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Ask HN: SWEs how do you future-proof your career in light of LLMs?

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Re: Ask HN: SWEs how do you future-proof your career in light of LLMs?

#271

Nothing because I’m a senior and LLM’s never provide code that pass my sniff test, and it remains a waste of time. I have a job at a place I love and get more people in my direct network and extended contacting me about work than ever before in my 20 year career. And finally I keep myself sharp by always making sure I challenge myself creatively. I’m not afraid to delve into areas to understand them that might look “…

The arrogance of comments like this is amazing.

I think it's an interesting psychological phenomenon similar to virtue signalling. Here you are signalling to the programmer in-group how good of a programmer you are. The more dismissive you are the better you look. Anyone worried about it reveals themself as a bad coder.

It's a luxury belief, and the better LLMs get the better you look by dismissing them.

Re: Ask HN: SWEs how do you future-proof your career in light of LLMs?

#272

Earlier quoted context omitted.

> Is there some expectation that these things won't improve? I definitely expect them to improve. But I also think the point at which they can actually replace a senior programmer is pretty much the exact point at which they can replace any knowledge worker, at which point western society (possibly all society) is in way deeper shit than just me being out of a job. > This is ego speaking. It definitely isn't. LLMs ar…

If a LLM (or any other tool) makes so that team of 8 can get the same results in the same time as it used to take a team of 10 to do, then I would count that as "replaced 2 programmers" - even if there's no particular person for which the whole job has been replaced, that's not a meaningful practical difference, replacing a significant fraction of every programmer's job has the same outcomes and impacts as replacing…

That's actually not accurate. See Jevons paradox, https://en.m.wikipedia.org/wiki/Jevons_paradox. In the short term, LLMs should have the effect of making programmers more productive, which means more customers will end up demanding software that was previously uneconomic to build (this is not theoretical - e.g. I work with some non-profits who would love a comprehensive software solution, they simply can't afford it, or the risk, at present).

Re: Ask HN: SWEs how do you future-proof your career in light of LLMs?

#273

Nothing because I’m a senior and LLM’s never provide code that pass my sniff test, and it remains a waste of time. I have a job at a place I love and get more people in my direct network and extended contacting me about work than ever before in my 20 year career. And finally I keep myself sharp by always making sure I challenge myself creatively. I’m not afraid to delve into areas to understand them that might look “…

My perspective is that if you are unable to find ways to improve your own workflows, productivity, output quality, or any other meaningful metric using the current SOTA LLM models, you should consider the possibility that it is a personal failure at least as much as you consider the possibility that it is a failure of the models.

A more tangible pitfall I see people falling into is testing LLM code generation using something like ChatGPT and not considering more involved usage of LLMs via interfaces more suited for software development. The best results I've managed to realize on our codebase have not been with ChatGPT or IDEs like Cursor, but a series of processes that iterate over our full codebase multiple times to extract various levels of resuable insights, like general development patterns, error handling patterns, RBAC-related patterns, extracting example tasks for common types of tasks based on git commit histories (i.e. adding a new API endpoint related to XYZ), common bugs or failure patterns (again by looking through git commit histories), which create a sort of library of higher-level context and reusable concepts. Feeding this into o1, and having a pre-defined "call graph" of prompts to validate the output, fix identified issues, consider past errors in similar types of commits and past executions, etc has produced some very good results for us so far. I've also found much more success with ad-hoc questions after writing a small static analyzer to trace imports, variable references->declarations, etc, to isolate the portions of the codebase to use for context rather than RAG-based searching that a lot of LLM-centric development tools seem to use. It's also worth mentioning that performance quality seems to be very much influenced by language; I thankfully primarily work with Python codebases, though I've had success using it against (smaller) Rust codebases as well.

Re: Ask HN: SWEs how do you future-proof your career in light of LLMs?

#274

Earlier quoted context omitted.

> Nothing because I’m a senior and LLM’s never provide code that pass my sniff test, and it remains a waste of time. I am constantly surprised how prevalent this attitude is. ChatGPT was only just released in 2022. Is there some expectation that these things won't improve? > LLM’s never provide code that pass my sniff test This is ego speaking.

> I am constantly surprised how prevalent this attitude is. ChatGPT was only just released in 2022. Is there some expectation that these things won't improve? Is there any expectations that things will? Is there more untapped great quality data that LLMs can ingest? Will a larger model perform meaningfully better? Will it solve the pervasive issue of generating plausibly sounding bullshit? I used LLMs for a while, I…

> Is there any expectations that things will?

Seems like an odd question. The answer is obviously yes: There is a very pervasive expectation that LLM's will continue to improve, and it seems odd to suggest otherwise. There is hundreds of billions of dollars being spent on AI training and that number is increasing each year.

> Is there more untapped great quality data that LLMs can ingest?

Why wouldn't there be? AI's are currently trained on the internet but that's obviously not the only source of data.

> Will a larger model perform meaningfully better?

The answer to this, is also yes. It is well established that, all else being equal, a bigger model is better than a smaller model, assuming that the smaller model hasn't already captured all of the available information.

Re: Ask HN: SWEs how do you future-proof your career in light of LLMs?

#275
I think it might be the opposite. It's not advisable to give advice to young SWEs when you might be one yourself out some.

Junior devs aren't going away. What might improve is often the gap between where a junior dev is hired and the effort and investment to get them to the real start line of adding value, before they hop ship.

AI agents will become their coding partners, that can teach and code with the Junior Dev, it will be more reliable contributions to a code base, and sooner.

By teach and code with, I mean explaining so much of the basic stuff, step by step, tirelessly, in the exact way each junior dev needs, to help them grow and advance.

This will allow SWE's to move up the ladder and work on more valuable work (understanding problems and opportunities, for example) and solve higher level problems or from a higher perspective.

Specifically the focus of Junior Devs on problems, or problems sets could give way to placing them in opportunities to be figured out and solved.

LLMs can write code today, not sure if it can manage clean changes to an entire codebase on it's own today at scale, or for many. Some folks probably have this figured out quietly for their particular use cases.

Re: Ask HN: SWEs how do you future-proof your career in light of LLMs?

#276

Nothing because I’m a senior and LLM’s never provide code that pass my sniff test, and it remains a waste of time. I have a job at a place I love and get more people in my direct network and extended contacting me about work than ever before in my 20 year career. And finally I keep myself sharp by always making sure I challenge myself creatively. I’m not afraid to delve into areas to understand them that might look “…

My perspective is that if you are unable to find ways to improve your own workflows, productivity, output quality, or any other meaningful metric using the current SOTA LLM models, you should consider the possibility that it is a personal failure at least as much as you consider the possibility that it is a failure of the models. A more tangible pitfall I see people falling into is testing LLM code generation using s…

Sometimes if it's as much work to setup and keep the tech running compared to writing it, it can be worth thinking about the tradeoffs.

A person with experience knowing how to push LLMs to output the perfect little function or utility to solve a problem, and collect enough of them to get somewhere is the interesting piece.

Re: Ask HN: SWEs how do you future-proof your career in light of LLMs?

#277

Earlier quoted context omitted.

> Is there some expectation that these things won't improve? I definitely expect them to improve. But I also think the point at which they can actually replace a senior programmer is pretty much the exact point at which they can replace any knowledge worker, at which point western society (possibly all society) is in way deeper shit than just me being out of a job. > This is ego speaking. It definitely isn't. LLMs ar…

> LLM’s never provide code that pass my sniff test If that statement isn't coming from ego, then where is it coming from? It's provably true that LLM's can generate working code. They've been trained on billions of examples. Developers seem to focus on the set of cases that LLM's produce code that doesn't work, and use that as evidence that these tools are "useless".

> It's provably true that LLM's can generate working code.

Yeah for simple examples, especially in web dev. As soon as you step outside those bounds they make mistakes all the time.

As I said, they're still useful because roughly correct but buggy code is often quite helpful when you're programming. But there's zero chance you can just say "write me an driver for the nRF905 using Embassy and embedded-hal" and get something working. Whereas I, a human, can do that.

Re: Ask HN: SWEs how do you future-proof your career in light of LLMs?

#278
Another thing I want to note is; even if I get replaced by AI, I think I'd be sad for a bit. I think it'd be a fun period to try to find a "hand-focused" job. Something like a bakery or chocolatier. I honestly wouldn't mind if I could do the same satisfying work but more hands-on, rather than behind a desk all day

Re: Ask HN: SWEs how do you future-proof your career in light of LLMs?

#279

I don't worry about it, because: 1) I believe we need true AGI to replace developers. 2) I don't believe LLMs are currently AGI or that if we just feed them more compute during training that they'll magically become AGI. 3) Even if we did invent AGI soon and replace developers, I wouldn't even really care, because the invention of AGI would be such an insanely impactful, world changing, event that who knows what the…

To replace all developers, we need AGI yes. To replace many developers? No. If one developer can do the same work as 5 could previously, unless the amount of work expands then 4 developers are going to be looking for a job. Therefore, unless you for some reason believe you will be in the shrinking portion that cannot be replaced I think the question deserves more attention than “nothing”.

I think counting the number of devs might not be the best way to go considering not all teams are equally capable or skilled in each person, and in enterprises, some people are inevitably hiding in a project or team.

Comparing only the amount of forward progress in a codebase and AI's ability to participate or cover in it might be better.

Re: Ask HN: SWEs how do you future-proof your career in light of LLMs?

#280

Earlier quoted context omitted.

If a LLM (or any other tool) makes so that team of 8 can get the same results in the same time as it used to take a team of 10 to do, then I would count that as "replaced 2 programmers" - even if there's no particular person for which the whole job has been replaced, that's not a meaningful practical difference, replacing a significant fraction of every programmer's job has the same outcomes and impacts as replacing…

Fav anecdote from ages ago: When hand-held power tools became a thing, the Hollywood set builder’s union was afraid of this exact same thing - people would be replaced by the tools. Instead, productions built bigger sets (the ceiling was raised) and smaller productions could get in on things (the floor was lowered). I always took that to mean “people aren’t going to spend less to do the job - they’ll just do a bigger…

This is a good example of what could happen to software development as a whole. In my experience large companies tend to more often buy software rather than make it. Ai could drastically change the "make or buy" decision in favour of make. Because you need less developers to create a perfect tailored solution that directly fits the needs of the company. So "make" becomes affordable and more attractive.
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