Do people in the software community realize how much the industry is going to totally transform in the next 5 years ? I can't imagine people actually typing code by hand anymore by that time.
But I also note that all the examples I have seen are with relatively simple projects started from scratch (on the one hand it is out of this world wild that it works at all), whereas most software development is adding features/fix bugs in already existing code. Code that often blows out the context window of most LLMs.
I can 100% imagine this. What I suspect developers will do in the future is become more proficient at deciding when to type code and when to type a prompt.
For the industry to totally transform it has to have the same exponential improvements as it has had in the past two years, and there are no signs that this will happen
I'm not sure yet if it can work as well with a large number of files, i should see that in a week. But for sure, this seems to be only a matter of scale now.
Granted, i picked a very unoriginal problem (a basic form-oriented website), but we're just at the very beginning.
The thing is, once you're used to that kind of productivity, you can't come back.
You're assuming we'll see the same exponential improvements as it has had in the past two years, and there are no signs that this will happen
> The thing is, once you're used to that kind of productivity, you can't come back.
Somehow everyone who sees "amazing unbelievable productivity gains" assumes that their experience is the only true experience, and whoever says otherwise lies or doesn't have the skills or whatever.
I've tried it with Swift and Elixir. I didn't see any type of "this kind of productivity" for several reasons:
- one you actually mentioned: "working with it more like i would work with a junior dev, and slowly iterating on the features"
It's an eager junior with no understanding of anything. "Slowly iterating on features" does not scream "this kind of productivity"
- it's a token prediction machine limited by it's undocumented and unknowable training set.
So if most of its data comes from 2022, it will keep predicting tokens from that time even if it's no longer valid, or deprecated, or superseded by better approaches. I gave up trying to fix its invalid and or deprecated output for a particular part of code after 4 attempts, and just rewrote it myself.
These systems are barely capable of outputting well-known boilerplate code. Much less "this kind of productivity" for whatever it means
The world isn’t just startups with brand new code. I agree it’s going to have a big impact though.
It’s great for boilerplate, that’s about it.
I'm using Claude sonnet 3.5 with cursor. This week I got it to:
- Modify a messy and very big file which managed a tree structure of in-game platforms. I got it to convert the tree to a linked list. In one attempt it found all the places in the code that needed editing and made the necessary changes.
- I had a player character which used a thruster based movement system (hold a key down to go up continuously). I asked the ai to convert it to a jump based system (press the key for a much shorter amount of time to quickly integrate a powerful upward physics force). The existing code was total spaghetti, but it was able to interpret the nuances of my prompt and implement it correctly in one attempt
- Generate multiple semi-complex shader lab shaders. It was able to correctly interpret and implement instructions like "tile this sprite in a cascading grid pattern across the screen and apply a rainbow color to it based on the screen x position and time".
- generating debug menus and systems from scratch. I can say things like "add a button to this menu which gives the player all perks and makes them invincible". More often then not it immediately knows which global systems it has to call and how to set things up to make it work first go. If it doesn't work first attempt, the generated code is generally not far off
- generating perks themselves - I can say things like "give me a list of possible abilities for this game and attempt implementing them". 80% of its perk ideas were stupid, but some were plausible and fit within the existing game design. It was able to do about 50%-70% of the work required to implement the perk on its own.
- in general, the auto complete functionality when writing code is very good. 90% of the time I just have to press tab and cursor will vomit up the exact chunk of code I was about to type.
Really? That's possibly the easiest task you could have asked it to do.
In what world is this "the easiest task" ??
I could do all this in my sleep when I was in my second year of career, and now I'm in my 24th year (god, I'm old).
What you described isn't just easy, it's trivial, and extremely boilerplate-y. That's why these automated token prediction machines are reasonably good at it.
You created something from scratch that used several boilerplate components with general use cases.
The amount of times professional devs do this is probably almost nil on the scale of the world.