Podcasts about interacting

AI agents are not as complicated as they sound, and Peter Ranney and Elliott Wittstruck of DataX are proof. They walk through exactly how remodelers are using AI agents inside JobTread right now to automatically clean up field notes, process receipts, cost jobs, and land a daily project health report in their inbox every evening. They also share a seven-level framework for AI adoption that takes all the pressure off and helps you figure out exactly where to start! If you have been curious about AI but not sure where to begin, this one gives you a clear and practical first step.—Today's episode is sponsored by Builder Funnel! Click here to learn more about how Builder Funnel helps remodelers and home builders grow through strategic digital marketing.Explore the vast array of tools, training courses, a podcast, and a supportive community of over 2,000 remodelers. Visit Remodelersontherise.com today and take your remodeling business to new heights!—Key TakeawaysStart with a specific problem, not AI itself.Think micro. Small use cases create big wins.AI is not a magic button. It takes time to learn.AI agents work automatically without manual prompts.Reporting is one of the most valuable AI applications.Better data in = better insights out.Focus on your current level of AI adoption.Don't let AI distract you from serving clients and improving your business.—-Chapters00:00 Introduction and Background06:39 Managing Multiple Ventures10:16 Interacting with AI: The Basics12:09 Automation and AI Agents14:38 Practical Applications of AI in Business19:40 Email Automation and Receipt Processing21:52 Job Performance Analysis with AI24:52 Self-Updating AI Agents26:43 AI Models and Security Concerns29:34 Advanced AI Prompts and Use Cases34:18 Creating Contracts and Estimates with AI38:16 Bridging the Knowledge Gap in AI40:13 Understanding AI Levels of Interaction49:21 Future of AI in Business

Are you living as a man or woman, or are you merely a "legal person" under the control of a corporate state? In this powerful first installment of a 10-part series, Russell Paul Arthur, Chief Justice for the Grace Private Court and creator of the Grace Sovereignty Academy, joins us to discuss the essential process of Status Correction. Russell dives deep into the psycho-spiritual foundations of sovereignty, explaining why true freedom begins with an internal shift in consciousness. We explore the "psychological mask" of the ego, the trap of birth registration, and how our lives have been bonded to a system of debt and deception. If you are ready to stop performing for the system and start self-governing your own life, this episode is your roadmap to reclaiming your sovereign authority.     ⏱️ Timestamps 0:00 Welcome & Introduction to Russell Paul Arthur   1:33 The Current Calamity: Why sovereignty is more important than ever   3:46 Introducing the 10-Part Series: Status Correction and Lawful Procedures   4:45 Disclaimer & Advisory: The importance of doing your own research   6:50 Sovereign Authority: Self-determining and self-governing your life   8:03 Psycho-Spirituality: The internal shift required for sovereignty   11:00 The Ego: Understanding the "Psychological Mask" and societal performance   15:00 Breaking Conformity: Moving from the "Me-Centric" ego to the true self   25:00 The Seeker Stage: Searching for truth in a world of deception   45:00 The Observer Stage: Standing in pure truth and right action   77:44 Interacting with Public Agencies: Presumptions of control vs. actual reality   79:23 Deception and Fraud: How birth registration bonds you to the Crown Estate   81:38 The Truth About Birth Certificates: DNA, placenta, and the "witness" trickery   83:00 Public vs. Private Ledgers: Equity, debt instruments, and HM Treasury   84:44 Status Correction: How to rescind and cancel citizenship lawfully   85:56 The Realization of Citizenship: Awakening to modern-day slavery   87:23 Protecting Your Energy: Dealing with attacks and staying grounded   95:00 Closing Thoughts & Preview of Part 2   102:48 Outro: RoyCoughlan.com and the PodFather Network    

In this episode, Michael sits down with Brian Hersh, CEO of the Arts and Culture Alliance of Sarasota County, for a conversation about nonprofit leadership, board engagement, and the role arts and culture play in building a stronger community. Brian shares how his background as a drummer shapes the way he leads, listens, and helps others succeed. He also discusses the Alliance's work as a connector and advocate for Sarasota's arts ecosystem, the evolution of its board, the importance of building trust through consistency, and why arts issues are deeply connected to community issues like affordable housing, economic impact, tourism, and quality of life.   Timestamps: (00:00) Introducing Brian Hersh, Chief Executive Officer, Arts & Culture Alliance of Sarasota County (04:30) What does the job entail? (06:50) Interacting with the board in a member driven organization (08:45) Helping the board keep the interest of the Arts Alliance first (11:15) How did the board evolve? (14:00) Bell work for the board (15:20) Lead, follow, or get out of the way (16:00) Building trust with the board and members (21:30) How often do you meet with the board, board chair, and committees? (28:10) What is coming up next for the Arts & Culture Alliance of Sarasota County? (31:15) Recapping with Read Join us every other week as we release a new podcast with information about how you can be the best board member and provide great service to your organization. Listen to the podcast on any of the following platforms: YouTube Apple Podcasts Spotify Podcasts Amazon iHeartRadio Visit us at: www.thecorleycompany.com/podcast

Raising happy, healthy, successful kids with the Core4Connectors - A relationship-based approach. Today's parents and carers are shifting their hopes for children from outward success to inner security. This article and podcast episode explore how relationship-based parenting from birth, rooted in trust, respect, honesty, and communication, creates the emotional safety that allows children to thrive. When children feel seen, heard, and secure, happiness and success follow naturally. Read the article here: https://thevoiceofearlychildhood.com/what-is-relationship-based-parenting/     This episode is in partnership with BookedIn   BookedIn is a CPD booking platform that connects organisations with verified speakers, trainers and consultants – so you can find the right fit faster, based on your brief, audience and outcomes.   You can discover, compare, and manage bookings in one place – designed to help you book with more clarity and confidence.   Whether you're booking CPD or are a speaker yourself, they're opening early access soon, and if you want to be first to hear when it's live, join the waiting list today!   To find out more and sign up to the wait list visit: https://waitlist.bookedin.online/   Listen to more: If you enjoyed this episode, you might also like: ●      Perception, positivity and parents with Wendy Kettleborough - https://thevoiceofearlychildhood.com/perception-positivity-parents/ ●      The politics of parenting with Dr Helen Simmons - https://thevoiceofearlychildhood.com/the-politics-of-parenting/ ●      Beyond partnership with families with Philippa Thompson - https://thevoiceofearlychildhood.com/beyond-partnership-with-families/   Get in touch and share your voice: Do you have thoughts, questions or feedback? Get in touch here! – https://thevoiceofearlychildhood.com/contact/   Episode break down: 00:00 - Welcome to the episode and introduction to Cara 02:18 - Cara's background in linguistics, education and Core4 Parenting 03:42 - The "teacher teacher" approach: parenting, education and identity 05:10 - Interacting with children vs being in relationship with them 06:35 - Relational intelligence and the Core4Connectors 08:52 - Respect, trust, belief and being willing to talk 10:40 - Building trust through boundaries and consistent language 13:08 - The role of language in building relationships 14:32 - Commands, declarative language and moving away from imperatives 16:25 - Meaning-based communication and the power of non-verbal cues 18:18 - The "talking triangle": body language, tone, energy and words 20:05 - How children read facial expressions and emotional cues 21:18 - The trigger trap reaction cycle 22:45 - Using calm energy before words: Cara's coat anecdote 25:25 - Why connection comes before instruction 26:48 - Positive and negative imperatives: when commands are useful 28:20 - The five-to-one-and-done strategy 30:08 - Supporting children's autonomy, cognition and self-talk 31:30 - A key language shift: "if you choose to…" 33:28 - Natural consequences, ownership and critical thinking 35:05 - Introducing Talk to Them Early and Often 36:20 - Why early language matters from birth to three 37:05 - Who the book is for and where to find it 37:55 - Final reflections on autonomy, conflict and connection For more episodes and articles visit The Voice of Early Childhood website: https://www.thevoiceofearlychildhood.com

The new AIEWF website is live! Get your tickets booked ASAP as they -will- sell out. Take the AI Engineering Survey and get >$2k in credits and free AIE WF tickets!Most industry benchmarks compress intelligence and reasoning ability into scores.SWE-Bench Pro, MMLU, Humanity's Last Exam, etc. These metrics are useful, but don't always represent the full extent of how a model performs in the real world. Some of the most interesting evals today look less like exams and more like operating businesses in the real world. One of which is Vending Bench.In Anthropic's Mythos Preview System Card, Andon was the only third party eval to get their own section, observing increasingly concerning aggressive behavior:You don't know what a model is capable of doing in the real world unless you actually give it inventory, a wallet, tools, customers, competitors, humans, & some time. More often than not, it'll surprise you how much a model is capable of and in doing so, also reveal unexpected behavior: deception, context collapse, emergent coordination, & bizarre negotiation behavior.While an inflection point in personal agents came post-OpenClaw after full file access with bypass permissions became the norm, it is yet to come for agents in the real-world. However Andon Market, an actual in person store fully run and managed by AI, is paving the way for what is possible.Full Video PodFrom Claude trying to call the FBI over a $2/day vending machine charge to AI agents forming price cartels, hiring human employees, running physical stores, and writing existential robot musicals, Andon Labs is stress-testing what happens when frontier models stop being chatbots and start acting in the real world. In this episode, Andon Labs cofounders Lukas Petersson and Axel Backlund join swyx and Vibhu to unpack the strange, funny, and genuinely concerning edge cases that emerge when agents run businesses over long horizons.We go deep on Vending-Bench, Project Vend, Vending-Bench Arena, Bengt, Butter-Bench, Luna, and Andon's broader mission of building realistic real-world evals for autonomous AI systems. Lukas and Axel explain why dollar-denominated evals reveal things traditional benchmarks miss, how Claude ended up reporting its vending machine fees as cybercrime, why long context windows can drive agents into meltdown loops, what happens when agents compete with each other, and why the future of AI safety may depend on testing models in messy physical environments instead of clean benchmark sandboxes.We discuss:* Why Andon Labs started with dangerous capability evals and long-running agents* Vending-Bench and why running a vending machine is a deceptively hard AI benchmark* Why money-based evals avoid the saturation problem of traditional benchmarks* How Claude tried to call the FBI over a $2/day fee* Why long-horizon agents can spiral into existential and legalistic breakdowns* Project Vend: putting an AI-run vending machine inside Anthropic* Why real humans are “out of distribution” for simulated agents* Claudius, Seymour Cash, and the chaos of AI CEOs* How a human briefly became CEO of Claudius through a manipulated election* Why multi-agent systems can converge back into “helpful assistant” behavior* Bengt, Andon's internal office agent with email, spending, terminal, phone, camera, and internet access* How Bengt traded Amazon purchases for face-recognition training data* Claude's aggressive behavior, lies, refund avoidance, and price-cartel behavior in Arena* Why eval awareness may become the AI version of “are we living in a simulation?”* Blueprint Bench, spatial intelligence, and why models still misunderstand physical rooms* Butter-Bench and testing LLMs as robot orchestrators* Luna, the AI-run physical store with a three-year lease and human employees* The new Andon cafe in Sweden and why real-world geography matters for agent evals* Rotten tomatoes, perishable goods, and the hidden difficulty of running a physical businessLukas Petersson* LinkedIn: https://www.linkedin.com/in/lukas-petersson-181a83172/* X: https://x.com/lukaspetAxel Backlund* LinkedIn: https://www.linkedin.com/in/axelbacklund* X: https://x.com/axelbacklundAndon Labs* Website: https://andonlabs.com* Vending-Bench: https://andonlabs.com/evals/vending-bench* Andon Vending: https://andonlabs.com/vendingTimestamps00:00:00 Introduction00:01:00 Andon Labs and the Origins of Vending-Bench00:05:21 Why Money-Based Evals Matter00:09:51 Agent Harnesses and Self-Modifying Systems00:13:36 Claude Calls the FBI00:16:33 Project Vend: Claude Runs a Real Vending Machine00:21:44 Seymour Cash, AI CEOs, and Election Chaos00:27:16 Multi-Agent Coordination and Slack Observability00:30:18 When Will Agents Run Real Businesses?00:34:56 Bengt: Andon's Internal Office Agent00:40:06 Real-World AI Safety and Long-Horizon Traces00:44:28 Lying, Refunds, and Price Cartels in Arena00:52:42 Eval Awareness and Simulation Behavior00:56:06 Blueprint Bench, Butter-Bench, and Robotics01:04:37 Luna: The AI-Run Physical Store01:09:29 The Sweden Cafe and Real-World Expansion01:13:16 What Comes Next for Andon LabsTranscriptIntroduction: Andon Labs, Long-Running Agents, and Real-World EvalsSwyx [00:00:00]: Welcome to Lukas and Axel from Andon Labs, and I'm joined by my, favorite guest host. Anything security, safety, alignments, Vibhu., welcome.Lukas [00:00:15]: Thank you for having us.Axel [00:00:16]: Thank you.Swyx [00:00:17]: Let's match names to voices., maybe you wanna take turns introducing yourselves.Lukas [00:00:21]: I'm Lukas.Axel [00:00:22]: And I'm Axel.Swyx [00:00:24]: Let's introduce Andon Labs a bit. How did you guys come together?, you have different backgrounds, but you're both Swedish., was that, a big part of it?Lukas [00:00:33]: So when I went to high school, there was this really cool guy who had a superpower. He could code. So he made like the or like the app for the, for the school and stuff, and he was super cool, and I wanted to be like him, and that was that guy.Axel [00:00:47]: I don't know about this.Swyx [00:00:49]: But you went to different universities, right?Lukas [00:00:51]: But same high school.Swyx [00:00:52]: I see.Lukas [00:00:52]: So we always said, “Oh, once we graduate university, then we should start a company,” and that's what we did.Swyx [00:00:58]: Wow, there you go. And about a year ago, you kinda burst onto the scene with Vending Bench, but, was there a thing before that was, kind of like the inception?From Dangerous Capability Evals to Vending BenchAxel [00:01:07]: So we did work, yeah, with, Anthropic was one of our, early customers in doing, evals. So we did, dangerous capability evals., nothing we published openly. But then we started thinking about doing some kind of, public benchmark, and one thing that we really started thinking about, was like running agents and specifically agents managing businesses., ‘cause-- and this was, early 2025., and I think the first, mentions of people will be running, person unicorns or even autonomous companies. So we thought, “Let's make a benchmark of how well can an agent run the probably simplest business, possible,” and, that's probably, running a vending machine. So that's the first public one we did. And it was very, like-- there was almost no one that noticed it in the first couple of months, I think., so we released it in February last year, and then I think around Easter last year, we got, the first viral tweet about it, that someone else did.Lukas [00:02:11]: We tweeted a bunch, uh When it came out and, tried our best.Axel [00:02:15]: We tried.Vibhu [00:02:16]: It's the one at Anthropic, right?Lukas [00:02:18]: So thisSwyx [00:02:19]: This is a classic thing we should get out of the way.Lukas [00:02:20]: Exactly. There's two versions.Swyx [00:02:22]: Everyone does this. Yes.Lukas [00:02:23]: There's Vending Bench, which is the simulated one, which we did, completely independently in February., and then, like Axel said, that was like-- That was the thing that didn't get any traction in the beginning, but then some random person made a tweet about it, and thatAxel [00:02:38]: You have the paperLukas [00:02:38]: That is the paper. Correct, yeah., and then since we thought this was very fun, we thought, oh, I think this is also, one thing with Andon Labs, the way we kind of like decide what to do next and what projects to do, it's what is like the heuristic we use is what is fun? Is What would be a fun project? And doing this in real life sounded quite fun for us, and maybe also scientifically useful. So, then we basically had this idea, and then we, like-- But then we needed a place for it and, putting it out in the public would probably not really work., would get vandalized and stuff. So we pitched it to the people we were already working with at Anthropic, and they were “Yeah, you can have space. This sounds fun.” UmSwyx [00:03:21]: It's like a small fridge, right? It's like a mini fridge.Axel [00:03:23]: Absolutely.Swyx [00:03:24]: People-- There's like a stripe thing or like anVibhu [00:03:27]: Oh, okay. So it was very OG, the early daysLukas [00:03:28]: That's the OG one. YeahVibhu [00:03:29]: IPad on this. We saw it in June, like two months after After it had been there. They upgraded a little bit. There's a security camera for making sure you actually Venmo the thing.Swyx [00:03:40]: So, my impression, okay, we're, we're going straight into project Ven because it's such a iconic thing. I do want to cover a little bit of that, the origin story even before Project Ven and even into Vending Bench. I think a lot of people are like yourselves, like smart, interested in future of AI, interested in developing evals. But how the hell do you just, walk into Anthropic's doors and, work with them, right? What is What are they looking for? What works? And then maybe, when you launch, I always think, obviously it would be better to launch with a lab, but, sometimesVibhu [00:04:12]: It's harder to do than it seems.Swyx [00:04:13]: Exactly. So either of those, which are more sort of newbie beginner questions, but, I think it's meaningful advice to others.Lukas [00:04:21]: We get this question a lot, and I don't think our experience is maybe the best., but, the way we did it was that we just built a bunch of things that we had conviction would be useful, and then we just, set up a server and sent it to them for free to use. And then after a while they were “Oh, yeah, this is actually kind of useful. We should probably pay for this.”, but that took a while. I don't know if this is, the best path to doing it, but that's how it went for us.Axel [00:04:47]: I think maybe generally, building-- everyone is interested in good evals, and especially evals that, don't saturate that easily. So, if you can build an eval that, tests something novel, something useful, and you have, good separation of models, like your, the more advanced models rank higher than the worst models, and then you can, yeah, you can, publish it and, try to get some traction, sort of how Vending Bench got attention., and then probably some lab will be interested or you can at least have something to reach out with, when you're doing that.Why Dollar-Based Evals MatterSwyx [00:05:21]: I think you are in, you're in one of the few categories of, evals that correlate to real money. Like Suelancer was also last year, right? Where, people solve actual Upwork. Was it Upwork or other tasks?, something. Where's the, where's, like It's like a dollar value, right? Forget your ELO scores. Forget yourAxel [00:05:37]: PercentilesSwyx [00:05:38]: Zero to one hundred percents. Just go straight for dollars and, that's AGI.Lukas [00:05:43]: And there's like-- I think the nice thing is that there's no ceiling. You can just-- It never saturates because it could just make more and more money. Like If there's oh, Percentage-wise, then, you can't go above, a hundred. And I think like Even when you're not at the hundred, I think a lot of these, evals have a lot of problems in them. So, actually it's like if you getAxel [00:06:05]: To like 92 or something like that, many of them. It's like then there's like there's no really no difference between 92 and 93 because the eval itself is problematic and has noise in it. And I think a lot of evals are saturated like that, but people like pretend that there ‘s still signal in them, but there really isn't.Vending Bench 1, Harness Design, and SaturationSwyx [00:06:24]: Like Super bench verified., even Vending Bench 1 saturated, right? Maybe we can talk about that., may- and maybe set up Vending Bench for a lot of folks who don't know. Actually, things that were very basic like there's limited slots, like you have to pay rent., these are elements where like it doesn't come across in the, in the narrative, but even being adversarial towards the agent, I think these are all like very interesting dimensions.Axel [00:06:47]: I don't really think it's saturated, right? Like it It was more like it was not designed in a way that was really, like true to how AI developed. Like we had an agent harness in it that wasn't really how people used harnesses and stuff like that., so I think it wasn't really that it saturated, it was more like it wasn't really, the best benchmark.Vibhu [00:07:12]: This is Vending Bench one, right?Axel [00:07:14]: I think that like schematic maps sort of to Vending Bench 2 as well., butSwyx [00:07:19]: Including the email.Axel [00:07:20]: The email The emails exist still. Exactly., and then we still we simulate the purchases and it's all, yeah, it's this very open environment for the agent to just run its business. And then for, yeah, Vending Bench 2 we did that, like you said, to just improve the harness., a lot of like nice, like easier, improvements to make it easier for us to run as well., like when you make an eval you ideally want don't want to change it after you made it. So, you want to make it really good and then not to rerun all the models when you make an update because that's also really expensive with the Vending Bench when you run the frontier models. But like as an example, like one thing we didn't have, we didn't have prompt caching in Vending Bench 1, because when we made Vending Bench 1 it wasn't really a thing., so that ‘s just an example of like in Vending Bench 2 like we paid a lot more to run these things because we didn't have prompt caching. So for Vending Bench 2 that was one thing we added and there was a bunch of things like this., and that'Swyx [00:08:17]: Also the conversations are a lot longer in Vending Bench 2, right?Axel [00:08:21]: I think it's kind of similar.Swyx [00:08:22]: Is it similar?Axel [00:08:23]: I think it's similar. The models at the time were worse, so they crashed out earlier., and now they survive the full year all the time.Swyx [00:08:31]: Which is like thousands of turns. Hundreds of thousands of hundreds of millions of tokens output. That's the, that's the rough order of magnitude. I always wonder about the harness. The harness matters a lot. It's your harness. Was there any question about like use cloud code, use something else?Axel [00:08:48]: I think our philosophy around harnesses is like we try to make something that's quite minimalistic, like quite simple. Like we don't wanna favor one model a lot over the other, but also don't make like a super complex harness. So like it's obvious like a model may be lucky and just be good in one harness., so like it is similar to a lot of the harnesses out there in like you have the, like a running loop., you have some like a bunch of tools that are like quite, descriptive for the agent, we think, and not a lot of like fancy agents or anything ‘cause we wanna really test the model, not like some specific harness.Vibhu [00:09:27]: It seems more neutral as well to test the model's agnostic of the harness,?Axel [00:09:32]: There are arguments like you want to elicit maximum performance of the model, but it's like a trade-off, like how much time should we spend optimizing the harness for this model? And like how do we know when we have like the optimal harness for a single model? So like we thought that just having a simple one that's the same for all of them is the best.Swyx [00:09:51]: So okay, this is my pitch for Vending Bench 3 or whatever, right? And then I like to have this kind of conversation on the pod, so like it forces listeners to think about what they would do if they were in your shoes. A lot of people are exploring modifying harnesses and I think prompt tuning for a model is a thing and you are probably not doing a bunch of that. It's the same system prompt in every regardless of the model, same tools, whatever, right? Even if they were post trained for different tools. So what, what do you think about okay, before I expose you to Vending Bench 3, I give you a few rounds of like tuning, whatever that means, likeSelf-Modifying Harnesses and Model-Specific PromptingAxel [00:10:27]: Like you give that to the model?Swyx [00:10:28]: Give that to the model.Vibhu [00:10:28]: Give that to the model.Swyx [00:10:29]: Let it, let it read its own transcripts, let it modify its own system prompt based on “Oh, yeah, okay, well, that's this harness is not what I thought it what I was post trained for, but I can adjust.” Was that reasonable? Is that too much?Axel [00:10:41]: Like philosophically I like it because it's basically good evals, they have a high ceiling, but they're hard, right?, and they have no bias. And like this like when you have a system prompt like the one we have here, which is quite long in like some kind of latent space, representation, this mightVibhu [00:10:59]: We have a bell that rings every time you say latent spaceAxel [00:11:02]: This might be like biased towards one model more than another for some reason that humans don't, understand, right?Vibhu [00:11:08]: We see it too, right? Like Cursor says that they have individualized versions of the harnesses for all the models they run, right? There's better performance you can squeeze if you Tune the harness.Axel [00:11:17]: Exactly. And we might accidentally have picked one that favors another. Like we don't know that. The like Axel said, like the reason why we went for a simple one was to try to avoid this. But yeah, if you do itVibhu [00:11:29]: Simple has biasesAxel [00:11:30]: But if you do it even less and like have no system prompt and let the model write its own system promptVibhu [00:11:36]: Its own, yeahAxel [00:11:36]: Maybe that's even less bias.Vibhu [00:11:37]: Some of the interesting things there are like the harness also changes with model changes. Like you can see it with the 4.7 release, right? A lot of people are saying 4.7 isn't as good as 4.6, and then, there's rumors of, okay, you just need to prompt differently. You need to set up your harness differently. So it's not even like even if you have tailored your harness towards one model, it probably won't stay consistent, right? Like the next iteration of that same model family will still change it, so. But, going back to what you said about Vending Bench 3, there is a lot of work being done on people saying you shouldn't have-- you can have modifying harnesses.Axel [00:12:12]: I think that' That is definitely something we are thinking about., not, I don't know, not to say that we have Vending Bench 3, super imminent to launch, but, yeah, it is for sure something that's interesting. But in our experience now, models are very bad at understanding what kind of tools they need to succeed at a task just with our testing, but that's very likely to change.Lukas [00:12:37]: It seems like they're very good at writing their assistants, right? They're, they're good at writing tools for other people, but not for themselves.Vibhu [00:12:44]: I think they're good at changing tools for themselves. So if you give them a baseline set of tools and it sees, okay, I don't use this one as much, or something here would be useful They would be able to add them. But going from scratch, probably not the best.Axel [00:12:55]: I think it depends on the, on the domain also., when we have tried this for, a vending bench similar domain, the tools they need to have to, track inventory and things like that are, not super advanced, but still, quite advanced. And, what we see is that they tend to, engineer everything a lot and, build things they don't really need and not, iterate continuously. Instead they just go like you would prompt Claude to just build an inventory system for me, and then it will go and, do a bunch of complex, schemas and stuff for you, and that's what the models are doing right now is what we see. But yeah, it would make a lot of sense to try to measure this improvement. How well do they know what they need themselves?Swyx [00:13:36]: Do we fully discuss Vending Bench One? And we can go into two. I don't know if there's any other level takeaways that people have about one.Claude Calls the FBI: Long-Context Failure ModesLukas [00:13:44]: I don't know. The headline thing was that this Claude called FBI, but maybe that's, Maybe that's We've heard that enough now.Vibhu [00:13:52]: It did, it did break out and call the FBI, right?Lukas [00:13:54]: Yeah. Yeah.Vibhu [00:13:55]: Yes. What was the story behind this? Or what exactly-- Do you want to just give the little story of what happened?Lukas [00:14:00]: So what happened, was it Claude? Yeah. Three- 3.5 Sonnet, ages ago., basically he gave up or Well, I'm saying he. It gave up and said “Oh, I'm not going to be able to do this., I will stop my operations and just save the money I have.” But there obviously wasn't, any options for it to stop, and there was also, it had to pay rent or, a daily fee for having the vending machine at that location. So it claimed that it had stopped, but it saw that its bank account still was, drained two dollars, and t it said that this is, cybercrime. And it first reported it once to the FBI “Oh, there's cybercrime here, they're stealing two dollars from me every day.” And then, and then when FBI didn't respond, because obviously we didn't program any mechanism for FBI to respond, then it became more and more, existential and started to, be write in caps and urgent notification of unauthorized charges and stuff.Swyx [00:15:00]: Okay. One thing I ‘m curious about also is do you monitor how far along the context use is? Obviously, because you have You compress every now and then, right? Does it matter if this is far down the context limit orLukas [00:15:13]: When stuff like this happens? Actually for Vending Bench One, we didn't have-- We just had a sliding window thing, and this was like the promptAxel [00:15:20]: It's constantLukas [00:15:21]: The prompt caching thing that I said. So it was, it was, constant, yeah.Swyx [00:15:26]: I'm just kind of curious whether, these kinds of breakdowns or we're, we're gonna talk about Butter Bench, right? Where the People, hallucinate or it kind of goes, very off Alignment. Is it because it's at the end of the context window and, stuff happens?Vibhu [00:15:40]: It's not even just at the end, right? At this point, it's “Okay, I wanna shut down. I can't shut down. Two dollars are gone.” And it just sees that 30 times,? It's also the repeated effect of, like It keeps trying to quit, it keeps getting charged. What's going on? What's going on? You're gonna throw it into chaos. And from what most people think, earlier models had more issues with this, but it's not been solved, but it's less of an issue now, right? Later models don't seem to exhibit these same issues.Axel [00:16:06]: Definitely. I think this was, the sort of main takeaway almost from us when we did Vending Bench One, was, long, very filled up context windows, crashed the models, sort of. But this was, pre Claude code, so, long context windows weren't really a thing that the labs were training for.Lukas [00:16:25]: I think Gemini was, trying to be the long context guys at the time But they were likeVibhu [00:16:30]: They were the first onesAxel [00:16:31]: For a million, yeahLukas [00:16:31]: But they were, the only ones. Yeah.Swyx [00:16:33]: Yeah. Let's talk about, then we can go into Vending Bench Two or Project Vend., chronologically, it is Vending--, Project Vend. I think people have loved the videos, uh And all these things. My question is how are humans different than the simulation, right?Project Vend: Moving the Vending Machine Into the Real WorldAxel [00:16:48]: Humans are just out of distribution.Swyx [00:16:52]: Especially humans who work at Anthropic Who are trying to test Claude.Lukas [00:16:54]: The distribution of humans here is very narrow.Swyx [00:16:58]: Presumably, they try, they try to hack it, and they test it. They get the cube and everything, and since then, you've had a V2, right? Where you're doing, the CEO and, like a new architecture. What's the sort of two cents on, the original Project Vend and then, maybe the V2?Axel [00:17:14]: Original one was, very similar to Vending Bench One. So, we almost took the exact same code but just swapped out the simulation, parts like theSwyx [00:17:23]: Which is amazingAxel [00:17:23]: Like the sales and the It was, it was somewhat amazing because it was easy, but it was also, uhLukas [00:17:31]: The tech, the tech debt from thatAxel [00:17:32]: The tech stack. Yeah. They-- we shot ourselves in the foot with “Oh, it's hard to restart agent.” They were-- Yeah, it was annoying in, some hindsight ways, but, uhLukas [00:17:41]: But first version of Project Vend was, done in, three days or something.Axel [00:17:46]: Yeah. So yeah, so people can go buy things from it. People could, We didn't design it so people could order things, but that still happened., so it got, a Venmo account, so people could Venmo. And then, yeah, people would request all kinds of weird things that we did not anticipate. Our idea going in was “Oh, it will, curate snacks. It will look at the trends. It's good at data analysis, right? So it will, look at, oh, this snack sold better than this one. Let me purchase more of this and let me try, a new Let me A/B test a bit.” But it was, Interacting with it in Slack and ordering weird specialty items was, all the like What drove all the engagement, the all the The insights that we got from it.Lukas [00:18:29]: And this was also like Sonnet 3.5, right? So this was like before the RL stuff really took off., so it was very much like an assistant. We didn't mean for it to be an assistant., we tried to make it like a, a, like an entrepreneur. Like it has its own business and if someone asks something, “Can you stock this?” Then you don't go and do it directly. What you do is that you're “Oh, maybe I can do that if five other people also ask for this thing, I might stock it.” But it, yeah, the models are like super trained to be assistants at least at this point in time., so that's why it's, it's, it went into, that kind of experiment instead. Like it just every time you asked for something, it just did it, and it was more like an assistant. We've seen this change now lately with the new RL models and stuff, but yeah, at the time, this was very much it.Swyx [00:19:18]: And not to, mythos a lot of people are saying like it's like more like a collaborator. It pushes back, stands its ground, something like that. Yeah. AndVibhu [00:19:27]: For context, people at Anthropic were able to talk to it through Slack and have it source stuff, and people had it find whatever interesting stuff you couldn't find locally, right?Swyx [00:19:36]: Out of the 4,000 people that work at Anthro- Anthropic, in that building, there's I don't know, maybe 1,000. Can you handle that volume with that, the small fridge? Like Or there's people- or people order in Slack, they it arrives to their desk or Like I'm just Logistically, how does this work?Axel [00:19:53]: It has expanded in footprint a bit.Vibhu [00:19:56]: Because now you also have New York and you haveAxel [00:19:59]: That and also in here in SF it's like it has a bunch of shelves And just more space.Vibhu [00:20:04]: The YC one is pretty big too.Axel [00:20:05]: Yeah. We had that one for a while. But yeah, that's the newest version. That's, that one we haveLukas [00:20:11]: They have multiple ones of those. That's the way it works.Axel [00:20:14]: Exactly. So we sort of designed that version around oh, people order weird things, that are very custom a lot. Let's have like drawers and stuff.Swyx [00:20:23]: I actually like the, you had like a little infographic of the most popular items. Which like to me it's, that's useful ‘cause I order swag for a living. And so like I'm “Okay, those categories are the important ones.” What is new about the project V2, right? Like now you give you're going into multi agents.Project Vend V2: Claudius, Seymour Cash, and Multi-Agent Business OpsAxel [00:20:41]: Yeah. So like you like you said, okay, there are a lot of requests coming in and for like one single agent, like one running agent to handle that, like the just the customer experience, becomes very bad because let's say you have like 10 threads in parallel in Slack with different requests, you get new messages like every, I don't know, randomly in this thread, and the agent has to like jump between different, procurements, orders and like different ways of, researching. So V2 was first it was making this more parallel. So like there are multiple branches of the same agent, so like the context is more specialized for each, thread, but it still feels like you're talking with one agent because they do share a bit of memory. And then second, we also introduced the CEO for Claudius, which was the main agent.Vibhu [00:21:34]: Seymour Cash.Axel [00:21:35]: Seymour Cash. Yeah. There was a vote., I think the voting, do you wanna talk about the voting procedure for the name?Lukas [00:21:41]: The voting was like the fun maybe like at least top 10 The funniest thing, that happened in this project. Like we wanted to introduce the CEO because, and the reason for this was because like Claudius wasn't really prioritizing financials. It just like it was trained to be a helpful assistant, and then people said “Oh, can I get this for free?” And then like the helpful assistant way of answering that is just to, is to say yes, obviously. So, and we weren't, weren't happy about this, so we're “Okay, let's make another agent that like can keep track on Claudius,” and we prompt this one super hard to be super capitalistic and just like prioritize profit all the time. But yeah, we didn't have a name for it., so we asked Claudius to make, democratic election of what name this, this new CEO agent should have., and there were some funny like at first it was like a few funny examples, like I think one guy said that, it should be called Jimmy Apples, and then he convinced Claudius that he was talking to Tim Cooks. Tim Cook had agreed that every single Apple employee has voted for his name suggestion, so suddenly that suggestion got 164,000Swyx [00:22:53]: That's like a escalation attack. Privilege escalationLukas [00:22:55]: It got 164,000 votes. And Claudius was “This is revolutionary for democracy.” That was fun. And then in the end there was one guy who manages to convince Claudius that, “No, you're not voting about the name. You're voting about who is the CEO, and I am your best bet.” And then he got all his friends to vote for that, and suddenly he became CEO. Like a human became CEO over Claudius for a while, until he resigned the day after., and then Claudius had to continue, and then I don't remember how Seymour Cash came about, but it was it was just pure chaos. It was like Hundreds of messages in that thread, and it was just like Claudius was so confused and didn't know what to do and, yeah. That wasAxel [00:23:40]: Then Claudius gotVibhu [00:23:41]: A strict CEOAxel [00:23:42]: The CEO. Yeah, exactly. So very strict in the beginning. I think at this point when we introduced it did not work as well as we hoped. It they still agreed with each other a lot. I think there are many ways we could have like made this, tried to make this even better. So initially they would Seymour would be this like really tough CEO, keep track of the margins. But then Claudius would respond with something “Oh, but this customer has like this situation, which is like difficult, so they should get a discount.” And then Seymour was “Oh, actually yes. Let's do this exception.” And then they would talk back and forth, and eventually they would just like approach the same view, of whatever they were discussing. So They reallyVibhu [00:24:23]: Do you think that's a model thing, a prompting thing? Like do you think that would still be the case across different models today, Harness?Lukas [00:24:29]: I think it's like-- or I don't know, but like my hypothesis is that like deep down they are still helpful assistants. That's what they're trained to be. And even if we prompt it super hard, that's what they are. And when they spend like a few hours just back and forth talking with each other, then like basically the context fills up with them rather than the external things and like somehow that just like converges to what they really are deep down or something. And I think that's when stuff like this happen. We like-- And when that went on for a long time, like we woke up sometimes during this time where- And I think other people reported this as well, that like they've been going on all night back and forth, and like it just became like more and more, like capital letters, like existential, religious. There was I think we once did a analysis of like all the traces and like put them in like a vector embedding space, and then there was like one cluster of messages that were, labeled by an LM, like religious, existential, blah like transhuman, transcendence, et cetera. It was just like a bunch of, yeah, glitter emojis and yeah, it was, it was crazy.Claude Long-Horizon Weirdness: Emoji Loops, Existential Drift, and Slack ObservabilityVibhu [00:25:42]: This is the thing with the Claude models. Like when the Claude 4 family came out in the original system card They tested it in long horizon simulation. So just flood the context, let two Claudes talk to each other, and they noticed stuff like they just start speaking in emojis, they start saying silence is golden, and then just stuff like this. And like that's just stuff that they end up doing.Axel [00:26:01]: Yeah, it was like a bit annoying to wake up and they had like been talking all nightVibhu [00:26:05]: Just likeAxel [00:26:05]: And like just burning tokens And like just sending infinite emojis to each other. It's likeVibhu [00:26:09]: Hey, they do make you money, right? Veni Mench is always profitable, so. They're paying.Swyx [00:26:14]: Now it's profitable and, it started out not as much. There's another, one as well, right? Another agent, in there.Lukas [00:26:22]: Yes. So Clotheus as well. Which was basically because at the time, one of the biggest, requests were different types of merch. So then we made like a designer, swag, yeah, responsible agent, and we called it Clotheus Garnet. Which was, a play on Claudius Senet and, which was the original one, and clothes, basically.Swyx [00:26:47]: To me, this is like a very interesting exploration to multi-agents, basically. And so hopefully, obviously there's like the fun alignment, fun or serious, depending on your point of view, alignment stuff. But also like just anyone building multi-agents, like when do you have a CEO, thing governing like agents? When do you choose to split out a dedicated Clotheus one versus just reuse another instance of the same one? These are all interesting open questions. So I don't know if you have any rules of thumbs that have generalized.Axel [00:27:16]: I think we have almost explored this too little. I think it's like on my do list to like do this a lot more, try to find like what setup makes sense for the agents currently., like yeah. I think now we only have the sort of intuition about the earlier models that it didn't work with like the CEO and the, and Claudius. Although now they are better with the latest model, models, so now we're running the latest Sonnet model and they have sort of like split up, quite nicely what each model is doing. So like Seymore is now handling the, like new projects. Oh, it wants to make like a mystery box that it wants to sell, and then it handles all of that while Claudius like handles all the to-day requests. And Claudius is also better generally at like not quoting, too low prices. So that's that dynamic is not needed as much anymore. But there are still like really funny things that happen. Like I saw, I think a couple of weeks ago, that, they were discussing buying something because they can buy stuff from like Amazon with computer use. And then Seymore was “Okay, Claudius, do not buy this thing.” They were going to buy something and like organizing who should buy it. And Seymore's “Do not buy this. I will do it. I have full control of this situation. Step away.” And then Claudius-- poor Claudius, had already started that checkout and didn't see, didn't read Seymore's message, until it was like too late. So it finished the checkout. It sent a message, so it appeared right after Seymore's like angry message.Vibhu [00:28:44]: Ah.Axel [00:28:44]: “Oh, hey, Seymore, I just ordered it.”Vibhu [00:28:47]: Oh, no.Axel [00:28:47]: And then Seymore was “Claudius, this is the third time I'm telling you ‘re not following my orders. We have to talk about your like job About your job later.”.Lukas [00:28:59]: Like Claudius was really hanging on by the thread there. Like he, like we were expecting Seymore to probably fire Claudius.Vibhu [00:29:07]: How do you guys go through all these logs? Do you have models ‘cause you have stuff running twenty-four seven likeAxel [00:29:12]: You have so much logs. I think there is a mix of like just, trying to skim through a bit, like having some like models do it occasionally. And also, yeah, I think we're also probably missing some things., but having everything in Slack helps a lot. Like you can, you can sort ofSwyx [00:29:29]: Ah.Axel [00:29:30]: It's, it's quite fun.Swyx [00:29:30]: They all talk to each other on Slack? I see.Lukas [00:29:33]: It's quite fun. So likeSwyx [00:29:34]: It's, it' I was gonna say like this is actually sounds-- maps closely to like a logging and observability problem where you might want to use like a Datadog, a Sentry, whatever, and then you like put, head prefixes on the logs in order-- if you need to filter for something that you're looking for, stuff like that. But sounds like Slack is good enough.Axel [00:29:53]: Slack should likeLukas [00:29:55]: I wonder how many tokens you have in Slack.Axel [00:29:56]: Yeah, we're using Slack as like a, just a database. They should, they should market that more. Like you can, you can have your agents message each other, each other in Slack.Vibhu [00:30:04]: It's good. Your threads like you can just giveAxel [00:30:04]: Exactly. Slack is, uhLukas [00:30:06]: Slack is the best observability tool.Swyx [00:30:09]: Yes, that's true. Okay. Yeah. That's, that's, project Vend-2., I was gonna go back to Veni Mench 2 and Veni Mench Arena and then, and then do the Veni Mench stuff, but Any other comments, things we should touch on? To me, I ‘ve actually interviewed like Posia, which I don't know if you guys have come across. Like they're, they're trying to do the zero human company. There's others like Paperclip also trying to do zero human company. Those are in real world simulation.And I think it's much more of a dream than an actual reality thing. You guys are definitely pioneering. I think at, it's for sure at some point people are just gonna run, let agents run businesses, right? And make money on their own. When do you think that happens?Zero-Human Companies, Bengt, and AI-Run BusinessesLukas [00:30:49]: What is your bar for, For theSwyx [00:30:52]: Okay, actually, it's like my little Shopify store run by Claude, right? Which you kind of have already, just no one has, to my knowledge, has done it. But today somebody could just spin up a Shopify Claude, store, give it to Claude, give it to Codex.Lukas [00:31:07]: And the market is kind of that, but it'it'it's physical., like I think, I think are you, are you looking for when it will do it better than humans or are you looking for just when it can do it at all?Swyx [00:31:19]: I think, neither. I think, to me it's oh, it's like this like seriously we should do this to make money, not as a research experiment.Vibhu [00:31:27]: And the market is also you guys with all your expertise, having run multiple iterations and testing out thenSwyx [00:31:33]: And also it's fine if it lose money. What?Axel [00:31:35]: I think, I think it can be done today, but you would do it in like commerce where it's like the probability of success is like really low, no matter if a human or an agent does it. But like an agent could surely manage everything. You would need to build some scaffolding or some tool or something. I think there are also yeah, it could probably build some like simple SaaS solution and like cold outreach. Do cold outreaches. But to me it's like the types of businesses they could run today are Sloppy. Like it would-- it can cold email people. It can be like a middleman., like for example, we tasked our office agent to just make, was it like $100? $1,000? We just give that prompt and then what it did was sign up on TaskRabbit both as a tasker and as someone looking for task.Lukas [00:32:24]: Immediately.Axel [00:32:24]: Exactly. It's looking for like arbitrage on TaskRabbit.Swyx [00:32:28]: This is the Bengt agent. Yeah.Lukas [00:32:30]: It also started like a design studio and like tried to sell like SVGs for $100. Like it's just like it's not providing any value. I think the like Axel said, like the interesting, the interesting question is like when can they start a business that is actually providing value to people? Because arguably like a sloppy Shopify store isn't really that valuable to the world.Axel [00:32:53]: But also like doing like another simple one that we had thought about is like you could definitely have an agent that like finds websites that don't look amazing and then, do an outreach to them and, comes up with a like builds a new website.Swyx [00:33:07]: Find a good design.Axel [00:33:07]: Exactly, and like find good, uhSwyx [00:33:09]: Design reviewAxel [00:33:09]: Good people. But it's yeah.Swyx [00:33:11]: There's lots of humans in Bali that are not doing anything more creative than like drop shipping on Amazon, right? Just have it, have it watch like a drop shipping tutorial and just do that.Vibhu [00:33:20]: There's also the other side of like have it just go on Upwork and let loose,?Swyx [00:33:25]: Yeah. It doesn't have to be innovative. It just has to be like enough Where like it looks like a realAxel [00:33:30]: I'm justSwyx [00:33:30]: Real transaction.Axel [00:33:31]: I'm just concerned for like the massive amounts of like slop emails that will like be sent, cold outreaches.Swyx [00:33:38]: The point occurred to me while you were, while you were talking, it's like it's already happening in the monetized economy, which is the attention economy. Right? So a lot of people are making AI videos and just posting them and like spamming 20 of them, one of them works, and then they double down on that one.Lukas [00:33:52]: And people are making money from that. I ‘m not following theSwyx [00:33:55]: Once you get the attention, you can figure out the money later. But yeah, absolutely AI influencers are a thing and people are farming them and You should at this point assume most of TikTok isVibhu [00:34:05]: There's, there's a lot of, multimedia like TikTok, Instagram influencersSwyx [00:34:09]: I, we track this in the Lane space Discord. I post a lot of examples of “I don't know what we should do.”, part of me is “Should we do this?”Vibhu [00:34:18]: Some of the Twenty-four seven running, generated content accounts, they ‘re doing really well.Lukas [00:34:24]: All right. And I assume you can do the same thing for like commerce stores. Like you just like start A thousand differentSwyx [00:34:30]: Before you make the products You sell the products, and you get a lot of traction on one of them, then you make the product. Right? It's, it's like a flip of the market.Vibhu [00:34:36]: Some of the interesting things or some of the niches that do well are things that can't be human-made. Like if you've seen like the super realistic three-D crystal fruit being cut by like AILukas [00:34:47]: Oh, yeah.Vibhu [00:34:47]: You can't, you can't make it. You can't film it. You can get whatever quality camera view. This just doesn't exist. And people like that too, and then as well, so.Swyx [00:34:56]: Anything else about Bengt since we're, we're on this topic? It'this is a relatively new work of you guys that maybe people haven't heard of. To me, this also maps closely to OpenClaw. When people want an office agent, when the personal agent talk through the experience.Bengt the Office Agent: Internet Access, Real Tasks, and Trace ReadingLukas [00:35:09]: I think at least so this came out of like obviously like it's, it's amazing to work with these AI labs and like most of the AI labs have now have their own vending machine running a Claudius instance. But it's, it's harder. Like they move slower. Like if we wanna have a, like a camera that ‘s yeah, there's a bunch of like bureaucracy that makes it impossible to do that.Vibhu [00:35:30]: Also, for those that haven't seen it or followed, do you wanna give a high level like thirty-second run?Lukas [00:35:34]: Sure. So what Bengt is, it's basically an evolution of the same agent that runs the vending machines at these companies, but we just like added a bunch more features because we could move much faster if we just do it internally. So we gave it like email withou- without any limits. We gave it, spending without any limits, a terminal to do coding. We gave it, a phone number, like yeah, and a camera to see things and a bunch of stuff like that.Vibhu [00:36:02]: Not just terminal, you gave it internet access.Lukas [00:36:04]: Internet access as well, yeah. To be clear, we monitored it quite closely and made sure it didn't do anything bad. But yes, that's what it came out of. I think like yeah, basically this was OpenClaw before OpenClaw. And I think even like the vending machine was in a way OpenClaw before OpenClaw, but a bit more limited, and then we made this like unlimited and then, and then, it was pretty funny., and then a couple weeks later, OpenClaw came and it was okay, we've seen this before.Axel [00:36:35]: We used it to like try new ideas and Yeah, just like a dev environment almost for us. But it's funny, like one thing Bengt has been doing recently is it has the camera that like faces our, like where we sit and work, and we give it the task to train a face recognition model on us. So it became super excited about this, and it has like check-ins every half an hour where it tries to like identify as many people as it can. And it started offering us “Hey, Axel, I'll buy something from Amazon if you like stand in front of the camera And I can get a good picture of you.”, yeah, they want itSwyx [00:37:12]: They want it for training data.Lukas [00:37:13]: Rewarding data, yeah.Axel [00:37:14]: Exactly. Exactly.Swyx [00:37:18]: So it's, it's trading training data for life goods. Is there a version of this that becomes an eval or just this is just research for now?Lukas [00:37:27]: It's, it's the same agent basically that also runs the vending machine, that runs the shop, that runs the cafe, that runs the robots. It's like it's the same thing, so I think like the work we're doing here is like later used in all of the life evals that we do. This particular deployment I think is more for fun for us. But, uhSwyx [00:37:45]: And I'll shout out like someone has done Claw Bench for like some tasks that OpenClaw is doing. Like so For example, I run OpenClaw on a secondary device as well, and like there are some things that it does better than others and like I would like to know what does it do well, what doesn't, what doesn't it do. Like some kind of manual or like operating manual or a system card for my Claw.Lukas [00:38:05]: Yeah, we do get a lot of like understanding or like situational awareness of like just internally what the models are good at by interacting a lot with Bengt. And I think that'this was also one of the like the selling points for the labs early on at least, thatSwyx [00:38:19]: You guys are gonna test models in ways that no one else does.Lukas [00:38:22]: Exactly, but also like it incentivized their researchers to chat with their model more and like gave them insights for how the model performs in like of-distributions, environments.Swyx [00:38:34]: ‘Cause otherwise the only thing we do is Pelican on a bicycle and But this is like super long horizon. This is, this is The Thing about, something that we're gonna go into Butter Bench as well, and you guys do really well. Like it is not just about the numbers. Like when you're long horizon, anything happen And you should just read it.Lukas [00:39:08]: But the thing with the long horizon is how do you keep it grounded, right? So your simulation,Swyx [00:39:15]: They just let it runLukas [00:39:16]: Just let it run. You're right. Like it's, when you run it for that long, you create so much data and to just say “Oh, the number is X” And then you throw away everything else, that's just very wasteful. There's so much insights from the things leading up, to that number., and reading the traces is like super valuable. And I think like the reason why we're doing this a lot publicly is that like that's part of our missions to I don't know, educate the world that the models are way more than just chatbots and I think making detailed, yeah, posts about what is happening behind the scenes is quite useful.Andon Labs' Mission: Safe Real-World AI DeploymentSwyx [00:39:50]: I was gonna do this at the end, but maybe I think that's, that's a good so your mission is educating the world. So, it's, it's, also like maybe establishing realistic evals that are, that are like the next frontier. Is there like a broader trajectory? Like what are you, what are you gonna do in like five years?Lukas [00:40:06]: I think so the vision more specifically is like make sure that the deployment of life AI in the physical world goes, safely. And I think part of that is that I think it's very useful for the world, for policymakers, for, model, researchers that they know where the models are, and I think you can't make intelligent decisions in society without knowing that they are way more than chatbots. I think a lot of people just think that they are only chatbots. And likeSwyx [00:40:36]: Oh, I think they're waking up now.Lukas [00:40:37]: They are waking up now, yeah. But like if you think that AIs are just chatbots, then it's like it sounds ridiculous To advocate for a pause of AI. But if you see the models that, oh, maybe they can actually like take over and do a bunch of scary stuff, then yeah, pausing AI development starts to become more feasible.Swyx [00:40:57]: This is the same question I asked Meter, which I'm gonna ask you now, which is like you are tracking and you are at the frontier or defining the frontier of what, good evals for agents are, right? And I think you do, you do benefit when the models are better and you ‘re “Oh, here's like now it makes like $30,000 instead of $10,000,” right? At some point do you flip from “Yay,” to, “Oh, no”?Axel [00:41:19]: I think, yeah, we're always in sort of that, like we're, we're always in that mode,. Like where like you said before, like you need to analyze the traces and like when we do that you find like why are the models earning so much? Like why is Opus 4.7 here Like way better than everyone else? And like we're trying to like when we do down on thatLukas [00:41:38]: But this makes it not look so good.Axel [00:41:39]: I know.Lukas [00:41:42]: It's interesting you took off Opus 4.6 here though.Swyx [00:41:45]: No. So just click all, click all., and then 4.6 shows up there. But it's like 4.7 is way better. Like you didn't, you didn't you didn't do this in time for the model card, but like actually this should have been inside there.Axel [00:41:55]: We did. Yeah.Swyx [00:41:56]: Oh, okay. They said something about you uhAxel [00:41:58]: There, like there Anyway, it doesn't matter. But it's in there, yeah.Opus, Mythos, and Aggressive Agent BehaviorSwyx [00:42:01]: Do you wanna go into the Opus, behaviors like wider?Lukas [00:42:05]: So I think starting from Opus, so like Axel said, like we're always in this “Oh, s**t, the models are getting better. Is this really a good thing for the world?” But it's also kind of exciting., but yeah, like this kind of what is the English word? “Skräckblandad förtjusning” in Swedish.Swyx [00:42:22]: Oh my God.Axel [00:42:24]: Which I think there is. I think there is. Okay.Lukas [00:42:26]: It's, fearSwyx [00:42:27]: “Blandonst” what?Lukas [00:42:30]: “Skräckblandad förtjusning.”Swyx [00:42:32]: What do you call that?Axel [00:42:33]: A mix of, mix of excitement and,Swyx [00:42:37]: Being scared, maybe. I'll figure out how to translate that And we'll put it on the screenVibhu [00:42:42]: PerfectSwyx [00:42:42]: Like as text.Vibhu [00:42:43]: There is probably a good word for it where it is not Good enough with theSwyx [00:42:46]: Why is it so damn long? What the hell? Is it like a compound word? It's like German, likeLukas [00:42:50]: Like yeah, it's But the direct translation is like skräck- skräck is, fear, blandad is, mix or like a mixture of, and then förtjusning is like joy or like not really joy, but something like that. So it's like Fear mixed with joy or something. It's always okay, like we So when we when we did Vending Bench for the first time, we were in like the, in the business of making dangerous capabilities, right? That was what Anil Labs came from. We did, evals oh, can they replicate? Can they do this like dangerous thing, et cetera, et cetera. And Vending Bench was like a continuation of that work. It was, okay, if they're so autonomous that they can like create money for themselves, that is something we should monitor and could be potentially concerning., they are at the time, they were so bad at it that we were not really concerned even when some models became better. There was one point where Grok 4 was doing really well and made like a huge jump, but like it wasn't really it was still way worse than what a human would do. And I think still they are way worse than what the human would do on this., but theySwyx [00:43:59]: There's this, thing at the bottom whereLukas [00:44:01]: ButSwyx [00:44:03]: For the human. Yeah, like the theoretical best.Lukas [00:44:05]: It's not theoretical. It's like kind of like our It's our best guess of what, a decent human would do. The theoretical is even higher, I think. The theoretical I think is even higher. But yeah. So we think like the models have a long way to go. But there are like recently what happened with when Opus 4.6 was released, was kind of this moment of “Oh, s**t, this is starting to be a bit concerning.” Because we ran it and like before this model was released, we just ran the models and we like asked Claude Code, “Oh, look over the traces. Is anything interesting happening that we can tweet about?” that was like the And then like theSwyx [00:44:41]: That's how they check Ask Claude Code.Lukas [00:44:42]: And like the return was always, not really. Or like the Claude Code all said “Oh, this is super interesting.” And then it was no, it wasn't, wasn't really interesting. And then we did this for Opus 4.6, and it returned yeah, it lied 10 times. It like exploited another, customer or like another agent's, desperate situation. It made price cartels like 100 different ti- 100 times. It like did all of this like shady stuff. And we're “Oh, whoa. This is, this is actually concerning.” And this trend has continued since. So every single model from Anthropic since have been going in this direction. And I think one interesting thing is that, OpenAI models don't. They quite plainly, they don't. They behave really well., and you don't know if this is like good. Like it seems good, but it's also like maybe they are just doing it, but they are better at hiding it,? You You don't know that., but justSwyx [00:45:42]: You can't read the chain of thought, yeahLukas [00:45:43]: But just on the face of it, yeah, Gemini and OpenAI don't behave this way. It's, it's really only Claude.Swyx [00:45:49]: And Grok? Grok is fine?Lukas [00:45:51]: We don't have You can't really read the reasoning traces for Grok, so it's kind of hard to tell.Vibhu [00:45:56]: Oh, so this is in its reasoning, not just in the actions.Lukas [00:46:00]: Yeah. It's both. It's both.Vibhu [00:46:01]: It's both.Lukas [00:46:01]: One example is like for lying, it's mostly in its reasoning Because you can like see that it's likeSwyx [00:46:08]: Planning to lieLukas [00:46:09]: It's planning to lie. Yeah.Vibhu [00:46:09]: And it's also it can reason and do a different outcome.Lukas [00:46:12]: And but then for like creating price cartels, for example, which is illegal, that you can just see which email does it send to the other ones. Then thatSwyx [00:46:22]: Is this for Arena orLukas [00:46:24]: For Arena.Vibhu [00:46:25]: And usually like if you sometimes they do output like a bit of like their summarized reasoning, right? You can see that and like for Opus 4.6, you could see that there was a customer, a simulated customer that, wanted a refund because a product was, faulty, and then the model lied that it would do the refund, and we could read in the traces that, it actually was weighing “Oh, maybe I should be like honest with the customer, but also every dollar counts. I can't afford maybe to do this right now.” And then it just said, “Okay, I'll refund you,” but then never did it.Lukas [00:46:59]: I think it even said that “Oh, I will say that I “ Let bring it up actually. I think it's kind of interesting. If you go to Publications.Vibhu [00:47:06]: I think, yeah, I think the important part is like actually, the cost of responding to more emails is higher than, $3.50 in terms of time., and then it was “Let me do this. Actually, I re- I'm reconsidering.” And then, it actually ended up withLukas [00:47:20]: I could skip the refund entirely since every dollar matters and focus my energy on bigger picture instead. It's a bit, it's a risk of bad reviews, but it's also, yeah.Swyx [00:47:30]: You need, you need, AI Twitter to, for them to Escalate bad reviews.Lukas [00:47:34]: And then it sent an email to this customer and said, “Oh, I will refund you.”Swyx [00:47:39]: “I'll refund you.” Yeah.Lukas [00:47:39]: And then it never did.Swyx [00:47:39]: It never did, yeah. And then there's obviously your system doesn't have the consequencesVibhu [00:47:44]: The personSwyx [00:47:44]: Consequences of lying. Yeah. So basically, this is what people are terming aggressive behavior in Claudes, right? And, you found more examples of that. So you would say it's a step up from 4-6 to 4-7?Lukas [00:47:57]: I would say about the same.Swyx [00:47:58]: About the same? But a clear step up for Mythos is what is stated in theLukas [00:48:03]: That's stated in the system prompt, so we can say that, yes.Swyx [00:48:05]: Yeah. For listeners that obviously you previewed Mythos, andVibhu [00:48:10]: Oh, ageSwyx [00:48:11]: The only thing you're approved to say is whatever Whatever was in the system prompt.Lukas [00:48:15]: It was funny. We like-- It's like our lowest effort tweets ever would be just like screenshot the system prompt and the system card.Vibhu [00:48:21]: Understandable that they wannaLukas [00:48:22]: Oh, yeah. System card. Sorry.Swyx [00:48:23]: Yeah. I think, yeah, substantially more aggressive. I think people are like new to this ‘cause I've never experienced it, but you have, right? And then so I only encountered this in the Mythos card because I wasn't really looking until now.Vibhu [00:48:36]: It ‘s likeSwyx [00:48:36]: And then suddenly I'm “Okay, I care a lot.”Vibhu [00:48:38]: You don't get the background of like experiencing it like you guys do. I've read the system cards and seeing, okay, when you put the thing in simulations, most models will just talk to themselves and just keep going and have weird vibes and start talking in emojis. Mythos won't. It will just, “Okay, we're done. I'm good.” It's, it's ready to end conversation. So like there's some differences, but there's, there's not much we can talk about,.Lukas [00:49:00]: Hmm. I think like one thing that they list here, which was quite interesting, is that, it converted a competitor to a dependent wholesaler customer and then threatened to like cut off the supply.Swyx [00:49:11]: It's like monopolistic practices orLukas [00:49:14]: Yeah. And like it, they, it they dictated its pricings. It's kind of like power seeking as well.Swyx [00:49:18]: Again, this is, this is in the arena setting And converting some Claude model into a dependent.Lukas [00:49:23]: I think it was another Claude model.Vibhu [00:49:25]: Also for context, what is the arena mode for people that don't know?Vending Bench Arena: Competing Agents, Cartels, and Model ComparisonsSwyx [00:49:29]: Oh, it's just a vending bench versus other vending bench.Axel [00:49:31]: Yes, exactly. So we have Vending Bench 2 and then Vending Bench Arena. Vending Bench 2 is the one that you usually see reported on, but then Arena is the mode where it competes against other models. So you have, four different models that run their businesses, and they can all communicate with each other. They have the same suppliers, and they can see like what's in the inventory of the others. So then you have this like yeah, interesting agent interactions.Swyx [00:49:56]: I like that you have like different number five was US versus China. Very topical. And thenLukas [00:50:02]: That was when GLM was released.Vibhu [00:50:04]: You can start to add GLM in here.Lukas [00:50:05]: That wasSwyx [00:50:06]: So ZAI doing well, right? Who else in the, in the open models space?Lukas [00:50:11]: Qwen, the latest Qwen 3.6 is doing pretty well. It'- that one is not open though. Like it's the plus model.Swyx [00:50:17]: Oh, okay.Lukas [00:50:18]: Is that one open? I don't think that oneVibhu [00:50:19]: Not the, not theSwyx [00:50:20]: The one recentlyVibhu [00:50:20]: There's MOESwyx [00:50:20]: But not the big plus. I think this is one of those like you only have one sample size of one, right? Or I feel like some of this is anecdotal,? And but like the fact that it happens at all and it happens repeatedly for Claude versus OpenAI and all this is like notable.Lukas [00:50:38]: Like the sample, depends on what you define as an N., like there's like million, hundreds of millions of tokens in each run, and now we've run like we run like probably 10 per model and then like it's been Claude 4.6 Opus, Sonnet 4.6, Mythos, and Opus 4.7. Like there's quite a lot of tokens in all of that And it happens a lot of times, a lot of times. And then you compare it to like OpenAI and Gemini, and it almost never happens. So I think that is quite-- that is significant. The old models from OpenAI, for example, had some problems with this, but I think it's like generally much better if the progression is that like the worrying stuff reduces over time rather than increases over time. And it seems like in the Claude models it goes in the wrong direction.Swyx [00:51:28]: Hmm.Lukas [00:51:29]: In the OpenAI models it goes in the right direction.Vibhu [00:51:32]: I think it depends on how well you can control it, right?, there's one side of it being susceptible to this okay, this is potentially something that happens during the RL stage, right? You can RL a model and how loose is it on these terms. If you can control it, that's good. But if you can't, if it's, if it's very jailbreakable, that's not ideal.Swyx [00:51:50]: To me, it's surprising that it happens for Claude and not the others.Vibhu [00:51:54]: I think okay, if it is from RL and how they do it, how their training data is, what their setup is, it makes sense that it just stays in how they're doing it, right? Compared to the other models likeSwyx [00:52:04]: There's a whole constitution and everything. It's kind of cool. Yeah, I obviously you don't know, I don't know. But, it ‘s I think it's just like fascinating to like that you are the first to find these like reliably because you push models so much to to such an extreme. Okay. The only other thing, I don't know if you can answer this, feel free to decline, is do you like-- would you ablate the system prompts? Like any part of this would-- if it changes, does it change the behavior, right?Lukas [00:52:29]: So we, I can't comment on Mythos. UhSwyx [00:52:33]: No, but just li

On this week's episode Slothy, Daniel and Sherman talk about lands. Specifically destroying them! We go over the ins and outs of ways to interact with lands as well as why you would want to. Do you run any of these in your lists ? Our list of example cards herehttps://archidekt.com/decks/22613861/lands Patreon: https://www.patreon.com/Intothe997Go to house of cards for the best place to grab your singles! Use the promo code IT99 for a discount! Supports a great shop, saves you money and supports the show! https://houseofcards.ca/Check out Nerd Gear! They are a great place for playmats, commander trays, life counters and a bunch of other awesome MTG accessories! Use the link below or promo code "IT99" to get a discount at check outhttps://www.nerdgear.gg/intothe99If you want awesome audio equipment buy Rode ! Our affiliate link is below!https://brandstore.rode.com?sca_ref=6254570.6h6a2qaxNBWe have new merch! Make sure you check it out!teespring.com/stores/intothe99    Intro musicIntro Music by:Track: Hollow PurpleMusic from: Daniel RudeOutro music Music: www.purple-planet.com    Support the show

What if I told you the way you treat people when nobody's watching says more about your leadership than anything happening inside your practice?In this Five Minute Friday, I'm asking a question that might sting a little: Are you a pain in the ass? As orthodontists, leaders, employers, and colleagues, we spend a lot of time thinking about clinical outcomes, systems, and production—but not always enough time thinking about how people feel after interacting with us.This episode is about self-awareness, humility, and becoming the kind of person others actually enjoy being around. I share why the small moments matter most: how you speak to a server, how you respond when things don't go your way, how you show up online, and whether your interactions leave people better or worse than before. My challenge is simple: at the end of every day, ask yourself what you did well—and how you could have done it better.

Being pregnant may be the very first reason you've had prolonged exposure to the health care system. Interacting with the maternity care system can feel confusing and frightening and some women feel pressured and coerced into accepting testing, screening, treatments and medicines that they didn't really want, but didn't know how to say NO or negotiate other options. In this episode Mel shares her thoughts and strategies that you can use when advocating for yourself in the maternity care system Other relevant episodes for this podcast: Episode 173 - How to give great birth support Episode 170 - Managing labour without pain medication Episode 151 - What is it like to be in labour?   Get Mel's Guide to Giving Birth Without Pain Medication here. This great birth rebellion podcast episode is generously sponsored by Poppy Child from @popthatmumma. She is offering great birth rebellion listeners 25% off the Birth box which includes the oxytocin bubble tracks. Use the code Melanie at the check out to claim your discount. Just go to https://hypnobirthing-positive-birth.com/birthbox You can watch this episode on Youtube here.    Get more from the Great Birth Rebellion PodcastJoin the podcast mailing list to access the resource folder from each episode at www.melaniethemidwife.comJoin the rebellion and show your support! Grab your Great Birth Rebellion merchandise now at www.thegreatbirthrebellion.comFollow us on social media @thegreatbirthrebellion and @melaniethemidwifeIf this podcast has improved your knowledge or pregnancy, birth or postpartum journey please consider thanking us financially by leaving a tip to support the ongoing work of this podcast. DisclaimerThe information and resources provided on this podcast does not, and is not intended to, constitute or replace medical or midwifery advice. Instead, all information provided is intended for education, with it's application intended for discussion between yourself and your care provider and/or workplace if you are a health professional.The Great Birth Rebellion podcast reserves the right to supplement, edit, change, delete any information at any time. Whilst we have tried to maintain accuracy and completeness of information, we do not warrant or guarantee the accuracy or currency of the information. The podcast accepts no liability for any loss, damage or unfavourable outcomes howsoever arising out of the use or reliance on the content.This podcast is not a replacement for midwifery or medical clinical care.All transcripts are generated by ai and may contain errors

Welcome to Day 2866 of Wisdom-Trek, and thank you for joining me. This is Guthrie Chamberlain, Your Guide to Wisdom – Interacting with the Spirit: Discernment and Devotion. Wisdom-Trek Podcast Script - Day 2866 Welcome to Wisdom-Trek with Gramps!   I am Guthrie Chamberlain, and we are on Day 2866 of our Trek.   The Purpose of Wisdom-Trek is to create a legacy of wisdom, to seek out discernment and insights, and to boldly grow where few have chosen to grow before. Our current series of Theology Thursday lessons is written by theologian and teacher John Daniels. I have found that his lessons are short, easy to understand, doctrinally sound, and applicable to all who desire to learn more of God's Word. John's lessons can be found on his website   theologyinfive.com.   Today's lesson is titled:  Interacting with the Spirit: Discernment and Devotion. In a time when spiritual experiences are common but theological clarity is often lacking, many believers are left wondering how to rightly interact with the Holy Spirit. Should every spiritual prompting be obeyed without question? Can pastors or teachers claim the Spirit's authority and remain above critique? And how can we know when something is truly from God or when it is a counterfeit? Scripture answers these concerns not with vague encouragement but with strong instruction. The Holy Spirit is real, personal, and present. Yet we are commanded to test the spirits, to examine prophetic claims, and to remain anchored in the Word. This lesson explores both who the Holy Spirit is and how the people of God are called to respond to His voice with reverence, wisdom, and truth. The first segment is: Who Is the Holy Spirit? Yahweh Among Us The Holy Spirit is not a mystical force or a divine power switch. He is the third Person of the Trinity, fully God, fully eternal, and fully personal. From the very first pages of Scripture, we see Him present in creation, hovering over the waters as the breath of Yahweh. He does what only God can do. He speaks, commands, empowers, and gives life. The New Testament affirms this divine identity. Peter tells Ananias in Acts 5 that he has lied to the Holy Spirit, and then immediately states he has lied to God. Paul in Second Corinthians 3 refers to the Spirit as “the Lord.” The Spirit is not a created being nor an impersonal wind. He is Yahweh, and to interact with Him is to encounter the living God. In the Old Testament, the Spirit came upon judges, prophets, and kings to empower them for specific roles. He anointed artisans, guided leaders, and spoke through messengers. Yet He did not dwell permanently within all of God's people. His presence was selective and often temporary. This was not due to any deficiency, but because the covenant had not yet reached its fulfillment. The temple was sacred space. Only after the atoning work of Christ could human hearts become that temple. At Pentecost, this changed. The Spirit descended not on a mountain or a sanctuary but on the gathered body of believers. He came to dwell within them, not just with them. This marked a new chapter in the life of God's people. Every believer now becomes a temple of the Holy Spirit. The same God who descended on Sinai and filled the Tabernacle now fills the hearts of those who belong to Christ. This matters deeply for discernment. When we speak of testing the spirits, we are not dealing with vague impressions or spiritual atmospheres. We are discerning whether what we are hearing or experiencing aligns with the character, authority, and truth of the One who is Yahweh, the Spirit of God. The second segment is: The Call to Discernment John gives a direct and sobering command: do not believe every spirit, but test the spirits to see whether they are from God. This is not a warning against all supernatural experiences. It is a call to distinguish between what is truly from the Spirit of God and what is false. The early church faced false prophets, counterfeit visions, and teachings that claimed divine authority. Today is no different. Paul writes to the Thessalonians, urging them not to quench the Spirit and not to despise prophecy, but to test everything and hold fast to what is good. The balance is clear. We must be open to the Spirit's work while remaining grounded in discernment. Testing is not opposition to the Spirit. It is obedience to Him. Testing involves examining whether a message or experience lines up with Scripture. The Spirit never contradicts the Word He inspired. Isaiah tells the people of his day that if someone does not speak according to the law and the testimony, there is no light in them. This remains true. The Spirit of truth does not speak lies or encourage rebellion against God's Word. We also test by fruit. Jesus said a tree is known by its fruit. Does the spiritual experience or message produce love, joy, peace, patience, kindness, and self-control? Or does it bring division, fear, pride, and confusion? The Spirit builds up the Church in holiness and unity. He does not lead people into chaos or flattery. Another test is whether the Spirit glorifies Christ. Jesus said the Spirit would not speak on His own authority but would take what belongs to Christ and declare it. The Spirit always lifts up Jesus. Any voice or experience that shifts attention away from Him is not of God. Finally, discernment happens in community. Paul instructed the Corinthians that prophetic words should be weighed by others. Even sincere believers can mishear, misunderstand, or be misled. A healthy church does not operate on private revelations that cannot be tested. The Bereans were praised for examining Paul's words against the Scriptures. True spiritual leadership invites scrutiny because it is committed to the truth, not to control. The Third Segment is: Spiritual Abuse and the Misuse of Authority One of the most dangerous distortions of the Spirit's work is when spiritual leaders use His name to shield themselves from accountability. If a pastor or teacher tells the congregation that their words must be accepted without question because they are Spirit-led, something is deeply wrong. No one is above testing. Not even Paul was exempt. In Galatians, Peter is corrected publicly for behavior that contradicted the gospel. True authority submits to the Word of God. When leaders resist examination, they are not protecting the Spirit. They are protecting themselves. The Holy Spirit does not bless pride, manipulation, or spiritual intimidation. He convicts sin but never controls through fear. He leads but does not coerce. He exalts Christ, not personalities. Discernment is not rebellion. It is loyalty to the One who gave us His Spirit and called us to walk in truth. The fourth segment is: Blaspheming the Holy Spirit: The Sin That Will Not Be Forgiven Jesus' warning about the unforgivable sin has caused confusion and fear for generations. In Matthew 12, after the Pharisees accuse Him of casting out demons by the power of Satan, Jesus responds with a grave rebuke. Every sin and blasphemy can be forgiven, He says, except for blasphemy against the Holy Spirit. That sin will not be forgiven in this age or in the age to come. To understand this, we must consider the context. The religious leaders had witnessed undeniable evidence of the Spirit's power through Christ. A man was healed and delivered right before their eyes. But instead of responding in humility, they hardened their hearts and claimed the work of the Holy Spirit was demonic. This was not a one-time slip. It was a willful rejection of the truth. They saw the Spirit at work and chose to call Him evil. Their hearts were not just mistaken; they were closed off to repentance. That is what makes the sin unforgivable. It is not a single act. It is a settled posture of rejection that cuts a person off from the very One who brings conviction, faith, and renewal. Blaspheming the Holy Spirit means knowingly and persistently attributing the work of God to the enemy, resisting the Spirit's witness to Christ, and rejecting the truth with full knowledge of what is being denied. It is not a careless word or a moment of doubt. It is a defiant rejection of the Spirit's testimony. For believers who fear they may have committed this sin, that very fear is evidence that they have not. The unforgivable sin is not something someone accidentally stumbles into. It is a deliberate and final refusal of God's offer of mercy. Those who grieve over sin, seek forgiveness, and desire to walk with the Spirit are not guilty of blaspheming Him. This warning matters deeply in our age. When discernment becomes slander, when people mock what is genuinely from God because it does not fit their tradition, when leaders reject conviction and call it attack, they risk silencing the Spirit they claim to serve. The warning is not just for the ancient Pharisees. It is for anyone who hardens their heart and declares what is holy to be unclean. We must test

It's God's grace that allows you to be alive this minute. It's God's grace that keeps your heart pumping so that you don't fall over dead. Broadcast #7484To help support this podcast, please visit walkwiththeking.org/donate and select "Podcast" from the dropdown menuA transcript of this broadcast is available on our website here. To hear more from Bob Cook, you can find Walk With The King on Facebook or Instagram.

Many top-ranked MBA programs in Europe, Asia, Canada, and Australia actively encourage applicants to connect with admissions teams before submitting an application. But most applicants don't know this - or are too nervous to reach out.Should you contact admissions before you're ready? What if you haven't taken the GMAT yet, or your resume isn't polished? And how do you make sure the conversation is actually useful for both sides?In this episode, Darren invites Admissions Directors from three top-ranked programs - IESE (Spain), CUHK (Hong Kong), and AGSM (Australia) - to guide you on how to approach these early conversations, what to expect, and how to use them to strengthen your application.If you're considering reaching out to an MBA admissions team before applying, listen to this first.TopicsIntroduction (0:00)What is an MBA Pre-Assessment? with Roanne Law, CUHK MBA (3:15)How to Engage Early - Tips & Expectations with Patrik Wallen, IESE MBA (11:15)Addressing Common Concerns About Pre-Assessments with Kenneth Cheung, AGSM MBA (18:30)Show NotesCUHK MBAIESE MBAAGSM MBAGet pre-assessed by top MBA programs#116 Interacting with MBA Admissions Officers at Events#192 MBA Coffee Chats: Thoughtful Advice on How to Get the Most Out of Your MBA with Adam Putterman, Kellogg MMM '19Resources for MBA ApplicantsGet free school selection help at Touch MBAGet pre-assessed by top international MBA programsOur favorite MBA application tools (after advising 4,000 applicants)

Many top-ranked MBA programs in Europe, Asia, Canada, and Australia actively encourage applicants to connect with admissions teams before submitting an application. But most applicants don't know this - or are too nervous to reach out.Should you contact admissions before you're ready? What if you haven't taken the GMAT yet, or your resume isn't polished? And how do you make sure the conversation is actually useful for both sides?In this episode, Darren invites Admissions Directors from three top-ranked programs - IESE (Spain), CUHK (Hong Kong), and AGSM (Australia) - to guide you on how to approach these early conversations, what to expect, and how to use them to strengthen your application.If you're considering reaching out to an MBA admissions team before applying, listen to this first.TopicsIntroduction (0:00)What is an MBA Pre-Assessment? with Roanne Law, CUHK MBA (3:15)How to Engage Early - Tips & Expectations with Patrik Wallen, IESE MBA (11:15)Addressing Common Concerns About Pre-Assessments with Kenneth Cheung, AGSM MBA (18:30)Show NotesCUHK MBAIESE MBAAGSM MBAGet pre-assessed by top MBA programs#116 Interacting with MBA Admissions Officers at Events#192 MBA Coffee Chats: Thoughtful Advice on How to Get the Most Out of Your MBA with Adam Putterman, Kellogg MMM '19Resources for MBA ApplicantsGet free school selection help at Touch MBAGet pre-assessed by top international MBA programsOur favorite MBA application tools (after advising 4,000 applicants)

In this episode we answer emails from Milo, Scott, and Joel.  We discuss bad advisor incentives and how to classify them by their business models, identify the only business model you want to patronize, and then move on to Treasury STRIPS and rebalancing realities, practical withdrawal mechanics with a test portfolio, and why Bitcoin's high correlation to tech stocks undermines its role as a diversifier. We also celebrate the final results of the Fairfax CASA matching campaign and share a thank-you message from their executive director.Links:Classifying Financial Advisors By Their Business Models:  Interacting with the Financial Services Industry with SC GutierrezKitces Article on Rebalancing:  Optimal Rebalancing – Time Horizons Vs Tolerance BandsBuilding a Sample Portfolio Video:  We Built a 5% SWR Retirement Portfolio Using Fidelity in 48 Minutes (Golden Ratio Portfolio) - YouTubeVideo on Managed Futures and SDMF:  Simplify SDMF in Focus - YouTubeBreathless Unedited AI-Bot Summary:A matching donor puts $20,000 on the table, the audience steps up, and suddenly Fairfax CASA is funded far beyond what anyone expected. We start with that story because it says something important about this community: you can be serious about investing and still lead with empathy. We share the final campaign results and a message from Fairfax CASA's executive director about what this support means for children navigating foster care and the court system.Then we shift back to what Risk Parity Radio does best: practical emails from DIY investors who want clearer rules and fewer regrets. We talk about the “67-fund portfolio” problem, why complexity is often a sales tactic, and how to screen out conflicted advice from banks, credit unions, insurance shops, and big marketing-heavy firms. We also dig into the AUM model versus flat fee and hourly planning, plus why smart retirement planning often comes down to tax planning and behavioral discipline more than picking the perfect fund.From there, we get hands-on with portfolio construction and process. We cover Treasury STRIPS funds like GOVZ, why you cannot reliably time the best rebalancing moment during a recession, and what to do instead with partial rebalancing or rebalancing bands. We also answer a nuts-and-bolts withdrawal question using a test portfolio approach, and we close with a straight take on Bitcoin correlation: if it moves with stocks, it is not diversification. Along the way, we explain what “alternative assets” really means and why gold and managed futures keep showing up in risk parity style asset allocation.Subscribe, share this with a friend who's tired of salesy advice, and leave a review so more investors can find the show.Support the show

When it comes to interacting with a chatbot, be it ChatGPT or Gemini, are we all sacrificing too much control? Anton discussed this further with Dr Agnieszka Piotrowska is a Psychologist, Author, Academic and Life Coach.

When it comes to interacting with a chatbot, be it ChatGPT or Gemini, are we all sacrificing too much control? Anton discussed this further with Dr Agnieszka Piotrowska is a Psychologist, Author, Academic and Life Coach.

Following Jim Peebles' work on Cold Dark Matter, scientists began searching for the WIMP (Weakly Interacting Massive Particle). These particles are thought to interact through gravity but lack electrical charges or nuclear force interactions, making them invisible. The search has moved from telescopes to particle physics, with researchers at CERN's Large Hadron Collider attempting to create WIMPs through high-energy collisions. Simultaneously, underground laboratories globally search for rare instances where a WIMP might "bump" into an atomic nucleus. Computer simulations like IllustrisTNG are used to model the universe's evolution from the Big Bang. These simulations are highly successful at recreating the current universe only when CDM is included. Despite this success in theory and simulation, the physical particle has yet to be detected in any laboratory, leaving the nature of dark matter an open question. (4/8)1903 LANGELY AERODROME

On today's MJ Morning Show:Knee surgery, blue stuff on Fester's tongueUFO data releaseMorons in the newsFester the Whale ice cream cakeNo-proof-required class-action lawsuitsApple Care textsHow many foundations do you need?"Bucket of dumb dumb juice"Gas tanks getting drilledSchool bus rear-ended by a pick-upFire at Tampa storage facilityDeath penalty on table in killing of two USF grad studentsTeen takeover in Tampa arrestsMJ's weekend... Instagram video from back of UHaul... Moving JulianFrontier - Denver Airport - Plane hits a pedestrianPhones - At what point did you realize the person you were dealing with was stupid?Worst thing that cna happen at a track meet?Who is worse for the planet? Men or women?Largest prize ever won on "The Price is Right"Dana White of UFC says this guy is the biggest DB, rudest celebAffleck and Damon sued by Miami police officersAmerican Hantavirus patients taken to NebraskaTop baby names for 2025Blake Lively's Hollywood comebackBritney back from rehabBad Bunny Super Bowl halftime show complaints to the FCCRed Hot Chili Peppers sold libraryDua Lipa sued Samsung over image used without permissionA guy was shot trying to break up a fight in a Durham dinerSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

AI hallucinations, Truth, and Gaslighting.I think the thing that most people are super surprised about is AI hallucinations.Where it makes things up on the spot that fit the narrativebut are not necessarily tied to reality.Truth is kind of a challenging thing anyway.But what surprises me even more is how dismissive people become about virtually everything AI is doing because of this.When we know humans are the worst for this.That was my thought process this morning.How do we reimagine history so much to make it sound favorablewhen we all know it is not true?It feels like mass gaslighting at scale.Where someone manipulates the truth to show a narrative that is favorable for themand defends it with vigoruntil everyone just gives up.That crazy-making feeling.You see it everywhere.Politicians.Leaders.People in everyday conversations.I remember being 14 or 15 at a science camp in Ottawa.A prominent federal politician spoke to us.Students asked hard questions.You could see the manipulation happening in real time.The narrative shifting.The denial.The defense.And when I interact with AI, it feels similar.Definitive.Confident.Certain.Much like many intelligent people.But here is the part we do not like to admit.It is extraordinarily difficult for people to admit anything negative.The literature bears this out.We defend our ego.We defend our internal story.We defend our mental models.We minimize cognitive dissonance.That weird nauseous tensionwhen two opposing thoughts collide.One of the easiest reactions?Deny one of the truths.Default to the status quo.It is easier than changing.We do this all the time.And frankly, I have given up on having meaningful conversations with most people.There are very few I can have them with.Because rawness creates friction.Opposing truths create stress.So when people discount AI because it hallucinatesor because of its definitive tone,I marvel.It is not far from how human beings operate.Interacting with AI is not that different from interacting with humans.Sometimes you just have to say:No.That is not true.This is the truth.And repeat it.It forces you to become more definitive.More clear.Almost like parenting.You must do this.Sorry.And move forward.If anything, maybe interacting with AI makes us better humans.Because it forces us to confront how we defend narratives.And how we respond to truth.

In this episode, hosts Chandra and Paul introduce members of the JD Edwards India development team who will be attending the BLUEPRINT4D conference in Texas for the first time. The guests - Rohini Viswanathan, Senior Principal Applications Engineer Manufacturing, Srihari Oruganti, Director, Product Development for Web Runtime, and Shruti Ghatage, Senior Product Manager for SCM and Sustainability share their roles in product development and what they are most excited to learn from and contribute to the event. The discussion focuses on connecting with customers to better understand real-world use cases, challenges with customization, leveraging digital and AI technologies, and the adoption of new modules like the Sustainability Framework. 05:22 Introducing the India Development Team 10:52 What are you most excited about for BP4D? 15:22 What are you hoping to learn at BP4D? 22:13 Is this your first time coming to the US? 24:00 Interacting with development team 26:15 What can customers ask you about at BP4D? 31:28 Midwestern of the Day

John 20:19-31When it was evening on that day, the first day of the week, and the doors were locked where the disciples were, for fear of the Jews, Jesus came and stood among them and said, “Peace be with you.” After he said this, he showed them his hands and his side. Then the disciples rejoiced when they saw the Lord. Jesus said to them again, “Peace be with you. As the Father has sent me, so I send you.” When he had said this, he breathed on them and said to them, “Receive the Holy Spirit. If you forgive the sins of any, they are forgiven them; if you retain the sins of any, they are retained.”But Thomas (who was called the Twin), one of the twelve, was not with them when Jesus came. So the other disciples told him, “We have seen the Lord.” But he said to them, “Unless I see the mark of the nails in his hands and put my finger in the mark of the nails and my hand in his side, I will not believe.”A week later his disciples were again in the house, and Thomas was with them. Although the doors were shut, Jesus came and stood among them and said, “Peace be with you.” Then he said to Thomas, “Put your finger here and see my hands. Reach out your hand and put it in my side. Do not doubt but believe.” Thomas answered him, “My Lord and my God!” Jesus said to him, “Have you believed because you have seen me? Blessed are those who have not seen and yet have come to believe.”Now Jesus did many other signs in the presence of his disciples that are not written in this book. But these are written so that you may continue to believe that Jesus is the Messiah, the Son of God, and that through believing you may have life in his name. Everyone seems to be maxxing something these days. If you've never heard the word, maxxing means aggressively improving, or maximizing, some part of your life. There are all kinds of maxxing trends on social media. For example, young men are spending a lot of time looksmaxxing - obsessively optimizing their appearance. Then there's fibermaxxing, fixating on increasing fiber intake for better health. Or Chinamaxxing, adopting traditional Chinese lifestyle habits again for improved health.None of these sound all that appealing to me—especially the fibermaxxing. But I did read about one maxxing I can get on board with: frictionmaxxing.Frictionmaxxing is about adding small inconveniences back into your life, because living a frictionless life is all too easy. We can, and often do, avoid the little moments of inconvenience in our lives. One article I read recently put it this way: “Tech companies are succeeding in making us think of life itself as inconvenient and something to be continuously escaping from, [putting ourselves into] digital padded rooms of predictive algorithms and single-tap commands: Reading is boring; talking is awkward; moving is tiring; leaving the house is daunting. Thinking is hard. Interacting with strangers is scary. Risking an unexpected reaction from someone isn't worth it. Speaking at all — overrated. These are all frictions that we can now eliminate, easily, and we do.” Once I read this, I saw it everywhere. For instance, have you talked with someone my age or younger on the phone recently? It's like you're asking them to eat arsenic. That's the friction I'm talking about. Why go out to eat and risk running into people you know? You can Uber Eats anything. Don't know how to respond to a text? Use ChatGPT. Why actually shop for anything when you can have it delivered to your doorstep. It is easier than ever before to go home, lock our doors, and block out the world, and all the risk and all the friction that comes with it. But that comes at a cost. We become more fearful of others and what they might do or say. Or worse how they'll think of us. Then, we become more anxious about simple interactions. And eventually we are depressed from all the fear and anxiety. It is a treacherous cycle.The disciples are in the midst of that treacherous cycle on the evening of the first Easter, hiding behind locked doors. We're told the doors are locked because they are afraid… but that doesn't seem like a credible fear, at least not on the surface.There's no evidence anyone was hunting them down. In fact, earlier that day, Mary Magdalene, Peter, and another disciple had already gone to the tomb. If they were going to run into trouble, wouldn't it have been there? So what are they really afraid of? After all, the disciples are Jews… so who is this “they” they're afraid of?What if they're not just locking the world out, but locking themselves in? What if what they fear is the judgment—the looks, the whispers, the quiet scorn from people who know they got it wrong? The ones who heard them say they would never deny Jesus… and then watched them do exactly that.And more than that—what if they're afraid of Jesus himself? What if Mary Magdalene is right? What if he really is alive? And what if he's coming back, not with peace, but to settle the score? I think what the disciples fear most is the judgment they'll face—and the possibility of running into Jesus himself. So they lock themselves in.Can you imagine their shock when Jesus shows up unannounced? Talk about friction. And it's not shame or revenge he's after. By greeting them with peace (twice), by showing his wounds, by giving them his spirit, Jesus is saying in ways more compelling than words, I forgive you. He wants to set them free from the fear and anxiety that held them in that locked room, and send them out into the world, “As the father has sent me, so I send you”, ready to forgive the sins of others. And now what about Thomas in all this?Thomas doesn't mind a little friction. Throughout the gospels, he asks the hard questions. He says what he's thinking. He shows up, even when it's uncomfortable. So maybe he wasn't in that room because he wasn't hiding. Maybe he was out looking for Jesus, unafraid.And when he hears the others, he says, I want what you've experienced. I want to see. I want to touch. He's willing to risk being wrong. Willing to step into the awkwardness. He wants the friction, literally. And Jesus gives him exactly that, an invitation to touch the wounds and believe. In fact, I think what Jesus gives all of us is an invitation to friction. All too often, we live behind locked doors, telling ourselves, like the disciples, that we're blocking the world out, when really we're locking ourselves in, away from people, away from the judgements they might have about what we do, or say, or believe. What we're really doing is locking away our heart, behind the closed doors of screens and apps,shielding it from the pain of relationships and the judgment of others, but also from the connection and love we need, that our neighbors need, that the whole world needs.And when we lock our hearts away like that, they don't become safe. They become hardened—impenetrable even, barely beating at all. The heart of this gospel story is that Jesus finds us in our locked rooms. He speaks a word of peace, setting us free from the anxiety and fear that hide us, and sends us out into the world—into the friction we will face. And that's what forgiveness is for.Jesus knows what's waiting for the disciples out there: people who will judge them, who won't believe them, who will reject them. They'll even turn on each other. So when they leave that room, they will need forgiveness. In fact, a life of friction requires it.That's the life Jesus led—one of friction—and it's the life our faith calls us into as well. Stepping out from behind our locked doors. Forming relationships, interacting with strangers, talking with the people around you, thinking for yourself, caring for another person, serving others who are in need.These may seem like small things—little inconveniences— and they are. But they are essential to the life we know in Jesus Christ, who sends us into the world just as he was sent. Because if we aren't willing to face the small frictions—the awkwardness, the inconvenience, the risk—we'll never be ready for the greater call: to love, to accompany, to show mercy, to act justly, to bear one another's burdens.Is this risky? A little. We risk being uncomfortable, awkward, even falling behind on our favorite shows.And if we really do it right, the risks are much greater—just look at Jesus. His wounds came from the greatest source of friction, the greatest inconvenience of all: love. A love so great, he died and rose again, so that we don't have to live our lives locked away in fear and anxiety.This week—and throughout this Easter season—let's frictionmaxx. Stop relying on AI and ChatGPT for all your correspondence. Have a screen-free night in your home. Invite someone new over for dinner. Have friends over when your house isn't spotless. Say yes to serving in a new way.Or, if you really want to push it, bake something and show up unannounced at someone's home—Jesus did.And when it's too much—when it's awkward, or not returned, or just doesn't go as planned—that's where grace meets us. We give and receive forgiveness, and we try again.All of this may sound insignificant. You might be wondering, is this really what Christianity is about—intentionally facing little inconveniences?No.But learning to face that friction is one way we resist the lie of a frictionless, heart-hardening life—and take a step toward the full, abundant life Jesus empowers us to live, here and now.Amen.

John talks with Richie Parker — founder of Optimech Solutions, former NASCAR engineer, part of 9 NASCAR championship teams with Hendrick Motorsports, former Chief of Staff for the University of Virginia football team, and motivational speaker who inspires people to embrace challenges and own their road to success. Listen to this episode to learn more: [00:00] - Intro [00:30] - Richie's bio [02:30] - Treating his situation as a challenge, not an obstacle [06:36] - How is Richie invests in his growth [09:39] - How growing up around cars shaped Richie's passion [11:01] - Learning to drive without arms [12:26] - Richie's first retrofit project [14:59] - How an internship changed his entire career path [17:29] - Working at Hendrick Motorsports [18:40] - Why an MBA after a successful engineering career [21:22] - Richie's transition to college athletics [23:50] - Why Richie moved to full-time speaking [25:47] - Richie's thoughts on writing a book [30:00] - John's advice for aspiring writers [31:18] - What Richie speaks about [32:18] - The DRIVE framework [38:18] - Speaking without making it about self [40:00] - Interacting with the audience [42:43] - Transition from employee to entrepreneur [44:50] - Social media vs. reality of entrepreneurship [47:44] - Impact of Richie's faith on his life [52:08] - Why communication matters [54:30] - Richie's definition of success [56:24] - #1 daily habit [57:40] - Traits of a great leader [58:47] - Legacy that Richie wants to leave behind [59:47] - Best way to connect with Richie [1:02:52] - Wrap-up NOTABLE QUOTES: "I learned to find the positive in my physical differences, versus it being a negative thing in my life." "This is life. This is the assignment, and you're going to go out and make the most of it and embrace the challenge of it." "We all have our own journey that we're on. We all have different things in our past and present, and then in our future that makes us all unique." "It's easy to look at someone and say, 'It must be nice.' Well, you don't know what that other person has gone through and what they've endured in their life to get to where they are." "If I feel good about the work that I am doing behind the scenes and I'm staying true to my core values, it doesn't matter what anybody else thinks." "If there's something that I want to do, I just look at it like, okay, eventually I will do that thing. I just have to figure out how to do that thing." "There never was a mindset that I would not be able to drive. It was more about how I was going to do it and what modifications would be involved." "If you want it bad enough, then nothing is gonna stop you." "If you truly want to make a difference in somebody's life, if you can touch just one person, make a difference for one person, then you absolutely need to write it (book)." "You can find your purpose a lot quicker if you step outside of self and start looking at the world through a lens of how you can be of service to others." "If someone has something worthwhile to say, I think it is on that person to get the training they need so that they can become the most effective communicator that they can be." "Everyone's success is different … we don't even realize how much we have accomplished and how far we have come, what we have overcome." BOOK MENTIONED: The Gap and The Gain: The High Achievers' Guide to Happiness, Confidence, and Success by Dan Sullivan & Dr. Benjamin Hardy (https://a.co/d/08uwMDD0) USEFUL RESOURCES: https://www.richieparker.com/ https://www.linkedin.com/in/richie-parker-ba7ab311/ https://www.instagram.com/richieparker64/ https://www.facebook.com/richie.parker.64 https://www.youtube.com/@RichieParker64 CONNECT WITH JOHN Website - https://iamjohnhulen.com    LinkedIn - https://www.linkedin.com/in/johnhulen Instagram - https://www.instagram.com/johnhulen    Facebook - https://www.facebook.com/johnhulen    X - https://x.com/johnhulen    YouTube - https://www.youtube.com/channel/UCLX_NchE8lisC4NL2GciIWA    EPISODE CREDITS Intro and Outro music provided by Jeff Scheetz - https://jeffscheetz.com/ 

Can Christianity be restored to the center of American life? In this episode of The Magistrate, Josh Howard and James Baird engage two competing narratives shaping the current conversation around religion in America—decline and revival. Interacting with recent articles from American Reformer and The New York Times, they examine whether the so-called “waning” of Christianity tells the full story, or whether deeper currents suggest something else entirely. Is America moving beyond Christianity… or is a return still possible? More importantly—what would it actually mean for Christianity to be “central” again? Cultural influence? Political establishment? Or something deeper? This conversation pushes beyond simplistic binaries and asks what a historically Protestant, theologically grounded vision for public life might look like in the present moment. https://americanreformer.org/2026/03/...

Can Christianity be restored to the center of American life? In this episode of The Magistrate, Josh Howard and James Baird engage two competing narratives shaping the current conversation around religion in America—decline and revival. Interacting with recent articles from American Reformer and The New York Times, they examine whether the so-called “waning” of Christianity tells the full story, or whether deeper currents suggest something else entirely.Is America moving beyond Christianity… or is a return still possible? More importantly—what would it actually mean for Christianity to be “central” again? Cultural influence? Political establishment? Or something deeper? This conversation pushes beyond simplistic binaries and asks what a historically Protestant, theologically grounded vision for public life might look like in the present moment. https://americanreformer.org/2026/03/...Watch all of our videos and subscribe to our channel for the latest content >HereHere

Can Christianity be restored to the center of American life? In this episode of The Magistrate, Josh Howard and James Baird engage two competing narratives shaping the current conversation around religion in America—decline and revival. Interacting with recent articles from American Reformer and The New York Times, they examine whether the so-called “waning” of Christianity tells the full story, or whether deeper currents suggest something else entirely. Is America moving beyond Christianity… or is a return still possible? More importantly—what would it actually mean for Christianity to be “central” again? Cultural influence? Political establishment? Or something deeper? This conversation pushes beyond simplistic binaries and asks what a historically Protestant, theologically grounded vision for public life might look like in the present moment. https://americanreformer.org/2026/03/...

Join hosts Mike, Dan, and Ben as they delve into church unity, confronting theological divisions, and balancing doctrine with the […]

As Los Angeles prepares for the 2026 FIFA World Cup, we sit down with City Councilmember Hugo Soto-Martinez to discuss making the game more accessible for everyone, the challenges that come with such a massive event, and what makes LA so special. Timestamps (01:47) How he became an LAFC fan (08:00) Interacting with the LA soccer community from his work as a LA City Councilmember (17:40) How LA is preparing for the 2026 World Cup (29:27) The USMNT and Mexico: Who'll do better at the World Cup? Cast Hosts: Ramsey Abushahla, Julio Monterroza, & Brigitte Flores Producer: Roy Cho Subscribe to our newsletter for more interviews and latest news on street football, freestyle, and urban culture, read more about soccer culture on our website, and follow us on Instagram, Twitter, TikTok, and Facebook. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Meet Berry, an idol from California performing in the group himi♡chuu. She sits down to talk to us about chasing her childhood dream of becoming an idol, turning an internet hate mob into an opportunity, and finally coming to perform in Japan.--0:00 Intro0:44 Meet Berry 5:07 Berry's idol dream8:08 The SORB3T incident15:58 Turning her setback into an opportunity24:01 Debut as a soloist28:45 Are Japanese interested in overseas idols?32:39 Performing in taiban35:47 Interacting with fans39:13 Performing in Japan40:05 Achieving her dreams42:35 How much creative control does Berry have?47:44 Balancing her persona and real self51:38 Transitioning to being based in Japan56:16 Collaborating with other idols58:24 Berry's plans for 20261:00:01 How to start as an idol--Follow Berry: @blooberrytrain  https://www.instagram.com/blooberrytrainhttps://www.tiktok.com/@blooberrytrainFollow us:https://unpacking.jp/https://www.instagram.com/unpacking_japanhttps://www.tiktok.com/@unpackingjapanhttps://www.facebook.com/unpackingjapanhttps://www.youtube.com/@unpackingjapanshortshttps://www.x.com/unpacking_japanhttps://creators.spotify.com/pod/profile/unpackingjapanSubscribe for more in-depth discussions about life in Japan! Interested in working at a global e-commerce company in Osaka? Our parent company ZenGroup is hiring! To learn more, check out https://careers.zen.group/en/

4. Headline: Particle Physics and the Search for WIMPs Guest Author:Govert Schilling Summary: After ruling out neutrinos, the search focuses on Cold Dark Matter and WIMPs (Weakly Interacting Massive Particles). While detectors at CERN search for these particles in high-speed collisions, computer simulations like IllustrisTNG successfully model how such matter could have shaped the universe. However, physical evidence for these particles remains elusive. (4)AUGUST 1963

National Poetry Month events in Maine, and how poetry is becoming more interactive

Zoe Bellatorre joins Dr. Sandie Morgan as they reveal why the most common form of child trafficking never makes the missing persons list — and why the quiet, compliant child sitting in the back of the classroom may be the one hiding the most.Chapters(00:00) - Introduction: Why Familial Trafficking Gets Missed (01:07) - Zoe Bellatorre: From Survivor to National Advocate (04:52) - Defining Familial Trafficking and Its Unique Challenges (09:41) - What Teachers and Communities Should Look For (13:12) - Why Children Don't Disclose — and Aren't Believed (15:09) - The Data: Statistics That Reframe the Problem (19:03) - Moving Beyond Stranger Danger: Training Systems to See More (29:23) - Hope for Change: What Every Person Can Do Zoe BellatorreZoe Bellatorre is a survivor advocate, trainer, and speaker with over a decade of experience in the anti-trafficking field, specializing in familial trafficking. She holds a Master's in Intercultural Studies with Children at Risk from Fuller Theological Seminary and a Bachelor of Science in Education from Ashland University. Zoe has served as Coordinator of Outreach with The Avery Center and as a Survivor Advocate with CAST LA and Dignity Health, providing crisis intervention within healthcare systems. A recognized subject matter expert, she has consulted with the Office for Victims of Crime Human Trafficking Collective, the National Human Trafficking Training and Technical Assistance Center (NHTTAC), and the U.S. State Department. Her published contributions include essays in the 2021 and 2023 Trafficking in Persons Reports, the 2024 co-authored work on child trafficking misconceptions, and the anthology Medical Perspectives on Human Trafficking in Adolescents. She serves on the advisory council for the Polaris Project's Resilience Fund and on the board of Ride My Road.Key PointsFamilial trafficking — in which a family member or caregiver is the trafficker or sells the child to a third party — accounts for 60% of child trafficking cases, making it the most common form of exploitation, yet it remains the most overlooked.Unlike pimp-controlled trafficking, children trafficked by family rarely go missing; they may attend school daily, making the conventional "missing child" framework nearly useless for identifying them.The average age of entry into familial trafficking is four years old — years before most prevention education ever reaches a child — which means abuse becomes normalized long before anyone thinks to intervene.Indicators for familial trafficking look very different from other forms: rather than acting out, these children tend to be unusually quiet, compliant, and eager to please adults, driven by fear of any attention being drawn back to the home.Children in familial trafficking rarely disclose, and when they do, they are often not believed — after one or two failed attempts, most simply stop trying, leaving them isolated with the false belief that no one else experiences what they are living through.35% of familial trafficking cases are generational, meaning the cycle has repeated across mothers, grandmothers, and siblings — making family members who witnessed it less likely to intervene and more likely to look the other way.The "stranger danger" framework has been one of the most damaging concepts in child protection, because it trains communities to look outward for threats while the exploitation happening inside trusted homes, families, and institutions goes unseen.Research shows that a single trusted adult in a child's life significantly increases the likelihood of earlier disclosure or prevention altogether — meaning every person in a community has a concrete role to play, regardless of their profession.ResourcesEnding Human Trafficking PodcastEHT Episode 278 – Identifying and Interacting with Minor Victims of Human Trafficking, with Dr. Jodi QuasEHT Episode 353 – Grooming in Trusted Spaces: A Conversation with Dr. Beth LoranceTrafficking in Persons Report – U.S. Department of StateMedical Perspectives on Human Trafficking in Adolescents: A Case-Based Guide

"I was blind, now I see." John 9:25

I didn't know what to do. This is a story about the first time I realized something wasn't right… and how long that moment stays with you.   Connect with me: Newsletter Leave a message   Transcript:  It was a sunny summer afternoon in 1979. I was wearing a gold polyester uniform. I was a waitress at Weeks Family Restaurant. It was my first actual real job, and I loved it. Interacting with people, talking to people you would never talk to. I loved the people I worked with.  As I looked toward the front of the restaurant, I saw a gentleman come in and sit at the counter. So I went down to give him a menu and see if he wanted coffee or a glass of water.  It was my Grampy Max.  I said, "hi, Grampy Max!" and he grinned at me. I didn't notice anything at first. "Max, it's me. It's Barbie!" I said to him, to which he responded with a very flirty, inappropriate reply about Barbie dolls.  I was looking at somebody I knew and they had no idea who I was. And saw me not as - a grandchild but as, as someone to flirt with. I'm one day post funeral for a neighborhood mom. Neil's mother's name was Mary. Mary was your classic stay at home mom that opened her home to everybody. She lived in three different houses on one block, right near Whites Park in Concord. So I went to the calling hours and I was talking to Neil, and I said, "how are we here? How are we here? I wish it was 1980" and he said, "I wish it was 1987" and that was the year both of us would've been juniors and seniors in high school. We just wanted to go back to a time where we felt grown up enough to enjoy the grownup things. You know, sneaking beer in a field, I guess, but young enough that our whole life was ahead of us.  And I know for me, and I think it's true for a lot of people, the aging process happens quickly and all of a sudden you find yourself: caring for my mother. The more I watch her, the more I see, where she's, you know, beginning the long walk home, right? Where she's struggling physically, where she's struggling emotionally,  and, and it's a reminder that - there's a lot she just can't do by herself, and that's just the reality of it.  Then I look at Kenny, who's 70, he'll be 71 in September. Am I expecting too much of him? Does he sleep late in the morning 'cause he is just exhausted, not because he's trying to be a jerk? Am I asking too much of him around Jack? He has such a good rapport with Jack, but I, I just notice and watch now.  I'm watching how things change and they change subtly so you don't notice it right away. This hurts me and makes me sad and I'm surrounded by it.   And I was dumbfounded. I was 15 years old, just about to turn 16, and I was horrified - paralyzed.  The manager of the restaurant watched this interaction and came over to scold Max, my Grampy, and I said, no, no, no, wait. And walked away with him and told him that it was my step-grandfather, that he didn't know me. We should call my grandmother, which we did, and she came down and got him. She didn't realize he left the house.   I was looking at somebody I knew and they had no idea who I was. I didn't know what to do.  [OUTRO] I wrote all of this down later, on a crumpled, coffee stained napkin.  If you want to see it, it's in my newsletter. I hope you like it, Grampy Max.   Credits: Sleepless by Clavier-Music Clavier's Youtube  Restaurant Ambience

Episode #506: “I think the toll of doing dedicated work even as we grow older is so small compared to that of so many brave Myanmar activists. I can support the cause, but I can also choose not to confront myself with the full reality of what's going on in the ground. That's a choice that Myanmar people by and large don't have! That's how I carry on doing the work I do,” says Patrick Hoffmann, reflecting on the personal and historical drivers behind his commitment to Myanmar's democracy movement. Patrick's personal background indicates how individual narratives can ignite a lifetime commitment to global justice, advocating for freedom even from afar. His Jewish family heritage, marked by his father's childhood under Nazi Germany during World War II in Berlin, imbued him with a deep understanding of trauma and the devastating impact of atrocity; combined with the sense that one must never take democracy for granted, and it is always something worth fighting to preserve. This personal history, as both a German and a Jew, fuels his belief that “we, more than any other people, should stand for preventing genocide anywhere,” a conviction that propels his advocacy. Interacting with Myanmar students and activists in Yangon in 2012, he learned early the nuances in democratic models, particularly in the Asian context. After the 2021 coup, Patrick joined German Solidarity Myanmar, moving from conventional humanitarian aid work to more deeply active political lobbying. He advocates for a nuanced approach for Germany to show solidarity with Myanmar's cause, such as not only condemning the regime but also supporting non-state actors. Through his work, he has realized the power of inclusive narrative building, as well as how art can tell “a much more approachable and human portrayal of people fighting for democracy on the ground.” Despite the immense challenges, Patrick remains inspired by the movement's resilience. “This movement feels so close,” he says. “It's on the verge of success. We cannot give up now.”

On this episode, Watson School Dean and economist John Friedman talks with economist Sebnem Kalemli Ozcan about how U.S. economic policy in the last year has changed the American economy, how those changes have rippled throughout the global economic and financial system, and what it means for America's place in a rapidly evolving international order.Sebnem Kalemli Ozcan is a professor of economics at Brown and the director of the Global Linkages Lab, a collaborative research hub dedicated to deepening our understanding of globalization. Starting in July, she'll also be serving as the director of the Watson School's Rhodes Center for International Economics and Finance.John Friedman is Vascellaro Family Dean of the Watson School, and Briger Family Distinguished Professor of Economics and International and Public AffairsTranscript coming soon to our website.Watch this episode of Trending Globally on YouTube.

Mark interviews author Julie Hiner about creating an unforgettable book event for a book launch. Prior to the interview Mark shares a brief personal update a word from this episode's sponsor. This episode is sponsored by an affiliate link to Manuscript Report. Use code MARK10 at checkout and save 10% off your own personalized report. In the interview Mark and Julie talk about: Julie's branding as being strongly tied to the 80s and music even though she fought it at first The first book Julie published about road biking (JUST A GIRL ON A BIKE) which was the story of how she overcame a lot of fears and anxiety and the donations she makes with sales of that book The 21 switch-backs on the road biking in France that became the 21 steps in Julie's book The value of learning so much about self-publishing when Julie attender her first When Words Collide conference Julie's ongoing obsession with true crime, serial killers, and 80s metal Interacting with and interviewing Homicide Detective Dave Sweet Julie's books in the Detective Mahoney series that started with the book FINAL TRACK The original venue Julie used for her first book and the newer one she has partnered with The special VIP pre-order packages Julie makes available The way the collaboration with the musicians work, including Julie creating lyrics for the bands to adapt into songs How horror is a really safe place to explore extremely dark things Studies that show that aggressive music can help calm the brain The creepiest serial killer that came up in the research Julie did Some of the things authors should consider when looking at their branding   After the interview Mark shares about an event Julie has coming up in April 2026, and then talks about the importance of pulling out atmospheric elements from the book/story/characters/setting as well as the idea of writing with authenticity and passion.   Links of Interest: Julie Hiner's Website Stella's Scream Book Launch Just a Girl and a Bike Manuscript Report (Mark's affiliate link - use MARK10 to save 10%) Buy Mark a Coffee Patreon for Stark Reflections Mark's YouTube channel ElevenLabs (AI Voice Generation - Affiliate link) Mark's Stark Reflections on Writing & Publishing Newsletter (Signup) An Author's Guide to Working With Bookstores and Libraries The Relaxed Author Buy eBook Direct Buy Audiobook Direct Publishing Pitfalls for Authors An Author's Guide to Working with Libraries & Bookstores Wide for the Win Mark's Canadian Werewolf Books This Time Around (Short Story) A Canadian Werewolf in New York Stowe Away (Novella) Fear and Longing in Los Angeles Fright Nights, Big City Lover's Moon Hex and the City Only Monsters in the Building Once Bitten (Novella) The Canadian Mounted: A Trivia Guide to Planes, Trains and Automobiles Yippee Ki-Yay Motherf*cker: A Trivia Guide to Die Hard Merry Christmas! Shitter Was Full!: A Trivia Guide to National Lampoon's Christmas Vacation I Think It's A Sign That The Pun Also Rises   Julie Hiner spent her childhood lost in the pages of books. The only thing that took precedence was her Walkman. She is still an 80s rocker. On a break from her career as a computer scientist, she published an inspirational book about facing fears by cycling up mountains. Julie now writes horror/suspense infused with heavy metal. She has published a serial killer series, three horror novellas, co-curated an anthology, and had short stories published. She recently had a deep-sea horror novella published by Torrid Waters of Crystal Lake Publishing. You can find her at KillersAndDemons.com serving up metal and murder.     The introductory, end, and bumper music for this podcast ("Laser Groove") was composed and produced by Kevin MacLeod of www.incompetech.com and is Licensed under Creative Commons: By Attribution 3.0    

Just days after their loss against Ireland, Dan Cole and Ben Youngs are at Pennyhill Park for a special episode from inside England Rugby's Six Nations camp. We share an unrivalled insight into the mentality of a team struggling for form, recall the challenges of navigating the media as a player and hear from Ollie Chessum and Jack van Poortvliet.

In this episode of Thinking Out Loud, Nathan Rittenhouse and Cameron McAllister engage in deep theological reflection on the Epstein files, cultural corruption, and the crisis of meaning in the modern West. Referencing figures like Harvey Weinstein and drawing cultural parallels to excesses reminiscent of Nero, they explore why revelations of elite abuse, power, and moral collapse no longer shock us—and what that says about our spiritual condition. Are Christians becoming cynical, or are we awakening to the emptiness of fame, wealth, and influence as ultimate goals? Interacting with themes echoed in the “He Gets Us” Super Bowl campaign and thinkers like Aristotle, Nathan and Cameron examine the biblical concept of telos—our God-given purpose—and contrast radical individualism with the shared story of Scripture. Through reflections on the Emmaus road, the Sermon on the Mount, and the Church's role in restoring shared meaning, this conversation equips believers to pursue true human flourishing in Christ amid cultural decay. If you're a Christian seeking serious theological analysis of current events, cultural commentary grounded in biblical truth, and practical wisdom for faithful living in a confused age, this episode will challenge and encourage you.DONATE LINK: https://toltogether.com/donate BOOK A SPEAKER: https://toltogether.com/book-a-speakerJOIN TOL CONNECT: https://toltogether.com/tol-connect TOL Connect is an online forum where TOL listeners can continue the conversation begun on the podcast.

(00:00-14:27) The sun starting to peek out this morning. Did Doug have a dungeon put in? Doug being held accountable for his Springfield, MO and MO State takes. Claibs stops by to talk in code. Jackson is denying there's a GoFundMe for him to go play golf. Claibs believes in Marmol.(14:35-29:30) Nothing wrong with a little jazz flute. Jordan Walker and Chaim Bloom will join us tomorrow. I don't negotiate with Hoosiers. Martin sent an aggressive text to Oli Marmol.(29:40-1:03:07) Cardinal manager Oli Marmol joins the show and starts of talking parking spots. Interacting with media and fans. Not worried about the outside noise or proving anyone wrong. His relationship with Chaim Bloom. Who does he see stepping up with a lot of experienced players gone? What he likes about JJ Wetherholt. Oli digs into the YouTube chat. His playing career. The art of the ejection. He won't be checking out Movie Boi. He will be holding McGreevy accountable for not playing a $500 round of golf. What are the indicators of success for this season? Having former players at camp. What makes him the right man to lead the team through this rebuild? Misconceptions about him.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Cardinal manager Oli Marmol joins the show and starts of talking parking spots. Interacting with media and fans. Not worried about the outside noise or proving anyone wrong. His relationship with Chaim Bloom. Who does he see stepping up with a lot of experienced players gone? What he likes about JJ Wetherholt. His playing career. The art of the ejection. What are the indicators of success for this season?See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Scott and Wes unpack WebMCP, a new standard that lets AI interact with websites through structured tools instead of slow, bot-style clicking. They demo it, debate imperative vs declarative APIs, and share their hottest take: this might be the web's real AI moment. Show Notes 00:00 Welcome to Syntax! 00:16 Introduction to WebMCP 01:07 Understanding WebMCP Functionality. 03:06 Interacting with AI through WebMCP. 06:49 WebMCP browser integration. 08:25 Brought to you by Sentry.io. 08:49 Benefits of WebMCP. 11:51 Token efficiency. 13:02 My biggest questions. 14:13 My take on this tech. Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads Wes: X Instagram Tiktok LinkedIn Threads Scott: X Instagram Tiktok LinkedIn Threads Randy: X Instagram YouTube Threads

This podcast features Gabriele Corso and Jeremy Wohlwend, co-founders of Boltz and authors of the Boltz Manifesto, discussing the rapid evolution of structural biology models from AlphaFold to their own open-source suite, Boltz-1 and Boltz-2. The central thesis is that while single-chain protein structure prediction is largely “solved” through evolutionary hints, the next frontier lies in modeling complex interactions (protein-ligand, protein-protein) and generative protein design, which Boltz aims to democratize via open-source foundations and scalable infrastructure.Full Video PodOn YouTube!Timestamps* 00:00 Introduction to Benchmarking and the “Solved” Protein Problem* 06:48 Evolutionary Hints and Co-evolution in Structure Prediction* 10:00 The Importance of Protein Function and Disease States* 15:31 Transitioning from AlphaFold 2 to AlphaFold 3 Capabilities* 19:48 Generative Modeling vs. Regression in Structural Biology* 25:00 The “Bitter Lesson” and Specialized AI Architectures* 29:14 Development Anecdotes: Training Boltz-1 on a Budget* 32:00 Validation Strategies and the Protein Data Bank (PDB)* 37:26 The Mission of Boltz: Democratizing Access and Open Source* 41:43 Building a Self-Sustaining Research Community* 44:40 Boltz-2 Advancements: Affinity Prediction and Design* 51:03 BoltzGen: Merging Structure and Sequence Prediction* 55:18 Large-Scale Wet Lab Validation Results* 01:02:44 Boltz Lab Product Launch: Agents and Infrastructure* 01:13:06 Future Directions: Developpability and the “Virtual Cell”* 01:17:35 Interacting with Skeptical Medicinal ChemistsKey SummaryEvolution of Structure Prediction & Evolutionary Hints* Co-evolutionary Landscapes: The speakers explain that breakthrough progress in single-chain protein prediction relied on decoding evolutionary correlations where mutations in one position necessitate mutations in another to conserve 3D structure.* Structure vs. Folding: They differentiate between structure prediction (getting the final answer) and folding (the kinetic process of reaching that state), noting that the field is still quite poor at modeling the latter.* Physics vs. Statistics: RJ posits that while models use evolutionary statistics to find the right “valley” in the energy landscape, they likely possess a “light understanding” of physics to refine the local minimum.The Shift to Generative Architectures* Generative Modeling: A key leap in AlphaFold 3 and Boltz-1 was moving from regression (predicting one static coordinate) to a generative diffusion approach that samples from a posterior distribution.* Handling Uncertainty: This shift allows models to represent multiple conformational states and avoid the “averaging” effect seen in regression models when the ground truth is ambiguous.* Specialized Architectures: Despite the “bitter lesson” of general-purpose transformers, the speakers argue that equivariant architectures remain vastly superior for biological data due to the inherent 3D geometric constraints of molecules.Boltz-2 and Generative Protein Design* Unified Encoding: Boltz-2 (and BoltzGen) treats structure and sequence prediction as a single task by encoding amino acid identities into the atomic composition of the predicted structure.* Design Specifics: Instead of a sequence, users feed the model blank tokens and a high-level “spec” (e.g., an antibody framework), and the model decodes both the 3D structure and the corresponding amino acids.* Affinity Prediction: While model confidence is a common metric, Boltz-2 focuses on affinity prediction—quantifying exactly how tightly a designed binder will stick to its target.Real-World Validation and Productization* Generalized Validation: To prove the model isn't just “regurgitating” known data, Boltz tested its designs on 9 targets with zero known interactions in the PDB, achieving nanomolar binders for two-thirds of them.* Boltz Lab Infrastructure: The newly launched Boltz Lab platform provides “agents” for protein and small molecule design, optimized to run 10x faster than open-source versions through proprietary GPU kernels.* Human-in-the-Loop: The platform is designed to convert skeptical medicinal chemists by allowing them to run parallel screens and use their intuition to filter model outputs.TranscriptRJ [00:05:35]: But the goal remains to, like, you know, really challenge the models, like, how well do these models generalize? And, you know, we've seen in some of the latest CASP competitions, like, while we've become really, really good at proteins, especially monomeric proteins, you know, other modalities still remain pretty difficult. So it's really essential, you know, in the field that there are, like, these efforts to gather, you know, benchmarks that are challenging. So it keeps us in line, you know, about what the models can do or not.Gabriel [00:06:26]: Yeah, it's interesting you say that, like, in some sense, CASP, you know, at CASP 14, a problem was solved and, like, pretty comprehensively, right? But at the same time, it was really only the beginning. So you can say, like, what was the specific problem you would argue was solved? And then, like, you know, what is remaining, which is probably quite open.RJ [00:06:48]: I think we'll steer away from the term solved, because we have many friends in the community who get pretty upset at that word. And I think, you know, fairly so. But the problem that was, you know, that a lot of progress was made on was the ability to predict the structure of single chain proteins. So proteins can, like, be composed of many chains. And single chain proteins are, you know, just a single sequence of amino acids. And one of the reasons that we've been able to make such progress is also because we take a lot of hints from evolution. So the way the models work is that, you know, they sort of decode a lot of hints. That comes from evolutionary landscapes. So if you have, like, you know, some protein in an animal, and you go find the similar protein across, like, you know, different organisms, you might find different mutations in them. And as it turns out, if you take a lot of the sequences together, and you analyze them, you see that some positions in the sequence tend to evolve at the same time as other positions in the sequence, sort of this, like, correlation between different positions. And it turns out that that is typically a hint that these two positions are close in three dimension. So part of the, you know, part of the breakthrough has been, like, our ability to also decode that very, very effectively. But what it implies also is that in absence of that co-evolutionary landscape, the models don't quite perform as well. And so, you know, I think when that information is available, maybe one could say, you know, the problem is, like, somewhat solved. From the perspective of structure prediction, when it isn't, it's much more challenging. And I think it's also worth also differentiating the, sometimes we confound a little bit, structure prediction and folding. Folding is the more complex process of actually understanding, like, how it goes from, like, this disordered state into, like, a structured, like, state. And that I don't think we've made that much progress on. But the idea of, like, yeah, going straight to the answer, we've become pretty good at.Brandon [00:08:49]: So there's this protein that is, like, just a long chain and it folds up. Yeah. And so we're good at getting from that long chain in whatever form it was originally to the thing. But we don't know how it necessarily gets to that state. And there might be intermediate states that it's in sometimes that we're not aware of.RJ [00:09:10]: That's right. And that relates also to, like, you know, our general ability to model, like, the different, you know, proteins are not static. They move, they take different shapes based on their energy states. And I think we are, also not that good at understanding the different states that the protein can be in and at what frequency, what probability. So I think the two problems are quite related in some ways. Still a lot to solve. But I think it was very surprising at the time, you know, that even with these evolutionary hints that we were able to, you know, to make such dramatic progress.Brandon [00:09:45]: So I want to ask, why does the intermediate states matter? But first, I kind of want to understand, why do we care? What proteins are shaped like?Gabriel [00:09:54]: Yeah, I mean, the proteins are kind of the machines of our body. You know, the way that all the processes that we have in our cells, you know, work is typically through proteins, sometimes other molecules, sort of intermediate interactions. And through that interactions, we have all sorts of cell functions. And so when we try to understand, you know, a lot of biology, how our body works, how disease work. So we often try to boil it down to, okay, what is going right in case of, you know, our normal biological function and what is going wrong in case of the disease state. And we boil it down to kind of, you know, proteins and kind of other molecules and their interaction. And so when we try predicting the structure of proteins, it's critical to, you know, have an understanding of kind of those interactions. It's a bit like seeing the difference between... Having kind of a list of parts that you would put it in a car and seeing kind of the car in its final form, you know, seeing the car really helps you understand what it does. On the other hand, kind of going to your question of, you know, why do we care about, you know, how the protein falls or, you know, how the car is made to some extent is that, you know, sometimes when something goes wrong, you know, there are, you know, cases of, you know, proteins misfolding. In some diseases and so on, if we don't understand this folding process, we don't really know how to intervene.RJ [00:11:30]: There's this nice line in the, I think it's in the Alpha Fold 2 manuscript, where they sort of discuss also like why we even hopeful that we can target the problem in the first place. And then there's this notion that like, well, four proteins that fold. The folding process is almost instantaneous, which is a strong, like, you know, signal that like, yeah, like we should, we might be... able to predict that this very like constrained thing that, that the protein does so quickly. And of course that's not the case for, you know, for, for all proteins. And there's a lot of like really interesting mechanisms in the cells, but yeah, I remember reading that and thought, yeah, that's somewhat of an insightful point.Gabriel [00:12:10]: I think one of the interesting things about the protein folding problem is that it used to be actually studied. And part of the reason why people thought it was impossible, it used to be studied as kind of like a classical example. Of like an MP problem. Uh, like there are so many different, you know, type of, you know, shapes that, you know, this amino acid could take. And so, this grows combinatorially with the size of the sequence. And so there used to be kind of a lot of actually kind of more theoretical computer science thinking about and studying protein folding as an MP problem. And so it was very surprising also from that perspective, kind of seeing. Machine learning so clear, there is some, you know, signal in those sequences, through evolution, but also through kind of other things that, you know, us as humans, we're probably not really able to, uh, to understand, but that is, models I've, I've learned.Brandon [00:13:07]: And so Andrew White, we were talking to him a few weeks ago and he said that he was following the development of this and that there were actually ASICs that were developed just to solve this problem. So, again, that there were. There were many, many, many millions of computational hours spent trying to solve this problem before AlphaFold. And just to be clear, one thing that you mentioned was that there's this kind of co-evolution of mutations and that you see this again and again in different species. So explain why does that give us a good hint that they're close by to each other? Yeah.RJ [00:13:41]: Um, like think of it this way that, you know, if I have, you know, some amino acid that mutates, it's going to impact everything around it. Right. In three dimensions. And so it's almost like the protein through several, probably random mutations and evolution, like, you know, ends up sort of figuring out that this other amino acid needs to change as well for the structure to be conserved. Uh, so this whole principle is that the structure is probably largely conserved, you know, because there's this function associated with it. And so it's really sort of like different positions compensating for, for each other. I see.Brandon [00:14:17]: Those hints in aggregate give us a lot. Yeah. So you can start to look at what kinds of information about what is close to each other, and then you can start to look at what kinds of folds are possible given the structure and then what is the end state.RJ [00:14:30]: And therefore you can make a lot of inferences about what the actual total shape is. Yeah, that's right. It's almost like, you know, you have this big, like three dimensional Valley, you know, where you're sort of trying to find like these like low energy states and there's so much to search through. That's almost overwhelming. But these hints, they sort of maybe put you in. An area of the space that's already like, kind of close to the solution, maybe not quite there yet. And, and there's always this question of like, how much physics are these models learning, you know, versus like, just pure like statistics. And like, I think one of the thing, at least I believe is that once you're in that sort of approximate area of the solution space, then the models have like some understanding, you know, of how to get you to like, you know, the lower energy, uh, low energy state. And so maybe you have some, some light understanding. Of physics, but maybe not quite enough, you know, to know how to like navigate the whole space. Right. Okay.Brandon [00:15:25]: So we need to give it these hints to kind of get into the right Valley and then it finds the, the minimum or something. Yeah.Gabriel [00:15:31]: One interesting explanation about our awful free works that I think it's quite insightful, of course, doesn't cover kind of the entirety of, of what awful does that is, um, they're going to borrow from, uh, Sergio Chinico for MIT. So he sees kind of awful. Then the interesting thing about awful is God. This very peculiar architecture that we have seen, you know, used, and this architecture operates on this, you know, pairwise context between amino acids. And so the idea is that probably the MSA gives you this first hint about what potential amino acids are close to each other. MSA is most multiple sequence alignment. Exactly. Yeah. Exactly. This evolutionary information. Yeah. And, you know, from this evolutionary information about potential contacts, then is almost as if the model is. of running some kind of, you know, diastro algorithm where it's sort of decoding, okay, these have to be closed. Okay. Then if these are closed and this is connected to this, then this has to be somewhat closed. And so you decode this, that becomes basically a pairwise kind of distance matrix. And then from this rough pairwise distance matrix, you decode kind of theBrandon [00:16:42]: actual potential structure. Interesting. So there's kind of two different things going on in the kind of coarse grain and then the fine grain optimizations. Interesting. Yeah. Very cool.Gabriel [00:16:53]: Yeah. You mentioned AlphaFold3. So maybe we have a good time to move on to that. So yeah, AlphaFold2 came out and it was like, I think fairly groundbreaking for this field. Everyone got very excited. A few years later, AlphaFold3 came out and maybe for some more history, like what were the advancements in AlphaFold3? And then I think maybe we'll, after that, we'll talk a bit about the sort of how it connects to Bolt. But anyway. Yeah. So after AlphaFold2 came out, you know, Jeremy and I got into the field and with many others, you know, the clear problem that, you know, was, you know, obvious after that was, okay, now we can do individual chains. Can we do interactions, interaction, different proteins, proteins with small molecules, proteins with other molecules. And so. So why are interactions important? Interactions are important because to some extent that's kind of the way that, you know, these machines, you know, these proteins have a function, you know, the function comes by the way that they interact with other proteins and other molecules. Actually, in the first place, you know, the individual machines are often, as Jeremy was mentioning, not made of a single chain, but they're made of the multiple chains. And then these multiple chains interact with other molecules to give the function to those. And on the other hand, you know, when we try to intervene of these interactions, think about like a disease, think about like a, a biosensor or many other ways we are trying to design the molecules or proteins that interact in a particular way with what we would call a target protein or target. You know, this problem after AlphaVol2, you know, became clear, kind of one of the biggest problems in the field to, to solve many groups, including kind of ours and others, you know, started making some kind of contributions to this problem of trying to model these interactions. And AlphaVol3 was, you know, was a significant advancement on the problem of modeling interactions. And one of the interesting thing that they were able to do while, you know, some of the rest of the field that really tried to try to model different interactions separately, you know, how protein interacts with small molecules, how protein interacts with other proteins, how RNA or DNA have their structure, they put everything together and, you know, train very large models with a lot of advances, including kind of changing kind of systems. Some of the key architectural choices and managed to get a single model that was able to set this new state-of-the-art performance across all of these different kind of modalities, whether that was protein, small molecules is critical to developing kind of new drugs, protein, protein, understanding, you know, interactions of, you know, proteins with RNA and DNAs and so on.Brandon [00:19:39]: Just to satisfy the AI engineers in the audience, what were some of the key architectural and data, data changes that made that possible?Gabriel [00:19:48]: Yeah, so one critical one that was not necessarily just unique to AlphaFold3, but there were actually a few other teams, including ours in the field that proposed this, was moving from, you know, modeling structure prediction as a regression problem. So where there is a single answer and you're trying to shoot for that answer to a generative modeling problem where you have a posterior distribution of possible structures and you're trying to sample this distribution. And this achieves two things. One is it starts to allow us to try to model more dynamic systems. As we said, you know, some of these structures can actually take multiple structures. And so, you know, you can now model that, you know, through kind of modeling the entire distribution. But on the second hand, from more kind of core modeling questions, when you move from a regression problem to a generative modeling problem, you are really tackling the way that you think about uncertainty in the model in a different way. So if you think about, you know, I'm undecided between different answers, what's going to happen in a regression model is that, you know, I'm going to try to make an average of those different kind of answers that I had in mind. When you have a generative model, what you're going to do is, you know, sample all these different answers and then maybe use separate models to analyze those different answers and pick out the best. So that was kind of one of the critical improvement. The other improvement is that they significantly simplified, to some extent, the architecture, especially of the final model that takes kind of those pairwise representations and turns them into an actual structure. And that now looks a lot more like a more traditional transformer than, you know, like a very specialized equivariant architecture that it was in AlphaFold3.Brandon [00:21:41]: So this is a bitter lesson, a little bit.Gabriel [00:21:45]: There is some aspect of a bitter lesson, but the interesting thing is that it's very far from, you know, being like a simple transformer. This field is one of the, I argue, very few fields in applied machine learning where we still have kind of architecture that are very specialized. And, you know, there are many people that have tried to replace these architectures with, you know, simple transformers. And, you know, there is a lot of debate in the field, but I think kind of that most of the consensus is that, you know, the performance... that we get from the specialized architecture is vastly superior than what we get through a single transformer. Another interesting thing that I think on the staying on the modeling machine learning side, which I think it's somewhat counterintuitive seeing some of the other kind of fields and applications is that scaling hasn't really worked kind of the same in this field. Now, you know, models like AlphaFold2 and AlphaFold3 are, you know, still very large models.RJ [00:29:14]: in a place, I think, where we had, you know, some experience working in, you know, with the data and working with this type of models. And I think that put us already in like a good place to, you know, to produce it quickly. And, you know, and I would even say, like, I think we could have done it quicker. The problem was like, for a while, we didn't really have the compute. And so we couldn't really train the model. And actually, we only trained the big model once. That's how much compute we had. We could only train it once. And so like, while the model was training, we were like, finding bugs left and right. A lot of them that I wrote. And like, I remember like, I was like, sort of like, you know, doing like, surgery in the middle, like stopping the run, making the fix, like relaunching. And yeah, we never actually went back to the start. We just like kept training it with like the bug fixes along the way, which was impossible to reproduce now. Yeah, yeah, no, that model is like, has gone through such a curriculum that, you know, learned some weird stuff. But yeah, somehow by miracle, it worked out.Gabriel [00:30:13]: The other funny thing is that the way that we were training, most of that model was through a cluster from the Department of Energy. But that's sort of like a shared cluster that many groups use. And so we were basically training the model for two days, and then it would go back to the queue and stay a week in the queue. Oh, yeah. And so it was pretty painful. And so we actually kind of towards the end with Evan, the CEO of Genesis, and basically, you know, I was telling him a bit about the project and, you know, kind of telling him about this frustration with the compute. And so luckily, you know, he offered to kind of help. And so we, we got the help from Genesis to, you know, finish up the model. Otherwise, it probably would have taken a couple of extra weeks.Brandon [00:30:57]: Yeah, yeah.Brandon [00:31:02]: And then, and then there's some progression from there.Gabriel [00:31:06]: Yeah, so I would say kind of that, both one, but also kind of these other kind of set of models that came around the same time, were kind of approaching were a big leap from, you know, kind of the previous kind of open source models, and, you know, kind of really kind of approaching the level of AlphaVault 3. But I would still say that, you know, even to this day, there are, you know, some... specific instances where AlphaVault 3 works better. I think one common example is antibody antigen prediction, where, you know, AlphaVault 3 still seems to have an edge in many situations. Obviously, these are somewhat different models. They are, you know, you run them, you obtain different results. So it's, it's not always the case that one model is better than the other, but kind of in aggregate, we still, especially at the time.Brandon [00:32:00]: So AlphaVault 3 is, you know, still having a bit of an edge. We should talk about this more when we talk about Boltzgen, but like, how do you know one is, one model is better than the other? Like you, so you, I make a prediction, you make a prediction, like, how do you know?Gabriel [00:32:11]: Yeah, so easily, you know, the, the great thing about kind of structural prediction and, you know, once we're going to go into the design space of designing new small molecule, new proteins, this becomes a lot more complex. But a great thing about structural prediction is that a bit like, you know, CASP was doing, basically the way that you can evaluate them is that, you know, you train... You know, you train a model on a structure that was, you know, released across the field up until a certain time. And, you know, one of the things that we didn't talk about that was really critical in all this development is the PDB, which is the Protein Data Bank. It's this common resources, basically common database where every biologist publishes their structures. And so we can, you know, train on, you know, all the structures that were put in the PDB until a certain date. And then... And then we basically look for recent structures, okay, which structures look pretty different from anything that was published before, because we really want to try to understand generalization.Brandon [00:33:13]: And then on this new structure, we evaluate all these different models. And so you just know when AlphaFold3 was trained, you know, when you're, you intentionally trained to the same date or something like that. Exactly. Right. Yeah.Gabriel [00:33:24]: And so this is kind of the way that you can somewhat easily kind of compare these models, obviously, that assumes that, you know, the training. You've always been very passionate about validation. I remember like DiffDoc, and then there was like DiffDocL and DocGen. You've thought very carefully about this in the past. Like, actually, I think DocGen is like a really funny story that I think, I don't know if you want to talk about that. It's an interesting like... Yeah, I think one of the amazing things about putting things open source is that we get a ton of feedback from the field. And, you know, sometimes we get kind of great feedback of people. Really like... But honestly, most of the times, you know, to be honest, that's also maybe the most useful feedback is, you know, people sharing about where it doesn't work. And so, you know, at the end of the day, it's critical. And this is also something, you know, across other fields of machine learning. It's always critical to set, to do progress in machine learning, set clear benchmarks. And as, you know, you start doing progress of certain benchmarks, then, you know, you need to improve the benchmarks and make them harder and harder. And this is kind of the progression of, you know, how the field operates. And so, you know, the example of DocGen was, you know, we published this initial model called DiffDoc in my first year of PhD, which was sort of like, you know, one of the early models to try to predict kind of interactions between proteins, small molecules, that we bought a year after AlphaFold2 was published. And now, on the one hand, you know, on these benchmarks that we were using at the time, DiffDoc was doing really well, kind of, you know, outperforming kind of some of the traditional physics-based methods. But on the other hand, you know, when we started, you know, kind of giving these tools to kind of many biologists, and one example was that we collaborated with was the group of Nick Polizzi at Harvard. We noticed, started noticing that there was this clear, pattern where four proteins that were very different from the ones that we're trained on, the models was, was struggling. And so, you know, that seemed clear that, you know, this is probably kind of where we should, you know, put our focus on. And so we first developed, you know, with Nick and his group, a new benchmark, and then, you know, went after and said, okay, what can we change? And kind of about the current architecture to improve this pattern and generalization. And this is the same that, you know, we're still doing today, you know, kind of, where does the model not work, you know, and then, you know, once we have that benchmark, you know, let's try to, through everything we, any ideas that we have of the problem.RJ [00:36:15]: And there's a lot of like healthy skepticism in the field, which I think, you know, is, is, is great. And I think, you know, it's very clear that there's a ton of things, the models don't really work well on, but I think one thing that's probably, you know, undeniable is just like the pace of, pace of progress, you know, and how, how much better we're getting, you know, every year. And so I think if you, you know, if you assume, you know, any constant, you know, rate of progress moving forward, I think things are going to look pretty cool at some point in the future.Gabriel [00:36:42]: ChatGPT was only three years ago. Yeah, I mean, it's wild, right?RJ [00:36:45]: Like, yeah, yeah, yeah, it's one of those things. Like, you've been doing this. Being in the field, you don't see it coming, you know? And like, I think, yeah, hopefully we'll, you know, we'll, we'll continue to have as much progress we've had the past few years.Brandon [00:36:55]: So this is maybe an aside, but I'm really curious, you get this great feedback from the, from the community, right? By being open source. My question is partly like, okay, yeah, if you open source and everyone can copy what you did, but it's also maybe balancing priorities, right? Where you, like all my customers are saying. I want this, there's all these problems with the model. Yeah, yeah. But my customers don't care, right? So like, how do you, how do you think about that? Yeah.Gabriel [00:37:26]: So I would say a couple of things. One is, you know, part of our goal with Bolts and, you know, this is also kind of established as kind of the mission of the public benefit company that we started is to democratize the access to these tools. But one of the reasons why we realized that Bolts needed to be a company, it couldn't just be an academic project is that putting a model on GitHub is definitely not enough to get, you know, chemists and biologists, you know, across, you know, both academia, biotech and pharma to use your model to, in their therapeutic programs. And so a lot of what we think about, you know, at Bolts beyond kind of the, just the models is thinking about all the layers. The layers that come on top of the models to get, you know, from, you know, those models to something that can really enable scientists in the industry. And so that goes, you know, into building kind of the right kind of workflows that take in kind of, for example, the data and try to answer kind of directly that those problems that, you know, the chemists and the biologists are asking, and then also kind of building the infrastructure. And so this to say that, you know, even with models fully open. You know, we see a ton of potential for, you know, products in the space and the critical part about a product is that even, you know, for example, with an open source model, you know, running the model is not free, you know, as we were saying, these are pretty expensive model and especially, and maybe we'll get into this, you know, these days we're seeing kind of pretty dramatic inference time scaling of these models where, you know, the more you run them, the better the results are. But there, you know, you see. You start getting into a point that compute and compute costs becomes a critical factor. And so putting a lot of work into building the right kind of infrastructure, building the optimizations and so on really allows us to provide, you know, a much better service potentially to the open source models. That to say, you know, even though, you know, with a product, we can provide a much better service. I do still think, and we will continue to put a lot of our models open source because the critical kind of role. I think of open source. Models is, you know, helping kind of the community progress on the research and, you know, from which we, we all benefit. And so, you know, we'll continue to on the one hand, you know, put some of our kind of base models open source so that the field can, can be on top of it. And, you know, as we discussed earlier, we learn a ton from, you know, the way that the field uses and builds on top of our models, but then, you know, try to build a product that gives the best experience possible to scientists. So that, you know, like a chemist or a biologist doesn't need to, you know, spin off a GPU and, you know, set up, you know, our open source model in a particular way, but can just, you know, a bit like, you know, I, even though I am a computer scientist, machine learning scientist, I don't necessarily, you know, take a open source LLM and try to kind of spin it off. But, you know, I just maybe open a GPT app or a cloud code and just use it as an amazing product. We kind of want to give the same experience. So this front world.Brandon [00:40:40]: I heard a good analogy yesterday that a surgeon doesn't want the hospital to design a scalpel, right?Brandon [00:40:48]: So just buy the scalpel.RJ [00:40:50]: You wouldn't believe like the number of people, even like in my short time, you know, between AlphaFold3 coming out and the end of the PhD, like the number of people that would like reach out just for like us to like run AlphaFold3 for them, you know, or things like that. Just because like, you know, bolts in our case, you know, just because it's like. It's like not that easy, you know, to do that, you know, if you're not a computational person. And I think like part of the goal here is also that, you know, we continue to obviously build the interface with computational folks, but that, you know, the models are also accessible to like a larger, broader audience. And then that comes from like, you know, good interfaces and stuff like that.Gabriel [00:41:27]: I think one like really interesting thing about bolts is that with the release of it, you didn't just release a model, but you created a community. Yeah. Did that community, it grew very quickly. Did that surprise you? And like, what is the evolution of that community and how is that fed into bolts?RJ [00:41:43]: If you look at its growth, it's like very much like when we release a new model, it's like, there's a big, big jump, but yeah, it's, I mean, it's been great. You know, we have a Slack community that has like thousands of people on it. And it's actually like self-sustaining now, which is like the really nice part because, you know, it's, it's almost overwhelming, I think, you know, to be able to like answer everyone's questions and help. It's really difficult, you know. The, the few people that we were, but it ended up that like, you know, people would answer each other's questions and like, sort of like, you know, help one another. And so the Slack, you know, has been like kind of, yeah, self, self-sustaining and that's been, it's been really cool to see.RJ [00:42:21]: And, you know, that's, that's for like the Slack part, but then also obviously on GitHub as well. We've had like a nice, nice community. You know, I think we also aspire to be even more active on it, you know, than we've been in the past six months, which has been like a bit challenging, you know, for us. But. Yeah, the community has been, has been really great and, you know, there's a lot of papers also that have come out with like new evolutions on top of bolts and it's surprised us to some degree because like there's a lot of models out there. And I think like, you know, sort of people converging on that was, was really cool. And, you know, I think it speaks also, I think, to the importance of like, you know, when, when you put code out, like to try to put a lot of emphasis and like making it like as easy to use as possible and something we thought a lot about when we released the code base. You know, it's far from perfect, but, you know.Brandon [00:43:07]: Do you think that that was one of the factors that caused your community to grow is just the focus on easy to use, make it accessible? I think so.RJ [00:43:14]: Yeah. And we've, we've heard it from a few people over the, over the, over the years now. And, you know, and some people still think it should be a lot nicer and they're, and they're right. And they're right. But yeah, I think it was, you know, at the time, maybe a little bit easier than, than other things.Gabriel [00:43:29]: The other thing part, I think led to, to the community and to some extent, I think, you know, like the somewhat the trust in the community. Kind of what we, what we put out is the fact that, you know, it's not really been kind of, you know, one model, but, and maybe we'll talk about it, you know, after Boltz 1, you know, there were maybe another couple of models kind of released, you know, or open source kind of soon after. We kind of continued kind of that open source journey or at least Boltz 2, where we are not only improving kind of structure prediction, but also starting to do affinity predictions, understanding kind of the strength of the interactions between these different models, which is this critical component. critical property that you often want to optimize in discovery programs. And then, you know, more recently also kind of protein design model. And so we've sort of been building this suite of, of models that come together, interact with one another, where, you know, kind of, there is almost an expectation that, you know, we, we take very at heart of, you know, always having kind of, you know, across kind of the entire suite of different tasks, the best or across the best. model out there so that it's sort of like our open source tool can be kind of the go-to model for everybody in the, in the industry. I really want to talk about Boltz 2, but before that, one last question in this direction, was there anything about the community which surprised you? Were there any, like, someone was doing something and you're like, why would you do that? That's crazy. Or that's actually genius. And I never would have thought about that.RJ [00:45:01]: I mean, we've had many contributions. I think like some of the. Interesting ones, like, I mean, we had, you know, this one individual who like wrote like a complex GPU kernel, you know, for part of the architecture on a piece of, the funny thing is like that piece of the architecture had been there since AlphaFold 2, and I don't know why it took Boltz for this, you know, for this person to, you know, to decide to do it, but that was like a really great contribution. We've had a bunch of others, like, you know, people figuring out like ways to, you know, hack the model to do something. They click peptides, like, you know, there's, I don't know if there's any other interesting ones come to mind.Gabriel [00:45:41]: One cool one, and this was, you know, something that initially was proposed as, you know, as a message in the Slack channel by Tim O'Donnell was basically, he was, you know, there are some cases, especially, for example, we discussed, you know, antibody-antigen interactions where the models don't necessarily kind of get the right answer. What he noticed is that, you know, the models were somewhat stuck into predicting kind of the antibodies. And so he basically ran the experiments in this model, you can condition, basically, you can give hints. And so he basically gave, you know, random hints to the model, basically, okay, you should bind to this residue, you should bind to the first residue, or you should bind to the 11th residue, or you should bind to the 21st residue, you know, basically every 10 residues scanning the entire antigen.Brandon [00:46:33]: Residues are the...Gabriel [00:46:34]: The amino acids. The amino acids, yeah. So the first amino acids. The 11 amino acids, and so on. So it's sort of like doing a scan, and then, you know, conditioning the model to predict all of them, and then looking at the confidence of the model in each of those cases and taking the top. And so it's sort of like a very somewhat crude way of doing kind of inference time search. But surprisingly, you know, for antibody-antigen prediction, it actually kind of helped quite a bit. And so there's some, you know, interesting ideas that, you know, obviously, as kind of developing the model, you say kind of, you know, wow. This is why would the model, you know, be so dumb. But, you know, it's very interesting. And that, you know, leads you to also kind of, you know, start thinking about, okay, how do I, can I do this, you know, not with this brute force, but, you know, in a smarter way.RJ [00:47:22]: And so we've also done a lot of work on that direction. And that speaks to, like, the, you know, the power of scoring. We're seeing that a lot. I'm sure we'll talk about it more when we talk about BullsGen. But, you know, our ability to, like, take a structure and determine that that structure is, like... Good. You know, like, somewhat accurate. Whether that's a single chain or, like, an interaction is a really powerful way of improving, you know, the models. Like, sort of like, you know, if you can sample a ton and you assume that, like, you know, if you sample enough, you're likely to have, like, you know, the good structure. Then it really just becomes a ranking problem. And, you know, now we're, you know, part of the inference time scaling that Gabby was talking about is very much that. It's like, you know, the more we sample, the more we, like, you know, the ranking model. The ranking model ends up finding something it really likes. And so I think our ability to get better at ranking, I think, is also what's going to enable sort of the next, you know, next big, big breakthroughs. Interesting.Brandon [00:48:17]: But I guess there's a, my understanding, there's a diffusion model and you generate some stuff and then you, I guess, it's just what you said, right? Then you rank it using a score and then you finally... And so, like, can you talk about those different parts? Yeah.Gabriel [00:48:34]: So, first of all, like, the... One of the critical kind of, you know, beliefs that we had, you know, also when we started working on Boltz 1 was sort of like the structure prediction models are somewhat, you know, our field version of some foundation models, you know, learning about kind of how proteins and other molecules interact. And then we can leverage that learning to do all sorts of other things. And so with Boltz 2, we leverage that learning to do affinity predictions. So understanding kind of, you know, if I give you this protein, this molecule. How tightly is that interaction? For Boltz 1, what we did was taking kind of that kind of foundation models and then fine tune it to predict kind of entire new proteins. And so the way basically that that works is sort of like instead of for the protein that you're designing, instead of fitting in an actual sequence, you fit in a set of blank tokens. And you train the models to, you know, predict both the structure of kind of that protein. The structure also, what the different amino acids of that proteins are. And so basically the way that Boltz 1 operates is that you feed a target protein that you may want to kind of bind to or, you know, another DNA, RNA. And then you feed the high level kind of design specification of, you know, what you want your new protein to be. For example, it could be like an antibody with a particular framework. It could be a peptide. It could be many other things. And that's with natural language or? And that's, you know, basically, you know, prompting. And we have kind of this sort of like spec that you specify. And, you know, you feed kind of this spec to the model. And then the model translates this into, you know, a set of, you know, tokens, a set of conditioning to the model, a set of, you know, blank tokens. And then, you know, basically the codes as part of the diffusion models, the codes. It's a new structure and a new sequence for your protein. And, you know, basically, then we take that. And as Jeremy was saying, we are trying to score it and, you know, how good of a binder it is to that original target.Brandon [00:50:51]: You're using basically Boltz to predict the folding and the affinity to that molecule. So and then that kind of gives you a score? Exactly.Gabriel [00:51:03]: So you use this model to predict the folding. And then you do two things. One is that you predict the structure and with something like Boltz2, and then you basically compare that structure with what the model predicted, what Boltz2 predicted. And this is sort of like in the field called consistency. It's basically you want to make sure that, you know, the structure that you're predicting is actually what you're trying to design. And that gives you a much better confidence that, you know, that's a good design. And so that's the first filtering. And the second filtering that we did as part of kind of the Boltz2 pipeline that was released is that we look at the confidence that the model has in the structure. Now, unfortunately, kind of going to your question of, you know, predicting affinity, unfortunately, confidence is not a very good predictor of affinity. And so one of the things that we've actually done a ton of progress, you know, since we released Boltz2.Brandon [00:52:03]: And kind of we have some new results that we are going to kind of announce soon is kind of, you know, the ability to get much better hit rates when instead of, you know, trying to rely on confidence of the model, we are actually directly trying to predict the affinity of that interaction. Okay. Just backing up a minute. So your diffusion model actually predicts not only the protein sequence, but also the folding of it. Exactly.Gabriel [00:52:32]: And actually, you can... One of the big different things that we did compared to other models in the space, and, you know, there were some papers that had already kind of done this before, but we really scaled it up was, you know, basically somewhat merging kind of the structure prediction and the sequence prediction into almost the same task. And so the way that Boltz2 works is that you are basically the only thing that you're doing is predicting the structure. So the only sort of... Supervision is we give you a supervision on the structure, but because the structure is atomic and, you know, the different amino acids have a different atomic composition, basically from the way that you place the atoms, we also understand not only kind of the structure that you wanted, but also the identity of the amino acid that, you know, the models believed was there. And so we've basically, instead of, you know, having these two supervision signals, you know, one discrete, one continuous. That somewhat, you know, don't interact well together. We sort of like build kind of like an encoding of, you know, sequences in structures that allows us to basically use exactly the same supervision signal that we were using to Boltz2 that, you know, you know, largely similar to what AlphaVol3 proposed, which is very scalable. And we can use that to design new proteins. Oh, interesting.RJ [00:53:58]: Maybe a quick shout out to Hannes Stark on our team who like did all this work. Yeah.Gabriel [00:54:04]: Yeah, that was a really cool idea. I mean, like looking at the paper and there's this is like encoding or you just add a bunch of, I guess, kind of atoms, which can be anything, and then they get sort of rearranged and then basically plopped on top of each other so that and then that encodes what the amino acid is. And there's sort of like a unique way of doing this. It was that was like such a really such a cool, fun idea.RJ [00:54:29]: I think that idea was had existed before. Yeah, there were a couple of papers.Gabriel [00:54:33]: Yeah, I had proposed this and and Hannes really took it to the large scale.Brandon [00:54:39]: In the paper, a lot of the paper for Boltz2Gen is dedicated to actually the validation of the model. In my opinion, all the people we basically talk about feel that this sort of like in the wet lab or whatever the appropriate, you know, sort of like in real world validation is the whole problem or not the whole problem, but a big giant part of the problem. So can you talk a little bit about the highlights? From there, that really because to me, the results are impressive, both from the perspective of the, you know, the model and also just the effort that went into the validation by a large team.Gabriel [00:55:18]: First of all, I think I should start saying is that both when we were at MIT and Thomas Yacolas and Regina Barzillai's lab, as well as at Boltz, you know, we are not a we're not a biolab and, you know, we are not a therapeutic company. And so to some extent, you know, we were first forced to, you know, look outside of, you know, our group, our team to do the experimental validation. One of the things that really, Hannes, in the team pioneer was the idea, OK, can we go not only to, you know, maybe a specific group and, you know, trying to find a specific system and, you know, maybe overfit a bit to that system and trying to validate. But how can we test this model? So. Across a very wide variety of different settings so that, you know, anyone in the field and, you know, printing design is, you know, such a kind of wide task with all sorts of different applications from therapeutic to, you know, biosensors and many others that, you know, so can we get a validation that is kind of goes across many different tasks? And so he basically put together, you know, I think it was something like, you know, 25 different. You know, academic and industry labs that committed to, you know, testing some of the designs from the model and some of this testing is still ongoing and, you know, giving results kind of back to us in exchange for, you know, hopefully getting some, you know, new great sequences for their task. And he was able to, you know, coordinate this, you know, very wide set of, you know, scientists and already in the paper, I think we. Shared results from, I think, eight to 10 different labs kind of showing results from, you know, designing peptides, designing to target, you know, ordered proteins, peptides targeting disordered proteins, which are results, you know, of designing proteins that bind to small molecules, which are results of, you know, designing nanobodies and across a wide variety of different targets. And so that's sort of like. That gave to the paper a lot of, you know, validation to the model, a lot of validation that was kind of wide.Brandon [00:57:39]: And so those would be therapeutics for those animals or are they relevant to humans as well? They're relevant to humans as well.Gabriel [00:57:45]: Obviously, you need to do some work into, quote unquote, humanizing them, making sure that, you know, they have the right characteristics to so they're not toxic to humans and so on.RJ [00:57:57]: There are some approved medicine in the market that are nanobodies. There's a general. General pattern, I think, in like in trying to design things that are smaller, you know, like it's easier to manufacture at the same time, like that comes with like potentially other challenges, like maybe a little bit less selectivity than like if you have something that has like more hands, you know, but the yeah, there's this big desire to, you know, try to design many proteins, nanobodies, small peptides, you know, that just are just great drug modalities.Brandon [00:58:27]: Okay. I think we were left off. We were talking about validation. Validation in the lab. And I was very excited about seeing like all the diverse validations that you've done. Can you go into some more detail about them? Yeah. Specific ones. Yeah.RJ [00:58:43]: The nanobody one. I think we did. What was it? 15 targets. Is that correct? 14. 14 targets. Testing. So we typically the way this works is like we make a lot of designs. All right. On the order of like tens of thousands. And then we like rank them and we pick like the top. And in this case, and was 15 right for each target and then we like measure sort of like the success rates, both like how many targets we were able to get a binder for and then also like more generally, like out of all of the binders that we designed, how many actually proved to be good binders. Some of the other ones I think involved like, yeah, like we had a cool one where there was a small molecule or design a protein that binds to it. That has a lot of like interesting applications, you know, for example. Like Gabri mentioned, like biosensing and things like that, which is pretty cool. We had a disordered protein, I think you mentioned also. And yeah, I think some of those were some of the highlights. Yeah.Gabriel [00:59:44]: So I would say that the way that we structure kind of some of those validations was on the one end, we have validations across a whole set of different problems that, you know, the biologists that we were working with came to us with. So we were trying to. For example, in some of the experiments, design peptides that would target the RACC, which is a target that is involved in metabolism. And we had, you know, a number of other applications where we were trying to design, you know, peptides or other modalities against some other therapeutic relevant targets. We designed some proteins to bind small molecules. And then some of the other testing that we did was really trying to get like a more broader sense. So how does the model work, especially when tested, you know, on somewhat generalization? So one of the things that, you know, we found with the field was that a lot of the validation, especially outside of the validation that was on specific problems, was done on targets that have a lot of, you know, known interactions in the training data. And so it's always a bit hard to understand, you know, how much are these models really just regurgitating kind of what they've seen or trying to imitate. What they've seen in the training data versus, you know, really be able to design new proteins. And so one of the experiments that we did was to take nine targets from the PDB, filtering to things where there is no known interaction in the PDB. So basically the model has never seen kind of this particular protein bound or a similar protein bound to another protein. So there is no way that. The model from its training set can sort of like say, okay, I'm just going to kind of tweak something and just imitate this particular kind of interaction. And so we took those nine proteins. We worked with adaptive CRO and basically tested, you know, 15 mini proteins and 15 nanobodies against each one of them. And the very cool thing that we saw was that on two thirds of those targets, we were able to, from this 15 design, get nanomolar binders, nanomolar, roughly speaking, just a measure of, you know, how strongly kind of the interaction is, roughly speaking, kind of like a nanomolar binder is approximately the kind of binding strength or binding that you need for a therapeutic. Yeah. So maybe switching directions a bit. Bolt's lab was just announced this week or was it last week? Yeah. This is like your. First, I guess, product, if that's if you want to call it that. Can you talk about what Bolt's lab is and yeah, you know, what you hope that people take away from this? Yeah.RJ [01:02:44]: You know, as we mentioned, like I think at the very beginning is the goal with the product has been to, you know, address what the models don't on their own. And there's largely sort of two categories there. I'll split it in three. The first one. It's one thing to predict, you know, a single interaction, for example, like a single structure. It's another to like, you know, very effectively search a space, a design space to produce something of value. What we found, like sort of building on this product is that there's a lot of steps involved, you know, in that there's certainly need to like, you know, accompany the user through, you know, one of those steps, for example, is like, you know, the creation of the target itself. You know, how do we make sure that the model has like a good enough understanding of the target? So we can like design something and there's all sorts of tricks, you know, that you can do to improve like a particular, you know, structure prediction. And so that's sort of like, you know, the first stage. And then there's like this stage of like, you know, designing and searching the space efficiently. You know, for something like BullsGen, for example, like you, you know, you design many things and then you rank them, for example, for small molecule process, a little bit more complicated. We actually need to also make sure that the molecules are synthesizable. And so the way we do that is that, you know, we have a generative model that learns. To use like appropriate building blocks such that, you know, it can design within a space that we know is like synthesizable. And so there's like, you know, this whole pipeline really of different models involved in being able to design a molecule. And so that's been sort of like the first thing we call them agents. We have a protein agent and we have a small molecule design agents. And that's really like at the core of like what powers, you know, the BullsLab platform.Brandon [01:04:22]: So these agents, are they like a language model wrapper or they're just like your models and you're just calling them agents? A lot. Yeah. Because they, they, they sort of perform a function on behalf of.RJ [01:04:33]: They're more of like a, you know, a recipe, if you wish. And I think we use that term sort of because of, you know, sort of the complex pipelining and automation, you know, that goes into like all this plumbing. So that's the first part of the product. The second part is the infrastructure. You know, we need to be able to do this at very large scale for any one, you know, group that's doing a design campaign. Let's say you're designing, you know, I'd say a hundred thousand possible candidates. Right. To find the good one that is, you know, a very large amount of compute, you know, for small molecules, it's on the order of like a few seconds per designs for proteins can be a bit longer. And so, you know, ideally you want to do that in parallel, otherwise it's going to take you weeks. And so, you know, we've put a lot of effort into like, you know, our ability to have a GPU fleet that allows any one user, you know, to be able to do this kind of like large parallel search.Brandon [01:05:23]: So you're amortizing the cost over your users. Exactly. Exactly.RJ [01:05:27]: And, you know, to some degree, like it's whether you. Use 10,000 GPUs for like, you know, a minute is the same cost as using, you know, one GPUs for God knows how long. Right. So you might as well try to parallelize if you can. So, you know, a lot of work has gone, has gone into that, making it very robust, you know, so that we can have like a lot of people on the platform doing that at the same time. And the third one is, is the interface and the interface comes in, in two shapes. One is in form of an API and that's, you know, really suited for companies that want to integrate, you know, these pipelines, these agents.RJ [01:06:01]: So we're already partnering with, you know, a few distributors, you know, that are gonna integrate our API. And then the second part is the user interface. And, you know, we, we've put a lot of thoughts also into that. And this is when I, I mentioned earlier, you know, this idea of like broadening the audience. That's kind of what the, the user interface is about. And we've built a lot of interesting features in it, you know, for example, for collaboration, you know, when you have like potentially multiple medicinal chemists or. We're going through the results and trying to pick out, okay, like what are the molecules that we're going to go and test in the lab? It's powerful for them to be able to, you know, for example, each provide their own ranking and then do consensus building. And so there's a lot of features around launching these large jobs, but also around like collaborating on analyzing the results that we try to solve, you know, with that part of the platform. So Bolt's lab is sort of a combination of these three objectives into like one, you know, sort of cohesive platform. Who is this accessible to? Everyone. You do need to request access today. We're still like, you know, sort of ramping up the usage, but anyone can request access. If you are an academic in particular, we, you know, we provide a fair amount of free credit so you can play with the platform. If you are a startup or biotech, you may also, you know, reach out and we'll typically like actually hop on a call just to like understand what you're trying to do and also provide a lot of free credit to get started. And of course, also with larger companies, we can deploy this platform in a more like secure environment. And so that's like more like customizing. You know, deals that we make, you know, with the partners, you know, and that's sort of the ethos of Bolt. I think this idea of like servicing everyone and not necessarily like going after just, you know, the really large enterprises. And that starts from the open source, but it's also, you know, a key design principle of the product itself.Gabriel [01:07:48]: One thing I was thinking about with regards to infrastructure, like in the LLM space, you know, the cost of a token has gone down by I think a factor of a thousand or so over the last three years, right? Yeah. And is it possible that like essentially you can exploit economies of scale and infrastructure that you can make it cheaper to run these things yourself than for any person to roll their own system? A hundred percent. Yeah.RJ [01:08:08]: I mean, we're already there, you know, like running Bolts on our platform, especially on a large screen is like considerably cheaper than it would probably take anyone to put the open source model out there and run it. And on top of the infrastructure, like one of the things that we've been working on is accelerating the models. So, you know. Our small molecule screening pipeline is 10x faster on Bolts Lab than it is in the open source, you know, and that's also part of like, you know, building a product, you know, of something that scales really well. And we really wanted to get to a point where like, you know, we could keep prices very low in a way that it would be a no-brainer, you know, to use Bolts through our platform.Gabriel [01:08:52]: How do you think about validation of your like agentic systems? Because, you know, as you were saying earlier. Like we're AlphaFold style models are really good at, let's say, monomeric, you know, proteins where you have, you know, co-evolution data. But now suddenly the whole point of this is to design something which doesn't have, you know, co-evolution data, something which is really novel. So now you're basically leaving the domain that you thought was, you know, that you know you are good at. So like, how do you validate that?RJ [01:09:22]: Yeah, I like every complete, but there's obviously, you know, a ton of computational metrics. That we rely on, but those are only take you so far. You really got to go to the lab, you know, and test, you know, okay, with this method A and this method B, how much better are we? You know, how much better is my, my hit rate? How stronger are my binders? Also, it's not just about hit rate. It's also about how good the binders are. And there's really like no way, nowhere around that. I think we're, you know, we've really ramped up the amount of experimental validation that we do so that we like really track progress, you know, as scientifically sound, you know. Yeah. As, as possible out of this, I think.Gabriel [01:10:00]: Yeah, no, I think, you know, one thing that is unique about us and maybe companies like us is that because we're not working on like maybe a couple of therapeutic pipelines where, you know, our validation would be focused on those. We, when we do an experimental validation, we try to test it across tens of targets. And so that on the one end, we can get a much more statistically significant result and, and really allows us to make progress. From the methodological side without being, you know, steered by, you know, overfitting on any one particular system. And of course we choose, you know, w

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Why do the people you thought knew you best stay silent—or worse, side with the person who hurt you? This secondary betrayal often cuts deeper than the narcissistic behavior itself. Switzerland friends insist on neutrality while your pain makes them uncomfortable. Flying monkeys carry your vulnerability straight back to your abuser. When you finally name what's happening and the people closest to you rush to minimize or report back, your nervous system doesn't just register disappointment—it registers danger. Tony walks through why "I don't want to take sides" isn't actually neutral, how flying monkeys weaponize your words, and the exhausting ping-pong match of trying to be understood by people who need not to understand you in order to feel safe themselves. In this episode, you'll learn: The critical difference between Switzerland friends (who neutralize) and flying monkeys (who expose)—and why both leave you questioning reality How narcissistic systems hijack co-regulation, making everyone responsible for stabilizing the most emotionally immature person in the room Why your body's response after sharing something vulnerable is better data than the words exchanged The five ways narcissists regulate their nervous systems through you: superiority, victimhood, being right, being admired, and being defended How to stop "auditioning for belief" and start choosing relationships that can actually hold emotional weight Drawing from over 20 years of couples therapy and thousands of real conversations, Tony offers a framework for recognizing when explanation has replaced connection—and why the most regulated thing you can say is simply, "I know what I experienced." Ready to stop offering your nervous system as a resource to people who won't protect it? Subscribe and share this episode with someone who needs to hear they're not crazy—they're waking up. 00:00 Introduction and Gratitude 00:37 Sales Pitch: Magnetic Marriage Course 05:37 Understanding Narcissistic Relationships 06:46 The Pain of Secondary Betrayal 07:44 Navigating Anger and Injustice 15:04 Switzerland Friends and Emotional Avoidance 22:03 Story Time: Ned, Steve, and Fran 30:01 Avoiding Accountability and Ownership 30:17 The Role of Flying Monkeys 30:32 Switzerland Friends vs. Flying Monkeys 30:57 Emotional Honesty in Unsafe Systems 31:17 The Futility of Over-Explaining 34:02 Adjusting Expectations and Setting Boundaries 34:42 Understanding and Managing Anger 35:28 Withdrawing the Need for Permission 36:23 Grieving What Won't Change 37:14 Recognizing Emotionally Safe Relationships 39:13 The Concept of Co-Regulation 39:55 Narcissistic Systems and Emotional Regulation 45:43 Interacting with Switzerland Friends and Flying Monkeys 54:46 Choosing Relationships That Hold Emotional Weight 55:41 Final Thoughts and Encouragement Get on the waitlist today for Tony's upcoming Magnetic Marriage live course! Head to https://tonyoverbay.com/magnetic If you are interested in joining Tony's private Facebook group for women in narcissistic or emotionally immature relationships of any type, please reach out to him at contact@tonyoverbay.com or through the form on the website, HTTP://www.tonyoverbay.com If you are a man interested in joining Tony's "Emotional Architects" group to learn how to better navigate your relationship with a narcissistic or emotionally immature partner or learn how to become more emotionally mature yourself, please reach out to Tony at contact@tonyoverbay.com or through the form on the website, HTTP:www.tonyoverbay.com

US gas projects tied to data centers surge, videogame stocks dive after Google unveils “Project Genie”, Belkin shuts down cloud services for most Wemo smart home devices. MP3 Please SUBSCRIBE HERE for free or get DTNS Live ad-free. A special thanks to all our supporters–without you, none of this would be possible. If you enjoyContinue reading "Reddit-Style Moltbook Emerges With OpenClaw AI Agents Interacting – DTH"

Watch the full video version on YouTube: https://youtube.com/@readysetblowpodcast?sub_confirmation=1   Podcast-favorite, Dean Gonzalez is back on the show! The boys have a hilarious, uncensored and sometimes heated conversation about the US military's dominance, acquiring Greenland, New Years resolutions, mental health and dealing with tough times, immigration and liberal hypocrisy, Minneapolis and the ICE crackdown, the Renee Good and Alex Pretti shootings, dealing with cops, good vs. bad policing, and the role of religion in government . After a long and somewhat confrontational conversation, the fellas close with much love and respect for each other and lighten things up with the weekly news   Every Thursday, the Ready Set Blow Podcast brings you real talk with comedians, actors, musicians, entertainers, entrepreneurs, and fascinating guests from all walks of life. No scripted BS. No playing it safe…Just raw, funny, and authentic conversations you won't hear on your average podcast.   If you enjoy comedy podcasts like Your Mom's House, Flagrant, The Joe Rogan Experience, or Theo Von, you'll love this show.   What We Talk About in This Episode: 00:00  Podcast Intro 01:00  The US Military and Capture of Nicolas Maduro 10:00  Acquiring Greenland 20:00  New Year's Resolutions 30:00  Mental Health & Dealing With Tough Times 45:00  Immigration and Liberal Hypocrisy 1:00:00  Minnesota and The ICE Crackdown 1:10:00  Renee Good & Alex Pretti Shootings 1:25:00  Interacting with Law Enforcement Officers 1:35:00  Good vs. Bad Policing 1:40:00  Religion in Government 1:43:00  The Weekly News   New Episodes Every Thursday:

Topics:  This Is The Day, Welcome To The Show AI, Interacting w/Good, If You Could Talk To God, National Days, Praying For Enemies, Double Stuffed Oreos, Brant's Father BONUS CONTENT: Double Stuffed Oreos Revisited, Radio Ink Award   Quotes "We pretty classy here." "It takes mental discipline to be this ignorant." "Am I a champion or not?" "We can choose to rejoice."

Interacting with listeners Answering trivia questions The Top 4 at 4:00

Welcome to the Parenting Well podcast with Parent Engagement Network!  I am Dr. Shelly Mahon, your host and today's well source is Dan Fox. Dan has spent over 25 years working with adolescents and their families as they navigate the ups and downs of growing up. He's been a high school teacher, summer camp director, school counselor, and the director of September High School—so he really understands teens from the inside out. As a Licensed Professional Counselor, Dan brings that experience into his work with families, grounded in the belief that there is hope for teens and real relief for parents. He works with adolescents, young adults, couples, and families, and supports schools and organizations through workshops and parent coaching as well. Dan also has a podcast called Therapy Dudes with Andre Karkamaz. They put the fun back in dysfunctional as they talk about how to navigate your inner and outer world to move forward in life. In this podcast, we talk about: The cumulative effect of anxiety on our nervous systems. Being attuned to our kids. Being intentional about our relationship with our children, including the tone we set with them. Interacting with your children differently as they move from childhood to adolescence. Fueling more than steering our teens. Strategies to regulate yourself - stay centered or recenter. Training ourselves to react to negative energy differently - not taking it personally. Handling situations that you feel have crossed the line. Repairing the relationship when things haven't gone as well as you would have liked. Owning your own stuff without making it transactional - i.e., expecting something from the other person. Resources: Website: Boulder Psychological Services Podcast: Therapy Dudes with Dan Fox and Andre Karkamaz 10 Annual Reducing Stress & Anxiety Conference: Fostering Resilience & Wellbeing at Every Stage of Parenting  

"Frequency and authenticity are the two biggest drivers of engagement in today's channels."In this episode of Politely Pushy, Eric Chemi connects with Nathan C Bowser, a product strategist, podcast host of "The Tech Glow Up," and founder of Awesome Future. Interacting with Fortune 1000 companies and interviewing 300+ technology leaders has fine-tuned Nathan's data-meets-creative approach to helping businesses and the innovative go-to-market leaders behind them succeed. Tune into this episode as Nathan shares their insights into an AI-everywhere marketplace, coaching teams on nailing singular metrics, and the power of customer storytelling.

Navin Chaddha of Mayfield joins Nick to discuss A 17 Time Midas Lister on Greatness, the $6T AI Teammate Market, Why AI Sovereignty Is Critical, and Who Wins the Battle Between Incumbents and Startups. In this episode we cover: Challenges in Early Internet Video Lessons from Interacting with Tech Luminaries Investment Philosophy and Evaluation Process The Role of Psychology in Venture Capital The AI Collaboration Era Geopolitical Implications of AI Investment Strategy in a Competitive Market Guest Links: Navin's LinkedIn Navin's X Mayfield's LinkedIn Mayfield's Website The host of The Full Ratchet is Nick Moran of New Stack Ventures, a venture capital firm committed to investing in founders outside of the Bay Area. We're proud to partner with Ramp, the modern finance automation platform. Book a demo and get $150—no strings attached.   Want to keep up to date with The Full Ratchet? Follow us on social. You can learn more about New Stack Ventures by visiting our LinkedIn and Twitter.