I don't see how your position is compatible with the constant hype about the ever-growing capabilities of LLMs. Either they are improving rapidly, and your intuition keeps getting less and less valuable, or they aren't improving.
I don't see how your position is compatible with the constant hype about the ever-growing capabilities of LLMs. Either they are improving rapidly, and your intuition keeps getting less and less valuable, or they aren't improving.
They're improving rapidly, which means your intuition needs to be constantly updated.
Things that they couldn't do six months go might now be things that they can do - and knowing they couldn't do X six months ago is useful because it helps systematize your explorations.
A key skill here is to know what they can do, what they can't do and what the current incantations are that unlock interesting capabilities.
A couple I've learned in the past week:
1. Don't give Claude Code a URL to some code and tell it to use that, because by default it will use its WebFetch tool but that runs an extra summarization layer (as a prompt injection defense) which loses details. Telling it to use curl sometimes works but a guaranteed trick is to have it git clone the relevant repo to /tmp and look at the code there instead.
2. Telling Claude Code "use red/green TDD" is a quick to type shortcut that will cause it to write tests first, run them and watch them fail, then implement the feature and run the test again. This is a wildly effective technique for getting code that works properly while avoiding untested junk code that isn't needed.
Now multiply those learnings by three years. Sure, the stuff I figure out in 2023 mostly doesn't apply today - but the skills I developed in learning how to test and iterate on my intuitions from then still count and still keep compounding.
The idea that you don't need to learn these things because they'll get better to the point that they can just perfectly figure out what you need is AGI science fiction. I think it's safe to ignore.
Personally I think this is an extreme waste of time. Every week you're learning something new that is already outdated the next week. You're telling me AI can write complex code but isn't able to figure out how to properly guide the user into writing usable prompts?
A somewhat intelligent junior will dive deep for one week and be on the same knowledge level as you in roughly 3 years.
No matter how good AI gets we will never be in a situation where a person with poor communication skills will be able to use it as effectively as someone who's communication skills are razor sharp.
But the examples you've posted have nothing to do with communication skills, they're just hacks to get particular tools to work better for you, and those will change whenever the next model/service decides to do things differently.
I'm generally skeptical of Simon's specific line of argument here, but I'm inclined to agree with the point about communication skill.
In particular, the idea of saying something like "use red/green TDD" is an expression of communication skill (and also, of course, awareness of software methodology jargon).
Ehhh, I don't know. "Communication" is for sapients. I'd call that "knowing the right keywords".
And if the hype is right, why would you need to know any of them? I've seen people unironically suggest telling the LLM to "write good code", which seems even easier.
I sympathize with your view on a philosophical level, but the consequence is really a meaningless semantic argument. The point is that prompting the AI with words that you'd actually use when asking a human to perform the task, generally works better than trying to "guess the password" that will magically get optimum performance out of the AI.
Telling an intern to care about code quality might actually cause an intern who hasn't been caring about code quality to care a little bit more. But it isn't going to help the intern understand the intended purpose of the software.
I'm going to resist the temptation to spend more time coming up with more examples. I'm sorry those weren't to your liking!
Why do you bother with all this discussion? Like, I get it the first x times for some low x, it's fun to have the discussion. But after a while, aren't you just tired of the people who keep pushing back? You are right, they are wrong. It's obvious to anyone who has put the effort in.
Trying to have a discussion with people who aren't actually interested in being convinced is exhausting. Simon has a lot more patience than I do.
[flagged]
I feel like both of these examples are insights that won't be relevant in a year.
I agree that CC becoming omniscient is science fiction, but the goal of these interfaces is to make LLM-based coding more accessible. Any strategies we adopt to mitigate bad outcomes are destined to become part of the platform, no?
I've been coding with LLMs for maybe 3 years now. Obviously a dev who's experienced with the tools will be more adept than one who's not, but if someone started using CC today, I don't think it would take them anywhere near that time to get to a similar level of competency.
I base part of my skepticism about that on the huge number of people who seem to be unable to get good results out of LLMs for code, and who appear to think that's a commentary on the quality of the LLMs themselves as opposed to their own abilities to use them.
> huge number of people who seem to be unable to get good results out of LLMs for code
Could it be, they use other definition of "good"?
I suspect that's neither a skill issue nor a technical issue.
Being "a person who can code" carries some prestige and signals intelligence. For some, it has become an important part of their identity.
The fact that this can now be said of a machine is a grave insult if you feel that way.
It's quite sad in a way, since the tech really makes your skills even more valuable.