Podcasts about orchestrator

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

Sprache, Touch, Gesten, Haptik, KI-Assistenten, moderne Fahrzeuge entwickeln sich zunehmend zu multimodalen Systemen. Doch genau hier beginnt eines der größten Missverständnisse aktueller HMI-Entwicklung: Mehr Interaktionskanäle bedeuten nicht automatisch bessere User Experience. In dieser Folge des Mensch-Technik Podcast geht es um die eigentliche Herausforderung multimodaler Automotive HMIs: nicht Technologie zu demonstrieren, sondern echten Wert zu schaffen. Warum sind physische Stellteile trotz Digitalisierung weiterhin relevant? Weshalb schlägt Kontext fast immer Technologie? Welche Rolle wird KI künftig bei der Orchestrierung von Interaktion spielen? Und warum entsteht gute Multimodalität häufig durch Reduktion statt Addition? Die beste Modalität ist nicht die modernste, sondern diejenige, die in einer konkreten Situation den größten Wert für den Menschen erzeugt. Fünf zentrale Themen der Folge: 1. Multimodalität als Systemarchitektur statt Feature-Sammlung 2. Kontext statt Technologie-Fetischismus 3. The Revenge of the Analog im Fahrzeugcockpit 4. KI als adaptiver Orchestrator von Interaktion 5. Warum echte UX durch Vereinfachung entsteht Eine Folge über Technologie, menschliche Wahrnehmung und die Frage, wie sich Interaktion im Fahrzeug der Zukunft wirklich anfühlen sollte.

Was leistet der Außendienst im Jahr 2026 wirklich noch? Viele Teams halten an der klassischen Trennung von Außendienst und Innendienst fest, obwohl Kundinnen und Kunden längst hybride Kaufwege gehen. Wir schauen nüchtern hin: Wo stiftet Präsenz vor Ort echten Wert, und wo genügt Remote? Und welche Setups ersetzen starre Zuständigkeiten durch ein spielfähiges Team um das Buying Center herum? Mehr als zuhören: In der Community Vertrieb & Verkauf wird mitgedacht und mitdiskutiert. Kostenfrei beitreten: https://stephanheinrich.com/skool Die alte Trennung ist ortsbezogen, die moderne Trennung aufgabenbasiert. Qualifizieren, Verstehen, Beweisen, Verhandeln, Entscheiden begleiten. Außendienst wird zum Orchestrator für Menschen und Momente, in denen Nähe zählt.  Lokale Verfügbarkeit wirkt, wenn Risiko, Komplexität oder politische Gemengelage hoch sind. Vor Ort lassen sich nonverbale Signale lesen, Nebendialoge führen, Vertrauen verdichten. In Projekten zeigt sich oft: Ein guter Tag beim Kunden spart Wochen Mailverkehr. Alternativen zur Aufteilung von Innendienst und Außendienst: kleine Account Teams mit klaren Rollen. SDR und BDR für Erstkontakt, Account Executive für Dealführung, Presales für Machbarkeitsnachweis, Customer Success für Wirkung nach dem Kauf. Hybrider Außendienst: 80 Prozent remote, 20 Prozent fokussierte Besuche mit messbarem Ziel. Jeder Termin hat eine Hypothese, eine Agenda und einen nächsten Schritt. Datenbasiertes Routing: Wir reisen, wenn Kaufsignale da sind. Mehrere Entscheider aktiv, kritische Fragen im Raum, interne Deadline beim Kunden. Kein Pflichtbesuch, sondern ein Besuch mit Wirkung. Für Menschen mit technischem Hintergrund: erst verstehen, dann zeigen. Statt erklärungsbedürftig als Ausrede zu nutzen, präzise Erkundungsfragen stellen. Wer nur überzeugen will, wirkt belehrend. Haben Sie sich das schon einmal gefragt: Woran wird intern wirklich entschieden und wer sagt am Ende Ja? Regionale Nähe neu gedacht: kleine Fachtreffen beim Kunden, Werksführungen und Lunch & Learn mit Partnern. Außendienst als Gastgeber einer Fachcommunity im Gebiet. Der Begriff aussendienst mag alt klingen, der Nutzen bleibt, wenn er richtig eingesetzt wird. Hier können Sie sich einen für Sie passenden Gesprächstermin auswählen: https://stephanheinrich.com/trainingsanfrage/

AI is starting to touch your editing workflow whether you're ready for it or not, and in this episode, we sit with that tension you're probably feeling right now, part curiosity, part hesitation, part “do I really need to learn this too?” We talk through what AI editing actually looks like today, from tools that can add captions and visuals to the idea of handing off entire workflows, and where that still falls short when it comes to shaping a real listening experience. The PMC cast and crew get honest about what happens when you rely too much on automation, how easy it is to start sounding like everyone else, and why your judgment still matters more than any tool you plug in. Here's the thing, you know that moment when you wonder if skipping the effort might cost you something you can't quite name, that's what we keep coming back to. By the end, you might not feel settled about where this is all going, but you'll have a clearer sense of what's yours to hold onto as everything around it starts to change.Episode Highlights:[01:58] Show Updates and Evaluations[04:22] AI Editing Teaser[05:47] Claude as Orchestrator[09:09] Tool Limits vs. Claude Code[11:35] Imagination and Creativity[12:21] What Is a Preset[14:56] Audience Tips and Presets[19:31] Best AI Editing Tasks[24:10] Spotlight Clip Promo[25:54] Community Spotlight Pitch[26:29] Will Editors Disappear?[27:51] AI Makes Us Dumber?[31:08] Cookie-Cutter Podcasting[34:12] Cognitive Offloading Explained[38:13] Education and Reading Decline[40:22] Human Connection Still Matters[42:47] Bots Taking Over Content[44:06] Nuance Over Doomerism[46:51] AI Editing: Key TakeawaysLinks & Resources: The Loud Quiet Podcast:podcastingmorningchat.com/loudquietGet a Podcast Evaluation from the Podcasting Morning Chat:https://podcastingmorningchat.com/evalGet Your Podcast Featured on our Show:podcastingmorningchat.com/spotlight"How I Fully Automated My Video Editing (Claude Code)":https://www.youtube.com/watch?v=G0EH0xdy2-EThe Podcasting Morning Chat: ⁠⁠www.podcastingmorningchat.com⁠⁠Ways to Watch or Listen:⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠https://www.podcastingmorningchat.com/joinus/Meet the PMC Cast and Crew:⁠⁠https://podcastingmorningchat.com/people⁠⁠Join The Empowered Podcasting Facebook Group:⁠⁠www.facebook.com/groups/empoweredpodcasting⁠⁠⁠Book A Free Call With Marc: https://calendly.com/ironickmedia/freestrategycallJoin us every other Monday at 7 AM ET for the Obsession Worthy Podcasts:⁠⁠⁠http://podcastingmorningchat.com/owp/⁠⁠Join us LIVE every weekday morning at 7 am ET (US) on ⁠Clubhouse⁠: ⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠https://podcastingmorningchat.com/clubhouse⁠⁠EPC3 Speaker Application:⁠⁠ ⁠https://empoweredpodcasting.com/speakersPowered by⁠⁠⁠ ⁠iRonickMedia.com⁠⁠⁠⁠ and⁠ ⁠ContentCreatorsAccountant.com⁠⁠Send in your mailbag questions:⁠⁠⁠⁠ https://www.podcastingmorningchat.contact/⁠⁠⁠⁠ or ⁠marc@ironickmedia.com⁠Want to be a guest on The Podcasting Morning Chat? Send me a message on PodMatch, here: https://podmatch.com/hostdetailpreview/1729879899384520035bad21b

On this episode of Talking Shop we are joined by Nicolaas Kroone, Co-founder and Creative Director of Bloom Studios. As retailers struggle to feed an "infinite" hunger for content, Nicolaas is helping brands move beyond the bottleneck of traditional production. We discuss the rise of "logistic-free shoots," why every product needs a photorealistic digital twin, and how marketing managers are shifting from being on-set directors to orchestrators of AI systems. We also explore how to protect a brand's visual soul in a world of automation and why the "human touch" is still the only thing an algorithm can't replace.

In this episode of “On the Aisle,” host Tom Alvarez Zooms in with Polish‑American Broadway music director and pianist Eugene Gwozdz, tracing his journey from a musical immigrant family in Fort Worth, Texas, to becoming a go‑to collaborator for stars like Betty Buckley, Chita Rivera, Ariana DeBose, and more. Eugene shares wild behind‑the‑scenes stories about helping Buckley learn “Memory,” sight‑reading early workshops of In the Heights, building tribute concerts at Casa Mañana, and shaping new musicals from a Dolly Parton show to a Spanish‑flu piece he composed while waiting in the hospital for a heart transplant. With dishy name‑drops, emotional “pinch me” moments, and a powerful message about following your dreams no matter what, this conversation is a masterclass in resilience, artistry, and the unexpected paths a life in music can take.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Frank Vitagliano, CEO of the Global Technology Distribution Council Every few years, someone announces the end of distribution. Direct sales was going to kill it. Then e-commerce. Then cloud. Then hyperscaler marketplaces. And yet here we are. Frank Vitagliano is CEO of the Global Technology Distribution Council, the industry consortium representing 21 of the world’s leading technology distributors, collectively responsible for more than $180 billion in annual IT sales. He spent more than 30 years in the channel as a vendor executive at IBM, Juniper Networks, and Dell – where he served as VP of Global Distribution Sales and Strategy – and as president and CEO of solution provider Computex Technology Solutions, before taking the helm at GTDC in 2019. In this episode, Vitagliano talks about why distribution keeps enduring through waves of disruption that should, on paper, have displaced it. His framing: distribution has evolved from what he calls “bank and warehouse” into the orchestrator of the IT ecosystem – the entity that connects vendors, solution providers, and end users in ways that no single vendor or hyperscaler marketplace can replicate on its own. He also gets into what distribution’s digital platform investments actually change – including GTDC’s recent research showing that 86% of suppliers are using or evaluating digital platforms – and why Vitagliano believes AI-enabled opportunity identification is “the game changer” that will define distribution’s next chapter. Vitagliano also draws on his vendor-side experience to explain what he wasn’t getting from distribution at Dell, and why platforms and data are finally closing that gap. This episode pairs with our solo essay on why reports of distribution’s demise have always been overstated. Read Full Transcript Robert Dutt: Hello and welcome to In The Channel from ChannelBuzz.ca, bringing news and information to the Canadian IT channel for the last 16 years. I’m Robert Dutt, editor of ChannelBuzz.ca and your host for the show. If you caught my recent solo episode on why reports of distribution’s demise have always been overstated, you know this is a topic I’ve been thinking about a lot lately. Distribution has survived every wave of disruption the IT industry has thrown at it – direct sales, e-commerce, cloud, marketplaces – and it keeps evolving. I want to explore that further, and I couldn’t think of a better person to do that than my guest today. Frank Vitagliano is CEO of the Global Technology Distribution Council, the industry consortium representing 21 of the world’s leading technology distributors, collectively responsible for more than $180 billion in annual IT sales. Frank has been in the channel for more than 30 years, holding senior executive roles at IBM, Juniper Networks, and Dell, where he served as VP of Global Distribution Sales and Strategy. He’s also been on the solution provider side as president and CEO of Computex Technology Solutions. He’s a member of the IT Industry Hall of Fame, and he’s one of the few people who’s seen distribution from every angle – vendor, solution provider, and the advocacy side. We talk about why distribution keeps enduring, what it actually does that’s harder to replicate than it looks from the outside, how digital platforms are fundamentally changing the distributor’s role, and what the next chapter looks like. Let’s get right into it. My chat with Frank Vitagliano. [Music] Robert Dutt: Frank, thanks so much for taking the time. It’s nice to catch up with you. Frank Vitagliano: Hi, Rob. Good to see you again. Robert Dutt: I guess to throw it open – you’ve changed roles since last we spoke, and I know you’re well-established at GTDC now, but because it’s an industry organization for distribution, can you tell us a little bit about the mandate of GTDC and what you guys are focused on over there? Frank Vitagliano: Absolutely. So GTDC has been around for quite some time. It stands for Global Technology Distribution Council – it kind of doesn’t exactly roll off your tongue – and it was established more than 20 years ago by some of the major distributors. Initially, the idea was to educate the financial community on the role and value of IT distributors, and this was before they were all public companies. There were a lot of questions about what do they do, how do they do it, why are they needed. For the first five or six years, GTDC focused on educating that group. After most of them went public, it wasn’t really necessary, because companies certainly did a better job of talking to the financial community themselves through earnings calls. So we pivoted to focusing on the supplier community and educating them on the role and value of distribution. You might think, why does the supplier community need to be educated on distribution? A couple of reasons. One, as you know, Rob, every day there are more players coming into the space, particularly in the cybersecurity area. You can’t count the number of cybersecurity vendors out there, and typically they’re all started by and run by very smart technical folks who really don’t know that much about taking a product to market. So that’s one reason. And the second group that continually needs a refresher is the broader vendor community, because most vendors rotate their people through a number of jobs. You start in direct sales, then you move to channel, and you don’t really understand distribution because it tends to be the least well understood aspect of channels. A lot of people view it as a cost element rather than a value driver. We’ve got about 21 members globally representing more than $180 billion of IT sales annually, so all of the major distributors are part of GTDC. We run three big conferences a year – one in Europe, one in Asia, one in North America. We do a lot with research and content – we had a particularly good year last year and released four strong papers on topics important to the vendor community. I do a podcast series every three weeks with members of the ecosystem. And we have global relationships with major data companies – IDC in North America, Context and Nielsen IQ GFK in Europe – where we collect POS data from distributors weekly and compile it into reports that the vendor community uses to track market share and pricing. So those are the major aspects of who we are and what we do. Robert Dutt: You sit in an interesting place. You’ve done the vendor side with Dell, IBM, Juniper. You ran a solution provider business at Computex. And now you lead the organization that represents and promotes the world’s largest distributors. Pretty rare trifecta. I’m curious how each of those vantage points shaped the way you think about distribution’s role today. Frank Vitagliano: That’s an excellent question, and it was actually one I had to answer when I first took the role at GTDC, because most of the folks previously involved in running the organization had worked in distribution, and I never have. But I had the advantage of working extraordinarily closely with distributors over the years. Back in my IBM days – and this is way in the way-back machine – I was involved in the early days of authorizing distributors to sell IBM PCs. It goes back that far. I understood early on the value they provided in getting a vendor’s product to market. Back then, you could argue it was a bank and a warehouse, which is how a lot of people still think about distribution. But it continued to evolve from a bank and a warehouse to a support mechanism that included pre-sale, post-sale, and solution support, to where it is today – a completely digitized route to market that I think orchestrates the IT ecosystem, because distribution sits right in the middle of it. Upstream is the vendor community, which I view as partners with distribution, and downstream are the customers, the solution providers. The second thing you mentioned is that I spent a couple of years running a solution provider, so I was a customer of distribution, not just a partner. It’s really something to have that perspective, because I thought I knew a lot about what distribution did, but I learned a lot more as a customer. And the last thing I’ll mention is that I have watched distribution evolve over the last 40 years – from a bank and a warehouse to what they are today. It’s incredible what they’ve been able to do to keep pace with technological changes and changes in how people buy technology. And they’ve continued to do it while maintaining the core function that a lot of people still consider critically important: help me get my product to market. That evolution has made me genuinely passionate about what they do and how they’ve done it. And it’s continuing now as they evolve into this new digital world with platforms and AI. Robert Dutt: In the time that I’ve covered the channel space, there’s always been a case being made for “well, this is finally the end of distribution” – it was direct sales, e-commerce, cloud, marketplaces, and yet here we are. You’ve touched on the nature of distribution’s evolution. What was the key through-line in that? What is it that distribution was able to do that allowed it to adapt through those waves of change? Frank Vitagliano: I think it’s one thing primarily. They listened very well to their customers – the solution providers – in terms of what they needed, and they listened and collaborated very well with the suppliers. At the end of the day, that’s the most important aspect of what they’ve been able to do. As the technology shifted – from hardware to services to SaaS, to the changing business models in terms of how products are delivered – they’ve been able to watch the evolution, watch the requirements, and adapt. The platforms that are now being built started probably six or seven years ago with very significant investments in an environment where, as you know, it’s a tight margin model. We’re not talking about hundreds of millions of extra dollars for investment. But they started making these major investments because they saw the requirements, and their customers pulled them in that direction. Another great example is that four or five years ago, the supplier community started pushing consumption models. And distributors have done an amazing job of combining their core capabilities – what they’ve been doing well for 40 years – with investments in key areas that keep them relevant. As for the disintermediation discussion, we’ve been hearing that forever. We heard it with the transition from hardware to software to SaaS. We heard it in the cloud transition. Now we hear it with hyperscalers. And the reality is it hasn’t happened. It won’t happen. You can go look at the earnings of the major distributors in 2025 and say, well, that certainly isn’t a business being disintermediated. That’s a business that’s thriving. And the secret is they’ve done an excellent job of understanding what’s required with their core constituency – the vendor community upstream and their customers downstream. Robert Dutt: One thing I’d add, maybe more from a solution provider point of view, is that a lot of the disintermediation predictions were tending to describe distribution as a transaction – the bank and the warehouse, a single point in the supply chain that can be removed. But there’s more to distribution than that transaction. There’s the ecosystem side – the way distribution has made itself stickier through things like partner communities. Can you talk about what distribution does that’s harder to replicate than simply having a bank and a warehouse? Frank Vitagliano: Absolutely. About three or four years ago, we started talking about distribution as the orchestrator of the ecosystem. You can look at that and say, okay, that’s a catchy marketing term, but what does it really mean? What it means is that there needs to be somebody within the IT ecosystem that connects all the pieces, and distribution is the logical point for that. When I do presentations about what distribution does, I can put up a chart with an enormous number of activities, and typically people’s eyes start to glaze over. But what I tell people is: these are all the activities that are part of getting a solution – not a product, but a solution – from the suppliers involved. And typically there are four or five vendors in every solution. It isn’t as simple as putting a PC on someone’s desk anymore. It’s way more comprehensive when you include all the technology, including the whole cybersecurity conversation. Distribution has the ability to do that, and has been doing it in a multi-vendor world since day one. Then when you look at the customer side – and this is the piece I really learned when I became a customer of distribution – there’s a whole set of capabilities around end user experience. Whether it’s managing the myriad of subscriptions that are out there, the typical solution provider today – whether it’s an MSP, a professional services organization, or the hybrid organization that I ran, with hardware, managed services, and professional services all combined – when they look around for who can help them, distribution becomes the natural spot. Vendors are very focused on training solution providers to sell their specific product, not necessarily to sell a complete solution. So partners turn to distribution for that, including now for AI guidance – how do I deal with it, what areas should I focus on, how do I train my people, how do I educate my customers. And then you add the communities. Distributors support partner communities that let a small solution provider in Canada, for example, punch way above their weight – accessing capabilities they couldn’t afford on their own because the distributor and the community members are supporting them. Really big deal. Robert Dutt: Let’s talk about some of the recent research. Your report found that 86% of suppliers are either using or actively evaluating digital platforms for transactions, lifecycle management, and analytics. That feels like a pretty fundamental shift in what distribution even looks like day to day. What does that transition mean in practice, and what does a distributor’s digital platform need to do that a vendor or a marketplace can’t? Frank Vitagliano: There are three things it does – and they’re well on the way to doing all three, although the third one is still a bit in transition. The first is that the platform provides an incredible productivity enhancement to just doing business. The basics – getting a quote, getting an order through the system, figuring out the right solution. There are studies showing that in some cases what used to take four hours is now taking 30 to 40 minutes. The productivity gains are significant enough on their own that vendors who have committed and built the appropriate API integrations will tell you it’s worth the engagement for that reason alone. The second thing is that distributor marketplaces are multi-vendor marketplaces. You don’t get that when you’re dealing directly with a vendor – you get their marketplace. Maybe some ancillary support, but it’s not a true multi-vendor, compatibility-tested marketplace. It’s the same with the hyperscalers. Hyperscalers have done a fabulous job and collaborate very well with distribution, but at the end of the day, a hyperscaler is a direct vendor – the same category as an IBM, a Juniper, a Dell. They have more capabilities than a single-vendor marketplace, but they still can’t match the breadth of a distributor marketplace when it comes to multi-vendor compatibility testing, subscription management, training, and services. So it’s not instead of – it’s in addition to. Hyperscaler marketplace, great. Vendor marketplace, great. Distributor marketplace can provide all of that plus. The third thing – which I believe will ultimately be the big differentiator – is this: if you ask any solution provider what the number one thing is they want from distribution, they tend to overlook the basics distribution already provides and say: I want distributors to help me drive sales. I want them to help me identify and close opportunities. AI-enabled opportunity identification, based on years and years of history and data, is providing that in ways that weren’t possible five years ago, two years ago. When you can go to a partner and say: three years ago you sold this configuration to these customers, and those customers now need an upgrade or new security features – here’s the customer, here’s the opportunity, here’s how we can help you close it – that’s the game changer. And it’s starting to happen. Robert Dutt: You hit on a key point there, which is the scale piece. Everything they do, they do at scale. And one of the reasons some of what distribution does gets taken for granted is they’ve been doing it for a long time at scale extraordinarily efficiently – because with a one to two percent net margin model, you don’t have a choice. Frank Vitagliano: Exactly. You can’t run a one to two percent net business and not be able to do it efficiently at scale. The margins aren’t big enough to survive any other way. That’s right. Robert Dutt: We’ve talked about the changing nature of distribution over the decades. When you were on the vendor side building distribution strategies, what did you expect or want from distributors that you weren’t always getting? And has anything changed about what vendors should be expecting today versus ten years ago? Frank Vitagliano: Back then, I was getting the basics – no debate there. What I wasn’t getting, and what I think we now have the opportunity to get, is leverage from data. I’ve been working with distributors for 40 years, so they’ve got 40 years of information. And in many cases, the distributors knew the end users that product was going to – it would go through a solution provider but ship directly to an end user. All that data existed, but we were never able to harness it to get what I needed for opportunity identification. I got inside sales support, both technical and to some degree sales. I got outside sales teams supporting opportunities. But I wasn’t getting the insights I really needed to figure out how to grow to the next level. I was getting the support I needed. I wasn’t getting the sales capabilities I needed. That was always the gap. So when we’d design programs and allocate MDF dollars to distribution, I’d fund inside sales heads focused on IBM, Juniper, or Dell. But the outcome I was looking for was increased volumes, increased opportunities, increased sales. I took for granted that if I brought distribution an opportunity, they’d support it flawlessly and at scale – no question. But I wanted them to bring me opportunities. And I’m not saying it didn’t happen, because it did – but it wasn’t completely consistent or transparent. It required effort. What’s happening now with AI-supported platforms, you have the ability to do that in a way that wasn’t possible before. Smart people are designing these platforms in conjunction with the supplier community and customers, and it’s happening. That’s a huge deal. Robert Dutt: One of the things I keep thinking about with distribution in a country like Canada is that it plays differently here. Part of that is the geography – huge country, relatively few major centres spread throughout. But there’s also Canadian-dollar credit, local peculiarities in terms of language – you have to parler français to be successful in the Quebec market – and different regulatory considerations. How do you see distribution’s role being amplified in markets like Canada versus larger, more centralized markets like the US? Frank Vitagliano: That’s one of the real values the global distributors provide – they have experience dealing with markets beyond their home territory, and they bring that cross-border understanding wherever they operate. One of the papers we did last year talked about how to build a distribution strategy for the vendor community. If you’re a supplier thinking about Canada, what do you think about? How many distributors do I need – one, three, twelve? We go through this in the paper, which is available on our website. You think about the TAM of the market, the geography, where you need warehousing to reach major markets effectively. You think about whether you need global players, what combination of local players – because every market has what we call local heroes who are really strong within their community. And then domain specialists – do you need a cybersecurity-focused distributor in addition to your broadline relationships? A lot of vendors think the more distributors they have, the more business they’ll generate. That’s not true. More distributors fighting with each other becomes a cost issue for the vendor and a margin issue for everyone. Building a thoughtful distribution strategy is genuinely important – and the nuances of a market like Canada are exactly the kind of thing that gets missed by vendors who don’t think it through. Fortunately, it’s all laid out in the paper for those who want the framework. Robert Dutt: My last question. We’ve talked about being at the early stages of the platform evolution and where it has the potential to go. If we’re sitting here again in five years, what does distribution look like? And what should partners and vendors be paying attention to right now? Frank Vitagliano: Good question. When I first took the job at GTDC in 2019, right before the pandemic, I was hearing all this discussion about disintermediation – hyperscalers were coming into play, cloud was a big deal, SaaS transition was accelerating. I took the job thinking: I don’t believe distribution is going to get disintermediated, but let’s go do a study. So we did Distribution 2025 – a five-year view of what we thought would happen. It turned out to be pretty accurate. A big part of that was validated by the pandemic, which made the value of distribution extraordinarily obvious. At the beginning of 2025 we did Distribution 2030, and we laid out what we see ahead. Clearly, a fully functioning AI-supported platform will be in place in five years. Not 86% of vendors – every vendor will be utilizing it. There will be major cybersecurity enhancements, and major opportunity identification scenarios where vendors will really begin to get that level of sales support from distribution with the existing basics still covered. I also see the enhancements creating stickiness. Today, any customer can work with any authorized distributor. The question is: how do you make your services sticky enough that they become primarily focused on one or two distributors rather than spreading everything around? Same on the supplier side – vendors will narrow to a smaller subset of distributors who can provide the level of service and platform integration they need. I don’t think it ever becomes exclusive, because most folks will view that as too risky. But it’ll certainly be a lot more thoughtful than “let me just sign up as many distributors as I can.” And I see potential for additional consolidation in the distributor market, particularly in Europe and Asia – not so much in North America, where five or six distributors already represent 90% of the business. But globally, I think we’ll see that over the next few years. Robert Dutt: Fun to have a talk about the ever-evolving nature of distribution. I appreciate your time, Frank. Frank Vitagliano: Rob, thank you for the platform. I love talking about it, as you can probably sense, because I’m genuinely passionate about what they do. I think distributors have done an excellent job over the years of doing what they needed to do to support existing business, while also making the investments required to ensure they weren’t left behind – they weren’t the Blockbuster or the Kodak of the IT space. You could argue that could have happened. But it isn’t happening. And it goes back to the number one point I made: they’re listening to their customers and their suppliers. Robert Dutt: It certainly has not happened. Frank, thank you. Enjoy the talk. Frank Vitagliano: Thanks, Rob. [Music] Robert Dutt: There you have it. Frank Vitagliano from the Global Technology Distribution Council. I’d like to thank Frank for his time and a really candid conversation. It’s not every day you get someone who sat on the vendor side, the solution provider side, and the industry body side, willing to talk openly about what distribution gets right, what it hasn’t always gotten right, and where it’s headed. A couple of things that stood out to me. Frank’s point about distribution evolving from what he called a bank and a warehouse into an ecosystem orchestrator – that’s not just branding. When you think about what it takes to stitch together multi-vendor solutions, handle consumption models, manage renewals, and now use AI to identify opportunities that individual vendors and solution providers can’t see on their own, that’s a fundamentally different value proposition than moving boxes and extending credit. And his candor about what he wasn’t getting from distribution when he was on the vendor side at Dell, and how platforms and data are finally closing that gap – I thought that was a really honest moment. The other thing I’d flag is his point about stickiness – the idea that vendors are going to narrow their distribution relationships and go deeper with fewer partners. That has real implications for solution providers too, in terms of which distributors they’re aligned with and what platforms they’re investing in. If you haven’t already, check out my solo episode on why distribution endures. It sets up a lot of the themes Frank and I explored here, and the two episodes work well together. If you’re enjoying In The Channel, I’d appreciate it if you’d follow or subscribe. You can find us on Apple Podcasts, Spotify, YouTube, and most podcast directories. And if you have a minute to leave a rating or a review, that goes a long way to helping other people in the channel community find the show. Until next time, I’m Robert Dutt for ChannelBuzz.ca, and I’ll see you in the channel.

In dieser spannenden Podcast-Episode spreche ich mit Andreas Decker, CPO der Solvd Group und Managing Director bei ControlExpert, über die Herausforderungen und Chancen der KI in der Kfz-Schadenbearbeitung. Andreas gibt uns tiefe Einblicke, warum viele interne KI-Projekte scheitern und wie radikale Spezialisierung der Schlüssel zum Erfolg ist. Hier sind die Highlights, die ich für dich zusammengefasst habe: Das Dilemma der Inhouse-KI: Wir sprechen darüber, warum viele Versicherer glauben, die komplette KI-Automatisierung im Schadenbereich selbst stemmen zu können, obwohl sie oft mit einem "digitalen Flickenteppich" aus Legacy-Systemen und zugekauften SaaS-Lösungen kämpfen. Andreas betont, dass ein echtes KI-Team weit über ein paar Data Scientists hinausgeht und eine komplette Organisation benötigt. Beschwerde-Explosion und fehlende Antworten: Ich frage Andreas, ob der Anstieg der Beschwerdezahlen beim Versicherungsombudsmann ein Indikator für die mangelnde Umsetzung von Technologie ist. Es zeigt sich, dass der häufigste Beschwerdegrund nicht etwa falsche Entscheidungen, sondern schlicht die fehlende oder verzögerte Kommunikation ist – ein Paradox in Zeiten, in denen jeder über KI und Kundenzentrierung spricht. Warum LLMs allein nicht reichen: Andreas erklärt eindrücklich, dass große Sprachmodelle wie "sehr gute Uni-Absolventen" sind – mit viel Wissen, aber ohne spezifische Praxiserfahrung und Kontext. Mit einer Genauigkeit von nur 54% bei versicherungsspezifischen Aufgaben sind sie unzureichend. Er beleuchtet die Notwendigkeit von spezialisierten Modellen, Data Factories und professionellem Operations-Management. Das "Schadendreieck knacken": ControlExpert hat sich zum Ziel gesetzt, das klassische Schadendreieck aus Kosten, Aufwand und Kundenzufriedenheit zu durchbrechen. Der Ansatz basiert auf drei Säulen: fundierte Fachkenntnis in der Kfz-Reparatur, exzellenter Kundenservice (24/7, multikanal, multilinguale, sofortige Status-Updates) und nahtlose Automatisierung durch neueste Technologie – alles orchestriert durch Agents. Ein spannendes Beispiel ist die User Experience, bei der eine künstliche Wartezeit die Akzeptanz erhöht hat! "Own the Brain, Rent the Body": Andreas plädiert für einen strategischen Ansatz: Kernelemente, die einen echten Wettbewerbsvorteil bringen (wie Vertrieb oder Risikomodelle), sollten intern entwickelt werden. Bereiche, wo Spezialisierung und Skalierung entscheidend sind (wie die Kfz-Schadenbearbeitung), sollten über externe Partner bezogen werden. Er betont zudem, dass jede Transformation zu 50% aus Technologie und zu 50% aus Change Management besteht. Dieses Gespräch hat mir wirklich die Augen geöffnet, wie komplex und gleichzeitig vielversprechend die Zukunft der Schadenbearbeitung ist. Hör dir die ganze Episode an, um Andreas' faszinierende Prognosen für die nächsten Jahre zu erfahren und zu verstehen, wie wir uns von repetitiven Aufgaben verabschieden und eine neue Ära der Effizienz und Kundenzufriedenheit einläuten können! Links in dieser Ausgabe Zur Homepage von Jonas Piela Zum LinkedIn-Profil von Jonas Piela Zum LinkedIn-Profil von Andreas Decker Stich aus der Masse hervor. Werde die Marke, die jeder Entscheider kennt. Echte Relevanz entsteht nicht durch Reichweite, sondern durch Vertrauen. Wir bringen deine Botschaft dorthin, wo die Zukunft gebaut wird. Vom Sponsored Podcast für maximale Awarenes über das Fachmagazin für echtes Print-Prestige bis hin zum direkten Lead bei unseren Boutique-Events. Klick jetzt direkt auf diesen Link oder geh auf insurancemedia.de/werbung.

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Vertrauen trifft Technologie: Wie sich das Maklergeschäft neu erfindet Wie verändert Digitalisierung das Immobilien- und Maklergeschäft – und was bleibt trotz KI, Plattformen und Social Media unverzichtbar? In dieser Episode von Sound of Finance sprechen wir mit Martin Englert, Geschäftsführer der LBS Immobilien GmbH Nordwest, über den tiefgreifenden Wandel im Immobilien-, Makler- und Baugeschäft. Aus seiner langjährigen Erfahrung gibt er fundierte Einblicke in aktuelle Markttrends, neue Kundenerwartungen und die Zukunft des Maklerberufs. Im Mittelpunkt steht ein scheinbarer Gegensatz: Während Technologie, digitale Plattformen und Künstliche Intelligenz Prozesse beschleunigen und neue Möglichkeiten eröffnen, bleibt Vertrauen der zentrale Erfolgsfaktor im Maklergeschäft. Wir diskutieren, wie sich das Geschäft von einem lokal geprägten, persönlichen Modell hin zu einem digitalen, komplexen System entwickelt hat – ohne seine menschliche Essenz zu verlieren. Weitere Schwerpunkte der Folge: • Warum digitale Sichtbarkeit heute Pflicht und kein Wettbewerbsvorteil mehr ist • Welche Rolle soziale Medien für Reichweite, Marke und Vertrauen spielen • Wie KI Effizienz hebt und die Beratung unterstützt – aber nicht ersetzt • Warum Kunden besser informiert, gleichzeitig aber oft stärker verunsichert sind • Weshalb der Makler der Zukunft immer mehr zum Orchestrator eines gesamten Ökosystems wird Zum Abschluss werfen wir einen Blick nach vorn: Welche Marktverschiebungen sind zu erwarten? Welche Kompetenzen werden künftig entscheidend? Und warum persönlicher Kontakt auch im digitalen Zeitalter unverzichtbar bleibt. Jetzt reinhören und erfahren, wie Technologie und Menschlichkeit gemeinsam die Zukunft der Immobilienbranche gestalten.

Most independent musicians think about ownership the wrong way.They think it means not signing to a major. They think it means keeping their masters. And sure, those things matter. But The Orchestrator — Denver-based jazz saxophonist, Guinness World Record holder, sold-out rooms from the Bluebird to Meow Wolf — is operating at a different level of that conversation. For him, ownership isn't a stance. It's infrastructure.“I can take all my music off Spotify if I want to. I can go sell it. I could throw it in the trash. I can never perform again. I own it. No one can exploit or benefit off of me in any way, shape or form without my explicit permission.”That's not a philosophy. That's a legal and financial position he built deliberately over years.What makes that possible isn't just owning your masters. It's understanding that the music business has multiple revenue streams running in parallel, and most artists only collect from one or two. This is not beyond your comprehension, it's simple administrative work that often gets overlooked. Regardless of the amount, keep in mind that if your music is being streamed, you're likely leaving that money to sit in the account of collection agencies. And, it is possible that this money, if left unclaimed for long enough, will end up paid to other parties. “People just don't know these things.”He's not bitter about it. He's just clear-eyed. The system isn't designed to walk you through this. So most artists don't find out until they're already leaving money on the table — or until someone in the room asks if they have a set list and they don't know why.His message is simple: you made the time to make the music, make sure you are in line to receive the fruits of that work as they arrive.He saw it happen. Someone played Red Rocks. Incredible. And had no idea what they were leaving behind when the show was over.If this is your first time here, ProducerHead is a podcast and publication for independent musicians who think seriously about the work. Subscribe free below.From Episode: 037. Building Complete Creative Independence | feat. The Orchestrator Get full access to ProducerHead at producerhead.substack.com/subscribe

In this episode, Jack Cochran welcomes back Manisha Raisinghani, Founder and CEO of SiftHub, exactly one year after her first appearance on the show. Together they explore the dramatic shift in how AI is being used by solutions engineers. Manisha shares what it means for SEs to become Forward Deployed Engineers, how vibe coding is changing live customer interactions, and what enterprise leaders are getting wrong about AI adoption. She also offers concrete first steps for SEs and SE leaders who feel like they're behind. Thank you to SiftHub for sponsoring this episode! Visit sifthub.io to learn more. Follow Us Connect with Jack Cochran: https://www.linkedin.com/in/jackcochran/ Connect with Manisha Raisinghani: https://www.linkedin.com/in/manisha-raisinghani/ Links and Resources Mentioned Join Presales Collective Slack: https://www.presalescollective.com/slack SiftHub: https://sifthub.io/ Sol/Con (PSC Annual Conference, Chicago): https://www.presalescollective.com/solcon-2026  Paytm (referenced in board discussion): https://paytm.com Key Topics Covered How AI Shifted from Co-Pilot to Workflow Orchestrator Vibe Coding and the Forward Deployed Engineer Democratizing Institutional Knowledge AI Transformation at Enterprise Scale Making SE Value Visible with Data Where to Start if You Feel Behind Timestamps 00:00 Welcome & Intro 03:58 One year later: the headline shift in AI and SE workflows 07:20 Vibe coding — what it is and why SEs should care 10:23 The rise of the Forward Deployed Engineer 14:01 How SiftHub has evolved: from retrieval to reasoning 20:23 Lessons from the Paytm board on enterprise AI adoption 24:43 Sales: the most blamed, least understood function 28:40 First steps for SEs and SE leaders who feel behind  

Ann Marcuson and Elliot Mackenzie are playing Mrs Winthrop and Dickon in The Secret Garden at York Theatre Royal.Marking the first major UK staging the musical in over a decade, John Doyle is directing this actor-musician production and working alongside Catherine Jayes as Musical Supervisor and Orchestrator.Ann and Elliot are reuniting onstage for this production, having previously performed together in the West End cast of The Curious Case of Benjamin Button which won the 2025 Olivier Award for Best New Musical.Ann's theatre credits include: These Demons (Theatre503), Two Ladies (Bridge Theatre), The Mighty Walzer (Royal Exchange Theatre), The Curious Incident of the Dog in the Night-Time (National Theatre/Gielgud Theatre), The Family Reunion (Donmar Warehouse), I Dreamt I Dwelt in Marble Halls (Watermill Theatre/Tricycle Theatre), Bloodhound, The Glass Slipper (Northern Stage), Little Wolf's Book of Badness and Hard Love (Hampstead Theatre).Elliot's theatre credits include: Lord of the Rings, Whistle Down the Wind (Watermill Theatre), Beautiful: The Carole King Musical (UK tour), Million Dollar Quartet (UK tour/India tour), The Misadventures Of Pinocchio, Dick Whittington, Aladdin (Queen's Theatre Hornchurch), Zog (Rose Theatre Kingston/UK tour) and A Christmas Carol (Rose Theatre Kingston).In this episode, Ann and Elliot discuss what it has been like to work on The Secret Garden, the art of actor-musicianship and their whirlwind journey with Benjamin Button.The Secret Garden runs at York Theatre Royal until 4th April. Visit www.yorktheatreroyal.co.uk for info and tickets.This podcast is hosted by Andrew Tomlins @AndrewTomlins32 Thanks for listening! Email: andrew@westendframe.co.uk Visit westendframe.co.uk for more info about our podcasts. Hosted on Acast. See acast.com/privacy for more information.

Sadie St. Lawrence joins me to unpack her concept of the "AI Orchestrator," explaining how it shifts our mindset from being a musician to a conductor in the age of AI. She shares insights from her work at the Human-Machine Collaboration Institute (HMCI), detailing how her team is building AI-powered solutions and tackling complex problems. We also chat about the common pitfalls in AI adoption, from unfounded fears to "work slop," and why foundational systems thinking remains paramount.

Dani Howard is originally from Hong Kong but she's now a renowned British composer and orchestrator. Her works have been performed by the London Symphony, BBC Symphony and Royal Philharmonic among others. She won a Royal Philharmonic Society Award for her Trombone Concerto. Her debut album features the Royal Liverpool Philharmonic and trombonist Peter Moore. Dani was Resident Artist with the National Youth Orchestra of Great Britain. And she was recently appointed as “Celebrated Composer” with the Bournemouth Symphony Orchestra.My featured song is my recent release, “Ma Petite Fleur String Quartet”. Spotify link.—-----------------------------------------------------------The Follow Your Dream Podcast:Top 1% of all podcasts with Listeners in 200 countries!Click here for All Episodes Click here for Guest List Click here for Guest Groupings Click here for Guest TestimonialsClick here to Subscribe Click here to receive our Email UpdatesClick here to Rate and Review the podcast—----------------------------------------CONNECT WITH DANI:www.danihoward.com—----------------------------------------ROBERT'S LATEST RELEASE:“MA PETITE FLEUR STRING QUARTET” is Robert's latest release. It transforms his jazz ballad into a lush classical string quartet piece. Praised by a host of classical music stars.CLICK HERE FOR YOUTUBE LINKCLICK HERE FOR ALL LINKS—---------------------------------------ROBERT'S RECENT SINGLE“MI CACHIMBER” is Robert's recent single. It's Robert's tribute to his father who played the trumpet and loved Latin music.. Featuring world class guest artists Benny Benack III and Dave Smith on flugelhornCLICK HERE FOR YOUTUBE LINKCLICK HERE FOR ALL LINKS—--------------------------------------ROBERT'S LATEST ALBUM:“WHAT'S UP!” is Robert's latest compilation album. Featuring 10 of his recent singles including all the ones listed below. Instrumentals and vocals. Jazz, Rock, Pop and Fusion. “My best work so far. (Robert)”CLICK HERE FOR THE OFFICIAL VIDEOCLICK HERE FOR ALL LINKS—----------------------------------------Audio production:Jimmy RavenscroftKymera Films Connect with the Follow Your Dream Podcast:Website - www.followyourdreampodcast.comEmail Robert - robert@followyourdreampodcast.com Follow Robert's band, Project Grand Slam, and his music:Website - www.projectgrandslam.comYouTubeSpotify MusicApple MusicEmail - pgs@projectgrandslam.com  

Spending more time fixing your AI outputs then you're saving? You're not alone. The trap? You're in operator mode. Falling for the industry status quo like upskilling and human-in-the-loop. The real winners in the AI race? Companies that have changed the human-AI relationship. How? Join us for Volume 4 of our Start Here Series as we uncover what you need to know. Human-AI Collaboration: Best practices for working alongside AI -- An Everyday AI Chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:Human-AI Collaboration Best Practices 2026Shift from Operator to Orchestrator RolesHuman-in-the-Loop Limitations ExplainedExpert-Driven AI Review Loops vs. Generic OversightOrchestrating AI Agents for Business ProductivityBuilding Reusable AI Context and SkillsElevating AI Champions on TeamHuman Strengths vs. AI Strengths in WorkflowsAvoiding Augmentation Debt and Workflow PitfallsMindset Shifts for Effective AI ManagementTimestamps:00:00 "Everyday AI: Start Here"03:23 "AI Shift: Operator to Orchestrator"06:35 "Unlearn to Harness AI"11:15 "AI Surpassing Human Collaboration"15:11 Expert-Driven AI Process Loops18:10 "Expert Collaboration Boosts AI ROI"23:59 "Outsmarting AI Through Expertise"26:30 "Navigating AI Success Strategies"31:19 "Embrace AI, Elevate Your Team"32:18 "Embrace AI, Elevate Humanity"Keywords: Human-AI collaboration, AI best practices, working alongside AI, human-AI relationship, AI orchestration, AI orchestrator, shift from operator to orchestrator, agentic workflows, AI agents, digital agents, expert-driven loops, expert oversight, senior partners with AI, context engineering, AI processes, context vaults, AI skills files, company data, chain of thought review, large language models, AI-powered workflows, AI expertise, AI in business, AI productivity, AI risk management, human in the loop, upskilling, reskilling, unlearning, AI mindset shift, augmented intelligence, multi-agent systems, AI automation, organization AI strategy, context quality, AI champion, domain experts, AI team integration, competitive advantage with AI, process redesign for AI, AI-powered decision making, accountability in AI, empathy in AI, ambiguous decision-making, novel judgment.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Ready for ROI on GenAI? Go to youreverydayai.com/partner 

פרק מספר 510 של רברס עם פלטפורמה, שהוקלט ב-6 בינואר 2026. אורי ורן מקליטים בכרכור ומארחים את טל (מאזין ותיק!) מחברת Rhino Federated Computing לשיחה על עולם של חישוב מבוזר, פרטיות רפואית, הצפנות הומומורפיות ונוסטלגיה ל-SETI@home (ולא AI! טוב, גם…).

In this episode, Chandra and Paul kick off the new year with a discussion about JD Edwards' mobile app strategy. They address a frequently asked question: why doesn't JD Edwards offer more mobile apps? Paul explains that JD Edwards shifted from providing a suite of mobile apps to an "enablement strategy," allowing customers to build their own fit-for-purpose mobile solutions using tools like Orchestrator and Oracle Visual Builder. The hosts highlight the continued availability of a PO Approval mobile app and encourage listeners to submit ideas for new apps via Oracle's enhancement request process. 04:16 JDE Shifts to Enablement Strategy 09:31 Streamlining Integration Through Orchestrator 10:35 The One Mobile App JDE has 12:11 Learn JDE Mobile Platform and Solutions 14:00 PO Approvals in the Product Catalog 17:40 Midwesternism Resources Learn JDE – Mobile Platform and Solutions: https://docs.oracle.com/cd/E84502_01/learnjde/mobile.html JD Edwards EnterpriseOne Mobile Strategy: https://support.oracle.com/epmos/faces/DocumentDisplay?_afrLoop=392297712248224&id=2652952.1&_afrWindowMode=0&_adf.ctrlstate=wjgjptbmd_4 Getting Started with Oracle Visual Builder and Orchestrator for JD Edwards: https://mylearn.oracle.com/ou/learning-path/getting-started-with-visual-builder-and-orchestrator-for-jd-edwards/128274 PO Approval Mobile Application: https://apexapps.oracle.com/pls/apex/f?p=103254:8:121656864344537::NO::P8_FEATURE_ID,P8_PRODUCT_ID,P8_LAYER_ID:263789653039579542459653088815394116340,208136276869697198410960926144257606832,208126803266041475109735961320172569774 Convert PO Approval Mobile Progressive Web Application (PWA) to Web PWA App: https://apexapps.oracle.com/pls/apex/f?p=103254:8:121656864344537::NO::P8_FEATURE_ID,P8_PRODUCT_ID,P8_LAYER_ID:77131914658374572604531825412304839676,208136276869697198410960926144257606832,208126803266041475109735961320172569774

Surojit Chatterjee is CEO of Ema, an agent platform build AI employees. They have raised $61M in funding from Accel, Section 32, and others. Before Ema, he was the chief product officer at Coinbase. And before that, a VP at Google. Surojit's favorite book: Man's Search for Meaning (Author: Viktor Frankl)(00:01) Welcome(00:07) Defining the “AI Employee”(02:23) Lessons from Google: Building for Scale(06:59) Coinbase CPO: Hypergrowth & Product Leadership(09:24) Market Framing: Why “AI Employee” vs Copilot(14:29) Platform Building Blocks (Agents, Orchestrator, Fusion, Governance)(19:26) Trust, Security, and On-Prem Deployment(23:11) Model of Models: How Fusion Picks & Combines LLMs(29:10) What Infra Is Still Missing (Eval at Scale, Speed)(32:10) Rapid Fire Round--------Where to find Surojit Chatterjee: LinkedIn: https://www.linkedin.com/in/surojitchatterjee/--------Where to find Prateek Joshi: Website: https://prateekj.com Research Column: https://www.infrastartups.comLinkedIn: https://www.linkedin.com/in/prateek-joshi-infiniteX: https://x.com/prateekj

Larry Blank is a prolific Composer, Conductor and Orchestrator across Theatre, TV and Film. He is a 3x Tony Nominee and a 6x Drama Desk nominee. His works include orchestrations of Catch Me If You Can, White Christmas, Fiddler On The Roof and La Cage Aux Folles. He was co-orchestrator for the stage and film versions of The Producers, and additional TV credits include The Grammy Awards and The Academy Awards.My featured song is “New York City Groove” from the album Made In New York by my band Project Grand Slam. Spotify link.—-----------------------------------------------------------The Follow Your Dream Podcast:Top 1% of all podcasts with Listeners in 200 countries!Click here for All Episodes Click here for Guest List Click here for Guest Groupings Click here for Guest TestimonialsClick here to Subscribe Click here to receive our Email UpdatesClick here to Rate and Review the podcastClick here for Robert's “Dream Inspire” App—----------------------------------------CONNECT WITH LARRY:www.larryblankmusic.com—----------------------------------------ROBERT'S NEW “DREAM INSPIRE” APPYour personalized Coach to Motivate, Pursue and Succeed at Your DreamCLICK HERE—----------------------------------------ROBERT'S LATEST SINGLE:“MI CACHIMBER” is Robert's latest single. It's Robert's tribute to his father who played the trumpet and loved Latin music.. Featuring world class guest artists Benny Benack III and Dave Smith on flugelhornCLICK HERE FOR YOUTUBE LINKCLICK HERE FOR ALL LINKS—--------------------------------------ROBERT'S LATEST ALBUM:“WHAT'S UP!” is Robert's latest compilation album. Featuring 10 of his recent singles including all the ones listed below. Instrumentals and vocals. Jazz, Rock, Pop and Fusion. “My best work so far. (Robert)”CLICK HERE FOR THE OFFICIAL VIDEOCLICK HERE FOR ALL LINKS—----------------------------------------Audio production:Jimmy RavenscroftKymera Films Connect with the Follow Your Dream Podcast:Website - www.followyourdreampodcast.comEmail Robert - robert@followyourdreampodcast.com Follow Robert's band, Project Grand Slam, and his music:Website - www.projectgrandslam.comYouTubeSpotify MusicApple MusicEmail - pgs@projectgrandslam.com  

Deliver the win, then ring the bell. Make small mistakes fast; make big learnings faster. Think global, act local — but don't go native. Do the nemawashi before the meeting, not during it. Your salary is earned in the stores: go to the gemba. A 28-year Domino's veteran, Martin Steenks began at 16 as a delivery expert in the Netherlands. He rose to store manager, multi-unit supervisor, then franchisee, building his operation to eight stores by 2019. After selling his stores, he became Head of Operations for Domino's Netherlands, then CEO of Domino's Taiwan in 2021, and subsequently CEO of Domino's Japan. Previously he was Chief Orchestrator in Japan, focusing on operational excellence, culture, and scalable execution in one of Domino's most exacting service markets. He is known for hands-on store work, cross-training, "Friday F-Up" learning rituals, the Grow & Prosper bell for micro-wins, and quarterly "Go Gemba" days that connect HQ functions with frontline realities.  Martin Steenks' leadership arc runs from a three-minute job interview at 16 to orchestrating Domino's Japan — one of the brand's most demanding markets for service quality. The connective tissue is execution discipline: he has run stores, supervised regions, built and exited an eight-store franchise, owned national operations, and led two country P&Ls. That breadth gives him pragmatic empathy for franchisees and HQ alike, which he leverages to align incentives, simplify operations, and insist that every back-office salary is ultimately "earned in the stores." Japan sharpened his leadership. Coming from low-context, fast-moving Dutch and Australian business styles into high-context Japan, he learned that meetings signalling agreement can still stall without prior nemawashi — the groundwork with middle management and other stakeholders. He now invests in pre-alignment, translating intent into culturally legible action: fewer big-room debates, more quiet lobbying, more ringi-sho style consensus building for irreversible decisions, and a clear bias to test-and-learn for reversible ones. Rather than trying to "change the culture," he adjusted himself — becoming more patient while preserving speed by separating decision types and sequencing alignment before action. His operating system is human and tangible. He set a weekly rhythm of learning with a "Friday F-Up" session, where leaders share mistakes and what was learned — a radical move in a high uncertainty-avoidance culture. He celebrates micro-wins with the Grow & Prosper bell to make progress visible, sustaining morale during long transformations. He bridged HQ–store gaps with Go Gemba: each quarter, every function works a store shift; IT discovers why a workflow fails at the point of sale, marketing sees campaign friction at Friday night peak, finance hears cost-to-serve realities. He personally worked in stores four to five days a month, especially during crunch periods like Christmas, leading by example and rebuilding trust through competence. Marketing localisation is equally pragmatic. Deep discounting can signal poor quality to Japanese consumers; "customer appreciation weeks" preserve value perception while rewarding loyalty. Community building is pushed to the store level — managers engage local clubs and schools to turn footfall into fandom. Cross-training makes delivery experts confident product explainers at the door, restoring a human touch in a world where >90% of orders arrive online. Ultimately, Steenks' playbook blended cultural fluency with decision intelligence. He aligned stakeholders through nemawashi, codified learning rituals, chose language and campaigns that respected local signals, and keeps strategy tethered to the edge where pizzas are made, boxed, and delivered hot. The title "Chief Orchestrator" wasn't just whimsy; in a business of many specialists, he conducted tempo, harmony, and timing — the difference between noise and music.  What makes leadership in Japan unique? Japan's high service standards and high-context communication demand leaders who are both exacting and empathetic. Success depends on pre-work: nemawashi with middle managers, thoughtful ringi-sho style consensus for high-impact choices, and visible demonstrations of respect for the frontline. Uniforms (like Domino's iconic race jacket for store managers) and rituals create shared identity that motivates in a group-oriented culture. Why do global executives struggle? Low-context leaders often misread meeting "yeses" as commitment. Without groundwork, nothing moves. Impatience backfires in high uncertainty-avoidance environments; public criticism shuts people down. Leaders must separate reversible from irreversible decisions, secure alignment offline, and then move decisively. They should also avoid copy-pasting global marketing: in Japan, steep discounts can be read as "lower quality," eroding trust. Is Japan truly risk-averse? Japan is less risk-loving than many markets, but teams will take smart risks when safety and learning are explicit. Stanks normalises small, fast experiments, celebrates micro-wins, and protects people when bets misfire. This reframes risk as controlled uncertainty with upside — a shift from avoidance to improvement. What leadership style actually works? Lead from the front and the shop floor. Work stores every month. Tie HQ metrics to store impact. Use rituals — Friday F-Up, the Grow & Prosper bell — to institutionalise learning and momentum. Celebrate teams more than individuals, and praise privately when cultural norms warrant it. Think global, act local, but don't "go native": retain an outsider's clarity about pace and standards. How can technology help? Digital tools amplify decision intelligence when paired with gemba reality. Store-level dashboards, route optimisation, and digital twins of peak-hour operations can test scenarios before rollouts; telemetry from ovens, makelines, and delivery routes can reveal bottlenecks that nemawashi then resolves across functions. Tech should reduce operational complexity, not add it. Does language proficiency matter? Fluency helps, but intent matters more. Demonstrating effort — basic greetings, store-floor Japanese, and culturally aware email etiquette — earns trust. Tools that translate bidirectionally unlock participation, but leaders still need to read context and invest time with the middle layer. What's the ultimate leadership lesson? Do the cultural homework, orchestrate alignment before action, and keep your hands in the dough — literally. When people see you respect their craft, protect their learning, and tie strategy to execution, they'll go all-in. Timecoded Summary [00:00] Origin story: hired at 16 as a delivery expert in the Netherlands; stayed through school; first — and only — job interview; early leadership as store manager, then multi-unit supervisor. [05:20] Entrepreneurship chapter: buys a struggling store; builds to eight locations with his wife's support; sells in 2019 to become Head of Operations for the Netherlands, trading entrepreneurial freedom for strategic impact. [12:45] Asia leadership: becomes CEO Taiwan in 2021, then moves to Japan; discovers that despite common Domino's DNA, markets differ; Japan's service bar is the highest. [18:10] Cultural recalibration: early meetings show apparent agreement but slow follow-through; learns nemawashi and middle-layer alignment; patience becomes a leadership muscle; adopts "Chief Orchestrator" title to reflect cross-functional reality. [24:00] Store-first operating system: cross-training (makeline ↔ delivery ↔ service); >90% of orders online makes the delivery interaction critical; community outreach by store managers; hands-on leadership with 4–5 store days per month and peak-period shifts. [31:30] Learning rituals: Friday F-Up meeting reframes failure as fuel; Grow & Prosper bell celebrates micro-wins to sustain momentum; public recognition calibrated to cultural comfort; Domino's manager jacket signals identity and pride in Japan. [38:05] Marketing localisation: avoid pure discounting (quality signal risk); position as "customer appreciation"; test premium, limited campaigns; keep operations simple for peak. [43:20] Bridging HQ and field: quarterly Go Gemba embeds IT/Finance/HR/Marketing in stores; internal surveys (anonymous) surface issues; visible follow-through flips scepticism to trust. [49:40] Leadership philosophy: lead by example, protect experimenters, separate reversible vs irreversible decisions, and use decision intelligence (telemetry, digital twins) to derisk change while moving faster. Author Credentials Dr. Greg Story, Ph.D. in Japanese Decision-Making, is President of Dale Carnegie Tokyo Training and Adjunct Professor at Griffith University. He is a two-time winner of the Dale Carnegie "One Carnegie Award" (2018, 2021) and recipient of the Griffith University Business School Outstanding Alumnus Award (2012). As a Dale Carnegie Master Trainer, Greg is certified to deliver globally across all leadership, communication, sales, and presentation programs, including Leadership Training for Results. He has written several books, including three best-sellers — Japan Business Mastery, Japan Sales Mastery, and Japan Presentations Mastery — along with Japan Leadership Mastery and How to Stop Wasting Money on Training. His works have also been translated into Japanese, including Za Eigyō (ザ営業), Purezen no Tatsujin (プレゼンの達人), Torēningu de Okane o Muda ni Suru no wa Yamemashō (トレーニングでお金を無駄にするのはやめましょう), and Gendaiban "Hito o Ugokasu" Rīdā (現代版「人を動かす」リーダー). In addition to his books, Greg publishes daily blogs on LinkedIn, Facebook, and Twitter, offering practical insights on leadership, communication, and Japanese business culture. He is also the host of six weekly podcasts, including The Leadership Japan Series, The Sales Japan Series, The Presentations Japan Series, Japan Business Mastery, and Japan's Top Business Interviews. On YouTube, he produces three weekly shows — The Cutting Edge Japan Business Show, Japan Business Mastery, and Japan's Top Business Interviews — which have become leading resources for executives seeking strategies for success in Japan. 

How to Dream, Drive, and Deliver Plan D: How Disruptors Turn Problems into Possibilities Disruptors aren't pyromaniacs—they're builders. Mike Maddock has launched seven companies, advised Fortune 100 innovators, and co-chaired the Gathering of Titans at MIT. In this episode, we unpack his Plan D: the mindsets, rooms, and questions that turn pressure into breakthroughs—and why your "ghost" might be your greatest edge. Key Points + Reflection Questions: See possibilities where others see problems. Disruptors reframe first, act second. Reflect: Where am I labeling something a "problem" that might be a doorway? Engineer the room. Great peer groups have diverse lenses: Operator, Strategist, Rainmaker, Visionary, Futurist, Orchestrator. If your room is lopsided, your answers will be too. Reflect: Which lens is missing on my leadership team? Outlaw advice; share experience. "Here's what I did, what happened, and what I'd do differently" preserves agency and truth. Reflect: What experience can I share instead of advice? Accountability by consent. Ask, "Do you want to be held accountable?" before forcing metrics. Reflect: Where am I imposing accountability before readiness? Your ghost is fuel—know it. The chip on your shoulder can be an edge if you name it and measure it. Reflect: What ghost is chasing me—and how do I chase it instead? Decision frameworks that travel. Use a simple 2×2 (Value vs. Cost) and the value-rank approach for hard, competing-values calls. Reflect: Which 2×2 priorities am I acting on this quarter? Ask better questions (1-3-1). One issue, three options, one recommendation—or one core question, three leading questions, one most important question. Reflect: What's the single most leveraged question I can ask today? Money Learning: Wealth isn't a number; it's the ability to do what you want, when you want, with whom you want—without being distracted by money. Many entrepreneurs can make money but still obsess over it. Systems create freedom; obsession steals it. Key Takeaway: Engineer your environment and questions; the right room + the right prompts turn pressure into possibility. Guest Bio: Mike Maddock is an entrepreneur, author, and coach known as the "Idea Monkey" for his boundless creativity and disruptive thinking. He has founded seven companies—including Maddock Douglas, an innovation consultancy that served more than a quarter of the Fortune 100, and Flourish Advisory Boards, a peer community for growth-minded CEOs. A contributor to Forbes and Inc., and author of four books including Plan D, Mike helps leaders reframe problems into possibilities and rediscover the joy of doing what they want, when they want, with whom they want—without being distracted by money. Links: www.mike-maddock.com www.FlourishAdvisoryBoards.com www.maddockdouglas.com LinkedIn: https://www.linkedin.com/in/gmichaelmaddock/ Podcast: Your Seat at the Table Want help building a leadership "room" around your Profit? Book a Profit Assessment and we'll align your numbers, seats, and systems. #Entrepreneurship #Leadership #Innovation #PlanD #IdeaMonkey #BusinessGrowth #RicherSoul #WealthWithPurpose #PeerGroups #GrowthMindset Watch the full episode on YouTube: https://www.youtube.com/@richersoul Richer Soul Life Beyond Money. You got rich, now what? Let's talk about your journey to more a purposeful, intentional, amazing life. Where are you going to go and how are you going to get there? Let's figure that out together. At the core is the financial well-being to be able to do what you want, when you want, how you want. It's about personal freedom! Thanks for listening! Show Sponsor: http://profitcomesfirst.com/ Schedule your free no obligation call: https://bookme.name/rockyl/lite/intro-appointment-15-minutes If you like the show please leave a review on iTunes: http://bit.do/richersoul https://www.facebook.com/richersoul http://richersoul.com/ rocky@richersoul.com Some music provided by Junan from Junan Podcast Any financial advice is for educational purposes only and you should consult with an expert for your specific needs.

Digital Shelf Institute's Lauren Livak Gilbert joins us in the future: By 2030, successful commerce professionals will function as orchestrators rather than specialists. This transformation requires organizations to shift from hierarchical pyramid structures to more dynamic, amoeba-like models that can pivot rapidly to solve problems and capitalize on opportunities. P.S. Listen to part two of this conversation on the Digital Shelf Institute podcast. Catch Digital Shelf Institute's report, Reinventing the Organization for Omnichannel Success.“It Depends.” (And “Culture Means Everything”)Key takeaways:Stop asking where ecommerce sits - The right question is how to fundamentally change how organizations work to match how consumers actually shop across all channelsCommerce leaders must become orchestrators - Future success requires professionals who can coordinate across sales, marketing, supply chain, and customer experience rather than operating in functional silosAI enables strategic thinking - By automating remedial tasks like content creation and competitive analysis, AI frees up human talent for higher-value strategic work and cross-functional collaborationChief Growth Officers represent true omnichannel leadership - Unlike Chief Digital Officers (which were transitional roles), CGOs who own the entire consumer journey represent the permanent future of commerce organizationJoint business planning must integrate all functions - Brands showing up as one unified company to retail partners will become essential for maintaining competitive advantage and accessing new opportunities[00:16:39] "We shouldn't be asking, ‘where does ecommerce sit?' We should be asking, ‘how are we fundamentally changing how we work to match the way that the consumer shops?'" -- Lauren[00:12:01] "Instead of being a digital marketer and like a traditional marketer, you're just a marketer who understands digital in store and every single other channel, including social commerce that you're seeing your consumer shop at." -- Lauren[00:23:47] "The true definition of omnichannel is having a leader who is accountable for the entire consumer journey. That's what a Chief Growth Officer is." -- Lauren[00:42:46] "Because commerce is culture." -- LaurenAssociated Links:Check out Future Commerce on YouTubeCheck out Future Commerce+ for exclusive content and save on merch and printSubscribe to Insiders and The Senses to read more about what we are witnessing in the commerce worldListen to our other episodes of Future CommerceHave any questions or comments about the show? Let us know on futurecommerce.com, or reach out to us on Twitter, Facebook, Instagram, or LinkedIn. We love hearing from our listeners! Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Doug Besterman is a much in-demand orchestrator, arranger, and composer whose works span Broadway, film, television, and concert stages. Doug's been nominated for the Tony Award six times, winning for The Producers, Thoroughly Modern Millie, and Fosse. With a career that includes numerous other iconic productions such as Young Frankenstein, Sister Act, and Anastasia, Doug has helped shape the sound of modern musical theater. Recent Broadway credits include Death Becomes Her, SMASH, and BOOP! Internationally, his work has been heard in London, Berlin, and Hamburg in productions such as Rocky, Sister Act, Charlie and the Chocolate Factory, and Guys and Dolls.Doug's film and TV orchestration credits include Mary Poppins Returns, the live action films of Aladdin, Beauty and the Beast, and The Little Mermaid, Chicago, Frozen,Smash, and Schmigadoon. He's contributed to countless live broadcasts and awards shows, including the Oscars, Tonys, Emmys, and Kennedy Center Honors.As a composer, Doug has written scores for Breathe, Little Did I Know, The Big One-Oh, and the new musical Crumbs. His arrangements have been performed by extraordinary artists ranging from Barbra Streisand to Beyoncé, and by ensembles including the Boston Pops and the U.S. Military Academy Band. Doug continues to champion musical storytelling across all media, including through the Arrival Arts Initiative.Doug's Website: https://dougbesterman.com/Facebook: https://www.facebook.com/DougBestermanMusic/Instagram: https://www.instagram.com/dougbesterman/ Arrival Arts:Website: https://www.arrivalartsinitiative.org/Facebook: https://www.facebook.com/profile.php?id=61577010220015Instagram: https://www.instagram.com/arrival_arts_initiative/  

In this episode, hosts Chandra and Paul chat with Nick Velharticky, Principal Tech Support Engineer at Oracle, about the "Learn JDE" resource and how it can benefit JD Edwards users. The conversation dives into the differences between the Oracle Knowledge Management (MOS) site and LearnJDE, likening LearnJDE to a mall or IKEA for JDE knowledge—great for exploring new features, releases, and tutorials, not just troubleshooting. He demonstrates how users can compare releases, access rich resources like tutorials and technical papers, and use LearnJDE for building business cases for upgrades. The episode rounds out with insights on the power of Orchestrator for automation, strong community culture in JD Edwards. 07:56 Introduction to Nick Velharticky 12:39 The Power of Curiosity 23:33 LearnJDE vs. Knowledge Garden: Doctor's Office or Shopping Mall? 26:20 LearnJDE landing page overview 29:09 Release Comparison Tool 32:31 Integrated Tools 35:35 Platform Certifications 37:10 Learning Paths and Tutorials 40:33 Orchestrator Resources 52:08 Premiere 10-Year Support Commitment 54:00 Midwesternism of the Day Resources: LearnJDE: https://learnjde.com/ If you have concerns or feedback on this episode or ideas for future episodes, please contact us at thejdeconnection@questoraclecommunity.org

In this episode, hosts Chandra and Paul dive into all the details about the upcoming INFOCUS conference, taking place September 9–11 in Denver. They walk listeners through the event agenda, highlighting everything from roadmaps and educational sessions to special interest group (SIG) meetings, hands-on workshops, and networking opportunities. Chandra and Paul emphasize the technical depth and practical value of the sessions, especially for those interested in JD Edwards innovations like Orchestrator, automation, upgrades, and AI integrations. Along the way, they call out must-attend presentations, fun social events, and a bit of friendly banter about party planning and conference “bling.” The episode is packed with tips for making the most of INFOCUS, and wraps up with encouragement to get involved—whether it's through asking questions, attending SIGs, or just enjoying the community spirit. 02:20 The fun begins Monday evening 03:20 Tuesday Sessions 09:55 Wednesday Sessions 18:59 Thursday Sessions Resources: Please check the INFOCUS app for the latest up-to-date schedule. Quest Oracle Community: https://questoraclecommunity.org/events/conferences/infocus/ If you have concerns or feedback on this episode or ideas for future episodes, please contact us at thejdeconnection@questoraclecommunity.org

Send us a textYou're significant—but you're not the Savior.In this humbling and hope-filled episode, Chelsey explores 1 Corinthians 3 and what it means to live with right-sized significance. Whether you feel over-responsible or stuck behind fear disguised as humility, this episode will reframe your role in God's divine plan.Inside, you'll hear:Why some of us overinflate our role—and how to surrender the pressureWhy others fill “sandbags” to avoid obedience (yep, just like in Up)How to trust God as the Orchestrator of every detail, timeline, and heartWhat it means to plant, water, or just be faithful in your portionThis episode is a reset for the woman who knows she's called—but needs the reminder that God is the one who brings the growth. Support the showChelsey Holm | the Wife Coach "I help Christian wives flourish deeply as YOU in marriage, motherhood, and life."Grab my free training here: https://chelsey.coach/highlevelwife-freetraining2x certified Coach (John Maxwell Leadership, Kristen Boss SSLS)10+ years coaching experienceNASM-certified in Personal Training and NutritionMom of 5, Army wife 16 yearsSupport the show!The Chelsey Holm Podcast (The Chelsey Holm Podcast) It's hard to give your best when you don't feel your best- replenish your health with Ready Set Wellness: https://us.shaklee.com/site/chelseynoel/Nutrition/Ready-Set-Wellness/Ready-Set-Wellness-Bundle/p/89599

In this episode, hosts Chandra and Paul continue their discussion from the previous week, diving deeper into JD Edwards Orchestrator. They offer an accessible introduction to what the Orchestrator is, who can benefit from it, and some prerequisites needed to use it (like being on a certain Tools release and having specific servers configured). Paul and Chandra break down the main components of Orchestrator—including form requests, data requests, report requests, watch lists, connectors, custom requests, messages, rules, cross references, whitelists, and how to schedule orchestrations—explaining their functionality and practical applications. They emphasize how Orchestrator enables automation, integration, and monitoring within JD Edwards, making complex processes easier and opening doors for business process improvements without heavy custom coding. 06:19 Are you set up for Orchestrator? 08:31 Chandra asks Paul, how would you describe the Orchestrator? 13:54 Chandra asks ChatGPT, how would you describe the Orchestrator? 18:40 Orchestrator Components 35:34 Midwesternism of the Day Resources: Orchestrator Guide: https://docs.oracle.com/en/applications/jd-edwards/cross-product/9.2/eotos/jd-edwards-enterpriseone-orchestrator-overview.html LearnJDE: Orchestrator Quest E1 Tech SIG: https://questoraclecommunity.my.site.com/s/group/0F9UT00000005w90AA/jde-e1-tech-sig Quest Orchestrator Content Center: JD Edwards Orchestrator - Quest Oracle Community The Orchestrator Enthusiasts LinkedIn Group - (25) JDE Orchestrator Enthusiasts | Groups | LinkedIn

Zusammenfassung der Folge:Die Folge dreht sich um das Thema agentenbasierte KI (Agentic AI) und ihre Auswirkungen auf Leadership, Organisation und Gesellschaft. Alois und Oliver nehmen die Hörer:innen mit von den Ursprüngen einfacher LLM-Agenten bis hin zu komplexen Multi-Agenten-Systemen, die Aufgaben in Teams übernehmen und zunehmend eigenständig handeln.Kernaussagen & Learnings:Von Einzelagenten zu Agenten-Teams: KI-Agenten übernehmen nicht nur einzelne Aufgaben, sondern organisieren sich wie ein "Dorf" mit unterschiedlichen Rollen. Daraus entstehen neue Möglichkeiten, aber auch neue Komplexitäten und Risiken (z.B. Fehlerverstärkung).Leadership neu gedacht: Wenn KI-Agenten Aufgaben, Management und sogar ganze Firmen steuern, verschiebt sich die Rolle des Menschen immer mehr zum Orchestrator, der Ziele und Werte vorgibt – aber auch das Risiko trägt, sich selbst überflüssig zu machen.Demokratisierung vs. Zugangshürden: Tools wie Vibe-Coding ermöglichen es vielen, eigene KI-Lösungen zu bauen. Gleichzeitig entstehen neue Risiken durch mangelnde Checks & Balances und Datenschutzprobleme.Moving Targets & Paradigmenwechsel: Die Geschwindigkeit, mit der sich KI und Organisationsformen entwickeln, erfordert ständige Lernbereitschaft und Anpassung – klassische Führungsmodelle und Organisationsstrukturen werden zunehmend hinterfragt.Wohin führt das alles? Agentic AI ist ein Zwischenschritt auf dem Weg zu mächtigeren, vielleicht sogar generalisierenden KI-Systemen (AGI). Die großen Fragen zu gesellschaftlicher Verantwortung, Nachhaltigkeit und Governance sind noch offen.Fazit:Im Zeitalter der agentischen KI verschieben sich Leadership, Organisation und Verantwortung rasant. Führung wird zum Moving Target: Wer nicht bereit ist, Rollen und Routinen immer wieder neu zu denken, läuft Gefahr, abgehängt zu werden.

In this episode, hosts Chandra and Paul discuss thechallenges of context switching in their professional roles and share updatesabout upcoming JD Edwards events, including recaps and resources made availablefrom the recent Blueprint 4D conference. Chandra talks about her journey inbuilding her first JD Edwards Orchestrator orchestration, highlighting both thechallenges and learning resources for newcomers. The conversation features adeep dive into where to find Orchestrator training, documentation, communitygroups, and practical advice from both hosts and other community members. Theyalso touch on the value of sharing knowledge within the JDE community.00:50 Context Shifting05:20Quest's BP4D Content and Blogs 10:21Chandra builds her first Orchestration 11:40Seeking Orchestrator Resources 15:25Orchestrator Training Tips and Resources 25:53 Midwesternism ofthe Day Resources:LearnJDE: Orchestrator Quest E1 Tech SIG: https://questoraclecommunity.my.site.com/s/group/0F9UT00000005w90AA/jde-e1-tech-sig QuestOrchestrator Content Center: JD Edwards Orchestrator - Quest OracleCommunity TheOrchestrator Enthusiasts LinkedIn Group - (25) JDEOrchestrator Enthusiasts | Groups | LinkedIn If you have concerns or feedback on thisepisode or ideas for future episodes, please contact us at thejdeconnection@questoraclecommunity.org

What if your intern had a better executive assistant than your CEO? In this episode of TNW, Aydin sits down with Nick Sonnenberg—Wall Street veteran, bestselling author, and founder of Leverage—to explore how AI agents are radically transforming how work gets done. Nick demos a real AI-powered assistant that can handle email, prep meeting briefs, and even outperform human teammates. You'll hear how he structures agent orchestration, optimizes prompts as IP, and envisions a future where everyone at every level has a digital assistant.What You'll Learn:- Why every employee (not just execs) should have their own AI assistant- How AI agents can be “managed” like employees—with hierarchy and QA- The role of MCP (Model Context Protocol) in unlocking personalized, high-context automation- Why context is king in building truly useful AI workflows- Why prompt libraries should be treated like company IPTimestamps:00:00 – Imagine if interns had better EAs than execs01:00 – Nick's background: From high-frequency trading to AI consulting02:20 – The origin of Leverage and the obsession with efficiency03:10 – Inbox Zero, RAD framework, and AI-powered email agents04:50 – What is MCP and why it matters06:20 – The CPR framework: Communicate, Plan, Resource08:30 – Orchestrator agents and agent 11:00 – QA as the new job for every role12:00 – Why execs are adopting AI faster than junior employees13:40 – The “Sniper Agent” and building executive briefs16:00 – Personalized, context-rich email drafting20:00 – Prompt optimization strategy as a business asset22:30 – Real-time battle card generation and agent chaining23:30 – Custom summaries using frameworks, not just transcripts24:00 – Behind the scenes: building and deploying agentsTools & Technologies Mentioned:Claude – Anthropic's AI model, used here with MCP and custom agentsMCP (Model Context Protocol) – Allows secure access to private data and agent orchestration within AI platformsAsana – Project management tool integrated with agentsZapier / NADN / Crew AI – Automation platforms for building AI workflowsPerplexity – Used to scan public web/news as part of the AI brief generationCoda / Notion – Popular tools for knowledge capture, now evolving into AI-integrated workflowsHubSpot – CRM used to integrate and personalize AI-generated contentFellow – AI meeting intelligence tool for smarter call summariesNick's website: https://www.getleverage.ai/Subscribe at⁠ thisnewway.com⁠ to get the step-by-step playbooks, tools, and workflows.

What does it take to release 200 digital singles in a single day and earn a Guinness World Record in the process? More importantly, what can that teach us about building a truly sustainable music career?In this episode, I sit down with The Orchestrator, a self-taught saxophonist whose approach to the music business is as methodical as it is inspiring. His philosophy centers on complete independence: owning your masters, understanding every revenue stream, and building passive income that allows you to create art without compromise.The Orchestrator shares his journey from learning Antonio Carlos Jobim classics to developing a bossa nova and jazz-focused catalog that generates sustainable income. But this isn't just about music. The Orchestrator highlights the importance of financial literacy, strategic thinking, and maintaining artistic integrity in an industry that often demands the opposite.Support ProducerHeadIf you value ProducerHead and want to support the show, consider a premium subscription (just $6 per month, even less on an annual basis). For less than a plug-in, you can directly support and grow the show while keeping the core content free for everyone.Three Game-Changing Takeaways1. Complete Ownership Creates FreedomOwning your masters, publishing, and trademarks isn't just about money—it's about maintaining the power to pivot and make decisions without outside interference. True artistic and financial independence starts with ownership.2. Diversify Your Revenue StreamsStreaming royalties from distributors represent only a fraction of what your music can earn. Register with PROs, SoundExchange, and Songtrust to collect all available royalties and build sustainable passive income.3. Financial Stability Enables Better ArtWhen your bills are covered by passive income, you can focus on creating art for art's sake rather than compromising your vision for short-term financial gains.Support ProducerHeadIf you value ProducerHead and want to support the show, consider a premium subscription (just $6 per month, even less on an annual basis). For less than a plug-in, you can directly support and grow the show while keeping the core content free for everyone.What We CoverThe Craft:* His daily saxophone practice routine and self-taught approach* Learning classic tunes and developing unique variations* The influence of Stan Getz and Antonio Carlos Jobim on his soundThe Business:* Complete breakdown of music royalty percentages and splits beyond streaming* Why he chose long-term passive income over performance-based career paths* The importance of trademarking your business and catalog ownershipThe Strategy:* Grassroots promotion and direct fan engagement through social media* Leveraging streaming data and algorithms for strategic marketing* The story behind his Guinness World Record and what it taught him about differentiationThe Philosophy:* Building financial independence to maintain artistic integrity* Investing in businesses that align with personal values* Working smart vs. working hard in music promotionEssential Resources MentionedMust-Read Books:* "All You Need to Know About the Music Business" by Donald Passman* "The Plain and Simple Guide to Music Publishing" by Randall D. WixsonAlbums That Shaped His Sound:* "A Love Supreme" by John Coltrane* "Getz/Gilberto" by Stan Getz and João Gilberto* "The Chronic" by Dr. DreRevenue Collection Services:* SoundExchange (international performance royalties)* Songtrust (publishing royalties)* ASCAP (performing rights organization)* BMI (performing rights organization)* Sticker Mule (promotional stickers for fan engagement)Chapter Timestamps* 0:00 - Intro and music business disclaimer* 2:24 - Saxophone practice routine deep dive* 4:44 - Learning Jobim and the Stan Getz influence* 11:57 - Music ownership philosophy and trademark importance* 16:33 - Choosing passive income over performance careers* 19:26 - Complete breakdown of music royalty splits* 25:18 - Grassroots promotion and social media strategies* 35:53 - Building sustainable income through streaming diversification* 45:58 - The Guinness World Record strategy* 52:46 - Building financial freedom with integrity (2000 days sober)* 1:00:02 - The story behind releasing 200 songs in one day* 1:04:50 - Self-taught saxophone journey* 1:12:36 - Breaking free from corporate control and honoring Black culture in electronic musicConnect with The Orchestrator* Website: theorchestratormusic.com* Instagram: @theorchestratormusic* Listen on: Spotify* Listen on: Apple MusicSupport ProducerHeadIf you value ProducerHead and want to support the show, consider a premium subscription (just $6 per month, even less on an annual basis). For less than a plug-in, you can directly support and grow the show while keeping the core content free for everyone.A Final ThoughtThe Orchestrator's approach challenges the conventional wisdom that musicians must choose between artistic integrity and financial success. His story proves that with the right knowledge, strategic thinking, and commitment to ownership, you can build a career that serves both your creative vision and your financial goals.The question isn't whether you can afford to own your masters—it's whether you can afford not to.What resonated most with you from this conversation? Share it with the community.This episode was co-produced, engineered and edited by Matthew Diaz. From ProducerHead, this is Toru, and in a way, so are you. Peace. Get full access to ProducerHead at producerhead.substack.com/subscribe

Hosts Chandra and Paul recap their experiences at the Blueprint 4D conference in Las Vegas, highlighting the increased attendance, energetic participation, and the influx of new faces within the JD Edwards (JDE) community. Chandra shares insights from wrapping up the Emerging Leaders Program, including rewarding group projects and building connections through vulnerability and teamwork. The hosts discuss the Executive Forum, focusing on leadership, rapid change management, data governance, communication styles, and the rising importance of empathy in IT. They note a significant conference shift: AI-related sessions outnumbered Orchestrator topics for the first time, reflecting AI's growing impact and the challenges of adoption and documentation. Customer case studies showcased successful strategies for keeping JDE systems current and capitalizing on new features. The episode also features Chandra's favorite fun session involving magic and comedy, and the duo's reflections on their own live podcast event. 00:48 The Struggle Is Real 07:06 ELP Wrap Up 11:10 Executive Forum Highlights 16:16 Highlights from the Chris Laping's Keynote Sessions 19:36 JDE Keynote Highlight: 2005 Called — It Wants Its ERP Back 22:17 Is it true there were More AI Sessions than Orchestrator Sessions? 28:39 Customer Success Stories 32:00 Magic, Laughter, and Lingo Bingo 35:54 Midwesternism of the Day If you have concerns or feedback on this episode or ideas for future episodes, please contact us at thejdeconnection@questoraclecommunity.org.

Bridging the Gap Between AI and Business Data // MLOps Podcast #325 with Deepti Srivastava, Founder and CEO at Snow Leopard.Join the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletter// AbstractI'm sure the MLOps community is probably aware – it's tough to make AI work in enterprises for many reasons, from data silos, data privacy and security concerns, to going from POCs to production applications. But one of the biggest challenges facing businesses today, that I particularly care about, is how to unlock the true potential of AI by leveraging a company's operational business data. At Snow Leopard, we aim to bridge the gap between AI systems and critical business data that is locked away in databases, data warehouses, and other API-based systems, so enterprises can use live business data from any data source – whether it's database, warehouse, or APIs – in real time and on demand, natively. In this interview, I'd like to cover Snow Leopard's intelligent data retrieval approach that can leverage business data directly and on-demand to make AI work.// BioDeepti is the founder and CEO of Snow Leopard AI, a platform that helps teams build AI apps using their live business data, on-demand. She has nearly 2 decades of experience in data platforms and infrastructure.As Head of Product at Observable, Deepti led the 0→1 product and GTM strategy in the crowded data analytics market. Before that, Deepti was the founding PM for Google Spanner, growing it to thousands of internal customers (Ads, PlayStore, Gmail, etc.), before launching it externally as a seminal cloud database service. Deepti started her career as a distributed systems engineer in the RAC database kernel at Oracle.// Related LinksWebsite: https://www.snowleopard.ai/AI SQL Data Analyst // Donné Stevenson - https://youtu.be/hwgoNmyCGhQ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our Slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Deepti on LinkedIn: /thedeepti/Timestamps:[00:00] Deepti's preferred coffee[00:49] MLflow vs Kubeflow Debate[04:58] GenAI Data Integration Challenges[09:02] GenAI Sidecar Spicy Takes[14:07] Troubleshooting LLM Hallucinations[19:03] AI Overengineering and Hype[25:06] Self-Serve Analytics Governance[33:29] Dashboards vs Data Quality[37:06] Agent Database Context Control[43:00] LLM as Orchestrator[47:34] Tool Call Ownership Clarification[51:45] MCP Server Challenges[56:52] Wrap up

Guests are Nick Eberts and Jon Li. Nick is a Product Manager at Google working on Fleets and Multi-Cluster and Jon is a Software Engineer at Google working on AI Inference on Kubernetes. We discussed the newly announced Multi Cluster Orchestrator (MCO) and the challenges of running multiple clusters.   Do you have something cool to share? Some questions? Let us know: - web: kubernetespodcast.com - mail: kubernetespodcast@google.com - twitter: @kubernetespod - bluesky: @kubernetespodcast.com   News of the week Etcd has released version 3.6.0 Kubernetes 1.33 is now available in the Rapid channel in GKE Kyverno 1.14.0 was released Links from the interview Nick Eberts on LinkedIn Jon Li on LinkedIn MCO Blog MCO Repo Cluster Inventory API ClusterProfile API Gemma 3 vLLM Sample (deploy on Google Cloud using Terraform and Argo CD) Hello World Sample (deploy on Google Cloud using Terraform and Argo CD) Gateway API Inference Extension

The annual Milken Institute Global Conference in Los Angeles is an "intellectual banquet" of stimulating content. Over the course of three days more than 1,000 speakers take the stage to share insights on the latest business, political, healthcare and cultural trends. How it all comes together is both fascinating and daunting. On this episode of Press Profiles we sit down with Conrad Kiechel, the Milken Institute's executive director of global programming and events, to discuss assembling the jigsaw puzzle that is the global conference. From landing bold face names such as Scott Bessent, Jason Sudeikis, Jensen Huang and Patrick Dempsey, to creating unique and insightful panels. We also explore Conrad's career journey and how his background as a journalist taught him to be curious and tenacious - two imperative traits that help him pull off the conference each year.   

“We're going beyond just contact center AI—this is CX-first orchestration across the entire enterprise,” said Elizabeth Tobey, Vice President of Marketing at NICE, as the company recently revealed its latest innovation—CXone Empower Orchestrator—and walked away with two top honors at Enterprise Connect 2025: Best CX Innovation and Best of Show. In a live Technology Reseller News podcast, Tobey shared how Empower Orchestrator marks a new era for enterprise AI—one that is measurable, scalable, and tied to tangible outcomes. CXone Empower Orchestrator is built around an AI-powered co-pilot design that allows organizations to analyze, automate, and optimize workflows across the full customer journey. “It's not just about improving one part of the customer experience,” Tobey noted. “It's about connecting the front office and back office, and turning insights into immediate, scalable action.” The result is greater agility and faster time-to-value, with automation tasks that once took days or weeks now completed in minutes. Critically, Empower Orchestrator also features a continuous feedback loop, even when tasks are escalated to live agents. “Every facet of the organization gets better through this data,” said Tobey. “This is AI moving from hype to highly functional.” Tobey emphasized NICE's commitment to a CX-first philosophy, noting that the platform touches every part of the business: from sales and marketing to fulfillment and support. “Customer experience and customer service incorporate the entire business,” she said. “Every interaction matters—and that's what we're connecting.” Beyond the enterprise, the launch presents new opportunities for NICE's global partner network. “Partners are seeing this as a way to help customers move faster,” Tobey shared. “And they're excited to be part of it.” The announcement also coincided with a milestone: the eighth anniversary of Amazon Connect, originally launched at Enterprise Connect in 2017. For NICE—and for Tobey, who was part of that original AWS launch team—it's a full-circle moment that reflects how far CX technology has come. Learn More Explore CXone Empower Orchestrator and NICE's full suite of AI-powered CX solutions at NICE.com. #NICE #CXone #EnterpriseConnect2025 #AI #CustomerExperience #Automation #CXInnovation #ContactCenterAI #WorkflowOrchestration #ChannelPartners #DigitalTransformation

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  Today we welcome Jesse Butler and Nic Slattery to talk about the Kubernetes Resource Orchestrator, or KRO. Jesse works as a principal product manager at AWS and Nic is a Product Manager at Google. The Kubernetes Resource Orchestrator is a new cloud agnostic tool meant to simplify Kubernetes resources for devs and platform admins.   Do you have something cool to share? Some questions? Let us know: - web: kubernetespodcast.com - mail: kubernetespodcast@google.com - twitter: @kubernetespod - bluesky: @kubernetespodcast.com   News of the week Kubernetes JobSets: An open-source API for managing distributed jobs as a single unit. Integrates with Kueue for better resource utilization. Kubernetes Blog: Introducing JobSet Kueue Project Google Cloud Next '24: Happening in Las Vegas, April 9-11. The Kubernetes Podcast team will be there! Google Cloud Next Kagent: A new open-source AI agent framework built on Microsoft's Autogen, designed for automating operations and troubleshooting in Kubernetes. kagent.dev Links from the interview Kubernetes Resource Orchestrator (KRO) KRO Announcement Blogs Google Cloud Blog- Simplify the developer experience on Kubernetes with KRO AWS Open Source Blog - Introducing kro: Kube Resource Orchestrator AWS Open Source Blog - Kube Resource Orchestrator, From Experiment to Community Project Reddit thread - anyone tried kro for kubernetes resource management yet? The New Stack: Kubernetes Gets a New Resource Orchestrator in the Form of Kro InfoQ: Cloud Giants Collaborate on New Kubernetes Resource Management Tool CRD (Custom Resource Definition): Kubernetes CRDs - A mechanism within Kubernetes to extend the API. Knative: Knative.dev - A Kubernetes-based platform for building serverless applications. Terraform: Terraform.io - Infrastructure as code software. Helm: Helm.sh - A package manager for Kubernetes. KPT (Kubernetes Package Tool): KPT - A tool for packaging and managing Kubernetes configurations. Crossplane: cncf.io/projects/crossplane - An open-source project for managing cloud resources through Kubernetes. Common Expression Language (CEL): cel.dev - A powerful expression language. kubebuilder: kubebuilder on GitHub - A framework to build Kubernetes controllers, details available in Kubernetes documentation.

Monica Lynch is one of the most respected and knowledgeable record executives in the music business.She talks about her arrival in New York from Chicago as a teenager, becoming Tom Silverman's first hire at TommyBoy and her rising quickly to ultimately become president at TommyBoy. She talks about her relationships with everyone from Afrika Bambaata, Queen Latifah, Naughty By Nature, Whizz Kidd, De La Soul, House Of Pain, Coolio, Digital Underground, Jock Jams and the other projects she guided.This is a must listen podcast

Pastor Vic Prietto

We're kicking off season 4 with Chanel Chambers, VP of Marketing at Lakeside Software. As a product marketing leader and past music teacher, she's joining us to break down the role of product marketing, why it's central to a business's success, and how PMMs can increase their influence. From jazz improvisation to AI-driven business strategies, Chanel shares fresh insights on how product marketers can start treating their role with a business holistic approach and how to create credibility internally AND externally. We're talked about:

Florian Rappl, solutions architect at smapiot GmbH, joins the podcast to give insights on micro frontends, discussing the misconceptions, architectural patterns, challenges, and exciting benefits of using micro frontends over traditional monolithic applications. Links https://florian-rappl.de https://github.com/FlorianRappl https://x.com/FlorianRappl https://bsky.app/profile/florianrappl.bsky.social https://www.linkedin.com/in/florian-rappl https://dev.to/florianrappl We want to hear from you! How did you find us? Did you see us on Twitter? In a newsletter? Or maybe we were recommended by a friend? Let us know by sending an email to our producer, Emily, at emily.kochanekketner@logrocket.com (mailto:emily.kochanekketner@logrocket.com), or tweet at us at PodRocketPod (https://twitter.com/PodRocketpod). Follow us. Get free stickers. Follow us on Apple Podcasts, fill out this form (https://podrocket.logrocket.com/get-podrocket-stickers), and we'll send you free PodRocket stickers! What does LogRocket do? LogRocket provides AI-first session replay and analytics that surfaces the UX and technical issues impacting user experiences. Start understand where your users are struggling by trying it for free at [LogRocket.com]. Try LogRocket for free today.(https://logrocket.com/signup/?pdr) Special Guest: Florian Rappl.

Pianist, Composer, Orchestrator and Believer,  Stanton Lanier  joins Joseph to discuss how to find your purpose NOW.  GUEST LINKS  StantonLanier.com  SUBSCRIBE  BrokenCatholic.com  Music By: StantonLanier.com | Purple-Planet.com

On today's episode, guest James Henderson joins the Packet Pushers to discuss Cisco’s Network Service Orchestrator (NSO). NSO’s role in network automation, its declarative management approach, and the challenges it presents are some of the things James shares with the hosts. They also cover operational requirements, deployment challenges, and performance considerations, in addition to discussing... Read more »

On today's episode, guest James Henderson joins the Packet Pushers to discuss Cisco’s Network Service Orchestrator (NSO). NSO’s role in network automation, its declarative management approach, and the challenges it presents are some of the things James shares with the hosts. They also cover operational requirements, deployment challenges, and performance considerations, in addition to discussing... Read more »

On today's episode, guest James Henderson joins the Packet Pushers to discuss Cisco’s Network Service Orchestrator (NSO). NSO’s role in network automation, its declarative management approach, and the challenges it presents are some of the things James shares with the hosts. They also cover operational requirements, deployment challenges, and performance considerations, in addition to discussing... Read more »

Damon gets to talk to Andrew about The Percussion People, Drum Corps, Cordis, composing, contracting, orchestrating, Ohio, Jeopardy and much more! There's also other segments!