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Andy Zonneveld is a dairy farmer from Laton, CA. His family of five includes 8-year-old son Andrew. He and Andrew enjoy collecting sports cards. As Andy says, "we're amateurs." And they were... until they weren't. Sometimes in life, remarkable things happen when we absolutely never expect it.  Please Like, Comment and Follow 'The Christopher Gabriel Program' on all platforms:    The Christopher Gabriel Program is available on the KMJNOW app, Apple Podcasts, Spotify, YouTube or wherever else you listen to podcasts.  ---  The Christopher Gabriel Program   | Website | Facebook | X | Instagram |   ---  Everything KMJ   KMJNOW App | Podcasts | Facebook | X | Instagram   See omnystudio.com/listener for privacy information.

Andy Zonneveld is a dairy farmer from Laton, CA. His family of five includes 8-year-old son Andrew. He and Andrew enjoy collecting sports cards. As Andy says, "we're amateurs." And they were... until they weren't. Sometimes in life, remarkable things happen when we absolutely never expect it.  Please Like, Comment and Follow 'The Christopher Gabriel Program' on all platforms:    The Christopher Gabriel Program is available on the KMJNOW app, Apple Podcasts, Spotify, YouTube or wherever else you listen to podcasts.  ---  The Christopher Gabriel Program   | Website | Facebook | X | Instagram |   ---  Everything KMJ   KMJNOW App | Podcasts | Facebook | X | Instagram   See omnystudio.com/listener for privacy information.

Editor's note: One of the top reasons we have hundreds of companies and thousands of AI Engineers joining the World's Fair next week is, apart from discussing technology and being present for the big launches planned, to hire and be hired! Listeners loved our previous Elicit episode and were so glad to welcome 2 more members of Elicit back for a guest post (and bonus podcast) on how they think through hiring. Don't miss their AI engineer job description, and template which you can use to create your own hiring plan! How to Hire AI EngineersJames Brady, Head of Engineering @ Elicit (ex Spring, Square, Trigger.io, IBM)Adam Wiggins, Internal Journalist @ Elicit (Cofounder Ink & Switch and Heroku)If you're leading a team that uses AI in your product in some way, you probably need to hire AI engineers. As defined in this article, that's someone with conventional engineering skills in addition to knowledge of language models and prompt engineering, without being a full-fledged Machine Learning expert.But how do you hire someone with this skillset? At Elicit we've been applying machine learning to reasoning tools since 2018, and our technical team is a mix of ML experts and what we can now call AI engineers. This article will cover our process from job description through interviewing. (You can also flip the perspectives here and use it just as easily for how to get hired as an AI engineer!)My own journeyBefore getting into the brass tacks, I want to share my journey to becoming an AI engineer.Up until a few years ago, I was happily working my job as an engineering manager of a big team at a late-stage startup. Like many, I was tracking the rapid increase in AI capabilities stemming from the deep learning revolution, but it was the release of GPT-3 in 2020 which was the watershed moment. At the time, we were all blown away by how the model could string together coherent sentences on demand. (Oh how far we've come since then!)I'd been a professional software engineer for nearly 15 years—enough to have experienced one or two technology cycles—but I could see this was something categorically new. I found this simultaneously exciting and somewhat disconcerting. I knew I wanted to dive into this world, but it seemed like the only path was going back to school for a master's degree in Machine Learning. I started talking with my boss about options for taking a sabbatical or doing a part-time distance learning degree.In 2021, I instead decided to launch a startup focused on productizing new research ideas on ML interpretability. It was through that process that I reached out to Andreas—a leading ML researcher and founder of Elicit—to see if he would be an advisor. Over the next few months, I learned more about Elicit: that they were trying to apply these fascinating technologies to the real-world problems of science, and with a business model that aligned it with safety goals. I realized that I was way more excited about Elicit than I was about my own startup ideas, and wrote about my motivations at the time.Three years later, it's clear this was a seismic shift in my career on the scale of when I chose to leave my comfy engineering job at IBM to go through the Y Combinator program back in 2008. Working with this new breed of technology has been more intellectually stimulating, challenging, and rewarding than I could have imagined.Deep ML expertise not requiredIt's important to note that AI engineers are not ML experts, nor is that their best contribution to a tech team.In our article Living documents as an AI UX pattern, we wrote:It's easy to think that AI advancements are all about training and applying new models, and certainly this is a huge part of our work in the ML team at Elicit. But those of us working in the UX part of the team believe that we have a big contribution to make in how AI is applied to end-user problems.We think of LLMs as a new medium to work with, one that we've barely begun to grasp the contours of. New computing mediums like GUIs in the 1980s, web/cloud in the 90s and 2000s, and multitouch smartphones in the 2000s/2010s opened a whole new era of engineering and design practices. So too will LLMs open new frontiers for our work in the coming decade.To compare to the early era of mobile development: great iOS developers didn't require a detailed understanding of the physics of capacitive touchscreens. But they did need to know the capabilities and limitations of a multi-touch screen, the constrained CPU and storage available, the context in which the user is using it (very different from a webpage or desktop computer), etc.In the same way, an AI engineer needs to work with LLMs as a medium that is fundamentally different from other compute mediums. That means an interest in the ML side of things, whether through their own self-study, tinkering with prompts and model fine-tuning, or following along in #llm-paper-club. But this understanding is so that they can work with the medium effectively versus, say, spending their days training new models.Language models as a chaotic mediumSo if we're not expecting deep ML expertise from AI engineers, what are we expecting? This brings us to what makes LLMs different.We'll assume already that our ideal candidate is already inspired by, and full of ideas about, all the new capabilities AI can bring to software products. But the flip side is all the things that make this new medium difficult to work with. LLM calls are annoying due to high latency (measured in tens of seconds sometimes, rather than milliseconds), extreme variance on latency, high error rates even under normal operation. Not to mention getting extremely different answers to the same prompt provided to the same model on two subsequent calls!The net effect is that an AI engineer, even working at the application development level, needs to have a skillset comparable to distributed systems engineering. Handling errors, retries, asynchronous calls, streaming responses, parallelizing and recombining model calls, the halting problem, and fallbacks are just some of the day-in-the-life of an AI engineer. Chaos engineering gets new life in the era of AI.Skills and qualities in candidatesLet's put together what we don't need (deep ML expertise) with what we do (work with capabilities and limitations of the medium). Thus we start to see what Elicit looks for in AI engineers:* Conventional software engineering skills. Especially back-end engineering on complex, data-intensive applications.* Professional, real-world experience with applications at scale.* Deep, hands-on experience across a few back-end web frameworks.* Light devops and an understanding of infrastructure best practices.* Queues, message buses, event-driven and serverless architectures, … there's no single “correct” approach, but having a deep toolbox to draw from is very important.* A genuine curiosity and enthusiasm for the capabilities of language models.* One or more serious projects (side projects are fine) of using them in interesting ways on a unique domain.* …ideally with some level of factored cognition, e.g. breaking the problem down into chunks, making thoughtful decisions about which things to push to the language model and which stay within the realm of conventional heuristics and compute capabilities.* Personal studying with resources like Elicit's ML reading list. Part of the role is collaborating with the ML engineers and researchers on our team. To do so, the candidate needs to “speak their language” somewhat, just as a mobile engineer needs some familiarity with backends in order to collaborate effectively on API creation with backend engineers.* An understanding of the challenges that come along with working with large models (high latency, variance, etc.) leading to a defensive, fault-first mindset.* Careful and principled handling of error cases, asynchronous code (and ability to reason about and debug it), streaming data, caching, logging and analytics for understanding behavior in production.* This is a similar mindset that one can develop working on conventional apps which are complex, data-intensive, or large-scale apps. The difference is that an AI engineer will need this mindset even when working on relatively small scales!On net, a great AI engineer will combine two seemingly contrasting perspectives: knowledge of, and a sense of wonder for, the capabilities of modern ML models; but also the understanding that this is a difficult and imperfect foundation, and the willingness to build resilient and performant systems on top of it.Here's the resulting AI engineer job description for Elicit. And here's a template that you can borrow from for writing your own JD.Hiring processOnce you know what you're looking for in an AI engineer, the process is not too different from other technical roles. Here's how we do it, broken down into two stages: sourcing and interviewing.SourcingWe're primarily looking for people with (1) a familiarity with and interest in ML, and (2) proven experience building complex systems using web technologies. The former is important for culture fit and as an indication that the candidate will be able to do some light prompt engineering as part of their role. The latter is important because language model APIs are built on top of web standards and—as noted above—aren't always the easiest tools to work with.Only a handful of people have built complex ML-first apps, but fortunately the two qualities listed above are relatively independent. Perhaps they've proven (2) through their professional experience and have some side projects which demonstrate (1).Talking of side projects, evidence of creative and original prototypes is a huge plus as we're evaluating candidates. We've barely scratched the surface of what's possible to build with LLMs—even the current generation of models—so candidates who have been willing to dive into crazy “I wonder if it's possible to…” ideas have a huge advantage.InterviewingThe hard skills we spend most of our time evaluating during our interview process are in the “building complex systems using web technologies” side of things. We will be checking that the candidate is familiar with asynchronous programming, defensive coding, distributed systems concepts and tools, and display an ability to think about scaling and performance. They needn't have 10+ years of experience doing this stuff: even junior candidates can display an aptitude and thirst for learning which gives us confidence they'll be successful tackling the difficult technical challenges we'll put in front of them.One anti-pattern—something which makes my heart sink when I hear it from candidates—is that they have no familiarity with ML, but claim that they're excited to learn about it. The amount of free and easily-accessible resources available is incredible, so a motivated candidate should have already dived into self-study.Putting all that together, here's the interview process that we follow for AI engineer candidates:* 30-minute introductory conversation. Non-technical, explaining the interview process, answering questions, understanding the candidate's career path and goals.* 60-minute technical interview. This is a coding exercise, where we play product manager and the candidate is making changes to a little web app. Here are some examples of topics we might hit upon through that exercise:* Update API endpoints to include extra metadata. Think about appropriate data types. Stub out frontend code to accept the new data.* Convert a synchronous REST API to an asynchronous streaming endpoint.* Cancellation of asynchronous work when a user closes their tab.* Choose an appropriate data structure to represent the pending, active, and completed ML work which is required to service a user request.* 60–90 minute non-technical interview. Walk through the candidate's professional experience, identifying high and low points, getting a grasp of what kinds of challenges and environments they thrive in.* On-site interviews. Half a day in our office in Oakland, meeting as much of the team as possible: more technical and non-technical conversations.The frontier is wide openAlthough Elicit is perhaps further along than other companies on AI engineering, we also acknowledge that this is a brand-new field whose shape and qualities are only just now starting to form. We're looking forward to hearing how other companies do this and being part of the conversation as the role evolves.We're excited for the AI Engineer World's Fair as another next step for this emerging subfield. And of course, check out the Elicit careers page if you're interested in joining our team.Podcast versionTimestamps* [00:00:24] Intros* [00:05:25] Defining the Hiring Process* [00:08:42] Defensive AI Engineering as a chaotic medium* [00:10:26] Tech Choices for Defensive AI Engineering* [00:14:04] How do you Interview for Defensive AI Engineering* [00:19:25] Does Model Shadowing Work?* [00:22:29] Is it too early to standardize Tech stacks?* [00:32:02] Capabilities: Offensive AI Engineering* [00:37:24] AI Engineering Required Knowledge* [00:40:13] ML First Mindset* [00:45:13] AI Engineers and Creativity* [00:47:51] Inside of Me There Are Two Wolves* [00:49:58] Sourcing AI Engineers* [00:58:45] Parting ThoughtsTranscript[00:00:00] swyx: Okay, so welcome to the Latent Space Podcast. This is another remote episode that we're recording. This is the first one that we're doing around a guest post. And I'm very honored to have two of the authors of the post with me, James and Adam from Elicit. Welcome, James. Welcome, Adam.[00:00:22] James Brady: Thank you. Great to be here.[00:00:23] Hey there.[00:00:24] Intros[00:00:24] swyx: Okay, so I think I will do this kind of in order. I think James, you're, you're sort of the primary author. So James, you are head of engineering at Elicit. You also, We're VP Eng at Teespring and Spring as well. And you also , you have a long history in sort of engineering. How did you, , find your way into something like Elicit where, , it's, you, you are basically traditional sort of VP Eng, VP technology type person moving into a more of an AI role.[00:00:53] James Brady: Yeah, that's right. It definitely was something of a Sideways move if not a left turn. So the story there was I'd been doing, as you said, VP technology, CTO type stuff for around about 15 years or so, and Notice that there was this crazy explosion of capability and interesting stuff happening within AI and ML and language models, that kind of thing.[00:01:16] I guess this was in 2019 or so, and decided that I needed to get involved. , this is a kind of generational shift. And Spent maybe a year or so trying to get up to speed on the state of the art, reading papers, reading books, practicing things, that kind of stuff. Was going to found a startup actually in in the space of interpretability and transparency, and through that met Andreas, who has obviously been on the, on the podcast before asked him to be an advisor for my startup, and he countered with, maybe you'd like to come and run the engineering team at Elicit, which it turns out was a much better idea.[00:01:48] And yeah, I kind of quickly changed in that direction. So I think some of the stuff that we're going to be talking about today is how actually a lot of the work when you're building applications with AI and ML looks and smells and feels much more like conventional software engineering with a few key differences rather than really deep ML stuff.[00:02:07] And I think that's one of the reasons why I was able to transfer skills over from one place to the other.[00:02:12] swyx: Yeah, I[00:02:12] James Brady: definitely[00:02:12] swyx: agree with that. I, I do often say that I think AI engineering is about 90 percent software engineering with like the, the 10 percent of like really strong really differentiated AI engineering.[00:02:22] And that might, that obviously that number might change over time. I want to also welcome Adam onto my podcast because you welcomed me onto your podcast two years ago.[00:02:31] Adam Wiggins: Yeah, that was a wonderful episode.[00:02:32] swyx: That was, that was a fun episode. You famously founded Heroku. You just wrapped up a few years working on Muse.[00:02:38] And now you've described yourself as a journalist, internal journalist working on Elicit.[00:02:43] Adam Wiggins: Yeah, well I'm kind of a little bit in a wandering phase here and trying to take this time in between ventures to see what's out there in the world and some of my wandering took me to the Elicit team. And found that they were some of the folks who were doing the most interesting, really deep work in terms of taking the capabilities of language models and applying them to what I feel like are really important problems.[00:03:08] So in this case, science and literature search and, and, and that sort of thing. It fits into my general interest in tools and productivity software. I, I think of it as a tool for thought in many ways, but a tool for science, obviously, if we can accelerate that discovery of new medicines and things like that, that's, that's just so powerful.[00:03:24] But to me, it's a. It's kind of also an opportunity to learn at the feet of some real masters in this space, people who have been working on it since it was, before it was cool, if you want to put it that way. So for me, the last couple of months have been this crash course, and why I sometimes describe myself as an internal journalist is I'm helping to write some, some posts, including Supporting James in this article here we're doing for latent space where I'm just bringing my writing skill and that sort of thing to bear on their very deep domain expertise around language models and applying them to the real world and kind of surface that in a way that's I don't know, accessible, legible, that, that sort of thing.[00:04:03] And so, and the great benefit to me is I get to learn this stuff in a way that I don't think I would, or I haven't, just kind of tinkering with my own side projects.[00:04:12] swyx: I forgot to mention that you also run Ink and Switch, which is one of the leading research labs, in my mind, of the tools for thought productivity space, , whatever people mentioned there, or maybe future of programming even, a little bit of that.[00:04:24] As well. I think you guys definitely started the local first wave. I think there was just the first conference that you guys held. I don't know if you were personally involved.[00:04:31] Adam Wiggins: Yeah, I was one of the co organizers along with a few other folks for, yeah, called Local First Conf here in Berlin.[00:04:36] Huge success from my, my point of view. Local first, obviously, a whole other topic we can talk about on another day. I think there actually is a lot more what would you call it , handshake emoji between kind of language models and the local first data model. And that was part of the topic of the conference here, but yeah, topic for another day.[00:04:55] swyx: Not necessarily. I mean , I, I selected as one of my keynotes, Justine Tunney, working at LlamaFall in Mozilla, because I think there's a lot of people interested in that stuff. But we can, we can focus on the headline topic. And just to not bury the lead, which is we're talking about hire, how to hire AI engineers, this is something that I've been looking for a credible source on for months.[00:05:14] People keep asking me for my opinions. I don't feel qualified to give an opinion and it's not like I have. So that's kind of defined hiring process that I'm super happy with, even though I've worked with a number of AI engineers.[00:05:25] Defining the Hiring Process[00:05:25] swyx: I'll just leave it open to you, James. How was your process of defining your hiring, hiring roles?[00:05:31] James Brady: Yeah. So I think the first thing to say is that we've effectively been hiring for this kind of a role since before you, before you coined the term and tried to kind of build this understanding of what it was.[00:05:42] So, which is not a bad thing. Like it's, it was a, it was a good thing. A concept, a concept that was coming to the fore and effectively needed a name, which is which is what you did. So the reason I mentioned that is I think it was something that we kind of backed into, if you will. We didn't sit down and come up with a brand new role from, from scratch of this is a completely novel set of responsibilities and skills that this person would need.[00:06:06] However, it is a A kind of particular blend of different skills and attitudes and and curiosities interests, which I think makes sense to kind of bundle together. So in the, in the post, the three things that we say are most important for a highly effective AI engineer are first of all, conventional software engineering skills, which is Kind of a given, but definitely worth mentioning.[00:06:30] The second thing is a curiosity and enthusiasm for machine learning and maybe in particular language models. That's certainly true in our case. And then the third thing is to do with basically a fault first mindset, being able to build systems that can handle things going wrong in, in, in some sense.[00:06:49] And yeah, the I think the kind of middle point, the curiosity about ML and language models is probably fairly self evident. They're going to be working with, and prompting, and dealing with the responses from these models, so that's clearly relevant. The last point, though, maybe takes the most explaining.[00:07:07] To do with this fault first mindset and the ability to, to build resilient systems. The reason that is, is so important is because compared to normal APIs, where normal, think of something like a Stripe API or a search API or something like this. The latency when you're working with language models is, is wild, like you can get 10x variation.[00:07:32] I mean, I was looking at the stats before, actually, before, before the podcast. We do often, normally, in fact, see a 10x variation in the P90 latency over the course of, Half an hour, an hour when we're prompting these models, which is way higher than if you're working with a, more kind of conventional conventionally backed API.[00:07:49] And the responses that you get, the actual content and the responses are naturally unpredictable as well. They come back with different formats. Maybe you're expecting JSON. It's not quite JSON. You have to handle this stuff. And also the, the semantics of the messages are unpredictable too, which is, which is a good thing.[00:08:08] Like this is one of the things that you're looking for from these language models, but it all adds up to needing to. Build a resilient, reliable, solid feeling system on top of this fundamentally, well, certainly currently fundamentally shaky foundation. The models do not behave in the way that you would like them to.[00:08:28] And yeah, the ability to structure the code around them such that it does give the user this warm, reassuring, Snappy, solid feeling is is really what we're driving for there.[00:08:42] Defensive AI Engineering as a chaotic medium[00:08:42] Adam Wiggins: What really struck me as we, we dug in on the content for this article was that third point there. The, the language models is this kind of chaotic medium, this, this dragon, this wild horse you're, you're, you're riding and trying to guide in the direction that is going to be useful and reliable to users, because I think.[00:08:58] So much of software engineering is about making things not only high performance and snappy, but really just making it stable, reliable, predictable, which is literally the opposite of what you get from from the language models. And yet, yeah, the output is so useful, and indeed, some of their Creativity, if you want to call it that, which is, is precisely their value.[00:09:19] And so you need to work with this medium. And I guess the nuanced or the thing that came out of Elissa's experience that I thought was so interesting is quite a lot of working with that is things that come from distributed systems engineering. But you have really the AI engineers as we're defining them or, or labeling them on the illicit team is people who are really application developers.[00:09:39] You're building things for end users. You're thinking about, okay, I need to populate this interface with some response to user input. That's useful to the tasks they're trying to do, but you have this. This is the thing, this medium that you're working with that in some ways you need to apply some of this chaos engineering, distributed systems engineering, which typically those people with those engineering skills are not kind of the application level developers with the product mindset or whatever, they're more deep in the guts of a, of a system.[00:10:07] And so it's, those, those skills and, and knowledge do exist throughout the engineering discipline, but sort of putting them together into one person that is That feels like sort of a unique thing and working with the folks on the Elicit team who have that skills I'm quite struck by that unique that unique blend.[00:10:23] I haven't really seen that before in my 30 year career in technology.[00:10:26] Tech Choices for Defensive AI Engineering[00:10:26] swyx: Yeah, that's a Fascinating I like the reference to chaos engineering. I have some appreciation, I think when you had me on your podcast, I was still working at Temporal and that was like a nice Framework, if you live within Temporal's boundaries, you can pretend that all those faults don't exist, and you can, you can code in a sort of very fault tolerant way.[00:10:47] What is, what is you guys solutions around this, actually? Like, I think you're, you're emphasizing having the mindset, but maybe naming some technologies would help? Not saying that you have to adopt these technologies, but they're just, they're just quick vectors into what you're talking about when you're, when you're talking about distributed systems.[00:11:03] Like, that's such a big, chunky word, , like are we talking, are Kubernetes or, and I suspect we're not, , like we're, we're talking something else now.[00:11:10] James Brady: Yeah, that's right. It's more at the application level rather than at the infrastructure level, at least, at least the way that it works for us.[00:11:17] So there's nothing kind of radically novel here. It is more a careful application of existing concepts. So the kinds of tools that we reach for to handle these kind of slightly chaotic objects that Adam was just talking about, are retries and fallbacks and timeouts and careful error handling. And, yeah, the standard stuff, really.[00:11:39] There's also a great degree of dependence. We rely heavily on parallelization because, , these language models are not innately very snappy, and , there's just a lot of I. O. going back and forth. So All these things I'm talking about when I was in my earlier stages of a career, these are kind of the things that are the difficult parts that most senior software engineers will be better at.[00:12:01] It is careful error handling, and concurrency, and fallbacks, and distributed systems, and, , eventual consistency, and all this kind of stuff and As Adam was saying, the kind of person that is deep in the guts of some kind of distributed systems, a really high, high scale backend kind of a problem would probably naturally have these kinds of skills.[00:12:21] But you'll find them on, on day one, if you're building a, , an ML powered app, even if it's not got massive scale. I think one one thing that I would mention that we do do yeah, maybe, maybe two related things, actually. The first is we're big fans of strong typing. We share the types all the way from the Backend Python code all the way to the to the front end in TypeScript and find that is I mean We'd probably do this anyway But it really helps one reason around the shapes of the data which can going to be going back and forth and that's really important When you can't rely upon You you're going to have to coerce the data that you get back from the ML if you want if you want for it to be structured basically speaking and The second thing which is related is we use checked exceptions inside our Python code base, which means that we can use the type system to make sure we are handling, properly handling, all of the, the various things that could be going wrong, all the different exceptions that could be getting raised.[00:13:16] So, checked exceptions are not, not really particularly popular. Actually there's not many people that are big fans of them. For our particular use case, to really make sure that we've not just forgotten to handle, , This particular type of error we have found them useful to to, to force us to think about all the different edge cases that can come up.[00:13:32] swyx: Fascinating. How just a quick note of technology. How do you share types from Python to TypeScript? Do you, do you use GraphQL? Do you use something[00:13:39] James Brady: else? We don't, we don't use GraphQL. Yeah. So we've got the We've got the types defined in Python, that's the source of truth. And we go from the OpenAPI spec, and there's a, there's a tool that you work and use to generate types dynamically, like TypeScript types from those OpenAPI definitions.[00:13:57] swyx: Okay, excellent. Okay, cool. Sorry, sorry for diving into that rabbit hole a little bit. I always like to spell out technologies for people to dig their teeth into.[00:14:04] How do you Interview for Defensive AI Engineering[00:14:04] swyx: One thing I'll, one thing I'll mention quickly is that a lot of the stuff that you mentioned is typically not part of the normal interview loop.[00:14:10] It's actually really hard to interview for because this is the stuff that you polish out in, as you go into production, the coding interviews are typically about the happy path. How do we do that? How do we, how do we design, how do you look for a defensive fault first mindset?[00:14:24] Because you can defensive code all day long and not add functionality. to your to your application.[00:14:29] James Brady: Yeah, it's a great question and I think that's exactly true. Normally the interview is about the happy path and then there's maybe a box checking exercise at the end of the candidate says of course in reality I would handle the edge cases or something like this and that unfortunately isn't isn't quite good enough when when the happy path is is very very narrow and yeah there's lots of weirdness on either side so basically speaking, it's just a case of, of foregrounding those kind of concerns through the interview process.[00:14:58] It's, there's, there's no magic to it. We, we talk about this in the, in the po in the post that we're gonna be putting up on, on Laton space. The, there's two main technical exercises that we do through our interview process for this role. The first is more coding focus, and the second is more system designy.[00:15:16] Yeah. White whiteboarding a potential solution. And in, without giving too much away in the coding exercise. You do need to think about edge cases. You do need to think about errors. The exercise consists of adding features and fixing bugs inside the code base. And in both of those two cases, it does demand, because of the way that we set the application up and the interview up, it does demand that you think about something other than the happy path.[00:15:41] But your thinking is the right prompt of how do we get the candidate thinking outside of the, the kind of normal Sweet spot, smooth smooth, smoothly paved path. In terms of the system design interview, that's a little easier to prompt this kind of fault first mindset because it's very easy in that situation just to say, let's imagine that, , this node dies, how does the app still work?[00:16:03] Let's imagine that this network is, is going super slow. Let's imagine that, I don't know, like you, you run out of, you run out of capacity in, in, in this database that you've sketched out here, how do you handle that, that, that sort of stuff. So. It's, in both cases, they're not firmly anchored to and built specifically around language models and ways language models can go wrong, but we do exercise the same muscles of thinking defensively and yeah, foregrounding the edge cases, basically.[00:16:32] Adam Wiggins: James, earlier there you mentioned retries. And this is something that I think I've seen some interesting debates internally about things regarding, first of all, retries are, can be costly, right? In general, this medium, in addition to having this incredibly high variance and response rate, and, , being non deterministic, is actually quite expensive.[00:16:50] And so, in many cases, doing a retry when you get a fail does make sense, but actually that has an impact on cost. And so there is Some sense to which, at least I've seen the AI engineers on our team, worry about that. They worry about, okay, how do we give the best user experience, but balance that against what the infrastructure is going to, , is going to cost our company, which I think is again, an interesting mix of, yeah, again, it's a little bit the distributed system mindset, but it's also a product perspective and you're thinking about the end user experience, but also the.[00:17:22] The bottom line for the business, you're bringing together a lot of a lot of qualities there. And there's also the fallback case, which is kind of, kind of a related or adjacent one. I think there was also a discussion on that internally where, I think it maybe was search, there was something recently where there was one of the frontline search providers was having some, yeah, slowness and outages, and essentially then we had a fallback, but essentially that gave people for a while, especially new users that come in that don't the difference, they're getting a They're getting worse results for their search.[00:17:52] And so then you have this debate about, okay, there's sort of what is correct to do from an engineering perspective, but then there's also what actually is the best result for the user. Is giving them a kind of a worse answer to their search result better, or is it better to kind of give them an error and be like, yeah, sorry, it's not working right at the moment, try again.[00:18:12] Later, both are obviously non optimal, but but this is the kind of thing I think that that you run into or, or the kind of thing we need to grapple with a lot more than you would other kinds of, of mediums.[00:18:24] James Brady: Yeah, that's a really good example. I think it brings to the fore the two different things that you could be optimizing for of uptime and response at all costs on one end of the spectrum and then effectively fragility, but kind of, if you get a response, it's the best response we can come up with at the other end of the spectrum.[00:18:43] And where you want to land there kind of depends on, well, it certainly depends on the app, obviously depends on the user. I think it depends on the, feature within the app as well. So in the search case that you, that you mentioned there, in retrospect, we probably didn't want to have the fallback. And we've actually just recently on Monday, changed that to Show an error message rather than giving people a kind of degraded experience in other situations We could use for example a large language model from a large language model from provider B rather than provider A and Get something which is within the A few percentage points performance, and that's just a really different situation.[00:19:21] So yeah, like any interesting question, the answer is, it depends.[00:19:25] Does Model Shadowing Work?[00:19:25] swyx: I do hear a lot of people suggesting I, let's call this model shadowing as a defensive technique, which is, if OpenAI happens to be down, which, , happens more often than people think then you fall back to anthropic or something.[00:19:38] How realistic is that, right? Like you, don't you have to develop completely different prompts for different models and won't the, won't the performance of your application suffer from whatever reason, right? Like it may be caused differently or it's not maintained in the same way. I, I think that people raise this idea of fallbacks to models, but I don't think it's, I don't, I don't see it practiced very much.[00:20:02] James Brady: Yeah, it is, you, you definitely need to have a different prompt if you want to stay within a few percentage points degradation Like I, like I said before, and that certainly comes at a cost, like fallbacks and backups and things like this It's really easy for them to go stale and kind of flake out on you because they're off the beaten track And In our particular case inside of Elicit, we do have fallbacks for a number of kind of crucial functions where it's going to be very obvious if something has gone wrong, but we don't have fallbacks in all cases.[00:20:40] It really depends on a task to task basis throughout the app. So I can't give you a kind of a, a single kind of simple rule of thumb for, in this case, do this. And in the other, do that. But yeah, we've it's a little bit easier now that the APIs between the anthropic models and opening are more similar than they used to be.[00:20:59] So we don't have two totally separate code paths with different protocols, like wire protocols to, to speak, which makes things easier, but you're right. You do need to have different prompts if you want to, have similar performance across the providers.[00:21:12] Adam Wiggins: I'll also note, just observing again as a relative newcomer here, I was surprised, impressed, not sure what the word is for it, at the blend of different backends that the team is using.[00:21:24] And so there's many The product presents as kind of one single interface, but there's actually several dozen kind of main paths. There's like, for example, the search versus a data extraction of a certain type, versus chat with papers, versus And each one of these, , the team has worked very hard to pick the right Model for the job and craft the prompt there, but also is constantly testing new ones.[00:21:48] So a new one comes out from either, from the big providers or in some cases, Our own models that are , running on, on essentially our own infrastructure. And sometimes that's more about cost or performance, but the point is kind of switching very fluidly between them and, and very quickly because this field is moving so fast and there's new ones to choose from all the time is like part of the day to day, I would say.[00:22:11] So it isn't more of a like, there's a main one, it's been kind of the same for a year, there's a fallback, but it's got cobwebs on it. It's more like which model and which prompt is changing weekly. And so I think it's quite, quite reasonable to to, to, to have a fallback that you can expect might work.[00:22:29] Is it too early to standardize Tech stacks?[00:22:29] swyx: I'm curious because you guys have had experience working at both, , Elicit, which is a smaller operation and, and larger companies. A lot of companies are looking at this with a certain amount of trepidation as, as, , it's very chaotic. When you have, when you have , one engineering team that, that, knows everyone else's names and like, , they, they, they, they meet constantly in Slack and knows what's going on.[00:22:50] It's easier to, to sync on technology choices. When you have a hundred teams, all shipping AI products and all making their own independent tech choices. It can be, it can be very hard to control. One solution I'm hearing from like the sales forces of the worlds and Walmarts of the world is that they are creating their own AI gateway, right?[00:23:05] Internal AI gateway. This is the one model hub that controls all the things and has our standards. Is that a feasible thing? Is that something that you would want? Is that something you have and you're working towards? What are your thoughts on this stuff? Like, Centralization of control or like an AI platform internally.[00:23:22] James Brady: Certainly for larger organizations and organizations that are doing things which maybe are running into HIPAA compliance or other, um, legislative tools like that. It could make a lot of sense. Yeah. I think for the TLDR for something like Elicit is we are small enough, as you indicated, and need to have full control over all the levers available and switch between different models and different prompts and whatnot, as Adam was just saying, that that kind of thing wouldn't work for us.[00:23:52] But yeah, I've spoken with and, um, advised a couple of companies that are trying to sell into that kind of a space or at a larger stage, and it does seem to make a lot of sense for them. So, for example, if you're trying to sell If you're looking to sell to a large enterprise and they cannot have any data leaving the EU, then you need to be really careful about someone just accidentally putting in, , the sort of US East 1 GPT 4 endpoints or something like this.[00:24:22] I'd be interested in understanding better what the specific problem is that they're looking to solve with that, whether it is to do with data security or centralization of billing, or if they have a kind of Suite of prompts or something like this that people can choose from so they don't need to reinvent the wheel again and again I wouldn't be able to say without understanding the problems and their proposed solutions , which kind of situations that be better or worse fit for but yeah for illicit where really the The secret sauce, if there is a secret sauce, is which models we're using, how we're using them, how we're combining them, how we're thinking about the user problem, how we're thinking about all these pieces coming together.[00:25:02] You really need to have all of the affordances available to you to be able to experiment with things and iterate rapidly. And generally speaking, whenever you put these kind of layers of abstraction and control and generalization in there, that, that gets in the way. So, so for us, it would not work.[00:25:19] Adam Wiggins: Do you feel like there's always a tendency to want to reach for standardization and abstractions pretty early in a new technology cycle?[00:25:26] There's something comforting there, or you feel like you can see them, or whatever. I feel like there's some of that discussion around lang chain right now. But yeah, this is not only so early, but also moving so fast. , I think it's . I think it's tough to, to ask for that. That's, that's not the, that's not the space we're in, but the, yeah, the larger an organization, the more that's your, your default is to, to, to want to reach for that.[00:25:48] It, it, it's a sort of comfort.[00:25:51] swyx: Yeah, I find it interesting that you would say that , being a founder of Heroku where , you were one of the first platforms as a service that more or less standardized what, , that sort of early developer experience should have looked like.[00:26:04] And I think basically people are feeling the differences between calling various model lab APIs and having an actual AI platform where. , all, all their development needs are thought of for them. , it's, it's very much, and, and I, I defined this in my AI engineer post as well.[00:26:19] Like the model labs just see their job ending at serving models and that's about it. But actually the responsibility of the AI engineer has to fill in a lot of the gaps beyond that. So.[00:26:31] Adam Wiggins: Yeah, that's true. I think, , a huge part of the exercise with Heroku, which It was largely inspired by Rails, which itself was one of the first frameworks to standardize the SQL database.[00:26:42] And people had been building apps like that for many, many years. I had built many apps. I had made my own templates based on that. I think others had done it. And Rails came along at the right moment. We had been doing it long enough that you see the patterns and then you can say look let's let's extract those into a framework that's going to make it not only easier to build for the experts but for people who are relatively new the best practices are encoded into you.[00:27:07] That framework, , Model View Controller, to take one example. But then, yeah, once you see that, and once you experience the power of a framework, and again, it's so comforting, and you can develop faster, and it's easier to onboard new people to it because you have these standards. And this consistency, then folks want that for something new that's evolving.[00:27:29] Now here I'm thinking maybe if you fast forward a little to, for example, when React came on the on the scene, , a decade ago or whatever. And then, okay, we need to do state management. What's that? And then there's, , there's a new library every six months. Okay, this is the one, this is the gold standard.[00:27:42] And then, , six months later, that's deprecated. Because of course, it's evolving, you need to figure it out, like the tacit knowledge and the experience of putting it in practice and seeing what those real What those real needs are are, are critical, and so it's, it is really about finding the right time to say yes, we can generalize, we can make standards and abstractions, whether it's for a company, whether it's for, , a library, an open source library, for a whole class of apps and it, it's very much a, much more of a A judgment call slash just a sense of taste or , experience to be able to say, Yeah, we're at the right point.[00:28:16] We can standardize this. But it's at least my, my very, again, and I'm so new to that, this world compared to you both, but my, my sense is, yeah, still the wild west. That's what makes it so exciting and feels kind of too early for too much. too much in the way of standardized abstractions. Not that it's not interesting to try, but , you can't necessarily get there in the same way Rails did until you've got that decade of experience of whatever building different classes of apps in that, with that technology.[00:28:45] James Brady: Yeah, it's, it's interesting to think about what is going to stay more static and what is expected to change over the coming five years, let's say. Which seems like when I think about it through an ML lens, it's an incredibly long time. And if you just said five years, it doesn't seem, doesn't seem that long.[00:29:01] I think that, that kind of talks to part of the problem here is that things that are moving are moving incredibly quickly. I would expect, this is my, my hot take rather than some kind of official carefully thought out position, but my hot take would be something like the You can, you'll be able to get to good quality apps without doing really careful prompt engineering.[00:29:21] I don't think that prompt engineering is going to be a kind of durable differential skill that people will, will hold. I do think that, The way that you set up the ML problem to kind of ask the right questions, if you see what I mean, rather than the specific phrasing of exactly how you're doing chain of thought or few shot or something in the prompt I think the way that you set it up is, is probably going to be remain to be trickier for longer.[00:29:47] And I think some of the operational challenges that we've been talking about of wild variations in, in, in latency, And handling the, I mean, one way to think about these models is the first lesson that you learn when, when you're an engineer, software engineer, is that you need to sanitize user input, right?[00:30:05] It was, I think it was the top OWASP security threat for a while. Like you, you have to sanitize and validate user input. And we got used to that. And it kind of feels like this is the, The shell around the app and then everything else inside you're kind of in control of and you can grasp and you can debug, etc.[00:30:22] And what we've effectively done is, through some kind of weird rearguard action, we've now got these slightly chaotic things. I think of them more as complex adaptive systems, which , related but a bit different. Definitely have some of the same dynamics. We've, we've injected these into the foundations of the, of the app and you kind of now need to think with this defined defensive mindset downwards as well as upwards if you, if you see what I mean.[00:30:46] So I think it would gonna, it's, I think it will take a while for us to truly wrap our heads around that. And also these kinds of problems where you have to handle things being unreliable and slow sometimes and whatever else, even if it doesn't happen very often, there isn't some kind of industry wide accepted way of handling that at massive scale.[00:31:10] There are definitely patterns and anti patterns and tools and whatnot, but it's not like this is a solved problem. So I would expect that it's not going to go down easily as a, as a solvable problem at the ML scale either.[00:31:23] swyx: Yeah, excellent. I would describe in, in the terminology of the stuff that I've written in the past, I describe this inversion of architecture as sort of LLM at the core versus LLM or code at the core.[00:31:34] We're very used to code at the core. Actually, we can scale that very well. When we build LLM core apps, we have to realize that the, the central part of our app that's orchestrating things is actually prompt, prone to, , prompt injections and non determinism and all that, all that good stuff.[00:31:48] I, I did want to move the conversation a little bit from the sort of defensive side of things to the more offensive or, , the fun side of things, capabilities side of things, because that is the other part. of the job description that we kind of skimmed over. So I'll, I'll repeat what you said earlier.[00:32:02] Capabilities: Offensive AI Engineering[00:32:02] swyx: It's, you want people to have a genuine curiosity and enthusiasm for the capabilities of language models. We just, we're recording this the day after Anthropic just dropped Cloud 3. 5. And I was wondering, , maybe this is a good, good exercise is how do people have Curiosity and enthusiasm for capabilities language models when for example the research paper for cloud 3.[00:32:22] 5 is four pages[00:32:23] James Brady: Maybe that's not a bad thing actually in this particular case So yeah If you really want to know exactly how the sausage was made That hasn't been possible for a few years now in fact for for these new models but from our perspective as when we're building illicit What we primarily care about is what can these models do?[00:32:41] How do they perform on the tasks that we already have set up and the evaluations we have in mind? And then on a slightly more expansive note, what kinds of new capabilities do they seem to have? Can we elicit, no pun intended, from the models? For example, well, there's, there's very obvious ones like multimodality , there wasn't that and then there was that, or it could be something a bit more subtle, like it seems to be getting better at reasoning, or it seems to be getting better at metacognition, or Or it seems to be getting better at marking its own work and giving calibrated confidence estimates, things like this.[00:33:19] So yeah, there's, there's plenty to be excited about there. It's just that yeah, there's rightly or wrongly been this, this, this shift over the last few years to not give all the details. So no, but from application development perspective we, every time there's a new model release, there's a flow of activity in our Slack, and we try to figure out what's going on.[00:33:38] What it can do, what it can't do, run our evaluation frameworks, and yeah, it's always an exciting, happy day.[00:33:44] Adam Wiggins: Yeah, from my perspective, what I'm seeing from the folks on the team is, first of all, just awareness of the new stuff that's coming out, so that's, , an enthusiasm for the space and following along, and then being able to very quickly, partially that's having Slack to do this, but be able to quickly map that to, okay, What does this do for our specific case?[00:34:07] And that, the simple version of that is, let's run the evaluation framework, which Lissa has quite a comprehensive one. I'm actually working on an article on that right now, which I'm very excited about, because it's a very interesting world of things. But basically, you can just try, not just, but try the new model in the evaluations framework.[00:34:27] Run it. It has a whole slew of benchmarks, which includes not just Accuracy and confidence, but also things like performance, cost, and so on. And all of these things may trade off against each other. Maybe it's actually, it's very slightly worse, but it's way faster and way cheaper, so actually this might be a net win, for example.[00:34:46] Or, it's way more accurate. But that comes at its slower and higher cost, and so now you need to think about those trade offs. And so to me, coming back to the qualities of an AI engineer, especially when you're trying to hire for them, It's this, it's, it is very much an application developer in the sense of a product mindset of What are our users or our customers trying to do?[00:35:08] What problem do they need solved? Or what what does our product solve for them? And how does the capabilities of a particular model potentially solve that better for them than what exists today? And by the way, what exists today is becoming an increasingly gigantic cornucopia of things, right? And so, You say, okay, this new model has these capabilities, therefore, , the simple version of that is plug it into our existing evaluations and just look at that and see if it, it seems like it's better for a straight out swap out, but when you talk about, for example, you have multimodal capabilities, and then you say, okay, wait a minute, actually, maybe there's a new feature or a whole new There's a whole bunch of ways we could be using it, not just a simple model swap out, but actually a different thing we could do that we couldn't do before that would have been too slow, or too inaccurate, or something like that, that now we do have the capability to do.[00:35:58] I think of that as being a great thing. I don't even know if I want to call it a skill, maybe it's even like an attitude or a perspective, which is a desire to both be excited about the new technology, , the new models and things as they come along, but also holding in the mind, what does our product do?[00:36:16] Who is our user? And how can we connect the capabilities of this technology to how we're helping people in whatever it is our product does?[00:36:25] James Brady: Yeah, I'm just looking at one of our internal Slack channels where we talk about things like new new model releases and that kind of thing And it is notable looking through these the kind of things that people are excited about and not It's, I don't know the context, the context window is much larger, or it's, look at how many parameters it has, or something like this.[00:36:44] It's always framed in terms of maybe this could be applied to that kind of part of Elicit, or maybe this would open up this new possibility for Elicit. And, as Adam was saying, yeah, I don't think it's really a I don't think it's a novel or separate skill, it's the kind of attitude I would like to have all engineers to have at a company our stage, actually.[00:37:05] And maybe more generally, even, which is not just kind of getting nerd sniped by some kind of technology number, fancy metric or something, but how is this actually going to be applicable to the thing Which matters in the end. How is this going to help users? How is this going to help move things forward strategically?[00:37:23] That kind of, that kind of thing.[00:37:24] AI Engineering Required Knowledge[00:37:24] swyx: Yeah, applying what , I think, is, is, is the key here. Getting hands on as well. I would, I would recommend a few resources for people listening along. The first is Elicit's ML reading list, which I, I found so delightful after talking with Andreas about it.[00:37:38] It looks like that's part of your onboarding. We've actually set up an asynchronous paper club instead of my discord for people following on that reading list. I love that you separate things out into tier one and two and three, and that gives people a factored cognition way of Looking into the, the, the corpus, right?[00:37:55] Like yes, the, the corpus of things to know is growing and the water is slowly rising as far as what a bar for a competent AI engineer is. But I think, , having some structured thought as to what are the big ones that everyone must know I think is, is, is key. It's something I, I haven't really defined for people and I'm, I'm glad that this is actually has something out there that people can refer to.[00:38:15] Yeah, I wouldn't necessarily like make it required for like the job. Interview maybe, but , it'd be interesting to see like, what would be a red flag. If some AI engineer would not know, I don't know what, , I don't know where we would stoop to, to call something required knowledge, , or you're not part of the cool kids club.[00:38:33] But there increasingly is something like that, right? Like, not knowing what context is, is a black mark, in my opinion, right?[00:38:40] I think it, I think it does connect back to what we were saying before of this genuine Curiosity about and that. Well, maybe it's, maybe it's actually that combined with something else, which is really important, which is a self starting bias towards action, kind of a mindset, which again, everybody needs.[00:38:56] Exactly. Yeah. Everyone needs that. So if you put those two together, or if I'm truly curious about this and I'm going to kind of figure out how to make things happen, then you end up with people. Reading, reading lists, reading papers, doing side projects, this kind of, this kind of thing. So it isn't something that we explicitly included.[00:39:14] We don't have a, we don't have an ML focused interview for the AI engineer role at all, actually. It doesn't really seem helpful. The skills which we are checking for, as I mentioned before, this kind of fault first mindset. And conventional software engineering kind of thing. It's, it's 0. 1 and 0.[00:39:32] 3 on the list that, that we talked about. In terms of checking for ML curiosity and there are, how familiar they are with these concepts. That's more through talking interviews and culture fit types of things. We want for them to have a take on what Elisa is doing. doing, certainly as they progress through the interview process.[00:39:50] They don't need to be completely up to date on everything we've ever done on day zero. Although, , that's always nice when it happens. But for them to really engage with it, ask interesting questions, and be kind of bought into our view on how we want ML to proceed. I think that is really important, and that would reveal that they have this kind of this interest, this ML curiosity.[00:40:13] ML First Mindset[00:40:13] swyx: There's a second aspect to that. I don't know if now's the right time to talk about it, which is, I do think that an ML first approach to building software is something of a different mindset. I could, I could describe that a bit now if that, if that seems good, but yeah, I'm a team. Okay. So yeah, I think when I joined Elicit, this was the biggest adjustment that I had to make personally.[00:40:37] So as I said before, I'd been, Effectively building conventional software stuff for 15 years or so, something like this, well, for longer actually, but professionally for like 15 years. And had a lot of pattern matching built into my brain and kind of muscle memory for if you see this kind of problem, then you do that kind of a thing.[00:40:56] And I had to unlearn quite a lot of that when joining Elicit because we truly are ML first and try to use ML to the fullest. And some of the things that that means is, This relinquishing of control almost, at some point you are calling into this fairly opaque black box thing and hoping it does the right thing and dealing with the stuff that it sends back to you.[00:41:17] And that's very different if you're interacting with, again, APIs and databases, that kind of a, that kind of a thing. You can't just keep on debugging. At some point you hit this, this obscure wall. And I think the second, the second part to this is the pattern I was used to is that. The external parts of the app are where most of the messiness is, not necessarily in terms of code, but in terms of degrees of freedom, almost.[00:41:44] If the user can and will do anything at any point, and they'll put all sorts of wonky stuff inside of text inputs, and they'll click buttons you didn't expect them to click, and all this kind of thing. But then by the time you're down into your SQL queries, for example, as long as you've done your input validation, things are pretty pretty well defined.[00:42:01] And that, as we said before, is not really the case. When you're working with language models, there is this kind of intrinsic uncertainty when you get down to the, to the kernel, down to the core. Even, even beyond that, there's all that stuff is somewhat defensive and these are things to be wary of to some degree.[00:42:18] Though the flip side of that, the really kind of positive part of taking an ML first mindset when you're building applications is that you, If you, once you get comfortable taking your hands off the wheel at a certain point and relinquishing control, letting go then really kind of unexpected powerful things can happen if you lean on the, if you lean on the capabilities of the model without trying to overly constrain and slice and dice problems with to the point where you're not really wringing out the most capability from the model that you, that you might.[00:42:47] So, I was trying to think of examples of this earlier, and one that came to mind was we were working really early when just after I joined Elicit, we were working on something where we wanted to generate text and include citations embedded within it. So it'd have a claim, and then a, , square brackets, one, in superscript, something, something like this.[00:43:07] And. Every fiber in my, in my, in my being was screaming that we should have some way of kind of forcing this to happen or Structured output such that we could guarantee that this citation was always going to be present later on that the kind of the indication of a footnote would actually match up with the footnote itself and Kind of went into this symbolic.[00:43:28] I need full control kind of kind of mindset and it was notable that Andreas Who's our CEO, again, has been on the podcast, was was the opposite. He was just kind of, give it a couple of examples and it'll probably be fine. And then we can kind of figure out with a regular expression at the end. And it really did not sit well with me, to be honest.[00:43:46] I was like, but it could say anything. I could say, it could literally say anything. And I don't know about just using a regex to sort of handle this. This is a potent feature of the app. But , this is that was my first kind of, , The starkest introduction to this ML first mindset, I suppose, which Andreas has been cultivating for much longer than me, much longer than most, of yeah, there might be some surprises of stuff you get back from the model, but you can also It's about finding the sweet spot, I suppose, where you don't want to give a completely open ended prompt to the model and expect it to do exactly the right thing.[00:44:25] You can ask it too much and it gets confused and starts repeating itself or goes around in loops or just goes off in a random direction or something like this. But you can also over constrain the model. And not really make the most of the, of the capabilities. And I think that is a mindset adjustment that most people who are coming into AI engineering afresh would need to make of yeah, giving up control and expecting that there's going to be a little bit of kind of extra pain and defensive stuff on the tail end, but the benefits that you get as a, as a result are really striking.[00:44:58] The ML first mindset, I think, is something that I struggle with as well, because the errors, when they do happen, are bad. , they will hallucinate, and your systems will not catch it sometimes if you don't have large enough of a sample set.[00:45:13] AI Engineers and Creativity[00:45:13] swyx: I'll leave it open to you, Adam. What else do you think about when you think about curiosity and exploring capabilities?[00:45:22] Do people are there reliable ways to get people to push themselves? for joining us on Capabilities, because I think a lot of times we have this implicit overconfidence, maybe, of we think we know what it is, what a thing is, when actually we don't, and we need to keep a more open mind, and I think you do a particularly good job of Always having an open mind, and I want to get that out of more engineers that I talk to, but I, I, I, I struggle sometimes.[00:45:45] Adam Wiggins: I suppose being an engineer is, at its heart, this sort of contradiction of, on one hand, yeah,

Dive into the world of Istanbul's venture capital scene with Laton Venture's Görkem Türk. Together we explore essential topics like investment strategies prioritizing founding teams' quality, the evolving role of culture in startups, and future trends in gaming such as AI and socialization-focused experiences. Don't miss out on industry insights. Sign up for our newsletter! www.deconstructoroffun.com/subscribe --- Send in a voice message: https://podcasters.spotify.com/pod/show/deconstructoroffun/message Support this podcast: https://podcasters.spotify.com/pod/show/deconstructoroffun/support

Geçmişinde Google Türkiye'nin oyun ve startup'lardan sorumlu sektör yöneticisi olarak görev alan Görkem, altı ay önce Laton Ventures'ı kurdu. Kısa sürede tahminlerinden büyük bir fon hacmine ulaşan Laton'un küresel çapta gerçekleştirmeyi hedeflediği yatırımlarından konuştuk. Konuğumuz Laton Ventures'ın kurucusu Görkem Türk. Görkem Türk: https://www.linkedin.com/in/gorkemturk/ Laton Ventures: https://laton.vc/ 00:00:00 - Swipeline Intro 00:00:30 - Daha önce ne yapıyordun? 00:01:39 - Fon kurmaya nasıl karar verdin? 00:06:00 - Ekip ve partnerler 00:10:01 - Sürpriz yatırımcı: Supercell 00:11:01 - Fon büyüklüğü 00:12:21 - Yatırım kıstaslarınız neler? 00:17:06 - Ortalama yatırım tutarı nedir? 00:19:13 - Operating VC olmak 00:21:20 - Kriterler 00:24:25 - Swipeline Outro

Laton Ke Bhoot Baaton Se Nahi Mante : Pujya Sant Shri Asharamji Bapu Satsang

Laton Ke Bhoot Baaton Se Nahi Mante : Pujya Sant Shri Asharamji Bapu Satsang

Laton Ke Bhoot Baaton Se Nahi Mante : Pujya Sant Shri Asharamji Bapu Satsang

A magyarok több mint fele tárol az otthonában használaton kívüli mobiltelefont Márkamonitor     2023-07-21 08:06:03     Mobiltech Telefon Ajándék Mobiltelefon Yettel A Yettel friss országos reprezentatív kutatása alapján a magyarok fele 3-4 évig használ egy mobiltelefont, ötödük viszont akár négy évnél is tovább. A válaszadók több mint felének volt már olyan telefonja, amit előtte már más is használt, és több mint harmada már maga is ajándékozott tovább családon belül készüléket.   A megkérdezettek leggyakrabba A cellájának köszönheti, hogy túlélte a 20. század legpusztítóbb vulkánkitörését Telex     2023-07-21 05:09:51     Tudomány Vulkán Pelé Már napok óta zúgolódott a Mont Pelée, mielőtt 1902. május 8-án kitört. A vulkán közelében fekvő Saint-Pierre városa porig égett. A katasztrófának egy túlélője volt, a bajkeverésről híres Ludger Sylbaris, aki a kitörés pillanatában egy bombabiztos, félig föld alatti magánzárkában tengődött. Amerikai nagykövet levelét megírta, Kína olvasta IT Business     2023-07-21 11:21:02     Infotech USA Kína Nagykövet Microsoft Peking Hacker E-mail A kínai kormányhoz köthető hackerek fértek hozzá Nicholas Burns Pekingbe akkreditált amerikai nagykövet e-mail fiókjához a Wall Street Journal értesülései szerint. A nagykövet mellett Daniel Kritenbrink, a kelet-ázsiai térségért felelős külügyminiszter-helyettes emailfiókját is feltörték a Microsoft által a hónap elején nyilvánosságra hozott, széle Nyúzottan ébredtél? Sebaj, a Microsoft Teams kisminkel! PCWorld     2023-07-21 09:10:44     Infotech Microsoft Smink Teams Már a Maybelline sminkjeit is feldobja az arcodra a Microsoft Teams, hogy virtuálisan szépítsen. Akkora hatással van az emberiség a Földre, hogy új földtörténeti korszakba léptünk Player     2023-07-21 04:57:09     Tudomány Az emberi tevékenység hatása már olyan mértékű, hogy az irányítja a Földön zajló folyamatokat. Activision-felvásárlás: a Sony után az FTC is enyhülni látszik Bitport     2023-07-21 13:09:00     Mobiltech FTC Kampány Microsoft Sony A napokban több győzelmet is elkönyvelhetett a Microsoft abban a kampányban, amelyet immár másfél éve folytat azért, hogy 68 milliárdért megvehesse az Activision Blizzard játékkiadót. Régen várt újítás jön a telefonokra First Class     2023-07-21 05:55:06     Mobiltech Telefon YouTube Előfordul, hogy a lehalkított mobilon elindított videó maximális hangerőn ordít, pont akkor, amikor a legkevésbé szeretnénk. Ezzel a jelenséggel számol le a Youtube, több felhasználónál már tesztelik a megoldást. Bungie: hiába nyert a bíróságon a stúdió, még mindig zaklatják a fejlesztőket theGeek     2023-07-21 05:14:00     Gaming Bíróság Kártérítés Hiába a fél millió dolláros ítélet, a Bungie dolgozóit továbbra is azért zargatják, mert ott dolgoznak, és a stúdió ezért kijelentette, hogy a közösségi csapata nem nagyon fog emiatt rövid időn belül aktívabb lenni. Nemrég 500 ezer dollárt nyert a bíróságon a Bungie egy Destiny 2-játékostól kártérítés formájában, mert az illető az egyik közösségi m Őrületesen jónak tűnik az új Pókember 24.hu     2023-07-21 10:08:06     Infotech Pókember A kedvcsinálót látva nagyon messzinek tűnik az október 20-án esedékes megjelenés. Dinamikus szigetté alakul a Mate 60 kamerakapszulája Mobilarena     2023-07-21 11:20:00     Mobiltech Legalábbis több forrás állítja, hogy a fejlett arcfelimerő rendszer középre kerül, a kivágás pedig kiegészítő funkciókat kap. A Tesla szuperszámítógépet épít Gyártástrend     2023-07-21 11:02:11     Cégvilág Elon Musk Tesla Elon Musk bejelentette, hogy a következő egy év során egymilliárd dollárt költ a Dojo nevű szuperszámítógép fejlesztésére. Az első amerikai űrhajós National Geographic     2023-07-21 03:35:42     Tudomány USA Világűr 1998. július 21-én hunyt el Alan Shepard, az első amerikai űrhajós, a második ember, aki a világűrben járt. Elon Musk 20,3 milliárd dollárt veszített csütörtökön a Tesla árfolyamesése miatt Növekedés     2023-07-21 09:50:00     Gazdaság Részvény Árfolyam Elektromos autó Elon Musk Tesla Elon Musk 20,3 milliárd dollárt veszített csütörtökön amiatt, hogy nagyot esett a tulajdonában lévő elektromos autókat gyártó Tesla részvényeinek ára - számolt be a Bloomberg.

A magyarok több mint fele tárol az otthonában használaton kívüli mobiltelefont Márkamonitor     2023-07-21 08:06:03     Mobiltech Telefon Ajándék Mobiltelefon Yettel A Yettel friss országos reprezentatív kutatása alapján a magyarok fele 3-4 évig használ egy mobiltelefont, ötödük viszont akár négy évnél is tovább. A válaszadók több mint felének volt már olyan telefonja, amit előtte már más is használt, és több mint harmada már maga is ajándékozott tovább családon belül készüléket.   A megkérdezettek leggyakrabba A cellájának köszönheti, hogy túlélte a 20. század legpusztítóbb vulkánkitörését Telex     2023-07-21 05:09:51     Tudomány Vulkán Pelé Már napok óta zúgolódott a Mont Pelée, mielőtt 1902. május 8-án kitört. A vulkán közelében fekvő Saint-Pierre városa porig égett. A katasztrófának egy túlélője volt, a bajkeverésről híres Ludger Sylbaris, aki a kitörés pillanatában egy bombabiztos, félig föld alatti magánzárkában tengődött. Amerikai nagykövet levelét megírta, Kína olvasta IT Business     2023-07-21 11:21:02     Infotech USA Kína Nagykövet Microsoft Peking Hacker E-mail A kínai kormányhoz köthető hackerek fértek hozzá Nicholas Burns Pekingbe akkreditált amerikai nagykövet e-mail fiókjához a Wall Street Journal értesülései szerint. A nagykövet mellett Daniel Kritenbrink, a kelet-ázsiai térségért felelős külügyminiszter-helyettes emailfiókját is feltörték a Microsoft által a hónap elején nyilvánosságra hozott, széle Nyúzottan ébredtél? Sebaj, a Microsoft Teams kisminkel! PCWorld     2023-07-21 09:10:44     Infotech Microsoft Smink Teams Már a Maybelline sminkjeit is feldobja az arcodra a Microsoft Teams, hogy virtuálisan szépítsen. Akkora hatással van az emberiség a Földre, hogy új földtörténeti korszakba léptünk Player     2023-07-21 04:57:09     Tudomány Az emberi tevékenység hatása már olyan mértékű, hogy az irányítja a Földön zajló folyamatokat. Activision-felvásárlás: a Sony után az FTC is enyhülni látszik Bitport     2023-07-21 13:09:00     Mobiltech FTC Kampány Microsoft Sony A napokban több győzelmet is elkönyvelhetett a Microsoft abban a kampányban, amelyet immár másfél éve folytat azért, hogy 68 milliárdért megvehesse az Activision Blizzard játékkiadót. Régen várt újítás jön a telefonokra First Class     2023-07-21 05:55:06     Mobiltech Telefon YouTube Előfordul, hogy a lehalkított mobilon elindított videó maximális hangerőn ordít, pont akkor, amikor a legkevésbé szeretnénk. Ezzel a jelenséggel számol le a Youtube, több felhasználónál már tesztelik a megoldást. Bungie: hiába nyert a bíróságon a stúdió, még mindig zaklatják a fejlesztőket theGeek     2023-07-21 05:14:00     Gaming Bíróság Kártérítés Hiába a fél millió dolláros ítélet, a Bungie dolgozóit továbbra is azért zargatják, mert ott dolgoznak, és a stúdió ezért kijelentette, hogy a közösségi csapata nem nagyon fog emiatt rövid időn belül aktívabb lenni. Nemrég 500 ezer dollárt nyert a bíróságon a Bungie egy Destiny 2-játékostól kártérítés formájában, mert az illető az egyik közösségi m Őrületesen jónak tűnik az új Pókember 24.hu     2023-07-21 10:08:06     Infotech Pókember A kedvcsinálót látva nagyon messzinek tűnik az október 20-án esedékes megjelenés. Dinamikus szigetté alakul a Mate 60 kamerakapszulája Mobilarena     2023-07-21 11:20:00     Mobiltech Legalábbis több forrás állítja, hogy a fejlett arcfelimerő rendszer középre kerül, a kivágás pedig kiegészítő funkciókat kap. A Tesla szuperszámítógépet épít Gyártástrend     2023-07-21 11:02:11     Cégvilág Elon Musk Tesla Elon Musk bejelentette, hogy a következő egy év során egymilliárd dollárt költ a Dojo nevű szuperszámítógép fejlesztésére. Az első amerikai űrhajós National Geographic     2023-07-21 03:35:42     Tudomány USA Világűr 1998. július 21-én hunyt el Alan Shepard, az első amerikai űrhajós, a második ember, aki a világűrben járt. Elon Musk 20,3 milliárd dollárt veszített csütörtökön a Tesla árfolyamesése miatt Növekedés     2023-07-21 09:50:00     Gazdaság Részvény Árfolyam Elektromos autó Elon Musk Tesla Elon Musk 20,3 milliárd dollárt veszített csütörtökön amiatt, hogy nagyot esett a tulajdonában lévő elektromos autókat gyártó Tesla részvényeinek ára - számolt be a Bloomberg.

From real black powder canons in Laton, concerts and car shows at the Clovis Veterans memorial District or the Avenue of Flags in Visalia. We went over a list of the many Memorial Day events that are taking place across the Central Valley. The Fresno City Council is on the verge of approving an ordinance that would prohibit the homeless from camping and blocking much of the city's sidewalks. Thursday, the council voted 6-1 to advance the ordinance. Council member Annalisa Perea cast the lone vote against the proposal. See omnystudio.com/listener for privacy information.

A Académica recebeu o Alverca e os ribatejanos levaram os três pontos, tendo beneficiado largamente de erros infantis da nossa equipa, que ainda assim conseguiu uma boa reacção na segunda parte, ainda que insuficiente para manter alguns dos pontos em disputa em Coimbra. Como consequência imediata, perdemos mais um lugar na tabela, mas as consequências não ficaram por aqui, uma vez que à hora a que gravámos recebemos a confirmação de que Zé Nando já tinha deixado o comando técnico da Académica. Neste momento, esperam-nos duas finais. A primeira no próximo domingo frente ao Oliveira do Hospital para a qual faremos a antevisão neste episódio.

Laton Spahr is the President & Portfolio Manager of ALPS Advisors, the innovative asset management, distribution, and fund services business within SS&C Technologies. In this podcast, Doug and Laton discuss the importance of understanding the spectrum of investment management, and how to get great investment ideas in the client's hands more efficiently. They also discussed: … Continue reading Episode 149 – Humanizing the Language of Wealth Management – With Laton Spahr →

5 - Juli az MVM ügyfélszolgálaton próbált elintézni egy átírást by Balázsék

5 - Juli az MVM ügyfélszolgálaton próbált elintézni egy átírást by Balázsék

Ayiye is kahawat ko deeply Samajhate hain .!

durée : 00:29:19 - LSD, la série documentaire - par : Perrine Kervran, Alain Lewkowicz - Du 16 au 22 juillet 1942, près de 13 000 Juifs furent enfermés dans le Vélodrome d'Hiver. Si aucune archive n'existe à ce jour sur les conditions d'existence à l'intérieur du Vel d'Hiv, ceux qui ont pu s'en échapper ou y pénétrer pour apporter de l'aide racontent l'horreur.

Welcome to episode 8 of The World Class Leadership Podcast! I'm your host Pierre Monice. Help me in welcoming our guest, Laton Smith. Laton Smith is a pastor from south Florida who's been in leadership the past 12 years. He's also a proprietary firm trader trading an account with $100k in assets. He also teaches others how to trade in his LoonEx Trading Academy. In his spare time he enjoys traveling and spending time with his family. His passion in life is to help others realize that it's not too late to go out and accomplish their dreams. You can keep up with Laton Smith by connecting with him on LinkedIn. ---- Please subscribe, share, and give this show a like if it served you! You can stay in touch with me @ https://linktr.ee/pierremonice

This week Laton, Gilles & Khadeem sat down to talk about watching new marvel Tv shows and movies. In this episode, the Conch Boyz touched on reality shows on Netflix. The Conch Boyz also give their predictions on the upcoming raising Kanan power book 3. The Boyz also shared their views on whether or not 50 cents is a top 10 rapper or not. Tune in to hear our views on these topics and many more. Subscribe anywhere you listen to podcasts! Timestamps: Marvel movies and Tv shows - 3:12 Reality Tv - 10:02 Candyman - 16:20 Power book 3 - 18:58 50 Cent debate - 22:18

This week Laton, Gilles & Khadeem sat down to talk about the production quality from older well acclaimed shows. In this episode The Conch Boyz also touched on the topic of athletes being able to smoke weed during calendar days of competing. The Conch Boyz also discussed our thoughts on Netflix Prices and NBA Finals mashup. Tune in to hear our views on these topics and many more. Subscribe anywhere you listen to podcasts! Timestamps: 2:15 - Our Take On Past Productions 16:38 - Weed For The Athletes 26:24 - Netflix Prices Gern To The Moon 31:05 - NBA Finals Mashup

Durante el transcurso de los años, en diversas líneas del culto de matriz africanista, hemos visto que se utilizan percusiones ritualisticas e instrumentos de llamado de varios materiales, desde el Marfil hasta el Laton, pasando por plata y bronce. Entonces, ustedes creen que puede seguir cambiando por ejemplo a utilizar el plástico como elemento? Ver Cinetas de plástico en nuestros rituales? --- Send in a voice message: https://anchor.fm/babaigbinola/message

This week Antoine, Laton, Gilles & Khadeem sat down to talk about movie trailers in the middle of the NBA playoffs. In this episode, the Conch Boyz touched on the potential vibrio outbreak on conch in the Bahamas. The Boyz also shared their views on reading subtitles or dub voiceovers on shows. The Conch Boyz also discussed music and the upcoming Verzuz between Bow Wow and Soulja Boy. Tune in to hear our views on these topics and many more. Subscribe anywhere you listen to podcasts! Timestamp: 1:20 - Nba playoff commercial breaks 5:27 - Conch vibrio outbreak 15:30 - Voiceover and dub shows 25:11 - Starbuck Bank 28:30 Bow Wow & Soulja Boy debate #motivation #struggle #hussle #consistency #consistencyiskey #marathon #ambitions #podcastlife #podcastlife

This week Antoine, Laton, Khadeem and Gilles, open the episode discussing reality TV, the use of music to push brands and nostalgia about going to parties while in highschool. The Conchboyz end the episode, with giving their opinion if males should have a dinner birthday party.Subscribe anywhere you listen to podcasts! Time stamps: 6:00 - Future line on Lori Harvey 9:55 - Reality TV contest 14:21 - The use of music to push brands 19:11 - highschool dress up days 28:46 - Birthday party nostalgia 37:05 - Can males have a dinner birthday party?

This week Laton, Gilles & Khadeem sat down to talk about the NBA Playoffs and the NBA HOF 2021 Class. In this episode The Conch Boyz also touched on the debacle that is The Joe Budden Podcast. Tune in to hear our views on these topics and many more.  Subscribe anywhere you listen to podcasts! Timestamps: 0:00 - NBA Playoffs 15:03 - Joe Budden Debacle

This week Laton, Gilles & Khadeem sat down to talk about rap and hip hop. In this episode, the Conch Boyz touched on 106 & park. The Conch Boyz also reminisced about childhood shows. The Boyz also shared their views on the new JCole album the offseason. Tune in to hear our views on these topics and many more. Subscribe anywhere you listen to podcasts! Timestamp: 7:00 - Eminem 8:20 - 106 & park 10:17 - Childhood shows 19:03 - J Cole offseason 32:59 - Nba talks

This week Khadeem and Laton, open up the episode with continuation on NFT explanation and give their takes on the Dj Khaled and Moneybagyo album. The Conch boyz cap of episode with their opinion on take care and 2014 Forest hill drive. Subscribe anywhere you listen to podcast Time stamps 0:00 - Khadeem schools on NFT 16:38 - Dj Khaled Album - Khaled Khaled 20:57 - Money bag you album 21:51 - Take care Vs 2014 forest hill drive 25:17 - jayz Hair style 28:48 - Khadeem gives us a history lesson

This week Laton & Khadeem sat down to talk about the conditions for Covid across the nation. In this episode The Conch Boyz also touched on the possibility of the COVID Vaccinated pill. The Conch Boyz also discussed our thoughts on Khaled Khaled and many other music releases. Tune in to hear our views on these topics and many more.  Subscribe anywhere you listen to podcasts! Timestamps: 4:30 - COVID Struggles 36:53 - Khaled Khaled & New Music 39:23 - NFT Sneak Peak

This week Laton, Gilles & Khadeem sat down to talk about how being unsigned affects your music career. In this episode, the Conch Boyz touched on the increasing number of covid cases in the world. The Conch Boyz also discussed the impacts of Bahamian socialites. The Boyz also shared their views on a viral tweet from The Game. Tune in to hear our views on these topics and many more. Subscribe anywhere you listen to podcasts! Timestamp: 5:23 - Unsigned Rappers 9:12 - Covid news 11:29 -Bahamian content 23:02 - The Game viral tweet 42:51 - Basketball rants

This week Antoine, Laton, Khadeem and Gilles, open up the episode with the new kanye west's sneaker deal, update on their thoughts on the NBA and show recognition to Bahamains who play professional sports. The Conch boyz cap of the episode with acknowledgement of Dmx passing. Subscribe anywhere you listen to podcasts! 0:00-Kanye deal vs Jordan Deal 9:45 - how the NBA looking? 27:08 - Bahamians in professional sports 37:38 - R.i.p DmX 51:06- The Queen

This week Antoine, Laton, Gilles & Khadeem sat down to talk about the conditions for Covid across the nation. In this episode The Conch Boyz also touched on the possibility of the Neuralink chip that is close to a roll out. The Conch Boyz also discussed our thoughts on a breakdown of the music industry and paid our respects to DMX. Tune in to hear our views on these topics and many more.  Subscribe anywhere you listen to podcasts! Timestamps: 2:30 - COVID Troubles; It’s All In The Details 14:49 - Neuralink…Y’all Getting Chipped Up 23:30 - People Will Buy Anything 32:20 - Music Industry Breakdown 39:02 - RIP DMX 44:10 - Kanye West Is Fashion ICON; Yezzy Jumped Over Jumpman

This week Antoine, Laton, Khadeem and Gilles, open up the episode with talking about the worth of live concerts, Texas lifting the mask mandate and the rise of streaming services. The boyz cap off the episode with their opinions on coming 2 America sequel. Subscribe anywhere you listen to podcasts! Timestamps 0:00 - worth of live concerts 4:08 - Texas lifting the Mask mandate 19:25 - Streaming Services on the rise 26:44 - Unknown Sequels 32:00 - Opinions on Coming 2 America sequel

This week Antoine, Laton, Gilles & Khadeem sat down to talk about how Youtube Took Down our video and almost gave our channel a strike. In this episode The Conch Boyz also touched on the All Star Preview and The Measure of Basketball Greatness. The Conch Boyz also discussed our thoughts on the new music that is coming out during the year of 2021. Tune in to hear our views on these topics and many more.  Subscribe anywhere you listen to podcasts! Timestamps: 3:30 - Dog House Challenge Nominees 7:57 - Youtube Took Down Our Video 13:45 - All Star Preview & The Measure Of Basketball Greatness 33:50 - New Music & ConchBoyzOVO

This week Laton, Gilles & Khadeem sat down to talk about Geography. In this episode, the Conch Boyz touched on having a good Internet service provider. The Conch Boyz also discussed how staying fit is key. The Boyz also shared their views on the dos and don'ts of a Bachelor/ Bachelorette party. Tune in to hear our views on these topics and many more. Subscribe anywhere you listen to podcasts! Timestamp: 6:06 - Canadian population 9:09 - Internet service Providers 16:20 - Exercising 23:58 - Bahamian geography 29:24 - Hogh School Sports 36:27 - Golfing and Tiger Woods accident 40:31 - Gyming and staying fit 47:12 - Ibra vs Lebron 52:52 - Bachelorette party gone wild 1:07:30 - covid police

This week Laton, Khadeem and Gilles, open up the episode with discussing copyright detection on Social media platforms and then they go back in time to recall the days of Hi5, MSN messenger and BBM. The boyz finish the episode on Meek Mill's lyrics on Kobe, then go on to give their opinions on Lori Harvey's movements. Time Stamps 2:20 - Copyright detection on Social media 8:00 - Hi5, MSN messenger, BBM 14:08 - The evolution of Trolling 20:00 - Meek Mill lyric about Kobe 42:51 - Did Lori Harvey have sex in her past relationships?

This week Laton, & Khadeem sat down with Wayde from Krew N Vybe to talk about working while you're podding. In this episode, the Conch Boyz and Krew touched on an opinion-based question about a husband and wife cheating. The Wayde N Conch Boyz also discussed the allegations against TI and Tiny. The Boyz N Vybes also shared their views on the difference money can make. Tune in to hear our views on these topics and many more. Subscribe anywhere you listen to podcasts! Timestamp : 4:10 - Working and Podding 12:20 - Who destroyed the family? 23:50 - Allegations against TI and Tiny 36:43 - Money Talks

This week Laton, Khadeem and Gilles, open up the episode with recapping the Keyshia Cole vs Ashanti versus, sponsored ads and which top rappers made a King impact on the industry. They finished the episode with J.Cole and Kendrick Lamar potential return and how your situation would change after winning the Mega Millions. Subscribe anywhere you listen to podcasts! Time stamps 0:00- Introduction of Rhone 1:40- Keyshia vs Ashanti recap 2:50- sponsored ads everywhere 7:22 - who makes king moves? 13:26- J.Cole and Kendrick return 16:04 - How your situation would change after winning the mega Millions?

This week Laton, Gilles & Khadeem sat down to talk about their VDAY plans and what the day entails. In this episode The Conch Boyz also touched on how social media monitors the things that are said by influencers as opposed to the general public. The Conch Boyz also discussed our thoughts on recent tv show and movie releases. Tune in to hear our views on these topics and many more.  Subscribe anywhere you listen to podcasts! Timestamps: 6:23 - VDAY Plans 17:23 - Social Media Has The Power 23:41 - Trump Pardons Kodak 31:40 - TV Shows & Movie Reviews

This week Antoine, Laton, Gilles & Khadeem sat down to talk about the nets trading for James Harden. In this episode, the Conch Boyz touched on Kyrie's disfunction in the NBA. The Conch Boyz also discussed Lori Harvey's new relationship with Micheal B Jordan. The Boyz also shared their views on the buss it challenge and new challenge culture #doghousechallenge. Tune in to hear our views on these topics and many more. Subscribe anywhere you listen to podcasts! Timestamp 3.37 - The nets new look 16:21 - Kyrie 21:17 - Lori Harvey and Micheal B Jordan 27:47 - Buss it challenge 32:41 - Ganster Drake 42:20 - Biden Inorgeration 01:00:36 - Koolaid Bain Tribute

This week on the Way of Champions Podcast we welcome Ron Adams, Golden State Warriors Asst. Coach. Ron is in his second season as an assistant coach for the Golden State Warriors, his 21st season overall on the bench as an NBA assistant. In his first year with the Warriors, Ron helped the team finish first in defensive rating (98.2) en route to an NBA Championship. During our discussion, we dive into Ron's philosophy of being a great assistant coach, navigating hard conversations with players, and working on a staff where another assistant coach is trying to undermine the head coach. Highlights and great quotes from the this episode: Ron talks about his journey as a coach "A good coach is someone who has a job." "There is no formula for good coaching." How has social media changed the halftime locker room experience? Why does Steve Kerr call Ron the "truth-teller"? What makes an assistant coach effective? ...what are your "ego needs"? How do you handle disagreements with the Head Coach? Having hard conversations with players Ron talks about his relationship with Kevin Durant How do you know when it's time to move on as an assistant? More About Ron Adams Adams, 68, owns previous coaching experience with Boston Celtics (2013-14), Chicago Bulls (2003-08, 2010-13), Oklahoma City Thunder (2008-2010), Milwaukee Bucks (1998-2003), Philadelphia 76ers (1994-96) and San Antonio Spurs (1992-94). During his time as an assistant coach, Adams has made 14 appearances in the NBA Playoffs. In addition to his coaching roles, the Laton, CA, native also served as a player personnel scout for the Portland Trail Blazers from 1996-98. Adams joined the Warriors following a one-year stint (2013-14) with the Celtics. Prior to working in Boston, he had spent the previous three seasons on Tom Thibodeau’s staff in Chicago. The Bulls made the playoffs in each of those three campaigns, including a trip to the 2011 Eastern Conference Finals. During this most recent stint with the Bulls, the team ranked in the top-three in opponent scoring each season, leading the league in the category in 2011-12. Before joining the NBA, Adams coached collegiately for 20 years with head coaching stints at Fresno State (1986-90) and his alma mater, Fresno Pacific (1972-75). He also served as an assistant coach at U.S. International, UC Santa Barbara, UNLV and at both Fresno schools. Additionally, Adams has coached professionally in Belgium and Japan, as well as for the Canadian national team. Adams and his wife, Leah, have a daughter, Hayley, and a son, Jared The Way of Champions Podcast is brought to you by Sports Refund. When kids sign up to play youth sports there are always fees involved. And parents pay those fees so their child can be on the team, not the injury report. That’s why I love Sports Refund, and parents will too. Sports Refund is low-cost sports fee insurance – NOT health insurance, sports fee insurance. So if your child becomes injured or sick and can’t play, you get your fees reimbursed for that lost time, from one game to an entire season. It’s that simple. This product not only saves families wasted fees, it saves injured athletes the stress of feeling like they’re wasting their parents’ money. The fees come back until the player comes back. So they can focus on fully recovering and making a healthy return to the game. Sports Refund has options for individuals and organizations. Ask your club if they offer it, or visit SportsRefund.com/Game to learn more and sign up today. That’s SportsRefund.com/Game. Can’t Play? Don’t Pay. With Sports Refund. Help Support the Podcast! Become a Podcast Champion! …and get FREE access to ALL of our online courses. If you love the podcast, we would love for you to become a Podcast Champion, (https://www.patreon.com/wayofchampions) for as little as a cup of coffee per month (OK, its a Venti Mocha), to help us up the ante and provide even better interviews, better sound, and an overall enhanced experience. Plus, as a $10 per month Podcast Super-Champion, you will have access to never before released and bonus material, including: Downloadable transcripts of the podcasts, so you don't have to crash your car trying to take notes! A monthly discussion with John, James, Jerry, and other special guests talking about the previous month's episodes and answering some of the FAQs we received that month A code to get free access to our online course called "Coaching Mastery," usually a $97 course, but yours for free for becoming a patron. Access to an online community of coaches like you who are dedicated listeners of the podcast, and will be able to answer your questions and share their coaching experiences. Thank you for all your support these past two years, and a special big thank you to all of you who become part of our inner circle, our patrons, who will enable us to take our podcast to the next level. https://www.patreon.com/wayofchampions

This week Laton, Khadeem and Gilles, open up the episode with completing their vodka preferences from the previous episode. They go on to discuss what was a good comedy movie and effects of drilling in the Bahamas. Subscribe anywhere you listen to podcasts! Time stamps 0:00 - Vodka preferences continued 5:20 - Tiffany Hardish 10:00 - What was a good comedy movie? 19:20 - Christmas movies 20:40 - Drilling in the Bahamas 29:43 - Shrimp, cockroaches of the sea

This week Laton, Khadeem and Gilles, open up the episode by talking about which Parrot is the correct Bahamian Parrot, Ashanti and Keyshia cole postponed versus and Grammys recent snubs. They cap the episode off by talking about spreading your self to thin and their vodka preferences.Subscribe anywhere you listen to podcasts! Time stamps 4:15 - What is the correct Bahamian Parrot 22:11- Ashanti vs Keyshia cole postponed 36:19 - Khadeem on fiver 37:30- Grammy snubs 41:00- Spreading yourself thin 43:35 - Vodka preferences

This week Laton, Gilles & Khadeem sat down to talk about the positive effects of living abroad. In this episode The Conch Boyz also touched on what is truly considered dating between two individuals. The Conch Boyz also discussed knowing your boundaries in the dating/talking/feeling stages. Tune in to hear our views on these topics and many more.  Subscribe anywhere you listen to podcasts! Timestamps: 1:09 - Trial 4 23:52 - Netflix Price Point Dilemma 34:24 - Netflix Monopoly on Media & Entertainment Industry

This week Antoine, Laton & Khadeem sat down to talk about the lil wayne and dj khalid collab on No Ceilings 3. In this episode the Conch Boyz touched on Donald Trump's transition out of office back to making money. The Conch Boyz also discussed how Viacom didn't want to pay Dave Chappell for the Chappell show on Netflix. The Boyz also shared their views on the recent Kevin Hart special on Netflix. Finally, they talked about the Joe Rogan podcast and a look back at the Jeezy vs Gucci versus. Tune in to hear our views on these topics and many more. Subscribe anywhere you listen to podcasts! Timestamp: 1:12 - No Ceilings 3 5:42 - Trump post-presidency 14:13 - Dave Chapell Vs Viacom 18:57 - Kevin Hart Special 28:06 - Joe Rogan podcast 35:16 - Gucci Vs Jeezy

This week Laton, Khadeem and Gilles, open up the episode by talking about the new Instagram features and online market place choices. They continue with their opinions on lil boosie's tweet and the best animated cartoon series.The episode is capped of with the discussion on how celebs relationships increase their hype and how women dresses have changed throughout the years.Subscribe anywhere you listen to podcasts! Time stamps 3:00 - new Instagram features 8:00 - Online market place preferences 16:00 - Lil boost tweet on Ps5 24:00 - funniest animated cartoons 33:30 - nostalgia on old shows 39:08 - relationships hype up celebs career 41:52 - changes in how women dresses throughout the years 43:00- Drake real Gallis

This week Laton, Gilles & Khadeem sat down to talk about the positive effects of living abroad. In this episode The Conch Boyz also touched on what is truly considered dating between two individuals. The Conch Boyz also discussed knowing your boundaries in the dating/talking/feeling stages. Tune in to hear our views on these topics and many more.  Subscribe anywhere you listen to podcasts! Timestamps: 1:10 - Political Talks 13:08 - Positive Effects Of Living Abroad 18:26 - You Replacing Your Girl With A New Girl On The Trip / What Is Dating? 32:18 - Knowing Your Boundaries in The Dating/Talking/Feeling Stage

This week Laton, Gilles & Khadeem sat down to talk about watching porn with your significant other. In this episode, the Conch Boyz touched on past and upcoming films and tv shows. The Conch Boyz also discussed the recent death of King Von the rapper. The Boyz also shared their views on how they would move as a high-level star. Tune in to hear our views on these topics and many more. Subscribe anywhere you listen to podcasts! Timestamp: 2:20 - Pornhub party 8:33 - Dc Movies 15:30 - Are actors getting paid for their reruns on Netflix? 19:00 - which shows were better? 22:37 - Joe Budden podcast 26:30 - Adult swim vs Nick at night 31:56 - R.I.P King Von

This week Laton, Khadeem,Gilles and Antoine. Talk about how things will change and be the same around Christmas time. The Conchboyz go into depth on some traditions they do in the Christmas season and if they will tell the future kids about Santa Clause. They also give their opinions on if your cloths should change as you age and if The Bahamas can put vat on Netflix. Subscribe anywhere you listen to podcasts! Timestamps 3:10 - How your Christmas looking? 24:00 - Christmas traditions 27:30 - Are you telling you kid about Santa clause? 34:30 - Does adapting to new trends effect us 42:41 - History of James Bond 55:55 - Vat on Netflix

This week Antoine, Laton, Gilles & Khadeem sat down to talk about the dilemma with the proposals culture of the modern day relationship. In this episode The Conch Boyz also touched on the concept of monogamy and divorce. The Conch Boyz also discussed if men are hard wired to cheat or is it something that is learned. Tune in to hear our views on these topics and many more.  Subscribe anywhere you listen to podcasts! Timestamps: 3:03 - The Woman & Proposal Dilemma 20:52 - Monogomy Or Divorce 24:30 - Are Men Hard Wired To Cheat 34:30 - Staying In a Relationship After Your Girlfriend Says No

In this episode of Made for Life, we're talking with Jonathan Laton about his military career, his tours of duty and all of the adventures he's had on the way. He's got some very interesting stories!