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Interviewing Engineers in the AI Era: Lessons from a Year of Rebuilding

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Re: Interviewing Engineers in the AI Era: Lessons from a Year of Rebuilding

#51

Post by xmcqdpt2 »

jacamera wrote: post #27

I'm very skeptical of that graph. What does it mean for code to be AI-generated vs human-generated? If I merge a PR that was opened by Claude do all the changes in it automatically count as AI-generated? How about the individual commits? If I'm reviewing locally and make manual changes but then have Claude create the commit it appears to be AI-generated but might not have been. The point is that these metrics are eas…

One of my least favourite feature of our AI era is that I can't even trust people to do basic math anymore. It used to be that a graph like that would mean someone had a (probably broken) metric that they put in an excel sheet and got a lineplot out of it. The metric might be broken but at least additions and subtractions etc were correct.

Now I don't even know! Colleagues send me graphs they made with Copilot and then we discover that the LLM did mental arithmetic or whatever to produce the results and they are wrong. Computers used to be able to do math !

Re: Interviewing Engineers in the AI Era: Lessons from a Year of Rebuilding

#52

Post by bonoboTP »

wild_egg wrote: post #43
ryanSrich wrote: post #30

Earlier quoted context omitted.

> This all sounds good on paper but I have an honest question: How do you develop judgement as a junior or mid level engineer to critique AI if you yourself never learned that skill by making mistakes in the first place? Shouldn't this be a pretty fundamental part of a degree? If you're in school right now for a CS or SWE related degree, I would imagine you're learning how code actually works, how the math actually w…

> Shouldn't this be a pretty fundamental part of a degree? If you're in school right now for a CS or SWE related degree, I would imagine you're learning how code actually works, how the math actually works, etc. CS and SWE are radically different subjects. Science vs Engineering. I'm sure it varies by school but many CS grads will have approximately zero exposure to engineering concepts or any of the latest and great…

In Europe, informatics degrees, or more specifically technical informatics or engineer-informatics is often the right balance if you want math but also practice. In Hungary they call it engineer-informatics, but officially translate it as Computer Engineering, but it's not really heavily about how to engineer a computer as a machine, though there is some architecture and flipflops etc. of course in the curriculum. It's housed under the electrical engineering faculty, and not under the math and natural sciences faculty, but they still have rigorous discrete math, analysis, graph theory, optimization theory etc. classes.

It always baffled me that US colleges seem not to offer such programs. Or maybe they do, they are just not prestigious enough.

Re: Interviewing Engineers in the AI Era: Lessons from a Year of Rebuilding

#53

Post by sarmadgulzar »

bonoboTP wrote: post #49
sarmadgulzar wrote: post #47

Earlier quoted context omitted.

Agreed. I’m not insinuating that colleges should teach all of these extremely domain-specific things. Most of the stuff would indeed be unnecessary for most students depending on what they do in the future. But that is my original point that you learn this on the job while making a mistake and being corrected by a senior mentor. AI wouldn’t call this out by itself unless you give it a very specific prompt which you a…

Not so sure. AI can flag non-best-practices like this quite easily from generic prompts. You just have to remember to ask it. I mean general review / audit prompts, like "are there any correctness bugs, or things that don't match best practices for efficiency, maintainability, security, or typical UX/API expectations?" And frontier models often flag such things. And it's just getting better. I would not confidently s…

Sure you can do it but I think you’ll agree with what I said about ownership. Whenever I ask AI to audit my codebase, it always plays it safe and gives me a few options to choose from. It just won’t give me a concrete answer unless I insist it to give only one but then I wouldn’t trust that answer that I basically forced out of it.

Re: Interviewing Engineers in the AI Era: Lessons from a Year of Rebuilding

#54

Post by Levitz »

ryanSrich wrote: post #30
sarmadgulzar wrote: post #26

This all sounds good on paper but I have an honest question: How do you develop judgement as a junior or mid level engineer to critique AI if you yourself never learned that skill by making mistakes in the first place? I’m not saying that all code should be typed by hand in 2026 but there are certain subtle things you learn only when you get into nitty gritty details especially related to security. Also today’s AI is…

> This all sounds good on paper but I have an honest question: How do you develop judgement as a junior or mid level engineer to critique AI if you yourself never learned that skill by making mistakes in the first place? Shouldn't this be a pretty fundamental part of a degree? If you're in school right now for a CS or SWE related degree, I would imagine you're learning how code actually works, how the math actually w…

These are two completely different types of mistakes.

LLMs are, by now, pretty good at not making "code doesn't work" and "math doesn't work" kind of mistakes when writing code. These are also "easy" things to get good at, you learn what each part does, understand the abstractions, ensure it makes sense, and go on with your day. Unit testing helps here.

LLMs are not that good at not making "this works wrong" kind of mistakes. Maybe the code compiles and does what it has to do, but maybe it's 2 lines of code with 6 lines of comments (looking at you Claude), maybe it defines three helper functions it doesn't really need, maybe it does something "here" when it should be doing that something "there" instead, maybe it finds itself in a framework and completely disregards how the framework is supposed to do things, etc etc. These are harder things to get good at and you WILL end up with an unreadable mess if you disregard caution and let the LLM go at it.

Re: Interviewing Engineers in the AI Era: Lessons from a Year of Rebuilding

#55

Post by layer8 »

bonoboTP wrote: post #50
layer8 wrote: post #40

Earlier quoted context omitted.

> internships where you've worked at a real company writing real code Though why would companies make that nontrivial investment if they can use AI instead? Furthermore, I’d argue that learning good judgement takes around a decade of full-time dev experience. In particular, experiencing the long-term consequences of one’s design and implementation decisions. You don’t get that just by college education and a few inte…

Do you get it from the job hopping every 1-3 years as typical in the US?

Less effectively, I presume. I’m not in the US.

Re: Interviewing Engineers in the AI Era: Lessons from a Year of Rebuilding

#56

Post by bonoboTP »

sarmadgulzar wrote: post #53
bonoboTP wrote: post #49

Earlier quoted context omitted.

Not so sure. AI can flag non-best-practices like this quite easily from generic prompts. You just have to remember to ask it. I mean general review / audit prompts, like "are there any correctness bugs, or things that don't match best practices for efficiency, maintainability, security, or typical UX/API expectations?" And frontier models often flag such things. And it's just getting better. I would not confidently s…

Sure you can do it but I think you’ll agree with what I said about ownership. Whenever I ask AI to audit my codebase, it always plays it safe and gives me a few options to choose from. It just won’t give me a concrete answer unless I insist it to give only one but then I wouldn’t trust that answer that I basically forced out of it.

Yes, I don't know how a junior brain would read it. I already have a map of roughly what kinds of things exist out there, even if my picture is blurry, so when I read a suggestion from the AI, I have a rough ballpark of what it means, and can ask followups and triage what is promising. If you never developed this, probably as a junior you have very little to base such decisions on.

Re: Interviewing Engineers in the AI Era: Lessons from a Year of Rebuilding

#57

Post by Ac1285asFa »

Regulatory compliance as attempted by Coinbase does not pay, and of course they had many fines for non-compliance already.

The whole point of Bitcoin is sanctions evasion. Tether moved from Deltec Bank (CIA) in the Caribbean to Lutnick's Cantor & Fitzgerald and El Salvador. That is where the action is.

So promoting the AI bullshit is for investors because that is what they want to hear. They are not going to vibe code financial transactions and get another $500M EU fine.

As an aside, it is interesting that the new "taste" talking point was already on Coinbase in mid-July.

Re: Interviewing Engineers in the AI Era: Lessons from a Year of Rebuilding

#58

Post by throwatdem12311 »

sarmadgulzar wrote: post #31
throwatdem12311 wrote: post #29

Earlier quoted context omitted.

You hire juniors but don’t let them use AI tools for a year :)

Stakeholders are not technical enough to understand that so CEOs won’t do it. We are already seeing it play out. Entry-level hiring has been consistently down ever since AI got mainstream.

The competence crisis in a few years is going to be apocalyptic.

Re: Interviewing Engineers in the AI Era: Lessons from a Year of Rebuilding

#59

Post by sarmadgulzar »

bonoboTP wrote: post #56
sarmadgulzar wrote: post #53

Earlier quoted context omitted.

Sure you can do it but I think you’ll agree with what I said about ownership. Whenever I ask AI to audit my codebase, it always plays it safe and gives me a few options to choose from. It just won’t give me a concrete answer unless I insist it to give only one but then I wouldn’t trust that answer that I basically forced out of it.

Yes, I don't know how a junior brain would read it. I already have a map of roughly what kinds of things exist out there, even if my picture is blurry, so when I read a suggestion from the AI, I have a rough ballpark of what it means, and can ask followups and triage what is promising. If you never developed this, probably as a junior you have very little to base such decisions on.

Exactly, I absolutely agree that AI supercharges seniors with judgment and conviction. They’re no longer constrained by how fast they can type.

But this is about what comes next i.e. seniors of tomorrow

If AI gets so good at software that vibe coding is the new norm and as Elon Musk says that AI will generate the machine code directly without any intermediate compilation or interpretation then we wouldn’t need seniors or juniors but I don’t think that’s coming any time soon, if at all!

Re: Interviewing Engineers in the AI Era: Lessons from a Year of Rebuilding

#60

Post by throwatdem12311 »

xmcqdpt2 wrote: post #46
throwatdem12311 wrote: post #29

Earlier quoted context omitted.

You hire juniors but don’t let them use AI tools for a year :)

I wish but at $work the push has been the opposite. I'm still allowed to write code, but interns and juniors are being told that they need to prompt all the time. I think upper management are hoping they can replace a possibly reticent or AI skeptical "old guard" with AI native Gen Zs. In practice, the young out of school kids are much less excited about AI than the 50 year olds, so I don't know how well it will work…

It is going to backfire spectacularly. It’s actually mind boggling to me how stupid business idiots actually are.

https://futurism.com/artificial-intelligence/zoomers-ai-sabo...

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