How AI will change software engineering – with Martin Fowler

What similar changes have you seen that could compare to some extent to AI in the technology field? >> It's the biggest I think in 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 highle languages. The biggest part of it is the shift from determinism to non-determinism and suddenly you're working in a non with an environment that's non-deterministic which completely changes. >> What is your understanding and take on vibe coding? I think it's good for explorations. It's good for throwaways, disposable 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 vibe coding, you're actually removing a very important part of something, which is the learning loop. What are some either new workflows or new software engineering approaches that you've kind of 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 LLMs enable, why refracting as 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 is presented by Statsig, the unified platform for flags, analytics, experiments, and more. Check out the show notes to learn more about them and our other season sponsor. If you enjoy 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. Uh I wanted to start with learning a little bit on how you got into software development which was what 40ish years ago. >> Yeah. It was yeah it would have been uh late 70s early 80s. Yeah. I mean like so many things it was kind of accidental really. Um at school I was clearly no good at writing because I got lousy marks for anything to do with writing. >> Really? >> Yeah. Oh absolutely. Um, but I was quite good at mathematics and that kind of thing and physics. So, I kind of leaned towards engineering stuff and I was interested in electronics and things cuz the other thing is I'm hopeless with my hands. I can't do anything requires strength or physical coordination. So, all sorts of areas of engineering and building things, you know, I've tried looking after my car and, you know, I couldn't get the the rusted nuts off or anything. You know, it was hopeless. So, but electronics is okay because that's all very, you know, it's more than in the brain than, you know, you need to be able to handle a soldering iron, but that was about as much as I needed to do. And then computers and it step easy. I don't even need a soldering iron. So, I kind of drifted into computers in that kind of way. And uh that was my route into into software development. Before I went to university, I had a year um at working with the UK at Atomic Energy Authority. Wow. or ukulele as as we call it. Um and I did some programming in forran 4 and um 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, computer science, I looked around and I thought, well, I could go into traditional engineering jobs, which weren't terribly well paid and weren't terribly high status, or I could go into computing where it looked like there was a lot more opportunity. And so I just drifted into computing. And and this was before the internet took off. This was >> what what what kind of jobs were there back then that that you could get into? What was and what was your first job? >> Well, my first job was with a consulting company Koopers and Librand or as I refer to them, Cheetum and Lightum and um we were doing advice on information strategy in 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 kind of drifted into that. I look at it back now and think, god, that was a lot of snake oil involved. But hey, it was my route into the 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 mid80s >> and and how how did you get into like object-oriented was back then back we're talking probably the mid80s 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 a two plus two 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 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 it kind of, you know, it's part of a snake oil as it were. I mean, that's a little bit cruel to call it snake oil because he had some very good ideas as well. Um but that kind of led me into that direction and and of course in time I've found out more about what object orientation was really about and uh that events led to my whole career >> in in the next 10 or 15 years. How did you make your way and eventually end up at Thought Works and and also you started to write some some books, you started to publish on the side. How did you go go from like someone who was brand new to the industry and kind of wideeyed and just taking it all in, learning things to starting to slowly become someone who was teaching others? >> Well, here again bundles of accidents, right? So, while I was at that consulting company, I met another guy that they had brought in to help them work with this kind of area, an American guy um who became the really the biggest mentor and influence upon my early career. His name is Jim Odell and he had been an early um adopter of information engineering and had worked with in that area and he was he saw the good parts of uh these ideas that these these folks were doing and he was an independent consultant and a teacher and so he spent a lot of his time doing work along those lines. I left Coopers and Librand after about a couple of years to actually join this the crazy company which is called PEK. Um and um I was with them for a couple of years. It was a small company. There was a grand total of four of us in the UK office and that was the largest office in the company. >> Wow. >> Kind of thing. Um and um so I did I saw a bit of you know having seen a big company's um craziness. I then saw a small company's craziness. did that for a couple of years and then I was in a position to go independent and I did um helped greatly by Jim Odell who was um who fed me a lot of work basically um and also by some other work I got in the U in the UK and that was great. I I remember leaving PEK and thinking that's it independence life for me. I'm never going to work for a company again. >> Famous last words. >> Exactly. And um I carried on. I did well as an independent consultant um throughout the '9s and during that time I wrote my first books. I moved to the United States in 93 um and I was doing very very happily and obviously got the rise of the internet, lots of stuff going on in the late 90s. It was a it was a good time and I ran into this company called Fort Works and they were just a client. I would just go there and help them out. Yeah. The story gets more. I had had met Kent Beck and worked with Kent at Chrysler, the famous C3 project, which is kind of the birth project of extreme programming. So I'd worked on that, >> seen extreme programming, seen the agile thing. So I'd got the object orientation stuff, I got the agile stuff, and then I came to Fort Works and uh they were in tackling a big project, a big project for them at the time. Still sizable, about 100 people working on the project. So, it's a sizable piece of work and it it was clearly going to crash and burn. Um, but I was able to help them um both see what was going on and how to avoid crashing and burning and they figured out how to sort of recover from the from the problem. Um, but then invited me to join them and I thought, hey, you know, join a company again maybe for a couple of years. They're really nice people. They're my favorite client. You know, I I always thought of it as 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, with a client like that, I might as well join them for a little while and and see what we can do." That was 25 years ago. >> Yeah. And then fast forward today, your title has been for I think over a decade, chief scientist. >> Since I joined, that was my title at join. >> Since you joined. So I have to ask what does a chief scientist at Thought Works 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 for some kind of public facing ideas kind of person. If I remember correctly, Grady Buch was chief scientist at Rational um at the time >> actually. True. >> And um and there were other people who had that title. So it was a it was a high looting very pretentious title but they felt it was necessary. It was weird because one of the things of Thoughtworks at that time was you could choose your own job title. Anybody could choose 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 um or loudmouth which is the one I most prefer. And one thing that Thought Works does every six months and the latest one just came out is the Thought Works Radar >> and this latest radar, it just came out I think a few days ago. It's the >> Today it was launched I think >> actually it was today. So by the time this is in production it will have been a few weeks but >> uh it's actually really really fresh. So I just looked at it and things that it it lists. But I'll just list a few things that I saw there and the adopting which is the the ones that they recommend using pre-commit hooks click house for database analytics vlm this is for learning LLM on on cloud or on on-rem in a really efficient way for trialing cloud code fast MCP which is an MC framework for MCB servers and they're they're also recommending a lot of different things related for example to AI and LMS to assess uh can you share a little bit of how Thorkworks comes up with this technology radar what's the process And it it feels very very kind of on the pulse every time like it feels close to the pulse of the industry and again I I talk with a lot of other people. How do people at Thought Works stay this close to what is happening in the industry? >> Okay. Yeah. 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 v or various levels of running the business and one of the leaders of that um 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 as to what was going on. We'd meet you know two or three times a year. She had me on the advisory board not so much for that reason but because I was very much sort of a public face of a 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 um Daryl Smith who was actually her TA at the time technical assistant um he um said we what we got all these projects going on it would be good to get some picture what kinds of technologies we're using and how useful they are and so as to better exchange ideas because we like so many companies we struggle to percolate good ideas around enough I mean even then when we're only just a few thousand it struggled and we're 10,000 now so yeah it's So we thought okay this is a nice idea and he came up with this idea of the radar metaphor and the rings of the radar that we see today and we had little meeting and we created the radar and it's it's a habit if we do something for internal purposes we try and just make it public >> and that's always been a strong part of the works ethos it's part of why I'm there of course is you know we just we talk about everything that we do and we share everything we we we give away our secret source all the time so we did that and people were very interested and so we continued doing it now the process has changed 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, um it's much harder to do that. And we've also created more of a process where people can submit blips, nominate them. A blip is being a point on the radar, an entry >> and um they will go to somebody that uh either connected through geographically or through the line of business or or technology or whatever and say, "Hey, we think this technology is interesting." They'll brief us 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 with our metaphors at times. Um and they and then at the meeting we'll decide which of these blips to put on the radar and not and obviously you get some crosspollination because somebody will say oh yeah I talked to somebody about this as well and and so it's very much this bottomup 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 just this this lineup 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. Um and that was an important part of microservices about 10 years ago because that came up in through that radar process and uh we got together with James Lewis and we ended up writing a good bit further about that. Um but that's really what happens is we go through this process of spotting this stuff. >> Yeah. And and the 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 they even like have 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 or maybe our our clients to consider or there's also I like that there's a hold maybe just be beware. We're we're not seeing great results with this and and here's the reasons for it. And yeah, I guess the reason it feels fresh is uh probably a lot of work that Thork works does is it feels cutting edge because it's all it's all about half of it or a third of it feels that it is around the hottest topic right now AI LMS and and all the techniques that people are trying to see if they work or are the things that we are seeing that actually starts to work. Yeah, I mean what I mean for works has basically got several thousand technologists all over the world doing projects of various kinds to 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 their heads and spreading it around both internally and to the industry as a whole. And you're right, I 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 radar thing 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 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 while for us it's a case if our clients are doing it then we we're going to find out about it right we have to use it >> of course the the radar is full with a lot of AI and LM related things because this is a huge change in in my professional career, it it feels by far the biggest technology innovation changes coming in. Looking back on your career, what similar changes have you seen that could compare to some extent to AI in the technology field? I >> it's the biggest I think 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 highle languages which is before my time right when we when first started coming up with cobalt and forran and the like I would imagine that would be a similar level of shift. >> So you started to work with forran and you probably knew people who were still doing assembly or at least knew knew some of people from that generation. >> There was a bit of assemble around when I was working still from from what you picked up around that time. uh what was that shift like in terms of mindset or or you know like because it it was a big change right you really needed to know the internals of the hardware and the instructions and the the different >> uh I I did very little assembly at university but it it's been very useful because I never want to do it again >> very wise but but what did you pick up in in terms of what needed to change and 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 were very specific to individual chips. You had the instructions were different on every chip. The you know as well things like registers where you access memory. You had these very convoluted ways of doing even the simplest thing because your only instruction was for something like move this value from a memory location to this register. Um and you so you've always got to be thinking in these very very low-level forms and even the very relatively poor um high level language like forran at least I can write things like conditional statements and loops else is in my conditional statements in forran 4 but I can at least 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 and then of course once I'm using forran I can be insulated to some degree away from the hardware I'm running on. I'm now am I running this on a main on a mainframe? Am I running this on a mini computer? I mean there's 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. Um that was really quite significant I think. I mean I only did it on small uh microprocessor like units because again it was the electronic engineering part right so we were fairly close to the metal anyway for some of that um but um you you definitely had that mind shift and I I think it's with LLMs it's a similar degree of mind shift although as I've you know written about it I 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 with an environment that's non-deterministic which completely changes you have to think about it Martin just talked about how AI is the most disruptive change since the move from assembly to highle languages that transition wasn't just about changing the language we use they required entirely new tool chains similarly AI accelerated development isn't just about shipping faster it's about measuring whether what you ship actually delivers value that's where modern experimentation infrastructure comes in and we're presenting sponsor stats can help. 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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 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 uh 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 the English language which will you know generate this code you're saying you don't think it's an abstraction jump why do you think this is >> I think there's a bit of an abstraction jump I think the abstraction jump difference is smaller than the determinism nondeterminism jump and it's it's worth remembering one of the key things about highle languages anguages which I didn't mention as 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 u more um expressive functional languages like lisp which didn't really have so much in I mean forran and cobalt you could do that to some extent um because because at least with forran you can create subutines and build abstractions out of that but you've got so many more tools for building abstractions when you've got the the abilities of more modern languages and that ability to build abstractions is crucial. >> So you can build a building block inside of the language that sets you and of course here we have like domain driven development later enables these things and so on. >> Exactly. I mean an old lisp adage 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 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 AI helps us a little bit in that because we can build abstractions a bit more easily a bit more fluidly but we have this problem and now 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 um to get around that. My colleague Unmesh Jooshi has been has written a couple of things um 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 the 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 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 that 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 that by obviously you're shrinking down the the token size because you've but you're also using a rigorous a much more rigorous notation to describe the problem. So maybe that's an angle of how we use LLM. What we have to come up is 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 in ubiquitous languages and also some of the stuff that I was working on a decade or so ago around domain specific languages and language workbenches. So I there's some fascinating stuff around there that be interesting to see how that plays out. >> Yeah. Yeah. And I guess is this the first time we're seeing a tool that is so wide in software engineering that is nondeterministic 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 not everywhere now every single developer is I mean if you're using code generation you are using non-deterministic things of course we're integrating them left and right trying out where it works. Is is it fair to say that this is probably the first time we're facing this challenge of deterministic computers which we know very well. We know their their limits and all those things and of course there's some race conditions and some exotic things but now we have >> exactly problem to solve for >> it's a whole new way of thinking. It's got some interesting parallels to other forms of engineering. Other forms of engineering you think in terms of tolerances and my wife's a structural engineer right? She always thinks in terms of what are the tolerances? How much 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, you know, go for the worst case. We need 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 skate too close to the edge because otherwise we're going to have some bridges collapsing. I I suspect we're going to do that particularly on the security side. We're going to have some noticeable crashes. I I fear um 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 software engineuring approaches that you've kind of observed or or aware of that that sound kind of exciting that we we can now now do with LMS or at least we can try to give them a goal that would have been impossible with, you know, our old deterministic toolkit, >> right? One area is one one that has 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. Um, but it's it's more than just that because it's also an ability to try explorations. Um, people can go, hey, I 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 for throwaway explorations for disposable little tools and things of that kind um and including stuff by people who aren't don't think of themselves as software developers. I think there's a whole area and you know 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 we'll that's that's really good on a completely opposite end of scale. U one area that's really interesting is helping to understand existing legacy systems. So my colleagues have have put a good bit of work in this um year or two ago. And basically the idea is you take the code itself um do the the essentially the the semantic analysis on on it. populate a graph database essentially with that kind of information and then use that graph database as kind of in a ragl 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 as 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 you if you're if you're doing any work with legacy systems you should be using LLMs in some way to help you understand >> so so in this ring in the thought force radar the the fewest things are in the adopt adopt says we strongly suggest that you look at this at least you know thought works themselves look at it there's only four items and one of them is yes uh to to to use genai to understand legacy code which to me tells that you have seen great success which is it's refreshing to hear by the way I did not hear this as much and I guess it helps at thought works 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 had done some really interesting work on on legacy code stuff um 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 the modernization of legacy systems because every big company that you know is older than a few years has got this problem. Y >> and they have it in spades >> and then especially just simple things people leave right as as as simple as that and having uh Gen AI that can help you make some progress is it's already better than making no progress. >> Exactly. So those are two areas that are clearly um right away I would say those are there's great success for using LLMs and then there's the areas that we're still figuring out. I mean, I'm certainly seeing some interest more in more and more interesting stuff as people try to figure out how to work with an LLM on a one-to-one basis to build decent quality software. We're seeing some definite signs of how you 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. Um, but you know, you can't trust a thing that they're they're doing. So, you've got to review everything very carefully when you play with the genie like that. The genie is GK Kent's term for it. Or or Dusty the uh sort of the anthropomorphic donkey, which is how Bita I love her take. >> Yeah. 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 the the advocates are talking about, but it is non-trivial. It's certainly worth learning how to to make some use of this and it's folks like Burgita or Kent or um Steve Jagg those are the 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 green field environment. So that leaves big questions in terms of a the brownfield environment. Well, we know that that LLMs 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 with James Lewis because he's in town as well this morning and he was commenting about he was playing with cursor and he's been was just building something like this and he said, "Oh, I I wanted to change the name of a class um in a not too big program and he sets it off to do that." and comes back an hour and a half later and has used you know 10% of his monthly allocation of tokens and all he's doing is changing the name of a class >> and and we actually in IDs we actually have functionality which which I I still remember when was cutting edge this was probably 20 years ago when Visual Studio it wasn't even Visual Studio it was Jet Brains who came out with an extension called ReSharper which helped refactor code and people paid serious money this was like $200 per year or something to get this plugin and now you could right click 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 again this was a a huge deal in fact uh in Xcode Apple's developer uh plat uh ID for a while when swift came out you couldn't do these refactors and it was you know people were like so it's interesting how some things are easy we've solved it and LMS are not very very efficient at not very good at it >> y >> yes and then I mean he did that just to see what it was going to be like right cuz he knows you can just I mean we've had this for a long 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 that's still really up in the air and then another area that's really up in the air both green field 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 be built with teams because even if and I don't think it will um AI makes us order of magnitude more productive We still need a team of 10 people to build what a team of 100 people needed to build and we will 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 got some an some answers some beginnings of answers and it's just a fascinating time to watch it all. >> You mentioned vibe coding. what what is your understanding and take on vibe coding? >> Well, when I use the term vibe coding, I used I try to go back to the original term which is basically you don't look at the output code at all. Maybe, you know, take a glance at it out of curiosity, but you you really don't care and maybe you don't look don't know what you're doing because you don't you've got no knowledge of programming. It's just spitting out stuff for you. So, that's my how I define vibe coding. Um, and my my take on it is kind of as I've indicated, I think it it's good for explorations. It's good for throwaways, disposable stuff. Um, 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, just this is a a silly anecdote, but I was working um my colleague Unesh, he's just wrote something that we published yesterday. And uh as part of doing this, we we create this little pseudo graph of capability over time kind of thing, which is, you know, one of those silly little pseudo graphs that helps illustrate a point. And he asked the uh at LLM to create this. He described the curves he wanted and produ came up with and put it up there. And I and he he committed it to our repo. And I was looking at it and thinking, yeah, that's a good good enough graph. I want to tweak it a little bit. I want to, you know, the labels are a bit far away from the lines they're labeling, so I'd like to bring them closer. So I open up the SVG of what the LLB has produced and oh 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 you know a dozen lines of SVG and SVG is not exactly a compact language right because it's XML but this thing was gobsmackingly um weird and 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 that's the difference and this is the the heart of the article that Unmesh wrote um that we published yesterday is when you're using vibe coding in this kind of way, you're actually removing a very important part of 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 loop program approach and unash's point, which I think is absolutely true, is you cannot shortcut that process. And what LLM 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 oh vibe coding as a consulting company, so many problems to fix for sure. But you are right on the learning the the the learning side both on on vibe coding and AI. One one thing that I'm noticing on on myself is it is so easy to you know give a prompt you get a bunch of output and you know you should be reviewing a lot of this code either yourself or 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 let let it go and this is also what I'm hearing when talking with software engineers is the ones who are working at companies which are adopting these tools which is pretty much every company it's there's a lot more code going out there, a lot more code to review, and they're asking, "How can I be vigorous at 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. Um I do I am very much paying attention to what Unmesh um is doing with this because his approach very much is that notion of let's try and build a language to talk to the LLM with 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. make sure do we create our own specialized language for working with whatever problem that we're working on and I think that actually brings another um we're talking about things we know LLM are useful for another thing and this is again something unme has highlighted is understanding an unfamiliar environment again I was chatting with James he was working with um he's he's working on a Mac with C which is not a language he's terribly familiar with using this game engine called God. >> Godo. Yeah. >> Yeah. Go. >> 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 I mean, I can't remember. I've I've certainly got to the point where I'm typing in to the L. Oh, well, how do I do so and so in R that I've I've done 20 times, but I still can't remember how to do it. And you and exploring and Umash makes a point again setting up initial environments. you know, give me a starting project, a sk a sample starting skeleton project so I can just get moving. Um, and so that kind of exploratory stuff and helping in an unfamiliar environment and just learning your way around an unfamiliar set of APIs and and coding ideas and the like. it can be quite handy for I >> I wonder if this is not all that new in the sense that I remember you know one of the last kind of big productivity boosts in the industry uh about 10 or 15 years ago was Stack Overflow appearing. So before Stack Overflow when you Googled for questions you bumped into this site called 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 and most of us I was a college student I just didn't pay right >> so you just couldn't find the answer and you were all frustrated but then Stack Overflow came along and suddenly you had code snippets that you could copy and of course what a lot of young people or 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 to more experienced engineers or developers, you started to tell the junior engineers like you need to understand that first like or even if it works, you need to understand why it works. You need to you should read the code. And I I feel we've been there was a few years where where we were going back and forth of people mindlessly copying pasting uh snippets. There were problems with uh I think there was a question about email validation and a 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 kind of been around this already. >> Yeah, it's a similar kind of thing, but >> maybe at a smaller scale. >> Yeah. But even more boosted and on steroids and with the question of, you know, how how are things going to populate in the future because who's going to be writing Stack Overflow answers anymore? >> Yeah. So, I I I wonder if what we're getting to is like you need to care about the craft. you you need to understand what the LLM's output is and it's there to help you and if you're not doing it I mean like you should but but if you're not you'll eventually be no better than someone just prompting it mindlessly. >> Exactly. Yeah. I mean it I mean I have no problem with taking something from the LLM and stick 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 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 LMS 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 Linear's internal weekly meetings called Quality Wednesdays, and I was completely blown away. This was a 30-minute meeting that happens weekly. In this session, the team went through 17 different quality improvements in half an hour. 17. 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Thousands of tiny improvements do add up and you feel the difference. When you use Linear, you're experiencing the results of literally hundreds of these quality Wednesday sessions. Thomas, their CTO, recently wrote a piece about this week 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.app/pragmatic. Because honestly, after seeing how they work up close, I understand why so many of the best engineering teams are switching. And now, let's get back to the importance of testing when working with LLMs. I mean, one of the people I I particularly uh focus on in this space is Simon Willis and something he stresses constantly is the importance of tests, but testing is a huge deal to to him and being able to make these things work. And of course, you know, Bea is is from Fort Works. We're very much an extreme programming company. So, she's steeped in in in testing as well. So, she will say the same thing. You got to really focus a lot on making sure that the tests work together. And of course, this is where the LLM struggle because you tell them to do the tests and I'm I'm only hearing problems or experiencing them myself like when the LLM tells me, "Oh, and I ran all the tests. Everything's fine. You got npm test five failures." H yeah, I I I see some improvements there by the way with with clock code also like other agents. But yes, it's the nondeterministic angle. Sometimes they can lie to you, which is weird, right? I'm I'm still not >> They do lie to you all the time. In fact, if if if they were truly a junior developer, which is how sometimes people like to say they should be characterized, I would be having some words with HR. >> Yeah. Like I the other day I just had this really weird experience, which is the simplest thing. I have a configuration file where I add just new items, a new JSON, you know, blob, and I I put the date of when I added it just in the comments saying added on, you know, October 2nd, added on November 1st. It's always a current date. And I told the LM, can you please add this configuration thing and add the current date? And it added it and it added it just copied the last date. And I said that is not today's date. I said, oh, I'm so sorry. You know, let me correct that for you. And it put yesterday's date. And and I I feel you need to get this experience to see that it can gaslight you for a simple thing of today's date which uh you know you know you could call a function whatnot but it's 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 etc so like in the end even for the simplest things you are as as a when you're a professional working on important stuff you should not trust Yeah, absolutely. Never. Yeah, it's got to you've got to don't trust, but do verify. >> Verify. Yes. Uh, speaking with developers at at Thought Works and and the people you're you're chatting with, what areas that they are successfully using LM's day-to-day though, like we we we did mention just right now testing. We we also mentioned things like prototyping, but do you see some other things where starting to become a bit of a routine? Like if if I'm doing this thing, let me reach for an LLM. it can probably help me >> that yeah I mean I'm I've mentioned many of that right the prototyping the legacy code understanding oh yes the fact that um you can use it to explore um new technology areas um potentially even new domains as long as you you know 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 Burgetta is exploring ing is spec development. There's this idea of what well you know LMS have 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 had iterations and so on. What is your take on this and do you have a bit of a dja vu because we we've heard this once right your your career started around this thing called waterfall development. So how how how are you seeing it similar but also different this time? Well, the the this 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 whether you talk again this is what you mean by speciment is it so much focusing on that or is it doing small bits of spec do the tight loop I mean to me the key thing is you want to you want to avoid the the waterfall problem of trying to build the whole spec first. It's got to be do a do 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 with these thin slices. What role a spec may play to drive in either case could be argued to be a spec form of spec driven development. But to me, what matters is the tight the tight loops, the the thin slices, that kind of thing. >> And I know big definitely agrees on that point. coming because she and you have to be the human in the loop verifying every time that's that's clearly crucial where the spectrum and 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 and that's you know I mentioned what the wood mesh was doing there using it to build an abstraction because essentially 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 from the codebase, right? We still want the ubiquitous language notion that it's the same language in our head as is 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 you know 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 in that direction. It >> it's interesting as new because I I feel we've never been able to use language as close to representing code ever or or like business logic and this is very new. >> Yeah. Although again people I mean there are plenty of people who take that kind of DSL like thinking into their programming and I would to I know people who would say yeah I would I would get to the point where I could write certain parts of the business logic in you know a programming language like 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 whether what was wrong or what was right in there. And this is just programming code, but it it 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. And so it's but that kind of thinking like trying to make an internal DSL of a programming language or maybe building your own external DSL DSL meaning domain specific language like if you're working with accountants, you're going to have the terms that they they use, the way they use it and so on. >> Yeah. And what you're trying to do, of course, is create that communication route where pe where a non-programmer can at least read what's going on and understand it enough to to be able to find what's wrong about it and and to suggest changes which may not be syntactically correct, but you can easily fix them because you 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 the interesting thing is whether whether LLMs will enable us 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 wrong this must be especially important in enterprises 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 may that be the project manager the technical pro etc. So you're saying that there could be a pretty interesting opportunity or or just an experiment uh with LM that maybe we can we can make this a bit easier for both sides. >> That is the world I'm most familiar with right is that world. I mean um one I mean I I 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 there's very complex business things going on that we've got to somehow interface in and of course also there's usually a much worse legacy system problem as well. Um and 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 I think we can all just think of banks of all the things cuz there's there's perfect storm, right? They have regulation that changes all the time. They have incidents that they want to avoid going future. They'll have special VIP, I don't know, accounts or whatever that they'll want to do. And of course, they have all these business units that all know their own rules and and frameworks. And they've been around since before technology. Some of some of the banks have been around for, you know, 100 plus years. >> Yeah. And remember, the banks tend to be more technological advanced than most other corporations in software. >> That's a good one. >> You're looking at the at the good bit when you're talking about banks. >> You you have worked with some with some of the less advanced folks as well. >> I mean, yeah, 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 uh you know they're they have to be extremely cautious. They are not allowed to touch LLMs at the moment because you know the the consequences of error when you're dealing with you know a major um government banking organization are pretty damn serious. So you've got to be really really careful about that kind of stuff. and and yeah, their constraints are very different and and it it it brought to mind this there's a an adage that says that to understand how the software development organization works, you have to look at the core business of the organization and see what they do. Interesting. I I was at this agile conference for the Federal Reserve in Boston and they took me a tour of the Federal Reserve, but where they handle the money. And so I saw the places where they bring in the 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 and send out the stuff again. And you look at the degree of care and control that they go through. own as you could imagine. I mean, when you're bringing in huge wges 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, "Yep, I can see why in the software development side that mindset percolates because they are used to the fact that they really have to be c careful about every little thing here." A lot of corporations of course have that similar notion. you're you're involved in an airline, you are really concerned about safety. You're really concerned about um getting people to their death that affects your whole way of thinking or ought to and it does and I guess this is a reason we are clearly seeing we always see a divide in 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 they have zero customers. They have everything to gain. They 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 the latest technology and they're here to break the rules and you know 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 and now it's a large enterprise you will just have different risk tolerance right >> exactly yeah >> but what what what 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 let's not touch this 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 in the technology curve for very good reason they're already aware of it or using it which probably means that it's everywhere now. Oh, it is. I mean, it is. I mean, we see it all over the place, but again, with that with with more caution in the enterprise world where they're saying, "Yeah, we we also see the dangers here." >> And then you're you're seeing kind of more nimble companies that you work with and the more enterprise focused. What would you say is the biggest difference between their relationship of of AI uh their approach? Is it is it this caution or are there other characteristics that the the the big more traditional less more riskaverse companies approach it differently? The the important thing to remember with any of these big enterprises is they are 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 like you know when I when I started at cheetah lightwe right and I was in this little bit that was being very very aggressively doing really wacky things, right? I mean you'll find that in any any big organization you'll find some small bits doing some stuff. Um, and so it's really the variation within an enterprise often is bigger than the variation between enterprises. >> Good to keep that in mind. So speaking about refactoring, LM are very good at refactoring and and you you've written the book back in 1999 called Refactoring. This is now the second edition which 20 years later it's it's been refreshed. And it's it's actually a really detailed book going through different code smells uh that could show that where the code is, techniques of of refactoring it. On the first page already, it has I really like this. It has a list of refactorings on I don't know how the publisher printed this because it's so so unusual, but it's it's it's right here on the table of contents. Why did you decide to write this book back in 1999? Can you bring us back on what the envir environment was like and what was the impact of the first edition of this book? Okay. So, I first came across refactoring at Chrysler. Yeah. When I was working with Kemp Beck, right early on in the project. Um, we we I remember in my hotel room, the 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 I'd already written and make it more understandable. I've always cared a lot about something being comprehensible. That's true in my pros writing and in my software writing. And so that I knew, but what he was doing was taking these tiny little steps and 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 blew my mind away. I thought, "Wow, this is a big big deal." But Kent was at the time his energy was to write the first extreme programming book, the white book. He didn't have the energy to write a 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. And partly to because I needed it for myself. How do I extract a a extract a method so as I don't screw it up? And so I would write careful notes on each one. And then each of those turned it the mechanics in the refactoring book would be that step. And then I' I'd make an example for each one. And that was the first edition in a book. And that 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 in the late 90s. And so that's what led to the um to the first book. And um the impact well I mean and also refactoring. I should also stress it wasn't invented by Kent. I mean it was very much um developed by um Ralph Johnson's crew at the University of Illinois the Bona Champagne. They built the first refactoring browser in Small Talk which is the first tool that did the automatic refactoring. that we talk about now. That was the original the refactoring browser built by um um I'm blanking on John Brandt and Don Roberts >> um did that and um then when the book came out that got more interest there was already some interest from the IBM visual age folks because they came out of Small Talk. the original versions of Visual Age were in fact built in Small Talk. Um, and so they were already aware of what was going on to some degree, but it was the Jet Brains folks that really caught the imagination because they put it into the early versions of Intelligj idea and really ran with it. Then you ran into it with ReSharper, of course. Um, and um, they really made the automated refactorings become something that people could rely on, but it's still good to know how to do them yourself because often you're in a language where you haven't got those refactorings available to you. So it's nice to be able to pull out that stuff and some of them aren't obviously in there and yeah so the impact it's had is refactoring became a word and of course like all of these words got horribly misused and people use refactoring to mean any kind of change to a program which of course it isn't because refactoring is very strictly these very small semantics pres 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 I I I think we've all had that story. At least I had a story where one of my colleagues or you know it could have been me but often times one of my colleagues would say like oh stand up saying like oh I'm I'm just going to do a refactoring and then next day oh I'm still doing the refactoring next day oh I'm still doing the refactoring and you know that that that missed a 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 um wanting to refresh um some of the things that were in it. Some there were some new things that I had. I was also concerned that I mean when you've got a book that's written in late 1990s Java, it it shows its age a bit. >> Yes. >> And although the core ideas I felt were sound and people could still use it, I felt 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 did I switch to another language and in in the end I decided to switch to JavaScript. I felt it would reach a broader 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 you don't wouldn't necessarily think of doing in an object-oriented language. But um it was mainly just to to get that refresh um 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 you specifically wrote that you you see refactoring as a key element in the software development life cycle and you've also talked about how when you refactor uh 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 or do you feel it's kind of like a little bit like being maybe refracting went a little bit out of style as some of those really innovative tools at the time like Jet Brains and others. They're maybe not as as uh kind of referenced even though they're everywhere. >> It's hard to say for me. Um because I mean again most of the interaction I have is with folks at Fort Works. 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 shake my head at >> how even refactoring is being described, let alone the lack of doing it in the and certainly in the kind of structured way, controlled way that that I like to do it because I like doing it quickly and effectively. And you know, 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 I have to it's at least been part of our language now. People talk about doing it. It's in these tools and they do it very effectively. The refactorings that they do, I mean, it's wonderful to work in 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 I'd have hoped for, but you know, that's often the way with these things. >> Looking ahead with AI tools, they 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 of refactoring thinking about the your intended meaning of of those small ongoing changes is going to be important? And are you already seeing some of this being important? >> I wouldn't say I'm already seeing it. Um, but I I can certainly expect it to be increasingly important. Um, 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 into a better state while keeping it working. Um, these tools at the moment can't definitely cannot refactor on their own. Although we've combined with other things. Adam Tornhill 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. Um 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 the the smallalness and the composability. combine those two and you can make a lot of progress. >> It's interesting because because right now if you want to refactor you need to have your IDE open for sure. And I mean the fast way is just just using the built-in tools or you moving things around. What what I found as well is describing it when I have a command line open with like cloth code or something similar. It's it's tough or I I spend more time explaining it than me doing that that small change. And I do wonder if uh if we will see more integrations in this end as well so that LMS can actually do it or some of them might do it automatically cuz as you say it it doesn't work out of the box but I think for any quality software that I mean we we all learn the hard way that if you just kind of leave it there and don't go back and don't change it up when your when your functions get just the simple things 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 um 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 with refactoring, 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 uh progress in that. I mean, particularly where you're talking about these automated changes across log large code bases." There was an example of this was it a year ago or so when one of these big companies talked about this massive change and made to change APIs and and clean up the code and they mentioned it as an LM thing, but it wasn't an LLM. It was a it was that different tool and I'm completely blanking on what the names of all of these things were. Oh, I'd have a 60-y old brain and I can't be able to remember anything anymore. It'll come to me at some point. But actually, it was it was a combination of, you know, maybe 10% LLM and 90% this other tool. Um, but that was it again provided that extra leverage that allowed them to to make the progress. I think 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 where there's some interesting interplay. Speaking about going on from refactoring to software architecture, you were very busy writing books around the early 2000. You wrote the book patterns of enterprise application architecture in 2002 and this was a collection of more than 40 patterns uh things like lazy load identity map template view and many others and I remember around this time there was your book about enterprise uh architecture patterns there was also the gang of four book there was a lot of talk when I was interviewing around that time on interviews they were asking me questions about how to do a factory pattern and singleton and 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 starting from the 2010s I 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 why was it important to to talk about it and and put it into the industry and how have you seen this change of where we stopped talking more on on patterns and why do you think it happened? >> Yeah, that I mean I've always found it a I mean what you're doing with patents is you're trying to create a vocabulary to talk more effectively about this kind of these kind of situations. I mean it's just like in you know in the medical world they come up with this jargon in Greek and Latin to more precisely talk about things that are quite involved and complex. Yes. >> And with patents 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 they do help communication flow more effectively. You know once people are familiar with that terminology. I mean you don't look at them as some kind of you know 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 you you very much got to understand the context of when to use them. And yeah, it's it's kind of a shame that some of the the wind has gone out of the sails of that perhaps because people were overusing them in terms of trying to use them as a sort of a like pinning medals on a chest. But it can still be very I mean I I mean I worked very recently with Unmesh on his book on dist patterns in 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 an 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. I it's hard for me to to get a sense of of why they can be they kind of became less fashionable. Maybe they'll become more fashionable again. Who knows? But I I 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 create these nouns that we can talk about things more precisely is a good way of part of doing that. I I wonder if because I I've I' I've seen I've worked at places where we we used these things and then places where we just like threw them out the window, no one was using it. And a difference was honestly just kind of the age and the attitude of the company cuz there was a sense at some point that the patterns there were for legacy companies. So startups would just start from a blank sheet of paper, you know, a whiteboard, you know, 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, the software architecture still exists, but you just put it on the whiteboard and you just drew a box or a circle and you didn't care about the arrows. And it was just uh I guess we we're not going to lock ourselves into existing ways of doing things. And it's a bit of an education as well like you do need to onboard to these things. You all need to have a shared understanding and maybe it's just a combination of of of these two things. And I guess it's a generational thing as well. you know, every every few years a new generation comes out and the same way where at some point uh I 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 my grandparents came on there like I I kind of like stopped using it when they started using it. So I I wonder if there there's like like these waves going back and forth because inside of these startups there is a language uh like you know lingo uh about how they talk about the architecture and it starts to form over time. You start to see it whether it's longer tenure 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 similar company where they take the jargon with them. >> Exactly. and and people will create these jarens. Um, and it's an inevitable part of communication. You need to you need to 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 that we can communicate a bit more widely. Um, but it's also quite natural for people to say, you know, within our little environment, we create our own little jargon. So, we don't take notice of that and and then you get the the mismatches that occur as you only you only really notice that as you cross these different environments. >> Grady BHC had a interesting take on this by the way. So I asked him about the same thing because he's he's been so much into software he still is into software architecture and he's progressed the field a lot and he said that what he thinks happened is that starting in like 20 cuz the patterns died out from mainstream industry I'll say again it's it's still in some pockets but around the 2010s 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 on premise backend services where you had most of your business logic later it moved to the cloud and Grady said that the these hyperscalers the cloud providers AWS for example they they built all these services that are really well architected so you can kind of use one after the other and it's it's well done you don't need to worry too much about your data storage you just use let's say Dynamob or or a managed Postgress service so suddenly architecture is not all that important because these blocks take it care of you. 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 architectured building block that you could use and you didn't have to reinvent the wheel. >> Yeah. or but I suspect there's still patterns of using these things and that's something I haven't delved into because I just haven't had the the opportunity to >> focus on that or more precisely I haven't had enough of my colleagues uh banging my banging me on the door with with uh draft articles to be able to publish on it. >> Well, one pattern that I do see is every every company you know names their system. Some have wacky names, some have logical names. But when you talk about architecture, you typically talk about like you know like like at at at Uber we had the the bank emoji service which called which was be migrated to Gulfream which was you know these all sound like doesn't make too much sense if if if you're from the outside. Sometimes they have like proper names they try with that the payment profile service but then there's a new version and that's now the payment pro that's PP PP2 anyway. But inside any 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 the lingo. >> Yeah, it is. It becomes that's again again part of the lingo of larger organizations and again you take a company that's been around for 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 interconnect. >> Well, one of the fascinating conversation that I had many years ago was someone very high up in American Express and we were talking about how uh he was responsible for rearchitecting their system to the next generation. And uh he was just getting ideas on how to socialize ideas and and get things out. And I asked how long have you been working on this? It has been 3 years. And I was like, "Okay, so we're we're like where are you are you like done?" He's like, "No, no, this is just a planning like like we're we're close to finishing the planning." And to me, it didn't compute because like in 3 years of planning. But again, once you I started to understand the the the scale of the business, how much money, how many legacy systems they have, half of half of what he did was talk with business stakeholders to convince them or get buy in. Um I guess this event eventually happens with like most companies except when you're at the younger company or digital first or tech first companies meaning founded in 2010 or later. You still don't see this but it it might come in 10 years. >> Oh yeah it it certainly will. Oh, it's interesting. It there's I remember chatting I was chatting with somebody who had joined a bank uh an established bank and they had 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 3 years now I think I can understand the problem I've got some idea of what 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 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 happen because so and so met so and so and had an arrow 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. and 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 cuz it's very hard to not get that that situation. And yeah, I mean, the Uber's lucky that it's only, you know, relatively young company, but it will be, you know, assuming it survives in 50 years time, it'll be like American Expresses, right? >> Yep. you can already see the the changes the the the layers of processes and so on which is kind of nec like it's necessary so as you grow speaking of uh change and iteration uh on and agile so you were part of the 17 people who created the agile manifesto and I previously asked Ken Beck about this who was another person involved can you tell me from your perspective what was the story there on on how you all came together how this pretty chaotic I I think day played out And what was the reception as as you recall back then? This was 2001, >> right? So I mean the the origin of it I always feel was actually a meeting we had that Kent 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 uh place near where Kent was living at the time in middle of nowhere Oregon and uh he also invited some people who weren't directly part of the extreme programming group folks like Jim Highmith um 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 well what do we do with this broader thing and how it overlaps with things like what the scrum people were doing and all that kind of stuff that's what led to the 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 Alistister wanted it in Utah and then Dave Thomas wanted to have it in Anguila in the Caribbean and for whatever reason we ended up in Utah um and the skiing and so we and we gathered together the people that we did and of course it was a case of who actually came along and because obviously lots of people were invited who didn't come um and I wasn't terribly involved with that although Bob 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 for works at the time. So, I probably did, but I don't remember. Um, and of the meeting itself, I actually don't remember very much of it, which is a shame. I I I, you know, curse myself for not writing a detailed journal of of those few days. Um, I'd love to know, you know, how did we come up with that this over that um structure for the values, for instance, which I think was really wonderful, but I have no idea how it got how that got put together. So, unfortunately, I get very vague about the actual doing of it. I do remember have a have a fairly clear memory although we should be wary about that. I'll come to that perhaps later about why of uh 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 the manifesto itself will be a complete useless and ignored of course but the exercise of writing it will be interesting it >> um and that was my reaction to it and matter how I felt about the manifesto I felt ah nobody will take any notice of this oh wow >> but um hey we're having fun writing it and we're going we're understanding each other etc. 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 it gets misused by by most of the time because there's there's that lovely quote from Alistister Cobin that your brilliant idea will either be ignored or misinterpreted and you don't get to choose which of the two it is. >> Well, it also helps that the manifest to us four different lines and so people just pick and choose which one they want to point. >> 12 principles. >> Oh, and the 12 principles which Yes. and and the fact that it says and says at the beginning we are uncovering um and that this is a continuous process and what the manifesto is just this is what we've got how how we got so far um so it's a snapshot of a point in time of where we were in 20201 yeah all sorts of subtleties to to the to the manifesto but um it I think it had an impact in the sense that I my feelings were was a certain way that we wanted to write software at Fort Works for our clients in 2000 000 and it was a real struggle because they didn't want to work the way we wanted to. We've said we want to put all this effort into writing tests and we was we want to have a build pro 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. You know, no, we got to we've got to have a big plan over five years and we'll spend two years doing a design and we'll produce a 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 the mentality of how things ought to be done. >> Yeah. That was just the common the commonly understood wisdom, right? >> Yeah. And our notion of no, we we'd like to do that entire 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 it 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 to much greater extent than we would 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 um on the whole I think we are a bit better off >> and and do you see like the the way you look especially when you look at the enterprise clients that that you have a lot more visibility to you see the definite change from like 25 years ago to like the the concepts 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 it's just common everywhere right can we say that or at least >> I would We've made significant progress, but compared to how we'd like it to be and where our vision is, it is still a pale shadow of what we want of what we wanted. I mean, and 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 c than we've been, but we have actually made material progress. And the thing is that we we were always in that situation where you know we're kind of nudging our our way forwards much at a much slower rate than we'd like to be. Yeah. Now of course AI is is coming and it it is now every it is everywhere and it will be everywhere and one things with 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 they want to see quality upfront. 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 driven development like getting a version of the software that is just great to start with. I don't know how the AI 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 sort of humans reviewing it is still the way to bet. What AI hopefully will allow us to do is to be able to do those slices faster um and maybe do a bit more in each slice, but we need to it's 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 cycled out those steps more rapidly. That's where I felt we've had our biggest gains is is through that more rapid cycle rather than trying to do more stuff in the same cycle as it were. And I I still get a sense of that when talking to people still saying, you know, can you look at all of the things that you do in software development and and increase the frequency? Do half as much but in half the time and and 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 the moment is improving cycle time. >> Yeah. And I I've been 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 this. They're also in their own interest to use their own tools. at Entrophic uh the cloud the cloud code team one of the creators of clot code Boris he shared how he did 20 prototypes of of a feature of how the progress bar when 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 and got feedback on and decided which one to go in two days and he and 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 proto protypes. So they were not just, you know, like on the paper, but they were inside. >> And to me, this was like, wow. Like if if you would have told me I built 20 prototypes and you asked me how long it took it, I would have said two weeks, maybe a week if you 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 show shared with small group, bigger group. So I I I feel I feel you're right on how we have not reached the limit of of how quickly can we look at things. >> Yeah, it comes back to feedback loops. I mean so much of it is trying how do we introduce feedback loops into the process? I mean 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 it comes back to you know we have to be learning about what it is we're trying to do. Speaking about learning uh and keeping up to date uh how do you learn about AI? How do you keep up to date with with what's happening? What approaches work for you? And what are approaches you see your colleagues uh 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 um are going on onto my site because my primary effort these days is getting good articles onto my site and 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. Haven't been doing 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 stack traces but it's only within this very very esoteric little area. Um so as a result I 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 you're very deeply involved in in the editing process for a lot of that material and that was 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 you know it's necessity only in the in the off time that I get to do that. Um and of course reading from where I feel are some of the better sources. I mean fortunately one of those better sources is Bita who has been um writing with me. So that's good. Um Simon >> he's excellent. Yeah. >> Spittita stuff is superb. Um Simon Willis I keep an eye on what he's doing all the time. Um I wish I had his energy work rate for getting stuff out. Actually I wish I had your energy you the man of stuff you get out these days. And so I look for sources like that. I'm always interested in what folks like Kent are up up to because let's face it, so much of my career has been leeching off Kent's ideas and um there's no reason to stop doing that if it's still working, right? Um and so those are the kinds of sources I mean then sometimes some books that come out that come through and and work through those. So a lot of it is in that kind of direction. I might even watch a video occasionally although I really hate watching videos. So yeah. So sounds like find the sources of the people you trust, the sources you trust. Again, your your blog I can very much recommend it because you have several people writing on it. Uh so you actually have a pretty good frequency of in-depth articles about interesting like I I I rarely see topics that have been discussed in depth and so I I enjoy checking checking out because of because of it. I mean one of the questions that I've been I've been pondering on is when asked of so how do you identify what a good source is of information and this is more general this is due to to our profession but of course due to to the world generally as we seem to be in an epistemological crisis of trying to understand what's going on in the world and and at some point I'm going to sit down and write this down and I'll get a more coherent um answer from it but part of what I'm always looking for is um 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 I remember one of my favorite early books when I was writing on the the um software architecture um I was des I remember desperately looking for something in the Microsoft world as opposed to something in the Java world there was a lot being written written in Java world. This is back around the late 90s. Lots of stuff was being written in Java land, not much in Microsoft land. And when I discovered this Swedish guy, Jimmy Nielson. 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. He was very tentative all the time, very much clear of this was how he was currently feeling, but he 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 false 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 you should never use microservices I mean those both of those arguments can completely discounted. It's when you say, "Ah, 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 on a sort of simple railroad and 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-offs, right? The the most common answer of of like how long will it take is it depends. It depends on on are we doing a prototype, it depends on on do I know the technology, etc. So if you if you're reading sources or if you're accessing sources where they tell you, okay, in my situation, you actually learn about their 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 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. a huge difference. Yeah. And then that's I mean and again you see it I mean we see it with we frankly we see it with clients a lot of clients say give us the answer give us the the the cookbook straightforward answer that I just need to apply. Yeah. 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 nuance involved. We we we keep going back to this like now more than 50 year old art the no silver bullets right one question uh 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 with learning I think you also mentioned or it might have been Umesh who mentioned with junior engineers it it it could be a bit iffy of if you're relying too much on AI will that hinder your learning because learning is important. If one of these engineers asked 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 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 if you're more junior is 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 a good experienced mentor is worth their weight in gold and in fact many ways it's worth prioritizing that above many other things that you when it comes to your career is is getting that met. I mean, again, me finding Jim Odell early on in my career was enormously valuable. The best thing that could have possibly happened to me was just blind luck. Um, but seek out somebody like that who can be your mentor. I mean, although we're peers in some ways, I often see think of Kemp Beck as a mentor. Um, because you know, we may be the same age or whatever, but his thinking is always leaping forwards. And so, watching what he's doing has been very val. So 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 do you giving me this advice? What are your sources? What what's leading you to say this? I mean I I remember this this is generally a good thing is whenever people are pro 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 where they're coming from. And you I think you have to do the same thing with the AI because in the end the AI is is it's it's just regurgitating something it saw 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, right? And but if you can find your way to the good stuff, then that can be much more useful. >> And looking at all this change that that's happening right now with AI LMS, how do you feel about the tech industry in in general? >> I mean, in in a broad sense, I'm positive because I' I still feel there's a hu so many huge things that can be done with technology and software. Um, and we are on, you know, we're still in a situation where demand is way more than we can imagine. But that's a long-term view. I mean at the moment we're in this very I'm going to say very str it's 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 um in uh the developed world depression. I mean we've seen a huge amount of job layoffs. I mean I 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, at Fort Works, we used to be growing at 20% a year all the time until about 2021. I mean, we've we've we've hit a wall, and we see our clients are just not spending the money on um 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 say there wasn't with blockchain and crypto. There's definitely stuff in AI, but exactly how it's going to pan out, who knows? And I mean, I went through this cycle with stuff in in the '9s and 2000. So, it's it's it's a repeat of that only at probably an order of magnitude more scale. Um, so all of 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 this is a much more macroeconomic thing. We have Looney driving the bus in the in the United States. We have all sorts of other pressures going on in internationally. Great uncertainty at the moment and that's affecting us because it means that businesses aren't investing. And while businesses aren't investing, it's hard for to to make much progress in in the software world. And so we have this weird mix of no investment pretty much depression in the in the software industry with an AI bubble going on. And they're both happening at the same time. >> And one of mass the other end, yeah, depends on where you are. Like I was in Silicon Valley and if you're an AI company, it all inside it looks all great. If you're outside, again, you can benefit from it, but it's it's it's a lot more careful. And if you're outside of this bubble, let's say you're at a a startup or or a company that is not in AI, it's it's just tough. So, you you have these these worlds happening. I mean, this is still, I think, an industry with plenty of potential in the future. I think it's a it's a good one to get into. It's not uh you know, the timing is not as great as it would be getting into this industry in say 2005. Um but you know it I still feel there's a there's a good profession here. I don't think AI is going to wipe out software development. Um I think it'll change it in a really manifest way like the change from assembly to high level languages did but the core skills are still there and the core skills of being a good software developer in my view are 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 and particularly communication with the users of software and crossing that divide which has always been the most critical um communication path >> and and you've also mentioned the expert general is becoming a lot more important which all of that when I looked into the details we'll link it in the the show notes the article that I think it was again >> unmesh has been on fire he's on fire but but it all all the traits seem to do nothing to do with AI. It's about curiosity. It's about going deep. Uh it's about going broad. It it it sounds like I'm 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 and I I do think that and it is it is always been communication and being able to collaborate effectively with people has always been to my mind the outstanding quality of what really makes the the very best developers. um come through certainly in the enterprise commercial world which is the one I'm most familiar with because every soft all the software we're writing for is for people who doing something very different to what we do. I remember when I was working in health service I mean I always said you know here I am doing this conceptual modeling of health care. I understand a huge amount about the process of of health care. You are not going to want me to treat whatever your medical problems are because I am never going to have that skill because I'm not a doctor. Yeah. >> 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 I'll fire and then you come what comes to mind. What is your favorite programming language and why? >> Um I would say at the moment my favorite programming language is Ruby because it's become it's I'm so familiar with it. I've been using it for so long. But the one that is my love is small talk without a doubt. Small talk. There was nothing as much fun as programming in Small Talk when I was able to do it in the uh in the '9s. That that was such a fantastic environment. >> You and Kenbeck and Kenbeck is is writing his Small Talk server. It's it's it's his baby. I I think he's making progress >> and and I mean there is still stuff going on. There is the Pharaoh project in Small Talk. And I keep thinking, you know, I if I could just take off some weeks and and stop everything else I was doing, maybe investigate, see what's going on in the small talk world again, cuz it was I mean, and has still so much power in that language. >> What are one or two books you would recommend? Uh, and why? >> So, a book I I do particularly like to recommend is Thinking Fast and Slow by Daniel Kaneman. I like it because um he does a really good job of trying to give you an intuition about numbers in 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 if we could understand um the statistical effects of what we see but also in life in general because I think uh our world would be a hell of a lot better if way more people understood a bit more about probability and statistics. Yeah. 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 probabil probability is and probabilistic reasoning has been the fact that I'm heavily into tabletop gaming where you have to constantly think in terms of probabilistics and um I I just honestly feel that knowing that is important and this book is I think a great way to get into that and so it was one of the best reads I've had in the last few years. Another book that I'd mention that is completely separate and is in challenging in a completely different way that I've been totally obsessed with is a book called The Power Broker. Um, so this is a book about uh a guy called Robert Moses who most people have never heard of but was the most powerful official in New York City for about 40 years um from about 192 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. Um how power works in a in a democratic society often in not in plain sight. Um and it's a fascinating book for that. It's also a fascinating book because it is so well written. There have been moments when I would just, you know, I've been reading a several page passage of something and I would just have to stop to just appreciate how brilliant what was just read was. And that's valuable because to be a better writer, and I think we all gain by being a better writer, it's really important to read really good writing. And his writing is magnificent. The downside is it's 1,200 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 he still hasn't finished the fifth. But again, there are moments when I was just gobsmacked by how brilliant the writing was and gossmacked by the way again power works in a democratic society and uh 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 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 would be the movie you would recommend? Right. Because I get it. So many different tastes and things. If I'm going to pick something that's I think not too complicated for someone to get into that I think is is still got quite a lot of richness at the moment, I think the game I'd 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 uh decision making in in the process. >> Well, Martin, thank you so much. It was great that we could make it happen in person as well. >> Yes, having that that worked out really well. I just happened to be in Amsterdam for something else and uh 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 biggest change with AI is about how we're going from deterministic systems to non-deterministic ones. This means that our existing software engineering approaches that were based on assuming a fully deterministic 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 liked 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 changed in the past 50 years check out related deep dives in the pragmatic engineer which are linked in the show notes below. 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