As someone who's been doing Infra stuff for two decades, this is very exciting. There is a lot of mindless BS we have to deal with due to shitty tools and services, and AI could save us a lot of time that we'd rather use to create meaningful value.

There is still benefit for non-Infra people. But non-Infra people don't understand system design, so the benefits are limited. Imagine a "mechanic AI". Yes, you could ask it all sorts of mechanic questions, and maybe it could even do some work on the car. But if you wanted to, say, replace the entire engine with a different one, that is a systemic change and has farther reaching implications than an AI will explain, much less perform competently. You need a mechanic to stop you and say, uh, no, please don't change the engine; explain to me what you're trying to do and I'll help you find a better solution. Then you need a real mechanic to manage changing the tires on the moving bus so it doesn't crash into the school. But having an AI could make the mechanic do all of that smoother.

Another thing I'd love to see more AI use of, is people asking the AI for advice. Most devs seem to avoid asking Infra people for architectural/design advice. This leads to them putting together a system using their limited knowledge, and it turns out to be an inferior design to what an Infra person would have suggested. Hopefully they will ask AI for advice in the future.

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nickpapciak 2 days ago | parent | next [-]

Glad you find it interesting. A surprising way people are using us right now has been people who are technical but don’t have deep infrastructure expertise, asking datafruit questions about how stuff should be done.

Something we’ve been dealing with is trying to get the agents to not over-complicate their designs, because they have a tendency to do so. But with good prompting they can be very helpful assistants!

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0xbadcafebee 2 days ago | parent [-]

Yeah it's def gonna be hard. So much of engineering is an amalgam of contexts, restrictions, intentions, best practice, and what you can get away with. An agent honed by a team of experts to keep all those things in mind (and force the user to answer important questions) would be invaluable.

Might be good to train multiple "personalities": one's a startup codebro that will tell you the easiest way to do anything; another will only give you the best practice and won't let you cheat yourself. Let the user decide who they want advice from.

Going further: input the business's requirements first, let that help decide? Just today I was on a call where somebody wants to manually deploy a single EC2 instance to run a big service. My first question is, if it goes down and it takes 2+ days to bring it back, is the business okay with that? That'll change my advice.

	▲	nickpapciak 2 days ago \| parent [-]
		Yes definitely! That's why we do believe the agents, for the time being, will act as great junior devs that you can offload work onto, while as they get better they can slowly get promoted into more active roles. The personalities approach sounds fun to experiment with. I'm wondering if you could use SAEs to scan for a "startup codebro" feature in language models. Alas this is not something we get to look into until we think that fine-tuning our own models is the best way to make them better. For now we are betting on in-context learning. Business requirements are also incredibly valuable. Notion, Slack, and Confluence hold a lot of context, but it can be hard to find. This is something that I think the subagents architecture is great for though.

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paool 2 days ago | parent | prev [-]

Funnily enough, the same scenario holds true for actual programmers vs vibe coders.

Even if you manage to prompt an app, you'll still have no idea how the system works.