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llmslave parent
My senior SWE job at FAANG has essentially turned into prompting Opus 4.5.

There is almost no reason to delegate the work, especially low level grunt work.

People disputing this are either in denial, or lacking the skill set to leverage AI.

One or two more Opus releases from anthropic and this field is cooked

drivebyhooting
What kind of work do you do that is simple enough that can be accomplished solely through prompting?
ivape
The golden handcuff type where you update documentation with new UI elements.
hu3
What kind of work do you do that CANT BE divided enough into tasks that can be accomplished mostly through prompting?
llmslave OP
distributed systems, log diving, deployments, etc
hactually
any sort of web tech based development.

Frontend, backend, animations, design, infra, distributed systems engineering, networking.

Capricorn2481
It's a troll account called llmslave made a couple months ago. Odds are low it's even a human.
gervwyk
It all depends on how you prompt. and the prompt system you’ve setup.. when done well, you just “steer” the code /system. Quite amazing to see it come together. But there are multiple layers to this.
extrabajs
> lacking the skill set to leverage AI

It possible that your job is simply not that difficult to begin with?

llmslave OP
yes, but so are most jobs like mine
wanderlust123
What job is so difficult that LLMs cant allow an experienced user an order of magnitude gain in efficiency?
wilsonnb3
An order of magnitude, really? An experienced user with an LLM is going to accomplish in 2026 what would have otherwise taken until 2036?
wanderlust123
Yes, I personally think so. In the hands of an experienced user you can crank out work that would take days or weeks even, and get to the meat of the problem you care about much quicker. Just churning out bespoke boilerplate code is a massive time saver, as is using LLMs to narrow in on docs, features etc. Even high level mathematicians are beginning to incorporate LLM use (early days though).

I cant think of an example where an LLM will get in the way of 90% of the stuff people do. The 10% will always be bespoke and need a human to drive forward as they are the ones that create demand for the code / work etc.

bdangubic
sounds about right
icedchai
The problem is many users are not experienced. And the more they rely on AI to do their work, the less likely they are to ever become experienced.

An inexperienced junior engineer delegating all their work to an LLM is an absolute recipe for disaster, both for the coworkers and product. Code reviews take at least 3x as long. They cannot justify their decisions because the decisions aren't theirs. I've seen it first hand.

wanderlust123
I agree totally; most people are no experienced, and there is a weird situation where the productivity gains are bifurcated. I have also seen a lot of developers unable to steer the LLM as they can’t pick up on issues they would otherwise have learned through experience. Interesting to see what will happen but probably gonna be a shit show for younger devs.
harisec
Unfortunatelly, i have the same experience.
samdoesnothing
The best part about your account is the people who don't understand the satire and unironically agree with you :D
bojan
It seems you've registered this account a couple of months ago only to basically repeat this opinion over and over (sprinkled with some anti-science opinions on top).

Really weird.

llmslave OP
the world has changed, have you caught up?
esseph
It hasn't really.

Now people can just search stack overflow quicker for the wrong answer, and even more confidently than ever before.

llmslave OP
its nothing like stack overflow
AuthAuth
Yeah its even more likely to give you a non working answer
petersellers
username checks out
trhway
great engineering effort was spent to make software at FAANG built on clear service oriented modular architectures, and thus easy to develop for. Add to that good organization of process where engineers spend most of their time doing actual dev work.

Enterprise software is different beast - large fragile [quasi]monoliths, good luck for [current] AI to make a meaningful fixes and/or feature development in it. And even if AI manages to speed up actual development multiple times, the impact would be still small as actual development takes relatively small share of overall work in enterprise software. Of course it will come here too, just somewhat later than at places like FAANG.

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