For the past year, David Crawshaw has intentionally sought ways to use LLMs while programming, in order to learn about them. He now regularly use LLMs while working and considers their benefits a net-positive on his productivity. David wrote down his experience, which we found both practical and insightful. Hopefully you will too!
Chapter 1: What is the purpose of the podcast 'Programming with LLMs'?
I'm Jared, and you're listening to The Change Log, where each and every week, we have conversations with the hackers, the leaders, and the innovators of the software world. We pick their brains, we learn from their mistakes, we get inspired by their accomplishments, and we have a lot of fun along the way.
For the past year, David Kroshaw has intentionally sought ways to use LLMs while programming in order to learn about them. He now regularly uses LLMs while working and considers their benefits a net positive on his productivity. David wrote down his experience, which we found both practical and insightful. Hopefully you will too.
But first, a quick mention of our partners at Fly.io, the public cloud built for developers who ship. Check them out at Fly.io. Okay, David Croshaw on the changelog. Let's do it.
Chapter 2: How does Brex use Retool for its operations?
Well, friends, before the show, I'm here with my good friend, David Shue, over at Retool. Now, David, I've known about Retool for a very long time. You've been working with us for many, many years. And speaking of many, many years, Brex is one of your oldest customers. You've been in business almost seven years.
I think they've been a customer of yours for almost all those seven years, to my knowledge. But share the story. What do you do for Brex? How does Brex leverage Retool? And why have they stayed with you all these years?
So what's really interesting about Brex is that they are an extremely operational heavy company. And so for them, the quality of the internal tools is so important because you can imagine they have to deal with fraud. They have to deal with underwriting. They have to deal with so many problems, basically. They have a giant team internally, basically just using internal tools day in and day out.
And so they have a very high bar for internal tools. And when they first started, we were in the same YC batch, actually. We were both at Winter 17. And they were, yeah, I think maybe customer number five or something like that for us. I think DoorDash was a little bit before them, but they were pretty early.
And the problem they had was they had so many internal tools they needed to go and build, but not enough time or engineers to go build all of them. And even if they did have the timer engineers, they wanted their engineers focused on building external facing software, because that is what would drive the business forward. Breck's mobile app, for example, is awesome.
The Breck's website, for example, is awesome. The expense flow, all really, you know, really great external facing software. So they wanted their engineers focused on that as opposed to building internal CRUD UIs. And so that's why they came to us. And it was awesome. Honestly, a wonderful partnership. It has been for seven, eight years now.
Today, I think Brex has probably around a thousand Retool apps they use in production. I want to say every week, which is awesome. And their whole business effectively runs now on Retool. And we are so, so privileged to be a part of their journey. And to me, I think what's really cool about all this is that we've managed to allow them to move so fast.
So whether it's launching new product lines, whether it's responding to customers faster, whatever it is, if they need an app for that, they can get an app for it in a day, which is a lot better than, you know, in six months or a year, for example, having to schlep through spreadsheets, etc. So I'm really, really proud of our partnership with Brex.
Okay, Retool is the best way to build, maintain, and deploy internal software, seamlessly connect to databases, build with elegant components, and customize with code, accelerate mundane tasks, and free up time for the work that really matters for you and your team. Learn more at retool.com. Start for free. Book a demo. Again, retool.com.
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Chapter 3: What can Tailscale offer for running LLMs?
Do you mean like deploying LLMs inside of the Tailscale product or how do you mean it wouldn't fit?
So what would Tailscale do with LLMs is the question I was asking from a Tailscale perspective. I think Tailscale is extremely useful for running LLMs yourself for a network backplane. In particular because of the sort of surprising nature of the network traffic associated with LLMs On the inference side, so you can kind of think about working with models from both a training and an inference.
These are sort of two sides of the coin here. And training is very, very data heavy and is usually done on extremely high bandwidth, low latency networks, InfiniBand style setups on clusters of machines in a single room. or if they're spread beyond the room, the next room is literally in the building next door. The inference side looks very different.
There's very little network traffic involved in doing inference on models in terms of bandwidth. And the layout of the network is surprisingly messy. This is because the nature of finding GPUs is tricky even still today. despite the fact that this has been a thing for years now. Very tricky. Yeah.
I feel I should try and explain it just because it's always worth trying to explain things, but I'm sure you all know this, which is that if you're running a service on a cloud provider that you chose years ago for very good reasons, All the cloud providers are very good at fundamental services, but they all have some subset of GPUs and they have them available in some places and not others.
And it's never quite what you're looking for. And if you are deciding to run your own model and do inference on it, you might find your GPU is in a region across the country or it's on a cloud provider that's different than the one you're using. or your cloud provider can do it, but it's twice the price of another one you can get.
And this leads to people ending up far more in sort of multi-cloud environments than they do in sort of traditional software. And so Tailscale actually is very useful there. So for users, I think it's a great fit. But what does the product actually need as like new features to support that? And the answer is, it actually is really great as it is today for that.
There's no specific. AI angle that you can add to the product and immediately make it more useful.
Yeah, I think that's right. I mean, there are, we came up with some proposals, but they're not exciting. Like they're, they would be very much, we'd be doing it because, you know, corporate in an, you know, at headquarters told us to find an angle for AI or something like that. And like, you know, we, we as a startup have the option of just not doing that. And so we didn't. So, yeah.
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Chapter 4: Why are LLMs challenging to integrate into products?
I mean, I love boring software. And so for me, the fact that you're having a boring experience is perfect. Yeah, man. No surprises. No surprises. Yeah. It's a product that's designed to enable you to do more things, not for you to spend your days having to configure it.
It is so smooth. The DevEx experience on this thing is bar none. I know my machines. I know where they're at. I know if they're out of date. It's pretty easy to do that kind of stuff. And as an avid user and a lover of Tailscale, again, not sponsored, just super passionate, I can't see how an LLM would fit in either. I just can't see how you would work in AI to make the platform better.
I mean, I haven't thought about it deeply besides the, this 20 ish minutes so far in the conversation, but I mean, give me some time and I might.
Yeah, if you come up with anything, let me know. I'm very excited about the idea of it. But software has to be, in some sense, true to itself. You have to think about its purpose when you're working on it and not step too far outside that. So I similarly wouldn't build a computer game in a tail scale. I don't think that would be a particularly good fit for a product. It's like an Easter egg.
As an Easter egg, it would be great, actually, like a little quiz game or something built into the terminal.
Once you have a hundred machines in your tail net, you get access to, you unlock a secret machine name that's on your tail net by default. There you go.
Right, it can ask questions like, what is the oldest machine on your tail net or something like that. That would be a lot of fun, actually.
There are some questions I would probably ask the tail net. Like there are actually some things I don't know about my tail net that I could discover via a chat LLM interface. I mean, there are some things I can see some value in, but I mean, does everybody want that or need that? Maybe, I don't know.
Yeah, I don't know either. I very much went looking for something I would use features like that for, and I didn't come up with anything. If you do come up with anything, again, I'd be very happy to hear about it.
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Chapter 5: How has GitHub Copilot influenced programming with LLMs?
And it often takes quite a while to roll out for more. And even if the company has a really good roadmap and a really good understanding of all of the ways they could use it, it can take a very long time to solve all of their problems with it. And that's assuming they have a really good understanding of all of the things it can do.
And the point you're making, Adam, that people often don't even realize all the great things you can do with it is true. And I'm sure a tool that helps people explore what they could do would have some effect on revenue. In terms of the technical side of it and the challenges, there are several challenges. In the very broad sense, the biggest challenge with LLMs is just the enormous amount of
what you might call traditional non-model engineering has to happen out the front of them to make them work well. It's surprisingly involved. I can talk to some things I've been working on over the last year to give you a sense of that. Beyond that, the second sort of big technical challenge is one of sort of Tailscale's core design principles is all of the networking is end-to-end encrypted. And
The main thing an LLM needs to give you insight is a source of data. And the major source of data would be what is happening on your network, what talks to what, how does it all work. And that means that any model telling you how you could change your networking layout or give you insight into what you could do would need access to data that we as a company don't have and don't want.
And so we're back to it would have to be a product you run locally. and have complete control over, which is absolutely, you know, my favorite sorts of products are that, you know, I like open source software that I can see the source code for, compile myself, run locally. That's how I like all things to be.
Trying to get there with LLMs in the state they are today is actually, I think, pretty tricky. I don't think I've seen an actually shipped product that does that really well for people. There's one. There's a developer tool that I hear a lot of good talk about that... I don't... I'm just trying to live search for it for you. Nope, that's the wrong one.
That's Magic Shell History, which also sounds really cool. I should use that one. Is that A2N? A2N, yeah. That one's awesome. Oh, you've used it? Oh, great. I'm a daily user.
No LLMs involved on that one.
Yeah, I thought that was the only one. There's another one that is... in the sort of agent space for developers as they're writing programs. And it helps you, it's, it's like a local Claude effectively. And it's, it's primarily built around helping you construct prompts really carefully for existing open models. And I've, it's come up several times and I'm sorry, it's fallen out of my head.
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Chapter 6: What are the benefits and challenges of using LLMs in programming?
Yeah, exactly. You can really feel it with the new reasoning models, O1, these things, because there's this pause at the beginning. It hurts.
The thinking phase. I'm like, come on. And it tells you what it's thinking, which is cool. Think faster.
I don't care what you're thinking. Give me the answer. I think it's actually kind of cool when you see that reason come up. I mean, you get to see, like, I feel like this is the closest we've glimpsed into the future that we've ever been able to by watching the reasoning in real time. You see the act of reasoning that it's happening. It explains the reasoning.
Users ask me this, so I'm going to think about that. Okay, I've thought about that, which causes this. And it's like this step process, and it reminds me of how I think, too. So I'm like, that's pretty dang cool. Yeah. But it's also a great trick. I agree.
Yeah, it is a ton of fun to watch. I agree. And it is a lot of insight into how the models work, too. Because the insides of the models are a large number of floating point numbers holding intermediate state. And it's very hard to get insight into those. But the words, you can read them. You can make some sense of them. So code completion is, I think, extremely useful to programmers.
It varies a lot depending on what you're writing and how experienced models are with it. And just... how sort of out on the edge of programming you are. If you're really out in the weeds, the models can get less useful. I used a model for writing a big chunk of AVX assembly a few months ago. And the model was both very good at it and very bad at it simultaneously.
And it was very different from the typical asking a model to help with programming experience. it would constantly get the order operations wrong or overcomplicate things or misunderstand. It was a very different experience than typical programming.
What model was this? How did you find it?
I used all of them for that. Okay. And this is what I meant by I'm spending a lot of time actively exploring the space. I'm putting far too much work into exploring the model space as I do work.
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