So I teach a couple of web architecture classes, and it’s been my experience that there is still a desire to learn how to truly build quality systems because they want to learn how to build quality systems.
I think our industry needs to transition to figure out what does it mean to build a quality system in the age of AI? It’s one thing to vibe code an application that works. But would you depend your livelihood on it? Would you get business funding and base the future of your team on it?
I think the challenge of the educational space is to figure out how to teach the judgement to build solid systems without relying on “hand coding”. For me I’ve been working on technology agnostic composable frameworks that help build that judgement. But honestly we are all just figuring this out together.
There will still be a need to be able to train system builders, the question really is what do they actually need to learn to be solid system builders in this AI era. And there’s a strong chance that pure programming language or tech stack learning may not be enough. This is what we educators need to figure out.
> But would you depend your livelihood on it? Would you get business funding and base the future of your team on it?
Yes. I stopped looking at the code about 9 months ago. It's easier/better/quicker to learn how to get the LLM to audit the code.
Investors don't care if the code is LLM-generated, they're mostly assuming that now anyway. The VC's I've talked to recently are screening pitches using an LLM, they're embracing this tech.
If I recruited a new dev to the team now, I would expect them to be able to run multiple agentic coding instances simultaneously. I would not expect them to be hand-crafting code any more.
I agree with you no investor or user cares how the code is built, and actually there’s nothing wrong with using LLMs to code. LLMs are the way to code now absolutely.
But, investors do care about the stability of the system they invested in. And how quickly it can be extended and expanded upon while still maintaining functionality. And they deeply care about scalability. I don’t believe this is a question about whether code is LLM generated. I think this is a question of having the ability to build a system that can be up and provide the value that investors expect. And the way you use AI and your knowledge of systems does play a role in that, especially over time.
And another point: it doesn’t have to be either or. Sometimes the prudent thing is to hand code a specific highly sensitive part of the system while your agents are building other things. The fact that these agents can do things in the background frees us with different possibilities. And also, sometimes hand coding some areas in the code that you are not familiar with builds your knowledge which the ln enables you to guide the agents more effectively later on. There’s benefits of both approaches and both approaches can be done simultaneously.
I used to be a freelancer working in startups. I've seen nightmare stuff cobbled together by student devs working on their mates' first startup that were completely unfit for purpose. Literally the entire thing needed to be rewritten from scratch in order for it to have more than two users. Still got funding.
kayashaolu2 · · focus · HN ↗
I think our industry needs to transition to figure out what does it mean to build a quality system in the age of AI? It’s one thing to vibe code an application that works. But would you depend your livelihood on it? Would you get business funding and base the future of your team on it?
I think the challenge of the educational space is to figure out how to teach the judgement to build solid systems without relying on “hand coding”. For me I’ve been working on technology agnostic composable frameworks that help build that judgement. But honestly we are all just figuring this out together.
There will still be a need to be able to train system builders, the question really is what do they actually need to learn to be solid system builders in this AI era. And there’s a strong chance that pure programming language or tech stack learning may not be enough. This is what we educators need to figure out.
marcus_holmes · · focus · HN ↗
Yes. I stopped looking at the code about 9 months ago. It's easier/better/quicker to learn how to get the LLM to audit the code.
Investors don't care if the code is LLM-generated, they're mostly assuming that now anyway. The VC's I've talked to recently are screening pitches using an LLM, they're embracing this tech.
If I recruited a new dev to the team now, I would expect them to be able to run multiple agentic coding instances simultaneously. I would not expect them to be hand-crafting code any more.
kayashaolu2 · · focus · HN ↗
But, investors do care about the stability of the system they invested in. And how quickly it can be extended and expanded upon while still maintaining functionality. And they deeply care about scalability. I don’t believe this is a question about whether code is LLM generated. I think this is a question of having the ability to build a system that can be up and provide the value that investors expect. And the way you use AI and your knowledge of systems does play a role in that, especially over time.
And another point: it doesn’t have to be either or. Sometimes the prudent thing is to hand code a specific highly sensitive part of the system while your agents are building other things. The fact that these agents can do things in the background frees us with different possibilities. And also, sometimes hand coding some areas in the code that you are not familiar with builds your knowledge which the ln enables you to guide the agents more effectively later on. There’s benefits of both approaches and both approaches can be done simultaneously.
marcus_holmes · · focus · HN ↗