Technology

Good engineers will produce more good code, because they ask the right questions, know what good looks like and check the output.

So what's the point then ?

There have been coding frameworks like game engines for decades, if system coding is beyond your ability.

If AI can't write usable code from the start its worthless.

Provlem being is that it is not an equal amplifier.

Good engineer spends a morning going back and forth and maybe gets something done that wild have taken them all day.

Bad engineer puts first draft slop in production in a couple of minutes.

Bad engineer gets to put out something every few minutes, good engineer works too make it actually right instead of merely looking specifically right.

Good engineers will produce more good code, because they ask the right questions, know what good looks like and check the output.

Best code is the code that is never written in the first place. But that concept is difficult to explain and measure for management purposes. Just because "AI" can automate well-structured, tested boilerplate for "good" developers does not mean all that boilerplate should be there.

I've read that because they have less and less experience actually writing code junior developers are having trouble analyzing the code produced.

It can work as you say, but some companies are pushing 10x (or whatever) with fewer people, so quality is guaranteed to go to shit.

AI is an amplifier.

So very much this ^^^.

If you put in the same time and effort creating software using AI that you would have put in coding by hand, in my experience you get better software, much more thorough documentation and automated testing, and fewer "oops" moments down the line. Not perfection, but better.

If you just give a loosely specified prompt and take the first functional looking thing that comes out, you can get code 10x faster than ever before, and it's going to be a 100x bigger mess to maintain.

A rule of thumb (aka useless constant applied to imaginary metrics) that my colleagues and I have found is: 80%. Work on an assumption that what you get back from each AI pass is about 80% good or right. Work to identify the 20% that needs more refinement, do another pass, now you're up to 96% good - and honestly probably already better than most first pass ready for a pull request code we used to submit 2 years back. Do a third pass on that and you've probably got something that's not going to give any trouble in all but some really rare cases, and you got it in about half the time you would have spent on lower quality output.

I have been trying, with limited success, to get our junior engineers to use AI to review their own code before submitting pull requests. Some do a single pass and their PRs are pretty good, one says he "doesn't believe in AI" and his code typically needs 3-4 review passes before it's even acceptable, even though he's clearly using AI to write the documentation. AI review is how they're finding all these zero day exploits in widely used products, it works, it finds maybe 80% of things you're looking for (if you keep the scope focused inside its context window capacity.) We are having slightly more success with all the junior engineers by having them submit 5-10 small pull requests per 2 week sprint instead of one big one. This not only helps human reviewers understand the bite sized chunks, it also means the AI reviews are more thorough. It also means the architectural definition steps are much more critical because review of tiny chunks misses more of the architectural level picture.

The biggest ethical question I have about using AI centers on management of management expectations. If management really thinks the human contribution value in software creation has disappeared overnight - I'd look for different management, because that ship just steered straight into an iceberg field. Some of them may pull off the Kessel run in less than 12 parsecs, but most won't.

Nah, good engineers are retiring, bad engineers are running rampant. You give yourself away calling us engineers, we were never, except for some yearly title increase instead of money. Just programmers, and that is fine. Engineer is a whole other thing from the steam age, my BSc was in Math, worked fine to get me in.

There was nothing wrong with the code in terms of structure or performance, but it was doing this really weird operation with an ID after DB insert

So there was plenty wrong with the code.

And it hasn't been fixed.

"Bro, its totally just you prompted it wrong"

Yeah these interaction are becoming quite fraught because there is extreme danger of AI use just shifting the burden from one person to another. That guy wrote the code super fast with AI, but you had to do an additional round trip during code review with him to eliminate that weirdness. He gets a bonus for doing twice the work in half the time, you get nothing.

blame your leadership

he’s not getting paid for good code

i dont understand that. i use ai for help reading through old stuff or to help me remember how tondo a thing i havent done in two years but blindly copy pasting blows my mind.

I worked with a guy that 100% used AI to dev everything. didn't even check to see if it would work before submitting a MR.

It got to the point that I stopped reviewing them and just rejected them outright with a simple comment, "doesn't work".

eventually he was fired. the evidence? the four months of shitty MRs he opened. the best part was, when I said "doesn't work", I was never wrong. none of his changes worked.

I mean, the pull requests will arrive.

Especially if they are security related, even if only 5% are anything, that's still quite a few actual bugs. 😞

It's a great tool... and it's only getting better.

But more importantly, it's not going away. You can hate it until the sun explodes but this technology exists now and the world isn't just going to forget about it. Your best bet is to figure out how to use it efficiently so that you don't end up the guy that pushes shitty AI code and instead the guy who was able to get a lot done by using AI in a meaningful way and produce good work

I see an LLM as my good-but-not-perfect assistant, there to code up boring bits "loop thru this data and extract...", "improve this bit of code please", and to help with errors "why does this code give that error".

I never let it do big slabs of code, and always run and check its code incrementally.

It is my code and the LLM just makes it easier to do. Thanks LLM.

I have coworkers with varying degrees of proficiency with AI. The ones that are better with it rein it in when it goes haywire. I have less of an issue with this. It still does awkward stuff here and there. The ones that are bad with it just commit the code for review and annoy me. When 60-80% or so of your PR can be refactored away then it’s a crap PR and honestly never should have been one. Don’t make me read through 2000 lines that should have been 400. It’s a waste of time. This mostly is it doing brain dead things like instead of passing a parameter into a method it makes 4 nearly identical methods.

I mostly use AI to implement a solution, not come up with the solution. I don’t care how fast you can type…AI can type faster. That also means I understand what it’s trying to build and how it should be going about it. That does mean you still have to use your brain and not offload your critical thinking to the machine which is what I see a lot of people doing with it.

In that regard I like AI but it’s still a love/hate relationship for sure. It both saves and costs me time just in different ways.