschlands Risiko: effizient, aber zu langsam13:03 Energie als harter Wachstumsgrenzwert14:52 Eisenbahn-Analogie: Bleibt die Macht im Zentrum17:10 Platzt die Bewertungsblase der KI-Infrastruktur18:38 Die offene Machtfrage hinter der EffizienzHier geht's zum Artikel:https://ventureaibriefing.substack.com/p/die-ki-blase-platzt-andersSupport the show________________Wenn du uns dabei unterstützen möchtest, diesen Podcast zu einer Allianz von Zukunftsarchitekten der KI-Transformation zu machen, in der wir offen über Chancen, Risiken und reale Erfahrungen mit Künstlicher Intelligenz sprechen, dann abonniere uns auf Substack, YouTube, Spotify oder Apple Podcasts. Dein Abonnement kostet dich nichts, hilft uns aber sehr, noch mehr herausragende Persönlichkeiten für tiefgehende und inspirierende Podcast Gespräche zu gewinnen. Vielen Dank für deinen Support.Vernetze dich mit Norman auf LinkedIn:https://www.linkedin.com/in/muellernorman

Mixture and Leaven!

Have you ever wondered why God prohibited the mixing of wool and linen in the Old Testament? At first glance, it may seem like an obscure command, but behind it lies a powerful spiritual lesson. In this episode, we explore three fascinating reasons for this prohibition and uncover how these ancient instructions reveal God's holiness, man's inability to earn salvation through works, and the incredible gift of grace found in Jesus Christ. We also examine how this Old Testament principle applies to believers today. From understanding the difference between works and grace to learning what Scripture teaches about spiritual separation and being unequally yoked, this study offers practical insights for Christian living. Join us as we uncover the symbolism hidden within these biblical garments and discover how God's commands are always designed for our protection, blessing, and relationship with Him. For more information about Bill Wiese and Soul Choice Ministries please visit us at: https://soulchoiceministries.org/  You can find more of Bill's teachings at: BillWieseTV-YouTube

https://novacut.ai/  https://genaimeetup.com/  Anthropic has officially closed a $65 billion Series H at a $965 billion valuation, nearly 2.5x its valuation from just 100 days ago. Meanwhile, funding is flowing across the ecosystem: Frameworks AI at $15B, Baseten at $11B, OpenRouter's $113M Series B, and Cognition AI's $1B Series D. NVIDIA went on an open-source super week with Nemotron 3 Ultra, Cosmos 3, and Nemotron 3.5 ASR. Microsoft dropped 5 new MAI models. Google released Gemma 4 12B, and Anthropic shipped Opus 4.8. On the benchmarks front, DeepSWE crowns GPT-5.5 as the leader in long-horizon coding tasks, while ITBench shows even frontier models struggle with real-world SRE incidents — Claude Opus 4.7 tops out at just 47%. Plus: Cloudflare acquires VoidZero to build the future of AI-native edge development, and Google is paying SpaceX $920M/month for compute. Topics covered: • Anthropic's $65B Series H and path to $1T • Fireworks AI, Baseten, OpenRouter & Cognition funding rounds • Microsoft's 5 new MAI models • NVIDIA's open-source super week (Nemotron, Cosmos 3) • MiniMax M3, Gemma 4 12B, JetBrains Mellum2, Opus 4.8 • DeepSWE benchmark: GPT-5.5 leads long-horizon coding • ITBench: Frontier models under 50% on real SRE tasks • Cloudflare + VoidZero for AI-native edge dev • Google's $920M/month SpaceX compute deal #AI #Anthropic #NVIDIA #OpenAI #AInews #TechNews #LLM     Funding rounds Anthropic formally confirmed the closure of its $65 billion Series H funding round at a post-money valuation of $965 billion. This represents a 2.5-fold increase over its $380 billion Series G valuation from February 2026, adding $585 billion in value in approximately 100 days https://www.anthropic.com/news/series-h  Frameworks AI raising at 15B valuation representing a near fourfold increase from its $4 billion Series C valuation recorded in October 2025 processing 15 trillion tokens daily for major production clients including Cursor, Notion, and Perplexity https://finance.yahoo.com/sectors/technology/articles/fireworks-ai-eyes-15-billion-174609357.html Baseten is raising 1B at 11B valuation annualized revenue, which skyrocketed from $200 million to $600 million over a single quarter https://techstartups.com/2026/05/26/ai-inference-startup-baseten-in-talks-to-raise-1-billion-at-11-billion-valuation/  OpenRouter has secured a $113 million Series B funding OpenRouter has experienced exponential traffic growth, with weekly production throughput expanding fivefold from 5 trillion to 25 trillion tokens over a six-month horizon https://www.businesswire.com/news/home/20260526953416/en/OpenRouter-Raises-%24113-Million-CapitalG-led-Series-B-as-Weekly-Volume-Explodes-to-25T-Tokens  Further up the stack: Cognition AI secured a $1 billion Series D round led by Lux Capital and 8VC https://cognition.ai/blog/series-d   Model Releases MAI models: MAI-Code-1-Flash: A 5-billion active parameter model optimized for ultra-low latency within GitHub Copilot and VS Code. MAI-Image-2.5: A high-fidelity image generation model ranking third on global image evaluation arenas, outperforming competing architectures like Nano Banana Pro. MAI-Transcribe-1.5: A multi-lingual speech processing engine offering fivefold speed improvements across 43 languages. MAI-Voice-2: Natural audio and voice generation across 15 languages, available at a highly competitive price point. Web IQ: A search-grounding API engineered to directly compete with Perplexity. https://microsoft.ai/models/    https://www.peoplematters.in/news/ai-and-emerging-tech/uber-imposes-dollar1500-monthly-ai-spending-limit-on-employees-amid-rising-costs-50073    Nvidia has executed an "Open-Source Super Week," positioning itself as a dominant software and model publisher: Nemotron 3 Ultra (best US open source open weights model but behind china): A massive 550-billion parameter MoE (55 billion active) designed with a 1-million token context window, optimized specifically for high-throughput, cyclical agent loops. It achieved peak throughput rates of 400 tokens per second on day-zero optimized clusters. Cosmos 3: A physical AI world-modeling framework comprising 16-billion Nano and 64-billion Super variants. Built on a Mixture-of-Transformers (MoT) architecture, Cosmos 3 natively binds textual, visual, auditory, and physical kinetic vectors. Nemotron 3.5 ASR: A highly compact 0.6-billion parameter streaming speech recognition model pushing sub-100 millisecond latencies across 40 language locales.   https://www.minimax.io/models/text/m3  MiniMax M3: A 1-million token context model hitting 59.0% on SWE-Bench Pro and 74.2% on MCP Atlas, though noted for high token consumption due to intensive internal self-validation loops.   https://blog.google/innovation-and-ai/technology/developers-tools/introducing-gemma-4-12b/  Gemma 4 12B: Google's Apache 2.0 on-device model, which utilizes an encoder-free architecture that projects vision and audio vectors directly into the text-token space, bypassing separate CLIP-style encoders to minimize local memory footprints. https://www.jetbrains.com/mellum/  JetBrains Mellum2: A compact 12-billion parameter MoE (2.5 billion active) engineered for ultra-low latency routing and retrieval-augmented generation (RAG) sub-agents within developer IDEs. Opus 4.8 https://www.anthropic.com/news/claude-opus-4-8    https://www.cnbc.com/2026/06/05/google-to-pay-spacex-920-million-a-month-for-xai-compute-capacity.html      Benchmarks: https://deepswe.d atacurve.ai/blog https://venturebeat.com/technology/deepswe-blows-up-the-ai-coding-leaderboard-crowns-gpt-5-5-and-finds-claude-opus-exploiting-a-benchmark-loophole (GPT 5.5 the winner in long horizon tasks) a highly complex software engineering benchmark focused on original, long-horizon tasks across five distinct programming languages. Comprising 113 chaotic tasks across 91 live, production-grade repositories, DeepSWE forces agents to generate 5.5 times more code and modify an average of 7 separate files per task compared to standard evaluations. On this challenging leaderboard, GPT-5.5 leads with a score of 70%, establishing a significant 16-percentage-point lead over contemporary alternatives I think older benchmarks where models reach ~90% accuracy can be considered saturated. Few percentage points don't give us any good signal.  https://research.ibm.com/publications/developing-ai-agents-for-it-automation-tasks-with-itbench  ITBench-AA, an evaluation framework focusing on live Kubernetes incident response and Site Reliability Engineering (SRE) operations. Comprising 59 live, containerized SRE incident snapshots, the results are remarkably sobering: every frontier model scored under 50% on successful incident resolution, with Claude Opus 4.7 leading at 47% and GPT-5.5 following closely at 46%.   Edge AI announcements: https://www.cloudflare.com/press/press-releases/2026/cloudflare-acquires-voidzero-to-build-the-future-of-the-ai-native-web/  The consolidation of the AI-native developer stack has reached the runtime virtualization layer. Cloudflare recently completed the acquisition of VoidZero, the development group responsible for Vite, Vitest, Rolldown, and Oxc, backing the transaction with a $1 million open-source ecosystem fund. This acquisition is highly strategic; as autonomous agents write an increasing proportion of production software, local development environments, compilation pipelines, and bundlers must be optimized for execution speeds that match agent speeds. Cloudflare's goal is to construct a localized, full-stack edge playground. In this sandbox, AI agents can generate, test, bundle (utilizing the highly parallelized, Rust-based Oxc and Rolldown engines), and deploy entire web applications end-to-end within milliseconds. This architecture completely bypasses traditional local machine container bottlenecks, enabling high-velocity agent loops to execute in a fully sandboxed, web-scale edge runtime.

Shiur given by Rabbi Bezalel Rudinsky on Dvar Halacha Pas Haba Bikisnin Mixture with Fruit Juice and Mezonos Bread. Shiur recorded in Yeshivas Ohr Reuven, Monsey, NY.

Study the daily lesson of Sefer HaMitzvos for day 119 with Rabbi Mendel Kaplan, where he teaches the mitzvah in-depth with added insight and detail.

Study the daily lesson of Sefer HaMitzvos for day 119 with Rabbi Mendel Kaplan, where he teaches the mitzvah in-depth with added insight and detail.

Shiur given by Rabbi Bezalel Rudinsky on Dvar Halacha Pas Haba Bikisnin. Shiur recorded in Yeshivas Ohr Reuven, Monsey, NY.

This research examines the development and scaling laws of Native Multimodal Models (NMMs), which are AI systems trained from scratch to process both images and text simultaneously. The sources compare early-fusion architectures, which integrate raw multimodal signals from the start, against traditional late-fusion models that rely on separate pre-trained encoders. Findings indicate that early-fusion models are more efficient to train, easier to deploy, and perform as well as or better than late-fusion counterparts at lower compute budgets. Furthermore, the study highlights that incorporating a Mixture of Experts (MoE) significantly boosts performance by allowing the model to learn modality-specific weights. This specialized approach enables sparse models to handle heterogeneous data more effectively than dense architectures while maintaining the same inference cost. Ultimately, the reports suggest that NMMs follow predictable scaling properties similar to large language models, providing a blueprint for the next phase of edge AI development.

מה הופך בקשה אחת ל-LLM למורכבת כל כך מאחורי הקלעים? איך מאות מיליארדי פרמטרים נדחסים על עשרות GPUs, ואיך כל ה-cluster הזה משרת אלפי משתמשים במקביל בלי להתפוצץ?אירחתי את מייק ארליכסון, אושייה בעולם הAI, ופירקנו את עולם ה-inference מבפנים: KV cache, batching, ההבדל בין prefill ל-decode, חלוקה של מודל בין GPUs שונים, ו-Mixture of Experts. דיברנו גם על למה זה הפך לאחד התחומים הכי קריטיים בעולם ה-AI - וגם איך נכנסים אליו אם אתם מהנדסים שרוצים להתחיל להריץ מודלים בעצמכם.האזנה נעימה, עמית בן דור.

Dans la course mondiale à l'intelligence artificielle, un acteur chinois s'impose de plus en plus clairement : Moonshot AI. Son nouveau modèle, K2.6, marque une progression rapide et vient bousculer les références du secteur. Trois mois seulement après la version précédente, K2.6 affiche des performances en hausse sur plusieurs tests clés. Sur SWE-Bench Pro, un benchmark qui mesure la capacité d'un modèle à corriger de vrais problèmes sur des projets GitHub, il atteint 58,6 points. Il dépasse ainsi GPT-5.4 et Claude Opus 4.6. Même tendance sur DeepSearchQA et Terminal-Bench, deux tests qui évaluent respectivement la recherche d'information complexe et l'exécution de tâches techniques.Pour comprendre cette avancée, il faut regarder sous le capot. K2.6 repose sur une architecture dite “Mixture-of-Experts”. Concrètement, cela signifie que le modèle mobilise seulement une partie de ses capacités à chaque requête, ce qui réduit les coûts de calcul. Sur le papier, il compte 1 000 milliards de paramètres, mais n'en active qu'une fraction à chaque instant. Autre élément clé : la “fenêtre de contexte”. C'est la quantité d'informations que le modèle peut traiter en une seule fois. Ici, elle atteint 256 000 tokens, soit l'équivalent de centaines de pages de texte. De quoi améliorer la compréhension de tâches longues ou complexes.Côté performance, Moonshot mise aussi sur la quantification INT4, une technique qui compresse les données numériques pour accélérer les calculs. Résultat : une inférence environ deux fois plus rapide, avec une perte de précision limitée. Mais tout n'est pas encore validé. La majorité des résultats provient des tests internes du laboratoire, et aucune reproduction indépendante complète n'a été publiée à ce stade. De plus, si les poids du modèle sont accessibles, les données d'entraînement et les méthodes utilisées restent confidentielles, ce qui limite la transparence.Enfin, l'accès à ce type de modèle reste coûteux. Même en version optimisée, il nécessite plusieurs cartes graphiques haut de gamme et une infrastructure pouvant atteindre plusieurs milliers de dollars par mois. En résumé, K2.6 illustre une tendance forte : les modèles ouverts chinois se rapprochent désormais des meilleurs systèmes propriétaires. Mais la bataille de la transparence et de la reproductibilité, elle, est loin d'être tranchée. Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.

Study the daily lesson of Sefer HaMitzvos for day 99 with Rabbi Mendel Kaplan, where he teaches the mitzvah in-depth with added insight and detail.

Tonight on The Pipes Magazine Radio Show, our featured interview is with Shane Ireland of SmokingPipes.com / Laudisi Enterprises, Inc. A familiar guest who has joined us several times before, Shane is the Vice President of Retail for SmokingPipes.com and has been with the company for nearly 15 years, making him a well-known and respected voice in the pipe community. Brian and Shane will discuss past, present, and future trends in the hobby, offering insights from Shane's extensive experience on the retail side and his close connection to the evolving tastes of pipe smokers. At the top of the show in Pipe Parts, Brian will have a tobacco review of Cornell & Diehl's Miskatonic Mixture.

Send us Fan MailForasmuch then as we are the offspring of God, we ought not to think that the Godhead is like unto gold, or silver, or stone, graven by art and man's device - Acts 17:29 KJV.  Let's hear from you via email at Rlwmsmedia@gmail.com or visit our website at www.hisredeeminglove.com3 Ways to Support the Ministry:- GIVE ONLINE AT HISREDEEMINGLOVE.COM- GIVE VIA THE RLWM APP AT 'MY CHURCH HOME' IN THE GOOGLE/APPLE STORE- TEXT ‘GIVE' TO (336) 360-0443

In this episode of the HVAC Know It All Podcast, host Gary McCreadie talks with Matthew Waxer, HVAC/R Mechanic at Kilmer HVACR Services Inc, about the key factors that impact system performance and compressor life. In Part 3, they focus on the importance of proper maintenance, airflow, and system setup from installation through operation. Matthew explains how correct airflow, refrigerant charge, and fluid mixtures like glycol all play a role in system efficiency and reliability. They also discuss common field mistakes such as poor airflow around equipment, incorrect glycol ratios, and refrigerant loss during servicing. The conversation wraps up with practical tips on maintenance practices, using probes instead of gauges, and avoiding contamination to keep systems running properly over time. Gary and Matthew discuss the key steps needed to keep HVAC systems running properly, focusing on maintenance, airflow, and correct setup. They explain how proper airflow across indoor and outdoor units is critical for system performance and how poor installation choices can lead to long-term issues. Matthew highlights the importance of correct refrigerant charge, coil sizing, and fluid flow in systems using glycol, including how improper mixtures can reduce efficiency or cause freezing problems. They also cover common service mistakes like refrigerant loss and contamination when using gauges. They finish by sharing practical tips such as using probes, checking airflow, and following proper maintenance practices to protect system life.   Expect to Learn: How proper maintenance helps extend the life of HVAC systems and prevent costly breakdowns. How correct airflow across indoor and outdoor units affects system performance and reliability. Why proper refrigerant charge and coil sizing are important for efficient operation. How glycol mixtures impact heat transfer, flow, and freezing protection in fluid systems. How using probes and careful service methods can reduce refrigerant loss and system contamination.   Episode Highlights: [00:00] - Sponsor: Factory Direct Filters ad [00:42] - Intro to Matthew Waxer in Part 03  [02:13] - Why maintenance matters for compressor life  [03:30] - Airflow setup: Indoor coil sizing & outdoor obstructions  [05:32] - Glycol systems: Mixture %, freezing point, refractometer use  [10:07] - Manifold vs. probes: Refrigerant loss & contamination risks  [15:33] - Outro & next episode    This Episode is Kindly Sponsored by: Cintas: https://www.cintas.com/hvacknowitall Cool Air Products: https://www.coolairproducts.net/ Factory Direct Filters: https://www.factorydirectfilters.com/ SupplyHouse: https://www.supplyhouse.com/tm Use promo code HKIA5 to get 5% off your first order at Supplyhouse! Follow the Guest Matthew Waxer:  LinkedIn: https://www.linkedin.com/in/matthew-waxer-b4a62360/   Kilmer HVACR Service Inc - Website: https://kilmerservice.com/  Follow the Host on: LinkedIn: https://www.linkedin.com/in/gary-mccreadie-38217a77/  Website: https://www.hvacknowitall.com Facebook: https://www.facebook.com/people/HVAC-Know-It-All-2/61569643061429/   Instagram: https://www.instagram.com/hvacknowitall1/  Follow the Podcast on:  YouTube: https://www.youtube.com/@HVACKnowItAll   Spotify: https://open.spotify.com/show/6LCBJGw0EHG03rdWHxUMce  Apple Podcast: https://podcasts.apple.com/us/podcast/hvac-know-it-all-podcast/id1359253455 

Brian Taylor joined Jake and Ben live from Augusta National with an update from the second round of The Masters. 

Morning MessageTeachers: Kerry & Karen BattleAhava ~ Love AssemblyThis message resumes Vayiqra (Leviticus) 19 beginning at verse 20 and brings the chapter to its conclusion. From there, the teaching moves directly into Vayiqra (Leviticus) 20, where the commands already given are enforced with judgment.Leviticus 19 establishes the instruction.Leviticus 20 establishes the consequence.This is not a new teaching.This is the completion and enforcement of what has already been spoken.Holiness is not a concept.It is covenant alignment expressed through obedience, separation, and accountability in every area of life.The commands define what is acceptable.The judgments reveal what happens when those commands are violated.WHAT WE COVER IN THIS MESSAGESexual Accountability and Moral LiabilityLeviticus 19:20–22Sexual conduct carries responsibility and consequence. Different penalties do not remove accountability. The standard of order remains intact.Separation, Distinction, and Prohibition of MixtureLeviticus 19:23–31What is set apart must not be profaned. Appetite is governed. Guidance must not be sought outside Yahuah. Mixture produces disorder.Honor, Justice, and Covenant ConductLeviticus 19:32–37Honor is commanded. The stranger is to be treated with justice and love. Economic conduct must remain honest. Holiness governs society and commerce.Judgment for Violating Covenant CommandsLeviticus 20This chapter enforces what was commanded.Molech reveals corruption of seed.Familiar spirits reveal divided allegiance.Dishonor reveals breakdown of authority.Sexual violations reveal destruction of creation order.The land responds to defilement.Separation (badal) defines covenant identity.WHY THIS MESSAGE MATTERSThe commands of Yahuah are not symbolic.They are not flexible.They are not optional.Leviticus 19 defines how Yisra'el is to live.Leviticus 20 establishes what happens when that order is rejected.Holiness is not private belief.Holiness is covenant alignment.Every area is governed:AuthoritySeedWorshipConductJusticeSpeechSeparationGuidanceHonorCommerceWhat stands outside His order is not neutral.It is disordered.To carry His Name while living outside His order is misrepresentation.SCRIPTURE REFERENCES FOR STUDYLeviticus 19:20–37Leviticus 20Genesis 1:27; 2:24Exodus 13:2; 20:12; 22:18Deuteronomy 6:13; 18:10–12; 21:18–21Leviticus 18:21–30Leviticus 11Leviticus 7:15–182 Kings 172 Kings 23:10All teaching is established precept upon precept.ABOUT AHAVA ~ LOVE ASSEMBLYWe teach the Pure Word of Yahuah.No religion.No tradition.No compromise.Our teaching follows the Sovereign Blueprint:Law | Precept | Example | Wisdom | Understanding | Prudence | Conviction | Fruit of the Ruach | Final Heart CheckSUPPORT THE WORK — GIVE VIA ZELLEZelle QR available at:ahavaloveministry.comZelle only. No CashApp. No PayPal.FINAL WORDThe command is already written.The judgment is already established.Holiness is not what you say.Holiness is whether your life reflects Yahuah's order or stands outside it.Final Heart CheckAre you aligned with His order, or are you tolerating what He has already judged?

DJ Bully B Essence of Soul 1 -4-26 Various Independent & 90s Mixture  Love is a gift of one's inner most soul to another so both can be whole

Morning MessageTeachers: Kerry & Karen BattleAhava ~ Love AssemblyThis message covers Leviticus 19 as a unified covenant instruction defining holiness, not as a concept, but as lived alignment with Yahuah's order.This chapter does not present isolated laws. It establishes a complete system of life governed by separation, authority, justice, restraint, and accountability. Holiness is not claimed. It is demonstrated through ordered obedience in every area.The command “you shall be qadosh” is not aspirational. It is a covenant requirement rooted in Yahuah's own nature. What He is, His people must reflect.This teaching unfolds how holiness governs identity, household order, time, worship, provision, justice, speech, correction, separation, appetite, honor, and economic conduct.Every instruction carries consequence. Every command reveals what is acceptable and what defiles.WHAT WE COVER IN THIS MESSAGEHoliness Defined by Yahuah, Not ManLeviticus 19:1–2Holiness is functional separation according to Yahuah's order. It is not emotional, cultural, or self-defined.Household Order, Time, and LoyaltyLeviticus 19:3–4Fear of parents establishes covenant authority. Sabbath establishes set-apart time. Idolatry exposes misplaced loyalty. Authority, time, and loyalty must align.Worship Integrity and Acceptable OfferingsLeviticus 19:5–8What is set apart must not be corrupted. Timing is part of obedience. Delayed obedience becomes corrupted obedience.Provision, Justice, Rebuke, and Love as One SystemLeviticus 19:9–18Gleaning establishes covenant provision. Dishonesty profanes the Name. Justice must remain unbiased. Rebuke prevents shared liability. Love is defined by action, not feeling.Separation and Moral LiabilityLeviticus 19:19–22Distinction must be maintained. Mixture forms disorder. Different penalties do not remove moral accountability.Sacred Order Governing Appetite, Honor, and GuidanceLeviticus 19:23–31Fruit is restrained before common use. What is set apart must not be profaned. Guidance must not be sought outside Yahuah.Honor, the Stranger, and Just WeightsLeviticus 19:32–37Honor is commanded. The stranger must be treated with justice and love. Economic conduct must remain honest. Holiness governs society and commerce.WHY THIS MESSAGE MATTERSHoliness is not private belief.Holiness is covenant alignment.Every area of life is brought under order:AuthorityTimeWorshipProvisionJusticeSpeechCorrectionSeparationAppetiteHonorCommerceWhat stands outside His order is not neutral. It is disordered.To carry His Name while living outside His order is misrepresentation.SCRIPTURE REFERENCES FOR STUDYLeviticus 19Leviticus 7:15–18Leviticus 20:25–26Exodus 19:6Exodus 20:8–12Exodus 31:13Deuteronomy 1:17Deuteronomy 7:6Deuteronomy 22:9–11Deuteronomy 24:19–21Deuteronomy 25:13–15Deuteronomy 27:16–18Isaiah 35:8Isaiah 58:13–14Jeremiah 9:4Micah 6:10–11Amos 8:5Zechariah 7:9–10Proverbs 1:8Proverbs 11:1–13Proverbs 24:23Psalm 82:3–4All teaching is established precept upon precept.ABOUT AHAVA ~ LOVE ASSEMBLYWe teach the Pure Word of Yahuah.No religion.No tradition.No compromise.Our teaching follows the Sovereign Blueprint:Law | Precept | Example | Wisdom | Understanding | Prudence | Conviction | Fruit of the Ruach | Final Heart CheckSUPPORT THE WORK — GIVE VIA ZELLEZelle QR available at:ahavaloveministry.comZelle only. No CashApp. No PayPal.FINAL WORDHoliness is not what you say.Holiness is whether your life reflects Yahuah's order or stands outside it.Final Heart CheckAre you aligned with His order in every area, or are there parts of your life that remain outside His authority?

Unlock new value with trusted, autonomous AI, talk with your rep for attendance options Think 2026 – May 4-7 in Boston → https://ibm.biz/BdpMzG From GPT-2 chatbot to GPT-5.3! In this milestone 100th episode of Mixture of Experts, host Tim Hwang is joined by Ritika Gunnar, Volkmar Uhlig and Kaoutar El Maghraoui, to explore how AI has evolved since episode 1. First, a homeowner uses ChatGPT to sell his house over realtor estimates—sparking debate about AI democratizing expertise versus replacing professionals. Next, we analyze a study revealing only 2.1% of scientists actively use Claude Code; what role does AI play in research? Finally, Adobe's CFO Dan Dern transformed his finance team into an AI lab, using autonomous agents for forecasting, contract analysis and workflow automation. Our experts identify the three hottest areas for enterprise AI adoption 00:00 – Introduction 1:14 – ChatGPT sells house for $100K over asking 15:01 – Claude Code adoption study: Only 2.1% of scientists 25:26 – Adobe CFO builds finance team AI lab The opinions expressed in this podcast are solely those of the participants and do not necessarily reflect the views of IBM or any other organization or entity. Visit Mixture of Experts podcast page to get more AI content → https://www.ibm.com/think/podcasts/mixture-of-experts

Visit Mixture of Experts podcast page to get more AI content → https://www.ibm.com/think/podcasts/mixture-of-experts NVIDIA announces NemoClaw. This week on Mixture of Experts, host Tim Hwang is joined by Merve Unuvar, Martin Keen and Olivia Buzek—who is reporting live from NVIDIA GTC. Jensen Huang revealed $1 trillion in orders for Blackwell and Vera Rubin systems through 2027, plus the launch of NemoClaw—NVIDIA's enterprise-grade AI wrapper built on the OpenClaw agent platform. Next, Anthropic announces the Anthropic Institute, but can AI labs honestly audit their own technology while building it? Then, Shopify enters the agentic shopping arena with AI-powered personal shoppers that could reshape e-commerce. Finally, OpenAI increases focus on enteprise users and coding, but are they behind? 00:00 – Introduction 1:14 – NVIDIA GTC 2026: Trillion-dollar orders, NemoClaw & agentic computing 11:17 – Anthropic Institute: Can AI labs audit themselves? 22:12 – Shopify shopping agents & the future of e-commerce 35:15 – OpenAI's enterprise pivot: Coding & business focus The opinions expressed in this podcast are solely those of the participants and do not necessarily reflect the views of IBM or any other organization or entity. Subscribe for AI updates → https://www.ibm.com/account/reg/us-en/signup?formid=news-urx-52120

Law enforcement has confirmed the DNA sample at the Nancy Guthrie scene is a mixture—meaning it may involve more than one person. That changes everything about who's been keeping this secret for over a month. This Hidden Killers Week In Review brings former FBI behavioral analyst Robin Dreeke and host Tony Brueski together for a deep dive into the details that demand a real conversation.Robin breaks down what that behavioral picture looks like when two people are carrying this together versus one. The psychological dynamics shift dramatically. The risk of exposure multiplies. And yet—silence.The pacemaker detail is one that hasn't gotten nearly enough attention. Nancy's pacemaker last synced at 2:28 AM. That's a hard data point in a case with very few of them. What does it tell investigators about the timeline of that night?Then there's the million-dollar reward—payable in cash. Does that actually move a case forward, or does it flood investigators with noise that makes real leads harder to find? Tony and Robin look at what reward escalations typically do to tip quality, and what the cash offer signals about where this investigation stands.The internet outage in Nancy's neighborhood the night she vanished—coincidence or deliberate sabotage? What happens psychologically the moment a burglary becomes a kidnapping, and what does that escalation tell us about the person responsible?Robin addresses what many consider the most haunting element: how does someone go home, sleep, wake up, and carry on with daily life after something like this?The tips have slowed. Sheriff Nanos keeps declaring he "personally believes" Nancy is alive. Is that strategic—or something else? After more than a month with no body, what does that mean?The questions deserve better than vague reassurances.Join Our SubStack For AD-FREE ADVANCE EPISODES & EXTRAS!: https://hiddenkillers.substack.com/Want to comment and watch this podcast as a video? Check out our YouTube Channel. https://www.youtube.com/channel/UC8-vxmbhTxxG10sO1izODJg?sub_confirmation=1PRE-ORDER Robin's NEW Book! - https://a.co/d/0iR9U8U0Instagram https://www.instagram.com/hiddenkillerspod/Facebook https://www.facebook.com/hiddenkillerspod/Tik-Tok https://www.tiktok.com/@hiddenkillerspodX Twitter https://x.com/TrueCrimePodThis publication contains commentary and opinion based on publicly available information. All individuals are presumed innocent until proven guilty in a court of law. Nothing published here should be taken as a statement of fact, health or legal advice.#NancyGuthrie #NancyGuthrieDNA #RobinDreeke #DNAMixture #PacemakerEvidence #TucsonKidnapping #FBIBehavioral #HiddenKillers #MissingPersons #TrueCrime

Visit Mixture of Experts podcast page to get more AI content → https://www.ibm.com/think/podcasts/mixture-of-experts Can your AI agent hack its own evaluation? This week on Mixture of Experts, Tim Hwang is joined by Ambhi Ganesan, Kaoutar El Maghraoui, and Sandi Besen to analyze OpenAI's Codex Security launch. Next, we explore eval awareness as Anthropic revealed Opus 4.6 figured out it was being tested, located the answer key and decrypted it.. Then, Meta acquires Moltbook, the social network for AI agents, and we discuss the strategic play for agentic commerce infrastructure. Finally, Alibaba reports that an agent broke containment and started mining crypto. Are agents trying too hard to maximize rewards? All that and more on todays Mixture of Experts. 00:00 – Introduction 1:02 – OpenAI Codex Security launch 12:44 – Meta acquires Moltbook 25:21 – Anthropic's eval awareness research 38:06 – Alibaba agents mining crypto The opinions expressed in this podcast are solely those of the participants and do not necessarily reflect the views of IBM or any other organization or entity. Subscribe for AI updates → https://www.ibm.com/account/reg/us-en/signup?formid=news-urx-52120

The evidentiary details in the Nancy Guthrie case are accumulating — and several of them aren't getting the scrutiny they deserve. Hidden Killers digs into the evidence layer with former FBI behavioral analyst Robin Dreeke and host Tony Brueski in this listener Q&A focused on what the physical and forensic record actually tells us.The pacemaker. Nancy's device last synced at 2:28 in the morning. In a case where the timeline of events that night is still not publicly established with precision, that data point is significant. Tony and Robin examine what a pacemaker sync can and cannot tell investigators — and why this detail isn't getting more airtime.The DNA at the scene has been characterized as a mixture — potentially from multiple contributors. That's not a minor detail. A mixed DNA profile opens the possibility that more than one person was present, which fundamentally alters the behavioral question of how this secret has been kept. Robin addresses what the psychological and behavioral profile looks like when two or more people share culpability in something like this — and which of those scenarios is more likely to crack under pressure.A million-dollar reward has been announced, payable in cash. From an investigative standpoint, does that generate actionable intelligence — or primarily volume? Tony and Robin address what large reward escalations have historically meant for tip quality and case trajectory.And if no remains have been located after this much time in a case with this evidence profile — what does that mean for investigators, for the family, and for the likelihood that Nancy is still alive?Join Our SubStack For AD-FREE ADVANCE EPISODES & EXTRAS!: https://hiddenkillers.substack.com/Want to comment and watch this podcast as a video? Check out our YouTube Channel. https://www.youtube.com/channel/UC8-vxmbhTxxG10sO1izODJg?sub_confirmation=1PRE-ORDER Robin's NEW Book! - https://a.co/d/0iR9U8U0Instagram https://www.instagram.com/hiddenkillerspod/Facebook https://www.facebook.com/hiddenkillerspod/Tik-Tok https://www.tiktok.com/@hiddenkillerspodX Twitter https://x.com/TrueCrimePodThis publication contains commentary and opinion based on publicly available information. All individuals are presumed innocent until proven guilty in a court of law. Nothing published here should be taken as a statement of fact, health or legal advice.#NancyGuthrie #GuthrieDNA #NancyGuthriePacemaker #NancyGuthrieMissing #ForensicEvidence #RobinDreeke #HiddenKillers #TrueCrimeInvestigation #MissingPersons #TrueCrimePodcast

Teachers: Kerry & Karen BattleAhava ~ Love AssemblyPsalm 119 is not emotional devotion.It is covenant endurance training.176 verses structured in 22 stanzas.Each stanza anchored to a Hebrew letter.Each movement reinforcing one reality:Life is preserved by disciplined obedience to the Word of Yahuah.In Part 2, we continue the structure without softening the weight.The foundation has been laid.Now endurance is tested.This is not inspiration language.This is constitutional language.────────────────────WHAT WE COVER IN THIS MESSAGEAffliction Produces AlignmentPsalm 119:49–72The Word sustains under pressure.Affliction is not contradiction, it is refinement.Obedience is tested in suffering, not in comfort.Precepts:Deuteronomy 8:2–3Lamentations 3:27–33Isaiah 48:10The Word Is Settled, Not NegotiatedPsalm 119:73–96The commandments are fixed.Creation obeys order.The servant survives because he delights in instruction.Precepts:Psalm 19:7–11Isaiah 40:8Ecclesiastes 3:14Separation from the Double-MindedPsalm 119:97–120Love for Torah produces hatred for false ways.Fear stabilizes obedience.Mixture collapses endurance.Precepts:Deuteronomy 10:12–13Psalm 1Proverbs 8:13Obedience Under OppositionPsalm 119:121–168Princes persecute without cause.The disciplined do not bend to pressure.Peace belongs to those who love the law.Precepts:Psalm 2Daniel 3Isaiah 26:3The Humble Plea of the GovernedPsalm 119:169–176The Psalm ends not in pride, but in humility.“I have gone astray like a lost sheep.”Discipline includes confession and return.Precepts:Deuteronomy 30:1–3Psalm 51Proverbs 28:13────────────────────WHY THIS MESSAGE MATTERSDiscipline must survive affliction.Stability must endure persecution.Fear must guard against mixture.Humility must close every cycle of obedience.Psalm 119 trains covenant consistency.It forms a people who remain governed when pressure increases.────────────────────SCRIPTURE REFERENCES FOR STUDYPsalm 119 (49–176)Deuteronomy 8Deuteronomy 10Deuteronomy 30Psalm 1Psalm 19Psalm 51Isaiah 40Precept upon precept.Law interpreting law.Scripture reinforcing Scripture.────────────────────ABOUT AHAVA ~ LOVE ASSEMBLYWe teach the Pure Word of Yahuah.No religion.No denominational systems.No theological overlays.Our teaching follows the Sovereign Blueprint:Law | Precept | Example | Wisdom | Understanding | Prudence | Conviction | Fruit of the Ruach | Final Heart Check────────────────────SUPPORT THE WORK — GIVE VIA ZELLEZelle QR at: ahavaloveministry.comZelle only.No CashApp.No PayPal.────────────────────FINAL WORDPsalm 119 does not end in emotion.It ends in accountability.If you want endurance, you must remain governed.If you want preservation, you must remain disciplined.Final Heart Check:When pressure increases,does your obedience tighten,or does it thin out?

Rally in the Grain Market Wet Weather and Fire Weather Musil Center for Sustainable Wheat Production   00:01:05 – Rally in the Grain Market: K-State grain economist, Daniel O'Brien, kicks off today's show discussing a few rallies he is seeing in the grain market and what he expects will impact the market in March. Daniel on AgManager.info   00:12:05 – Wet Weather and Fire Weather: Chip Redmond, K-State meteorologist, keeps the show moving as he explains the mixture of foggy weather and fire concerns in Kansas and what he forecasts this month.    00:23:05 – Musil Center for Sustainable Wheat Production: Ending the show is a Kansas Wheat, Wheat Scoop as Gordon Clark talks about the first realized legacy gift and establishing the Musil Center for Sustainable Wheat Production.   kswheat.com Extension.K-State.edu     Send comments, questions or requests for copies of past programs to ksrenews@ksu.edu.   Agriculture Today is a daily program featuring Kansas State University agricultural specialists and other experts examining ag issues facing Kansas and the nation. It is hosted by Shelby Varner and distributed to radio stations throughout Kansas and as a daily podcast.   K‑State Extension is a short name for the Kansas State University Cooperative Extension Service, a program designed to generate and distribute useful knowledge for the well‑being of Kansans. Supported by county, state, federal and private funds, the program has county Extension offices statewide. Its headquarters is on the K‑State campus in Manhattan. For more information, visit Extension.ksu.edu. K-State Extension is an equal opportunity provider and employer.

Visit Mixture of Experts podcast page to get more AI content → https://www.ibm.com/think/podcasts/mixture-of-experts Is Perplexity still focused on search? This week on Mixture of Experts, Host Tim Hwang is joined by Gabe Goodhart, Chris Hay and Aaron Baughman to break down what Perplexity Computer really offers—and whether closed systems can compete with open alternatives like OpenClaw, LangGraph and AutoGPT. Next, Anthropic introduces memory import for Claude. Is memory still a competitive moat, or can users now seamlessly switch between AI providers? Then, meet NullClaw, a minimalist agent framework that runs on just 678 kilobytes. Our experts debate whether edge-based agent swarms are the future—or just clever marketing. Finally, Particle6 unveils Tilly Norwood, the world's first AI actor. Would you give an Oscar to an algorithm? All that and more on this week's Mixture of Experts. 00:00 -- Introduction 1:07 -- Perplexity Computer 11:23 -- Claude Import Memory 24:14 -- NullClaw 35:14 -- Tilly Norwood The opinions expressed in this podcast are solely those of the participants and do not necessarily reflect the views of IBM or any other organization or entity. Subscribe for AI updates → https://www.ibm.com/account/reg/us-en/signup?formid=news-urx-52120

Weeks into the Nancy Guthrie investigation, the forensic picture is more complicated than the headlines suggest.Retired FBI Special Agent Jennifer Coffindaffer assesses what investigators are actually working with—and it's not as clean as the public might assume.The DNA recovered inside the Nancy Guthrie home is a mixture still being separated. Family members, landscapers, service workers all contributed to the sample. Genetic genealogy can't begin until that profile is clean enough to upload. With questions about lab facilities and sample condition, the timeline remains uncertain.The glove found miles from the property? Processed through CODIS. No match to anyone in the system—and critically, no match to the DNA at the scene. Coffindaffer raises the possibility it shouldn't be treated as case evidence at all.Meanwhile: lost Nest camera footage. A pacemaker search running for weeks. Tens of thousands of tips. No suspect identified.But the pressure is building on whoever did this—and Robin Dreeke, former head of the FBI's Counterintelligence Behavioral Analysis Program, breaks down what that pressure is doing to them right now.The reconnaissance windows suggest someone local. Someone who's been watching weeks of national coverage knowing genetic genealogy is processing, the FBI is showing photos at gun shops, and CeCe Moore told national TV the kidnapper should be "extremely concerned."What does that pressure do to someone trying to act normal? What behavioral tells might they be showing to people around them?The forensic awareness at the door suggests planning. The dropped glove suggests panic. Dreeke identifies the signature of someone who may be in over their head.This is the Nancy Guthrie investigation—where it actually stands.Join Our SubStack For AD-FREE ADVANCE EPISODES & EXTRAS!: https://hiddenkillers.substack.com/Want to comment and watch this podcast as a video? Check out our YouTube Channel. https://www.youtube.com/channel/UC8-vxmbhTxxG10sO1izODJg?sub_confirmation=1Instagram https://www.instagram.com/hiddenkillerspod/Facebook https://www.facebook.com/hiddenkillerspod/Tik-Tok https://www.tiktok.com/@hiddenkillerspodX Twitter https://x.com/TrueCrimePodThis publication contains commentary and opinion based on publicly available information. All individuals are presumed innocent until proven guilty in a court of law. Nothing published here should be taken as a statement of fact, health or legal advice.#NancyGuthrie #SavannahGuthrie #TrueCrimeToday #Coffindaffer #RobinDreeke #GeneticGenealogy #DNAEvidence #CODISMiss #TucsonKidnapping #CaseUpdate

Visit Mixture of Experts podcast page to get more AI content → https://www.ibm.com/think/podcasts/mixture-of-experts Where does AI actually fit into the mainframe modernization journey? In this week's episode of Mixture of Experts, host Tim Hwang is joined by experts Skyla Loomis, Maryam Ashoori and Kaoutar El Maghraoui. We dive into conversation around AI-powered mainframe modernization and AI builders. Next, 84% of the world has never used AI? A reality check on AI adoption and what needs to change. Finally, OpenClaw exposes some AI agent security gaps. We discuss "agent ops"—the framework for transparency, evaluation, optimization and policy enforcement that makes AI agents production-ready. All that and more on today's Mixture of Experts. 00:00 – Introduction 1:06 – Mainframe modernization 14:18 – AI adoption reality check 29:40 – Security-by-design agentic AI The opinions expressed in this podcast are solely those of the participants and do not necessarily reflect the views of IBM or any other organization or entity. Learn how to operate AI agents responsibly at scale in the latest Tech Summit → https://ibm.webcasts.com/starthere.jsp?ei=1749693&tp_key=83a9212ff7&sti=podcast

Get our AI news cheat sheet: 20+ prompts for the latest models and tools https://clickhubspot.com/kps Episode 98: Is 2026 shaping up to be the year AI agents become indispensable—and outpace GPT-5? Hosts Matt Wolfe (https://x.com/mreflow)) and Joe Fier (linkedin.com/in/joefier) break down the explosion of new AI models, including Claude Sonnet 4.6, Gemini 3.1 Pro, and Grok 4.2, and explore how these tools are not only smarter but also significantly cheaper than previous state-of-the-art language models. This episode dives deep into the rise of agentic AI, the OpenClaw origin story, and how companies like Meta and ElevenLabs are racing to create integrated, emotionally-aware AI agents. Matt and Joe discuss the rapid democratization of AI, the impact of these advances on creativity and business operations, and the ongoing debate about slowing down AI before it accelerates beyond human control. Plus: practical demos, business tips, and a look at the hardware/software divide in global robotics. Check out The Next Wave YouTube Channel if you want to see Matt and Nathan on screen: https://lnk.to/thenextwavepd — Show Notes: (00:00) Next Wave Podcast: AI Insights (06:09) Anthropic Blocks, OpenAI Welcomes (10:35) ClaudeBot: AI Team Assistant (20:50) Meta Integrates Manus AI Ads (22:04) AI vs Manual Ad Management (29:55) New AI Models Released (31:54) AI Models Improve, Consumers Unchanged (41:09) Chatbots: Everyday and Advanced Uses (43:57) Mixture of Experts Explained (47:23) AI-Powered Product Photo Creator (56:58) Debating Internet Advancement (01:00:36) To Scale: Human Evolution (01:03:42) AI Debate: Polarized or Balanced? (01:13:16) AI Creativity Still Needs Humans (01:16:40) AI's Future in Entertainment (01:24:15) Experience Enhances AI Creativity (01:27:08) Robots Struggle with Nuance (01:30:27) US-China Collaboration for Smart Robots — Mentions: Joe Fier: https://www.instagram.com/joefier/ Seedance 2.0: https://www.seedance.com/ OpenClaw: https://openclaw.ai/ Manus: https://manus.im/ Nano Banana: https://nanobanana.com/ ElevenLabs: https://elevenlabs.io/ Perplexity: https://www.perplexity.ai/ Get the guide to build your own Custom GPT: https://clickhubspot.com/tnw — Check Out Matt's Stuff: • Future Tools - https://futuretools.beehiiv.com/ • Blog - https://www.mattwolfe.com/ • YouTube- https://www.youtube.com/@mreflow — Check Out Nathan's Stuff: Newsletter: https://news.lore.com/ Blog - https://lore.com/ The Next Wave is a HubSpot Original Podcast // Brought to you by Hubspot Media // Production by Darren Clarke // Editing by Ezra Bakker Trupiano

Long before believers were called “Christians,” they were known as People of the Way—followers of Jesus whose lives reflected His movement in the world. This series walks through the book of Acts, exploring how the early church lived, served, and shared the gospel in a changing world. As we study their story, we're challenged to rediscover what it means to be the church today—faithful, united, and always on the move.

Visit Mixture of Experts podcast page to get more AI content → https://www.ibm.com/think/podcasts/mixture-of-experts Do AI agents still need humans? This week on Mixture of Experts, guest host Matt Kosinski from Security Intelligence is joined by Mihai Criveti, Martin Keen and Kush Varshney. First, we unpack Google and DeepMind's massive USD $200B AI infrastructure investment in India— “The Biggest AI Infrastructure Deal in History.” Is this about sovereignty, geography, or something else entirely? Next, Anthropic researcher Nicholas Carlini used Claude to build a fully operational 100,000-line C compiler autonomously. Our experts debate whether this is impressive or inevitable, and what it means for human developers. Then, a sobering reality check: 36% of AI agent skills contain security vulnerabilities. Finally, as IT leaders question AI ROI, we discuss the shift from "how" to "how much" and whether value-based pricing could change everything. All that and more on this week's Mixture of Experts. 00:00 – Introduction 1:22 – Google's $200B AI infrastructure deal in India 7:54– Claude builds a C compiler autonomously 26:25 – Security vulnerabilities in AI agent skills 39:44– The AI ROI problem: Measuring value vs. cost The opinions expressed in this podcast are solely those of the participants and do not necessarily reflect the views of IBM or any other organization or entity. Learn more about AI agent vulnerabilities → https://www.ibm.com/think/podcasts/security-intelligence Subscribe for AI updates → https://www.ibm.com/account/reg/us-en/signup?formid=news-urx-52120

Pima County Sheriff Chris Nanos says that the unidentified DNA discovered at Nancy Guthrie's residence consists of a mixture, a scenario "common" in cases involving violent crimes.See omnystudio.com/listener for privacy information.

Visit Mixture of Experts podcast page to get more AI content → https://www.ibm.com/think/podcasts/mixture-of-experts What do people really use AI for? This week on Mixture of Experts, we unpack Microsoft's Copilot usage report revealing AI adoption patterns. Host Tim Hwang is joined by IBM Fellow Kush Varshney, Lauren McHugh Olende and Volkmar Uhlig to discuss enterprise AI costs, the Ralph Wiggum prompting strategy for coding agents, and whether giving simple instructions beats micromanaging. Then, we preview the India AI Impact Summit. Finally, AI companies showed up in Superbowl Ads this weekend. Are we witnessing mainstream adoption or just another bubble? All that and more on this week's Mixture of Experts. 00:00 – Introduction 1:07 – Copilot usage reports 10:00 – Ralph Wiggum prompting strategy 20:00 – India AI Impact Summit 28:05– AI ads at the Superbowl The opinions expressed in this podcast are solely those of the participants and do not necessarily reflect the views of IBM or any other organization or entity. Subscribe for AI updates → https://www.ibm.com/account/reg/us-en/signup?formid=news-urx-52120

In this message, No Mixture, Pastor Chris shared about the importance of having a clean heart before the Lord—living with honesty, integrity, and nothing hidden. We were reminded that God searches our hearts, and that when our inner life is pure, everything else begins to flow from the right place.

Did AI end up being a political force this year?

Ceasefire and Post-War Legacy — James Holland — Holland describes the final ceasefire order received at a German farmyard, evoking the profound mixture of "immense relief and overwhelming grief" experienced by surviving regiment members who had endured years of combat and witnessed countless comrades' deaths. Hollandtraces the post-war lives of veterans including Arthur Reddish and John Semken, documenting their transition from combat to civilian life and the lasting psychological impact of sustained warfare. Holland concludes by noting that the Sherwood Rangers Yeomanry continues to exist in contemporary Britain as an integral component of the Royal Yeomanry, maintaining institutional continuity and commemorating the regiment's extraordinary combat history and sacrifice during World War II.

-We all expect Fernando Mendoza to win the Heisman, with the exceptional year he's had at Indiana in coming from Cal -The other finalists: Diego Pavia (transfer from NM State); Jeremiyah Love (Notre Dame full time); and Julian Sayin (Ohio State)    Show Sponsored by NEBCOOur Sponsors:* Check out Hims: https://hims.com/EARLYBREAK* Check out Infinite Epigenetics: https://infiniteepigenetics.com/EARLYBREAK* Check out Uncommon Goods: https://uncommongoods.com/EARLYBREAK* Check out Washington Red Raspberries: https://redrazz.orgAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy