The Future of AI Software Factories- OpenCLAW, Agent Orchestration & The Intelligence Age with Mark Smith [MVP]

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You're welcome everybody to another episode of the M665FM podcast.

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The podcast where we look on speakers Microsoft professional

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architects, consultiles, developers, IT leaders and decision makers.

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And yeah, we try to learn directly from every people in the Microsoft and from the Microsoft technologies.

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The days guest, I think it's someone who not really meets an interaction in the Microsoft community.

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And my guest is Mark Smith also known as the NZ 365 guy has been a Microsoft MVP for 15 years or over 15 years.

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Microsoft certified, okay, certified China founder of global based out of the Microsoft press book Microsoft 65 co palette adoption creator of the global success for 90 days mentoring challenge in the host of the.

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Yeah, over podcast, I think you have renamed it from the intelligent age podcast to Microsoft innovation podcast.

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Well, yeah, with more than 800 episodes and published since 2017 over the years, Mark has held thousands of professional worlds understand Microsoft technologies from dynamics 365 and power platform to today's rapidly evolving world of AI co palette in.

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Yeah, intelligent agents recently his focus has shift to one of the most exciting areas enterprise AI AI software factories and open claw where multi AI agent color,

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to design build and test to live a software ways called fundamental change how organizing create digital solutions.

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Yeah, welcome Mark. It's still amazing to have you here.

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Thank you. Thank you. It's a pleasure to be here.

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Yeah, so we've let the size normally you are the host.

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So, Mark, can you a little bit tell us how you how you become a part of the Microsoft ecosystem.

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Yeah, that's interesting. That started 30 years ago. So a long time ago, I was working for a company at the time that.

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Was involved in IT training. It was a company called New Horizons, which was headquartered in California.

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And I joined a company at that time that had the New Zealand franchise of that IT company.

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And I joined it because I wanted to learn web design.

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It was my goal. I had the company I was working for, which was in the medical industry at the time.

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I decided that we needed a website and started down that path. And back then,

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if you didn't have a super light as in from a file size on your website, you got criticized severely from the market.

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And so, you know, that was HTML, CSS back then you had to learn the whole everything and building sites.

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And so, this company advertised that they had a web design course that you could do over the course of a week, a full intensive, you know, six hour a day type web design course.

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And remember, this is at the the birjing of the internet. Back then, you know, Google wasn't even a thing.

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It was the search engines were were ultra vista and how was the other one as in.

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Fireball and you know, yeah, really, really old tech back then. And that was the start for me.

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And I joined, sorry, I signed up to do this course and then they told me that the course didn't exist.

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That they were aspirational and wanting to teach the course.

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And so from that, I joined the company.

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And as a salesperson of the training, and I sold the first 12 seats as a 12 seat classroom,

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I sold the first 12 seats and we didn't still didn't have a trainer.

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And so then they put me on train, the trainer out of the US at the work through the night.

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I learnt the web design course as the trainer.

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And then the next week, I delivered to the 12 students, individual students that I'd sold onto the course.

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So, and that was the start of it.

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And that was my start into IT at the time, really big, the big thing to get was that was.

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You know, by that, at that point, no value was switching over to Microsoft,

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been the big player in network operating systems.

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NT4 is what I started on from a tech perspective.

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And I was just immersed in it then for seven years.

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I taught courses on, I ended up teaching like a five day course on internet working,

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which covered everything around how packets move across the network,

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how they function down the OSIRM, which basically means how they get from your screen to the other side of the world.

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What's all the routes, you know, what is DNS, what is all the things that make the network infrastructure work.

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And that was, you know, my journey, I mean, I got exposed massively to Microsoft tech.

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At the time, I also bought some micro systems into the company,

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and I want a big contract for delivering all the Sun Microsoft system, system training.

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By that point, I'd moved into management and I became the general manager of the company over that seven years

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from starting off as the salesperson to getting into that position.

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And really, you know, I've been with Microsoft ever since.

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Yeah, you have worked across dynamics, power platform, corporate, corporate AI.

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How has your, your own career evolved alongside the Microsoft transformation?

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So it was, I, at this training company, our CRM was built on Fox Pro.

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Now, just to give you an idea, that means it was, it was never designed to use a mouse on it.

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It was designed to use keyboard shortcuts and, and key combinations.

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It was, it was designed pre-email.

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So you think there wasn't even an email address field?

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Well, not pre-email. It was never designed for email because email wasn't even a mainstream kind of construct.

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Then everything was phones.

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It was all around calling people, getting people on the phone and, and, you know,

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our draw card was, you offered a one day free training.

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And you've got to remember, this is the, the start of all tech.

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It was a start of using word in XR on PowerPoint.

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Now, I mean, our most popular course was exalt training.

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A two day, a two day exalt training course was the most popular course.

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But, you know, we, I was looking for a CRM that was a bit more modern.

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And the, the, the company, the pairing company, a new horizons wanted to go with the product called sales logic,

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back then.

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And Microsoft had just bought out MSCRM.

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And it started as MSCRM 1.0 in 2003. It started 2003.

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And by November 2003, it was MSCRM 1.2.

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And, and then they skipped too entirely and went to three.

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And that's when the dynamics name started to kick into play.

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And that was because they had bought other more ERP related products into the suite and that bazaar suite.

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And, you know, I got to say, I just fell in love with the tech.

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And for me, it wasn't about like a saw beyond the vision beyond CRM systems,

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which is to be able to build any type of system.

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And really, that was the original low code system that, that I touched from a Microsoft perspective.

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And you think SharePoint was only just coming out around the same time.

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It was, you know, very new SharePoint and content management systems at that time.

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They were all brand new things as well.

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I mean, our mobile phones were brand new.

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Like, you know, it was, it was a big deal to be able to get the web browser to work on your mobile phone back then.

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And so, yeah, it's been a long journey in tech.

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And so I've ridden the, the dynamic CRM and then Dynamics 365 journey right up to 10 years ago.

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But over 10 years ago, now Power Platform kicked in.

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And so, rode that journey and, but you know, one thing I've always observed about the smart people at work inside Microsoft.

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They always move to where the money where Microsoft is investing the money.

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And if you've just watched this space for a little while,

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then not really investing it in Dynamics 365,

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then not really investing in the Power Platform.

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And I might get some hate from that, but read the writing on the wall.

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Look at the people that have left Microsoft in recent times and look at the product teams that they've been involved in.

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The day of low code has been superseded by AI.

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And that, I can do a lot more with a lot deeper technical functionality and technical ability.

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And I low code just slows me down.

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A UX, that's, or UI, sorry, that's in the way slows me down.

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I'd much rather be at a COI level.

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I'd much rather be at a prompt level, MCP API level,

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to drive the results that I want, then spend hours wasted on configuration screens to try and make the software perform.

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How I wanted to perform.

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Yeah.

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What was, how you, one was the moment you realized I would fundamentally change software development.

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I mean, the day,

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ChatGBT became a thing in November 2022,

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I got it that day, right?

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I list on Reddit, I'm on X, I never left X from the Twitter days when a lot of people did.

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And so I heard about it and I was on it that day.

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And I called my wife over and said, well, check this out.

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What I was able to do.

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Now, at that point, Microsoft had already done the deal about two years earlier with OpenAI to acquire the IP.

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And, you know, I can remember Charles Lamana talking about the initial versions of GPC and in regards to power effects and things like that.

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And I started really getting interested in AI, I suppose, around 2018 when I was in London.

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And particularly, you know, Google were doing some great things and I always saw that what Google was doing was academic AI.

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In other words, it was like awesome stuff, but like, how do I use it?

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Right?

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And what I saw from Microsoft at those early days, and this is back in, this is pre GPC.

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So this is back 2018, 2019.

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And so really much more around ML and cognitive services and things like that.

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Microsoft was starting to really what I call created practical AI.

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In fact, they might have even been calling it practical AI.

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Like AI that really could move the dial in your business.

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And so, you know, that's where I started getting excited about it.

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I started writing blog posts.

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In fact, I'm sure you can go on my blog and find posts on AI from 2018,

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where I was writing and exploring and I was interviewing people on my podcast that were in AI,

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particularly across Europe at the time, because I was in the UK.

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And then, but it was, I suppose, there was an MVP session, like an MVP summit that I was, it was in COVID.

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And so it was still locked down.

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We couldn't travel from memory, or they just at the last minute allowed travel into the US at the end of the COVID type run.

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And Charles Lamana got on stage and talked about, you know, co pilot inside of the power platform.

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And you have been your ability to just speak into creation.

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And while he was doing it, he said, and by the way, in your MVP tenant, it's their available one.

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So while he's presenting, I'm on my laptop and I built an app while he's talking to manage seeds.

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And, you know, I've got a big garden, I've got a centropic forest that I'm building.

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And so, and one of the things I want to do is like when's the optimal time to plant seeds, you know,

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if for any type of species that I was wanting to plant, what was the best conditions, etc.

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And I was able to prompt that app into reality while he was doing the presentation.

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And for me, it was like, I could see the future and, you know, where it was going to go.

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And so I haven't been a Dynamics 365 or Power Platform MVP for two, three years now.

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So I pivoted it.

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I pivoted first into the next category I went into was AI platforms at Microsoft.

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And then in the last renewal cycle, I've actually gone into co pilot.

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And that's because, you know, writing the book, as you said, on co pilot adoption with my wife,

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has taken that pivoting journey and they switched AI platform MVP just to Foundry MVP.

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And, you know, bless Microsoft, but they get so enamored with their own product names

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that they forget that it means nothing in the market.

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And so, you know, I wasn't too much of a fan of being a Foundry MVP

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because you had to explain it to everybody what it meant.

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And I'm happy to go to co pilot because at least I think it has a higher profile, but also in more recent times,

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you know, I have got heavily into AI outside of Microsoft.

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And because I fundamentally feel Microsoft has lost its way in the last three years in the AI space as a whole.

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It went from a absolute, you know, when if you look at November, going into the new year,

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calendar, new year in 2023, I think there was a graphic that came out at that time showing Microsoft

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that a 64% market share in AI because of its relationship to open AI and the assets that it already had in Azure.

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And unfortunately, my observation, Microsoft was always good at chasing the market leader.

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They've never, ever, ever in my estimation, and I'm happy to be proved wrong, although it's irrelevant,

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not good at playing lead and owning and demanding a space.

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And I give you like three or four examples.

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There was no Valnetware.

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Then, he four came out and Microsoft dominated and ultimately took over that space.

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There was Lotus 123, right?

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Spreadsheets ex-out dominated that space took the market share.

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Word perfect, word, even let's go into gaming, right?

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There was PlayStation from Sony and then Microsoft jumped in with Xbox.

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So they're good at chasing a category.

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They don't seem to be good at leading a category.

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And what I feel that's happened in the last three years, their sales org, their marketing org,

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and even their R&D or product teams just went after the wrong goals.

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They didn't have clarity.

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They just thought if we chuck the name Copilot on everything and force it down people's

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throats that people had adopt.

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And of course at the start of this year, the data came out in the earnings report.

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That something like 3.4% adoption of Copilot globally, which is, if you're a startup,

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that is a multi-billion dollar business, about a $4 billion business.

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Nice.

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But you're the biggest company in the world at the time.

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That's not good enough.

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That's not how the game is played when you're the biggest company in town.

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And so I feel that they've lost their way.

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Now one thing I'll credit Microsoft, they tend to play a long game, right?

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And so they will iterate and iterate and probably ultimately get this right.

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But right now, they aren't.

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They're playing catch-up, they're playing follow me, and Throppaker releases co-work.

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Oh, now Microsoft's under deal with them because hey, this is the latest shiny thing.

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OpenClaw becomes a big deal then we've got Scout.

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And I'm just like, you know, and then they've released three or four models, the Maya models,

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and on the announcement that built, they're nowhere near on par with what's in market even now.

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They're like, all five, like the open models are ahead of them.

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And I know that that's the start.

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And they will probably win long term with this strategy.

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But I didn't want to be held up with only drinking Microsoft's cool aid for this AI era.

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So I have gone deep into a topic.

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I've gone deep into open AI.

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I've gone deep into open claw.

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I have nine different APIs into other models.

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So like I use Chinese models.

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I use European models.

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I don't want to be restricted to only one flavor or only one dish on the menu.

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Yeah, I think that's really interesting.

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I think also the start of co-pilot was really, really bad that start because it's, don't

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feel like an AI.

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It feels like clippy is back.

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It's only says you're okay.

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Yeah, I mean, I look at how much it got mocked and, and for that, that, that exact type of

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thing.

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And I think what they did is they didn't use the latest models from day one.

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They always used behind and so people were getting this AI experience and market with the

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consumer type apps.

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And Microsoft just sucked them comparison and their only kind of story was, hey, it's on

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your data.

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But nobody was experiencing the value of it being on your data.

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And another thing is companies data estates are so bad and full of so much human error,

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duplicated thousands of times.

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And what happened is that the results weren't great even if it's on your own data because

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you know accounting and set that spreadsheet out for the last five years every week and

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there's like 500 or 1000 copies of that spreadsheet all with the same formula error, all with

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the same formatting error or whatever other error.

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And then you go, AI, look at this and give me the correct answer and it goes, well, your

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pattern is fully human error.

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And so I'm just going to copy it and then everyone gets upset about hallucination and

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AI's sucks and stuff.

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And I'm like, it's just train on bad human data.

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Yeah, yeah, I think that's a real big topic.

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But have you also tried the Microsoft models?

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I think it's P4 and MIRR.

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So it's MIRR is as I'm building a product at the moment as an ISV that I am only using

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the MIRR models because I want it to be economical for the people that buy this product.

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And I wanted to 100% sit in Microsoft's ecosystem, in other words, on their infrastructure

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with no inference being handled off to our third party AI model.

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And the reason is trust.

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Microsoft has created a good trust profile with their customers.

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And you know, right now, if you're using co-pilot and you're using opus 5, for example, you

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know, if you go, you know, move away from the auto selector and choose your model and co-pilot,

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the compute or what's happening there is not even being done on Azure infrastructure.

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It's been handed off to AWS infrastructure.

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It's been computed outside Microsoft's ecosystem and been handed in, same for co-work.

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It's not been computed on Microsoft's infrastructure.

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And so for a lot of regulated industries, that's a problem.

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Data software, that's a problem.

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You know, it's not operating in my local data center.

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And even, you know, I'm in a little country called New Zealand.

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We have got no AI infrastructure by the hyperscalers in country save.

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AWS about three months ago put a bit of infrastructure in.

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So they're really the first move, even though Microsoft has its own data center in country.

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There's just not enough silicon to go around.

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And, you know, New Zealand, five million people total.

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We've got more sheep than we have people.

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It's not like a prime market where they're going to make a lot of money on AI.

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So I can see why it's not here.

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But it changes the profile in a regulated industry where let's say the government says,

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you're not allowed to send financial data outside the borders of the country.

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That creates a problem for people.

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And, you know, in the financial or regulated industry.

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So therefore, even co-pilot, you know, can be difficult unless they, you know,

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talk down and say, we don't want to throw up in the mix.

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Let's make it just GVT that's running on Azure and having that control.

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Yeah, I know it.

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I live in New Europe.

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We love Rayleigh.

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Yeah, what?

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I mean, just at the first month, New Rules kicked in in the EU AI Act, right?

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Now, new penalties just kicked in to play on the first of this month.

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So you know, yeah, it's a, it's a really, really hard topic here.

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What, what, what, I think you have the, this road, AI software factories.

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What, what does this mean?

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Yeah, so an AI software factory, you know, over my career, I've had, you know,

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teams of developers work for me on software projects.

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So I've had my architects, I've had developers, I've had my QA testing teams,

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and my project managers that, you know, have run the team.

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And I've always loved the concept of DevOps, although I've really struggled to ever

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see it fully implemented in the organizations I have been in.

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And that's partly because I've always worked for partners and you really don't run

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a DevOps for another customer.

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The whole idea is it's an in-house operational thing between the developer side of

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the organizational engineering and your business.

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So what I did is I sat down and I said, could I construct a, in my case, it's now a

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nine agent team that each one falls a role in the DevOps, Infinity Loop.

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And they are gated, they can't do any work outside their role.

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So, you know, I have, uh, ARC, and the ARC is my architect.

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I've scouted, my scout is my, um, takes requirements.

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And so it has a formalized requirements gathering process that it goes through with me.

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Hands-it-arc, ARC, you know, has to go out is very deterministic and very many elements

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I put into is about how they research, then I'm allowed to just start roofing on something

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without showing that they have researched it.

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And so one of the kind of, um, parts of the scaffold I have in place is I require them

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to always look at today's date and then they have to research the latest finding in

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whatever I'm doing.

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And so I don't want historic, oh, three years ago, this is how it was done.

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And that's why I'm architect me this way.

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Hell no, right?

293
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It's got to be how would we do it if we were starting today from scratch?

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How would the architecture look?

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And then, you know, it goes through, I've got Cody, does the actual code development.

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Now, I've got different models that are doing different that, that,

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a power these different agents.

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I have Tara, Tara is my, um, engineering manager.

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She's the orchestrator across all of them.

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She makes sure they hand off, um, I've built in Ralph, what's called Ralph loops, which is

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make sure that they keep, they keep going to they solve because agents often will go

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or that they will stop and ask just a benign question, but that stops them.

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I want to get up in the morning and a piece of software that I've specced is built.

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And it's now ready for me to get demoed.

305
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It's gone through its test cycle.

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Um, I have a security, a role that is security.

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It uses, um, um, um, Microsoft's red teaming resources as part of that.

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It does a red teaming process on my release cycle.

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It goes to Rex, my release manager.

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It goes to Wiki that does all the documentation for it.

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So it's this continuous loop.

312
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And then recently I built a maintenance system in that it's always checking the security

313
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posture of my environment.

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Or by the way, all this runs on Azure, I run the entire thing on the, um, and, um, and, um,

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I've got a lot of Azure resources coming into the mix now, you know, of, uh, started to

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have to add in blob storage.

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I've had to start adding in, you know, you've quickly find API key management becomes

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unwieldy.

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I think I'm over 300 API keys now.

320
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You don't want to be managing those, um, manually.

321
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And you never want them into being in plain text format, right?

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Because it's a security, um, vulnerability.

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And then what would often happen if they weren't encrypted, you would get an AI model

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that would output a key to screen and go, oops, oops, we need to rotate the keys because

325
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I fucked up type thing.

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And then I'd be like, well, how do I stop the AI making these stupid mistakes?

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And so, you know, um, they use things like key vault.

328
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You don't crypt, you, you, you abstract the way.

329
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And this is all the learning.

330
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So, but now I've got to kind of, I, I use, um, uh, uh, DevOps to, sorry, I don't use DevOps.

331
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I use, um, GitHub to run all my repos.

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The agents have, um, app, what's called app level access to those.

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So I can see the identity of who's doing what, um, across those.

334
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I have, I use the project management board, um, inside the company profile.

335
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So it spans all my repos.

336
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And, um, so I can get a real time dashboard.

337
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What cards, what tickets been worked on?

338
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Are they, you know, at what stage of that DevOps cycle are they in?

339
00:26:53,840 --> 00:26:59,480
Are built multiple dashboards that, you know, are built full routing tool for all my, um,

340
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all my, uh, AI models.

341
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And so it has a, a price comparison.

342
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At the moment, it's, it's, uh, it's selectable by agent.

343
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My next bill I'm going to make is I'm going to make it, um, route based on task.

344
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So in other words, it's going to choose the API or the LLM that it's going to use based

345
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on the task that it's actually doing.

346
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And so this is all around cost optimization, um, of the models.

347
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And so now I'm starting to build, and the crazy thing is the first bit of software I built

348
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was back in January.

349
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And it was because my kids' local skill wanted to run a fishing competition as a fundraiser.

350
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And someone told them that I knew something about AI and my girl had only just started five

351
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years old, started at school.

352
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And so they said, yeah, here's the, the paper that we used to run this competition.

353
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It's been running for 25 years as fishing comp.

354
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And I used AI to build out a full, uh, dashboard for at the event, somebody displayed on

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screens.

356
00:27:59,960 --> 00:28:03,960
I did apps that people out in their boats when they're fishing could measure and weigh

357
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and photograph their fish for a catch and release.

358
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I built this whole thing myself and it kind of stunned what I could do, you know, and, um,

359
00:28:15,080 --> 00:28:20,760
all that kind of my whole IT career, I've gone from being the lead of teams and stuff to

360
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not to be able to build myself.

361
00:28:23,040 --> 00:28:24,840
Have an idea, I'm always an ideas person.

362
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So I'm now able to quickly take an idea, iterate it into a production based app.

363
00:28:29,680 --> 00:28:39,120
And so the, the, um, the software factory is, you know, um, at the end of the day, uh, a DevOps

364
00:28:39,120 --> 00:28:48,080
team with Ralflux loops and, um, and very segregated role functions to make sure, you know,

365
00:28:48,080 --> 00:28:51,720
I was one of the, even the commands that said, you can never mark your own homework.

366
00:28:51,720 --> 00:28:54,360
A developer can never say, my shit's awesome, right?

367
00:28:54,360 --> 00:28:57,240
It has to be verified by my case, Vera.

368
00:28:57,240 --> 00:29:03,360
She's my verification and she's got a whole, uh, over 20 different release patterns that

369
00:29:03,360 --> 00:29:07,120
it must comply with before it will, the PR will go through.

370
00:29:07,120 --> 00:29:13,800
And so, um, yeah, I've, it's amazing what you can build once you start bringing, if you

371
00:29:13,800 --> 00:29:19,760
like, all your career experience together, knowing how software is developed and then

372
00:29:19,760 --> 00:29:22,920
being able to go, Hey, what have I made every single role in agent?

373
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What would that look like?

374
00:29:24,200 --> 00:29:25,680
And, um, it's been fun.

375
00:29:25,680 --> 00:29:28,680
It's built a lot of software in the last six months.

376
00:29:28,680 --> 00:29:29,680
Yeah.

377
00:29:29,680 --> 00:29:36,680
Uh, I also, uh, have done a lot of an AI factory, uh, Azure factory, uh, I don't know.

378
00:29:36,680 --> 00:29:39,280
They, they, they renamed it every time.

379
00:29:39,280 --> 00:29:43,240
Um, uh, and that's really amazing.

380
00:29:43,240 --> 00:29:51,120
I have built an, um, yeah, and a research tool, uh, with thousands of companies and all

381
00:29:51,120 --> 00:29:58,680
the employees and, uh, my idea was, uh, do a research for cyber security, uh, if all

382
00:29:58,680 --> 00:30:03,360
the best practices to the companies and they have started perfectly and then look, where,

383
00:30:03,360 --> 00:30:05,840
where did the cyber security risk come from?

384
00:30:05,840 --> 00:30:10,440
And I have checked, checked this and, yeah, you can ask every, every company and every, every

385
00:30:10,440 --> 00:30:17,000
user, uh, some questions and then it makes, uh, yeah, similar to a wall, yeah, and, uh,

386
00:30:17,000 --> 00:30:22,000
or enterprise, uh, will companies and that's really, really cool whether you can build, but,

387
00:30:22,000 --> 00:30:29,400
um, how autonomous, actually agents from, from your perspective.

388
00:30:29,400 --> 00:30:30,400
Sorry.

389
00:30:30,400 --> 00:30:31,400
Can you say that again?

390
00:30:31,400 --> 00:30:34,280
Autonomous, uh, um, or autonomous, autonomous.

391
00:30:34,280 --> 00:30:35,280
Yeah.

392
00:30:35,280 --> 00:30:38,640
Uh, how autonomous are the, these agents today from your perspective?

393
00:30:38,640 --> 00:30:46,960
So, so I, I have chat interfaces to about half my agents.

394
00:30:46,960 --> 00:30:50,400
So, so Tara, for example, is my interface.

395
00:30:50,400 --> 00:30:57,280
I have a direct chat interface to her and then she oversees the entire DevOps team.

396
00:30:57,280 --> 00:31:01,920
I don't have any direct interfaces with those individual DevOps teams.

397
00:31:01,920 --> 00:31:06,120
I can speak to any of them, but I speak through Tara to them.

398
00:31:06,120 --> 00:31:10,480
And so, because as I say, you know, there's, there's so much to learn.

399
00:31:10,480 --> 00:31:11,480
How do you handle memory?

400
00:31:11,480 --> 00:31:15,640
How do you make, you know, the difference between short term memory, um, medium term and

401
00:31:15,640 --> 00:31:20,040
long term memory, that's a whole thing you've got to sort out with your systems.

402
00:31:20,040 --> 00:31:22,200
That's why I spend a lot of time on the Wiki side.

403
00:31:22,200 --> 00:31:25,280
The Wiki is not for me to know what the documentation is.

404
00:31:25,280 --> 00:31:30,320
The Wiki is so that DevOps team can refer to and know what was built three months, five

405
00:31:30,320 --> 00:31:37,360
months, six months ago and build their, my system only became autonomous when I got Ralph

406
00:31:37,360 --> 00:31:43,600
Loops working correctly, um, which is this whole, I need to, you know, Ralph Loop is

407
00:31:43,600 --> 00:31:50,080
like forward slash goal, um, if you were in, in one of the other, one of the, um, frontier

408
00:31:50,080 --> 00:31:55,640
lab type products and the idea is you say, don't stop until you sort this out like to

409
00:31:55,640 --> 00:31:57,600
you have solved it.

410
00:31:57,600 --> 00:32:03,680
And so they will go and so I will go, I'll wake up in the morning and I will have over 50

411
00:32:03,680 --> 00:32:07,760
PR notifications from GitHub from through the night.

412
00:32:07,760 --> 00:32:09,280
They are working.

413
00:32:09,280 --> 00:32:12,120
They find a problem, verifies a problem in their code.

414
00:32:12,120 --> 00:32:14,760
They send it back and it goes through.

415
00:32:14,760 --> 00:32:20,240
Now I do have a jump out of loop if they just keep screwing up because I don't want to

416
00:32:20,240 --> 00:32:26,280
wedge my VM that I've got running everything and, you know, I've gone through that process

417
00:32:26,280 --> 00:32:29,880
where they get into a, uh, out of control loop.

418
00:32:29,880 --> 00:32:38,480
And so in that scenario outside of open claw, I created a, a entity called, uh, Ruru and

419
00:32:38,480 --> 00:32:41,360
Ruru in New Zealand is the name for Owl.

420
00:32:41,360 --> 00:32:46,800
So it's, it's the Maori name for Owl and what it does is it observes everything.

421
00:32:46,800 --> 00:32:52,480
It's observing my infrastructure, health, it's observing, um, all my API health and this

422
00:32:52,480 --> 00:32:56,120
is the API that I have built.

423
00:32:56,120 --> 00:33:02,040
And anytime it detects something, it sends me a notification around severity.

424
00:33:02,040 --> 00:33:07,400
So it's looking across 20 dimensions of my level one layer, level two is then looking

425
00:33:07,400 --> 00:33:11,680
for downtime, uh, can't reach for a certain period of time.

426
00:33:11,680 --> 00:33:16,080
And ultimately that goes up to a Zua notifications when Ruru dies.

427
00:33:16,080 --> 00:33:17,560
How do I know that Ruru's died?

428
00:33:17,560 --> 00:33:24,160
Well I use a Zua then through their notification system to say, hey, your CPU's gone through

429
00:33:24,160 --> 00:33:27,880
the roof, you run out of RAM or you run out of this space.

430
00:33:27,880 --> 00:33:33,120
And by the way, Ruru hasn't phoned home, you know, in a set frequency of time.

431
00:33:33,120 --> 00:33:34,840
So Ruru might be down.

432
00:33:34,840 --> 00:33:43,440
So Ruru is really about making sure I keep my system in top, um, you know, peak state at

433
00:33:43,440 --> 00:33:48,160
any time because the more code you build, right, the thing that bogs you down is maintenance

434
00:33:48,160 --> 00:33:50,080
then of that code.

435
00:33:50,080 --> 00:33:55,880
And so that's why Ruru and as part of that model, it sits outside or how I've designed

436
00:33:55,880 --> 00:34:02,560
it outside of open claw as an observer of open claw and of anything that could break.

437
00:34:02,560 --> 00:34:08,800
And so the minute it finds a break that it, it, it tries again and let's say it doesn't

438
00:34:08,800 --> 00:34:15,040
get the response or it, or it can repeat or prove the, um, the issue.

439
00:34:15,040 --> 00:34:19,160
It then raises a ticket straight away over on my DevOps board.

440
00:34:19,160 --> 00:34:24,680
Tara is then on her loop, which she's looking at the board every 15 minutes.

441
00:34:24,680 --> 00:34:27,080
She picks up the new ticket that's arrived.

442
00:34:27,080 --> 00:34:29,320
She then starts handing it through the DevOps cycle.

443
00:34:29,320 --> 00:34:33,680
So I can have an incident overnight, but when I get up in the morning, it's solved because

444
00:34:33,680 --> 00:34:39,800
it's been triaged through, um, and the outcomes been addressed and, um, built into the code.

445
00:34:39,800 --> 00:34:46,560
And so what could one of my drivers is, is around how do you create a, a self improving

446
00:34:46,560 --> 00:34:47,560
system?

447
00:34:47,560 --> 00:34:52,320
And this is a big thing with AI models at the moment, recursive self improvement.

448
00:34:52,320 --> 00:34:57,880
So I take that thinking and go, okay, how can I improve my DevOps team?

449
00:34:57,880 --> 00:35:02,080
What's the new learnings it's going to take from the last month and improve itself for the

450
00:35:02,080 --> 00:35:04,200
next month's worth of work?

451
00:35:04,200 --> 00:35:10,760
And so the DevOps team that I created five months ago is far superior now than it was then

452
00:35:10,760 --> 00:35:14,480
because it's actually always improving itself.

453
00:35:14,480 --> 00:35:18,920
And I've got this maintenance layer that is always scanning things like security, best

454
00:35:18,920 --> 00:35:21,520
in class security, best in class architecture.

455
00:35:21,520 --> 00:35:24,480
Um, I have all these different layers.

456
00:35:24,480 --> 00:35:32,760
And the minute it detects a maintenance issue or I'm using, you know, um, uh, a vulnerable

457
00:35:32,760 --> 00:35:36,880
piece of code or something like that, it then just raises a ticket straight away on the

458
00:35:36,880 --> 00:35:41,080
DevOps team and their job is to research and solve it.

459
00:35:41,080 --> 00:35:47,280
And so my, my system is super stable, super secure.

460
00:35:47,280 --> 00:35:52,280
And um, because I'm losing all the best in class security from Microsoft and I did that

461
00:35:52,280 --> 00:35:53,280
from day one.

462
00:35:53,280 --> 00:35:59,920
I originally built on Windows, um, first PM and I lasted three days until I went with a headless

463
00:35:59,920 --> 00:36:03,280
and put into environment and I tell you, I've never looked back.

464
00:36:03,280 --> 00:36:10,040
Um, it's just, yeah, I mean, I built full rag databases and all sorts for, for handling, um,

465
00:36:10,040 --> 00:36:11,040
cross agent.

466
00:36:11,040 --> 00:36:12,040
How do you get out?

467
00:36:12,040 --> 00:36:15,960
How do you tell agents that they know other agents are involved in your business?

468
00:36:15,960 --> 00:36:20,240
Like had to build a crew of a global address list that you would have in real life, but for

469
00:36:20,240 --> 00:36:21,240
agents.

470
00:36:21,240 --> 00:36:27,640
And I can know who else could help me to do something, um, built hundreds of skills.

471
00:36:27,640 --> 00:36:34,080
I don't like to take anybody else's, um, skills like if there's skill marketplaces and stuff.

472
00:36:34,080 --> 00:36:42,080
So what I typically do is I will point, um, my AIs at a, a, a repo where somebody's got

473
00:36:42,080 --> 00:36:47,880
a skill or, or some idea and I'll say research it, then go out and five, five competing products

474
00:36:47,880 --> 00:36:50,720
and research what their nuances.

475
00:36:50,720 --> 00:36:53,920
And now let's plan our skill and how we'll create it.

476
00:36:53,920 --> 00:37:00,280
So I don't have any dependencies on anybody else's, um, stuff to say politely.

477
00:37:00,280 --> 00:37:04,600
I don't want any risk like having an early days in the marketplace where you're getting

478
00:37:04,600 --> 00:37:09,840
some, you know, um, bad actors putting skills up to get, you know, access.

479
00:37:09,840 --> 00:37:13,480
I've never had those issues because I don't use anybody else's skills.

480
00:37:13,480 --> 00:37:18,440
I always, uh, research the concept of their skill, then build it myself.

481
00:37:18,440 --> 00:37:20,440
Awesome.

482
00:37:20,440 --> 00:37:26,440
So, so the soft, the system do is the requirement gathering, architecture, UX design, coding,

483
00:37:26,440 --> 00:37:29,760
testing, security reviews, documentation, and deployment.

484
00:37:29,760 --> 00:37:30,760
Yeah.

485
00:37:30,760 --> 00:37:37,240
So what's your, uh, my, my job is just keep feeding the new ideas.

486
00:37:37,240 --> 00:37:43,040
You know, for example, um, about a week ago, I started to build a full accounting system.

487
00:37:43,040 --> 00:37:48,400
Um, and, uh, but, you know, and like accounting software doesn't spin my, um,

488
00:37:48,400 --> 00:37:53,920
my wheels, but I need an account, I, you know, I've used their company for 15 years in my

489
00:37:53,920 --> 00:37:56,520
own business, um, called zero.

490
00:37:56,520 --> 00:38:00,840
Um, they're one in, they're New Zealand's most successful company on the global stage with

491
00:38:00,840 --> 00:38:01,840
its software.

492
00:38:01,840 --> 00:38:07,560
Build probably rocket labs is up there challenging them now, but each year the price goes up and

493
00:38:07,560 --> 00:38:09,560
accounting is accounting, right?

494
00:38:09,560 --> 00:38:12,760
The government doesn't change the rules around accounting all that often.

495
00:38:12,760 --> 00:38:16,840
If there's a change, it's just a new tax.

496
00:38:16,840 --> 00:38:21,840
But the price keeps going up for the SaaS service of kind of accounting has been done the same

497
00:38:21,840 --> 00:38:24,360
way in New Zealand for the last hundred years.

498
00:38:24,360 --> 00:38:28,120
It's probably going to be done the same way for the next period of time.

499
00:38:28,120 --> 00:38:32,080
And so I was just like, I'm sick of paying these SaaS fees.

500
00:38:32,080 --> 00:38:36,920
Why don't I build an accounting kernel that every type of business software, small business,

501
00:38:36,920 --> 00:38:37,920
not the enterprise.

502
00:38:37,920 --> 00:38:39,840
I'm not tackling that, that, that game.

503
00:38:39,840 --> 00:38:41,320
And I've opened source it.

504
00:38:41,320 --> 00:38:42,320
But I'm getting up open source.

505
00:38:42,320 --> 00:38:47,120
So I'm designed for New Zealand because I just want to give something back to the New Zealand

506
00:38:47,120 --> 00:38:50,960
market, but allow any business to point their AI at it.

507
00:38:50,960 --> 00:38:56,640
And it's, you know, from the core kernel of my, what I call the New Zealand ledger, it

508
00:38:56,640 --> 00:39:02,160
has full CLI access, full MCP access, full API access.

509
00:39:02,160 --> 00:39:06,760
But it's built with the idea that agents are probably going to be the main operator of

510
00:39:06,760 --> 00:39:09,320
this accounting system as well as humans.

511
00:39:09,320 --> 00:39:14,320
Where most software accounting software is designed for people and accountants.

512
00:39:14,320 --> 00:39:19,840
But by booting this core kernel, I can then allow any company to build their unique

513
00:39:19,840 --> 00:39:20,840
layer over the top.

514
00:39:20,840 --> 00:39:25,120
So if they're a project management business, they can build a project management module.

515
00:39:25,120 --> 00:39:30,080
If they've, you know, got warehousing, they can boot a warehousing module on top.

516
00:39:30,080 --> 00:39:35,520
But it's going to be AI first in how it's been engineered from the ground up.

517
00:39:35,520 --> 00:39:39,800
Yeah, that did.

518
00:39:39,800 --> 00:39:41,200
It's really cool.

519
00:39:41,200 --> 00:39:42,200
Awesome.

520
00:39:42,200 --> 00:39:44,440
Yeah, we also an expert in open claw.

521
00:39:44,440 --> 00:39:48,360
What problem do you solve, Zulford?

522
00:39:48,360 --> 00:39:55,640
So the big thing for me with open claw, right, is that open AI has been the main product I

523
00:39:55,640 --> 00:39:58,040
have been on since November, 2022.

524
00:39:58,040 --> 00:40:02,920
As soon as the $20 plan I was on it, as soon as the $200 plan I was on it.

525
00:40:02,920 --> 00:40:09,840
And then open AI started to make some really dick moves in the market, just fumbles back

526
00:40:09,840 --> 00:40:10,840
in 2025.

527
00:40:10,840 --> 00:40:13,760
And so I was like, okay, I'm going to go deep on on Thropic.

528
00:40:13,760 --> 00:40:19,240
So I went deep on on Thropic January this year, February.

529
00:40:19,240 --> 00:40:24,960
And then, and then, you know, and Thropic starts shooting itself in the foot, you know, they

530
00:40:24,960 --> 00:40:29,600
started screwing the model, right, because they ran out of compute.

531
00:40:29,600 --> 00:40:33,760
And so rather than tell the market what's going on, they start to throttle you back.

532
00:40:33,760 --> 00:40:37,280
And so all of a sudden, I'm looking at some code that it's producing.

533
00:40:37,280 --> 00:40:40,400
And I'm like, it's producing what looks like code.

534
00:40:40,400 --> 00:40:43,600
And then when you look at it, it's just, it's not there.

535
00:40:43,600 --> 00:40:44,600
It's saying it did it.

536
00:40:44,600 --> 00:40:46,320
And it's not there.

537
00:40:46,320 --> 00:40:52,520
And so what I loved about open claw is that I own the harness, right?

538
00:40:52,520 --> 00:40:57,040
I can plug, as I said, I've got nine different API connections, not even including open

539
00:40:57,040 --> 00:41:03,760
router. That's another connection I have that allows me to pull on Chinese models, European

540
00:41:03,760 --> 00:41:10,320
models in US models as as I see fit, but none of them own all my stuff, all my own stuff

541
00:41:10,320 --> 00:41:16,160
sits in Azure, all my own that whole DevOps team, I've changed the models behind various

542
00:41:16,160 --> 00:41:19,320
agents multiple times now, right?

543
00:41:19,320 --> 00:41:23,160
And the beauty is, I don't have to reinvent my system where I don't have to move the context

544
00:41:23,160 --> 00:41:27,640
out of one system and import it into another system because the context sits in my open

545
00:41:27,640 --> 00:41:29,160
claw environment.

546
00:41:29,160 --> 00:41:30,680
And that's the power of the system.

547
00:41:30,680 --> 00:41:35,360
I don't care what models I don't care, you know, whose model is ahead at the moment because

548
00:41:35,360 --> 00:41:39,480
I will always have the latest models and I will always be able to switch the minute

549
00:41:39,480 --> 00:41:43,720
now to work on what, but I don't have to set context every time.

550
00:41:43,720 --> 00:41:51,440
Context sits with me.

551
00:41:51,440 --> 00:41:59,520
I think I have to ask you that question.

552
00:41:59,520 --> 00:42:01,520
Adulity?

553
00:42:01,520 --> 00:42:02,520
Yeah.

554
00:42:02,520 --> 00:42:03,520
Okay.

555
00:42:03,520 --> 00:42:04,520
Okay.

556
00:42:04,520 --> 00:42:05,640
I can still hear you.

557
00:42:05,640 --> 00:42:13,000
You don't want to ask me about what I think about co-pilot studio.

558
00:42:13,000 --> 00:42:14,760
Here.

559
00:42:14,760 --> 00:42:16,640
It's not positive.

560
00:42:16,640 --> 00:42:24,920
I think co-pilot studio is an interim solution to try and gather up the low-code audience and

561
00:42:24,920 --> 00:42:31,780
give them something a bone, you know, because I remember the day the predecessor, what co-pilot

562
00:42:31,780 --> 00:42:36,280
studio is built on, was released and there's a blog post where I put a little chat bot

563
00:42:36,280 --> 00:42:45,680
on my site, you know, this would be back in 2020, I would say, or 2019, the end of 2019.

564
00:42:45,680 --> 00:42:50,240
And the thing is, once again, I don't want to low-code, agentic platform.

565
00:42:50,240 --> 00:42:56,680
I want an, I, I, I, I prompt, I want English as the engagement or language as the engagement

566
00:42:56,680 --> 00:42:57,680
layer.

567
00:42:57,680 --> 00:43:04,040
I don't want to know, did I plug in the right API at the right time and did I, you know,

568
00:43:04,040 --> 00:43:08,720
did I put the right, I just, I hate the product.

569
00:43:08,720 --> 00:43:09,720
I think it's shite.

570
00:43:09,720 --> 00:43:12,240
I think it's a product that doesn't need to exist.

571
00:43:12,240 --> 00:43:15,800
People, if they want to build stuff, just go straight to Foundry and build there.

572
00:43:15,800 --> 00:43:19,920
Nowadays, get your AI skills up that you can just work directly with Foundry.

573
00:43:19,920 --> 00:43:26,800
You don't need an interim solution with a, a stupid monetization model in my mind in

574
00:43:26,800 --> 00:43:27,800
place.

575
00:43:27,800 --> 00:43:33,000
It's Microsoft once again, fumbling to find relevance in the space with a solution.

576
00:43:33,000 --> 00:43:36,040
So you asked the question, sorry about the answer.

577
00:43:36,040 --> 00:43:38,040
No, no, no problem.

578
00:43:38,040 --> 00:43:39,960
So I'm a little bit more happy.

579
00:43:39,960 --> 00:43:44,640
I never have used co-pilot studio yet because I'm happy with AI Foundry.

580
00:43:44,640 --> 00:43:49,160
I never, I also never see the reason for, for, for, for this product.

581
00:43:49,160 --> 00:43:51,280
So, yeah.

582
00:43:51,280 --> 00:43:57,600
Um, I think one topic, especially when we talk about agents, it's, it's governance.

583
00:43:57,600 --> 00:44:03,000
How, how did you handle the, the governance stuff?

584
00:44:03,000 --> 00:44:04,640
So this is a cool thing.

585
00:44:04,640 --> 00:44:11,520
Microsoft have got really strong governance, um, uh, content available.

586
00:44:11,520 --> 00:44:17,640
And so you can take that governance content that they have available and plug it into

587
00:44:17,640 --> 00:44:18,640
your system.

588
00:44:18,640 --> 00:44:21,320
You can say to your AI, go read this, understand it.

589
00:44:21,320 --> 00:44:26,440
And I want to implement that exact type governance framework in, in what I am building.

590
00:44:26,440 --> 00:44:32,680
And so governance is slightly easier when you're only one person, right?

591
00:44:32,680 --> 00:44:33,840
That's governing everything.

592
00:44:33,840 --> 00:44:38,640
I put very strict gates around what has to have my eyeballs on it.

593
00:44:38,640 --> 00:44:46,080
Um, well, like I don't let, um, my open core environment, I built a, this, my only other

594
00:44:46,080 --> 00:44:59,240
open source product, I built a CLI into, um, into Microsoft graph from, um, open core.

595
00:44:59,240 --> 00:45:05,360
And I can access anything that's in the open, over the Microsoft graph, um, and that means

596
00:45:05,360 --> 00:45:12,240
that I can do from open core posts into teams, I can delete posts in teams, I can send messages

597
00:45:12,240 --> 00:45:17,440
to people inside organization and teams, I can read all my email, read all my calendar.

598
00:45:17,440 --> 00:45:23,520
Um, I've had that maybe for five months now, but I have never given it the skill to write

599
00:45:23,520 --> 00:45:24,720
an email.

600
00:45:24,720 --> 00:45:29,080
Um, I've never given it, so in other words, I've given it, uh, the ability to

601
00:45:29,080 --> 00:45:30,640
create appointments for me.

602
00:45:30,640 --> 00:45:35,600
So what I do is I have my agents, my agents actually can create appointments with me when it

603
00:45:35,600 --> 00:45:38,920
has to present a concept to me, uh, whatever agent it is.

604
00:45:38,920 --> 00:45:42,640
And so they go through, I have a main agent called Rook.

605
00:45:42,640 --> 00:45:47,400
So Rook is my, uh, personal, uh, like my PA agent.

606
00:45:47,400 --> 00:45:53,360
So not all my agents get access to my diary on my M365 calendar.

607
00:45:53,360 --> 00:45:54,360
Only Rook does.

608
00:45:54,360 --> 00:46:00,160
And so if somebody wants a time in my diary from my agent team, they have to broker that

609
00:46:00,160 --> 00:46:01,160
through Rook.

610
00:46:01,160 --> 00:46:06,880
And so Rook has a whole bunch of operational rules around how it can set messages for me,

611
00:46:06,880 --> 00:46:09,880
what times of the day I do and don't take appointments.

612
00:46:09,880 --> 00:46:16,040
Um, it will, I'll let it draft the email, but I don't let it draft the email inside of

613
00:46:16,040 --> 00:46:17,040
outlook.

614
00:46:17,040 --> 00:46:23,640
I have a draft is, uh, to a markdown file and then I copy and paste that markdown file

615
00:46:23,640 --> 00:46:26,400
into, uh, an email.

616
00:46:26,400 --> 00:46:29,680
And I'm, I'm the person that doesn't have a lot of email nowadays.

617
00:46:29,680 --> 00:46:35,160
I've got, you know, I've run for over 10 years on a zero inbox principle in my business

618
00:46:35,160 --> 00:46:36,160
career.

619
00:46:36,160 --> 00:46:40,720
Um, something to learn from a book called Getting Things Done Years ago.

620
00:46:40,720 --> 00:46:47,560
And so because I have my email so optimized, like I don't get spam, I don't get newsletter

621
00:46:47,560 --> 00:46:53,880
emails because I have a, uh, automations that automatically clean those out.

622
00:46:53,880 --> 00:46:59,880
They automatically put them into my, uh, folder called spam and then, um, I will then point

623
00:46:59,880 --> 00:47:03,440
the AI at that spam photo and is there any, I don't even go look at it.

624
00:47:03,440 --> 00:47:08,920
I say, is there anything I need to be, uh, you know, identifying here and I let it handle

625
00:47:08,920 --> 00:47:09,920
that.

626
00:47:09,920 --> 00:47:16,200
So I'm, I, I, I believe that within 18 months, I won't have email anymore.

627
00:47:16,200 --> 00:47:21,960
I won't have that need for email in the traditional sense that, sorry, let's change it.

628
00:47:21,960 --> 00:47:26,400
I will have email, I won't have outlook and I won't have replaced outlook with another

629
00:47:26,400 --> 00:47:28,200
mail program.

630
00:47:28,200 --> 00:47:34,720
An agent will make me aware of any, uh, email and an agent will, at that same agent will,

631
00:47:34,720 --> 00:47:39,760
you know, take a dictation from me, whatever and send under my approval.

632
00:47:39,760 --> 00:47:43,840
Um, I will find the eliminate outlook from my life.

633
00:47:43,840 --> 00:47:47,720
From me, eyeballing outlook.

634
00:47:47,720 --> 00:47:59,240
Um, what, what did you think about, um, I think that needs to invest heavily in AI and, uh,

635
00:47:59,240 --> 00:48:06,800
data centers and, um, I think there's something coming new that's, uh, and, uh, media, how

636
00:48:06,800 --> 00:48:09,440
development with Microsoft.

637
00:48:09,440 --> 00:48:16,160
So this, I think it makes 1000 terror flops and the new windows should be in a minimum,

638
00:48:16,160 --> 00:48:17,960
I think 20 or 40 terror flops.

639
00:48:17,960 --> 00:48:24,440
It's the, uh, environment for next window system.

640
00:48:24,440 --> 00:48:35,240
Did you think we, uh, in, in having AI on your, on our, our out sees, if you don't?

641
00:48:35,240 --> 00:48:43,120
Yeah, I, yeah, I'm convinced that AI is going to move, um, like it won't move away from

642
00:48:43,120 --> 00:48:44,120
the cloud, right?

643
00:48:44,120 --> 00:48:49,000
AI will be, you know, still will have optimization, the cloud, but what I do really believe,

644
00:48:49,000 --> 00:48:55,640
and I've run, um, models locally on my, I've got a, you know, a high spec, um, Nvidia card in

645
00:48:55,640 --> 00:48:57,160
my computer.

646
00:48:57,160 --> 00:49:04,600
Um, so I've run models locally and I think that we're going to run to a world very soon

647
00:49:04,600 --> 00:49:09,320
within the next 12 months where AI will run locally.

648
00:49:09,320 --> 00:49:13,840
That will be as good as what the current front end models are providing, but it will be local

649
00:49:13,840 --> 00:49:14,840
on device.

650
00:49:14,840 --> 00:49:21,880
And so I think compute on device and the ability to run on device as good as what the current,

651
00:49:21,880 --> 00:49:23,640
um, providers are right now.

652
00:49:23,640 --> 00:49:26,760
So I'm not going to say they're going to be as good as the day that you're running them

653
00:49:26,760 --> 00:49:31,760
locally as the model that we're running that day in the cloud, but I absolutely believe

654
00:49:31,760 --> 00:49:42,960
compute on the edge and AI at the edge will be our first class and will be a, um, will be

655
00:49:42,960 --> 00:49:44,120
in all sorts of devices.

656
00:49:44,120 --> 00:49:49,000
I believe, you know, my, my money is still even though they've fumbled the ball badly, is

657
00:49:49,000 --> 00:49:58,520
that Apple will nail AI at the edge on their devices and we will naturally see it on, um,

658
00:49:58,520 --> 00:50:01,920
you know, on the PC as well, AI on the edge in time.

659
00:50:01,920 --> 00:50:09,240
Um, and that will be a massive, uh, massive enabler, um, of what's possible.

660
00:50:09,240 --> 00:50:10,240
Yeah.

661
00:50:10,240 --> 00:50:16,600
Um, what, what did you think for, for the people that they starting now, like students, uh,

662
00:50:16,600 --> 00:50:17,600
or so?

663
00:50:17,600 --> 00:50:23,400
What, how would it, well, they prepare for the, for the AI first career?

664
00:50:23,400 --> 00:50:24,400
Okay.

665
00:50:24,400 --> 00:50:26,280
So they need to learn a couple of things.

666
00:50:26,280 --> 00:50:30,680
They need, and you know, prompt engineering has been around a long time, right?

667
00:50:30,680 --> 00:50:36,400
Prompt engineering is, if you're getting it now, you need to really set yourself up to

668
00:50:36,400 --> 00:50:40,040
get a good, a prompting, but you need to move beyond prompt engineering.

669
00:50:40,040 --> 00:50:44,000
You need to get into context engineering and you need to really understand what context

670
00:50:44,000 --> 00:50:45,240
engineering is about.

671
00:50:45,240 --> 00:50:49,040
And then you should probably take a third step and go into outcomes engineering, um, in

672
00:50:49,040 --> 00:50:50,040
understanding it.

673
00:50:50,040 --> 00:50:51,880
You need to understand first principles.

674
00:50:51,880 --> 00:50:53,800
You need to understand critical thinking.

675
00:50:53,800 --> 00:50:57,200
And when I say you need to understand it, you need to be trained on it.

676
00:50:57,200 --> 00:51:00,880
And you don't have to go to fancy provider, get AI to train you on these subjects.

677
00:51:00,880 --> 00:51:04,040
Get it to, um, do space, space training.

678
00:51:04,040 --> 00:51:09,240
So in other words, you learn the same thing repeated over time, making the distance larger

679
00:51:09,240 --> 00:51:15,640
each time so that it bakes it into your, your computer, um, up here.

680
00:51:15,640 --> 00:51:17,320
Um, so those are the first things.

681
00:51:17,320 --> 00:51:21,520
The next thing I'd say, learn about harnesses and you learn everything you can about

682
00:51:21,520 --> 00:51:25,360
harnesses and why harnesses are so, so important.

683
00:51:25,360 --> 00:51:31,760
Um, the third thing I'd say, um, don't get bended locked in the day of, you know, drinking

684
00:51:31,760 --> 00:51:38,120
just Microsoft, cool aid or just AWS, cool aid or, or, um, uh, Google Cloud Platform,

685
00:51:38,120 --> 00:51:42,600
GBC, that, that gone, use whatever tool, use all the tools.

686
00:51:42,600 --> 00:51:48,000
You know, if your, uh, got xenophobia and anti-China, sort that shit out.

687
00:51:48,000 --> 00:51:52,800
Like they are, they're gonna win this race.

688
00:51:52,800 --> 00:51:54,480
Just watch the writing on the wall.

689
00:51:54,480 --> 00:51:59,920
They're gonna win this race and the reason is they are getting better results on poor

690
00:51:59,920 --> 00:52:01,920
a silicon at the moment.

691
00:52:01,920 --> 00:52:05,360
Um, then they're, they're, well, they're getting, not better results.

692
00:52:05,360 --> 00:52:08,720
They're getting on par results where the people that have the best computer

693
00:52:08,720 --> 00:52:10,600
infrastructure in the best silicon in the world.

694
00:52:10,600 --> 00:52:15,400
So if they're doing that in inferior silicon, you wait to their fabs get up to speed

695
00:52:15,400 --> 00:52:21,960
and they're on par at the silicon layer, they're getting so much additional layers in their

696
00:52:21,960 --> 00:52:28,840
architecture right that I think just like they overtook the solar industry globally, just

697
00:52:28,840 --> 00:52:34,440
like they've overtaken the, the, the EV market with electric vehicles and every other thing

698
00:52:34,440 --> 00:52:39,400
in manufacturing, they are going to be the model providers of the future.

699
00:52:39,400 --> 00:52:45,000
So if you've got some, you know, thing racism or anything like that about them,

700
00:52:45,000 --> 00:52:48,280
and you, you want to get locked into that type of thinking?

701
00:52:48,280 --> 00:52:49,480
Good luck.

702
00:52:49,480 --> 00:52:55,640
Um, but yeah, I definitely won't be and I want to know what the Chinese models are doing.

703
00:52:55,640 --> 00:53:01,040
I want to know what the, the, um, models are doing that are, you know, predominantly now I

704
00:53:01,040 --> 00:53:05,240
think of France, um, coming out of Europe and I want to know what the American models are

705
00:53:05,240 --> 00:53:06,240
doing.

706
00:53:06,240 --> 00:53:09,840
Um, and I think honestly, we're gonna get to the point where there's gonna be a lot of

707
00:53:09,840 --> 00:53:16,640
models that are much more refined on specific datasets, um, rather than just one of these

708
00:53:16,640 --> 00:53:19,920
bigger ubiquitous models that we currently have.

709
00:53:19,920 --> 00:53:23,080
Just industry data in the world, but we don't use it really good.

710
00:53:23,080 --> 00:53:32,000
So I think we have, yeah, we are doing too many compliance stuff around it and so on.

711
00:53:32,000 --> 00:53:39,040
And yeah, we are, I don't know, it's feeling like, uh, we have invented the wheel and, and

712
00:53:39,040 --> 00:53:42,720
uh, the other built the cars, uh, actually.

713
00:53:42,720 --> 00:53:43,720
Yeah.

714
00:53:43,720 --> 00:53:44,720
Yeah.

715
00:53:44,720 --> 00:53:50,360
Uh, or what I also would ask you, you have renamed your podcast, uh, from the intelligent

716
00:53:50,360 --> 00:53:57,800
edge to, um, to the Microsoft innovation podcast that has the intelligent age, uh, stopped,

717
00:53:57,800 --> 00:54:02,520
uh, get me all stupid, uh, through all my, yeah.

718
00:54:02,520 --> 00:54:07,000
So the new podcast is called the intelligence age, the old one, the, the original podcast

719
00:54:07,000 --> 00:54:12,600
was the Microsoft business application podcast, um, and then it became the Microsoft

720
00:54:12,600 --> 00:54:19,840
innovation podcast and surprisingly, I've gone eight and a half years using Microsoft's

721
00:54:19,840 --> 00:54:22,680
name in a domain name.

722
00:54:22,680 --> 00:54:28,840
And on a podcast and never been pinged legally by Microsoft because if you look at that T's

723
00:54:28,840 --> 00:54:30,280
and C's, that's a no no.

724
00:54:30,280 --> 00:54:35,480
You don't get to use Microsoft's name, um, like that.

725
00:54:35,480 --> 00:54:43,000
And so it was actually, um, Claude that pointed it out to me, it said, well, one day, do you

726
00:54:43,000 --> 00:54:50,040
realize that you're in breach of Microsoft's legal thing by using their name like this?

727
00:54:50,040 --> 00:54:54,480
And I'm like, yeah, but I've never been pinged and it was just like, cause I was, so some

728
00:54:54,480 --> 00:55:00,640
of the things I do with AI is I look at, um, risk profiles, how do I do risk things in my

729
00:55:00,640 --> 00:55:07,080
life? I run a dashboard around what I call situational intelligence, which has nothing to do with

730
00:55:07,080 --> 00:55:13,680
the guy that just lost a shirt and with a situational intelligence company, um, like lost 42 billion

731
00:55:13,680 --> 00:55:14,680
dollars or something.

732
00:55:14,680 --> 00:55:16,080
This week, just passed.

733
00:55:16,080 --> 00:55:23,200
Um, but situational intelligence is a, uh, security, a kind of posture, which I learnt when

734
00:55:23,200 --> 00:55:26,800
I won this award with Microsoft years ago for crime, safety and justice.

735
00:55:26,800 --> 00:55:32,920
And it's ability to look at all factors going on in your environment and be aware of things

736
00:55:32,920 --> 00:55:37,760
that could become, um, high risk to you.

737
00:55:37,760 --> 00:55:42,400
And so I was working with the Australian federal government, um, at that time and we were

738
00:55:42,400 --> 00:55:48,400
looking at, for example, camera feed data is this person in the way they're loitering?

739
00:55:48,400 --> 00:55:50,800
Are they about to do something?

740
00:55:50,800 --> 00:55:55,360
We should track them from camera to camera just to be aware of what potentially is going

741
00:55:55,360 --> 00:55:59,400
on. But I go, okay, how do I do that in my own personal life?

742
00:55:59,400 --> 00:56:04,680
And so that's how this thing flagged that I was, I potentially could be legally pursued

743
00:56:04,680 --> 00:56:09,920
by Microsoft and so I was like, okay, let's back out using Microsoft's name, um, before I

744
00:56:09,920 --> 00:56:11,280
get asked to.

745
00:56:11,280 --> 00:56:17,520
And then the other thing is, is that I wanted to talk about more than Microsoft.

746
00:56:17,520 --> 00:56:22,880
I wanted to have more than Microsoft's voice on my podcast.

747
00:56:22,880 --> 00:56:28,680
And I wanted to, you know, for the last three or four years, I've released three or four shows

748
00:56:28,680 --> 00:56:29,680
a week.

749
00:56:29,680 --> 00:56:34,640
And so that was a phenomenal amount of recording logistics around scheduling, et cetera.

750
00:56:34,640 --> 00:56:38,880
Now when, and I haven't even started the new podcast, it doesn't start till about four

751
00:56:38,880 --> 00:56:39,880
weeks time.

752
00:56:39,880 --> 00:56:43,400
I've only done my first load of recordings this week ready to launch.

753
00:56:43,400 --> 00:56:47,600
Um, and I just recorded my first six episodes ready to go, but I'm only going to release

754
00:56:47,600 --> 00:56:48,600
one a week.

755
00:56:48,600 --> 00:56:55,720
So I have rather than mass producing podcasts, I am going down to, um, you know, slightly

756
00:56:55,720 --> 00:57:01,760
longer podcast, but really around things that deeply interest me.

757
00:57:01,760 --> 00:57:07,160
And if the world benefits from what deeply interests me, awesome, but I'm not like, you know,

758
00:57:07,160 --> 00:57:12,440
one of the things in a conversation I have with AI often is I don't want to be an influencer.

759
00:57:12,440 --> 00:57:14,840
I want to be a value creator.

760
00:57:14,840 --> 00:57:20,600
So everything that I do, podcasts, et cetera, is it creating value for other people?

761
00:57:20,600 --> 00:57:21,600
I will do it.

762
00:57:21,600 --> 00:57:25,800
I'm not trying to make, um, I'm not trying to influence others.

763
00:57:25,800 --> 00:57:28,040
I'm trying to create value for others.

764
00:57:28,040 --> 00:57:29,040
Yeah.

765
00:57:29,040 --> 00:57:31,080
And the, the, the value topic is it's good.

766
00:57:31,080 --> 00:57:32,080
Yeah.

767
00:57:32,080 --> 00:57:35,680
Uh, what value can we expect from the 90 days mentoring challenge?

768
00:57:35,680 --> 00:57:36,680
Yeah.

769
00:57:36,680 --> 00:57:40,800
So the 90 mentoring challenge, like I've had a lot of people go through it.

770
00:57:40,800 --> 00:57:43,840
Um, of the people that have been on it over 65 of them.

771
00:57:43,840 --> 00:57:47,680
And they've become Microsoft, MVP's that have gone through that program.

772
00:57:47,680 --> 00:57:52,200
Um, I was running it all year round and it just became a hard slog for me.

773
00:57:52,200 --> 00:57:55,320
So I've, I've run it once this year, January, February, March.

774
00:57:55,320 --> 00:57:56,520
Um, that's run.

775
00:57:56,520 --> 00:58:00,880
I will probably release the next one in January, February, March next year.

776
00:58:00,880 --> 00:58:03,080
I'll do it once a year.

777
00:58:03,080 --> 00:58:05,080
And I've got percolating.

778
00:58:05,080 --> 00:58:10,960
And I mean, that 90 mentoring challenge is designed explicitly for anybody with Dynamics 365

779
00:58:10,960 --> 00:58:15,480
or their careers in Dynamics 365 and the power platform.

780
00:58:15,480 --> 00:58:17,000
And, and it is full on.

781
00:58:17,000 --> 00:58:21,400
It's not an easy, um, a light process, right?

782
00:58:21,400 --> 00:58:26,800
It's 12 weeks where every week you have detailed lab assignments and things to do.

783
00:58:26,800 --> 00:58:30,160
It's like, you know, it's like drinking from a fire hose, that course.

784
00:58:30,160 --> 00:58:35,400
And it's been running since, I think 2018 is when I launched that and ran it every year

785
00:58:35,400 --> 00:58:39,880
apart from one year when I was working at IBM and I was running that 90 day mentoring challenge

786
00:58:39,880 --> 00:58:45,280
internally for IBM is around the world and I didn't want to, um, and do it out in the market.

787
00:58:45,280 --> 00:58:48,720
So I've got, you know, I keep getting people gone, the wait list for that.

788
00:58:48,720 --> 00:58:50,200
So that will carry on.

789
00:58:50,200 --> 00:58:53,480
Um, I just refreshed all the content recently.

790
00:58:53,480 --> 00:58:57,760
Um, in the, for this year, um, just because so much has changed.

791
00:58:57,760 --> 00:59:03,520
Um, maybe I will release a 90 day mentoring challenge.

792
00:59:03,520 --> 00:59:06,520
Maybe it's a lot of work for me to do.

793
00:59:06,520 --> 00:59:13,400
Um, you know, my basically rule of thumb for every hour that I present in that 90 mentoring

794
00:59:13,400 --> 00:59:18,600
challenge, I've spent over 12 hours getting to that one hour of output.

795
00:59:18,600 --> 00:59:28,880
And so I am considering doing, um, one specifically on AI, which will be targeted at tech people

796
00:59:28,880 --> 00:59:36,680
that really want to become super proficient in AI, uh, consulting.

797
00:59:36,680 --> 00:59:42,960
So, um, but I don't know, I'm not, I've gone, I'm fluctuating.

798
00:59:42,960 --> 00:59:47,480
I'm whether I release that right now, I'm building software that I'm going to release

799
00:59:47,480 --> 00:59:49,040
in the Microsoft marketplace.

800
00:59:49,040 --> 00:59:52,560
I see there's gaps that I, I feel I can fill.

801
00:59:52,560 --> 00:59:57,440
So I've really gone down the, you know, what software can I create that's world class?

802
00:59:57,440 --> 01:00:02,120
I'm working with Microsoft internal stakeholders at the moment on that, um, having what I'm

803
01:00:02,120 --> 01:00:05,960
building architecturally reviewed and, and, and peer reviewed and things like that.

804
01:00:05,960 --> 01:00:11,200
So that's probably my focus at the moment is, um, what software can I build?

805
01:00:11,200 --> 01:00:13,480
Yeah, that is awesome.

806
01:00:13,480 --> 01:00:16,480
I have a rapid firewall in every session.

807
01:00:16,480 --> 01:00:17,480
Awesome.

808
01:00:17,480 --> 01:00:20,760
So, um, let, let us start short, short answers.

809
01:00:20,760 --> 01:00:27,160
And so, if there anything you say, um, you will never be automated in the future.

810
01:00:27,160 --> 01:00:29,160
No.

811
01:00:29,160 --> 01:00:34,120
And, uh, what did you think is the biggest thing?

812
01:00:34,120 --> 01:00:36,000
Oh, hang on, hang on, I do, sorry, just switch.

813
01:00:36,000 --> 01:00:38,400
Spending time with my family and, and friends.

814
01:00:38,400 --> 01:00:40,440
I don't think that will be automated.

815
01:00:40,440 --> 01:00:42,440
Yeah, okay, good.

816
01:00:42,440 --> 01:00:45,680
Um, is there one Microsoft product?

817
01:00:45,680 --> 01:00:47,160
Everybody should learn now.

818
01:00:47,160 --> 01:00:52,440
Uh, I would say co-pilot, like, learn it properly.

819
01:00:52,440 --> 01:00:59,760
Like, don't stay on auto, learn about work IQ, the importance of it, learn how to be really

820
01:00:59,760 --> 01:01:04,040
proficient in it, make sure it knows your context super well.

821
01:01:04,040 --> 01:01:09,560
Um, I think, yeah, there's a lot of runway for people as an, if you're just in a, if you're

822
01:01:09,560 --> 01:01:14,640
in a role, that is an information worker role, not a tech role, you should absolutely

823
01:01:14,640 --> 01:01:20,280
learn that if you're in that, what would be classified in information worker type role.

824
01:01:20,280 --> 01:01:21,280
Um, and, yeah.

825
01:01:21,280 --> 01:01:22,280
Yeah.

826
01:01:22,280 --> 01:01:26,720
What, what did you think, um, it's about a large language models or specialized language

827
01:01:26,720 --> 01:01:27,720
models?

828
01:01:27,720 --> 01:01:28,720
Right now, LLM.

829
01:01:28,720 --> 01:01:29,720
Okay.

830
01:01:29,720 --> 01:01:35,720
Um, you know, um, small language models, it smells, they've, they've got, they will become

831
01:01:35,720 --> 01:01:39,560
more prominent, but right now, LLM is where it's at.

832
01:01:39,560 --> 01:01:43,120
What's the habit that makes you so successful?

833
01:01:43,120 --> 01:01:44,120
Curiosity.

834
01:01:44,120 --> 01:01:49,920
Um, is there an AI list you will bust?

835
01:01:49,920 --> 01:01:55,880
Uh, that AI will become a, uh, sky net.

836
01:01:55,880 --> 01:02:01,920
Um, coffee tea or energy thing, three new development.

837
01:02:01,920 --> 01:02:06,280
I have been a massive energy drinker, like massive.

838
01:02:06,280 --> 01:02:12,560
Um, we have an energy drink in New Zealand called V and I just, I love that stuff so much.

839
01:02:12,560 --> 01:02:16,640
Uh, um, but I always start like, I have my own burst of machine.

840
01:02:16,640 --> 01:02:20,520
I have proper coffee machine.

841
01:02:20,520 --> 01:02:23,480
I always probably drink two coffees in the morning before lunch.

842
01:02:23,480 --> 01:02:26,720
Um, but yeah, I, I, you know, I, I do it.

843
01:02:26,720 --> 01:02:32,080
I don't do energy now because of diet and, and, and health, but every now and again, I

844
01:02:32,080 --> 01:02:33,680
let myself have one.

845
01:02:33,680 --> 01:02:38,920
Uh, what is the best food everyone should try when they come to New Zealand?

846
01:02:38,920 --> 01:02:43,880
Oh, if you come to New Zealand, you've got to try what's called a honey, which is food

847
01:02:43,880 --> 01:02:47,000
that is cooked in the ground.

848
01:02:47,000 --> 01:02:48,000
Okay.

849
01:02:48,000 --> 01:02:49,000
Let's go.

850
01:02:49,000 --> 01:02:50,000
So it's interesting.

851
01:02:50,000 --> 01:02:56,800
And, um, uh, co-pilot or chat GPT.

852
01:02:56,800 --> 01:02:57,800
Depends.

853
01:02:57,800 --> 01:02:58,800
Yeah.

854
01:02:58,800 --> 01:03:06,280
So, um, thank you for, for, for, for staying here with me, uh, over there.

855
01:03:06,280 --> 01:03:09,400
So thank you so much for, for your time.

856
01:03:09,400 --> 01:03:18,840
Um, what will you say when people can, uh, yeah, um, or what is the key, key takeaway from

857
01:03:18,840 --> 01:03:24,720
you from this session for what the people should recognize?

858
01:03:24,720 --> 01:03:30,800
I always not going anywhere and you need to do everything you can to be the best person

859
01:03:30,800 --> 01:03:37,360
in your place or the most knowledgeable, the most proficient and, and, and really, knowledge

860
01:03:37,360 --> 01:03:39,800
does not replace skill.

861
01:03:39,800 --> 01:03:41,440
We have infinite knowledge.

862
01:03:41,440 --> 01:03:46,720
It's the people that can apply the knowledge that can say, I've done it rather than I know

863
01:03:46,720 --> 01:03:47,720
it.

864
01:03:47,720 --> 01:03:50,080
There's a big difference.

865
01:03:50,080 --> 01:03:55,640
And, um, yeah, but my last question is, you have so many guests on your podcast, uh, who

866
01:03:55,640 --> 01:04:01,440
should I invite to them to my podcast and what questions should I ask?

867
01:04:01,440 --> 01:04:07,000
Um, you should invite Charles Lamana.

868
01:04:07,000 --> 01:04:15,040
To your podcast and get him to based on everything he's seen behind the curtain, so to speak.

869
01:04:15,040 --> 01:04:22,280
Uh, where does he see the next, uh, 24 months to 36 months going in tech?

870
01:04:22,280 --> 01:04:24,040
Yeah, thank you.

871
01:04:24,040 --> 01:04:29,000
Uh, yeah, that was fantastic and, and really interesting conversation.

872
01:04:29,000 --> 01:04:34,280
So Mark, thank you so much for joining me here on the MC65FM podcast and, uh, sharing your

873
01:04:34,280 --> 01:04:40,440
experience across AI, adoption, Microsoft technologies, uh, OpenClaw and, yeah, software factories

874
01:04:40,440 --> 01:04:45,400
and, uh, also the old words about Microsoft co-pilot studio.

875
01:04:45,400 --> 01:04:52,440
And, yeah, I think we now understand what the intelligent age, uh, means and what's upcoming

876
01:04:52,440 --> 01:04:54,000
and, yeah, thank you.

877
01:04:54,000 --> 01:04:55,000
Thank you so much.

878
01:04:55,000 --> 01:05:01,200
It was, was really cool and I think, yeah, thanks for for listening and until next time,

879
01:05:01,200 --> 01:05:03,680
keep learning, keep building and stay curious.

880
01:05:03,680 --> 01:05:04,500
- Yeah.

881
01:05:04,500 --> 01:05:05,740
- Ta-jao.

882
01:05:05,740 --> 01:05:06,580
- Bye.

883
01:05:06,580 --> 01:05:14,580
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