I’ve used spicy auto-complete, as well as agents running in my IDE, in my CLI, or on GitHub’s server-side. I’ve been experimenting enough with LLM/AI-driven programming to have an opinion on it. And it kind of sucks.
All of that can be automated with tools built for the task. None of this is actually that hard to solve at all. We should automate away pain points instead of boiling the world in the hopes that a linguistic, stochastic model can just so happen to accurately predictively generate the tokens you want in order to save a few fucking hours.
The hubris around this whole topic is astounding to me.
I think you underestimate the amount of business logic contained in boilerplate. (Or maybe we’re just talking about different definitions of what boilerplate is). LLMs can understand that business need while most code generators cannot.
LLMs do not understand anything. There is no semantic understanding whatsoever. It is merely stochastic generation of tokens according to a probability distribution derived from linguistic correlations in its training data.
Also, it is incredibly common for engineers at businesses to have their engineers write code to automate away boilerplate and otherwise inefficient processes. Nowhere did I say that automation must always be done via open source tooling (though that is certainly preferable when possible, of course).
What do you think people and businesses were doing before all of this LLM insanity? Exactly what I’m describing. It’s hardly novel or even interesting.
OK sure if you want to be pedantic. The point is that LLMs can do things traditional code generators can’t.
You don’t have to like it or use it. I myself am very vocal about the weaknesses and existential dangers of AI code. It’s going to cause the worst security nightmares in humanity’s recorded history. I recommend to companies that they DON’T trust LLMs for their coding because it creates unmaintainable nightmares of spaghetti code.
But pretending that they have NO advantages over traditional code generators is utter silliness perpetuated by people who refuse to argue in good faith.
All of that can be automated with tools built for the task. None of this is actually that hard to solve at all. We should automate away pain points instead of boiling the world in the hopes that a linguistic, stochastic model can just so happen to accurately predictively generate the tokens you want in order to save a few fucking hours.
The hubris around this whole topic is astounding to me.
I think you underestimate the amount of business logic contained in boilerplate. (Or maybe we’re just talking about different definitions of what boilerplate is). LLMs can understand that business need while most code generators cannot.
LLMs do not understand anything. There is no semantic understanding whatsoever. It is merely stochastic generation of tokens according to a probability distribution derived from linguistic correlations in its training data.
Also, it is incredibly common for engineers at businesses to have their engineers write code to automate away boilerplate and otherwise inefficient processes. Nowhere did I say that automation must always be done via open source tooling (though that is certainly preferable when possible, of course).
What do you think people and businesses were doing before all of this LLM insanity? Exactly what I’m describing. It’s hardly novel or even interesting.
OK sure if you want to be pedantic. The point is that LLMs can do things traditional code generators can’t.
You don’t have to like it or use it. I myself am very vocal about the weaknesses and existential dangers of AI code. It’s going to cause the worst security nightmares in humanity’s recorded history. I recommend to companies that they DON’T trust LLMs for their coding because it creates unmaintainable nightmares of spaghetti code.
But pretending that they have NO advantages over traditional code generators is utter silliness perpetuated by people who refuse to argue in good faith.