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

I apologize for misinterpreting what you were saying -- I was clearly taking "for the cost of an hour of a software engineer" to mean something that you didn't intend.

> As an example, huggigface just posted an article showing this for math, where with some sampling you can get a 3B model to outperform a 70B one

This is not relevant to our discussion. Again, I'm reasonably sure that I'm not going to be convinced by any research demonstrating that X new tech can increase Y metric by Z%.

> Formalizing the criteria is not as hard as you're making it out to be. You can have an LLM listen to a conversation with the "customer", ask follow up questions and define a clear spec just like a normal engineer. If you doubt it open up chatGPT, tell it you're working on X and ask it to ask you clarifying questions, then come up with a few proposal plans and then tell it which plan to follow.

This is much more relevant to our discussion. Do you honestly feel this is an accurate representation of how you'd define the requirements for the pipeline you outlined in your post above? Keep in mind that we're talking about having LLMs work on already-existing large codebases, and I conceded earlier that writing boilerplate/base code for a brand new project is something that LLMs are already quite good at.

Have you worked as a software engineer for a long time? I don't want to assume anything, but all of your points thus far read to me like they're coming from a place of not having worked in software much.

Yes I've been a software engineer working in deep learning for over 10 years, including as an early employee at a leading computer vision company and a founder / CTO of another startup that built multiple large products that ended up getting acquired.

> I apologize for misinterpreting what you were saying -- I was clearly taking "for the cost of an hour of a software engineer" to mean something that you didn't intend.

I meant that unlike a software engineer, the LLM can do a lot more iterations on the problem given the same budget. So if your boss comes and says build me new dashboard page it can generate 1000s of iterations and use a human aligned reward model to rank them based on which one your boss might like best. (that's what the test time compute / sampling at inference does).

> This is not relevant to our discussion. Again, I'm reasonably sure that I'm not going to be convinced by any research demonstrating that X new tech can increase Y metric by Z%.

These are not just research papers, people are reproducing these results all over the place. Another example from a few minutes ago: https://x.com/DimitrisPapail/status/1868710703793873144

> This is much more relevant to our discussion. Do you honestly feel this is an accurate representation of how you'd define the requirements for the pipeline you outlined in your post above? Keep in mind that we're talking about having LLMs work on already-existing large codebases,

I'm saying this will be solved pretty soon, working with large codebases doesn't work well right now because last years models had shorter context and were not trained to deal with anything longer than a few thousand tokens. Training these models is expensive so all of the coding assistant tools like cursor / devin are sitting around and waiting for the next iteration of models from Anthropic / OpenAI / Google to fix this issue. We will most likely have announcements of new long context LLMs in the next 1-2 weeks from Google / OpenAI / Deepseek / Qwen that will make major improvements on large code bases.

I'd also add that we probably don't want huge sprawling code bases, when the cost of a small custom app that solves just your problem goes to 0 we'll have way more tiny apps / microservices that are much easier to maintain and replace when needed.

I think hardware manufacturers, including ones that produce chips, are way less encouraged to put things online and thus has a wide moat. "Classic" ones such as 6502 or 8086 definitely have way more material. "Modern" popular ones such as x86/64 too have a lot of material online. But "obscure" ones don't.

On software side, I believe LLMs or other AI can easily replace juniors who only knows how to "fill-in" the code designed by someone else, in a popular language (Python, Java, Javascript, etc.), in under 10 years. In fact it has greatly supported my data engineering work in Python and Scala -- does it always produce the most efficient solution? No. Does it greatly reduces the time I need to get to a solution? Yes, definitely!