121–130 of 208 posts
> "didn’t you say that you check all the AI generated code for Redis?"... Yes, I do
For important code (Redis) he is still reading the diffs.
> but: try it yourself, you will discover you can’t just say “implement XYZ” and see it working.
Yes, good software still requires engineering today.
Earlier quoted context omitted.
I do highly unusual, off the beaten path projects with AI. For instance, a game engine written entirely in LuaJIT, but allocating almost all data structures using CFFI, a very unusual, custom object-orientation DSL I designed myself, and that uses SDL3's SDL_gpu library to do all rendering, a ton of GLSL 4.6 compute shaders cross compiled to SPIR-V and Metal (which constraints what GLSL can be written), and is a hybr…
> All I can say is skill issue I know nothing about you or your code, but till you make your code + workflows public and have all of it reviewed / critiqued (so that other can replicate the performance), it's hard to accept it as a serious case study. For all we know, this could just as well be a severe case of Dunning-Kruger.
https://williamcotton.github.io/algraf
https://williamcotton.github.io/pdl
https://github.com/williamcotton/algraf
https://github.com/williamcotton/pdl
Check out the /docs dir in the repos for each plan file and the language specs.
They are my 4th and 5th DSLs and I learned quite a bit along the way, most importantly how to separate a WASM runtime wired up to a Monaco editor “lsp” with the same crate for editor-services that works with the actual system binary LSP.
I tried really hard to do this, but it turns out the models don't care about your ideas and want to do what's popular in their training data, so they will happily ignore anything you try to force down their throats, especially as context length grows or if you hit compaction. So to make best use of the models steer them down familiar paths, mention common pattern and frameworks, use popular packages and languages tha…
I do highly unusual, off the beaten path projects with AI. For instance, a game engine written entirely in LuaJIT, but allocating almost all data structures using CFFI, a very unusual, custom object-orientation DSL I designed myself, and that uses SDL3's SDL_gpu library to do all rendering, a ton of GLSL 4.6 compute shaders cross compiled to SPIR-V and Metal (which constraints what GLSL can be written), and is a hybr…
I've also had reasonable success with the models generating fairly idiomatic Objective-Smalltalk, my own language of which there are likely few to no examples in the training data.
I do steer them towards my own sample programs.
Ideas are a dime a dozen. All of us have half a dozen of what appear to be good ideas every. Execution matters, testing and sanity checking matters, actual engagement with users and iteration matters. Sure, we're reducing the cost of idea -> prototype to near zero (well, as long as tokens are free or nearly free), but that just means we now have mountains of throw away code, within which there may a gem or two. Nothi…
Earlier quoted context omitted.
In my experience, because as you say, the flexibility of these tools and how you engage with them is so broad, and how much the nuances of thay engagement can often matter, the only way to teach someone how to do what you do with them is to sit down and pair a program with them. Barring that, any instructions you could give would massively under-determined what they actually try as a result of your instructions, and…
That's a completely reasonable take. I wouldn't have any issue with a nuanced comment like that. My issue with your above comment is that it's a lot more arrogant than nuanced.
Earlier quoted context omitted.
I do highly unusual, off the beaten path projects with AI. For instance, a game engine written entirely in LuaJIT, but allocating almost all data structures using CFFI, a very unusual, custom object-orientation DSL I designed myself, and that uses SDL3's SDL_gpu library to do all rendering, a ton of GLSL 4.6 compute shaders cross compiled to SPIR-V and Metal (which constraints what GLSL can be written), and is a hybr…
things you described are not "highly unusual" in the way gp comment is describing. you are just remixing bunch of stuff. Thats the type of thing ai is really good at.
Also, what would be highly unusual to you?
For "ideas", substitute "code in a higher level language". At least if we actually want to control the ideas. Right now, with those ideas expressed only in English, our control is limited.
What LLM coding does is generate lots of code from a fairly small English (natural language) prompt.
If you look closely, I think you'll see that this is two processes:
1. Translate from fuzzy English to precise machine-executable language
2. Translate from high-level description to lots of low-level code.
Although these two are still pretty great when they are mashed-together, I think they'd be even better, lots better, if we could separate them.
But today we cannot.
I tried really hard to do this, but it turns out the models don't care about your ideas and want to do what's popular in their training data, so they will happily ignore anything you try to force down their throats, especially as context length grows or if you hit compaction. So to make best use of the models steer them down familiar paths, mention common pattern and frameworks, use popular packages and languages tha…
That runs the danger of putting the model out of distribution, of course, so it takes some experimenting.
I'm working on a Ruby project where - on purpose - I'm disallowing any frameworks other than what's standard Ruby. So no Rails or Sinatra, just the inbuilt Webrick web server. Initially it was hard to keep the model on the rails but as I've learned more it gets easier. That said, it's clear to me that the model pulls very strongly in familiar directions in terms of how it writes code, i.e. a lot of what gets written still ends up looking "Railsy" even though my learning project doesn't use it.
Earlier quoted context omitted.
I do highly unusual, off the beaten path projects with AI. For instance, a game engine written entirely in LuaJIT, but allocating almost all data structures using CFFI, a very unusual, custom object-orientation DSL I designed myself, and that uses SDL3's SDL_gpu library to do all rendering, a ton of GLSL 4.6 compute shaders cross compiled to SPIR-V and Metal (which constraints what GLSL can be written), and is a hybr…
> All I can say is skill issue I know nothing about you or your code, but till you make your code + workflows public and have all of it reviewed / critiqued (so that other can replicate the performance), it's hard to accept it as a serious case study. For all we know, this could just as well be a severe case of Dunning-Kruger.
https://github.com/alexispurslane/cursed/
https://github.com/alexispurslane/industrialworld/
Feel free to look at my past projects, prior to me starting to use AI (which started with gemini-ocr) to assess my baseline skill level.
Earlier quoted context omitted.
That's a completely reasonable take. I wouldn't have any issue with a nuanced comment like that. My issue with your above comment is that it's a lot more arrogant than nuanced.
That's fair, I think my point is there's a nuanced theory behind my choice to be rude, which is essentially "you need to experiment more, be more open minded, really work at it, and then you'll see what others are seeing, it isn't impossible." It's like Dark Souls, nobody can beat it for you, or teach you how to use dodge rolls, you've gotta build that skill yourself, and blaming the tools won't help.
IMHO as you get more senior in software, after a while the only interesting metric becomes: are you raising the level of the people around you?
EDIT: looking at your Github seems like you are still in university. So I'll say "age and experience issue" on your end ;)