What similar changes have you seen that could compare to some extent to AI in the technology field? So the biggest thing in my career, I think, if we look back at the history of software development as a whole, the comparable thing would be the shift from assembly language to the very first pineapple languages. The biggest part of it is the shift from determinism to non-determinism, and suddenly working in a non-environment that's non-deterministic, which completely changes everything. What is your understanding and take on by coding? I think it's good for explorations, it's good for our ways, because Oso will stuff, but you don't want to be using it for anything that's going to have any long-term capability.
When you're using by coding, you're actually removing a very important part of something What are some new workflows or new software engineering approaches that you've observed? One area that's really interesting is Martin Fowler is a highly influential author and software engineer in domains like Agile, Software Architecture, and Refactoring. He is one of the authors of the Agile Manifesto in 2001, the author of the popular book Refactoring, and regularly publishes articles on software engineering on his blog. In today's episode we discuss how AI is changing software engineering and some interesting and new software engineering approaches analogs enable. While your refactoring is a practice will probably get more relevant with AI coding tools, why design patterns seem to have gone out of style the last decade? What the impact of AI is on
Agile practices and many more. This podcast episode was presented by Statsig, the unified platform for flags, analogs, experiments, and more. Check out the show to learn more about them and our other seasoned sponsor. If you enjoyed the show, please subscribe to the podcast on any podcast platform and on YouTube. So Martin, welcome to the podcast. Well, thank you very much for having
me. I didn't expect to be actually doing it face to face with you. That was rather nice. It's all the better this way. I wanted to start with learning a little bit about how you got into software development, which was what, 40-ish years ago? Yeah, it was, yeah, it would have been
late 70s, early 80s, yeah. I mean, like so many things. It was kind of accidental, really. At school, I was clearly no good at writing because I got lousy marks for anything to do with writing. Really? Yeah, absolutely. But I was quite good at mathematics and that kind of thing, physics.
So I was kind of lean towards engineering stuff. And I was interested in electronics and things, because the other thing is I'm hopeless with my hands. I can't do anything requires strength for physical coordination. So all sorts of areas of engineering and building things, I'll try looking after my car and I can get the rest of the nuts off or anything. It's hopeless. So,
but electronics is OK, because that's all very, it's more than in the brain than you need to be able to handle a soldering iron, but that was not bad as much as I needed to do. And then computers and it's step easier. I don't even need a soldering iron. So I kind of drifted into computers in that kind of way. And that was my route into software development. Before I went to university, I had a
year at working with the UK Atomic Energy Authority, or Ucolalia, as we call it. And I did some programming in Fortran 4. And it seemed like a good thing to be able to do. And then when I finished my degree, which was a mix of electronic engineering and computer science, I looked around and I thought, well, I could go into traditional engineering jobs, which worked terribly well paid and went terribly high status, or I could go into computing where it looked like it was a lot more opportunity. And so I just drifted into computing. And this was before the internship,
was what kind of jobs were there back then that you could get into? And what was your first job? Well, my first job was with a consulting company, Cooper's and Leibrand, or as I referred to him, Sheetam and Leibrand. And we were doing advice on information strategy, the particular group I was with. Although that wasn't my job. My job was I was one of the few people who knew UNIX,
because I'd done UNIX at college. And so I looked after a bunch of workstations that they needed to do to run this weird software that they were running to help them do their strategy work. And then I got interested in the what they were doing with their strategy work and drifted into that. I look in the back now and think, God, that was a lot of snake oil involved, but hey, it was my route into the industry. And it got me early into the world of object oriented thinking.
And that was extremely useful to get into objects in the mid 80s. And how did you get into objects oriented was back then back we're talking probably the mid 80s. That was a very kind of radical thing. And you said you were working at a consulting company, which didn't seem like the most cutting edge. So how does it plus to get together? How did you get to
do cutting edge stuff? Because this little group was into cutting edge stuff. And they had run into this guy who had some interesting ideas, some very good ideas, as well as some slightly crazy ideas. And he packaged it up with the term object orientation, which wasn't really the case, but it was part of the snake oil as it were. I mean, that's a little bit cruel for school, it's not cool because he had some very good ideas as well. But that kind of led me into that direction. And of course,
at the time I found out more about what object orientation was really about. And that events led to my whole career. In the next 10 or 15 years, how did you make your way and eventually end up at ThoughtWorks? And also you started to write some books you started to publish on the side. Had to go from like someone who was brand new city industry and kind of wide eyed and just taking it all in, learning things to starting to slowly become someone who was teaching others.
Well, again, bundles of accidents, right? So while I was at that consulting company, I met another guy that led brought in to help them work with this kind of area, an American guy who became the really the biggest mentor and influence on my own career. His name is Jim O'Dell. And he had been an early adopter of information engineering and had worked with in that area. And he saw the good parts of these ideas that these folks were doing. And he was independent consultant and a teacher.
And so he spent a lot of his time doing work along those lines. I left Gippus and Iberin after about a couple of years to actually join the crazy company, which is called P-TEC. And I was with them for a couple of years. It was a small company that was a grand total of four of us in the UK office. And that was the largest office in the company. Wow. And so I saw a bit of
having seen a big company as craziness. I've been sort of small companies craziness, did that for a couple of years. And then I was in a position to go independent and I did helped greatly by Jim O'Dell, who was who fed me a lot of work, basically, and also by some other work I got in the UK. And that was great. I remember leaving P-TEC and thinking, that's it, independent of the life of me. I'm never going to work for a company again.
That's words. Exactly. And I carried on, I did well as an independent consultant throughout the 90s. And during that time, I wrote my first books. I moved to the United States in 1993 and I was doing very, very happily. And obviously you got the rise of the internet,
lots of stuff going on in the late 90s. It was a good time. And I ran into this company called ThoughtWorks. And they were just a client. I would just go there and help them out. The story gets
them all. I had a Met Chem back and worked with Kent Kreisler, the famous C-free project, which was going to the Birth Project of Extreme Programming. So I worked on that, seen extreme programming, seen the Agile things. I got the Object orientation stuff, I got the Agile stuff. I mean, I came to ThoughtWorks and they were tackling a big project, a big project for them at the time.
Still size of 100 people working on the project. So it's a sizable piece of work. And it was clearly going to crash and burn. But I was able to help them both see what was going on and how to avoid crashing and burning. And they figured out how to recover from the problem.
But they invited me to join them. And I thought, hey, join a company again maybe for a couple of years. They're really nice people. They're my favorite clients. I always thought of it.
Those other clients would say, these are really good ideas, but they're really hard to implement. And while ThoughtWorks would say, these are really good ideas. They're really hard to implement, but we'll give it a try. And they usually pulled it off. And so I thought, hey, we have a client like that. I might as well join him for a little while and see what we can do. That was 25 years ago.
Yeah. And then the password today, your title has been for, I think, over a decade, Chief Scientist. Since I joined, that was my title. Since I joined, so I have to ask, what does a Chief Scientist at ThoughtWorks do? Well, it's important to remember, I'm Chief of Nobody and I don't do any science.
The title was given because that title was used a fair bit around that time. But some kind of public facing ideas kind of person. If I remember correctly, Grady Booch was Chief Scientist at Rational at the time. That's actually true. And there were other people who had that title.
So it was a high-for-looting, very pretentious title, but they felt it was necessary. It was weird, because one of the things ThoughtWorks at that time was you could choose your own job title. Anybody could do whatever job title they like, but I didn't get to choose mine. I had to take the Chief Scientist one. They didn't like titles like Flagpole or Battering Ram or Labmouth, which is the one I could most prefer. And one thing that ThoughtWorks does every six months, and the latest one just came
out, is the ThoughtWorks radar. And this latest radar, it just came out a few days ago. It's the... Just today, it was launching. Actually, it was today. So by the time this is in production, it will
have been a few weeks. But it's actually really, really fresh. So I just looked at it and things that... It lists just a few things that I saw there and the adapting, which is the ones that they recommend using, pre-comit hooks, Clickhouse for database analytics, VLLM. This is for learning LMs on OnCloud or on Onprem in a really efficient way for trialing Cloud Code, FastMCP, which is an MCP, for frame-of-frame service. And they're also recommending a lot of different things related,
for example, to AI and LMs to assess. Can you share a little bit of how ThoughtWorks comes up with this technology radar? What's the process? And it feels very, very kind of on the pulse every time. Like it feels close to the pulse of the industry. And again, I talked with a lot of other people.
How do people at ThoughtWorks stay this close to what is happening in the industry? Okay. Well, this will be a bit of a story. Okay. So it started just over 10 years or so ago.
Its origin was one of the things that we've really pushed at FootWorks is to have technical people, practitioners really involved at various levels of running the business. And one of the leaders of that was our former CTO, Rebecca Parsons. So Rebecca became CTO, and she said, I want an advisory board who will keep me connected with what's going on in projects. So she created this technology advisory board. And it had a bunch of people whose job was to brief her, what was going on with me two or three times. She had me on the advisory board,
not so much for that reason, but because I was very much in public face of the company, she wanted me present and involved in that. And originally that was just our brief. We would just get together and we'd talk through this stuff. And then one of these meetings, Darryl Smith, who was actually her TA at the time, Technical Assistant, he said, well, we got all these projects going on. It would be good to get some picture of what kinds of technologies we're using and how useful they are. And so it's a better exchange idea. Because we struggled to
perky like good ideas around and off. I mean, even then, when we're only just a few thousand, it's struggled and we're 10,000 now. So it's hard. So we thought, okay, it was an outside game. He came up with this idea of the radar metaphor and the rings of the radar that we see today. And we had the meeting and we created the radar. And it's a habit that if we do something
for internal purposes, we try and just make it public. And that's always been a strong part of the footwork's ethos. It's part of why I'm there, of course, is we talk about everything that we do, we share everything. We give away our secrets all the time. So we did that and people were very interested. And so we continue doing it. Now the process has changed a bit over time. At that
original meeting, many of the people that were in the room were actually hands on on projects, advising clients all the time. Now, as we've grown an order of magnitude, it's much harder to do that. And we've also created more of a process where people can submit blips, nominate them, blip something, point on the radar in entry. And they will go to somebody that either connected through geographically or through the line of business or technology or whatever and say, hey, we think this technology is interesting. I'll brief this a little bit about it. And then
they brief the members of the what's now called the Doppler group, because we make a radar. Yeah, I mean, we can be a bit loose about metaphors at times. And then at the meeting, we'll decide which of these blips to put on the radar and obviously you get some cross-pollination because somebody's like, oh, yeah, I talked to somebody about this as well. And so it's very much this bottom up exercise. And that's how it's created now. So we will have these we will do blip gathering
sessions about a month or two before the radar meeting and gradually shake them up and then in the meeting itself, we go through them one by one. And for me, it's a bit weird because I'm so detached from the day to day these days that it's this line up of technologies and things. I have no idea what most of them are. But interesting to hear about. And sometimes I latch on to certain themes or something like that. And that was an important part of microservices about 10 years ago, because
that came up in through that radar process and got together with James Lewis and we ended up writing a good bit further about that. But that's really what happens is we go through this process of spotting this stuff. Yeah. And the radar analogy, I know some companies also take the idea, which by the way, ThoughtWorks encourages saying make your own radar, take it in your own company. You can I think leave and like up tools around it. I really like how ThoughtWorks never said like this
is the thing for the industry. They said this is the thing for us. This is what we see. This is what we recommend our team, our team members or maybe our clients to consider or there's also I like that there's a hold. Maybe just be where we're not seeing great results with this. And here's
the reason for it. And yeah, I guess the reason it feels fresh is probably a lot of work that ThoughtWorks does is it feels cutting edge because it's all about half of it or a third of its feels that it is around the hottest topic right now, AI, LMS and all the techniques that people are trying to see if they work or the things that we are seeing that actually starts to work. Yeah. I mean, what I mean, what's basically got several thousand technologists all over the world doing projects of various kinds, all sorts of different organizations. And the radar is a mechanism that we've discovered is a way of getting some of that information out of our heads and spreading around both internally and to the industry as a whole. And you're right, it is a recommended
thing for clients to do is to try and do their own radars. It's slightly different when it's a client right offing because sometimes there it can be more of a, this is what we think you should be doing with a bit more of a forcefulness to it than we would give. And also they can be a bit more choosy in the sense of they can say, yeah, we're just not interested in doing certain technologies. Well, for us, it's okay. So if that client's doing it, then we're going to find out about it, right?
We have to use it. Of course, the radar is full with a lot of AI and I'm really think because this is a huge change in in my professional career. It feels by far the biggest technology innovation change coming in. Looking back on your career, what similar changes have you seen that could compare to some extent to AI and the technology field? I mean, it's the biggest thing for my career.
I think if we looked back at the history of software development as a whole, the comparable thing would be the shift from assembly language to the very first high level languages, which is before my time, right? When we first started coming up with Coball and Fortran and the like, I would imagine that would be a similar level of shift. So you started to work with Fortran and you probably knew people who were still doing assembly or at least knew some people from that generation. There was a bit of assembly around when I was working still. From what you picked up around that time, what was that shift like in terms of mindset or because it was a big change, right? You really needed to know the
internals of the hardware and the instructions and the different, I did very little assembly at university, but it's been very useful because I never want to do it again. Very wise. But what did you pick up in terms of what needed to change and how it changed the industry, just moving from mostly assembly to mostly higher level languages? Well, I mean, for a start, as you said, things are very specific to individual chips. You had the instructions with different on every chip, as well as things like registers, where you access memory. You had these very convoluted ways of
doing even the simplest thing because your only instruction was something like move this value from the memory location to this register. So you've always got to be thinking in these very, very low level forms. And even the very relatively poor high level language like Fortran, at least I can write things like conditional statements and loops, else is in my conditional statements in Fortran for, but I can go if and I can get one statement, I can't do a block of statements, I have to use go tos, but you know, it's better than what you can do in assembly, right? And so there's a definite shift of moving away from the hardware to thinking in terms of something a bit more abstract. And I think that is a very, very big shift. I mean, of course, once I'm using Fortran, I can be insulated
to some degree away from the hardware I'm running on. I'm now, am I running this on a mainframe? Am I running this on a mini computer? I mean, there's issues because the language is always varied a little bit from place to place, but you've got a degree of decoupling there that was really quite significant, I think. I mean, I only did it on small microprocessor like units, because again, it was the electronic engineering part, right? So we were fairly close to metal anyway for some of that.
But you definitely had that mind shift. And I think it's with LLMs, it's a similar degree of mind shift, although as I've written about it, the interesting thing is the shift is not so much of an increase of a level of abstraction, although there is a bit of that. The biggest part of it is the shift from determinism to non-determinism. And suddenly you're working in a non-withal environment that's non-deterministic, which completely changes how to think about it. Martin just talked about how AI is the most disruptive change sent to move from assembly to high-level languages. That transition wasn't just about changing the language we use,
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and pro pricing for teams starts at $150 per month. To learn more and get a three day enterprise trial, go to statsick.com slash pragmatic. And now let's get back to the shift in abstraction with LLMs. Can we talk about that shift in abstraction? Because one very naive or naive way of looking at is saying like, well, we've had the three levels, right? We have assembly where you have commands
for the hardware, you need to be intimately aware of the hardware. We have high level programming languages starting with C later Java, later JavaScript, and where you don't need to be aware of the hardware, you're aware of the logic. And what you might say as well, we have a new abstraction is you have English language, which will generate this code. You're saying you don't think it's an abstraction jump. Why do you think this is? I think it is a bit of an abstraction jump. I think
the abstraction difference is smaller than the determinism on the terminus and jump. And it's worth remembering. One of the key things about high level languages, which I didn't mention, I was talking about earlier on, is the ability to create your own abstractions in that language. That is particularly important as you get to things like object orientation towards more expressive functional languages like this, which didn't really have so much info. In Fortran and Kabul, you could do that to some extent, because at least with Fortran, you can create subroutines and build abstractions out of that. But you've got so many more tools to build abstractions when you've got the
abilities of more modern languages. And that ability to build abstractions is crucial. And so you can build the building blocks inside of the language that sets you. And of course, here we have like domain driven development later, enables these things out and so on. Exactly. I mean, an old list advantage is really what you want to do is to create your own language
in Lisp and then solve your problem using the language that you've created. And I think that way of thinking is a good way of thinking in any programming language. You're both solving the problem and creating a language to describe the kinds of problems you're trying to solve in. And if you can balance those two nicely, that is what leads to very maintainable and flexible code. So the building of abstractions, that's I think to me a key element of high level languages.
And I helped us a little bit in that because we can build abstractions a bit more easily, a bit more fluidly. But we have this problem in there, we're talking about non-deterministic implementations of those abstractions, which is an issue. And we've got to sort of learn a whole new set of balancing tricks to get around that. My colleague, Anesh Joshi, has written a couple of things that I really been really enjoying about his thinking about how, because he's really pushing this using the LLM to co-build an abstraction and then using abstraction to talk more effectively to the LLM. And that I'm finding really, really interesting way of thinking about how he's working with that, because he's really pushing that direction. There's a thing I read in, and I can't
remember the book off the top of my head, we'll have to dig it out later. And I talked about how, apparently, if you can describe to an LLM a whole load of chess matches and describe it just in plain English, and the LLM, when you do that, the LLM can't really understand how to play chess. But if you take those same chess matches and describe the LLM to those chess matches in chess notation, then it can. And I thought that was really interesting that you advise, obviously, you're shrinking down the token size, but you're also using a rigorous, much more rigorous notation to describe the problem. So maybe that's an angle how we use LLM is what we have to come up with, it's a rigorous way of speaking, and we can get more traction that way. And of course, that has
great parallels in with the ideas of domain-driven design, because of languages, and also some of the stuff that I was working on a decade ago, read the main specific languages and language work benches. So I've done some fascinating stuff around there, but we interesting to see how that plays out. Yeah, and I guess, is this the first time we're seeing a tool that is so wide, so much software engineering that is non-deterministic, because we did have neural nets, for example, in the past, they were not, but they were more, I feel, the application of those was a lot more kind of niche. And not everywhere. Now, every single developer is, I mean, if you're using cogeneration, you are using non-deterministic things, of course, we're integrating them left and right, trying out where it works. Is it fair to say that this is probably the first time we're
facing this challenge of a deterministic computer, which we know very well, we know their limit and all those things. And of course, there are some race conditions and some exotic things. But now we have this problem to solve for. It's a whole new way of thinking. It's got some interesting parallels to the forms of engineering. The other forms of engineering you think in terms of tolerances,
and my wife's a structural engineer, right? She always thinks in terms of one of the tolerances, how much extra stuff do I have to do beyond what the math tells me? Because I need it for tolerances, because yeah, I mean, I mostly know what the properties of wood or concrete or steel are, but I've got to go for the worst case. We need to probably some of that kind of thinking ourselves, what are the tolerances of the non-determinism that we have to deal with and realizing that we can't get too close to the edge, because otherwise we're going to have some bridges collapsing. I suspect we're going to do that, particularly on the security side. We're going to have some
noticeable crashes, I fear, because people have got skated way too close to the edge in terms of the non-determinism of the tools they're using. Oh, for sure. But before we go into where we could crash, what are some either new workflows or new suffering and during approaches that you've observed or aware of that? That sounds kind of exciting that we can now do with LMSR. I think we can try to get them to go with what would have been impossible with our old deterministic toolkit.
One area is, one that's got lots of attention already, is the being able to knock up a prototype in a matter of days that's just way more than you could have done previously. So this is the vibe coding thing, but it's more than just that, because it's also an ability to try explorations. People can go, hey, I'm not really quite sure what to do with this, but I can spend a couple of days exploring the idea much, much more rapidly than I could have before. And so the throwaway explorations for disposable little tools or things of that kind, and including stuff by people who don't think of themselves as software developers, I think there's a whole area. And we can, with good reason, be very suspicious of taking that too far because there's a danger there. But we also realize
that as long as you treat that within its right bounds, that's a very valuable area. And I think that's really good. On a completely opposite end of scale, one area that's really interesting is helping to understand existing legacy systems. So my colleagues have put a good bit of work in Eurotube a go. And basically the idea is you take the code itself, do the essentially the semantic analysis on it, populate a graph database essentially with that kind of information, and use that graph database as kind of in a rag-like style. And you can begin to interrogate and say,
well, what happens to this piece of data? Which bits of code touch this data is it flows through the program? Incredibly effective. And in fact, if I remember correctly, we put actually understanding of legacy systems into the adopt ring, because we said, yeah, if you're doing any work with legacy systems, you should be using LLM's in some way to help you understand. So in the string, in the ThoughtWorks radar, the fewest things are in the adopt as we strongly suggest that you look at it, at least, you know, ThoughtWorks themselves look at it. There's only
four items and one of them is yes, to use JNI to understand legacy code, which to me tells that you have seen great success, which is it's a refreshing to hear, by the way, I did not hear this as much, and I guess it helps that ThoughtWorks. I'm sure you have to work with a lot of Well, I mean, it came from the fact that some of the folks who have done some really interesting work on legacy code stuff, happened to bump into and look at this and say, Hey, let's try this out. And they found it to be very effective. And it also has been an ongoing interest for many of us at ThoughtWorks because we have to do it all the time. And how do you effectively work with the modernization of legacy systems, because every big company that you know, is older than a few years has got this problem. And they have it in spades. And then especially just simple things,
people leave right as simple as that and having Jenny, I that can help you make some practices, it's already better than making no progress. Exactly. So those are two areas that are clearly right away, I would say those are great success using LLM's. And then there's the areas that we're still figuring out. I mean, I'm certainly seeing some interest more and more interesting stuff as people try to figure out how to work with LLM on a one to one basis to build decent quality software.
We're seeing some definite signs of how you got to work with very thin, rapid slices, small slices, you've got to treat every slice as a PR from a rather dodgy collaborator, who's very productive in the lines of code sense of productivity. But you know, you can't trust a thing that they're doing. So you've got to review everything very carefully. When you play with the genie like that, the genie goes against term for it or dusty the anthropomorphic donkey, which is how big it is. Yeah, I love that. But using it well, you can actually definitely get
some speed up in your process. It's not the kind of speed up that the advocates are talking about, but it is non trivial. It's certainly worth learning how to make some use of this. And it's folks like the Gita or Kent or Steve Yeg, those are the folks I think who are pushing this. We're still, I think, learning how to do this. Everyone is learning it. Absolutely. And it's still the
question and most of the experience we're gaining is building in a greenfield environment. So that leaves big questions in terms of a the brownfield environment. Well, we know that LLM's can help us understand legacy code. Can they help us modify legacy code in a safe way? It's still a question.
I mean, I was just chatting with James Lewis because he's in term as well this morning. And he was commenting about he was playing with cursor. And he's been just building something like this. And he said, Oh, I wanted to change the name of a class in a not too big program. And he sets it off to do that. And comes back and hour and a half later. And there's used 10% of his monthly allocation of
tokens. And all he's doing is changing the name of a class. And we actually in IDEs, we actually have which I still remember when I was cutting edge. This was probably 20 years ago when Visual Studio, it wasn't even Visual Studio was JetBrains who came out with an extencials re-sharp, which helped re-factor code and people paid serious money. This was like $200 per year or something to get this plugin. And now you could write like and say, rename class. And it
went and it built that the graph behind the scenes somehow it went and changed, you could rename variables. And this was a huge deal. In fact, in Xcode, Apple's developer Bob of ID for a while when it came out, you couldn't do these refactors. And it was, you know, people were like, so interesting how something's are easy. We've solved it. And Ellen's are not very efficient.
Adam not very good at it. Yeah. Yeah. And then I mean, he did that just to see what it was going to be like, because he knows you can just, I mean, we've had technology for a long time. So it's kind of amusing. I mean, but it's also to the point that when working with an existing system and modifying
an existing system, we're that's still a really up in the air. And in other areas, really up in the air, both Greenfield and Brownfield is what happens when you've got a team of people, because most software has been built by teams and will continue to build with teams. Because even if, and I don't think it will, AI makes us order a magnitude more productive, we still need a team of 10 people to build what a team of 100 people needed to build. And we'll always want this stuff. There's no sign of demand dropping for software. So we will always want teams. And then the question
is, of course, how do we best operate with AI in the team environment? And we're still trying to figure that one out as well. So there's lots of questions. We've got some answers, some beginnings of answers, and it's just a fascinating time to watch it all. You mentioned vibe coding. What is your understanding and take on vibe coding?
Well, when I use the term vibe coding, I use I try to go back to the original term, which is basically you don't look at the Apple code at all. Maybe I'll take a glance at it for curiosity, but you really don't care. And maybe you don't know what you're doing because you don't know what knowledge of programming is just spitting out stuff for you. So that's how I define vibe coding. And my take on it is kind of as I've indicated, I think it's good for explorations, it's good for all ways disposable stuff. But you don't want to be using it for anything that's going to have any
long term capability because it's I mean, again, it just if this is a silly anecdote, but I was working, my colleague, on Mesh, he just wrote something that we published yesterday. And as part of doing this, we create this little pseudo graph of capability over time kind of thing, which is, whatever, silly little pseudo graph that helps it a straight point. And he asked the LLM to correct this. He described the curves he wanted and put it up there and he committed it to our repo and I was looking at it thinking, yeah, that's a good enough graph. I want to tweak it a little bit. The
labels are a bit far away from the lines they're labeling, so I like to bring them closer. So I open up the SVG of what the LLM has produced. I mean, it was astonishingly how complicated and convoluted it was for something that I had written the previous one myself and I knew it was a dozen lines of SVG and SVG is not exactly the compact language, right? Because it's text of L. But this thing was gobsmackingly weird. I mean, that's the thing, when you vibe code stuff, it's going to produce God
knows what and often it really is and you cannot then tweak it a little bit. You have to basically throw it away and hope that you can generate whatever it is you're trying to tweak. And the other thing, of course, it's a difference and this is the heart of the article that I mentioned wrote, we published yesterday is when you're using vibe coding in this kind of way, you're actually removing a very important part of something which is the learning loop. If you're not looking at the output, you're not learning. And the thing is that so much of what we do is we come up with ideas, we try them out on the computer with this constant back and forth between what the computer does with what we're thinking, we're constantly going through that learning group program, proction, and then actually this point, which I think is absolutely true, is you cannot shortcut that process. And what LLMs do, they just kind of skim over all of that and you're not
learning. And when you're not learning, that means that when you produce something, you don't know how to tweak it and modify it and evolve it and grow it. All you can do is nuke it from orbit and start again. The other thing I've done occasionally with vibe coding is that, oh, vibe coding as a consulting company, so many problems. Yeah. For sure. But you are right on the learning side, both on vibe coding and AI. One thing that
I'm noticing on on myself is it is so easy to give a prompt, you get a bunch of output, and you know, you should be reviewing a lot of this code, either yourself or in a code review. But what I'm seeing on myself is I'm at some point, I start to get a bit tired and I just let it go. And this is also what I'm hearing when talking with software engineers is the ones who are working on companies, which are adopting these tools, which is pretty much every company. There's a lot more code going out there, a lot more culture review. And they're asking, how can I be vigorous that code reviews when there's just more and more of them than before? Have you seen approaches that
help people, both less experienced people and also more experienced engineers keep learning with these tools, just approaches that seem promising? Not a huge amount. I do. I am very much paying attention to what Amesh is doing with this. Because he's approached very much is that notion of, let's try and build a language to talk to the LLM, work with the LLM to produce a language to communicate to the LLM more precisely and carefully what it is that we're looking for. And I do feel
that is a promising and very much a more promising line attack. It would be to create our own specialized language for working with whatever problem that we're working on. And I think that actually brings another, we're talking about things we know LLM is useful for. Another thing, this is against something Amesh has highlighted, is understanding an unfamiliar environment. Again, I was chatting with James. He was working on a Mac with C Sharp, which is not a language he's
terribly familiar with, using this game engine called... Go. Yeah. And he doesn't know anything about this, right? But with the LLM, he can learn a bit about it because he can try things out. And if you take
it with that exploring sense, and I mean, I can't remember, I've certainly got to the point where I'm typing in to the other, oh, how do I do so and so in ARV? And I've done 20 times, but I still can't remember how to do it. And exploring, and Amesh makes a point again, setting up initial environments. Give me a starting project, a sample starting skeleton project, so we'll just get moving. So that kind of exploratory stuff, and helping in an unfamiliar environment, and just learning your way around an unfamiliar set of APIs and coding ideas and the like, it can be quite handy for.
I wonder if this is not all that new in the sense that I remember one of the last kind of big productivity boost in the industry about 10 or 15 years ago was Stack Overflow appearing. So before Stack Overflow, when you googled for questions, you bumped into the cycle of experts to change, and there was the question, and you had to pay money to see the answer, or you had to pay money to get an expert to answer. But usually there was nothing behind it, even if you paid, I was a college student, I didn't pay. So you can find the answer, and we're all frustrated, but then Stack Overflow came along, and suddenly you had code snippets that you'd copy. And of course, what a lot of young people are like less experienced developers, even like myself did, is you just take the code, put it in there, and see if it works. As you got some more experienced
engineers or developers, you started to tell the junior engineers, like, you need to understand that first. Or even if it works, you need to understand why it works, you need to read the code. And I feel we've been, there was a few years where we were going back and forth of people mindlessly copying pasting snippets. There were problems with, I think there was a question about email validation, the top voted answer was not entirely correct, and turns out that a good part of software and developers just use that one. I feel we've kind of been around this already.
Yeah, it's a similar kind of thing. But maybe at a smaller scale. But even more boosted in on steroids and with the question of how things are going to populate in the future, because there's going to be writing Stack Overflow answers anymore. So I wonder if what we're getting to is like, you need to care about the craft, you need to understand what the LLM's output is, and is there to help you? And if you're not doing it, I mean, like you should, but if you're not, you'll eventually be no better than someone just prompting it mindlessly. Exactly. I mean, I mean, I have no problem with taking something from the LLM and
putting it in to see if it works. But then once you've done that, understand why it works, as you say. And also look at it and say, is this really structured the way I'd like it to be? Don't be afraid to refactor it, don't be afraid to put it in. And then of course, the testing combo.
Anything you put in that works, you need to have a test for. And if you constantly are doing that back and forth with the testing process. Martin Fowler was just talking about the importance of testing when working with LLM's and in general, when building quality software. Speaking of the quality software, I need to mention our season sponsor, Linear. I recently sat on one of the linear's internal weekly meetings called quality Wednesdays, and I was completely blown away.
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Thomas, their CTO, recently wrote a piece about this weekly ritual, and I'll link it in the show notes below. If your team cares about craftsmanship and building products that people actually love using, check out Linear at Linear at Massage Pragmatic. Because honestly, after seeing how they work up close, I understand why so many of the best engineering teams are switching. Now, let's get back to importance of testing when working with LLMs. One of the people I particularly focus on in this space is Simon Willison. He stresses constantly his importance of tests. A testing is a huge deal
to him being able to make these things work. Of course, Bikit areas, some fault works, we're very much an expert in programming company. She's steeped in testing as well. She will say the same thing. You've got to really focus a lot on making sure that the tests work together. Of course, this is
where he had a lot of them struggle because he tells them to do the tests. I'm only hearing problems or experiencing them myself. When you have LLM tells me, I ran all the tests, everything's fine. You've got NPM test, five failures. I see some improvements there, by the way, with clock code, also like other agents, but yes, it's the non-deterministic angle. Sometimes they can light
you, which is weird. I'm still not. They do like you all the time. If they were truly a junior developer, which you say sometimes people like to say they should be characterized, I would be having some words of HR. The other day, I just had this really weird experience, which is the simplest thing. I have a configuration file where I add new items and new JSON blob, and I put the
date of when I added it, just in the comments saying, added on October 2nd, added on November 1st. It's always a current date. I told the LLM, can you please add this configuration thing and add the current date? It added, and it added, and it added, it just copied the last date. I said, that is not today's date. I said, oh, I'm so sorry, let me correct that for you. And I put yesterday's date.
And I feel you need to get this experience to see that it can gaslight you for a simple thing of today's date, which you could call a function, whatnot, but it's down to which, who knows which model I was using, how that model works, whether the company creating it is optimizing for token usage or not, etc, etc. So like in the end, even for the simplest things, you are, as a professional working on important stuff, you should not trust it. Yeah, absolutely. Never. Yeah. You've got to, you've got to don't trust, but do verify.
Verify yes. Speaking with developers at ThoughtWorks and the people you're chatting with, what are areas that they are successfully using LLM's day to day, though? We did mention just right now, testing, we also mentioned things like prototyping, but you see some other things where it's starting to become a bit of a routine. Like if I'm doing this thing, let me reach for an LLM. It can probably help me.
That, yeah, I mean, I've mentioned many of that, right? The prototyping, the legacy code understanding, the fact that you can use it to explore new technology areas, potentially even new domains, as long as you trust it significantly less than you would trust Wikipedia 10 years ago, those are the things that I'm hearing so far. Yeah. One interesting area that Vergata is exploring is speculative and development. There's this idea of what, well, you know, LMs have their own limitations, but what if we define pretty well what we want it to do, and give it this like really good specification, and you know, it can run with it, it can run long, it's rations and so on. What is your take on this? And do you have a bit of a deja vu, because we
refer this one's right? Your career started around this thing called waterfall development. So how are you seeing it somewhere, but also different this time? Well, the similar to waterfall is where people try and say let's create a large amount of spec, and not pay much attention to the code. And here, I mean, whether you talk about, again, this is what you mean by speculative and development. Is it so much focusing on that,
or is it doing small bits of spec, do the type loop? I mean, to me, the key thing is you want to avoid the waterfall problem of trying to build the whole spec first. It's got to be, the smallest amount of spec you can probably, you can possibly get to make some forward progress, cycle with that, build it, get it tested, get it in production, if possible, and then cycle where these thin slices, what role a spec may play to drive, in either case, could be argued to be a spec former spectra of development. But to me, what matters is the type loops, the type loops, the thin slices, that kind of thing. And I know I beg you to definitely agree on that point, I mean, because you have to be the human in the loop verifying every time, that's clearly crucial.
Where the spec development then ties in interesting, again, it comes back to this thinking of building domain languages and domain specific languages and things of that kind. Can we craft some kind of more rigorous spec to talk about them? I mentioned that the one that was doing there, using it to build an abstraction, because eventually, what we're saying is that it gives us the ability to build and express abstractions in a slightly more fluid form than we would be able to do if we were building them purely within the codebase itself. But we still don't want them to deviate too much on the codebase, or we still want the ubiquitous language notion that it's the same language in our head as it's in the code. And we're seeing the same names and they're doing the same kinds of things. The structure is clearly parallel. But obviously, the way we think is a bit
more flexible than the way the code can be. And then, can we blur that boundary a bit by using the LLM as a tool in that area? So that's the area that I think is interesting in that direction. It's interesting, it's new, because I feel we've never been able to use language as close to representing code ever or business logic. And this is very new.
Yeah. Well, again, plenty of people who take that kind of DSL-like thinking into their programming. Now, I know people who say, yeah, I would get to the point where I could write certain parts of the business logic in programming language, say Ruby, and show it to a domain expert, and they could understand it. They wouldn't feel the ability to be able to write it themselves, but they could understand it enough to point out what was wrong or what was right in there. And this is just programming code, but that requires a certain degree of the way you go about projecting the language in order to be able to get that kind of fluidity. But that kind of thinking, like,
trying to make an internal DSL of the programming language, or maybe building your own external DSL. And DDS-alming domain specific language. Like, if you're working with accountants, you're going to have the terms that they use the way they use it and so on. Yeah. And what you're trying to do, of course, is create that communication root where a non-programmer can at least read what's going on and understand it enough to be able to find what's wrong about it and to suggest changes, which may not be impacted correctly, you can easily fix them because you're as a programmer, you can see how to do that.
And that's the kind of goal, and some people have reached that goal in some places. So, an interesting thing is where the LLMs will label this to make more progress in that direction and see that happening more widely. And I guess this must be, I'm just assuming, correct me if I'm, this must be a special important enterprise. These very large companies where software developers are not the majority of people, let's say they're 10 or 20% of staff, and there's going to be accounting, marketing, special business divisions who all want software written for them, and they know what they want. And historically, there's been layers of people translating this, maybe the project manager, the technical product, etc. So you're saying that there could be a pretty interesting opportunity or
just an experiment with LLMs that maybe we can make this a bit easier for both sides. That is the world I'm most familiar with, right? It's that world. I mean, my sense is you're very familiar with the big tech company and the startup worlds, but this corporate enterprise world, of course, is a whole different kettle of fish because exactly the reason that you said, suddenly the software developers are a small part of the picture and is very complex business things going on that we've got to somehow interface in. And of course, also is usually a much worse system problem as well.
Yeah. And there's going to be regulation, there's going to be a history, there's going to be exceptions because of all the knowledge, I think we can all just think of banks of all the things. There's a perfect storm idea of regulation that changes all the time. They have incidents that they want to avoid to go into, they'll have special VIP accounts or whatever that they want to do. And of course, they have all these business units that all know their own rules and framework, and they've been around since before technology, some of the banks have been around for 100 plus years.
Yeah. And remember, the banks tend to be more technological advanced than most other corporations. That's a good one. You're looking at a good bit when you're talking about banks. You have worked with some of the less, as well as well.
I mean, retailers, airlines, government agencies, things of that kind. I mean, it was interesting. I was chatting with some folks working in the Federal Reserve in Boston, and they have to be extremely cautious. They are not allowed to touch LMLs at the moment, because the consequences of error when you're dealing with a major government banking organization are pretty damn serious. So you've got to be really, really careful about that kind of stuff.
And their constraints are very different. And it brought to mind, there's an adage that says that to understand, have a software development organization works, you have to look at the core business of the organization and see what they do. Interesting. I was at this agile conference at the Federal Reserve in Boston, and they took me to the Federal Reserve where they handle the money. And so I saw the places where they bring in the notes that have been brought in from the banks, and they kind of clean them and count them and all the rest of it and send out the stuff again.
And you look at the degree of care and control that they go through, as you could imagine. I mean, when you're bringing in huge watches of cash, and it has to be sorted and counted and all the rest of it, the controls have to be really, really strenuous. And you look at that and you look at the care with which they do all of this, and you say, yeah, I can see why in software development side, that mindset percolates because they are used to the fact that they really have to be careful about every little thing here. A lot of corporations, of course, have that some inertia. You're involved in an airline. You are really concerned about safety. You're really concerned about getting
people to adapt that affects your whole way of thinking or to. And it does. And I guess this is a reason we are clearly seeing, we always see the Biden technology usage, because you have the startups, which is a group of people, they just raised some funding or they have no funding. They have nothing to lose. They have zero customers, they have everything to gain.
They need to jump on the latest bandwagon. They want to try out the latest technologies, oftentimes build on top of them or sell tools to use a latest technology. And they're here to break the rules. And the midway when there's, when you start to have a few customers in a business, you're starting to be a bit more careful. And of course, you know, 50 or 70 years down the road, when the founders have gone and now it's a large enterprise, you will just have different resources, right? Exactly. Yeah. But what I find fascinating,
talking about this, that I'm unsure if there has been any new technology that has been so rapidly adopted everywhere. You mentioned that, let's say the federal reserve or some other government organizations might say, well, that's not such as yet, but they are also evaluating it. Sounds like it. So if they're, you know, they're the one of the most, I guess behind it, the technology curve for a very good reason, they're already aware of it or using it. It just probably means that it's everywhere now. Oh, it is. I mean, we see it all over the place. But again, with that,
with more caution in the enterprise world, we're saying, yeah, we also see the dangers here. And then you're seeing kind of more nimble companies that you work with and the more enterprise focus. What would you say that's the biggest difference between their relationship of AI, their approach? Is it this caution or are there other characteristics that the big, more traditional, less more risk-vers companies approach it differently? The important thing to remember with any of these big enterprises is I am not monolithic.
So it'll be small portions of these companies can be very adventurous and other portions can be extremely not so. And so what you'll see is small, I mean, when I started at Chitima Lightwam, right, and I was in this little bit that was being very, very aggressively doing really wacky things, right? I mean, if you'll find out in any big organization, you'll find some small bits doing some stuff. And so it's really the variation within its products often is bigger than the variation between enterprises. You've got to give that a mind. So speaking about refactoring,
LM's are very good at refactoring. And you've written the book back in 1999 called Refactoring. This is now the second edition, which 20 years later, it's been refreshed. And it's actually a really detailed book going through different code smells that could show that what were the code is techniques of refactoring it on the first page already. It has every list of refactoring on, I don't know how the publisher printed this because it's so unusual, but it's right here on Table of Contents. Why did you decide to write this book back in 1999? Can you bring us back on
what the environment was like and what was the impact of the first edition of this book? Okay, so I first came across refactoring, Caroisler, when I was working with Chembeck, right early on in the project, I remember in my hotel room, in a courtyard or whatever in Detroit, him showing me how he would refactor some small talk code. And what I mean, I was always someone who liked going back to my, something got already written and make it more understandable. I've always cared a lot about something being comprehensible. That's true in my prose writing and in my software writing. And so Vah knew, but what he was doing was taking these tiny little steps. And I was just
astonished at how small each step was, but how, because they were small, they didn't go wrong and they would compose beautifully and you could do a huge amount with this sequence of little steps. And that really blew my mind away. I thought, wow, this is a big, big deal. But Kent was at the time, his energy was the first extreme programming book, the white book. He didn't have the energy to write the refactoring book. So I thought, well, I'm going to do it then. And I started by, you know,
whenever I was refactoring something, I would write careful notes, apparently, because I needed it for myself. How do I extract a method? So I was, I don't screw it up. And so I would write careful notes on each one. And then each of those turned into mechanics in refactoring book would be that step. And then I'd make an example for each one. And that was the first
edition of the book. And I did it in Java, not in small talk, because Small Talk was dying sadly. And Java was the language of the future, the only programming language we'd ever need in the future, in the late 90s. And so that's what led to the first book. And the impact, well, I mean, and also refactoring, I should also stress, it wasn't invented by Kent. I mean, it was very much developed by Ralph
Johnson's crew at University of Illinois at the Bunnish Empire. And they got the first refactoring browser in Small Talk, which is the first tool that did the automatic refactoring. So we talk about that. That was the original refactoring browser built by, I'm blanking on John Brant and Don Roberts. And then when the book came out, that got more interest. It was already some interest from the IBM
visual age folks, because they came out of Small Talk, the original versions of visual age we've had built in Small Talk. And so they were already aware of what was going on to some degree, but it was the JetBrains folks that really caught the imagination, because they put it into the early versions of the idea and really ran with it, then you ran into it with ReSharper, of course. And they really made the automated refactoring become something that people could rely on. But it's still good to know how to do them yourself, because after you're in a language where you haven't got those refactoring is available to you. So it's nice to be able to pull that stuff and some of them obviously in there. And yeah, so the impact it's had is the refactoring became a word. And of course,
like all of these words got horribly misused and people used to refactoring to me any kind of change the program, which of course it isn't, because refactoring is very strictly these very small semantics, behavior, preserving changes that you make. Tiny, tiny steps. I always like to say each step is so small that it's not worth doing, but you string them together and you can really do amazing things with that. I think we've all had that story. At least I had a story where one of my colleagues or it could have a meat but oftentimes one of my colleagues would say like, oh, let's stand up saying like, oh, I'm just going to do a refactoring. And then next day, oh, I'm still
doing the refactoring. Like, I'm still doing the refactoring. And you know, that that is the part of the small changes for sure. What made you do a second edition for the book 20 years later in 2019, which was fairly recent? Well, it was a sense of wanting to refresh some of the things that were in it and some of the new things that I had. I was also concerned that,
I mean, when you got a book that's written in late 1990s Java, it shows its age a bit. And although the core ideas I felt was sound, people could still use it. I felt are you coming, giving it a more doing it in a more modern environment. And then the question was, which, you know, would I stay with Java or the does which don't have a language in your life, it's switched to Java script. I thought it was reach abroad audience that way, and also allow a less object oriented centered way of describing things. So instead of extract method, it's extract function, because of course, it's the same process
for functions. And also some things that you don't necessarily think of doing in an object oriented language. But it was mainly just to get that refresh, to redo the examples to really hopefully give it another 20 years of life because it's got to keep me going until I croak, you know. Yeah. So you published this book 25 years ago or 26 years ago, in the industry based on your interactions with developers, how has the perception of refactoring changed? Because in the book, you
specifically wrote that you see refactoring as a key element in a software development life cycle. And you've also talked about how when you refactor the overall cost of changing code over time can be a lot cheaper. Was there a time where there was a lot more uptake on this or is there still, or do you feel it's kind of like a little bit like being maybe refactor refactoring went a little bit out of style as some of those really innovative tools at the time, like JetBrains and others, they're maybe not as as kind of referenced, even though they're everywhere. It's hard to say for me, because I mean, again, most of the interaction I have is with folks at ThoughtWorks, they tend to be more clued up with this kind of stuff than the average developer. Certainly, I read plenty of things on the internet that make me just check my head at how even refactoring is being described, let alone the lack of doing it in a certain kind of structured way, controlled way. But I like to do it because I like doing it quickly and effectively. And
it's one of those things where the disciplined approach actually is faster, even though it may seem strange to describe it that way. But I mean, I have to, at least being part of our language, now people talk about doing it, it's in these tools and they do it very effectively, refactorings that they do. I mean, it's wonderful to work in an environment where you can actually automatically do so many of these things. And so I feel we've definitely made some progress, maybe not as much as others hoped for, but we're tough to know way with these things. Now, looking head with AI tools, they generate a lot more code a lot faster, so we're just going to have a lot more code. We already have a lot more code. How do you think the value of refactoring,
thinking about the year and 10 of the meaning of those small, ongoing changes is going to be important. And are you already seeing some of this being important? I want to say I'm already seeing it. But I can certainly expect it to be increasingly important, because again, if you're going to produce a lot of code of questionable quality, but it works. Then refactoring is a way to get it to a better state while keeping it working.
These tools at the moment can't definitely come up refactor on their own, although with combined with other things Adam Thornhill does some interesting stuff with combining LLMs with other tools to be able to get a much more effective route. And I think that kind of approach combining could be a good way to do it. But definitely the refactoring mindset and thinking, how do I make changes by basically boiling them down to really small steps that compose easily? That's really the trick of it, the smallness and the composability combine those two, and you can make a lot of progress. That's interesting, because right now, if you want to refactor, you need to have your IDE open for sure. And I mean, the fast way of just using the built-in tools or you moving things around.
But what I found as well is describing it when I have a command line open with like clock code or something similar. It's tough, where I spend more time explaining it than doing that small change. And I do wonder if we will see more integrations in this end as well, so that LLMs can actually do it, or some of them might do it automatically. Because as you say, it doesn't work out of the box. But I think for any quality software that we all learn the hard way that if you just leave it there and don't go back and don't change it up when your functions get just a simple thing, right? When your
function gets too long, when your class gets too long, you break it up. Otherwise, you're not going to understand it later. Yeah, it'll be interesting as well to see if it provides a way for us to control the tool. I mean, one of the things that interests me is where people are using LLMs to describe queries against relational databases that turn into SQL. You don't know how to get the SQL right, but if you type the thing at the LLM, it will give you back the SQL and you can then look at it and say, oh, this is right or not right and tweak it. And it gets you started, right? And so similarly,
we refactor it may allow you to get started and say, oh, these are the kinds of changes I'm looking at, and be able to make some progress in that. I mean, particularly where you're talking about these automated changes across a large code basis, there was an example of this. Was it a year ago or so? One of his big companies talked about this massive change that made to change APIs and clean up the code. And I'd mention it as an LLM thing, but it wasn't an LLM. It was a different tool, and I'm
completely blanking on what the names of all these things were. Oh, to have a 60 year old brain and I can't be able to remember anything anymore. It'll come to me at some point. But actually, it was a combination of maybe 10% LLM and 90% of this other tool. But that was, again, provided the extra leverage for a lad to make the progress. I mean, those kinds of things are really quite interesting,
using the LLM as a starting point to drive a deterministic tool. And then you're able to see what the deterministic tool is doing. That's, I think, going on from a factoring to software architecture, you were very busy writing books around the early 2000s. You wrote the book Patterns of Enterprise Application Architecture in 2002. And this was a collection of more than 40 patterns, things like lazy, old identity, app template for you and many others. And I remember around this time,
there was your book about an enterprise architecture patterns. There was also the Gang of four book. There was a lot of talk when I was interviewing around that time and interviews, they were asking me questions about how to do a factory pattern and singleton and all of these things. Software architecture was talked about, my sense was in a lot of places or a lot more. Then something happened.
Something started from 2010s. I no longer hear most technologists talk about patterns or architecture patterns. How have you observed this period of when the book came out? What was the impact of it and why was it important to talk about it and put it into the industry? And how have you seen this change of where we stopped talking more on patterns and why do you think it happened?
Yeah. I mean, I've always found it. I mean, what you're doing with patterns is you're trying to create a vocabulary to talk more effectively about this kind of these kinds of situations. I mean, it's just like in the medical world, they come up with this jargon in Greek and Latin to more precisely talk about things quite involved in complex. And we patterns what we're trying to do is trying to evolve that same kind of language except we're not doing it in Greek and Latin. I certainly
feel that they do help communication flow more effectively. Once people are familiar with that terminology, I mean, you don't look at them as some kind of, how many of them can you cram into the system you're building. It's more a sense of how can you use it to describe your alternatives and the options that you have. And also think about more about when to apply things or not apply them. I mean, patterns are only useful in certain contexts. So you very much got to understand the context of when
to use them. And yeah, it's kind of a shame that some of the windows gone out of the sales of that, perhaps because people were overusing them in terms of trying to use them as a sort of like pinning medals on a chest. But it can still be very, I mean, I worked very recently with Unlech on his book on Distributed Systems. And I felt that was a very good way of coming up with, again, a language to describe how we think about the core elements and better gain and understanding of how distributed systems work, which is an important aspect of how to deal with life these days, because we're all building these kinds of distributed systems. So I still feel that they can be a very good way of expressing that. It's hard for me to get a sense of why they can become
less fashionable. Maybe they'll become more fashionable again, who knows. But I'm always looking for ways to try to spread knowledge around and make things more understandable. And I do feel that this idea of trying to identify these and create these nouns that we can talk about things more precisely is a good way of part of doing that. I wonder if, because I've seen, I've worked with places where we use these things and then places where we just threw them out the window and no one was using it.
And a difference was honestly just the age and the attitude of the company, because there was a sense at some point that the patterns, they were for legacy companies. So startups would just start from a blank sheet of paper, a whiteboard, UML was a perfect example, where UML had pretty strict rules on how to do the arrows. And if you do that right, you could even generate code and do all these things. And at startups, their software should still exist, but you just put it on the whiteboard and you just do a box or a circle and you didn't care about the arrows. And it was just, I guess, we're not going to lock ourselves into existing ways of doing things. And it's a bit of an education as well.
You do need to onboard to these things. You all need to have a shared understanding. And maybe it's just a combination of these two things. I guess it's a generational thing as well. Every few years a new generation comes out and the same way where at some point, I was one of the first people in college where it was super cool to use Facebook and it was just all scholar students.
And then when my parents went on there, it was super uncool to use Facebook. Or what's about it? And while grandparents came on there, I kind of like, saw stop using it when they started using it. So I wonder if there's like these waves going back and forth, because inside of these startups, there is a language, like, lingo about how they talk about the architecture. And it starts to form over time. You start to see it whether it's longer
tenured people. You get more and more of the jargon except it's not in a book that anyone can read, but you have to go in there or go to a similar company where they take the jargon with them. Exactly. And people will create these juggins. And it's an inevitable part of communication.
You need to, you need to, you can't explain everything from first principles, requiring five paragraphs every single time. If you're using the term all the time, you just make a word out of it. And then everybody creates their own words. And all you're doing when you're coming up with a book like the patterns of distributed systems is you're trying to say, okay, here's a set of words with a lot of definition and explanation of them. And let's hope we can kind of converge on that. So
we can communicate a bit more widely. But it's also quite natural for people to say, you know, we've in our little environment, we try our own little jargon. So we don't take a notice of that. And then you get the mismatches that occur as you only really notice that as you cross these different environments. Grady Booch had an interesting take on this, by the way. So
I asked him about the same thing because he's been so much into software. He still is into a software infrastructure and he's progressed the field a lot. And he said that what he thinks happened is that starting in like two, because the patterns died out from mainstream industry, I'll say again, it's still in some pockets, but are in the two thousand cents. One interesting thing that happened around that time is cloud. The cloud started to get bigger AWS Google cloud and a lot of companies started to build similar things. They started to build either initially on-premise
back-end services where you had most of your business logic later moved to the cloud. And Grady said that these hyperscalers, the cloud providers, AWS, for example, they built all these services that are really well architected. So you can kind of use one after the other. And it's well done. You don't need to worry too much about your data storage. You just use let's say DynamoDB or a managed postgres
service. So suddenly architecture is not all that important because these blocks take care of you have these building blocks. And now you're talking about using this database on top of this system. His observation was maybe architecture was solved with a well-architected building block that you could use and you didn't have to reinvent the wheel. Yeah. Or but I suspect it's still patterns
of using these things. And that's something I haven't delved into because I just had the opportunity to focus on that. Or more precisely, I haven't had another of my colleagues banging my banging me on the door with draft articles to be able to publish on it. Well, one pattern that I do see as every company names their system somehow, wacky names, some have logical names. But when you talk about architecture, you typically talk about like, like at Edoubert, we had the the bank emoji service, which called, which was being migrated to Gulfstream, which was, you know, these all sound like doesn't make too much sense. If you're from the outside, sometimes they have like proper names
they try without the payment profile service, but then there's a new version and that's now the payment that's P P P two. Anyway, but inside of every company, like you will talk about these specific names and you will talk about how they work, how small they are, how large they are. And that's kind of, I feel that's oftentimes a lingo. Yeah, it is. It becomes, again, again, part of the lingo of larger organizations. And again, you take a company that's been around for a much longer than Uber. And of
course, that lingo is baked into the organization can take you several years just to figure out what the hell's going on, because it just takes you that long to learn all of these systems and how they connect. But one of the fascinating conversations that I had many years ago was someone very high up in American Express. And we were talking about how he was responsible for re-architecting their system to the next generation. And he was just getting ideas on how to socialize ideas and get things out and ask how long to be working on this. There's been three years. And I was like, okay,
so we're like, where are you? Are you like, Don is like, no, no, this is just a planning. We're close to finishing the planning. And to me, I didn't compute because like in three years we're planning. But again, once you start to understand the scale of the business, how much money, how many legacy systems they have, half of half of what he did was talking about business stakeholders to convince them or get buy in. I guess this eventually happens with like most companies, except when we
are at the younger company or digital first-tech first companies, meaning founded in 2010 or later, you still don't see this? But it might come in 10 years. Oh, yeah, it certainly will. I was interested in it. I remember chatting, I was chatting with somebody who had joined a bank, an established bank, and they joined from a startup. And one of their jobs was to modernize
the way the bank stuff was going. And the comment was, now we've been here three years. Now I think I can understand the problem. I've got some idea of what I can do, what can be done. But it just takes you that long to just really understand the land where you are in this new landscape, because it's big and it's been around a long time and it's complicated and it's not logical because it's built by humans, not by computers, and it's not a logical system. And there's all sorts of history
in there because all sorts of things happened because so-and-so met so-and-so and added around with so-and-so and all of these things kind of percolate over time. And this vendor came in here and was popular over here. And then the person who liked this vendor got moved to a different part of the organization, somebody else came in who wanted a different vendor. And all of this stuff builds up over time to a complicated mess. And any big company is going to have that kind of complicated mess because it's very hard to not get that situation. And yeah, I mean,
Uber's lucky but it's only a relatively young company. But assuming it survives in 50 years' time, it'll be like American Expresses, right? You can already see the changes, the layers of processes and so on, which is kind of like it's necessary as you grow. Speaking of change on iteration on an agile, so you were part of the 17 people who created the agile manifesto and I previously asked Ken back about this, who was another person involved. Can you tell me from your perspective what was the story there on how you all came together, how this pretty chaotic, I think day played out and what was the reception, as you were called back? And this was 2001.
Right. So, I mean, the origin of it, I always feel was actually a meeting we had that Ken ran about a year before we did the agile manifesto and it was a gathering of extreme programming folks who were working with extreme programming. And we had it at this place near where Ken was living at the time in middle of nowhere, Oregon. And he also invited some people who weren't directly part of extreme programming group, folks like Jim Hysmith along as well. And part of the discussion we had was should extreme programming be the relatively narrow thing that Kent was describing in the white book or should it be something more broad that had many of the similar kind of principles in mind. And Kent decided he wanted something more concrete and narrow. And then the question is,
what do we do with this broader thing and how it overlaps with what the scrum people were doing and all that kind of stuff. That's what led to the decided idea of getting together people from these different groups. And we had the argument about whether we were going to hold it in Utah, because Allison wanted it in Utah. I mean, Dave Thomas wanted to have it in Anguilla in the Caribbean and for whatever reason, we ended up in Utah, and skiing. And so we gathered together the people that we did. And of course, it was a case of who actually came along because obviously lots of
people invited who didn't come. And I wasn't terribly involved with that. Although, but Martin does insist that I was involved, got involved in, he mentioned some lunch in Chicago, which is very likely because I was going to Chicago all the time at four times. I probably did, but I don't remember. And of the meeting itself, I actually don't remember very much of it, which is a shame.
I cursed myself for not writing a detailed journal of those few days. I'd love to know how did we come up with that this over that structure for the values, for instance, which I think was really wonderful. But I have no idea how that got put together. So unfortunately, I get very vague about the actual doing of it. I do remember have a fairly clear memory, although we should be wary about that.
I'll come to that perhaps later about why of Bob Martin being the one who was really insistent on I want to make a manifesto and me thinking, Oh, well, yeah, we can do that. It'll manifest itself will be a complete useless and ignored, of course, but the exercise of writing it will be interesting. And that was my reaction to it. The matter I felt about the manifesto, I thought, nobody will take any notice of this. But hey, we're having fun writing it and we're understanding each other better. And that will be the value, right? We'll understand each other better.
And then of course, the fact that it made a bit of an impact was kind of a shock. And then of course, it gets misused by most of the time because there's that lovely quote from Alistair Coben, but your brilliant idea will either be ignored or misinterpreted. And you don't get to choose which of it is. Well, also how did the manifest to us for different lines? And so people just pick and choose which one they want to go to. I don't think it's called principles. Oh, and it's
called principles, which yes, and the fact that it says, and says at the beginning, we are uncovering that is a continuous process. And what the manual festival is just this is what we've got so far. So it's a snapshot of a point in time of where we were in 2000 2001. Yeah, all sorts of subtletists to the manifesto. But I think it had an impact in the sense that my feelings were was a certain way that we wanted to write software at ThoughtWorks for our clients in 2000. And it was
a real struggle because they didn't want to work the way we wanted to. We said we want to put all the sentiment into writing tests. And we want to have an automated build process. And we want to do these kinds of things. We want to be able to progress in small increments, all of these kinds of things, which were anathema. No, we've got to have a big plan over five years and we'll spend
two years doing a design, well, reduce the design. And then it'll get implemented over the next year or so. And then we'll start testing. Right? I mean, that was the mentality of how things ought to be done. Yeah, that was just the common the common the understood wisdom, right? Yeah, and our notion
of no, we'd like to do that in type process for a subset of requirements in one month, please, only a month. And of course, we really wanted to do it in a week, but you know, baby steps. And so to me, the great thing about agile is that we can actually go into organizations and operate much closer to the way that we'd like to be able to do our clients will let us work the way we want to the much greater extent than we were able to do back in 2000. And so that is the success. I just wanted the world to be safe for those people that wanted to work that way to be able to work that way.
Yeah, there's all sorts of other bad things that have happened as a result of all of this. But on the whole, I think we are a bit better off. And do you see like the way you look, especially when you look at the enterprise clients that you have a lot more visibility to, you see the definite change from like 25 years ago to like the concept of agile are way more accepted, like working with the customer, having a lot more incremental delivery, forgetting about these like very long pieces of work, like this is just common everywhere, right? Can we say that or at least I would say we've made significant progress, but compared to how we'd like it to be, and who our vision is, it is still a pale shadow of what we want, of what we wanted. I mean, I suspect most of the 17 that are still with us would agree with that. We still feel we can go much, much better than we've been, but we have
actually made material progress. And the thing is that we were always in that situation where we're kind of nudging our way forwards much slower right than we'd like to be. Yeah, now of course, AI is coming and it is everywhere and it will be everywhere. And one thing is with AI. So the core idea behind agile was that you make incremental improvements and the shorter the better. Now with, and you could then build software that incrementally
start to improve, but today with AI, especially with AI, there's going to be more software everywhere, there already is. And there's a sense that customers don't necessarily want to wait for incremental improvements. They want to see quality up front. Do you think that agile will work just as well with with with AI with even shorter increments? Or do you think we might start to think about like some different way to work with with AI putting on the quality lens up front as well and getting back to a little bit of, you know, the spec, the driven development, like getting a version of the software that is just great to start with. I don't know how the thing is going to play out,
because we're still in the early days. I still feel that building things in terms of small slices with the human, so there's humans reviewing it is still the way to bet. What I hopefully will allow us to do is to be able to do those slices faster and maybe do a bit more in each slice. But we need to, I'd rather get smaller, more frequent slices than more stuff in each slice. Improving the frequency is usually what we, I think we need to do and just cycle out those steps more rapidly. That's where I felt we've had our biggest gains is through that more rapid cycle rather than
trying to do more stuff in the same cycle as it were. And I still get a sense about when talking to people that still saying, you know, can you look at all of the other things that you do in software development and increase the frequency? Do half as much, but in half the time. And speed up that cycle. Look for ways to speed that through and also, you know, just look at where, what you're doing, look for the cues in your flow and figure out how to cut those cues down.
If you were able to get some ideas from idea to running code in two weeks, how do you get it down to a week? Just try to constantly improve that cycle time. And I still feel that that's our best form of leverage at a moment is improving cycle time. Yeah. And I'm talking with some of the leading AI labs on how they use it, because of course, they're going to be on the bleeding edge they will use as they are also in their own interest to use their own tools at and traffic. The cloud, the clock hold team, one of the creators of clock code,
Boris, he shared how he did 20 prototypes of a feature of how the progress bar, when you do a task, how it lists out different steps and how it shows you where it's at. And he built 20 different prototypes that he all tried out and got feedback on and decided which one to go in two days. And he showed me, so actually he has, he had videos, he just recorded these as he went the exact prompt that he used the output. And these were interactive prototypes. So they were not just, you know, like on the paper, but they were inside. And to me, this was like, wow, like, if you would have told me,
I built 20 prototypes and you asked me how long it took, I would have said two weeks, maybe a week if they were small, like paper prototypes, but as you can still speed it up and it is still manageable, some of them, he threw it away, some of them he showed it with a small group, bigger group. So I feel, I feel you're right on how we have not reached a limit of how quickly can we look at things. Yeah, it comes back to feedback loops. I mean, so much of it is trying to introduce feedback loops into the process. And then how do we tighten those feedback loops so we get the feedback faster, so that we're able to learn. Because in the end, again, it comes back to, you know, we have to be
learning about what it is we're trying to do. Speaking about learning and keeping up to date, how do you learn about AI? How do you keep up to date with what's happening, what approaches work for you? And what are approaches you see your colleagues follow who are also staying up with, you know, what's going on? Well, the main way I learn these days is by working with people who are writing articles that are going on to my site, because my primary effort these days is getting good articles onto my site. And my view is that I'm not the best person to write this stuff, because I'm
not doing the day-to-day production work. I haven't been doing them for a long time. The only production code I write is ironically the code that runs the website. I still write code. I still generate sacrifices, but it's only within this very, very esoteric little area. So as a result,
it's better for me to work with people who actually are doing this kind of work and help them get their ideas and what their lessons and express them to the as many people as possible. So I'm learning through the process of working with people to write their ideas down, which is a very interesting way of learning, because of course you're very deeply involved in the editing process for a lot of that material. And that's my primary form. I do do some experimentation when I get the chance, not as much as I'd like, but I do see that as a second priority to working with people. So it's necessity only in me, in the off time that I get to do that. And of course, reading from where I feel
are some of the better sources. I mean, fortunately, one of those better sources is the data who's been writing with me. So that's good. Simon was excellent. So I get to stuffy superb. Simon Wollison, I keep an eye on what he's doing all the time.
I wish I had his energy work right for getting stuff out. Actually, I wish I had your energy. You'd imagine stuff you get out these days. And so I look for sources like that. I'm always interested in what folks like came to up to because let's say, so much of my career has been leaching off-cents ideas. And there's no reason to stop doing that if it's still working. Right. And so
those are the kinds of sources. I mean, then sometimes some books that come out that come through and work through those. So a lot of it is in that kind of direction. I like to even watch a video occasionally, although I really hate watching videos. So it sounds like find the sources of the people you trust the sources you trust. Again, your blog, I can very much recommend it because
you have several people writing on it. So you actually have a pretty good frequency of in-depth articles about interesting. Like I rarely see topics that have been discussed in depth. And so I enjoy checking out because of it. I mean, one of the questions that I've been pondering on is when asked of how do you identify what a good source is of information? And this is more
general, this is due to our profession, but of course, due to the world generally, as we seem to be in a epistemological crisis of trying to understand what's going on in the world. And at some point, I'm going to sit down and write this down and I'll get a more coherent answer from it. But part of what I'm always looking for is a lack of certainty is I think a good thing. When people tell me, oh, I know the answer to this, I'm usually a good bit more suspicious. And I'm much more conscious of when people say, this is what I understand at the moment, but it's fairly unclear. I remember
one of my favorite early books when I was writing on the software architecture. I remember desperately looking for something in the Microsoft world, as opposed to something in the Java world. There was a lot being written in Java world. It's back around the late 90s. Lots of being stuff was being written in Java land, not much in Microsoft land. And when I discovered this Swedish guy, Jimmy Nielsen,
and his book was full of stuff that says, well, this is how I'm feeling about this is the way to approach this stuff. It was very tentative all the time, very much clear of this was how he was currently feeling, but you understood that things might change. I've since got to know Jimmy really well, and he's a fantastic guy. But what impressed me so much and what influenced me so much is I felt very much the degree to which, oh, this is somebody I can trust because they're not trying to give me this full sense of certainty and confidence. And I think that's important. Also, someone who's
keen to explore nuances and saying, well, this works in these circumstances, not if somebody tells me, oh, you should always use microservices or somebody says that you should never use microservices. I mean, those both arguments can be completely discounted. It's when you say, oh, these are the factors that you should be considering about whether to go in this direction or that direction. Whenever someone is stepping back and saying, ah, it's, it's a trade off, there's various things involved. Here's the factors you should go and it's not going to be a simple answer. You've got to dig into the nuances.
Then again, that increases my confidence because again, I'm feeling this is someone who's thinking these things through and not just coming on a sort of simple rail rod and going down it. And I guess with these sources, you can also trust that everything we do in software engineering, it's going to be trade off, right? The most common answer of like how long will it take is it depends, it depends on how are we doing a prototype, it depends on on doing what technology, etc. So if you're reading sources or if you're accessing sources where they tell you, okay, in my situation, you actually learn about the situation and you can figure out like, okay, in this specific case, for them, this worked or it didn't work. And later, you can probably apply it a bit better because again, it's very different if you're going to be working as a software engineer inside a highly regulated retailer that's 70 years old versus you've just started a brand new startup where go and knock yourself out zero customers, contact smart market difference. And again, you see it, we've
frankly, we see it with clients a lot of clients like, give us the answer, give us the cookbook straightforward answer that I just need to apply. If you're looking for that kind of cookbook answer, you're going to get in trouble because anybody who will tell you there's a cookbook answer, they either don't understand it or they're deliberately covering it up for you because there's always tons of new ones involved. We keep going back to this like now more than 50 year old, or the no silver bullets, right? One question I got from online, I asked what people would like to ask from you is what would your advice be today for junior software engineers who are starting out, there's all this AI stuff going on, we know with learning, I think you also mentioned, or it might have been who mentioned with junior engineers, it could be a bit iffy of if you're relying too much on AI, well, that hinder your learning because learning is important. If what is engineers ask you like, Hey, I'm a junior engineer, I'd like to eventually become a more experienced engineer. What tactics would you advise me, especially with AI tools? Should I rely on them? Should I
not? Is there something that might work better than other things? Well, I mean, certainly we have to be using AI tools and exploring their use the hard part with your more juniors, you don't have this sense of is to what extent is the output I'm getting good? And in many ways, the answer is what it's always been find some good senior engineers who will mentor you because that's the best way that you're going to learn this stuff. And a good experience mentor with the weight in gold. And in that many
ways, it's worth prioritizing that above many other things that you when it comes to your career is getting that meant. I mean, again, me finding Jim O'Dell early on in my career was enormously valuable. The best thing that could have possibly happened to me is just blind look, but seek out somebody like that who can be your mentor. I mean, if I were peers in some ways, I often see think of Camp Becca's a mentor because we may be the same age or whatever, but he's thinking he's always leaping forwards. And so watching what he's doing has been very fast again, find somebody like that.
The AI can be handy, but always remember it's gullible and it's likely to lie to you. So be probing on asking it, okay, why are you giving me this advice? What are your sources? What's leading you to save this? I mean, I remember this is generally a good thing is whenever people are giving you something is to say, what is leading you to say that? What is the background? What is the context you're coming
from? What are the things that are leading you to this point of view? And by probing that, you can get a better understanding of where they're coming from. And I think you have to do the same thing with the AI, because in the end, the AI is just regurgitating something. It's all on the internet. So the question is, did it see good stuff on the internet or did it see most of
the crap that's on the internet? And if you can find your way to do good stuff, then that can be much more useful. And looking at all this change out that's happening right now with AI LMs, how do you feel about the tech industry in general? I mean, in a broad sense, I'm positive because I still feel there's so many huge things that can be done with technology and software. And we are on, we're still in a situation where demand is way more than we can imagine. But that's a long term
for you. I mean, at the moment, we're in this very, I'm going to say very straight, life has always been a strange phase. I mean, in strange in different ways, the current strangeness is we're basically in a huge, certainly in the developed world depression. I mean, we've seen a huge amount of job layoffs. I mean, I've heard numbers banded around of quarter million, half a million jobs lost. I mean, it's
that kind of magnitude. I mean, we're seeing it. I mean, if it works, we used to be growing at 20% a year all the time until about 2021. I mean, we've heard a wall and we see our clients are just not spending the money on this stuff. I mean, AI is doing its own separate thing, but it's almost like a separate thing going on. And it's clearly bubbly. But we don't but the thing with bubbles is you never
know how big they're going to grow. You don't know how long it's going to take before they pop. And you don't know what's going to be after the pop. I mean, all this stuff is unpredictable. I do think there's value in AI in a way that's not what we've blocked Shane and crypto. There's definitely
stuff in AI. But exactly how it's going to pan out. Who knows? I mean, I went through this cycle. We did dot com stuff in the 90s and 2000s. So it's a repeat of that only probably an order of magnitude
more scale. So all that's going on. But really what's happening, the most important thing that's hit us is not AI. It's the end of zero interest rates. That's the big thing that really hit us. And that's
what the job losses started before AI because of that kicking in. And we don't know how that's going to change because this is a much more macro economic thing. We have a loonie on driving the bus in the United States. We have all sorts of other pressures going on internationally. Great uncertainty at the moment. And that's affecting us because it means the businesses aren't investing. And while businesses
aren't investing, it's hard to make much progress in the software world. And so we have this weird mix of no investment, pretty much depression in the software industry with an AI bubble going on. And they're both happening at the same time. And one of those masks very other. And yeah, it depends on where you are. Like, I was in Silicon Valley. And if you're an AI company at all, inside, it looks
all great. If you're outside, again, you can benefit from it. But it's, it's a lot more careful. And if you're outside of this bubble, let's say you're at a startup or company that is not an AI, it's just tough. So you have these worlds happening. I mean, is it still, I think, an industry
with plenty of potential in the future. I think it's a good one to get into. It's not, you know, the timing is not as great as it would be getting into this industry in say 2005. But you know, I still feel there's a good profession here. I don't think AI is going to wipe out software development. I think it'll change it in a really manifest way, like the change from
assembly to high level languages did. But there's core skills are still there. And the core skills are being a good software developer. In my view, it's still it's not so much about writing code. That's part of the skill. A lot of the skill is understanding what to write, which is communication,
particularly communication with the users of software. And crossing that divide, which has always been the most critical communication path. And you've also mentioned the expert general is becoming a lot more important, which all of that when I looked into the details will link it in the show, almost the article that I think it was again, I'm not sure. But all the traits seem to do nothing to do with AI. It's about curiosity. It's about going deep. It's about going broad. It sounds like I'm hearing
more and more people who are thinking longer of like what it means to be a standout software engineer. The basics don't seem to change. Right. Yeah. I do think that. And it is always been
communication and being able to collaborate effectively with people who's always been, to my mind, the outstanding quality of what really makes the very best developers come through. Certainly in the enterprise commercial world, which is the one I'm most familiar with, because every software we're writing for is the people who are doing something very different, what we do. I remember when I was working in health service, I always said, here I am doing this conceptual modeling of healthcare. I understand a huge amount about the process of healthcare. You are not going to want me to treat one of your medical problems, because I'm never going to have that skill, because I'm not a doctor. And so therefore the doctors have to be involved in the process.
So as closing, I just wanted to do some rapid questions where I'll fire in and then you come what comes to mind. What is your favorite programming language and why? I would say at the moment, my favorite programming language is Ruby, because I'm so familiar with it. I've been using it for so long. But the one that is my love is SmallTalk. We've added out.
SmallTalk, there was nothing as much fun as programming in SmallTalk when I was able to do it in the 90s. That was such a fantastic environment. You and Ken Beck and Ken Beck is writing his SmallTalk server. It's his baby. I think he's making progress. And I mean, there is still stuff going on.
There is the pharaoh project in SmallTalk. And I keep thinking, you know, I, if I could just take off some weeks and stop everything else I was doing, maybe investigate, see what's going on in the SmallTalk world again. Because it was, I mean, and still so much power in that language. One or two books he would recommend. And why? So a book I do particularly like to recommend is Thinking
Fast and Slow by Daniel Cayman. I like it because he does a really good job of trying to give you an intuition about numbers and spotting some of the many mistakes and fallacies we make when we're thinking in terms of probability and statistics. And this is important in software development, and because I mean, a lot of what we do is greatly enhanced by the fact that we could understand the statistical effects of what we see, but also in life in general. Because I think world will be a hell of a lot better if way more people understood a little bit more about probability and statistics than they do. I mean, I like most kids probably when they did maths at school, it was heavily calculus based. I really do feel that it would have been a lot better if,
you know, it was much more statistics based because that the knowledge of being able to use that well. I mean, one of the things that has helped me more with probability and probabilistic reasoning has been the fact that I'm a heavily inter tabletop gaming, where you have to constantly think in terms of probabilistic. And I just honestly feel that knowing that is important. And this book is, I think, a great way to get into that. And so one of the best reads I've had in the last few years, another book that I'd mentioned that is completely separate, and is in challenging in a completely different way that I've been totally obsessed with is a book called Power Broker. So this is a book
about a guy called Robert Moses, who most people never heard of, but was the most powerful official in New York City for about 40 years from about 1925 to 1960. He was never elected to any office, he controlled more money than the mayor or the governor of New York during that time. And this book is about how he rose to power, how power works in a democratic society, often not in plain sight. And it's a fascinating book for that. It's also a fascinating book because it is so well written.
There were moments when I would just, I've been reading a several page passage of something and I would just have to stop to just appreciate how brilliant what just read was. And that's valuable because to be a better writer, and I think we all gained by being a better writer, it's really important to read really good writing. And his writing is magnificent. But downside is, it's 1200 pages. It's a really long book. But I was enjoying it so much that I didn't mind.
And then once you go on from that, you move on to his second biography, because he's only written two biographies. And that's his currently five volume biography of Lyndon Baines Johnson, LBJ, which is equally brilliant. And I've been reading it, but it's a lot more to ask because it's four volumes so far and it still hasn't finished the fifth. But again, there are moments when I was just gobsmacked by how brilliant the writing was and gobsmacked by the way, again, power works in a democratic society. And I think to understand how our world works, these kinds of books are really, really valuable.
And finally, can you give us a board game recommendation? You are very heavily into board games. Your website has a list of them as well. Yeah, it's a tricky one because it's kind of like saying, I'm really interested in getting into watching movies, which will be the movie would recommend, right? Because I got a doubt so many different tastes and things. If I'm going to pick something
that I think not too complicated for someone to get into that I think is still got quite a lot of richness, at the moment, I think the game I pick out would be something called Concordia. It's fairly abstract in its nature, but it's easy to get into. And it's got quite a good bit of decision-making in the process. Well, Martin, thank you so much. It was great that we could make it happen in person as well. Yes, I mean, that worked out really well. It just happened to be an
Amsterdam for something else. And I know somebody in Amsterdam, so I thought I'd get in touch and we finally get the chance to meet face to face. It was amazing. Thank you. Thank you. Thanks very much to Martin
for this interesting conversation. One of the things that really stuck with me is how the single book exchange with AI is about how we're going from deterministic systems to non-deterministic ones. This means that our existing software engine approaches that were based on assuming a fully terminusic system, like testing, refactoring, and so on, this probably won't work that well. And we might need new ones unless we can make elements more deterministic. That is, I also like how Martin mentioned to us that the problem with vibe coding is that when you stop paying attention to the code generated, you stop learning, and then you stop understanding. And you might end up with
software that you have no understanding of, so be mindful in the cases when you are happy with this trade-off. For more reading on AI engineering best practices and an overview of how the software engineering field changes in the past 50 years, check out really the deep dives in the pragmatic engineer, which are linked in the show notes below. If you enjoyed this podcast, please do subscribe in our favorite podcast platform and on YouTube. This helps more people discover the podcast, and a special thank you if you're leaving a rating as well. Thanks and see you in the next one.