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[0] https://sketch.dev/blog/our-first-outage-from-llm-written-co...
After working with the latest models I think these "it's just another tool" or "another layer of abstraction" or "I'm just building at a different level" kind of arguments are wishful thinking. You're not going to be a designer writing blueprints for a series of workers to execute on, you're barely going to be a product manager translating business requirements into a technical specification before AI closes that gap…
After working with the latest models I think these "it's just another tool" or "another layer of abstraction" or "I'm just building at a different level" kind of arguments are wishful thinking. You're not going to be a designer writing blueprints for a series of workers to execute on, you're barely going to be a product manager translating business requirements into a technical specification before AI closes that gap…
So when things break or they have to make changes, and the AI gets lost down a rabbit hole, who is held accountable?
These models are nothing short of astounding. I can write a spec for an entirely new endpoint, and Claude figures out all of the middleware plumbing and the database queries. (The catch: this is in Rust and the SQL is raw, without an ORM. It just gets it. I'm reviewing the code, too, and it's mostly excellent.) I can ask Claude to add new data to the return payloads - it does it, and it can figure out the cache inval…
where's the catch? SQL is an old technology, surely an LLM is good with it
It's not a programming language if you can't read someone else's code, figure out what it does, figure out what they meant, and debug the difference between those things. "I prompted it like this" "I gave it the same prompt, and it came out different" It's not programming. It might be having a pseudo-conversation with a complex system, but it's not programming.
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Lots of horrifying things are inevitable because they represent "progress" (where "progress" means "good for the market", even if it's bad for the idea of civilization), and we, as a society, come to adapt to them, not because they are good, but because they are.
It's not a programming language if you can't read someone else's code, figure out what it does, figure out what they meant, and debug the difference between those things. "I prompted it like this" "I gave it the same prompt, and it came out different" It's not programming. It might be having a pseudo-conversation with a complex system, but it's not programming.
After working with the latest models I think these "it's just another tool" or "another layer of abstraction" or "I'm just building at a different level" kind of arguments are wishful thinking. You're not going to be a designer writing blueprints for a series of workers to execute on, you're barely going to be a product manager translating business requirements into a technical specification before AI closes that gap…
> what I'm seeing is that all of the skills that my training and years of experience have helped me hone are now implemented by these tools to the level that I know most businesses would be satisfied by. So when things break or they have to make changes, and the AI gets lost down a rabbit hole, who is held accountable?
My point is that SWEs are living on a prayer that AI will be perched on a knifes edge where there is still be some amount of technical work to make our profession sustainable and from what I'm seeing that's not going to be the case. It won't happen overnight, but I doubt my kids will ever even think about a computer science degree or doing what I did for work.
I ask the developer the simplest questions, like "which of the multiple entry-points do you use to test this code locally", or "you have a 'mode' parameter here that determines which branch of the code executes, which of these modes are actually used? and I get a bunch of babble, because he has no idea how any of it works.
Of course, since everyone is expected to use Cursor for everything and move at warp speed, I have no time to actually untangle this crap.
The LLM is amazing at some things - I can get it to one-shot adding a page to a react app for instance. But if you don't know what good code looks like, you're not going to get a maintainable result.
After working with the latest models I think these "it's just another tool" or "another layer of abstraction" or "I'm just building at a different level" kind of arguments are wishful thinking. You're not going to be a designer writing blueprints for a series of workers to execute on, you're barely going to be a product manager translating business requirements into a technical specification before AI closes that gap…
But if your job depends on taste, design, intuition, sociability, judgement, coaching, inspiring, explaining, or empathy in the context of using technology to solve human problems, you’ll be fine. The premium for these skills is going _way_ up.
It's not a programming language if you can't read someone else's code, figure out what it does, figure out what they meant, and debug the difference between those things. "I prompted it like this" "I gave it the same prompt, and it came out different" It's not programming. It might be having a pseudo-conversation with a complex system, but it's not programming.
Well I think the article would say that you can diff the documentation, and it's the documentation that is feeding the AI in this new paradigm (which isn't direct prompting).
If the definition of programming is "a process to create sets of instructions that tell a computer how to perform specific tasks" there is nothing in there that requires it to be deterministic at the definition level.