This over reliance on llms is crazy. People are going to forget how to code. Sometimes the llm makes up shit or uses the wrong version of the API. Sometimes it's easier to look up the documentation and write some code.
Years ago I interviewed at Rackspace. They did a data structures and algorithms type interview. One of the main questions they had was about designing a data structure for a distributed hash table, using C or equivalent, to be used as a cache and specifically addressing cache invalidation. After outlining the basic approach I stopped and said that I have used a system like that in several projects at my current and former jobs and I would use something like Redis, memcache, or even Postgres in one instance, and do a push to cache on write system rather than a cache server pulling values from the source of truth if it suspected it had stale data. They did not like that answer. I asked why and they said it’s because I’m not designing a data structure from scratch. I asked them if the job I am applying for involved creating cache servers from scratch and they said “of course not. We use Redis.” (It might have been memcache, I honestly don’t remember which datadore they liked). Needless to say, this wasn’t a fit for either of us. While I am perfectly capable of creating toy versions of these kinds of services, I still stand by using existing battle tested software over rolling your own.
If you worry about forgetting how to code, then code. You already don’t know how to code 99% of the system you are using to post this comment (Verilog, CPU microcode, GPU equivalents, probably MMU programming, CPU-specific assembly, and so on). You can get ahead of the competition by learning some of that tech. Or not. But technically all you need is a magnetized needle and a steady hand.
I was like well I'd probably just make a Set in Java and insert letters until it has a duplicate.
They didn't like that. So I was like well I guess I can make a double for-loop and check that way and they liked that ... It is weird how people like you to re-invent the wheel as opposed to just import one.
One is domain knowledge which is less important in the age Google search and StackOverflow (and even less so in the age of LLMs but I guess interview techniques haven't caught up yet).
The second is the ability to understand a nested for loop, and if a coder can't do that by the point they reach an interview, it can probably never be taught.
It could be argued that being able to think up using a set in this instance is also an important skill, and I agree. But nested for loops are foundational skills, if the interviewee has problems there it's a good thing to know about early.
It could also be argued that they should just say directly "solve this using loops" if that's what they want, and well, yeah.
My favorite way to interview people is to ask them about their work and personal projects and about what parts of those were tricky, why, and how they solved those challenges. This gets candidates to talk much more openly about what experience they have and we can discuss real world practical problems down to having them show pseudo code (or showing off their GitHub repos of specific projects) that can very efficiently tell you how they think. It’s the equivalent of “tell me about bridges you have designed” vs “here are popsicle sticks, design a toy bridge” approach.
The company itself also turned into a circle of hell so probably for the best.
Jokes aside, while I'm almost sure that the ability to code can be lost and regained just like training a muscle what I'm more worried is the rug pull and squeeze that is bound to happen sometime in the next 5 to 10 years unless LLMs go the way of Free Software GNU style. If the latter happens then LLMs for coding will be like calculators and such more or less and personally I don't know how more harmful that would be compared to the boost in productivity.
That said if the former becomes reality (and I hope not!) then we're in for some huge existential crises when people realize they can barely materialize the labour part of their jobs after doing the thinky part and the meetings part.
In time, even video and embodied training may be possible for amateurs, though that's difficult to contemplate today.
People into homelabs have been running AI tools on home servers for years.
And they're all too small and dumb to be useful for anything but the most basic tasks.
We definitely would not have Electron and that's a world I want to live in.
Things will probably continue in that general direction. And just like today, a small number of people who really know what they're doing keep everything humming so people can build on top of it. By importing 13 python libraries they don't completely understand, or having an AI build 75% of their CRUD app.
Your code should have tests the AI can use to test the code it wrote.
And thanks to MCP, you can literally point your LLM to the documentation of your preferred tool [1].
One of the skills I've developed is spinning (back) up on problems quickly while holding the overall in my head. I find with AI I'm just doing that even more often and I now have 5 direct reports (AI) added to the 6 teams of 8 I work with through managers and roadmaps.
Pretty much me with some IDEs and their code inspections and refactoring capabilities and run profile configurations (especially in your average enterprise Java codebase). Oh well.
With your leave, this is going up on my wall :)
I think the talent pipeline has contracted and/or will and overcorrect. But maybe the industry’s carrying capacity of devs has shrunk.
Which is a problem when exactly? When civilization collapses?