AI-generated code helps me learn and makes experimenting faster

17.19, Friday 27 Jan 2023

It’s one thing to keep tabs on generative AI, speculating about it here and in private client work – it’s another to experience the whoa moment for myself.

Code. I can take or leave AI art. ChatGPT mostly leaves me cold. But code!

GitHub Copilot is your AI pair programmer – it’s smart, code-aware autocomplete. Ok I get that. But let me try to explain what I experienced earlier this week…

I’m building a basic in-browser prototype so I can explore the UX around computing vision, gestures, and attention, just a lightweight personal investigation on the topics from yesterday.

The tech isn’t rocket science, but it isn’t something I know already. From experience this means that I probably need a day or so to learn enough to ask the right questions of StackOverflow and Google. Absorbing a domain like this means reading tutorials, specs, examples, etc. I can bully my way through most code given time.

I signed up for the Copilot 60 day free trial because why not. Installed the plug-in etc.

I opened my vanilla React project. I made an empty component that displayed “Hello, World!” in my browser preview, just to check everything was working.

Then I wrote a comment at the top of the file:

// A react component that activates the user’s webcam and displays the stream in a video element

And waited for the autocomplete: a bunch of code. And accepted it. And hit save. Less than half a second.

The browser preview refreshed – asked for webcam access – then I saw my own face staring back at me.

I got that feeling of the floor dropping away.

Look, I know the code isn’t rocket science. I know I could do this, eventually, and you could probably smash this out without looking, but I don’t really know React - I can’t write it idiomatically - and I don’t know about webcams in the browser, and I don’t know about the MediaStream API.

So this was a day of work in 10 minutes.

What is meant was that I could spend that day integrating hand pose detection and noodling with the actual micro-interactions. And now I have opinions about all of that!

Now, none of that Copilot-supplied code remains in my app.

What happened what that it helped me frame my problem. I was able to rapidly explore the edges of my knowledge, and figure out how to structure my questions and what I need to learn. My learning requirement is not obviated obviously…

…but as an epistemic journey my interaction with Copilot is insanely more efficient than doing it on my own.

So, after Tom Stafford, Copilot is an epistemic agent: it’s not query/response, which is a model which presupposes that I do not change; it scouts ahead and helps me build knowledge. I have a better mental model of my domain, I know more, than I did before I started.

btw when I refreshed the browser and saw my face there, the code working as-wished but not necessarily as-expected, my laptop felt haunted. I closed the lid to stop the face looking at me.

Then I opened it again to check the screen. Then took a breather. Then came back to write this.

Github Copilot radically lowers the cost of experimenting. That’s the value to me.

On ChatGPT for a sec because I dunked on it at the top:

Generated text is meh. It all reads like vapid SEO traffic-farming blog content. Automating away people’s jobs is… ugh, fine I guess? but let’s try to be more original. Stochastic text collisions can stimulate new ideas, sure, that’s another use… but if that’s your goal then flip a coin or use Oblique Strategies or the I Ching or something. Prompt injection attacks are funny and the engineering to avoid them will probably open up more interesting possibilities than the reverse. But not yet.

Ok but I still love large language models so why? I’ve been asking myself that. So here are five large language model applications that I find intriguing:

  1. Intelligent automation starting with browsers but this feels like a step towards phenotropics
  2. Text generation when this unlocks new UIs like Word turning into Photoshop or something
  3. Human-machine interfaces because you can parse intent instead of nouns
  4. When meaning can be interfaced with programmatically and at ludicrous scale
  5. Anything that exploits the inhuman breadth of knowledge embedded in the model, because new knowledge is often the collision of previously separated old knowledge, and this has not been possible before.

Between those starting points (which I should unpack I know), and spotting second-order effects where cheaper UX experiments is one such example, that’s where I’m spending my cycles rn.

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