233 karma · joined April 6, 2024
I enjoy what I do but I confess to being “in technology” because I need to earn a living not because it’s some sort of passion project. I think it’s pretty horrible to wish us all out of job so you can feel superior again.
The primary reason I became a software engineer at middle age was to make enough money and have good enough health insurance that she could have the freedom to leave a job that was killing her mentally and physically.
You can throw all the money, new techniques and technology you want to at the problem. It will not get better without fixing that fundamental issue.
Now you’ve given every parent a way to easily mass block all adult/social sites/apps if they want and no one’s privacy need be compromised.
I do try to continually improve my skill set, refresh on design patterns, etc. I’m currently employed and have been for the last six years. I don’t really know if I fall in category 3 or not.
What, in your opinion are the “foundational” CS skills the #3 people are missing?
If you mean obsolete in the sense of "no longer fit for purpose" I don't think that's true. They may become obsolete in terms of "can't do hottest new thing" but that's true of pretty much any technology. A capable local model that can do X will always be able to do X, it just may not be able to do Y. But if X is good enough to solve your problem, why is a newer better model needed?
I think if we were able to achieve ~Opus 4.6 level quality in a local model that would probably be "good enough" for a vast number of tasks. I think it's debatable whether newer models are always better - 4.7 seems to be somewhat of a regression for example.
If your child says they've learned their multiplication tables but they can't actually multiply any numbers you give them do they actually know how to do multiplication? I would say no.
Yet most developers I work with just use it reflexively. This seems like one of the biggest issues with the npm ecosystem - the complete lack of motivation to write even trivial things yourself.
That said, very sophisticated next word predictors can and sometimes do write good code. It’s amazing some of the things they get right and then can turn around and make the weirdest dumbest mistakes.
It’s a tool. Sometimes it’s the right tool, sometimes it’s not.
No one will be eager to employ “ai-natives” who don’t understand what the llm is pumping out, they’ll just keep the seasoned engineers who can manage and tame the output properly. Similarly, no one is going to hire a bunch of prompt engineers to replace their accountants, they’ll hire fewer seasoned accountants who can confidently review llm output.
I use LLMs with best practices to program professionally in an enterprise every day, and even Opus 4.6 still consistently makes some of the dumbest architectural decisions, even with full context, complete access to the codebase and me asking very specific questions that should point it in the right direction.