I just don't see how AI coding is going to replace devs AT ALL.
I'm trying my absolute best, but maybe the problem is I just have too much experience?
I can see all the absolutely wacky shit it's doing and I'm like WTF.
Aider for example. It's an AI dev system, with 80% coded with itself.
It doesn't even work with Azure deepseek. It has a bug. So I decided to use windsurf and claude 3.5 to try to fix the bug to see how it handles it.
Here's one of the things it wanted to do.
It wants to add this in model-settings.yml
- name: azure_ai/deepseek-r1
edit_format: diff
weak_model_name: gpt-4o-mini
use_repo_map: true
lazy: true
reminder: sys
examples_as_sys_msg: true
extra_params:
api_type: "azure"
api_key_env_var: "AZURE_API_KEY"
Pay attention to the last 2 extra_params.
For starters, wtf is with the string "AZURE_API_KEY"? That's just WRONG. That needs to be an env variable set in http://models.py.
Now, let's go to http://models.py and see what it simultaneously wants to change in there.
if http://self.name.startswith("azure_ai/"):
api_base = os.environ.get("AZURE_API_BASE")
api_version = os.environ.get("AZURE_API_VERSION")
api_key = os.environ.get("AZURE_API_KEY")
if not api_base or not api_version or not api_key:
raise ValueError("AZURE_API_BASE, AZURE_API_VERSION, and AZURE_API_KEY must be set for Azure AI models")
kwargs.update({
"api_type": "azure",
"api_base": api_base,
"api_version": api_version,
"api_key": api_key,
})
Ok, so it's getting the env variables, that's right.
But, it's now just adding api_type: "azure" again in the code, after adding it in model-settings.yml
And you know the best bit?
THATS NOT A VALID PARAM!
These are kwargs for litellm.completion. There is no param called api_type.
I can tell everyone that, people using AI to develop software are in for one heck of a treat dealing with bugs and maintainability.
The AI has absolutely no understanding of what's going on. It's hallucinating and missing important things. When it fixes one bug, it creates 10 more.
The only thing AI is good for is analysing codebases, and creating quick one-time small pieces of software.
You might think it saves you time, but what's happening is you're sprinting off when the race is 150 mile ultramarathon. You'll be tired 500m into the race, while the really slow jogger will just keep going and absolutely obliterate you.
I get that people are building stuff with it.. Rotating boxes and all that cool stuff.
But if anyone tries to actually build something that people are going to use, that needs to be maintained, updated and bug-fixed, they're going to get a massive reality check.
Software is NOT write once and forget.
Look, here's the codebase for vscode.
https://github.com/microsoft/vscode
Go visit issues.
"5k+"
And this is enterprise grade software written by humans at Microsoft.
Now imagine a few years down the line with all this AI slop.
I will say this.. There's going to be an absolute mountain of gold for experienced devs to fix shit.
And before anyone says AI is getting better..
It isn't.
It's the same next token prediction with the same problems, the same lost in the middle context window issues, the same lack of understanding of what it's doing. It just gets more sophisticated at pattern matching.
The only way to get it even close to the competence of a human is throwing a ton of compute at it, and passing everything back and forth, running 100's of inferences using an array of different fine-tuned models that do different tasks.
Yes it will improve, but it's still not going to have abilities that humans do.
